Engineering论文模板 – Individual Factors of Corruption in Engineering Projects: Quantitative Evidence from China

  1. Introduction

This section is divided into two parts: social context and theoretical background of corruption in general and the engineering industry.

  1. Social Context

As a global problem that spans social systems, corruption exists in countries with varying economic conditions, levels of social development and cultural traditions (Li et al., 2022). Although it differs in terms of severity and external characteristics, the damage and destructive nature of corruption are similar. The fight against it is a serious issue that needs to be addressed worldwide (Tanzi, 1998). It has been shown that rapid economic transformation and radical changes in social structures coincide with a high incidence of corruption, as was the case in the 18th century in the United Kingdom, the 19th century in the United States, the 1960s in South Korea, and the late 20th century in Russia. In a period of social and economic transition, China has also been plagued by corruption (Li et al., 2022; Troilo and Sun, 2010). Corruption is destructive and harmful in that it distorts industrial policy, reduces the efficiency of public investment, causes loss of tax revenue, weakens public finances, and hinders financial stability and political stability. For society as a whole, corruption is a deterrent to investment, infringes on public power, distorts the allocation of market resources, and significantly hinders economic growth, thus causing a loss to society and is often closely linked to poverty (Shan et al., 2019).

In 2020, the global construction industry market reached £968 billion, with a CAGR of 7.4% over the projected period to 2028. Over the projected period, the key revenue growth driver of the global construction industry is the increasing urbanization due to rising disposable income and a growing population (Emergen Research, 2021; Sohail and Cavill, 2008). However, the global construction industry is plagued by corruption, misuse of funds and bribery. Statistics show that as early as 2001, over £11.8 billion was wasted each year due to building collapses nationwide, not to mention untold millions of pounds lost to corruption in the execution of projects (Winch, 2001). According to the American Society of Civil Engineers (ASCE), the annual cost of corruption in the global construction industry is estimated at £260 billion. The Global Economic Crime Data Survey shows that one-third of the 184 construction companies from 44 countries are threatened by corruption and bribery (PwC, 2020). Transparency International’s 2005 Global Corruption Report highlighted the serious impact of corruption in the engineering sector. Also, large infrastructure projects are plagued by international bribery and call for greater transparency. When Transparency International (2011) last included industry sectors in its Bribery Perception Index (BPI), the construction sector ranked at the bottom of the list of all sectors in all types of bribery.

Figure 1: Bribery Perception Index 2011

Source: Transparency International (2011)

Researchers also agree that construction sector has been the domain with the hardest hit of corruption (Owusu et al., 2019). Engineering construction projects are labour and capital intensive, with a complicated structure of contractors and subcontractors, making it a major area of corruption (Khadim et al., 2021). As construction proceeds (from project initiation, bidding, subcontract management, project payment, material procurement, quality supervision, and completion acceptance), every step creates corruption opportunities (GIACC and Transparency International, 2008). Surveys and studies show that the engineering industry is the most corrupt area, be it the developed or developing countries, severely deteriorating the positive image of the sector (Mejía et al., 2020; Le et al., 2014a; Liu et al., 2017). For example, a survey by the Chartered Institute of Building (CIOB) in 2016 revealed that 48% of the 700 participants in the construction sector believe corruption is widespread in the UK. More than a third of construction professionals said they had the change to bribe at least once (Barnes, 2016).

With the development of the economy and the needs of society, China has invested a large amount of financial and social funds in construction projects. According to a 2018 report from Deloitte, Chinese construction and engineering companies ranked highest in 2018, with more than £133 billion in total revenue. China Railway Group and China Railway Construction followed closely behind.

However, with tremendous growth come great challenges. In its 2021 Corruption Perception Index report, Transparency International, a leading anti-corruption organisation, assessed the likelihood of corruption in 180 countries and territories all over the world and nineteen different sectors. With a score of 45 (where 100 is viewed as highly clean), China ranks 66th and is believed to be the fertile ground for corruption (Transparency International, 2022).

Figure 2: CPI 2021 Ranking List of Countries

Source: Transparency International (2022)

According to statistics in March 2021 presented to the National People’s Congress by the Procurator-General of the Supreme People’s Procuratorate, 15,346 people were charged with corruption just two years ago, and 12 state bureaucrats at the minister and governor level were investigated, demonstrating the severity of corruption in China. In 2020, 8,056 cases of crimes in the field of engineering construction were investigated and handled, accounting for more than 30% of the cases of similar crimes filed and investigated throughout the year, involving many key aspects such as planning and adjustment, bidding and tendering, use of funds and quality supervision (Xinhua, 2021). It is, therefore, crucial to explore the mechanisms of engineering corruption, its development and the corresponding measures to curb the issue. Behind the staggering number of corruption offences in this area are the harm and damage.

Abundant studies have summarised the dangers of corruption in engineering projects. Table 1 lists some of the current international scholarly research on the dangers of engineering project corruption. Most researchers believe in the adverse consequences of corruption in the engineering industry. For example, Sohail and Cavill (2008) believe that corruption reduce the excellency of infrastructure services in engineering and construction. They argue that both the public and commercial industry in countries with different economic power would benefit from a reduction in corruption. Works of low quality are a great threat to the safety of people’s lives and property. Corruption in engineering has led to the misuse and waste of funds. Many officials, in order to improve their personal job performance, have blindly launched projects without scientific proof, resulting in a needless loss of investment (Habib and Zurawicki, 2001). Beyond the physical damage, Park and Blenkinsopp (2011) and Zhang (2019) discuss the intangible cost of corruption: undermining public trust in governments.

Some consequences are still debatable. For example, a high possibility of corruption brings income inequality in the long run (Keneck-Massil et al., 2021). This conclusion differs from evidence from Aisa when Saha et al. (2021) claim that inequality is unaffected by low corruption. Finally, there are also cases when an advocate of the opposite view holds that corruption might fuel economic development (Chan et al., 2019).

Table 1: Research of Impacts of Corruption in Construction

SourceNegative Results
Boudreaux et al., 2017 ; De Rosa et al., 2015 ; Le et al., 2014b ; Owusu et al., 2017lower economic growth and productivity
Cooray et al., 2017; Epaphra and Massawe, 2017; Locatelli et al., 2017reduce government revenue
Owusu et al., 2017; Zou, 2008reduce lifespan of facility
Habib and Zurawicki, 2001cause inefficient use of public investment
Locatelli et al., 2017delay delivery time
Damoah et al., 2018; Gidado and Niazai, 2012 Locatelli et al., 2017; Sohail and Cavil, 2008result in defective or dangerous projects
Locatelli et al., 2017increase transition cost
Keneck-Massil et al., 2021; Sulemana and Kpienbaareh, 2018; Wong, 2016lead to income inequality
Le et al., 2014a; Locatelli et al., 2017 Zhang et al., 2017deter small enterprises from entering and create a monopoly
Boudreaux et al., 2017 ; Lavallée et al., 2008 Park and Blenkinsopp 2011; Zhang 2019erode trust in government employees and political institutions
Dakhil et al., 2017demotivate workers
SourcePositive Results
Locatelli et al., 2017promote competition
Chan et al., 2019fuel economic development in countries at transitional period
Dreher and Gassebner, 2011; Locatelli et al., 2017; Méon and Sekkat, 2005speed up delivery
Méon and Weill, 2010serve as efficient grease in ineffective institutions

The necessity of combating corruption is well acknowledged. Albert Einstein had a statement: “The World will not be destroyed by those that do evil but by those who watch them without doing anything.” Since recognising the seriousness of corruption, countries around the world have managed to reduce corruption through different approaches. Generally speaking, there has also been a significant shift in the methods used to fight against corruption, changing from passive punishment after corruption and reactive recovery of losses to proactive preventive measures.

Anti-corruption efforts in China, for example, were mainly focused on the symptoms prior to the 15th National Congress. However, after the 15th National Congress, anti-corruption efforts gradually entered a phase of addressing both the symptoms and the root causes. The government launched a comprehensive treatment to tackle the root causes. Finally, following the 18th National Congress, the central authorities have not only launched an intensive anti-corruption campaign but have also tried to improve the behaviour of officials and rid them of bureaucracy and extravagance. Today, the anti-corruption campaign has included traditional anti-corruption strategies and creative ways of fostering an atmosphere in a society where civil servants become aware of the healthy behaviour of officials. From the development of the three stages, the rationale behind China’s anti-corruption measures has shifted from governance to prevention, with increased vision and foresight (Cao, 2022). On the whole, that prevention is preferable and more effective than cure has become a universal understanding in the world.

However, scholars have also cautioned that eradicating corruption is a daunting task since not many successful and replicable examples are found in history (De Rosa et al., 2015). In the fight against corruption, some scholars have advocated that policies to address this issue can be developed through enhanced education and cultural changes, which empowers governments to eliminate corruption (H. Akbar and Vujić, 2014). Unfortunately, it may take decades to achieve all of these necessary processes and cultural changes, but projects need to be planned and delivered on an ongoing basis (Kyriacou et al., 2015). Hence, while the sociological and political science community are dealing with long-term issues such as policy change, the project management community should not hesitate to research specific corruption issues in projects (Locatelli et al., 2017).

  1. Theoretical Context

Corruption is a far-reaching problem and has a long history, yet theoretical and academic research on corruption is relatively recent, beginning with the study of corruption in political science and democratic theory 200 years ago. Theories have discussed corruption. Gravinetter (2007 as cited in Tazler et al., n.d.) tried to explain corruption based on constructivism. It argued that corruption is the impact of social factum and not a natural phenomenon. Social constructivism can be divided into two types. First, corruption can be seen within the communication of social systems. From this point of view, corruption is the condition when the system is diverted by the motivation to fulfil the inherent functional objectives. Second, corruption is the result of the interpretation of the social world by a person or individual.

In addition, a comprehensive review of the existing research on corruption reveals two main approaches: one is to consider corruption as the cause of the problem, i.e. the independent variable, such as the consequences of corruption, corruption and economic growth, corruption and future development, which is discussed in the previous section; and the other is to consider corruption as the affected outcome, i.e. the dependent variable, which refers mainly to research that explores the causes of corruption.

Research on corruption in engineering projects has centred around the search for the causes. It is generally accepted that the vulnerability of engineering projects to corruption is highly correlated to the large flow of investments involved and the absence of actual owners (Shan et al., 2019). Specifically, this is closely related to the characteristics of the project itself and the environment. In terms of the project’s own features, the one-time and one-off nature of the project, coupled with the large number of parties involved and the complexity of the decision-making process, makes corruption more likely to happen in an insidious environment; from the perspective of the environment, the construction market, which is dominated by a buyer’s market, encourages the prevalence of unfair competition that could lead to potential corruption.

The complexity of the problem of corruption in engineering projects also suggests that it must be the result of multiple factors acting systematically. The search for the causes of engineering corruption, therefore, remains an important area to be explored in project management.

Due to the significant consequences of corruption, there is an urgent need for an in-depth examination of ongoing fraudulent activities. Scholars and practitioners have put considerable effort into studying corruption and have attempted to improve the chances of success of anti-corruption measures. Existing studies have explored various corruption scenarios from political, legal, sociological, and economic perspectives to shed light on corrupt practices’ complexity, secrecy, and diversity. These studies identify the causes of corruption, contextual risk signals, anti-corruption measures (ACMs), the effectiveness of ACMs, and obstacles to effectively implementing ACMs in specific areas, such as in political and private environment or in the contract, implementation and management of infrastructure projects (Kenny, 2012; Locatelli et al., 2017; Owusu et al., 2020; Sundström, 2015).

However, these studies still have some limitations. First, they have mainly focused on corrupt types and coping strategies rather than studying the behavioural characteristics of corrupt subjects (Islam and Lee, 2016). Moreover, previous studies rarely analyse corruption cases from a quantitative perspective. Lastly, relevant studies available have been mainly conducted within the context of western countries, most of which have more extended democratic regimes and more transparent mechanisms, so corruption is somewhat less severe or widespread than that in socialist countries (Ivlevs and Hinks, 2015). Therefore, the results of these studies are not necessarily applicable to China.

Also, Johanna, a leading anti-corruption expert, argues that most researchers advocate a holistic reform programme to tackle corruption on the grounds that, because of its complexity, fighting corruption is like destroying Gordian knots; fragmented solutions do not seem to work. Nevertheless, even if we believe such a holistic programme would emerge, it would not clearly point the way to reform because we lack the required conditions of one kind or another, and therefore corruption continues. In order to find a break in this fog, results from empirical research are necessary (Johann Lambsdorff, 2008).

The rest of the dissertation is arranged as follows: Section 2 presents the literature review and the research aim; section 3 introduces the research methodology and the data analysis; section 4 discusses the findings, and section 5 summarises the conclusion and limitations.

The first two parts of this literature review introduce the concepts and causes of corruption in general. Afterward, it narrows down the scope to the engineering industry and focuses on factors at the individual level. Finally, the gap of knowledge is identified and research objectives raised.

With the acceleration of human processes, corruption has become an inevitable social problem. In fact, corruption is not only an academic topic that has been researched for many years; it is also a destructive socio-economic problem that has affected societies for many years and hindered socio-economic development. It is a global disease that exists across different social systems and countries with different cultures, economic conditions and levels of social development (Bowen et al., 2015; Kenny, C. 2009; Li et al., 2022). Therefore, study about corruptions are needed to elaborate about this global problems.

