Capital and Crime–Corruption Nexus in the Shadow of the Law: A Theoretical Analysis of Public Policy

Abstract

Corruption is a symptom of wider political dynamics intertwined with sectors prone to criminal activities. This arises due to the laxity of legal enforcement or a dysfunctional political system. This paper analytically demonstrates the nexus between organized crime and corruption in the presence of the public sector. The relevant questions at this juncture are: (i) How does capital investment in the industrial sectors affect the crime–corruption nexus? (ii) Why a more stringent law-and-order enforcement may produce counterproductive outcomes? and (iii) Whether the creation of alternative income opportunities in the legally approved sectors by the government will lower corruption and decriminalize society? What we will try to show is that when corruption becomes necessary to sustain the criminal sector, capital expansion and deterrence policy augment crime and corruption. This crucially depends on a multitude of general equilibrium factors including the labor relocation effect, capital relocation effect, and factor intensity of sectors.

Keywords

crime corruption capital general equilibrium public policy D50 K42 H39

Introduction, Motivation, and Interventions

Corruption is a symptom of wider political dynamics and electoral malpractice and is likely to thrive in conditions where law enforcement and legal institutions are weak or politically dysfunctional. When organized crime dominates in the public sector, corruption within these sectors is bound to grow. The word “crime” covers a variety of violations which are prohibited by the criminal law, and may include murder, robbery, assault, theft, or any other public offences. On the other hand, “corruption” is the manifestation of wider complex factors which the Cambridge dictionary defines as the dishonest or illegal behavior involving a person in a position of power. It can include public servants demanding bribes or favor in exchange for services, politicians using power to grant contracts to their sponsors or corporations bribing public officials to get lucrative deals (Transparency International).¹ It is quite obvious that corruption may result in grotesque misuse of power. Corruption and crime are intertwined and quite often they are used interchangeably. When the public sector is dominated by organized crime, corruption is bound to go beyond tolerable limits. Though corruption is illegal in general, it may be socially acceptable or tolerated. In this paper, crime is the illegal extraction of output produced by the public sector. This is a dominating factor in cases of operations of the public sector on a large scale in developing countries. Crime is sustained by a network of corruption, which includes inter alia bribes and similar instruments. The focus of the paper is on the nexus between crime and corruption in a developing country in which the public sector plays a propulsive role in the development process.

Let us consider a few illustrations of how crime and corruption feed each other. These illustrations are based on Indian experiences. In particular, we will consider three incidences namely, illegal coal plunder, theft of electricity, and illegal sand mining that would help to understand the nexus of crime and corruption in the public sector and how these two types of complex crime reinforce each other.

First, India is the second-largest coal-producing country in the world with coal production of 700,382 thousand metric tons in the year 2020 (U.S. Energy Information Administration Statistics).² Coal India Limited (CIL) is an Indian government-owned public-sector enterprise that has near-monopoly power in coal extraction and refining. CIL with its eight subsidiaries is the single largest coal mining company in the world. One of its major subsidiary units, Eastern Coalfield Limited (ECL), is located in the coal-rich states of Jharkhand and West Bengal, which is also a hub for local gangsters commonly referred to as “coal mafias” in the local language. These coal mafias are involved in the outright theft of coal from the open mines of ECL and pay off company officials, police, bureaucrats, and local politician to mine and transport coal illegally and evade punishment (Daniel and Williams 2013, Reuters report; Singh and White 2019).³ According to a report published by Singh, Chakraborty, and Katakey (2014), about 13 percent of annual coal produced by the CIL is stolen by the gangsters. This amounts to a yearly loss of about USD 23.02 crore for the company (Singh 2011). These gangsters employ people from the pool of local poor unskilled workers to plunder the coal from the mines.⁴ What happens next is that the pilfered coal is sold illegally to brick kilns, sponge-iron factories, roadside eateries, tea stalls, and households to meet their energy demand for cooking. The price charged to sell this pilfered coal varies from USD 0.1 to USD 0.38 (Singh 2013, The Guardian). The police, managers, contractors, and public officers are bribed to allow operations in this organized criminal sector.

Second, the menace of power theft is another such episode of crime and corruption that has led to substantial financial losses for the Government of India. Pilferers illegally resort to terminal tapping of the overhead lines of the low voltage side of the transformer. According to a report in the Economic Times (2019), in the Konkan region of Maharashtra state in India, 1949 cases of power theft were detected by the Maharashtra State Electricity Distribution Company Ltd. (MSEDCL). In the Haryana state, 5,500 cases of power theft had been detected by the Uttar Haryana Bijli Vitran Nigam (UHBVN) (Economic Times, 2021). This unauthorized leakage of electricity leads to an economic loss of around USD 255.83 crores annually for the entire nation (Chatterjee and Viriyam 2021). In the Meghalaya state of India, the industries located in Byrnihat are accused of stealing electricity provided by the public sector firm Meghalaya Energy Corporation Limited (MECL) with the help of the company officials by bribing them (Assam Tribune 2021).⁵ In this case, also, the pilferers can plunder electricity with the connivance of company officials, public servants, and police.

Third, illegal sand mining in India is another highly lucrative business. Sand mafias unlawfully mine sand from inland and riverbeds, which is estimated to be worth USD 16 million.⁶ Police personnel are bribed by the truck drivers carrying illegally mined sands which is USD 0.8–USD 1.5 per truck. Alternatively, officials are bribed by the transporters with an amount ranging from USD 80–USD 160 per sand-filled truck to obtain the official code number for unregistered trucks carrying sand (Rege 2015; Seshacharyulu 2013).

Now few explanatory remarks based on the above-mentioned illustrations for this paper are in order. First, the nature of the product which is pilfered is an intermediate good that is used as an input for further production in the final good sector. Second, this intermediate good is a publicly produced private good. These intermediate products are produced by the public sector undertakings or public sector enterprises (PSUs or PSEs, hereafter). According to the section 2 (45) of the Companies Act 2013, the Government of India, a PSE is a government-owned corporation to earn profit and maximize social welfare with the share of the government not less than 51 percent of the total paid-up capital.⁷ In the above-mentioned examples, CIL, MSEDCL, UHBVN, and MECL are such PSEs. Third, the stolen product is sold further illegally by the unlicensed sellers in the black market at competitively determined price. Fourth, this is a localized form of crime and corruption in the sense that these activities take place at the micro (or individual) level, which leads to a substantial loss of resources at an aggregate level.

The domain of literature on corruption had extensively analyzed its various forms that include the agency theory of corruption (Khalil, Lawarrée, and Yun 2010; Laffont and Tirole 1991; Strausz 1997), bureaucratic corruption (Acemoglu and Verdier 2000; Breen and Gillanders 2012; Mookherjee and Png 1995; Shleifer and Vishny 1993), bribe competition (Burguet, Ganuza, and Garcia Montalvo 2016; Finocchiaro Castro 2020), corruption and foreign investment (Egger and Winner 2005; Gillanders and Parviainen 2018a; Habib and Zurawicki 2002), corruption and environment (Dincer and Fredriksson 2018; Ganda 2020) political cronyism and corruption (Chang 2020; Chang and Chu 2015; Chaudhuri, Dastidar, and Mahata 2022), etc. However, the literature linking organized crime and corruption has been very scant. Blackburn et al. (2017) is an exception that analyzed the macroeconomic implications of organized crime, its interaction with corruption and its effect on economic growth. They found that if corruption leads to a higher (lower) expected payoff from extortion, then the growth-diminishing effect of crime is greater (smaller) in the presence of corruption. The intuition behind the results is that corruption operates as a tax on criminality, the outcome depends on the expected payoffs of the crime syndicate when having to pay bribes to corrupt law officers. On the contrary, in our paper, corruption is necessary to sustain the criminal sector. Besides this, unlike Blackburn et al. (2017), which is a partial equilibrium analysis, our model captures the general equilibrium linkage of the crime–corruption nexus with a micro-foundation that explains the genesis of the criminal sector.

