Economic freedom and corruption: new cross-country panel data evidence.

Author:Yamarik, Steven
  1. Introduction

    A growing literature examines the empirical relationship between economic freedom and corruption. The first and more prominent branch of this literature looks at the role played by economic freedom in explaining cross-country differences in corruption. By estimating determinants of economic freedom, Goldsmith (1999), Chafuen and Guzman (2000), Paldam (2002), and Shen and Williamson (2005) find that economic freedom is negatively related to corruption. Subsequent analysis shows that this negative relationship does not hold across all components of freedom (Goel and Nelson 2005); levels of income (Graeff and Mehlkop 2003); levels of corruption (Billger and Goel 2009); and the inclusion of fixed country effects (Saha, Gounder, and Su 2009).

    The second branch investigates the impact of corruption on economic freedom outcomes. There are two papers of note: Emerson (2006) and Apergis, Dincer, and Payne (2012). (1) Emerson develops a theoretical agency model that relates corruption to competition. He then estimates the determinants of competition and of corruption and finds a negative relationship between the two variables. Using education and democracy to instrument for corruption, he finds that greater corruption lowers competition. Emerson does not examine the effect of freedom on corruption due to his research question. Apergis, Dincer, and Payne use a panel error correction approach to examine the linkages between corruption, freedom, and other macroeconomic outcomes across US states. Their causality tests find that economic freedom causes less corruption and also that corruption causes less freedom. However, these state-level results may have limited applicability to countries due to their significantly wider range of corruption and freedom outcomes.

    In this paper, we examine the impact of economic freedom on corruption and freedom on corruption. We use the principal-agent-client (PAC) model of Aidt (2003) to identify potential causal linkages between corruption and the components of economic freedom. In our example, the regulator (principal) allocates entry licenses to firms (agents) following a process set by the client (government). If the government is assumed to be benevolent, then the "helping hand" of government sets the regulatory process, including the number of licenses, to maximize social welfare. In this case, economic freedom causes corruption. If, however the government is assumed to be nonbenevolent, then the grabbing hand of government intervenes to create economic rents. In this case, corruption causes economic freedom.

    We then estimate a two-equation system to test these linkages. We use a series of panel general method of moment (GMM) estimators where identification is achieved through the use of external (or excluded) and internal (lagged values) instruments. Our GMM results find that corruption lowers economic freedom, but that freedom does not significantly impact corruption. With regard to the components of freedom, we find that rule of law, open markets, and regulatory efficiency lower corruption, while limited government raises it.

    Our paper contributes to the literature on economic freedom and corruption in several important ways. First, we expand the sample coverage to over 160 countries compared to the typical sample of 50-100 countries or 50 US states. By including a much wider distribution of corruption and freedom outcomes, we limit potential sample selectivity bias and increase the power of our results. Second, we control for unobserved heterogeneity through the inclusion of regional or country effects. Potential unmeasured correlates are likely to occur between freedom and corruption given measurement error. Third, we identify causality between freedom and corruption using external and internal instruments. In particular, we use democracy, education, and resource rents interacted with democracy to instrument for corruption and geographical measures to instrument for economic freedom. Fourth, we examine the impact of corruption on the different components of economic freedom and each component's influence on corruption.

    The rest of the paper proceeds as follows. In section 2, we use the PAC model to generate the helping hand and grabbing hand theories of corruption. We present our econometric methodology, including our identification strategy, in section 3. We describe the data in section 4 and present our empirical results in section 5. We conclude with some policy implications in section 6. II.

  2. Corruption and Economic Freedom

    Corruption is the use of public office for private gains (Rose-Ackerman 1999; Treisman 2000). Given its clandestine nature, corruption cannot be directly observable, so it must be inferred through other means, such as surveys on corruption or by estimating a structural model (Dreher, Kotsogiannis, and McCorriston 2007). Economic freedom, on the other hand, is defined as the ability of individuals to work, produce, consume, and invest in any they please, and that freedom is both protected by the state and unconstrained by the state (Beach and Miles 2006). Economic freedom involves multiple rights and liberties that are quantified through different regulatory (and economic) policies.