The word “corruption” comes from the Latin word “corruptus,” meaning “broken” or “damaged” (Hogdson and Jiang, 2007). It is a phenomenon that is easy to notice but difficult to define. Scholars have studied corruption for a long time but have not developed a universally accepted definition (GIACC and Transparency International, 2008; Jiang, 2017). Researchers such as Goldsmith and Lambsdorff explain corruption as the overuse of power for personal earnings, including bribery and theft. Serra and Wantchekon (2012) agree with such a diverse manifestation by taking bribery, clientelism, nepotism, and more as corruption forms. GIACC expands the form of corruption to include cartels, embezzlement, extortion, fraud, and money laundering (2008). The word “power” in the previous definition refers to a broad category that is not limited to the government but also includes individuals in the private sectors (Goldsmith, 1999; Lambsdorff, 2005).

In the same vein, Transparency International (TI) used to define corruption as the exploitation of entrusted power by public servants in political or administrative matters to improperly or illegally benefit themselves or those close to them. “Exploitation” is defined as a departure from the normal obligations of a public actor or, more commonly, as the pursuit of interests at the arbitrary cost of the wider public interest. Unlike the previous definition, Transparency International (TI) does not emphasise the different types of corruption but rather the source of power, which in this case is granted by the public and therefore has a strong administrative dimension (2022).

Now, this definition changes to a simpler and more straightforward one, which is also consistent with that of the International Monetary Fund: “Corruption [is] the abuse of entrusted power for private gain” (Tanzi 1998). It suggests two layers: one layer is the abuse of public power for personal interest; the other is the mishandling of public power for the profits of the individual’s party, group, circle, and syndicate. This definition covers a broader scope, but it does not specify the subject of corruption.

Figure 3: Traditional Definition and Connotation of Corruption

The basic definitions connect corruption to the abuse of power, disclose how corruption occurs and offer strong explanatory power for reality. The use of corruption definitions to explain the mechanisms by which corruption happens, however, still has the following limitations. Firstly, these definitions place emphasis on power and ignore the impact of other factors on corruption. Second, the definitions do not thoroughly explain the specific mechanisms that contribute to corruptive behaviours. Both “power” and “private interest” exist objectively and are necessary circumstances for corruption to occur. It is the “abuse” of power that determines whether or not an act of corruption occurs. The essence of exploring the mechanisms by which corruption occurs is to identify through whom and by what means “power” is used for “private gain.”

Contrary to the various definitions of corruption, scholars of corruption in engineering projects rarely define it directly. In the literature, only the South African scholar Shakantu (2006) has explicitly defined project corruption, arguing that corruption in the construction industry refers to the manipulation and control of projects by contractors, engineers and owners through the exploitation of loopholes in construction rules. Meanwhile, he believes that the conditions of the project location determine the form and extent of corruption. To examine the relationship between productivity and corruption, De Rosa et al. (2015) narrowly defines corruption as “the occurrence of informal payments to government officials” to facilitate the daily running of enterprises. Despite these efforts, researchers agree that corruption in project management is an area less explored (Locatelli et al., 2017; Rizk et al., 2018; Shan et al., 2019).

This dissertation argues that it is necessary to define corruption in engineering projects at the beginning of the study in order to set the boundaries and lay the foundation for subsequent research. Based on the explanation of the definitions of corruption in the previous section, this study believes that the definition of corruption in engineering projects should include at least two aspects: on the one hand, acts of corrupt practices related to “power,” that is, improper behaviours of various parties involved in engineering projects abusing their power at different stages of the project construction for personal gains, such as inappropriate behaviours related to the use of power by leaders in charge of the project to manipulate bidding; on the other hand, corrupt practices related to “rights,” that is, all kinds of illegal and irregular practices of the participants in the project that cause damage to the public interest, such as contractors paying bribes and then reducing materials in the construction process to make up for their losses. These two aspects intersect but do not completely overlap.

Theories have been trying to explain corruption and why it happens. In this subsection, constructivism is chosen to discuss the root of corruption. Constructivism explained corruption as the impact of social factums and not a natural phenomenon.  Social constructivism can be divided into two types. First, corruption can be seen within the communication of social systems. In this approach, it is assumed that corruption can be inferred as any discrepancies in the codes and conducts from the government regulations related to the social structures. In other words, it can be said that neglecting certain procedures is similar to abusing the daily operations (Hiller, 2010 as cited in Tänzler, et al., 2016). As the impact, there is a disruption in a mode of communications within the organizations.

From this point of view, corruption is the condition when the system is diverted by the motivation to fulfil the inherent functional objectives (Tänzler, et al., 2016). This approach can be more rationale when explaining corruption cases in recent years. In a political situation, corruptions mean making any decision that does not depends on the system within an organization but depends on the economic calculation and is mostly related to individual obligation; in general, corruptions refer to the interruption in the functional logic of the systems due to objectives reasons. The definition of corruption in this approach is merely related to the social dimensions. It lacks individual dimensions that can explain why someone decides to do corruption. Therefore, the second approach can best complement social system theory in explaining the corruptions.

Second, corruption results from the interpretation of the social world by a person or individual (Tänzler, et al., 2016). Individuals interpret that corruption actions are related to their extrinsic motivations, not discrepancies in functional system logic. The corruptions are about personal interest for self-preservation. In addition, these theories explain about three main motivations that underline corruption. First, an actor is committed to any kind of corruption because he follows the daily routines of behaviour. Second, the actor is searching for the resources that are not provided by the organizations and decides to get the resources from other sources. Third, actors feel that there is a violation of the values of the intuitional roles.

  • External Causes of Corruption

There are some reasons why the construction industry is vulnerable to corruption. A huge investment can be such a big opportunity to have significant losses in this industry (Matthews, 2016). The multidimensional aspects of corruption in construction projects increase the corruption rate. Global Infrastructure of Anti-corruption Centre mentioned that each construction project should be unique and customized. Therefore, it will be hard to estimate the cost of the construction. Further, when corruption occurs, the involved parties would have difficulties in comparing the budget and cost, so the inflations due to corruption could be unnoticed (Matthew, 2016).

Khadim et al. (2021) believe that a comprehensive understanding of causes is necessary to eradicate corruption. Zhu (2021) mentioned many reasons to conduct corruption. It includes internal and external factors. At the organizational level, external causes of corruption would become the priority to discuss. It proves that multi-factors cause corruption in constructions project. There is no single cause of corruption, especially those that are related to contruction projects.

In broad terms, Tanzi (1998) analyses the causes of corrupt practices from two categories: the demand side (the public) and the supply side (public servants) (see table 2). From one side, the demands are related to the environment, nature of the job, and organisational culture caused the corruption. Those factors would improve the possibility of corruption. Meanwhile, the supply sides explain the factors that make the opportunities to conduct corruption directly. Like Tanzi (1998), Zhu (2021) stated that supervisory power is the critical aspect of corruption’s spreading. Tanzi (1998) explained that the power of institutions to control and supervise the work of their worker would cause the occurrence of corruption. Inadequate supervision mechanisms and power will give a chance for corruption to exist. For instance, Hao (1999 as cited in Zhu, 2021) explained that corruption could happen when the government failed in forming the legal mechanism and systematic way to prevent and punish the actor of corruption. The low level of ethical standard in conducting the business would be the mediator variable in explaining the cause of corruption.

Table 2: Summary of Corruption Causes

 Demand SideSupply Side
1the monopoly power from regulations and authorizationsprestige of bureaucracy
2characteristics of the tax systemincome level of public officials
3public expenditure decisionsthe punishment system
4purchases of goods and services at prices lower than market priceinstitutional supervisions and controls
5 transparency of legal systems
6 the influence of leaders

In the same manner, Pan and Tian (2017) highlight that the prestige of the government contributes to a higher rate of corruption in China. The government controls and allocates essential economic resources. Therefore, the incentive to obtain resources propels companies to bribe government officials so as to build a close relationship with the government. Another evidence is the experience from Chinese developers who have indicated that, without bribes, it is impossible to secure quality land (Zhang, Zhou, et al., 2015), especially in the case that land and state property are privatized in China (Li and Vendryes, 2018). In this sense, sometimes the payers of corruption are considered as “victims” (Justesen and Bjørnskov, 2014). Nevertheless, for the purpose of this dissertation, the distinction between victims and beneficiaries of corrupt practices is not very meaningful, as they both violate the rules and correspond with the definition of corruption.

Dong and Torgler (2013) have systematically explored the causes of Chinese corruption through provincial panel data, which is summarised in Table 2.2.1. The degree of openness refers to the share of imports in GDP; the relative income of government employees is compared with the average wage in the region; and fiscal decentralisation is the ratio of consolidated provincial expenditure per capita to consolidated central expenditure per capita. In terms of decentralisation, this finding confirms the impact of government size and decentralisation on corruption, in line with the results from Goel and Nelson (2010) and Fisman and the investigation of Gatti (2002a), albeit the latter using American data. As for the education level, Uslaner and Rothstein agree that (2016) higher education tends to lead to lower corruption in both the short-term and long-term. Unlike the above studies, Svensson (2003) and Cai et al. (2011) leverage data from firms instead of the state to identify the “micro” causes of corruptive behaviours in both China and Uganda.

Table 3: Summary of Frequently Cited Corruption Causes in China

1anti-corruption efforts
2level of education
3historical impacts left by Anglo-American church universities
4Openness
5exposure to press
6relative salaries of public servants
7body of women representatives in the legislature
8fiscal decentralisation

Neither of them discusses the influence of cultural or institutional features of a society, which is quite debatable. DeRosa et al. (2015) believe that corruption can be endemic in nature, making it difficult for general policy measures to stop it.

In summary, research on the causes of corruption is quite abundant, presenting a clear and rather comprehensive list of the causes. The story is slightly different in engineering project corruption.

A number of studies on corruption in the field of engineering and construction have been carried out by academics worldwide, each focusing on different perspectives, including forms, motivations, evidence from different countries, evidence from different stages, and anti-corruption strategies. Based on the forms of corrupt practices in the project, corruption can be divided into two categories if the parties involved are different. A “petty corruption” only contains abuse of delegated powers by middle and lower-level administrators and ordinary citizens. In contrast, a “grand corruption” engages individuals in the provincial government and related departments such as the courts (Transparency International, 2011).

Instead of focusing on individuals, DeRosa et al. (2015) define three forms of corruption on the basis of how it manifests. Bribery is the most typical type. Officials ask for informal payments or gifts to complete an official task (such as issuing a permit or awarding a contract) or circumvent regulations. In line with these statements, Herrera and Rodriguez (2003) found in her study that bribery is a very common type of corruptions, and the rates in governments office are linked to the strength of supervisors’ power over their subordinates. Secondly, state capture meets the definition of corruption when functionaries lobby for preferential treatment for certain private interests, entailing monetary bribes, kickbacks, or other benefits. Finally, a broad definition of corruption may include political patronage, cronyism, and nepotism. On top of these types, Le et al. (2014a) add that extortion or blackmailing the supply side using physical or financial threats is also considered corruption. They highlight that forms vary, be it bid rigging, unfair conduct, fraud or embezzlement. A more comprehensive list of twelve forms is summarised by Shan et al. (2019), including collusion, negligence, dishonesty and unfair conduct, conflict of interest, and front companies.

Some studies tried to examine the motivation in conducting the corruptions at the Micro-, Meso-, and Macro levels. From the limited research on determinants at the individual level, moral decadence stands out to be the major incentive to corrupt (Ahmed Kabiru, 2019). Greed to advance oneself and the desire for common power also appear frequently in studies, such as Abah and Nwoba (2016). From the macro-level, Akpa (2018) argues that if the society is more money and power-oriented, then citizens within that society are more prone to corruption. Dimant and Tosato (2017) hold a holistic view that the interactions of factors at different levels cause corruption.

Many countries have different evidence related to corruption. Bowen et al. (2015) investigate personal experiences and perceptions of corruption in the construction industry in South Africa and find the leading cause is a deficit of transparency in granting contracts. Through a survey, Rizk et al. (2018) understand that the Lebanese accept basic corruption, thus examining the reasons behind their acceptance in the construction industry. They conclude by proposing a lean-based framework that can reduce unethical practices and acknowledge that eliminating corrupt actions has a long way to go. For instance, there is no absolution agreement on how a country would have different rates corruptions, especially in engineering projects. United Nations Office on Drugs and Crime (UNODC) suspected that many country level factors contributed to the corruption rates. The economic conditions, country size, the nature of bureaucracy, and also the culture of government institutions.

Existing corruption studies identify specific phases of the project that are more likely to incur corruption, and some scholars elaborate on the evidence of corruption from different project phases. For instance, according to Sohail and Cavill’s (2008) study in Pakistan, common corrupt practices are associated with, among other processes, excavation with illicit equipment, illegal land purchase, disposal of unauthorised materials, prohibited connection to utilities and unlawful storage of construction materials. Researchers find that corruptive behaviours occur the most during the bid and tender phases (Bowen et al., 2015). One of the major forms of corruption identified in Iran includes procedural violations in awarding contracts. Other corrupt practices range from irrational decision-making to disregarding project management procedures (Hosseini et al., 2019).