There has been substantial empirical research which analyzed the effect of corruption on foreign capital. The majority of the studies found a negative long-run impact of corruption on foreign capital inflow (Asiedu 2006; Habib and Zurawicki 2002; Hines 1995; Tosun, Yurdakul, and Iyidogan 2014; Wei 2000). This has been explained as corruption can increase the cost of doing business to the point of making the business unsustainable. On the other hand, corruption can lead to economic expansion if it acts as a substitute for poor governance and can act as a helping hand to foster foreign capital inflow (Al-Sadig 2009; Egger and Winner 2005; Gillanders and Parviainen 2018a; Houston 2007). This helping hand argument is based on the greasing the wheels’ hypothesis. We take the opposite route in this paper from the existing set of literature. We analyze how capital injection into the economy affects corruption as well as crime.

Next, we analytically examined the popular perception which suggests that stronger law enforcement authorities can lower the incidence of crime and corruption by keeping a strong vigil over the parallel economy. Some of the earlier literature on deterrence theory includes Beccaria (1764) and Bentham (1781). Beccaria (1764) proposed that punishment must be proportionate to the crime committed. Bentham (1781) argued on similar lines based on a utilitarian approach and suggested that people can be deterred from harming others by establishing punishments. Both theories assumed that a potential criminal compares the expected benefit of committing a crime with the benefit of not committing it. The theory held that if the cost of committing the crime is increased such that the net benefit from crime falls, people will not commit the crime. Becker (1968) provided a formal treatment to the theory of crime, punishment and deterrence. It was suggested that an increase in income opportunities in the legal sector would shift the balance and make it more likely that a person would not commit a crime. The arguments and analysis put forward by Beccaria (1764), Bentham (1781), and Becker (1968) led to the economic theory of rational deterrence that involved three crucial instruments—the severity of punishment, certainty of being caught, and the speed of punishment. Therefore, the government can indirectly increase the cost of committing a crime by utilizing the above three instruments. This perception was evident in the study by Haddad and Moghadam (2011), which obtained a negative relationship between crime and deterrence factors using the panel data for Iran. A similar result was obtained by Meera and Jayakumar (1995) using crime data from Malaysia. On the other hand, Dari-Mattiacci and Raskolnikov (2021) found that neither higher sanctions, nor a greater probability of detection unambiguously increases deterrence. Hazra (2020) found a positive association between deterrence and crime in India.

Given all of these, the purpose of this paper is to demonstrate the genesis of crime and corruption and the intertwining relationship between them in a distortion-ridden developing economy in the presence of a public sector that produces a private intermediate good. Crime in this paper is limited to the size of the criminal sector proxied by the number of criminals who plunder the public sector output, while corruption is measured by the amount of bribes paid by the criminal sector to the officials to evade punishment. Given the pre-existence of economic distortions and dysfunctionalities of the legal institutions, what we will try to show is that the effect of capital expansion on crime and corruption crucially depends on a multitude of factors including the labor relocation effect, capital relocation effect, and factor-intensity of sectors. The existing literature by and large has explained the effect of corruption on capital inflow. In this paper, we have reversed the causality and attempted to examine the effect of capital inflow on the crime–corruption nexus. In so doing we have attempted to develop a five-sector general equilibrium model for a small open economy. We will also try to show how the popular perception that deterrence lowers crime, may not hold good rather it may be counterproductive if general equilibrium interlinkage effects are carefully dealt with. Besides this, we demonstrate the theoretical conditions under which “crime does not pay,” that is, despite a rise in the risk of getting caught, criminals may not get higher compensation for undertaking this risk. This is opposite to Becker (1968) and is discussed later in the comparative statics section. The extant literature, by and large, uses a partial equilibrium framework, our departure is that we use a general equilibrium framework without sacrificing the micro foundation. Our analysis reveals how corruption becomes necessary to sustain this localized form of organized crime. We obtained that capital investment in the industrial sectors worsens the incidence of corruption and augments the criminal sector, while an attempt to improve the enforcement of law-and-order in presence of economic vis-à-vis legal distortion produces counterproductive outcomes in terms of an expansion of the criminal sector. These findings are premised on the “relative factor compositions of the sectors” and “dualism in both the labor and capital market” which is a crucial feature of a developing economy. In this model, the agriculture sector is relatively unskilled labor-intensive compared to natural resources (land) than the public sector, whereas, the manufacturing sector is relatively capital intensive compared to skilled labor than the high-skilled service sector. Unlike the partial equilibrium framework, the above results in our general equilibrium analysis depict the multitude of cross effects and sectoral interdependence. Finally, we attempt to test the proposition of Becker (1968) whether an increase in legal income by providing agriculture price subsidy can curb the size of the criminal sector.

Description of the Economy

We consider a $5 \times 5$ small open distortion-ridden developing economy, that is, with five sectors and five factors of production. The factors of production are land, unskilled labor, skilled labor, traditional capital (capital of type $I$ ), and advanced capital (capital of type $F$ ). Both labor and capital in this economy are heterogeneous and imperfectly mobile across sectors. The economy is characterized by a competitive final product market and an imperfect intermediate (middle) product market. The production side of the economy is elucidated as follows. Sector 1 is the agriculture sector that produces output using land, unskilled labor, and capital of type I. Sector G is a public sector enterprise (PSE, henceforth) that employs land, unskilled labor and capital of type F to produce an intermediate good that is used as an input in the production of manufacturing output in sector 2. Sector 1 and sector G are the two primary natural resource-based industries that exploit the natural resource land to produce its output. Sector 2 is a secondary semi-skilled sector that produces a final manufacturing commodity using unskilled labor, the output of sector G and capital of type F. Sector 3 is a high-skilled service sector that uses skilled labor and capital of type F.

Sector 1, 2, and 3 are the three-traded sectors, while the sector G is a non-traded PSE. The product prices of the three traded sectors are internationally determined, while the price of the intermediate product produced by the public sector is administratively fixed at an exogenous level, ${\bar{P}}_{G}$ . Sector G has the natural monopoly in the production of intermediate output; thus, it enjoys absolute market power and earns a positive economic profit (e.g., in India, CIL, Bharat Heavy Electrical Limited, and Indian Railways are some of the state-owned monopolies). Government is the owner of sector G and utilizes this profit towards providing price support to agriculture (sector 1) in terms of a price subsidy.

In the factor market, labor earns a competitive (informal) return W in sector 1 while its counterpart earns a high-institutionally determined (formal) wage rate, $\bar{W}$ $(> W)$ in sector G and sector 2. Skilled workers earn a high-skilled wage rate, $W_{S}$ in sector 2 and 3. Capital of type I is perfectly mobile between sector 1 and C and earns return $r_{I}$ , while type F capital is mobile only across the formal sectors of the economy (sector 2, 3, and $G$ ) and earns return $r_{F}$ . Although there exists unemployment of unskilled labor within sector 2 and sector G owing to the rigid wage rate, however, there exists no open unemployment. Those workers who are unable to find a job in the high-wage-paying sectors are absorbed in the flexible wage sectors at a lower equilibrium wage rate, W. Land earns a competitive rent, R. All factors are fully employed. This completes the description of the sectors that produce its output with legal approbation.

