This article analysed the relationship between illicit financial flows (IFFs) and human development, as measured with the United Nations Human Development Index (HDI), using data for 56 low- and middle-income countries for the period 2002–2013. The main result was that, in the cluster of the most corrupt countries, the total effect of an annual 10% point increase in the ratio of IFFs to total trade would imply a 21.7 points decrease in the HDI level as a long-run effect. Although apparently small, this estimated long-run effect is three times greater than the annual average increase observed in the HDI over the period for the entire sample of countries.
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GFI noted that, of the total estimated annual outflows, close to 80% of the funds are moved offshore using trade misinvoicing (Cardamone 2015).
Three main motives are presented for exporting and importing firms to engage in trade misinvoicing: financial motives; circumventing exchange and customs controls; and minimizing the administrative burden (UNCTAD 2016).
Income enters the HDI as a proxy for a decent standard of living and as a surrogate for all human choices not reflected in the other two dimensions (UNDP 2000, p. 17).
In the classical linear regression model there are a wide range of circumstances in which the explanatory variables are correlated with the disturbance (errors), such that OLS regression does not yield consistent estimates. The empirical model proposed in this article includes at least two relevant examples of these circumstances: the inclusion in the model of a lagged dependent variable as an explanatory variable in the presence of serial correlation; and the existence of simultaneity bias due to the inclusion in the regression of endogenous variables as independent variables. For example, the estimation of model (1) allows us to quantify what effect (if any) a change in the “independent” IFFTit might have on the “dependent” variable HDIit, all else being equal. Thus, Eq. (1) defines a causal ordering among the variables: the value of HDIit follows from those of IFFTit and this implies that IFFTit should not be affected by changes in HDIit (i.e. IFFT should be exogenous). However, in most of the causal socio-economic relationships, the dependent variable and some independent variables are “mutually constitutive” (Gerring 2012). In these cases, the OLS estimator will collect both effects, giving biased and inconsistent estimates of parameters. Nevertheless, in this context of simultaneity between dependent and independent variables, it is possible to analyse what the independent impact of one particular factor on the dependent variable might be, or has been, “…by means of “exogenizing” one (or more) chosen causal factor (Gerring 2012, p. 250). There are two ways to do this (Stock and Watson 2011). One is to design and implement a controlled experiment (a treatment that is randomized and probably manipulated across treatment and control groups) by means of which the inverse causality channel may be cancelled. The alternative is to employ instrumental variable (IV) methods which generate consistent estimates provided that it is possible to find instruments that are (at least asymptotically) correlated with the endogenous explanatory variable (X) and uncorrelated with the error term. In other words, instrumentation is made possible by a set of instrumental variables (Z) that: are uncorrelated with the disturbance (are valid); are correlated with the endogenous regressor X (are relevant); and are not included in the original equation (Z have no causal impact on the dependent variable except through X). Thus, a relevant and valid instrument may capture the part of the variations of X which are exogenous. These exogenous variations can be employed at the same time to estimate the truth effect of X on the dependent variable by means of IV estimators (such as the Two-Step Least Squares (2SLS) estimator or GMM estimators).
A prospective instrument can be flawed in either of two weakening ways (Murray 2006). First, an instrument can itself be correlated with the error term (i.e. can be invalid). Second, an instrument can be only weakly correlated with the endogenous variable (i.e. can be a weak instrument). In these circumstances, the variance of IV estimators will be larger than OLS, so that even when the latter is inconsistent, there is no guarantee that the IV estimators will be closer to the truth (Deaton 1995).
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This research did not receive any specific Grant from funding agencies in the public, commercial, or not-for-profit sectors.
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Ortega, B., Sanjuán, J. & Casquero, A. Illicit Financial Flows: Another Road Block to Human Development in Low- and Middle-Income Countries. Soc Indic Res 142, 1231–1253 (2019). https://doi.org/10.1007/s11205-018-1942-z
- Illicit financial flows
- Human Development Index
- Low- and middle-income countries
- Panel data analysis