Once researchers became aware of the dangers of engineering corruption, they began to examine countermeasures. Many studies relating to anti-corruption measures have emerged, and their conclusion can be divided into proactive and reactive. Bowen et al. (2015) argue that corruption is rarely conspicuous information to the regulation departments. There is a lack of trust in the legal system, a fear that no detection or punishment will be undertaken, and a view that ‘whistle-blowers’ will not be appropriately protected. All of these are barriers to reporting. The construction industry and government authorities should take a more proactive approach—ACMs strategically designed to prevent potential corruption in public and commercial industries—to corruption and work closely to discover and report it. Existing studies consider transparency mechanisms an essential method of deterring corrupt behaviours (Le et al., 2014a; Zhang et al., 2016). According to Kenny (2012), exposing contract and implementation details regularly is a frequent strategy for increasing project transparency. In addition to ethical guidelines and transparency mechanisms, Owusu et al. (2018) point out the importance of training and development initiatives, listing thirteen nine specific ACMs through an analysis of selected publications.

Indeed, different institutions make it a priority to design a series of proactive measures. Internationally, the Global Infrastructure Anti-Corruption Centre and Transparency International have developed the Project Anti-Corruption System (PACS), which proposes a variety of anti-corruption measures regarding different projects, phases, key players and contract types. The International Federation of Consulting Engineers (FIDIC) and researchers have developed, among others, project disclosure rules, anti-corruption standards and ethical guidelines for engineers (FIDIC, 2011; GIACC and Transparency International, 2008; Owusu et al., 2018).

Beyond proactive measures, some researchers have also actively analysed the role of reactive measures, developed to impose necessary penalties on perpetrators as well as deliver justice, such as fear of punishment (Tabish and Jha, 2012). In studying a more specific sector—transport construction—Kenny (2009) argues that, in addition to monitoring construction, simple, transparent and robust enforcement and quality requirement are important tools in combating corruption in construction. This conclusion is consistent with Sohail and Cavill (2008) and Saenz, C., and Brown, H. (2018), suggesting that strict construction accountability should be enforced to reduce corruption.

Although research varies, the available surveys and studies, overall, seem to agree that engineering corruption has the following common features in recent years: 1) corrupt practices are complex and take many forms, such as tender rigging and collusive pricing, and not all corruption is the result of bribery; 2) corruption is highly concealed; 3) corruption cases appear in the whole construction process, entailing many stakeholders and every phase, as shown in the following table and 4) construction projects have a large scale of income (Bowen et al., 2015; Nordin et al., 2013; Tabish and Jha, 2012; Yap et al., 2019).

The prerequisite for measuring the consequences of a variety of factors is to identify the factors of engineering projects. The following section both lists and analyses the identification of the factors of project corruption in some existing studies. The first factor is age. Probably the most examined predictor in bribery is age. Even though researchers mention the difficulty of inconsistent age categories across different studies, the general pattern is that individuals at a young age are more likely to bribe officials in the public sector (Ivlevs and Hinks, 2015). On a similar note, the findings of Hernandez and McGee’s (2013) study suggest that as people get older, they become increasingly hostile to corruptive acts. Both of those contradictory results can be explained by logic. The young ages who are more likely to engage in bribery can be explained by their immature thoughts, so they make a shortcut to gain their interest by bribing the officials. Meanwhile, other results said that older people would likely have aggressivity to conduct corruption. Economic factors can explain it as the external motivations. Both of these findings are related to the social system approach and individual motivations.

The second factor is education. Mocan (2008) indicates that people with higher education levels are more likely to be asked to bribe because they are more likely to get in touch with government officials. As constructivism explains, personal interest would trigger corruption. The more educated people can have a better personal interest in government than the less educated people dol. Similarly, Dong and Torgler’s (2013) survey suggests that education positively affects corruption in China. They argue, for one thing, that less-educated workers in the early decades had more political power than people with better education (such as professors or teachers). Educated Chinese were unable to monitor government officials. As a result, it is unlikely that high levels of education were linked to a low rate of corruption in China’s provinces during the early period.

Third, gender is considered another factor of corruption. The common belief is that gender has a negative association with corruption, indicating that the more men in government positions, the higher the levels of corruption (Debski et al., 2018; Dollar et al., 2001; Mocan, 2008; Swamy et al., 2001; Torgler and Valey, 2010). However, Ivlevs and Hinks (2015) did not find evidence between a lower corruption possibility and a higher representation of females. Indeed, using data from 177 countries, Debski et al. (2018) prove that no direct association exists between female participation in politics and corruption. Even thoughwasre were some related research about gender and corruption, but it lacked of information why the differences between men and women could affected the corruption incidents rates (Rivas, 2008). There should be an explanation of what kind of inherent characteristics would differ between men and women in committing corruption.

Some corruption is related to the income factor. The constructivism theory obviously states economic factors. Some previous studies also examined the relationship between income factors and corruption. Opinions vary regarding the effects of income on curbing corrupt acts. Some researchers find that higher salaries, though not a sufficient condition for tackling corruption, are necessary for reducing it (Kaufmann and Vicente, 2011). Apergis et al., 2009 used panel data to empirically corroborate a negative relationship between income levels and corruptive exchanges. Therefore, to reduce — and ideally eradicate — corruption, it is necessary to improve the remuneration and basic benefits of government employees and implement a policy of using high salaries to manage integrity (Rose-Ackerman and Palifka, 2016). This study echoes Dong and Torgler’s (2013) finding: when officials are removed due to corruption, higher relative salary imply higher opportunity costs. However, a large body of literature also indicates that wealthier people are more prepared to pay bribes and consider them justifiable (Gatti et al., 2003; Hunt and Laszlo, 2012; Mocan, 2008). One possible reason for the difference could be the distinction between the demand and supply sides. This paper only examines the demand side of corrupt transactions to assess the determinants of individuals who actively require or passively receive bribes.

The location or region where bribery also happens matters. It is generally believed that corruption may be more prevalent in larger cities because economic activity is more extensive and more diverse, resulting in increased engagement with governments and firms. To understand the influence of the location on corruption, Mocan (2008) takes the city as a variable, defining small and middle-size cities with a population of 50,000 less and 50,000 to 1 million, respectively. In addition, in larger cities, people’s relationship with government officials is less intimate than in smaller regions, making them more likely to ask for or pay a bribe (Hunt 2004). However, there should be more explanation about the impact of region or city on corruption rates.

In addition, corrupt political leadership that continually circumvents the rule of law is associated with mishandling of public power and illegal compromises of trust (Akech, 2011; Ayodele et al., 2011; Bowen et al., 2015). From an organisational standpoint, corruption is institutionalised when senior leaders choose to neglect, tolerate or promote corruption (Tabish and Jha 2012). Executives’ personal relationships, position in political parties and championship for political activities as crucial variables shedding light on unethical behaviours. They found that managers’ cognition and social relationships substantially impact their decision to overlook the illegitimacy of corruptive behaviours and are reluctant to recognise the harmful effects of corrupt behaviour on society. Moreover, it is important to emphasise that culture within an organisation influences employees through a top-down process with employees tending to imitate the decision-making characteristics of their leaders, contributing to the spread of harmful organisational and societal norms (Gorsira et al., 2018). Therefore, it can be assumed that the higher the official position of the corrupt person and the more power he or she has, the greater the likelihood of corruption.

From the literature review, a list of key drivers describing those involved in a severer corruption is summarised in Table 4, which provides a clear clue to this empirical analysis. Determinants such as fiscal decentralisation are eliminated because they are not personal traits and do not contribute to this study.

Table 4: Identified Factors of Project Corruption from Literature Review

Key driversImpact on CorruptionSource
AgenegativeHernandez and McGee, 2013; Ivlevs and Hinks, 2015
EducationcontroversialApergis et al., 2009; Dong and Torgler, 2013; H. Akbar and Vujić, 2014; Mocan, 2008; Uslaner and Rothstein, 2016
GendernegativeHernandez and McGee, 2013
IncomenegativeApergis et al., 2009; Dong and Torgler, 2013; Kaufmann and Vicente, 2011; Rose-Ackerman and Palifka, 2016
RegionnegativeHunt 2004; Mocan 2008
RankpositiveAbah and Nwoba, 2016; Holliday, 2017; Tabish and Jha, 2012

In general, some progress has been made in corruption, especially in the construction area, focusing on identifying the leading causes and proposing various measures to combat it. However, there are gaps in the project management to be further explored.

2.5.1 A Lack of Research at Micro-level Focusing on Engineering Industry

There is limited research on individuals who commit crimes in project management, which contrasts with society’s high level of interest. Ivlevs and Hinks (2015) identified determinants like age, income and the degree of urbanisation, but the relationship is between determinants and economic crisis instead of the relationship with engineering corruption. Similarly, Uslaner and Rothstein (2016) discovered the impact of education level on corruption in general in seventy-eight countries, and the engineering project sector is not separated in the study. Even though scholars like H. Akbar and Vujić (2014) researched individual determinants related to the tender stage, the study used Russian data, which leads to the second gap.

2.5.2 A Lack of Research Targeting Engineering Corruption in China

China occupies a large market share in the engineering industry. The gargantuan market makes it especially vulnerable to bribery and the severer consequences. Major research into corruption in China is abundant but scarce neither in the engineering sector nor at the micro-level. De Rota et al. (2015) believe that corruption can be endemic in nature, tied to the deep-rooted culture of a society. For example, Troilo and Sun (2010) mention that grey zones in the transition between planned and market economies were exploited in China. Owusu et al. (2019) corroborate this claim by arguing that differences in occurrence and outcomes exist among different cultures.

Many European countries developed stricter anti-corruption approaches and relevant implementation tactics to address the identified causal determinants. As an illustration, the first United Kingdom Anti-Corruption Strategy 2017-2022 was enacted in December 2017 to offer strategies for government to better implement anti-corruption policies and actions. Other areas, such as some countries in Asia and Africa, still struggle to implement rigid anti-corruption measures to address these causes (Hunt and Laszlo, 2012). On the contrary, China’s economy can benefit from corruption (Chan et al., 2019; Dong and Torgler, 2013). Therefore, to ensure measures are effective in eradicating corruption, an analysis of the specific country is necessary. Therefore, in order to quantitatively understand the relationship between the characteristics of responsible subjects and the severity of corruptive practices, this study examines corruption cases officially released in China at a micro-level.

2.5.3 A Lack of Quantitative Analysis of Factors with Empirical Studies

The current research on the factors influencing corruption in engineering projects is mainly qualitative in its approach, with the analysis being mainly based on theoretical derivations from general corruption theories or direct analysis from typical cases of corruption in engineering projects (Damoah et al., 2018; Dimant and Tosato, 2017). Take the study from Abah and Nwoba (2016), for instance: they mainly used descriptive analysis through library research. Similar studies on the factors influencing corruption in engineering projects have been conducted on subjective means, such as interviews or questionnaires with stakeholders, to identify the motives behind corruption and their subsequent improvement based on these motivations (Ackerman and Palifka, 2016; Tabish and Jha, 2012). However, a correlation among the factors has not been widely studied. If anti-corruption agencies do not distinguish between primary and secondary factors but treat all factors equally, they can hardly achieve desired effects with anti-corruption measures. In other words, when they put concerted efforts into designing measures and mechanisms to target the dominant factors, they optimise the effectiveness of anti-corruption measures.

Despite abundant anti-corruption measures and strategies that have been put in place around the world, the prevention of corruption in construction projects remains bleak due to shortcomings in the implementation (Damoah et al., 2018). One of the obstacles is that some corruption prevention measures place too much emphasis on legal compliance. They lack monitoring and prevention of the behaviours of the responsible parties. Another reason for the ineffectiveness of the measures is that they are based on purely theoretical induction —that is, the “principal-agent theory”—without taking into account empirical analysis (Rothstein, 2018). Hence, they are superficial and abstract regulations that do not regulate the flexible people in the real world. The construction industry is not fighting corruption per se as the industry is fighting corruption for its own sake. Therefore, the industry needs to establish specific anti-corruption measures, which require a precise analysis of the determinants that breed corruption at the individual level and external and macro causes.

Moreover, as anti-corruption efforts can be costly, blindly addressing any causes researchers have found may waste funding. To these ends, a quantitative analysis of the factors influencing corruption in engineering projects is particularly important. The purpose of quantitative analysis is twofold: first, to find the correlation among factors and to identify the dominant factors; secondly, to identify the degree of each factor on the consequences of corruption and to guide the development of subsequent measures and mechanisms.

To these ends, this paper aims to analyse the individual characteristics and behavioural patterns of engineering corruption within the context of China through an empirical study of 80 cases.

Following the aim, three specific objectives are listed below:

Objective 1:  To determine the distributions of corruption in China’s construction project.

Objective 2: To examine the distribution of corruption cases in China based on individual factors, including age, region, education, rank, and length.

Objective 3: To investigate the current trends of corruption cases in the Engineering Field in China.

Objective 4: To evaluate the impact of individual factors (age, region, education, rank, and length) on the corruption amount.

This chapter illustrates how the study follows positivism and uses objective methods to analyse data. First, it reveals the rationales for the chosen method. Then, it demonstrates steps by step how data is collected. Finally, ethical issues are addressed.