The operation of the illegal (criminal) sector of the economy exists parallel alongside the legally authorized sectors and all transactions carried out within this sector are legally prohibited and officially remain unaccounted. This criminal sector (sector $C$ ) plunders the output produced in the public sector (sector $G$ ) with the help of criminals of size Z and capital of type I. The amount of public sector output plundered (leakage) is denoted by $X_{C}$ , which is locally traded in the black market at a domestically determined price $P_{C}$ . For example, according to reports by India Today and the Mint, in India’s mineral-rich state of Jharkhand, the pilfered coal is sold by the coal mafias in the black market to local factories, brick kilns, and households at $19 to $20 per quintal (see Singh 2011; Singh, Chakraborty, and Katakey 2014). The amount of pillaged output $X_{C}$ is conditional on the number of criminals involved, their efficiency denoted by $α (Z), α^{'} (Z) > 0$ and the probability of getting caught and punished denoted by $Φ (X_{C}, ρ) \in [0, 1] \forall X_{C}, ρ \geq 0$ . The probability of getting caught depends positively on the amount of loot $X_{C}$ and negatively on the rate of bribe per unit of output stolen, $ρ$ .⁸ This is because the higher the amount of pillaged output of the public sector the higher will be the chance of getting exposed to the law enforcement authority compared to petty pilferage. On the other hand, the higher the rate of bribes to the officials lowers will be the chances of getting caught. Each criminal is compensated with $W_{Z}$ for the services provided to the sector C. Thus, each worker in the economy has the option either to engage in the labor market as formal labor in sector G and sector 2 or as informal labor in sector 1 or participate in the criminal sector.⁹

Table 1 summarizes the general equilibrium structure of the representative developing economy.

Table 1.

The Structure of the Representative Economy.

Production sectors	Factors of production	Returns to factor
Sector 1 (Agriculture Sector)	Unskilled labor	$W$
	Capital type-I	$r_{I}$
	Land	$R$
Sector G (Intermediate Public Sector)	Unskilled labor	$\bar{W}$
	Capital type-F	$r_{F}$
	Land	$R$
Sector 2 (Semi-skilled Manufacturing)	Skilled labor	$W_{S}$
	Unskilled labor	$\bar{W}$
	Capital type-F	$r_{F}$
	Intermediate input produced in sector G	${\bar{P}}_{G}$
Sector 3 (High-skilled Service Sector)	Skilled labor	$W_{S}$
Sector 3 (High-skilled Service Sector)	Capital type-F	$r_{F}$
Sector C (Criminal Sector)	Criminals	$W_{Z}$
	Capital type-I	$r_{I}$
	Bribe	$ρ$

In what follows, we highlight the legal and economic specification of the sectors in three categories—formal, informal, and illegal production activities. An activity is defined as formal when it is subject to government regulations and complies with these regulations (Kanbur, Lahiri, and Svejnar 2012). Sectors 2, 3, and G are the formal sectors. On the other hand, the shadow economy or the unofficial economy constitutes activities that are not recorded in government statistics (Choi and Thum 2005; Gillanders and Parviainen 2018b). It is pertinent to note that not all types of sectors operating in the shadow economy are illegal. Kanbur, Lahiri, and Svejnar (2012) specify the informal sector as the complement of the formal sector and categorizes three types of the informal sector—evaders, avoiders, and outsiders. “Evaders” are subject to regulation and are not complying, “avoiders” are not subject to regulations but only because they have adjusted out of the bounds and “outsiders” are those to whom regulation does not apply. The former two are involved in illegal practices while the latter falls under the legal ambit. In this paper, sector 1 (agriculture) and sector C (criminal) both constitute the informal economy. This is because workers in sector 1 and C receive an informal wage rate with loose job contracts (or, no job contracts), while its counterpart in the other formal sector receives a high-institutionally determined wage rate. Sector 1 falls under the third type of shadow economy, that is, outsiders to which regulations do not apply. For example, in India, the regulation of minimum wage legislation or social security benefits does not apply to the informal sectors which are unincorporated firms employing ten or fewer workers (Ministry of Labour and Employment, Government of India 2013–2014; Srija and Shirke 2014). Sector C is an illegal sector (evaders). According to the System of National Accounts (2008), SNA 6.43 specifies that illegal production activities include those whose sale or purchase are either forbidden by the law or unlicensed/unauthorized production of legal goods. Sector C illegally plunders the public sector output and sells it in the open market without legal authorization at a price different from what the government regulated.

Endogenous Determination of the Number of Criminals

The economy is populated with ( $L + S$ ) number of agents, where, $L, S$ are the number of unskilled and skilled labor, respectively. Each unskilled labor decides whether to engage in the criminal sector or remain in the legal labor market based on their preferences and optimal behavior.

To construct the model, the following notations are used:

$P_{2}, P_{3}$ : World price of goods produced in sector 2 and 3, respectively.

$V^{l}, V^{Z}$ : Utility functions of the unskilled labor engaged in the legal production sectors and the criminals, respectively.

$κ_{i}, κ_{i}^{'}$ : Expenditure share of the $i$ th good by the unskilled labor and criminal, respectively.

$X_{i}$ : Level of output of the i th sector, where $i = 1, 2, 3, G, C$ .

$a_{j i}$ : Factor output coefficient of the $j$ th factor in the $i$ th sector for $j = L, N, I, F, S$ and $i = 1, 2, 3, G, C$ .

$x_{i}^{l}, x_{i}^{Z}$ : Consumption of the unskilled labor engaged in the legal production sectors and the criminals, respectively.

If the representative unskilled individual remains in the legal production sectors, then the utility function takes the following form.¹⁰

V^{l} = \sum_{i - {G}} κ_{i} \log (x_{i}^{l}); κ_{i} > 0, \sum_{i - {G}} κ_{i} = 1

(1)

where

i = {1, 2, 3, G, C}

. The expected income of the unskilled individual from participating in the legal production sectors is given by

{\frac{W (a_{L 1} X_{1}) + \bar{W} (a_{L G} X_{G} + a_{L 2} X_{2})}{\bar{L} - Z}} .

The individual consumes the product plundered by sector C besides consumption of the normal commodities. The price for the agricultural product is assumed to be numeraire. This leads to the following budget constraint.

x_{1}^{l} + \sum_{i - {1, G}} P_{i} x_{i}^{l} = {\frac{W (a_{L 1} X_{1}) + \bar{W} (a_{L G} X_{G} + a_{L 2} X_{2})}{\bar{L} - Z}}

(2)

The individual solves the following optimization problem:

\begin{aligned} M a x V^{l} = \sum_{i - {G}} κ_{i} \log (x_{i}^{l}) \\ {x_{i}^{l}}_{i - {G}} \\ subject to, x_{1}^{l} + \sum_{i - {1, G}} P_{i} x_{i}^{l} = {\frac{W (a_{L 1} X_{1}) + \bar{W} (a_{L G} X_{G} + a_{L 2} X_{2})}{\bar{L} - Z}} \end{aligned}

(3)

Solving equation (3) yields the following Marshallian demand function for

x_{C}

x_{C}^{l} = \frac{κ_{C} {\frac{W (a_{L 1} X_{1}) + \bar{W} (a_{L G} X_{G} + a_{L 2} X_{2})}{\bar{L} - Z}}}{P_{C}}

(4)

The aggregate demand for commodity C is given by the following expression:

X_{C}^{D} (P_{C}, W, X_{1}, X_{2}, X_{G}) = (\bar{L} - Z) x_{C}^{l} = \frac{κ_{C} {W (a_{L 1} X_{1}) + \bar{W} (a_{L G} X_{G} + a_{L 2} X_{2})}}{P_{C}}

(5)

Substituting the optimal values in equation (1) leads to the following indirect utility function of the unskilled labor engaged in the legal production sectors:

V^{l} (W, X_{i}, P_{C}, Z) = \log (\frac{\tilde{κ} (W (a_{L 1} X_{1}) + \bar{W} (a_{L G} X_{G} + a_{L 2} X_{2}))}{{(P_{C})}^{κ_{c}} (\bar{L} - Z)})

(6)

where

\tilde{κ} = \frac{{(κ_{1})}^{κ_{1}} {(κ_{2})}^{κ_{2}} {(κ_{3})}^{κ_{3}} {(κ_{C})}^{κ_{c}}}{{(P_{2})}^{κ_{2}} {(P_{3})}^{κ_{3}}} > 0

Similarly, if the individual participates in the criminal sector the utility function takes the following form:

V^{z} = \sum_{i = 1}^{3} {κ^{'}}_{i} \log (x_{i}^{z}); κ_{1}^{'}, κ_{2}^{'}, κ_{3}^{'} > 0

(7)

Each criminal is compensated with

W_{Z}

for the services provided to sector C; thus, the expected earning of each criminal is given by

W_{Z} [1 - Φ (X_{C}, ρ)]

. The following assumption depicts the properties of

Φ (X_{C}, ρ)

Assumption 1:

(i) $\frac{\partial Φ (X_{C}, ρ)}{\partial X_{C}} > 0$ and $\frac{\partial Φ (X_{C}, ρ)}{\partial ρ} < 0$ , (ii) $Φ (X_{C}, 0) = \bar{Φ} > Φ (X_{C}, ρ) > Φ (0, ρ) = 0$ , (iii) $0 \leq Φ (X_{C}, ρ) < 1$ .

