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Illicit Financial Flows: Another Road Block to Human Development in Low- and Middle-Income Countries

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Abstract

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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Notes

  1. GFI noted that, of the total estimated annual outflows, close to 80% of the funds are moved offshore using trade misinvoicing (Cardamone 2015).

  2. 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).

  3. 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).

  4. 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).

  5. 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).

References

  • African Development Bank, Organisation for Economic Co-operation and Development, United Nations Development Programme, United Nations Economic Commission for Africa (2012). (AfDB, OECD, UNDP, UNECA). African economic outlook 2012. Promoting youth employment. Paris: OECD Publishing.

  • Ajayi, S. (2015). Capital flight and economic development in Africa. In S. I. Ajayi & L. Ndikumana (Eds.), Capital flight from Africa: Causes, effects, and policy issues (pp. 55–80). Oxford: Oxford University Press.

    Google Scholar 

  • Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58, 277–297.

    Article  Google Scholar 

  • Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68, 29–51.

    Article  Google Scholar 

  • Barro, R. J. (1997). Determinants of economic growth a cross-country empirical study. Cambridge, MA: MIT Press.

    Google Scholar 

  • Beja, E. (2007). Brothers in distress: Revolving capital flows of Indonesia, Malaysia, and Thailand. Journal of Asian Economics, 18(6), 904–914.

    Article  Google Scholar 

  • Bhattacharyya, S., & Collier, P. (2014). Public capital in resource rich economies: is there a curse? Oxford Economic Paper, 66(1), 1–24.

    Article  Google Scholar 

  • Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87, 115–143.

    Article  Google Scholar 

  • Boyce, J., & Ndikumana, L. (2005). Africa’s debt: Who owes whom?”. In G. A. Epstein (Ed.), Capital flight and capital controls in developing countries (pp. 334–340). Cheltenham: Edward Elgar.

    Google Scholar 

  • Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics, methods and applications. New York, NY: Cambridge University Press.

    Book  Google Scholar 

  • Cardamone, T. (2015). Addressing illicit financial flows in the FfD and SDG processes. Resource document. Global financial integrity. http://www.gfintegrity.org/addressing-illicit-financial-flows-in-the-financing-for-development-ffd-and-sustainable-development-goal-sdg-processes.

  • Cardamone, T. (2016). Illicit flows and funding the SDG’s. Washington, DC: Global Financial Integrity.

    Google Scholar 

  • Cerra, V., Rishi, M., & Saxena, S. (2005). Robbing the riches: Capital flight, institutions, and instability. IMF working paper WP/05/199.

  • Chipalkatti, N., & Rishi, M. (2001). External debt and capital flight in the Indian economy. Oxford Development Studies, 29(1), 31–44.

    Article  Google Scholar 

  • Deaton, A. (1995). Data and econometric tools for development analysis. In K. J. Arrow & M. D. Intriligator (Eds.), Handbook of development economics. Part A (Vol. 3, pp. 1777–2463). Amsterdam and New York: North-Holland.

    Google Scholar 

  • Dickinson, B. (2014). Illicit financial flows and development. Comments on capital flight and tax havens: Impact on investment and growth in Africa. Revue d’Économie du Développement, 22, 143–149.

    Article  Google Scholar 

  • Gerring, J. (2012). Social science methodology. A unified framework (2nd ed.). Cambridge: Cambridge University Press.

    Google Scholar 

  • Hausman, J. A. (1978). Specifications tests in econometrics. Econometrica, 46, 1251–1272.

    Article  Google Scholar 

  • Heller, P. S. (2005). Understanding fiscal space. IMF Policy. Discussion paper PDP/05/4.

  • Janský, P. (2015). Updating the rich countries’ commitment to development index: How they help poorer ones through curbing illicit financial flows. Social Indicator Research, 124, 43–65.

    Article  Google Scholar 

  • Kar, D., & Spanjers, J. (2015). Illicit financial flows from developing countries: 2004–2013. Washington, DC: Global Financial Integrity.

    Google Scholar 

  • Kiviet, J. (1995). On bias, inconsistency and efficiency of some estimators in dynamic panel data models. Journal of Econometrics, 68, 63–78.

    Article  Google Scholar 

  • Lessard, D. R., & Williamson, J. (1987). Capital flight and third world debt. Washington, DC: Institute for International Economics.

    Google Scholar 

  • Lustig, N. (2017). Fiscal policy, income redistribution and poverty reduction in low and middle income countries. CEQ Institute working paper 54. Tulane University.

  • Mosselmans, I. (2014). Tax evasion: The main cause of global poverty (03/2014) at Resource document. http://blogs.lse.ac.uk/africaatlse/2014/03/07/tax-evasion-the-main-cause-of-global-poverty.

  • Murray, M. P. (2006). Avoiding invalid instruments and coping with weak instruments. Journal of Economic Perspectives, 20(4), 111–132.

    Article  Google Scholar 

  • Naylor, R. T. (1987). Hot Money: Peekaboo finance and the politics of Debt. Toronto: McClelland and Stewart.

    Google Scholar 

  • Ndikumana, L., & Boyce, J. K. (2003). Public debts and private assets: Explaining capital flight from Sub-Saharan African countries. World Development, 31(1), 107–130.

    Article  Google Scholar 

  • Ndikumana, L., & Boyce, J. K. (2011). Africa’s odious debts: How foreign loans and capital flight bled a continent. London: Zed Books.

    Google Scholar 

  • Ndikumana, L., Boyce, J. K., & Ndiaye, A. S. (2015). Capital flight from Africa: Measurement and drivers. In I. Ajayi & L. Ndikumana (Eds.), Capital flight from Africa. Causes, effects, and policy issues (pp. 15–54). Oxford: Oxford University Press.