Following positivism and deductive methods, both qualitative and quantitative analysis are employed for this dissertation. The analysis first summarises relevant corruptive subjects’ variables based on the current literature findings. Then, through preliminary statistical observations of the secondary data and by considering the availability of empirical data, the main variables of the final econometric model are identified.

This mixed-approach case analysis proves appropriate for this dissertation for the following reasons. This approach can lead to generating an objective analysis. Researchers often use perception indexes and questionnaires when studying anti-corruption measures and perceptions of corrupt practices (Jetter and Parmeter, 2018; Treisman, 2000). However based on personal experience, which is subject to inaccurate memory and under-reporting, such a perception index inevitably carries subjective information and assesses the extent of the corruption, which fails to reveal a reliable fact (Burguet et al.,2016; Lambsdorff, 1999; Treisman, 2000). For example, in a study of village road construction in Indonesia, Olken (2009) found a correlation between villagers’ subjective perceptions of corruptive behaviours and the severity of unethical practices estimated from the final review of expenditures, but this correlation was not very strong. These facts suggest that the subjective perception index is not a good tool for measuring the true extent of corruption and that it is often systematically biased.

Surveys or questionnaires allow for direct interviews with victims of corruption about the extent and details of the corruption they have faced. Some studies have been conducted using such data. For example, Davis’ (2004) used survey data to conclude that the bribes paid to facilitate the approval process for water pipeline construction in India were spread among consumers. The additional fee was £2 per consumer. In existing studies, surveys have mostly been conducted on businesses or individuals. The World Bank, for instance, has asked businesses in nearly twenty surveys how much of a “gift” is required to obtain water, electricity and communications services in the infrastructure sector.

Although survey-based data are more accurate than subjective perception-based data, the validity of such data is not very consistent due to limitations in survey instruments and the concealment of information by respondents. For example, Henderson and Kuncoro (2004) note that disparities in designing surveys and instrumentation can make a big difference in data results. Based on their survey of Indonesian firms, they estimate that firms spend 10.5% of their costs on bribes, whereas according to the results of the annual Indonesian small and medium-sized enterprise (SME) survey data, firms spend only 3% of their profits on bribes. It is also highlighted that, to avoid being caught in their corrupt practices, questionnaire respondents either do not mention their corruption or choose to lie about it (Gutmann et al., 2015). Although Hunt (2007) argues that the proportion of honestly reported corrupt practices tends to be higher in high-corruption countries because it is perceived as an unavoidable procedure, this fact actually reveals that self-reported corruption outcomes are still biased. Finally, this accuracy bias is amplified by the way questions are asked. Considering these facts, surveys and questionnaires are not used in this study.

This approach is the best approach with the available data. Since the primary purpose of this dissertation is to study individuals who commit corruptive practices and to understand individual characteristics, it is necessary to find data that record details of corruption. As indicated in Section 2, corruption is not directly observable because engineering corruption, or all kinds of it, often occurs in the shadows. Due to the nature of the unethical practice, detecting every instance of corruption and determining its full impact is challenging. Therefore, the obstacle in conducting empirical research on corruption is a lack of full-range, transparent and timely data in a particular geographical area or industry sector.

In similar research, Bakerr and Faulkner (1993) used court documents and publicly available evidence to find data related to illegal networks. Xu and Chen (2008) highlighted that the structural characteristics of the hidden network could be analysed through typical corruption cases and thus identify effective strategies to dismantle it. In the same vein, this study decides to address this problem by analysing the cases that have been identified and convicted. Convictions are preferred over illegal cases. This is because unlawful acts are not necessarily crimes or cases that have been investigated and prosecuted. Cases that have been examined and often penalised already give reasons for the corruption and list the type of corruption, which may serve as a foundation for future research on corruption prevention.

A mixed approach allows researchers to compare the results. This study aims to capture trends and features of corrupt people, which requires comparing people in different cases. Only data that contains similar basic information are ready to be compared and analysed. This study used only data from officially released records, such as samples from reliable books, websites such as China Judgements Online, Legal Daily, Chinese National Bureau of Corruption Prevention (NBCP) and more.

Of course, conviction data has its own limitations. Since the number of corruption cases depends largely on the detection ability, it may not provide a full spectrum of the project corruption reality, be completely random or comparable across countries. However, the first and third weaknesses are not relevant for this study because this dissertation does not attempt to estimate the excellency of judicial systems all over the world. The second weakness can be mitigated by using as many cases as possible. Recent years have witnessed a large number of typical cases being published due to China’s intensive efforts to tackle engineering corruption, providing an unprecedented opportunity for quantitative research in this area. The following table summarizes the common international methods for measuring corruption and identifies the advantages and drawbacks of each method.

Table 5: Advantages and Disadvantages of Different Methods

Perception Index
based on experts’ subjective assessment of the level of corruptiondata compiled from different sourcesexample: Corruption Control Index from the World Bank Institute; Corruption Perceptions Index from the Transparency InternationalAdvantage: cover many countries, time-series databased on the reputation and quality of the index publisher, relatively reasonable and stablemeasure only the subjective perception of the decision-maker, relatively simpleinfluence many institutional decisions (e.g., investment decisions, aid programs, etc.)
Disadvantage: subjective perceptions may deviate significantly from objective realityexperts’ subjective perceptions may be highly correlated with macro governance conditionsindicate micro situation, not sector or industry-specific data
Survey
based on interviews and investigations of people who have been involved in corrupt transactionsexample: the World Bank Institute’s Business Environment and Enterprise Performance Survey; Bangalore Citizen Report CardsAdvantage: based on personal experience of corruption, improved accuracyaccess to detailed evidence, such as corrupt transactions in different industries, sectors and types
Disadvantage: subject to anonymity, concealment, and level of personal awarenessnot as broad as the subjective index measure the amount of bribery rather than the impact of corruption
Indirect Data and Performance Result Index
include objective data on capital flow, sector or industry performance resultsexample: public expenditure tracking surveys; audit reports; performance evaluationAdvantage: relatively easy access to datadirectly evaluate outcomes rather than process
Disadvantage: examine the impact of overall governance performance rather than the impact of corruptioncostly and specialised
Judicial Inspection Report
based on convicted corruption casesexample: China’s annual Supreme People’s Procuratorate ReportAdvantage: based on actual corruption cases that occurred, highly accurateimportant details are available
Disadvantage: not suitable for cross-country comparisons

The methodology is designed to achieve the two objectives previously presented in Section 1. Objective 1 is partially addressed by the literature review in Section 2. This section investigates it further based Chinese project corruption cases. Objective 2 are answered in detail in Section 3 and Section 4.

This study takes the engineering corruption cases released in the past five years as samples. The primary bodies that are responsible for scrutinizing corruption are the Supreme People’s Procuratorate, the National Supervision Commission and the Ministry of Supervision bureaus. Therefore, the sample data are from the following reliable sources:

  • Analysis of Typical Cases in Engineering Construction Field and Corruption Prevention Guidelines compiled by the Office of the National Supervision Commission;
    • Cutting Off the Black Hand in Engineering Field — 50 Typical Cases of Leading Cadres Interfering in Engineering Construction Field published by the Office of the Ministry of Supervision bureaus in Engineering Construction Field
    • Judgements from the Supreme People’s Procuratorate and various local level people’s procuratorate
    • Press release

After verification, twenty-two cases do not meet the article’s research focus, and five cases involve a tremendous amount of corruption, distorting relevant calculations. Eighty typical cases of engineering corruption are finally kept. The specific description of each source type is shown in the following table.

Table 6: Sources of Cases

TypeSourcesNumber
caseAnalysis of Typical Cases in Engineering Construction Field and Corruption Prevention Guidelines18
caseCutting Off the Black Hand in Engineering Field — 50 Typical Cases of Leading Cadres Interfering in Engineering Construction Field50
judgementPkulaw database, China Judgements Online142
investigative journalismInternet, Legal Daily, Communist Party’s Central Commission for Discipline Inspection (CCDI)/
Total numbers80

Judgments: 122 judgments were identified through searching keywords — “bribery,” “construction,” “corruption,” “embezzlement,” “malfeasance”, and “project management” in the Pkulaw database China Judgments Online.

Investigative journalism: Based on the cases and information collected from the above three ways, complementary materials are further searched through the internet, including certain individuals’ education background and positions.

3.3.1 Date Sample

The study examines the collected corruption cases in engineering projects from a qualitative and quantitative standpoint. The analysis focuses on identifying forms and individual determinants of corruption in engineering projects from a qualitative perspective. The study used a quantitative approach to investigate the causes of corruption in engineering projects, the impact of case characteristics on corruption outcomes, and the underlying relationships implied by the phenomenon. The sample must meet the following characteristics to achieve the stated goals:

Firstly, the statistical requirements of the sample. A prerequisite for qualitative and quantitative analysis is that the number of cases selected reaches a specific size or meets the statistical requirements. The 80 cases selected for analysis in this study satisfy the size requirement. All conclusions are based on the analysis of these 80 cases.

Secondly, the study of the characteristics of corrupt transactions in engineering projects is the starting point, rather than considering only the basic information. A basic crime case usually includes several aspects such as subject, object, and legal liability, but in fact, the corruption cases of engineering projects contain more than those mentioned above. With this in mind, this study analyses the basic legal features of corruption in engineering projects. More importantly, based on the characteristics summarised in the literature review in Chapter 2, it analyses the regular features of corruption in engineering projects at the individual level. Conducting microscopic research achieves the objective of objectively analysing the project corruption problem and provides an in-depth analysis of the reasons affecting the occurrence of corruption. 

3.3.2 Data Standardisation

Based on these factors and the purpose of the study, this phase standardised the collected cases to facilitate subsequent analysis. Each case is considered a system of variables, including the basic case information and the basic profile of the perpetrator of the corruption. The following table shows specific variables extracted. Finally, all the results are interpreted qualitatively to identify the main characteristics of corruptive subjects in engineering projects.

Table 7: Standardisation of Corruptive Cases

Basic Case InformationProfile
NameTimeLengthChargeReasonAmountAgePositionRankEducationRegion

No female suspects were seen in the engineering corruption cases in this research, so gender is also removed from the list. Due to realistic or traditional reasons, the political power in China is dominated by males, and therefore leadership positions and resources are concentrated among males. Men’s advantages in decision-making and implementation of public affairs give them more opportunities to commit corruption in office. In addition, men’s social activities are more frequent than those of women. According to Chinese social customs and daily observations, men can usually escape the burden of family affairs and become more involved in social interactions, frequent drinking and networking. For these reasons, males are more likely to be surrounded by situations prone to unethical behaviours, which contrasts with what women face.

The income factor is also ignored because neither judgments nor the public information available on the Internet contains the real income of the perpetrators. China’s government employees are paid according to a single uniform pay scale, which is the same for all civil servants. The government has also provided employees with goods, services and allowances on top of the basic salary. Nearly all government employees received housing at subsidised prices before 2003, which makes it hard to calculate the total income. Even though this study mainly studies corruption cases in the recent five years, and at this period, housing is no longer common good provided, the actual income is still challenging to obtain.

The succeeding part discusses explanations for each variable.

3.3.3 Length

Even though previous studies do not mention this determinant, length is easy to calculate and may indicate a pattern. Therefore, this dissertation takes the length as a factor and analyses its impact on the amount. Most stakeholders who embezzle do not commit the crime only once but offer and demand bribes several times. In this study, the length of corruption measures the time between when a case first occurred and when it was reported.

3.3.4 Amount

Some individuals are involved in multiple crimes, including misappropriation, offering and receiving enormous bribes and owning assets from unexplained sources. The amount represents the total value of property illegally obtained.

3.3.5 Age

This study records the age of corruption offenders at the first offence.

3.3.6 Education

The education system in China has been organised into seven different levels: elementary school education begins at the age of six and lasts for six years. Junior secondary education follows primary school and lasts three years, marking the end of the compulsory education program. After finishing junior secondary school, graduates either attend either academic senior secondary school or vocational senior secondary school based on their ability and interest, known as Zhongzhuan. Bachelor’s degrees, master’s degrees and doctoral degrees belong to higher education. Given that all the data collected does not include PhDs, this category is not specifically discussed. In this study, the education variable is divided into six groups: 1) primary education, 2) junior secondary education, 3) senior secondary school, 4) vocational senior secondary school, 5) undergraduate and 6) graduate.

3.3.7 Rank

According to the Law of the People’s Republic of China on Civil Servants, the grades of civil servants are under the category of general management as in Table 8. The corruption cases only involve more than one participant (Li et al., 2022). For the simplicity, however, this study only considered the case of the lead person, who initiated the corruption behaviour.

Table 8: Ranks of Civil Servants in China

PositionRank
Premier1
Vice Premier, State Councillor2-4
Minister, Governor4-8
Vice minister, Vice Governor6-10
Bureau chief8-13
Deputy bureau chief10-15
Division chief12-18
Deputy division chief14-20
Section chief, responsible section member16-22
Deputy section chief, deputy responsible section member17-24
Section member18-26
Clerical staff19-27

Source: Law of the People’s Republic of China on Civil Servants, Article 19

3.3.8 Region

In China, the higher a city’s administrative level, the more project opportunities and politics are available. As a result, in the Chinese context, research on engineering corruption requires exploration of the economic situation in which the corruption occurs (Li et al., 2015). Despite the fact that the Chinese government does not recognise any of the classification lists of cities, the widely accepted criterion is to employ city tiers, which is developed by the influential financial journal Yicai and regarded as the most reputed in China. The firm looked at 337 Chinese cities, 119 of which made it to four city levels. It is worth noting that all these cities are at least county-level cities administratively. Beijing, Shanghai, Guangzhou and Shenzhen are China’s first-tier cities. The “New Tier 1” is the second tier, which consists of fifteen emerging cities. Tier 2 cities are composed of thirty cities, whereas Tier 3 cities are composed of seventy cities. This study is based on the most recent data for 2021 (Anon, 2020).