Assumption 1(i) implies that an increase in the amount of pillaged output increases the chance of the criminal sector getting exposed to the legal system or the vigilance authorities, while a higher bribe rate reduces the chance of getting caught. Assumption 1(ii) reveals that if no bribe is paid then the probability of getting caught is relatively high, that is, $\bar{Φ}$ than if any bribe would have been paid. Finally, assumption 1(iii) represents the weak (distorted) legal system that prevails throughout.¹¹

Given assumption 1, the individual solves the following constrained-optimization problem while engaged in the criminal sector:

\begin{aligned} M a x V^{z} = \sum_{i = 1}^{3} {κ^{'}}_{i} \log (x_{i}^{z}) \\ {x_{i}^{z}} \\ s u b j e c t t o, x_{1}^{z} + P_{2} x_{2}^{z} + P_{3} x_{3}^{z} = W_{Z} [1 - Φ (X_{C}, ρ)] \end{aligned}

(8)

Solving equation (8) leads to the following indirect utility (optimal value) function:

V^{Z} (W_{Z}, X_{C}, ρ) = \log (\tilde{κ^{'}} W_{Z}) + \log (1 - Φ (X_{C}, ρ))

(9)

where

\tilde{κ^{'}} = \frac{{({κ^{'}}_{1})}^{κ_{1^{'}}} {({κ^{'}}_{2})}^{κ_{2^{'}}} {({κ^{'}}_{3})}^{κ_{3^{'}}}}{{(P_{2})}^{κ_{2^{'}}} {(P_{3})}^{κ_{3^{'}}}} > 0

The marginal individual is indifferent where the expected income from engaging in the criminal sector is equal to the expected wage income from participating in the labor market that is obtained by juxtaposing equations (6) and (9). This leads to the following labor relocation equilibrium between the legal product sectors and the criminal sector:

\tilde{κ} {\frac{W (a_{L 1} X_{1}) + \bar{W} (a_{L G} X_{G} + a_{L 2} X_{2})}{{(P_{C})}^{κ_{c}} (\bar{L} - Z)}} = \tilde{κ^{'}} {W_{Z} [1 - Φ (X_{C}, ρ)]}

(10)

The General Equilibrium Analogue

The following notations are used to represent variables and parameters:

$s$ : agriculture price subsidy. $K_{I}$ : Traditional capital stock.

$K_{F}$ : Advance capital stock. $N$ : Land endowment.

$λ_{j i}$ : physical share of the jth factor in the i th sector.

$θ_{j i}$ : value share of the jth factor in the i th sector.

$k_{C}$ : unit capital share in the criminal sector.

$x_{G 2} = \frac{X_{G} - X_{C}}{X_{2}}$ : public sector output available per unit sector 2’s output net of the plundered output.

$''$ : proportional change of variables and parameters.

The price-average cost parity condition in sectors 1, 2, and 3 are represented by the following expressions:

W a_{L 1} + R a_{N 1} + r_{I} a_{K 1} = 1 + s

(11)

W_{S} a_{S 2} + \bar{W} a_{L 2} + {\bar{P}}_{G} x_{G 2} + r_{F} a_{K 2} = P_{2}

(12)

W_{S} a_{S 3} + r_{F} a_{K 3} = P_{3}

(13)

The public sector has a natural monopoly in the production of intermediate input $X_{G}$ ; thus, it earns a profit that is a markup (denoted by, $m > 0$ ) over its cost of production which leads to the following profit-maximizing condition:

k (\bar{W} a_{L G} + R a_{N G} + r_{F} a_{K G}) = {\bar{P}}_{G}

(14)

where

k = 1 + m, f o r m > 0, k > 1

The criminal sector (sector $C$ ) incurs the cost of hiring criminals ( $W_{Z} Z$ ), payment for bribing the officials ( $ρ P_{C} X_{C}$ ), and the cost of capital ( $r_{I} k_{C} X_{C}$ ). The pillaged output is sold in the black market at a locally determined price $P_{C}$ . Thus, the break-even condition for the criminal is given by the following expression:

W_{Z} (1 - Φ) Z + ρ P_{C} X_{C} + r_{I} k_{C} X_{C} = P_{C} X_{C}; ρ \in [0, 1]

(15)

Bribes are central to the criminal syndicates conniving with the police and the judiciary to reduce the risk of detection and prosecution. This can foster corruption at all levels of public office. In this model, the total value generated by the criminal sector from selling the stolen PSEs output is given by

P_{C} X_{C}

. On one hand, a fraction of

P_{C} X_{C}

, that is,

(1 - ρ) P_{C} X_{C}

is spent on hiring criminals and making payments to the capitalist of type I. On the other hand, the remaining fraction of

P_{C} X_{C}

that is,

ρ P_{C} X_{C}

is spent on bribes paid to the officials to evade punishments. Thus, the total amount of corruption in this economy is measured by the amount

ρ P_{C} X_{C}

The factor market equilibrium conditions are obtained as follows:

a_{K 1} X_{1} + k_{C} X_{C} = K_{I}

(16)

a_{K 2} X_{2} + a_{K 3} X_{3} + a_{K G} X_{G} = K_{F}

(17)

a_{N 1} X_{1} + a_{N G} X_{G} = N

(18)

a_{L 1} X_{1} + a_{L 2} X_{2} + a_{L G} X_{G} = L - Φ Z - (1 - Φ) Z

(19)

a_{L 1} X_{1} + a_{L 2} X_{2} + a_{L G} X_{G} = L - Z

(19.1)

a_{S 2} X_{2} + a_{S 3} X_{3} = S

(20)

We make the following assumptions:

Assumption 2:

(i) $λ_{L N}^{1 G} = (λ_{L 1} λ_{N G} - λ_{L G} λ_{N 1}) > 0$ , (ii) $λ_{K S}^{23} = (λ_{K 2} λ_{S 3} - λ_{S 2} λ_{K 3}) > 0$ ; $θ_{S K}^{32} = (θ_{S 3} θ_{K 2} - θ_{S 2} θ_{K 3}) > 0$ and (iii) $λ_{K 1} λ_{N G} - λ_{N 1} {\frac{λ_{K Z}}{1 + (\frac{Φ}{1 - Φ}) ε_{X_{C}}^{Φ}}} > 0$ .

Assumption 3:

(i) $α (Z) \in (0, 1] \forall Z > 0$ , (ii) $α (0) = 0$ , (iii) $α^{'} (Z) > 0$ .

Assumption 2(i) and 2(ii) implies that sector 1 is relatively unskilled labor-intensive compared to the land than sector G and sector 2 is relatively more capital-intensive compared to skilled labor than sector 3 in both physical and value sense, respectively. 2(iii) is required as a sufficient condition to solve the values of equilibrium changes in the variables of the economy.