    Google Scholar 

  • Ngaruko, F. (2012). Size and dynamics of the middle class in Africa. Tunis: African Development Bank.

    Google Scholar 

  • Nkurunziza, J. D. (2012). Illicit financial flows: A constraint on poverty reduction in Africa. ACAS Bulletin, 87, 15–21.

    Google Scholar 

  • O’Hare, B., & Curtis, M. (2014). Health spending, illicit financial flows and tax incentives in Malawi. Malawi Medical Journal, 26(4), 133–137.

    Google Scholar 

  • O’Hare, B., Makuta, I., Bar-Zeev, N., Chiwaula, N. L., & Cobham, A. (2014). The effect of illicit financial flows on time to reach the fourth Millennium Development Goal in Sub-Saharan Africa: A quantitative analysis. Journal of the Royal Society of Medicine, 107(4), 148–156.

    Article  Google Scholar 

  • Organisation for Economic Co-operation and Development (OECD). (2013). Measuring OECD responses to illicit financial flows. Paris: OECD.

    Google Scholar 

  • Organisation for Economic Co-operation and Development (OECD). (2014). Better policies for development 2014: Policy coherence and illicit financial flows. Paris: OECD.

    Google Scholar 

  • Reed, Q., & Fontana, A. (2011). Corruption and illicit financial flows. The limits and possibilities of current approaches. Chr. Michelsen Institute (CMI). U4 no. 2.

  • Ritter, I. (2015). Illicit financial flows: An analysis and some initial policy proposals. Berlin: Friedrich-Ebert-Stiftung.

    Google Scholar 

  • Rodriguez, M. A. (1987). Consequences of capital flight for Latin American debtor countries. In D. R. Lessard & J. Williamson (Eds.), Capital flight and third world debt (pp. 129–152). Washington, DC: Institute for International Economics.

    Google Scholar 

  • Rodrik, D. (2017). Populism and the economics of globalization. NBER working paper no. 23559. Cambridge: National Bureau of Economic Research.

  • Roodman, D. (2009). How to do xtabond2: An introduction to difference and system GMM in Stata. The Stata Journal, 9(1), 86–136.

    Article  Google Scholar 

  • Staiger, D., & Stock, J. H. (1997). Instrumental variables regression with weak instruments. Econometrica, 65(3), 557–586.

    Article  Google Scholar 

  • Stiglitz, J. (2000). Capital market liberalization, economic growth, and instability. World Development, 28, 1075–1086.

    Article  Google Scholar 

  • Stock, J. H., & Watson, M. M. (2011). Introduction to econometrics (3rd ed.). New York, NY: Prentice Hall.

    Google Scholar 

  • Tanzi, V. (2001). Globalization and the work of fiscal termites. Finance and Development, 38(1), 34–37.

    Google Scholar 

  • United Nations (UN). (2014). Report of the open working group of the general assembly on sustainable development goals. A/68/970. New York, NY: United Nations Department of Economic and Social Affairs.

    Google Scholar 

  • United Nations (UN). (2015). Addis Ababa action agenda of the third international conference on financing for development. New York, NY: United Nations Department of Economic and Social Affairs Financing for Development Office.

    Google Scholar 

  • United Nations Conference on Trade and Development (UNCTAD). (2014). Economic development in Africa: Catalysing investment for transformative growth in Africa. TD/B/61/4.

  • United Nations Conference on Trade and Development (UNCTAD). (2016). Trade misinvoicing in primary commodities in developing countries: The cases of Chile, Côte d’Ivoire, Nigeria, South Africa and Zambia. New York and Geneva: UNCTAD.

    Google Scholar 

  • United Nations Development Programme (UNDP). (2000). Human development report 2000. New York, NY: Human Rights and Human Development.

    Book  Google Scholar 

  • United Nations Development Programme (UNDP). (2013). Humanity divided: Confronting inequality in developing countries. New York, NY: UNDP.

    Google Scholar 

  • United Nations Development Programme (UNDP). (2016). Human development report 2016. New York, NY: Human Development for Everyone.

    Google Scholar 

  • United Nations Office on Drugs and Crime (UNODC)/World Bank. (2007). Stolen asset recovery (StAR) initiative: Challenges, opportunities and action plan. Washington, DC: The International Bank for Reconstruction and Development/The World Bank.

    Google Scholar 

  • Windmeijer, F. (2005). A finite sample correction for the variance of linear efficient two-step GMM estimators. Journal of Econometrics, 126, 25–51.

    Article  Google Scholar 

  • Winters, J. A. (2011). Oligarchy. New York, NY: Cambridge University Press.

    Book  Google Scholar 

  • World Bank. (2014). Illicit financial flows and the post-2015 development agenda. Washington, DC: World Bank.

    Google Scholar 

  • World Bank. (2017). World development report 2017. Governance and the law. Washington, DC: World Bank.

    Book  Google Scholar 

  • Wu, D. (1973). Alternative tests of independence between stochastic regressors and disturbances. Econometrica, 41, 733–750.

    Article  Google Scholar 

  • Zheng, Y., & Tang, K. T. (2009). Rethinking the measurement of capital flight: an application to Asian economies. Journal of the Asia Pacific Economy, 14(4), 313–330.

    Article  Google Scholar 

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  1. Departamento de Economía Aplicada (Estructura Económica), Universidad de Málaga, Campus El Ejido, 29071, Málaga, Spain

    Bienvenido Ortega, Jesús Sanjuán & Antonio Casquero

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  1. Bienvenido Ortega
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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

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