This dissertation uses only open secondary data for further analysis. All data is available on the Internet and books freely. Permission for further use in this dissertation is implied, which does not violate any Intellectual Property law. The Data Collection section acknowledges the ownership of the original data. The data file is be kept safe with a password on the author’s computer, which prevents any unauthorised access or damage from accidental loss. At the initial transcription, all identifying information such as names, positions and regions are anonymised into Case ID and specific numbers to protect privacy and confidentiality. A log of all replacements is stored separately from the anonymised data file. The data is used for this research paper only.

This chapter first discusses the features of engineering corruption cases and guilty individuals.

Through the statistical analysis of the corruption cases of engineering projects, this dissertation summarises that project corruption in China has the following general characteristics:

4.1.1 Length

Figure 4 describes the distribution of the length of the crime in the 80 collected cases. It shows that corruption cases mainly span 61 to 180 months, or 5 to 10 years, accounting for more than half of all cases. The cases between 10 to 15 years account for 21%, ranking second. The shortest case lasted less than a year, while the longest lasted up to 25 years. On average, it takes 8.24 years to detect corruptive behaviours. Armed with these figures, it is evident that corruption in engineering projects can go unnoticed for years.

Figure 4:  Distribution of the Length

4.1.2 Charges

The primary offences involved in the cases analysed in this statistical exercise are bribery, embezzlement, misappropriation of public funds, abuse of authority and unexplained source of large amounts of property. The number of cases for each offence is shown in the table below. In terms of charges, the cases counted were mainly focused on the offence of bribery, with 72 cases accounting for 90% of all cases, followed by 15 cases of embezzlement, accounting for 18.75%. It is worth noting that 20 cases, accounting for 25% of the total, involved two or more offences, with the largest number of offences involving all five offences, which also illustrates the complexity of the corrupt practices of corrupt individuals.

Figure 5: Distribution of Charges

Based on the charges involved, bribery offences characterised by trading in power and money account for 94% of the total number of cases, which can basically be regarded as the predominant form of corruption acts in engineering. The reasons are most tied to the features of China’s construction market. With the increasingly fierce competition in the engineering construction market, enterprises need not only advanced technical equipment, excellent construction quality and a trustworthy corporate reputation, but also good interpersonal relations, which can be difficult to achieve without the exchange of interest. Therefore, in engineering construction, the accepted norm is that every building project, from tender to completion, requires the builder to pay a particular proportion of the overall investment. Bribe-givers are increasing, delivering benefits in the form of emotional investments to key targets under the guise of friends in the hope of sustaining a long-term, stable relationship.

4.1.3 Features of the Crime

The features of the crime refer to that the perpetrators of the corruption act alone or in groups. The number of instances committed by more than one person outnumbers the number of cases by a single individual, indicating that corruption is more likely to occur in groups.

Table 9: Features of the Crime

FeatureNumberPercentage
Single3746.25%
In group4353.75%

According to the analysis of details in the cases, the characteristics of an engineering project determine the group nature of corruption in this sector. Engineering projects involve various phases, including initiation, bidding and tendering, funding, completion and acceptance. These procedures are usually complex and time-consuming. To guarantee that each stage proceeds smoothly, contractors frequently bribe the government officials responsible for these procedures. From the analysis of six typical cases with more than eight people, this dissertation found that a corrupt leader founds a coalition of corrupt followers. This unethical leader has a long tenure in the department (over fifteen years) and lacks oversight. His corrupt behaviour also lowers the moral defences of his subordinates. When they become familiar with project leaders, a criminal network of corruption is formed, and the negative influence from the leader is passed around in the network (Bowen et al., 2015; Le et al., 2014a). The network continues until most, if not all, of the followers, become corrupt.

4.1.4 Amount

In a market economy, choices in the face of economic interests become a measure of rationality; albeit not the only criteria, it is widely applicable and highly explanatory. Therefore, the amount of the crime is chosen as a measure of the return of the crime to examine how the perpetrator made a choice at the time of the crime. The amounts involved in all cases are shown in the table. The absolute amount of money varies. The lowest amount was ¥70,000 (£8,300), and the highest amount was over ¥20 million (£2,6 million). The average corruption amount was ¥3.94 million (£470,000). In terms of the distribution of the amount involved, the cases were mainly in a million level, with 43 cases or 53.75%, followed by the lakh level, with 24 cases or30%. The bar chart shows the distribution of the amount of money involved in the cases more clearly.

Table 11: The Distribution of the Amount of Money

Amount (Chinese yuan in thousand)NumberPercentage (%)
below 10033.75
100-1,0002430
1,000-10,0004353.75
over 10,0001012.5

Figure 6: The Distribution of the Amount of Money

Corruption cases in engineering projects usually involve a large amount of money, owing to the fact that most construction projects require huge investments and produce large profits. Consequently, some project contractors are willing to bribe key personnel in order to obtain the chance to undertake the project, smooth the path for construction progress and acquire high returns.

Based on statistics, most engineering corruption cases in China occur between the ages of 40 and 55. This age group accounts for 67.5%. According to the relevant Chinese laws and practices, cadre at the bureau level are usually shifted to the second line or retired at the age of 60, and those at the county level at 55. In addition, most corrupt subjects who first engage in corrupt practices are over 40 years old. Only about 3.75% of those who engage in corrupt activities are over the age of 60. Case ID 46, then at the age of 66, the Xi’an Municipal Planning Bureau’s chief engineer was the oldest person to commit his crime. He had been corrupted for 86 months, which means he was 73 years old when he was arrested. Case ID 69, 31, the director of the Yunnan branch of the Construction Comprehensive Survey and Design Institute Co., was the youngest person to commit the crime. He had been corrupting for 96 months and was 42 when his crime was first exposed. The average age of incidence is 46. The age of crime decreases significantly after the age of 55.

Table 10: Age of Perpetrators

agebelow 4040-4546-5051-5556-60Over 60
number1520171783
percentage18.752521.2521.25103.75
cumulative percentage18.7543.756586.2596.25100

Figure 7: Age of Perpetrators

4.2.2 Education

The academic background of the persons involved in the case is divided into four categories in order: below vocational senior secondary school, vocational senior secondary school, undergraduate school and graduate school. The distribution of each type of education is shown in the following table. The total number of people with vocational senior secondary school, undergraduate and graduate degrees exceed three-quarters of the total figure. Therefore, it is safe to conclude that the probability of highly educated people in engineering project corruption cases is increasing. Nine of the cases investigated even earned graduate degrees.

Table 12: Education of Perpetrators

Educational LevelNumberPercentage
Below is a vocational senior secondary school1721.25%
Vocational senior secondary school2126.25%
Undergraduate school3341.25%
Graduate school911.25%

4.2.3 Region

The distribution of corruption in each region is shown in the following chart. Tier 4 cities with 29% reach the highest proportion. Tier 5 and Tier 3 cities are not far behind, with 23% and 21%, respectively, ranking in second place. These three regions account for the vast majority of corruption cases. Of all the cases researched, only six occurred in Tier 1 cities, all of which were in Beijing.

Figure 8: Corruption in Different Regions

4.2.4 Psychological Reasons

The root cause of the motivation for corruption is generally considered to be the profit-seeking nature of corrupt individuals (Li et al., 2022). However, through an analysis of the summary of key facts, grounds appeal against sentences and convictions in judgments, the author found that some corrupt individuals had a high degree of integrity and self-discipline at the beginning of their tenure. It was the subsequent internal or external circumstances that led to corruption. For example, 33.75% of corrupt individuals believed that their motivation for corruption stemmed from a feeling of imbalance, believing that there was a large gap between their personal income and that of contractors, developers and other people in business with whom they had close business transactions; 26.25% of corrupt people believed that they had made a significant contribution to the organisation and that they deserved the rewards came from corruption. 16.25% of corrupt individuals believed that they should be paid for the convenience they bring with their power. 47.5% of the corrupt could not resist the temptation of bribe-givers in their social circle. 8.75% of the subjects chose to be corrupt because they had difficulties in their lives, such as the need to buy a house, the need for their children to go to a prestigious school or financial difficulties, and the help from bribe-givers could help them overcome these difficulties. It also worthies of mention that the failure of personal defence can be caused by a combination of these reasons.

4.2.5 Rank

The positions of the those involved in the cases were assigned in accordance with the standards for national civil servants, in descending order of rank: national, provincial, bureau, divisional and sectional levels. Table 13 shows the number of cases at each level. The highest number of perpetrators was found at the division level, with more than half of the cases. Individuals at the sectional level make up 23.75% of the total, while officials at the bureau level account for less than 20%.

Table 13: Rank of Distribution of Personnel

RankNumberPercentage
Provincial level22.5%
Bureau level1417.5%
Division level4556.25%
Section level1923.75%

At each level, the status of employees is, in descending order, set as follows: directors, senior leaders, junior leaders and ordinary staff. The number of cases in each category is shown in Table 14.

Table 14: Power of Distribution of Personnel

RankNumberPercentage
Director4455%
Senior Leader2328.75%
Junior Leader1215%
Ordinary Staff11.25%

Leading cadres account for a significant proportion of the persons investigated and punished—more than half. They are usually the decision-makers and play a decisive role in all aspects of construction. The power in their hands attracts businesses, allowing for trading power for personal gain. This is a direct effect of the concentration of power and inadequate supervision. As long as they have certain real power, staff of ordinary status might violate rules and abuses power at hand.

Based on the analysis of the characteristics of the cases and perpetrators of corruption in the previous two sections, this thesis summarises that the following features characterise the current corruption cases in the engineering field in China, which addresses objective 3.

The first feature is corruption crime remains high. More specifically, the field of crime is no longer confined to the construction sector and the related departments but has spread to sectors unrelated to the construction industry, such as higher education institutions and grass-roots governments; the areas have also expanded beyond construction projects into building maintenance, street renovation, engineering wiring, locks and security product procurement. These findings echo discoveries from previous literature (Chan et al., 2019; Le et al., 2014a). The subjects of the crimes include staff of administrative and institutional management departments such as planning, land, municipalities, education and health, as well as staff from construction, supervision, survey and design and construction units. There is also a new trend of more corruption cases in public utilities. As state-owned departments still have a monopoly on the construction of infrastructures such as water, electricity, gas and telecommunications, almost every construction project requires a supplement part of municipal public infrastructure in the implementation process. As a result, some staff members of public utilities take advantage of their monopoly position to accept bribes from builders under various disguises. It is safe to assume that there will be corruption wherever there is a project.

The second feature is collusion cases have become the normal form of corruption cases. Due to the prevalence of unwritten rules in the industry, relationships, favours, and greetings are much more important than technology, equipment and reputation. It has become the general consensus of the participants in the process that contractors must first invest money to bid. This unwritten rule is an unavoidable procedure to get the project, and the bribe-taker also takes it for granted that he will be paid for doing a favour for others. The construction of each project involves many phases and many people, including intermediates, bribe-offerors and bribe-takers, who may accept bribes from many sources. Suppose engineering corruption cases are considered as a large network. In that case, everyone in it is a node, and the authorities may be able to pull a string of people from whichever they choose to investigate further.

The last feature indicates that the subject of the crime is highly educated. In the 80 cases studied, most people involved were highly educated. These highly educated people are generally the best in their construction profession, and some have titles such as senior engineer and professor. However, their professional skills were not used to improve their work but rather as a tool for the perpetrators to run corrupt deals and find loopholes in the system.

Following the empirical literature and the analysis of the characteristics of the 80 cases and the characteristics of the perpetrators, six variables were extracted: duration of the crime, rank, age, education, region and amount of money involved. In this study, the first five variables are set as independent variables and the amount of money involved in the case is the dependent variable. A multiple regression analysis models between the independent and dependent variables is established to investigate the correlation between the above six factors.

Table 11: Summary of Variables Used in the Model

 VariablesDefinition of variables
Degree of the project corruptiony: Amountthe total corruption amount (measured in Chinese Yuan)
Basic Profilex1: Agethe age at the first corruption
x2: Regiontier 1 city=1, new tier 1=2, tier 2=3, tier 3=4, tier 4=5, tier 5=6
x3: Educationbelow vocational senior secondary school=1, vocational senior secondary school=2, undergraduate=3, graduate = 4
x4: Rankbased on the rank of civil servants
Features of the Corruptive Behaviourx5: Lengththe time of detection minus the first corruption (measured in months)

If the five independent variables influence the dependent variable, the OLS regression model is estimated as follows.

y = a + b1x1 + b2x2 + b3x3 + b4x4 + b5x5

In the above equation, a, b1, b2, b3, b4 and b5 are parameters. The standardised data was entered into SPSS data files, some of which are intercepted in Table 15.

The VIF value represents the severity of multicollinearity and is used to test whether the model is highly correlated with the explanatory variables. The VIF values are all less than 10, so the model is not subject to multicollinearity and is well constructed.