The amount of pillaged output ( $X_{C}$ ) depends on the proportion of $X_{G}$ that is theft by Z number of criminals is denoted by $α (Z)$ . In other words, $α (Z)$ represent both proportions of public sector output that is pillaged or the efficiency of each criminal. Assumption 3 defines the properties of $α (Z)$ . Thus, $X_{C}$ is given by the following equation:

X_{C} = [1 - Φ (X_{C}, ρ)] α (Z) X_{G}

(21)

Given that the pillaged output is sold in the local (domestic) market; thus, the market-clearing condition for commodity

X_{C}

is as follows:

X_{C} = X_{C}^{D} (P_{C}, W, X_{1}, X_{2}, X_{G})

(22)

The government uses its profit generated by the public sector to finance the cost of agriculture price subsidy; equation (23) depicts the government’s balanced budget condition.

(k - 1) (\bar{W} a_{L G} + R a_{N G} + r_{F} a_{K G}) (X_{G} - X_{C}) = s X_{1}

(23)

Determination of General Equilibrium Variables and Their Properties

In this general equilibrium system, there are fifteen main endogenous variables, viz., $W, W_{S}, W_{Z}, k, R, r_{I}, r_{F}, P_{C}, X_{1}, X_{2}, X_{3}, X_{G}, X_{C}, Z$ , and $X_{C}^{D}$ , with the same number of independent equations (i.e., equations (5), (10)–(18), (19.1), and (20)–(23)). On solving the simultaneous equations (16), (18), (19.1) and (21) the values of $X_{1}$ , $X_{G}, X_{C}$ , and Z are obtained in terms of $X_{2}$ . The final equilibrium values of $X_{2}$ and $X_{3}$ are obtained from solving equations (17) and (20). This leads to the determination of final equilibrium values of $X_{1}$ , $X_{G}, X_{C}$ , and Z. Thus, output levels are determined independently of the price system. $W_{S}$ and $r_{F}$ are solved from equations (12) and (13). Equation (14) solves for R. k is solved from equation (23). Finally, the equilibrium values of $W, W_{Z}$ , $r_{I}$ , $P_{C}$ , and $X_{C}^{D}$ are obtained from solving the simultaneous equations (5), (10), (11), and (22). This completes the determination of general equilibrium values and description of the working of the model. Non-reversibility of factor-intensity ranking and Walrasian stability for the non-traded commodity ensures the stability of the general equilibrium system. The model is non-decomposable; however, $W_{S}$ , $r_{F}$ , and R are determined independently of the endowment system, thus is invariant to change in factor endowment levels.¹²

The model can be further decomposed into subsystems. A subsystem is a miniature form of Hecksher-Ohlin that comprises sectors among which at least two factors are (perfectly or imperfectly) mobile which confirms the Hecksher-Ohlin properties. In the present setup, sectors 2 and 3 form a subsystem with respect to capital type F and skilled labor, sectors 1 and C are another subsystem with respect to capital type I and unskilled labor, and sectors 1 and G form a subsystem with respect to land and unskilled labor. This completes the description of the properties of the model.

Comparative Statics

In this section, we perform a few comparative static exercises pertaining to our analysis.

Capital, Crime, and Corruption

An increase in capital investment in the industrial sectors (sectors 2, 3, and $G$ ) is implied by an increase in the parameter $K_{F}$ . This may be due to foreign capital account liberalization, which leads to an inflow of foreign capital that raises the capital endowment in the domestic economy, or an influx of capital by the government or some international agency such as the IMF or the World Bank. Given the pre-existence of economic distortions and dysfunctionalities of the legal institutions, what could be the effect of such an expansion of domestic capital endowment on crime and corruption besides other economic outcomes? Provided the sufficient conditions (S1)–(S3) (see Appendix B), we take the total derivative of equations (5), (10)–(18), (19.1), and (20)–(23), substituting $\hat{s} = 0$ and $\hat{ρ} = 0$ and assuming ${\hat{K}}_{F} > 0$ leads to the result in propositions 1–3.

The intuitive explanation can be offered as follows. An increase in capital endowment $K_{F}$ leads to a higher capital availability in the subsystem comprising sectors 2 and 3, which causes an expansion of the capital-intensive sector 2 and contraction of sector 3 given assumption 2(ii). This is the Rybczynski effect (RE, henceforth). The expanding sector 2 draws out unskilled labor from the other subsystem comprising sector 1 and G. This causes a fall in the availability of unskilled labor to sector 1 and G, which causes the relatively labor-intensive sector 1 to contract and sector G expand. This is the Rybczynski-type effect (RTE, henceforth).¹³ The following proposition is immediate.

Proposition 1:

An increase in capital investment in the industrial sectors leads to (i) contraction of the agriculture sector, (ii) the high-skilled sector contracts while the semi-skilled sector expands, and (iii) the public sector expands.

The contracting sector 1 releases capital type I, which gets absorbed in sector C, which leads to an expansion of sector C. This effect arises due to the relocation of capital across the sectors and can be termed as capital relocation effect (CRE, henceforth). It follows from equation (21) that on one hand, an expansion of sector C (rise in $X_{C}$ ) escalates the demand for criminals, Z, while, on the other hand, an increase in $X_{G}$ due to an expansion of sector G for given level of $X_{C}$ causes a fall in demand for criminals, Z. This is because a higher amount of $X_{G}$ can be plundered even with a smaller number of criminals as the scale of public sector operation expands. The former effect dominates the latter causing a net increase in Z provided assumption 2(iii) and a sufficient condition (S1). An increase in Z and an increase in $X_{G}$ causes a rise in $X_{C}$ . This effect along with CRE leads to a relatively higher increase in $X_{C}$ compare to $X_{G}$ . Therefore, the net availability of $X_{G}$ (denoted by, $x_{G 2}$ ) to sector 2 falls.

An increase in Z leads to the relocation of labor across the sectors which can be termed as the labor relocation effect (LRE, henceforth). As Z rises, the availability of unskilled labor $(L - Z)$ falls to the subsystem comprising sector 1 and G. This leads to an RTE that causes contraction of sector 1 and an expansion of sector G, which reinforces the CRE effect. These effects are summarized in Proposition 2.

Proposition 2:

The scale of operation of the criminal sector expands, the probability of getting caught by legal institutions rises and the number of unskilled individuals choosing to become criminals escalates due to capital augmentation in the industrial sector. Output plundered by the criminal sector accentuates.

From the earlier discussion, it is found that the net availability of public sector output falls ( $x_{G 2}$ falls). Thus, the per unit use of $X_{G}$ in sector 2 falls. This lowers the expenditure on the intermediate input, which causes a rise in the net price of commodity 2, that is, $(P_{2} * - x_{G 2} {\bar{P}}_{G})$ rises (equation (12)). Since sector 2 and 3 forms a subsystem, a rise in $(P_{2} * - x_{G 2} {\bar{P}}_{G})$ leads to a Stolper–Samuelson type effect, which causes a rise in $r_{F}$ and fall in $W_{S}$ .¹⁴ From the balance budget condition in equation (23), a fall in $X_{1}$ lowers the fiscal expenditure on subsidies, while a fall in net output (also net revenue) of the public sector lowers the fiscal revenue of the government. Thus, to maintain the balanced-budget condition, k must fall provided the fall in fiscal expenditure dominates the decrease in fiscal revenue. It follows from equation (14) that a fall in k must be accompanied by a rise in R to restore the profit-maximizing condition in sector G. The expansion of the high-wage paying sector causes an expansion of demand for $X_{C}$ , which leads to an increase in its price, $P_{C}$ to equilibrate the market given the Walrasian stability condition. A rise in total earnings of the criminal sector ( $P_{C} X_{C}$ ) leads to an expansion of demand for the capital of type I which inflates its return, $r_{I}$ . In sector 1, a rise in the cost of capital $(r_{I})$ and a rise in land rent $(R)$ increase the average cost of production; thus, the net unit revenue shortfalls of its unit cost. Thus, demand for unskilled labor in sector 1 falls, which causes a fall in W, which restores the equilibrium condition in equation (11). In the labor relocation equilibrium in equation (10), on one hand, the high-wage paying sector 2 and G expands, which augments the expected returns to the unskilled labor in the legal production sectors (LHS rises) and a rise in $Φ$ lowers the expected return in the criminal sector (RHS falls). The above two effects lead to a movement of labor from the criminal sector to the legal labor market. On other hand, a rise in $P_{C}$ lowers the real expected returns in the legal labor market (LHS of equation (10)). This causes the opposite movement of labor to the criminal sector. Thus, the effect on $W_{Z}$ hinges on the above multitude of cross effects. If the former two effects dominate the latter then labor supply in the criminal sector falls which causes a rise in $W_{Z}$ and vice-versa.