    1. Theoretical Model

    We use the basic principal-agent-client (PAC) model of Aidt (2003) to identify potential linkages between corruption and economic freedom. In PAC models, the principal (government) sets the rules governing the regulatory relationship between the regulator (regulator) and the clients (private agents) (Klitgaard 1998 and Lambsdorff 2002). We focus in our example on the licensing of firms into a market with potential safety concerns, such as the markets for food or pharmaceuticals.

    The government sets the licensing rules, including the total number of (one unit) licenses A. The r regulators implement these rules by choosing which firms receive a license and which firms do not. Each regulator earns a government wage of w and foregoes a wage of [w.sub.0] [greater than or equal to] 0 in the private sector. To introduce heterogeneity, a fraction ([gamma]) of all regulators are assumed to be honest, while the remainder (1 - [gamma]) are dishonest. The honest regulators choose those firms to license on the basis of some observable safety criteria, while the dishonest regulators will choose the less-safe firm by falsification if the bribe raises those regulators' expected private returns (Becker 1968).

    For exposition purposes, we list the parameters of the model in table 1. We link each regulatory parameter to a corresponding component of economic freedom and then to a predicted impact on corruption. The number of licenses [lambda] records competition and thus corresponds positively to the Open Markets component. The fraction of honest regulators [gamma] and the private wage rate w capture business and labor freedoms contained in the Regulatory Efficiency component. The government wage rate w and the number of regulators r relate negatively to Limited Government, since the size of government, measured by either revenue or expenditure, is positively related to government employment and government wages (Kraay and Van Rijckeghem 1995).

    Depending upon the motives of government, the PAC model can be solved for the two main theories of corruption (Aidt 2003, 2016). The helping hand theory of corruption assumes that the government is benevolent in that it chooses a licensing process to maximize social welfare. With potential negative externalities in the marketplace, government selects a number of licenses, [[lambda]], lower than the quantity obtained under free competition, [[lambda].sub.fc]. As a result, a firm with a license will earn a positive economic profit: [pi]([[lambda]]) > 0.

    The government (principal) delegates the licensing of firms to the regulators (agent) due to expertise or private information. These regulators are either honest or dishonest. Although the government cannot observe the motives of the regulators, it does possess a monitoring device like auditing that discovers a falsified application with probability p. Discovery of corruption results in the regulator being dismissed and paying a fine of f and the firm paying a penalty of g. These three parameters (p, f, g) correspond to the Rule of Law component.

    A firm has an incentive to offer a bribe, b, to a dishonest regulator in exchange for a license. This licensed firm gains n if not caught, but pays g if caught, for an expected return of [pi]([[lambda]]) - p x g. Assuming for simplicity that the regulator has all bargaining power, the equilibrium bribe [b.sup.*] is max{[pi]([[lambda]]) - p x g, 0}. This equilibrium bribe [b.sup.*] will be negatively related to the number of licenses [[lambda]] since entry into the licensed market reduces economic profits.

    A dishonest regulator earns a government wage w plus the bribe b if not caught and earns a private sector wage [w.sub.0] but pays f if caught. The expected return is (1 - p)(w + b) + p([w.sub.0] - f). A dishonest regulator will only accept a bribe if the expected return exceeds the guaranteed government wage w from honest reporting. Therefore, bribing will occur if, and only if

    (1 - p)b + p([w.sub.0] - w - f) > 0 (1)

    where [b.sup.*] = [pi]([[lambda]]) and [pi]'([[lambda]]) [w.sub.0], bribery and thus the incidence of corruption are a negative function of the government wage w, the penalty f, and the number of licenses [[lambda]]; and a positive function of the private sector wage [w.sub.0]. The level of corruption also depends positively on the number of regulators r and the fraction of dishonest regulators (1 - [gamma]).

    The important takeaway for our purposes is that the regulatory parameters determine the actual level of corruption under the helping hand theory. Each of these parameters corresponds to a component of economic freedom. With a benevolent...

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