Table 15: File of the Data Collected

The results in Tables 16 and 17 can be obtained from the multiple linear regression analysis in SPSS.

Table 16 shows that the coefficient of determination, R-squared, is 0.531, and the adjusted coefficient of determination is 0.501. The R-squared value indicates that the model’s regression equation can explain only 53.2% of the amount involved in the data. The model is a relatively good fit in terms of the coefficient of determination.

The F-test is used to determine whether there is a significant linear relationship, and the main concern in linear regression is whether the F-test is passed or not. From the analysis of the results of the F-test, the significance p-value for the F statistic is 0.000<0.05, which is significant at the level of 0. The original hypothesis that the regression coefficient is 0 is rejected, so the model basically meets the requirements.

Table 16: Linear Regression Result 1

Number of obsR-squaredAdj.R-squaredFSig.
80.0000.5320.50116.8340.000

The regression results show that the regression coefficient of x1 is 0.098, which is significantly positive at the 1% level, indicating a positive a between age and the amount of corruption. As the age increases by one year, the level of corruption increases by ¥980 (£117), indicating that the higher the age, the higher possibility of corruptive behaviours involving a large amount of money.

The regression results show that the regression coefficient of x3 is -0.253, which is significantly negative at the 1% level, indicating the negative relationship between the education level and the amount of corruption. As education increases by unit (from undergraduate to graduate, for example), the level of corruption decreases by ¥2530 (£302), indicating that the higher the education, the lower the amount level of corruption.

The regression results show that the regression coefficient of x4 is -0.144, which is significantly negative at the 10% level, indicating a negative relationship between the rank of the person in the workplace and the amount of his corruption. As the rank increases by unit (from section level to division level, for instance), the level of corruption decreases by ¥1440 (£172), indicating that the higher the rank, the lower the level of corruption.

The regression results show that the regression coefficient of x5 is 0.005, which is significantly positive at the 5% level, indicating a positive relationship between the length of the corruption and its amount. As the corruption case goes unnoticed for one more month, the amount of corruption increases by ¥50 (£6), indicating that the longer it takes to detect unethical behaviours, the higher the level of corruption.

Finally, the regression coefficient for x2 is not significant, indicating no significant relationship between the region and the level of corruption.

Table 17: Linear Regression Result 2

Table 18 shows the regression results using the ordered probit regression method in stata 15.1. The regression results show that the regression coefficient of age is 0.111, which is significantly positive at the 1% level, indicating that age and the level of corruption have a positive relationship. The regression coefficient of education is -0.313, which is significantly negative at the 1% level, indicating a negative relationship between education and the amount of corruption. The regression coefficient of 0.007 for the length of crime is significantly positive at the 1% level, indicating that the higher the length of crime, the higher the level of corruption. In addition, the regression coefficients for rank and region are not significant, indicating that there is no significant relationship between rank and region and the level of corruption. It is evident that the ordered probit regression results are not significantly different from the linear regression results above, which indicate robust results.

Table 18: Ordered Probit Regression

The combined analysis in Section 4 leads to the following conclusions:

  1. The length of corruption and the age of the individuals have a positive relationship with the amount of money involved. In particular, the longer the duration of the crime and the older the age, the higher the amount of corruption.
  2. There is a negative relationship between education and the amount of corruption. Although the current trend of corruption in engineering projects shows a trend of individuals with a high level of education (conclusion from 4.3), better education leads to lower amounts of corruption.
  3. Job rank of the person and the city of residence are not the main factors influencing the amount involved in corruption, but they reveal that tier 4 cities and division-level employees have been hit especially hard by corruption in construction.
  4. Discussion

This section is composed of three major sections. First, it reviews the research process and connects the findings with the relevant theory examined earlier. Second, possible future suggestions are promoted based on the findings. Finally, the limitations are listed to point to future research direction.

This study found that many cases of corruptions were occurred in the construction projects. It was aligned with study results from Transparency International (2011) which stated that the construction industry’s catastrophic corruption situation has not improved since it is still widely regarded as the most corrupt sector (Transparency International 2011). Engineering corruption is an exceptionally large risk that has great repercussions on construction projects. However, this study’s results found that corruption in engineering projects can be unnoticed for years. Most of the cases had been identified after 61 to 180 months. The majority of the cases were bribery, embezzlement, misappropriation of public funds, abuse of authority, and unexplained source of large amounts of property. The distributions of the corruption cases in the construction project remained similar to the study conducted by Herrera and Rodriguez (2003) and De Rosa et al. (2015). It can be explained in the previous study that bribery is likely to occur because of weak supervisory control. Moreover, the payment from bribery was significantly larger than the officer’s salary Herrera and Rodriguez (2003).

This current study also found that most of the corruption occurred in the groups. This result was closely related to studies conducted by Bowen et al. (2015) and Le et al. (2014a). Those studies stated that corruptions were most likely to occur when teams were familiar with the project leaders. It was also related to leaders and their behaviours. It can be explained because of negative influences of the workplace that triggered the criminal network of corruption. The previous study could explain this finding. Dong et al. (2012) stated that corruption is influenced by the level of peers and other individuals’ perceived behavior. In other words, it can be said that the actor will be more willing to engage in corruption when the peers and close individuals perceived such kind of behavior.

This study also found that Corruption cases in engineering projects usually involve a large amount of money. It can be explained because most construction projects require huge investments and produce large profits (Matthews, 2016). Consequently, some project contractors are willing to bribe key personnel to obtain the chance to undertake the project, smooth the path for construction progress and acquire high returns.

This current study aimed to examine both social system factors and individual factors that were related to corruption. As constructivism theory explained, the individual factors are complimentary of the social system approach in explaining corruption behaviour. In the social system approach, corruption is the condition when the system is diverted by the motivation to fulfil the inherent functional objectives. Meanwhile, based on individual dimensions, corruption results from the interpretation of the social world by a person or individual (Tänzler, et al., 2016). In this study, we examined both approach by evaluating five factors that have possibility to influence the social system and individual’s motivation in predicting the corruptions behaviour. Age of actor and education of actor were considered to individual dimensions that were examined in this study. Region when the corruption took place, rank of civil servants, and the length of corruptions were considered to be influenced by social system approach that can disturbed the functional logic in organization.

Multiple regression showed that the model was significant in predicting the number of corruptions. It showed that the predictor variables (age, education, rank, length of corruption, and region) impacted the amount of corruption incidents. Age contributed to the occurrence of corruption. Age The average age found in this study was 46 years old. However, the incidents were reduced when age 55. These findings were not aligned with the previous study conducted by Ivlevs and Hinks (2015) that stated that young age is more likely to commit corruption. Hernandez and McGees (2013) suggested that people get older and become more hostile to get involved in corruptive acts. Moreover, age has positively related to amount of corruption. As the age increases by one year, the level of corruption increases by ¥980 (£117), indicating that the higher the age, the higher possibility of corruptive behaviours involving a large amount of money. The older the people, the more likely people to engage in corruption behaviour. In addition, the older the people, they have possibility to engage in higher amount of corruption. We linked these results with the economic motivation that influence corruption esp,ecially in project management. Older employees would have higher needs financially, so they have a higher level of economic motivation. It interacted with other social factors to interrupt the functional logic in organizations.

Education was found to significantly predict the amount of corruption. As education increases by unit (from undergraduate to graduate, for example), the level of corruption decreases by ¥2530 (£302), indicating that the higher the education, the lower the amount level of corruption. These findings were not aligned with Dong and Torgler’s (2013) and Mocan (2018). Some scholars argued that people with higher education levels are more likely to be asked to bribe because they are more likely to get in touch with government officials. In contrary, our result did not find similar conclusion. The less educated people were more likely to engage in corruption. There are many possibilities of this contradictory results. The first one is the proportion of sample. In this study, most of the samples were undergraduate. The number of samples were reduced for graduate level. Thus, a careful consideration should be taken in conclude the results. Second, as we noticed that the data were collected from noticed corruptions. As previously mentioned, there are many of unnoticed corruption within construction project. Further, it can be related with the political power. The more-educated employee had more political power than people with lower education because of their position in the organization. Therefore, Educated Chinese were able to monitor government officials. As a result, it is likely that high levels of education were linked to a low rate of corruption in China’s provinces during the early period.

The rank of the person in the workplace was negatively associated with the amount of corruption. As the rank increases by unit (from section level to division level, for instance), the level of corruption decreases by ¥1440 (£172), indicating that the higher the rank, the lower the level of corruption. The power can explain it that higher rank employees might have. If it is linked to the constructivism theory, higher-rank employees would have the possibility to interrupt bigger functional logic in the organization. So, the amount of corruption would become larger than the employee of lower rank. It could be logical if this finding were linked to the salary or annual income of the civil servant in China (Wu & Gong, 2012). The empirical evidence found an association between the salary and the corruption incidents. They assumed that even if civil servants’ salaries were high, their expectations would not differ from those who have higher salaried. This condition made them expect additional compensation from the informal sources. Those can be the root of corruption. 

In addition, the length of corruption was positively associated with the number of corruptions. As the corruption case goes unnoticed for one more month, the amount of corruption increases by ¥50 (£6), indicating that the longer it takes to detect unethical behaviors, the higher the level of corruption. It indicated the internal motivation of the actors. The unnoticed behavior becomes daily routines that can strengthen the personal interest and motivation to commit the corruption act. However, there was no significant relationship between the region and the level of corruption. This result was not aligned with a previous study (Mocan, 2008). In other words, factors contributing to corruption might not differ between regions in China. A further study will be needed to evaluate the region-specific factors leading to China’s corruption.

By thoroughly reviewing papers on corruption in general and engineering corruption in specific, this dissertation identifies the scope of current research on corruption, such as the concept of corruption, causes of engineering corruption at the macro-level, processes prone to corruption and anti-corruption mechanisms. While gaining more comprehensive knowledge about the academia of corruption in construction, the author also finds a limitation: paltry studies on the criminal subjects in engineering corruption. More specifically, no empirical research has been conducted on their behaviours, nor has any quantitative relationship between behavioural characteristics and the severity of engineering corruption been carried out at the micro-level. However, identifying the causal factors of engineering corruption is one of the most important branches of engineering management research, as revealed in Owusu et al. ’s (2019) metaphor of corruption as sickness. Causes of corruption from multiple levels must be accurately diagnosed in order to direct specific drugs (anti-corruption measures) to treat it. This thesis, therefore, attempts to contribute to this area by analysing reliable data released by official sources with the following steps, which fulfil the three objectives initially set out in this dissertation.

An initial list of factors to be analysed was derived by examining the literature. Secondly, the study further identified available factors by designing a standardisation template. Thirdly, a search for engineering corruption-related cases in three sources was conducted. By further examining the text retrieved carefully and supplementing information from the news on the Internet, this dissertation exploited 80 cases from 2015 to 2021 with complete information needed. Then, the information obtained was filled in the standardisation template and analysed descriptively from multiple perspectives. Next, based on the descriptive analysis, five variables (amount of corruption, age, region, education, rank and length) for the econometric model were identified, and regression analysis was conducted. Finally, this thesis draws the following significant conclusions on behavioural patterns of individuals in engineering corruption. Gender, the type of unit, the amount of the crime, the amount of the sentence and the type of behaviour all play a greater or lesser role in the perpetrator’s choice to commit an embezzlement offence.

Corruption involves huge amounts of money and has a long latent period, which is in line with findings in corruption in construction. It occurs commonly among people aged 40 to 55 and has a negative relationship with age. This result contradicts the existing literature that older people are increasingly hostile to corruption (Hernandez and McGee 2013; Ivlevs and Hinks, 2015). One possible explanation is that issues related to power also need to be taken into account. According to the law on promotion in government employees, promotion to section level cadres is generally limited to those between the ages of 40 and 55. People who are younger than this do not have the power base to set up rents, making the proportion of corrupt subjects before the age of 40 look low. Also, officials in their 50s who are close to retirement might think that their power are going to be null and void, which either increases the desire to corrupt or a latent decay in ethical values (Ochulor and Bassey, 2010).

This study found no linear relationship between the city where the corruption occurred, and the amount involved in corruption and that more underdeveloped cities were more prone to corruption. This argument contradicts the literature—despite only a few mentioning this factor— that suggests that corruption is high in cities with better economies (Hunt, 2004; Mocan 2008). One possible reason for this is that the literature suggests that in large cities, people tend to be indifferent to each other and, therefore, easier to ask for or offer bribes. In China, however, human relations are a vital component of society. When people are closer to each other, they are more likely to cover for each other, and supervisors may become part of the corruption coalition, as demonstrated in cases where some of the corrupt people were even members of the judiciary. This finding also proves the commonly accepted truth in project management that regulatory comprise is one of the leading causes of corruption (Owusu et al., 2019).

The impact of education on corruption is controversial. Some scholars believe that higher education can lead to lower corruption. Scholars such as H. Akbar and Vujić (2014) have suggested that corruption can be tackled through increased education. Different evidence has also been found in empirical studies by Mocan (2008) and Dong and Torgler (2013), who assert that higher education does not prevent corruption due to frequent contact with officials. The quantitative analysis on education in this dissertation suggests that an increase in the level of education of individuals can reduce the amount of corruption. Dong and Torgler’s (2013) interpretation is that highly educated people in the past ten years have not had the power to monitor leaders in government, which is true. However, this study uses cases of 2015 to 2021; less educated government officials may have retired, and newly promoted individuals with high educational backgrounds have entered engineering-related fields. It is also important to note that while this thesis found that most corrupt individuals have high levels of education, one of the key reasons for this is that civil servants in China today are subject to competitive screening and increased overall education levels.