Proposition 3:

An increase in the stock of capital lowers the expected return to the unskilled labor in the legal production sectors which makes the legal labor market less attractive relative to the criminal sector. However, capitalists gain, while the market power of the public sector deteriorates.

Policy Analysis

Two alternative policy measures are analyzed in terms of an attempt to improve the law-and-order measures and an increase in agriculture price subsidies. Popular perception suggests that stronger law enforcement authorities can lower the incidence of crime and corruption by keeping a strong vigil over the parallel economy. On the other hand, a subsidy to the agriculture sector causes it to expand; thus, employment opportunities in the legal commodity-producing sectors improve which incentivizes unskilled workers to keep themselves away from engaging in the criminal sector. This is based on Becker (1968) that an increase in legal income can be an economic deterrence to lower crime. In what follows, this common perception is tested against the analytical framework in this paper.

Deterrence and Stringent Law Enforcement. The perception that a rise in detection rate reduces the size of the so-called criminal sector was evident in the study by Haddad and Moghadam (2011), which obtained a negative relationship between crime and deterrence factors using the panel data for Iran. A similar result was obtained by Meera and Jayakumar (1995) using crime data from Malaysia. On the contrary, Hazra (2020) found a positive association between deterrence and crime in India. This suggests that the deterrence factors have mixed effects on the incidence of crime in developing economies. What we find in this section is a possibility of expansion of the criminal sector in the wake of the rise in detection rate.

In this paper, the bribe rate $ρ$ is a distortion in the legal system. A stronger law enforcement agency is implied by an exogenous fall in the value of $ρ$ . This improves the probability of getting caught by the vigilant authorities which is implied by a rise in $Φ$ . Thus, the fraction of pillaged output falls, as a rise in $Φ$ implies a rise in the rate of confiscation. This incentivizes sector C to hire more criminals to increase the amount of pillaged output, which implies an increase in $Z$ (equation (21)) that leads to the LRE. This causes a fall in the availability of unskilled workers that leads to an RTE causing contraction of sector 1 and an expansion of sector G. Contracting sector 1 releases capital of type I which gets absorbed by sector C causing it to expand (equation (16)). On the other hand, the expanding public sector G draws out capital leading to a CRE. This causes contraction of sector 2 and an expansion of sector 3, owing to an RTE. Contraction of sector 2 leads to the release of unskilled workers, which gets absorbed in the subsystem comprising sector 1 and G. This causes an expansion of sector 1 and contraction of sector G, which further leads to contraction of sector C. The use of Z falls as $X_{C}$ falls. If the former LRE dominates the latter CRE under certain sufficient conditions, then it leads to a rise in the equilibrium value of Z, $X_{G}$ , $X_{C}$ , $X_{3}$ and a fall in $X_{1}$ and $X_{2}$ . The effect on factor prices remains the same as in the case of rise in $K_{F}$ .¹⁵ The effect on $W_{Z}$ depends on the expected earnings from the legal labor market and the criminal sector. First, a rise in $X_{C}$ and fall in $ρ$ increases the chances of getting caught (rise in $Φ$ ). This lowers the expected earnings in the criminal sector; thus, labor moves to the legal labor market. On the other hand, the high-wage paying public sector expands while sector 2 contracts. The expected wage in the legal labor market may fall if the contraction of sector 2 dominates the expansion rate of sector G. This will deflate the expected return in the legal labor market, thus driving out labor from the legal sectors to get engaged in the criminal sector. This will increase the supply of labor in the criminal sector. If this latter effect dominates the former effect, then labor supply in sector C will increase, which will lead to a fall in $W_{Z}$ . Thus, we get that despite a rise in the chance of detection, the reward to criminals ( $W_{Z}$ ) may fall. This result is opposite to Becker (1968) who found that the real income of the criminals in risky activities is, at the margin, relatively higher for taking a higher risk with an increase in the chance of getting detected. Thus, in Becker (1968) for such risk-averse criminals, “crime pays.” However, in this exercise, given the general equilibrium sector interlinkages, we have demonstrated the conditions in which “crime does not pay.” In other words, the wage rate of criminal plummets despite a higher risk of getting caught. The following proposition is immediate.

Proposition 4:

An improvement in law-and-order that curbs down the rate of bribes leads to counterproductive results in terms of accentuating the amount of pillaged output, expansion of the criminal sector and may augment the number of criminals.

Agriculture Price Subsidy. The question that is raised in this section is whether an agriculture price subsidy can act as friction to restrict unskilled workers from joining the criminal sector. To obtain the effect of a rise in agriculture price subsidy, we substitute $\hat{s} > 0$ in equations (24)–(38).

The intuition can be explained as follows. From equation (23) we find that a rise in “ $s$ ” augments the fiscal expenditure of the government, implied by an increase in $s X_{1}$ . Thus, k must rise to maintain the balanced-budget condition in equation (23). The returns to land, R falls in response to a rise in k to restore the profit-maximizing condition in sector 2 (equation (14)). A fall in the value of R lowers the unit cost of production in sector 1, while a rise in s increases the unit revenue in this sector. This causes demand for unskilled labor to rise which leads to a rise in W (equation (11)). A rise in income of the unskilled workers escalates the demand for commodity C, which inflates the equilibrium price in this sector, $P_{C}$ . From equation (15), a rise in $P_{C} X_{C}$ implies a rise in revenue in the criminal sector; thus, the unit revenue exceeds the unit cost. This leads to an increase in demand for capital of type I, which causes a rise in $r_{I}$ . Thus, it follows from equation (11) that W must fall. The former (direct) effect due to an increase in s on W dominates the latter (indirect) effect due to rise in $r_{I}$ under the sufficient condition (S4), which leads to an increase in the final equilibrium value of W. Finally, $W_{Z}$ rises from the labor relocation equilibrium in equation (10), provided the real unskilled informal wage given by $(W / P_{C})$ rises subject to the condition (S5). The following proposition is immediate.

Proposition 5:

An increase in agriculture price subsidy generates no effect on the size of the criminal sector and the number of criminals, the output of other sectors remains unchanged; however, the amount of corruption inflates.