These characteristics above reflect, on the one hand, the lack of effective and adequate supervision in the construction sector and the lack of efficient efforts to combat engineering corruption (Shan et al., 2019). On the other hand, they indicate that engineering corruption is highly concealed, employs diverse means and is difficult to manage and monitor. Therefore, in order to create a more transparent and healthy industry, anti-corruption strategies in the field of engineering construction require precise control and long-term supervision. When project management is increasingly precise in identifying the causes, as this thesis attempts to do, then limited anti-corruption resources can bring maximum returns.

Although the sample size for this paper is not large enough, it is still represented as the cases were selected from the typical cases published by the Public Prosecutor’s Office. The findings, therefore, have commonalities that are useful for policy implications. Not only do perpetrators choose different offences because of their free will, but different acts of corruption (embezzlement or misappropriation of public funds), region, educational factors, age and other external factors also influence the choice of the perpetrator. Therefore, a more realistic and differentiated system of countermeasures against engineering corruption can be constructed regarding people, organisations and institutions. While corruption cannot be completely eliminated from the industry, the findings help regulators to develop effective measures to curb its exaggeration. This dissertation promotes the following practical recommendations.

First, to strengthen the targeted rectification of corruption problems in construction in fourth-, fifth and third-tier cities. At present, the remediation of prominent problems in construction mostly starts from general construction processes nationwide but ignores the differences between regions and cities at different levels. This negligence leads to the phenomenon that the relevant governance or countermeasures of the central government are not specific nor practical enough. The findings of the study suggest that as fourth-, fifth and third-tier cities face large-scale urban construction and inadequate supervision, the problem of corruption may become severer and special measures should be developed.

Secondly, in the process of appointing and promoting cadres, the auditing of their positions during their term of office should be strengthened to eliminate the phenomenon of “promotion with illness” — following Owusu et al.’s (2009) metaphor. This research shows a direct positive correlation between the severity of corruption and the latency periods of corruption. The longer a person serves, the amount of money involved in corruption becomes larger, also easier to breed collective corruption. With regard to the proposed appointment and promotion of personnel, regulators should avoid the appeasement practice of using success to offset mistakes. Timely investigation and handling of engineering corruption cases are necessary to curb the deterioration and spread of corruption.

This study has identified and presented a number of micro-level determinants, analysing the exact severity of each factor. It should be noted, however, that albeit the pragmatic efforts made to perform this research, more work remains to be done to gain additional insight based on the results of this study. The identification and analysis of causal factors in this study is merely the first step towards a more extensive investigation. Determining the source of a problem is always considered to be the very first step towards resolving it. Similarly, in the field of corruption in the construction industry, understanding the causal characteristics of individuals can be viewed as a practical step towards developing a more accurate and appropriate response in combating corruption. 

First, further empirical testing of these factors can be carried out in specific project segments of the engineering project. Identifying the influence of each factor on specific processes can help to establish priorities for future management. For example, officials understand which segments immediately require developing strategic anti-corruption measures and which segments can be left alone for the time being.

Furthermore, in this phase of empirical analysis of the 80 cases, the method of multiple linear regression models in statistics was used to analyse the current crime trends in corruption cases in the engineering domain. It confirms that the basic variables of the legal cases can indicate the pattern of engineering corruption cases, while other motives or variables need to be further analysed, laying the foundation for the subsequent research. Therefore, it is recommended that more relevant factors in this area and their quantitative relationship with the intensity and severity of corruption be investigated empirically and in depth.

For example, due to difficult-to-obtain data, income is a factor mentioned in much of the literature but not explained in this study. Several factors argue in favour of excluding income from this analysis. First, the exact salaries of corrupt individuals are not disclosed in legal judgments, nor is the data disclosed on the internet or investigative journalism. Also, the complex salary structure of Chinese government officials makes it difficult to calculate specific figures. All these reasons prevented this dissertation from conducting a regression analysis of income. In order to reveal income and its impact on corruption and further determine its quantitative relationship, subsequent research could start by investigating the income of corrupt actors. As the publicly disclosed criminals are already in prison or arrested, it is not easy to conduct a direct questionnaire. Future studies could therefore start by surveying the salaries of people in the same or similar positions and adjusting the numbers accordingly to obtain more accurate information, such as inflation adjustments.

In addition, as mentioned earlier, culture plays an important role in the initiation of unethical practices. This study uses cases from China to analyse its context, and arguments are impacted by China’s social, economic and cultural background, which may limit the finding’s generalisation. Therefore, future research can be performed in different regions across the world.

Finally, due to corruption’s dynamic nature and complexity, the determinants identified may change over time; therefore, continuous research is needed to extend knowledge and awareness in this domain.

References

Ahmad, O., Boschi-Pinto, C., Lopez Christopher, A., Murray, J., Lozano, R. and Inoue, M. 2013. AGE STANDARDIZATION OF RATES: A NEW WHO STANDARD [Online]. [Accessed 7 March 2022]. Available from: https://www.who.int/healthinfo/paper31.pdf.

Ahmed Kabiru, S. 2019. An Appraisal of Legal and Institutional Framework of Corruption Eradication in Nigeria. Turk Turizm Arastirmalari Dergisi. 1(6), pp.1–9.

Akech 2011. Abuse of Power and Corruption in Kenya: Will the New Constitution Enhance Government Accountability? Indiana Journal of Global Legal Studies. 18(1), p.341.

Akpa, O. 2018. Neuroeconomics of Corruption: Feelings, Brain and the Nigerian Narratives. Doctoral Dissertation, Claremont Graduate University.

Anon 2020. Tier 1 Cities in China – Definition and Rankings. The Biggest Cities in China. [Online]. Available from: https://thebiggestcitiesinchina.com/tier-1-cities-in-china-definition-and-rankings/.

Apergis, N., Dincer, O.C. and Payne, J.E. 2009. The Relationship between Corruption and Income Inequality in U.S. states: Evidence from a Panel Cointegration and Error Correction Model. Public Choice. 145(1-2), pp.125–135.

Ayodele, A.O., Ogunbode, A. and Ibironke, E.A. 2011. Corruption in the construction industry of Nigeria: causes and solutions. undefined. [Online]. [Accessed 21 March 2022]. Available from: https://www.semanticscholar.org/paper/Corruption-in-the-construction-industry-of-Nigeria-Ayodele-Ogunbode/10c389d4380b044d58f9b42a7083bbcca72dfefa.

Baker, W.E. and Faulkner, R.R. 1993. The Social Organization of Conspiracy: Illegal Networks in the Heavy Electrical Equipment Industry. American Sociological Review. 58(6), p.837.

Barnes, D. 2016. Corruption in the UK Construction Industry | CIOB. www.ciob.org. [Online]. [Accessed 16 March 2022]. Available from: https://www.ciob.org/industry/research/Corruption-UK-Construction-Industry.

Boudreaux, C.J., Nikolaev, B.N. and Holcombe, R.G. 2017. Corruption and destructive entrepreneurship. Small Business Economics. 51(1), pp.181–202.

Bowen, P., Edwards, P. and Cattell, K. 2015. Corruption in the South African construction industry: experiences of clients and construction professionals. International Journal of Project Organisation and Management. 7(1), p.72.

Cao, Z. 2022. How-to China: China’s further development hinges on sustained anti-corruption efforts – Expert. www.chinadaily.com.cn. [Online]. [Accessed 16 March 2022]. Available from: https://www.chinadaily.com.cn/a/202201/18/WS61e5edefa310cdd39bc81925_1.html.

Chan, K.S., Dang, V.Q.T. and Li, T. 2019. The evolution of corruption and development in transitional economies: Evidence from China. Economic Modelling. 83(2), pp.346–363.

Collins, J.D., Uhlenbruck, K. and Rodriguez, P. 2009. Why Firms Engage in Corruption: A Top Management Perspective. Journal of Business Ethics. 87(1), pp.89–108.

Cooray, A., Dzhumashev, R. and Schneider, F. 2017. How Does Corruption Affect Public Debt? An Empirical Analysis. World Development. 90, pp.115–127.

Dakhil, A., Naji, Z. and Al-Zuwair, S. 2017. Factors Affecting Construction Labour Productivity in Iraq Using Basra City as a Case Study. Kufa Journal of Engineering.

Damoah, I.S., Akwei, C.A., Amoako, I.O. and Botchie, D. 2018. Corruption as a Source of Government Project Failure in Developing Countries. Project Management Journal. 49(3), pp.17–33.

Davis, J. 2004. Corruption in Public Service Delivery: Experience from South Asia’s Water and Sanitation Sector. World Development. 32(1), pp.53–71.

De Rosa, D., Gooroochurn, N. and Görg, H. 2015. Corruption and Productivity: Firm-level Evidence. Jahrbücher für Nationalökonomie und Statistik. 235(2).

Debski, J., Jetter, M., Mösle, S. and Stadelmann, D. 2018. Gender and corruption: The neglected role of culture. European Journal of Political Economy. 55, pp.526–537.

Deloitte 2018. Global Construction Industry Overview. Deloitte United States. [Online]. Available from: https://www2.deloitte.com/us/en/pages/energy-and-resources/articles/global-construction-industry-overview.html.

Dimant, E. and Tosato, G. 2017. Causes and Effects of Corruption: What Has Past Decade’s Empirical Research Taught Us? A Survey. Journal of Economic Surveys. 32(2), pp.335–356.

Dollar, D., Fisman, R. and Gatti, R. 2001. Are women really the “fairer” sex? Corruption and women in government. Journal of Economic Behavior & Organization. 46(4), pp.423–429.

Dong, B., Dulleck, U. and Torgler, B., 2012. Conditional corruption. Journal of Economic Psychology, 33(3), pp.609-627.

Dong, B. and Torgler, B. 2013. Causes of corruption: Evidence from China. China Economic Review. 26, pp.152–169.

Dreher, A. and Gassebner, M. 2011. Greasing the wheels? The impact of regulations and corruption on firm entry. Public Choice. 155(3-4), pp.413–432.

Emergen Research 2021. Construction Market Report by Size, Share and Growth 2020-2028. www.emergenresearch.com. [Online]. [Accessed 16 March 2022]. Available from: https://www.emergenresearch.com/industry-report/construction-market.

Epaphra, M. and Massawe, J. 2017. Corruption, governance and tax revenues in Africa. Business and Economic Horizons. 13(4), pp.439–467.

Fernanda Rivas, M., 2008. An experiment on corruption and gender. Elektronische Ressource]. Working Paper of the University of Granada. Verfügbar unter: http://www. ugr. es/~ teoriahe/RePEc/gra/wpaper/thepapers08_10. pdf [01.03. 2012].

FIDIC 2011. Integrity Management System (FIMS) Guidelines 1st ed.

GIACC and Transparency International 2008. Anti-corruption Training Manual (Infrastructure, Construction and Engineering Sectors) [Online]. [Accessed 3 March 2022]. Available from: https://giaccentre.org/wp-content/uploads/2019/10/GIACC.TRAININGMANUAL.INT_.pdf.

Gidado, K. and Niazai, G. 2012. Causes of project delay in the construction industry in Afghanistan. research.brighton.ac.uk. [Online], pp.63–74. [Accessed 16 March 2022]. Available from: https://research.brighton.ac.uk/en/publications/causes-of-project-delay-in-the-construction-industry-in-afghanist#:~:text=The%20findings%20show%20that%20the%20main%20critical%20factors.

Goel, R.K. and Nelson, M.A. 2010. Measures of corruption and determinants of US corruption. Economics of Governance. 12(2), pp.155–176.

Goldsmith, A.A. 1999. Slapping the Grasping Hand. American Journal of Economics and Sociology. 58(4), pp.865–883.

Gorsira, M., Steg, L., Denkers, A. and Huisman, W. 2018. Corruption in Organizations: Ethical Climate and Individual Motives. Administrative Sciences. 8(1), p.4.

Granovetter, M., 2007. The social construction of corruption. On capitalism, 15.

Gutmann, J., Padovano, F. and Voigt, S. 2015. Perception vs. Experience: Explaining Differences in Corruption Measures Using Microdata. SSRN Electronic Journal.

H. Akbar, Y. and Vujić, V. 2014. Explaining corruption. Cross Cultural Management: An International Journal. 21(2), pp.191–218.

Habib, M. and Zurawicki, L. 2001. Country-level investments and the effect of corruption — some empirical evidence. International Business Review. 10(6), pp.687–700.

Hao, Y., 1999. From rule of man to rule of law: An unintended consequence of corruption in China in the 1990s. Journal of Contemporary China, 8(22), pp.405-423.

Henderson, J. and Kuncoro, A. 2004. Corruption in Indonesia [Online]. [Accessed 13 March 2022]. Available from: https://www.nber.org/system/files/working_papers/w10674/w10674.pdf.

Hernandez, T. and McGee, R.W. 2013. The Ethics of Accepting a Bribe: An Empirical Study of Opinion in the USA, Brazil, Germany and China. SSRN Electronic Journal.