Conclusion

The paper analytically examined the intertwining relationship between organized crime and corruption from a developing economy perspective distorted by labor and capital market imperfections. In so doing, we have developed a $5 \times 5$ general equilibrium model in presence of an intermediate input-producing public sector. Organized crime in this paper takes the form of plundering of output produced by the public sector. This is a dominating feature of less-developed economies with a dysfunctional legal system. Thus, the criminal sector does not add value to production because it involves illegal/forceful transfer of output produced in the public sector. However, the criminal sector generates income and spending in the economy by selling the plundered output in the open market at unregulated prices. First, we have laid down a micro foundation to provide the economic rationale behind the decision of the unskilled individuals to engage as criminals which have been derived in terms of labor-relocation equilibrium condition from the individual’s optimizing behavior. The marginal individual is found to balance the expected earnings from the criminal sector net of punishment and the earnings from engaging in the legal labor market. This partial equilibrium result is in line with the majority of existing literature on deterrence theory (Becker 1968; Haddad and Moghadam 2011). However, later in our analysis, we have shown that the general equilibrium interlinkage effect can contradict the deterrence measures. Corruption in this analysis is defined as the aggregate amount of bribes paid to the law enforcement authorities which sustains with the help of the criminal sector. The existing literature by and large has explained the effect of corruption on capital inflow. In this paper, we have reversed the causality and attempted to examine the effect of capital inflow on the crime–corruption nexus. We found that an increase in capital endowment causes an expansion of the criminal sector and augments the amount of corruption and the number of criminals accentuates. The policy response in terms of a stringent law-and-order leads to counterproductive outcomes, which reimpose the effect due to capital expansion, while the policy of agriculture price subsidy does not affect the size of the criminal sector and the number of criminals, the output of other sectors remains unchanged, however, this inflates the amount of corruption. The results of the paper are based on general equilibrium outcomes, which we have decomposed into labor relocation effect, capital relocation effect and factor-intensity of sectors. The paper can be extended to incorporate the intertwining relationship between electoral malpractice and the crime–corruption nexus by explicitly modelling the optimal behavior of political agents in power who can bend regulation in favor of their cronies.

Footnotes

Acknowledgments

The authors are grateful to the editor and the anonymous referee for their comments and revisions. The authors are also indebted for the constructive comments received from Dr. Jayanta Kumar Dwibedi at 2nd Annual Economics Conference, St. Xavier’s University, Kolkata, India; Dr. Amarjyoti Mahanta at GRM 2021, Indian Institute of Technology, Guwahati, India; Prof. Oguzhan Dincer, Dr. Robert Gillanders, Dr. Eric C.C. Chang, and Mr. Nicolás Jaramillo at 3rd Workshop on Corruption, Institute of Corruption Studies, Illinois State University, USA; Dr. Biswajit Nag at 7th EIITF Conference, Indian Institute of Foreign Trade, Kolkata, India; and Dr. Shivangi Chandel at ACEP 2021, OP Jindal Global University, Haryana, India, which helped improve and clarify this paper. The authors remember (Late) Prof. Sarbajit Chaudhuri with great fondness and respect for encouraging diverse issue-based research in the domain of applied general equilibrium models. This work is in memory of (Late) Prof Sarbajit Chaudhuri. However, the usual disclaimer applies.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article

ORCID iDs

Rohan Kanti Khan

Sushobhan Mahata

Notes

Author Biographies

Rohan Kanti Khan: Rohan Kanti Khan is an Assistant Professor of Applied Economics at Maulana Abul Kalam Azad University of Technology, India and a PhD Scholar at the Department of Economics, University of Calcutta, India. His main research interests are corruption, crime and labour economics, with a special interest in household modelling and child labour. His research methodology includes applied general equilibrium models and micro-theoretical models. He has published a recent article in the journal Research in Economics on gendered aspects of COVID-19.

Sushobhan Mahata: Sushobhan Mahata holds a position as a Junior Research Fellow (UGC, New Delhi) at the Department of Economics, University of Calcutta, India. His main research interests are in corruption, crime, labour economics, and political economics, with a special interest in household modelling and development economics. His research methodology includes applied game-theoretic models, applied general equilibrium models and micro-theoretic models. He has published several articles, including a recent article in the journal Managerial and Decision Economics (Wiley) on cronyism and corruption.

Ranjanendra Narayan Nag: Ranjanendra Narayan Nag (PhD) is an Associate Professor at the Department of Economics, St. Xavier's College (Autonomous), Kolkata, India. His main research interest includes open economy macroeconomics, general equilibrium and international trade. He has published over fifty journals some of which include the South Asian Journal of Macroeconomics and Public Finance, Keio Economic Studies, Global Business Review, Indian Economic Review, Macroeconomics and Finance in Emerging Market Economies, and The Journal of Applied Economic Research among others.

References

Acemoglu

Verdier

. 2000. “The Choice between Market Failures and Corruption.” American Economic Review 90 (1): 194–211. https://doi.org/10.1257/AER.90.1.194.

Al-Sadig

2009. “The Effect of Corruption on FDI Inflows.” Cato Journal 29 (2): 267–94. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2264898

Asiedu

2006. “Foreign Direct Investment in Africa: The Role of Natural Resources, Market Size, Government Policy, Institutions and Political Instability.” The World Economy 29 (1): 63–77. https://doi.org/10.1111/j.1467-9701.2006.00758.x.

Assam Tribune. 2021. “Probe unearths massive power theft at Byrnihat Industrial Area.” https://assamtribune.com/probe-unearths-massive-power-theft-at-byrnihat-industrial-area/.

Bang

N. P.

Singh

. 2013. "The Untold Coal Story: Jharkhand's Cycle Pullers Work for a Pittance." Firstpost. https://www.firstpost.com/india/the-untold-coal-story-jharkhands-cycle-pullers-work-for-a-pittance-1224821.html.

Beccaria

1764. An Essay on Crimes and Punishments. W.C. Little and Company.

Becker

G. S.

1968. “Crime and Punishment: An Economic Approach.” Journal of Political Economy 76 (2): 169–217. https://doi.org/10.1086/259394

Bentham

1781. An Introduction to the Principles of Morals and Legislation. Batoche Books, Kitchener.

Blackburn

Neanidis

K. C.

Rana

M. P.

. 2017. “A Theory of Organized Crime, Corruption and Economic Growth.” Econ Theory Bull 5: 227–45. https://doi.org/10.1007/s40505-017-0116-5.

10.

Breen

Gillanders

. 2012. “Corruption, institutions and regulation.” Economics of Governance 13 (3): 263–85. https://doi.org/10.1007/S10101-012-0111-0.

11.

Burguet

Ganuza

Garcia Montalvo

. 2016. The Microeconomics of Corruption. A Review of Thirty Years of Research. Economics Working Paper, 908, May.

12.

Chang

E. C. C.

2020. “Corruption Predictability and Corruption Voting in Asian Democracies.” Public Choice 184 (3–4): 307–26. https://doi.org/10.1007/S11127-019-00760-X/FIGURES/4.

13.

Chang

E. C. C.

Chu

Y. H.

. 2015. “Corruption and Trust: Exceptionalism in Asian Democracies?” The Journal of Politics 68 (2): 259–71. https://doi.org/10.1111/J.1468-2508.2006.00404.X.

14.

Chatterjee

Viriyam

. 2021. “Power Theft Management: The Indian Chapter-Is It Time for Refurbishment." Indian Journal of Projects, Infrastructure and Energy Law. https://ijpiel.com/index.php/2021/05/10/power-theft-management-the-india-chapter-is-it-time-for-a-refurbishment/

15.

Chaudhuri

Biswas

. 2016. “Endogenous Labour Market Imperfection, Foreign Direct Investment and External Terms-of-Trade Shocks in a Developing Economy.” Economic Modelling 59 (C): 416–24. https://doi.org/10.1016/j.econmod.2016.08.011.

16.

Chaudhuri

Dastidar

K. G.

Mahata

. 2022. “Cronyism and Corruption in India: A Game Theoretic Analysis.” Managerial and Decision Economics 2021 (6): 1–12. https://doi.org/10.1002/mde.3550.

17.

Choi

J. P.

Thum

. 2005. “Corruption and the Shadow Economy.” International Economic Review 46 (3): 817–836: https://doi.org/10.1111/j.1468-2354.2005.00347.x.

18.

Daniel

F. J.

Williams

. 2013. Special Report - “Coal Mafia” Stokes India’s Power Crisis. Dhanbad: Reuters.

19.

Dari-Mattiacci

Raskolnikov

. 2021. “Unexpected Effects of Expected Sanctions.” The Journal of Legal Studies 50 (1): 35–74.

20.

Dincer

O. C.

Fredriksson

P. G.

. 2018. “Corruption and Environmental Regulatory Policy in the United States: Does Trust Matter?” Resource and Energy Economics 54: 212–25. https://doi.org/10.1016/J.RESENEECO.2018.10.001.

21.

Economic Times. 2019. "Maharashtra: MSEDCL Detects over 1900 Power Theft Cases.” December 25. https://energy.economictimes.indiatimes.com/news/power/nshik-msedcl-detects-over-1900-power-theft-cases/72962699.

22.

Economic Times. 2021. "5,508 Power Theft Cases Detected in Haryana during Raids over Two Days.” Published on July 11. https://energy.economictimes.indiatimes.com/news/power/5508-power-theft-cases-detected-in-haryana-during-raids-over-two-days/84316920.

23.

Egger

Winner

. 2005. “Evidence on Corruption as an Incentive for Foreign Direct Investment.” European Journal of Political Economy 21 (4): 932–52. https://doi.org/10.1016/J.EJPOLECO.2005.01.002.

24.

Finocchiaro Castro

2020. “To Bribe or Not to Bribe? An Experimental Analysis of Corruption.” Italian Economic Journal 7 (3): 487–508. https://doi.org/10.1007/S40797-020-00129-W/FIGURES/6.

25.

Ganda

2020. “The Influence of Corruption on Environmental Sustainability in the Developing Economies of Southern Africa.” Heliyon 6 (7): e04387. https://doi.org/10.1016/J.HELIYON.2020.E04387.

26.

Gillanders

Parviainen

. 2018a. “Experts’ Perceptions Versus Firms’ Experiences of Corruption and Foreign Direct Investment.” The Manchester School 86 (2): 195–218. https://doi.org/10.1111/MANC.12199.

27.

Gillanders

Parviainen

. 2018b. “Corruption and the Shadow Economy at the Regional Level.” Review of Development Economics 22 (4): 1729–43. https://doi.org/10.1111/RODE.12517.

28.

Habib

Zurawicki

. 2002. “Corruption and Foreign Direct Investment.” Journal of International Business Studies 33 (2): 291–307. https://doi.org/10.1057/palgrave.jibs.8491017.

29.

Haddad

G. K.

Moghadam

H. M.

. 2011. “The Socioeconomic and Demographic Determinants of Crime in Iran (a Regional Panel Study).” European Journal of Law and Economics 32 (1): 99–114.

30.

Hazra

2020. “What Does (and Does Not) Affect Crime in India?” International Journal of Social Economic 47: 4. https://doi/10.1108/IJSE-03-2019-0206/full/html

31.

Hines

J. R.

1995. Forbidden Payment: Foreign Bribery and American Business after 1977. NBER Working paper 5266. Cambridge, MA: National Bureau of Economic Research.

32.

Houston

2007. “Can Corruption Ever Improve an Economy?” Cato Journal 27 (3): 325–342. http://www.cato.org/sites/cato.org/files/serials/f...2007/11/cj27n3-2.pdf.

33.

Kanbur

Lahiri

Svejnar

. 2012. “Informality, Illegality and Enforcement: Introduction.” Review of Development Economics 16 (4): 511–511. https://doi.org/10.1111/rode.12000.

34.

Khalil

Lawarrée

Yun

. 2010. “Bribery Versus Extortion: Allowing the Lesser of Two Evils.” The RAND Journal of Economics 41 (1): 179–98. https://doi.org/10.1111/J.1756-2171.2009.00095.X.

35.

Labour Bureau. 2013–2014. Report on Employment in Informal Sector and Conditions of Informal Employment. Ministry of Labour and Employment, Government of India.

36.

Laffont

J.-J.

Tirole

. 1991. “The Politics of Government Decision-Making: A Theory of Regulatory Capture.” The Quarterly Journal of Economics 106 (4): 1089–127. https://doi.org/10.2307/2937958.

37.

Lahiri-Dutt

Williams

D. J.

. 2005. “The Coal Cycle: Small-Scale Illegal Coal Supply in Eastern India.” Resources, Energy, and Development 2 (2): 93–105. https://doi.org/10.3233/red-120019.

38.

Meera

A. K.

Jayakumar

M. D.

. 1995. “Determinants of Crime in a Developing Country: A Regression Model.” Applied Economics 27 (5): 455–60.

39.

Mookherjee

Png

I. P. L.

. 1995. “Corruptible Law Enforcers: How Should They Be Compensated?” The Economic Journal 105 (428): 145–59. https://doi.org/10.2307/2235324.

40.

Mukhopadhyay

Chaudhuri

. 2013. “Economic Liberalisation, Gender Wage Inequality and Welfare.” The Journal of International Trade and Economic Development 22 (8): 1214–39. https://doi.org/10.1080/09638199.2012.654400

41.

Rege

2015. “Not Biting the Dust: Using a Tripartite Model of Organized Crime to Examine India’s Sand Mafia.” International Journal of Comparative and Applied Criminal Justice 40 (2): 101–21. https://doi.org/10.1080/01924036.2015.1082486.

42.

Seshacharyulu

2013. Night Life on Krishna River High with Sand Mining. Indian Express, November18. http://www.newindianexpress.com/states/andhra_pradesh/Night-life-on-Krishna-river-high-with-sand-mining/2013/11/18/article1896842.ece.

43.

Shleifer

Vishny

R. W.

. 1993. “Corruption.” Quarterly Journal of Economics 108 (3): 599–617. https://doi.org/10.2307/2118402.

44.

Singh

2013. "India's Coal Cycle Wallahs: People Have No Alternative but to Steal from Mines." The Guardian, International edition. https://www.theguardian.com/global-development/2013/sep/06/india-coal-cycle-wallahs.

45.

Singh

White

B. H.

. 2019. “The Criminal Economics and Politics of Black Coal in Jharkhand, 2014.” In The Wild East: Criminal Political Economies in South Asia, edited by White

B. H.

Michelutti

, 35–67. London: University College London (UCL) Press.

46.

Singh

2011. "In Jharkhand, Coal Robbery Has Become a Way of Life." India Today. https://www.indiatoday.in/india/east/story/coal-theft-rampant-in-jharkhand-136450-2011-06-28.

47.

Singh

R. K.

Chakraborty

Katakey

. 2014. "Stolen Coal Fuelling Varanasi's Underground Economy." The Mint. https://www.livemint.com/Industry/FnNRBxuvtaRIXcbi5Od74M/Stolen-coal-fuelling-Varanasis-underground-economy.html.

48.

Srija

Shirke

S. V.

. 2014. An Analysis of Informal Labour Market in India. Economy Matter, Confederation of Indian Industry. https://www.ies.gov.in/pdfs/CII%20EM-october-2014.pdf.

49.

Strausz

1997. “Collusion and Renegotiation in a Principal–Supervisor–Agent Relationship.” Scandinavian Journal of Economics 99 (4): 497–518. https://doi.org/10.1111/1467-9442.00078.

50.

System of National Accounts. 2008. European Commission-United Nations-International Monetary Fund-Organization for Economic Co-operation and Development-World Bank, New York, 2009. https://unstats.un.org/unsd/nationalaccount/docs/sna2008.pdf ISBN 978-92-1-161522-7

51.

Tosun

M. U.

Yurdakul

M. O.

Iyidogan

P. V.

. 2014. “The Relationship between Corruption and Foreign Direct Investment Inflows in Turkey: An Empirical Examination.” Transylvanian Review of Administrative Sciences 10 (42): 247–57. https://rtsa.ro/tras/index.php/tras/article/download/100/96

52.

Wei

2000. “How Taxing is Corruption on International Investors?” The Review of Economics and Statistics 82 (1): 1–11. https://doi.org/10.1162/003465300558533