Herrera, A.M. and Rodriguez, P., 2003. Bribery and the nature of corruption. Michigan. State University, Department of Economics.

Hiller, P., 2010. Understanding corruption: How systems theory can help (pp. 64-82). B. Budrich.

Hogdson, G.M. and Jiang, S. 2007. The Economics of Corruption and the Corruption of Economics: An Institutionalist Perspective. Journal of Economic Issues. 41(4), pp.1043–1061.

Hosseini, M.R., Martek, I., Banihashemi, S., Chan, A.P.C., Darko, A. and Tahmasebi, M. 2019. Distinguishing Characteristics of Corruption Risks in Iranian Construction Projects: A Weighted Correlation Network Analysis. Science and Engineering Ethics. 26(1).

Hunt, J. 2004. Trust and Bribery: The Role of the Quid Pro Quo and the Link with Crime. SSRN Electronic Journal.

Hunt, J. and Laszlo, S. 2012. Is Bribery Really Regressive? Bribery’s Costs, Benefits, and Mechanisms. World Development. 40(2), pp.355–372.

Islam, A. and Lee, W.-S. 2016. Bureaucratic Corruption and Income: Evidence from the Land Sector in Bangladesh. The Journal of Development Studies. 52(10), pp.1499–1516.

Ivlevs, A. and Hinks, T. 2015. Bribing Behaviour and Sample Selection: Evidence from Post-Socialist Countries and Western Europe. Jahrbücher für Nationalökonomie und Statistik. 235(2), pp.139–167.

Jetter, M. and Parmeter, C.F. 2018. Sorting through global corruption determinants: Institutions and education matter – Not culture. World Development. 109, pp.279–294.

Jiang, G. 2017. Corruption Control in Post-Reform China. Singapore: Springer Singapore.

Johann Lambsdorff 2005. Consequences and causes of corruption what do we know from a cross-section of countries? Passau Gruppe Der Volkswirtschaftlichen Professoren Der Wirtschaftswiss. Fak. Der Univ.

Johann Lambsdorff 2008. The institutional economics of corruption and reform : theory, evidence and policy. Cambridge: Cambridge University Press.

Justesen, M.K. and Bjørnskov, C. 2012. Exploiting the Poor: Bureaucratic Corruption and Poverty in Africa. SSRN Electronic Journal.

Kaufmann, D. and Vicente, P.C. 2011. Legal Corruption. Economics & Politics. 23(2), pp.195–219.

Keneck-Massil, J., Nomo-Beyala, C. and Owoundi, F. 2021. The Corruption and Income Inequality Puzzle: Does Political Power Distribution Matter? Economic Modelling. 103, p.105610.

Kenny, C. 2012. Publishing construction contracts to improve efficiency and governance. Proceedings of the Institution of Civil Engineers – Civil Engineering. 165(5), pp.18–22.

Kenny, C. 2009. Transport Construction, Corruption and Developing Countries. Transport Reviews. 29(1), pp.21–41.

Khadim, N., Taseer, S., Jaffar, A., Musarat, M. and Ilyas, U. 2021. Effects of Corruption on Infrastructure Projects in Developing Countries. International Journal on Emerging Technologies. 12(1), pp.284–295.

Kyriacou, A.P., Muinelo-Gallo, L. and Roca-Sagalés, O. 2015. Construction corrupts: empirical evidence from a panel of 42 countries. Public Choice. 165(1-2), pp.123–145.

Lavallée, E., Razafindrakoto, M. and Roubaud, F. 2008. Corruption and trust in political institutions in sub-Saharan Africa [Online]. [Accessed 16 March 2022]. Available from: https://hal.archives-ouvertes.fr/hal-01765960/document#:~:text=More%20specifically%2C%20we%20set%20out%20to%20test%20the.

Le, Y., Shan, M., Chan, A.P.C. and Hu, Y. 2014a. Investigating the Causal Relationships between Causes of and Vulnerabilities to Corruption in the Chinese Public Construction Sector. Journal of Construction Engineering and Management. 140(9), p.05014007.

Le, Y., Shan, M., Chan, A.P.C. and Hu, Y. 2014b. Overview of Corruption Research in Construction. Journal of Management in Engineering. 30(4), p.02514001.

Li, H., Wei, Y.D., Liao, F.H. and Huang, Z. 2015. Administrative hierarchy and urban land expansion in transitional China. Applied Geography. 56, pp.177–186.

Li, J., Shen, Q. and Gao, W. 2022. Characterization of Group Behavior of Corruption in Construction Projects Based on Contagion Mechanism H. Fu, ed. Computational Intelligence and Neuroscience. 2022, pp.1–16.

Li, S. and Vendryes, T. 2018. Real estate activity, democracy and land rights in rural China. China Economic Review. 52, pp.54–79.

Liu, J., Zhao, X. and Li, Y. 2017. Exploring the Factors Inducing Contractors’ Unethical Behavior: Case of China. Journal of Professional Issues in Engineering Education and Practice. 143(3), p.04016023.

Locatelli, G., Mariani, G., Sainati, T. and Greco, M. 2017. Corruption in public projects and megaprojects: There is an elephant in the room! International Journal of Project Management. 35(3), pp.252–268.

Matthews, Peter. 2016. This is why the construction is so corrupt. World Economic Forum.

Mejía, G., Sánchez, O., Castañeda, K. and Pellicer, E. 2020. Delay causes in road infrastructure projects in developing countries. Revista de la construcción. 19(2), pp.220–234.

Méon, P.-G. and Sekkat, K. 2005. Does corruption grease or sand the wheels of growth? Public Choice. 122(1-2), pp.69–97.

Méon, P.-G. and Weill, L. 2010. Is Corruption an Efficient Grease? World Development. 38(3), pp.244–259.

Mocan, N. 2008. What Determines Corruption? International Evidence from Microdata. Economic Inquiry. 46(4), pp.493–510.

Niazai, G. and Gidado, K. 2017. Analysis of Causes of Delay in Any Construction Project. International Journal of Modern Trends in Engineering & Research. 4(2), pp.128–134.

Nordin, R.M., Takim, R. and Nawawi, A.H. 2013. Behavioural Factors of Corruption in the Construction Industry. Procedia – Social and Behavioral Sciences. 105, pp.64–74.

O. Ph.D, A., Emmanuel and S, I., Aloysius 2017. Government, Democracy and Dysfunctional Governance in Nigeria. IOSR Journal of Humanities and Social Science. 22(06), pp.83–92.

Ochulor, C.L. and Bassey, E.P. 2010. Analysis of Corruption from the Ethical and Moral Perspectives. European Journal of Scientific Research. 44(3), pp.466–476.

Olken, B.A. 2009. Corruption perceptions vs. corruption reality. Journal of Public Economics. 93(7-8), pp.950–964.

Owusu, E.K., Chan, A.P.C., DeGraft, O.-M., Ameyaw, E.E. and Robert, O.-K. 2018. Contemporary Review of Anti-Corruption Measures in Construction Project Management. Project Management Journal. 50(1), pp.40–56.

Owusu, E.K., Chan, A.P.C. and Shan, M. 2017. Causal Factors of Corruption in Construction Project Management: An Overview. Science and Engineering Ethics. 25(1), pp.1–31.

Owusu, E.K., Chan, A.P.C., Yang, J. and Pärn, E. 2020. Towards corruption-free cities: Measuring the effectiveness of anti-corruption measures in infrastructure project procurement and management in Hong Kong. Cities. 96, p.102435.

Park, H. and Blenkinsopp, J. 2011. The roles of transparency and trust in the relationship between corruption and citizen satisfaction. International Review of Administrative Sciences. 77(2), pp.254–274.

PwC 2020. Fighting fraud: A never-ending battle PwCs Global Economic Crime and Fraud Survey 2 0 2 0 [Online]. [Accessed 16 March 2022]. Available from: https://www.pwc.com/gx/en/forensics/gecs-2020/pdf/global-economic-crime-and-fraud-survey-2020.pdf.

Rizk, R., Sobh, D., Yassin, A.A.A. and Hamzeh, F. 2018. Studying the Mindset of Corruption in the Construction Industry – A Lean Perspective. 26th Annual Conference of the International Group for Lean Construction.

Rose-Ackerman, S. and Palifka, B.J. 2016. Corruption and government: causes, consequences, and reform, second edition. Cambridge (Reino Unido) [Etc.] Cambridge University Press.

Rothstein, B. 2018. Fighting Systemic Corruption: The Indirect Strategy. Daedalus. 147(3), pp.35–49.

Saenz, C. and Brown, H. 2018. The disclosure of anticorruption aspects in companies of the construction sector: Main companies worldwide and in Latin America. Journal of Cleaner Production. 196, pp.259–272.

Saha, S., Beladi, H. and Kar, S. 2021. Corruption control, shadow economy and income inequality: Evidence from Asia. Economic Systems. 45(2), p.100774.

Shakantu, W. 2006. Corruption in the construction industry: forms, susceptibility and possible solutions: industry issues. undefined.

Shan, M., Le, Y., Chan, A.P.C. and Hu, Y. 2019. Corruption in Construction: A Global Review In: M. Shan, Y. Le, A. P. C. Chan and Y. Hu, eds. Springer Link [Online]. Singapore: Springer, pp.9–22. [Accessed 16 March 2022]. Available from: https://link.springer.com/chapter/10.1007%2F978-981-13-9550-5_2.

Sohail, M. and Cavill, S. 2008. Accountability to Prevent Corruption in Construction Projects. Journal of Construction Engineering and Management. 134(9), pp.729–738.

Sulemana, I. and Kpienbaareh, D. 2018. An empirical examination of the relationship between income inequality and corruption in Africa. Economic Analysis and Policy. 60, pp.27–42.

Sundström, A. 2015. Covenants with broken swords: Corruption and law enforcement in governance of the commons. Global Environmental Change. 31, pp.253–262.

Swamy, A., Knack, S., Lee, Y. and Azfar, O. 2001. Gender and corruption. Journal of Development Economics. 64(1), pp.25–55.

Tabish, S.Z.S. and Jha, K.N. 2012. The impact of anti-corruption strategies on corruption free performance in public construction projects. Construction Management and Economics. 30(1), pp.21–35.

Tänzler, D., Maras, K. and Giannakopoulos, A., 2016. The social construction of corruption: Theoretical reflections. In The Social Construction of Corruption in Europe (pp. 31-48). Routledge.

Tanzi, V. 1998. Corruption Around the World Causes, Consequences, Scope, and Cures. IMF Staff Papers. 45(4).

Torgler, B. and Valey, N.T. 2010. Gender and Public Attitudes toward Corruption and Tax Evasion. Contemporary Economic Policy. 28(4), pp.554–568.

Transparency International 2011. Bribe Payers Index 2011 – Publications. Transparency.org. [Online]. [Accessed 16 March 2022]. Available from: https://www.transparency.org/en/publications/bribe-payers-index-2011.

Transparency International 2022. Corruption Perceptions Index 2021 – Publications. Transparency.org. [Online]. [Accessed 28 February 2022]. Available from: http://www.transparency.org/en/publications/corruption-perceptions-index-2021.

Treisman, D. 2000. The causes of corruption: a cross-national study. Journal of Public Economics. 76(3), pp.399–457.

Troilo, M. and Sun, Z. 2010. The limits of China’s growth. Chinese Management Studies. 4(3), pp.273–279.

Winch, G.M. 2001. Governing the project process: a conceptual framework. Construction Management and Economics. 19(8), pp.799–808.

Wong, M.Y. 2016. Public Spending, Corruption, and Income Inequality: a Comparative Analysis of Asia and Latin America. International Political Science Review. 38(3), pp.298–315.

Wu, Alfred M., and Gong, Thing. 2012. Does Increased Civil Service Pay Deter Corruption? Evidence from China. Review of Public of Personnel Administration.

Xinhua 2021. 最高人民检察院工作报告(第十三届全国人民代表大会第四次会议 张军 2021年3月8日)_中华人民共和国最高人民检察院. www.spp.gov.cn. [Online]. [Accessed 15 March 2022]. Available from: https://www.spp.gov.cn/spp/gzbg/202103/t20210315_512731.shtml.

Yap, J.B.H., Skitmore, M., Gray, J. and Shavarebi, K. 2019. Systemic View to Understanding Design Change Causation and Exploitation of Communications and Knowledge. Project Management Journal. 50(3), pp.288–305.

Zhang, B., Le, Y., Xia, B. and Skitmore, M. 2017. Causes of Business-to-Government Corruption in the Tendering Process in China. Journal of Management in Engineering. 33(2), p.05016022.

Zhang, H., Song, Y., Tan, S., Xia, S., Zhang, H., Jiang, C., Xiong, D., Cheng, G., Zhang, L. and Lv, Y. 2019. Anti-corruption efforts, public perception of corruption, and government credibility in the field of real estate: An empirical analysis based on twelve provinces in China. Cities. 90, pp.64–73.

Zhu, J., 2021. Out of China’s Reach: Globalized Corruption Fugitives. The China Journal, 86(1), pp.90-113.

Zou, Patrick.X.W. 2008. Strategies for Minimizing Corruption Risks in Construction Industry in China. Journal of Construction in Developing Countries. 11(2).

Scroll to Top