¿Puede la tecnología reducir la corrupción? Nueva evidencia empírica utilizando datos de panel

Autores/as

  • Brayan Tillaguango Universidad Nacional de Loja
  • Roberto Erazo Universidad Nacional de Loja

Palabras clave:

Tecnología, Corrupción, Datos de panel, Mundo

Resumen

La tecnología en los últimos años se ha convertido en una herramienta capaz de potenciarel ámbito social, económico y político. El objetivo de esta investigación es examinarel efecto de la tecnología sobre la corrupción a nivel global y en grupos de países usandotécnicas econométricas de datos de panel. Los datos fueron obtenidos del World DevelopmentIndicators (2018) para 72 países durante el periodo 2001-2018. Nuestros resultadosmuestran que el gasto en tecnología es un mecanismo clave para combatir la corrupcióntanto a nivel global como en los países de ingresos altos. Las acciones deben ir desde elcontrol y monitoreo de las actividades gubernamentales hasta una interacción más directaEstado-Sociedad. Por lo tanto, es imperativo que los gobiernos inviertan y destinen unmayor grado de componentes tecnológicos enfocado en los sectores más vulnerables yentidades propensas a ser corruptas.

Citas

Andersen, T. B. (2009). E-Government as an anti-corruption strategy. Information Economics

and Policy, 21(3), 201-210.

An, W., & Kweon, Y. (2017). Do higher government wages induce less corruption? Cross-

country panel evidence. Journal of Policy Modeling, 39(5), 809-826

Banco Mundial (2017) https://datahelpdesk.worldbank.org/knowledgebase/articles/378834-

how-does-the-world-bank-classify-countries

Bašná, K. (2019). Income inequality and level of corruption in post-communist European

countries between 1995 and 2014. Communist and Post-Communist Studies.

Batzilis, D. (2019). Electoral competition and corruption: Evidence from municipality audits in

Greece. International Review of Law and Economics, 59, 13-20.

Beekman, G., Bulte, E., & Nillesen, E. (2014). Corruption, investments and contributions to

public goods: Experimental evidence from rural Liberia. Journal of public

Economics, 115, 37-47.

Bertot, J. C., Jaeger, P. T., & Grimes, J. M. (2010). Using ICTs to create a culture of

transparency: E-government and social media as openness and anti-corruption tools for

societies. Government Information Quarterly, 27(3), 264-271.

Bertot, J. C., Jaeger, P. T., & Grimes, J. M. (2010). Using ICTs to create a culture of

transparency: E-government and social media as openness and anti-corruption tools for

societies. Government information quarterly, 27(3), 264-271.

Borsky, S., & Kalkschmied, K. (2019). Corruption in space: A closer look at the world's

subnations. European Journal of Political Economy.

Bindu, N., Sankar, C. P., & Kumar, K. S. (2019). From conventional governance to e-

democracy: Tracing the evolution of e-governance research trends using network

analysis tools. Government Information Quarterly.

Breusch, T. S., & A. R. Pagan. 1980. The Lagrange multiplier test and its applications to model

specification in econometrics. Review of Economic Studies 47: 239-253.

Charoensukmongkol, P., & Moqbel, M. (2014). Does investment in ICT curb or create more

corruption? A cross-country analysis. Public Organization Review, 14(1), 51-63.

Choudhury, S. (2015). Governmental decentralization and corruption revisited: accounting for

potential endogeneity. Economics Letters, 136, 218-222.

De Chiara, A., & Livio, L. (2017). The threat of corruption and the optimal supervisory

task. Journal of economic behavior & organization, 133, 172-186.

Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series

with a unit root. Econometrica: Journal of the Econometric Society, 1057-1072

Drukker, D. M. 2003. Testing for serial correlation in linear panel-data models. The Stata

Journal (3)2, 1-10.

Duerrenberger, N., & Warning, S. (2018). Corruption and education in developing countries:

The role of public vs. private funding of higher education. International Journal of

Educational Development, 62, 217-225

Duerrenberger, N., & Warning, S. (2018). Corruption and education in developing countries:

The role of public vs. private funding of higher education. International Journal of

Educational Development, 62, 217-225.

Dumitrescu, E. I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous

panels. Economic modelling, 29(4), 1450-1460.

Elbahnasawy, N. G. (2014). E-government, internet adoption, and corruption: an empirical

investigation. World Development, 57, 114-126.

Ferreira, I., Cunha, S. R. L., Amaral, L., & Camões, P. J. (2014). ICT for governance in

combating corruption: the case of public e-procurement in Portugal. In 8th International

Conference on Theory and Practice of Electronic Governance (ICEGOV2014) (Vol.

, pp. 109-112). Association for Computing Machinery.

Garrido-Rodríguez, J. C., López-Hernández, A. M., & Zafra-Gómez, J. L. (2019). The impact of

explanatory factors on a bidimensional model of transparency in Spanish local

government. Government Information Quarterly, 36(1), 154-165.

Greene, W. H. 2012. Econometric Analysis. 7th ed. Upper Saddle River, NJ: Prentice Hall.

Greene, W. Econometric Analysis. New York:Prentice-Hall. 2000.

Glaeser, E. L., & Saks, R. E. (2006). Corruption in america. Journal of public Economics, 90(6-

, 1053-1072.

Gans-Morse, J., Borges, M., Makarin, A., Mannah-Blankson, T., Nickow, A., & Zhang, D.

(2018). Reducing bureaucratic corruption: Interdisciplinary perspectives on what

works. World Development, 105, 171-188

Gans-Morse, J., Borges, M., Makarin, A., Mannah-Blankson, T., Nickow, A., & Zhang, D.

(2018). Reducing bureaucratic corruption: Interdisciplinary perspectives on what

works. World Development, 105, 171-188.

Im, K. S., Pesaran, M. H., & Shin, Y. (2003). Testing for unit roots in heterogeneous

panels. Journal of econometrics, 115(1), 53-74

Jain, P. K., Kuvvet, E., & Pagano, M. S. (2017). Corruption’s impact on foreign portfolio

investment. International Business Review, 26(1), 23-35.

Jha, C. K., & Sarangi, S. (2017). Does social media reduce corruption?. Information Economics

and Policy, 39, 60-71.

Junxia, L. (2019). Investments in the energy sector of Central Asia: Corruption risk and policy

implications. Energy Policy, 133, 110912.

Kanyam, D. A., Kostandini, G., & Ferreira, S. (2017). The mobile phone revolution: have

mobile phones and the internet reduced corruption in Sub-Saharan Africa?. World

Development, 99, 271-284.

Kim, S., Kim, H. J., & Lee, H. (2009). An institutional analysis of an e-government system for

anti-corruption: The case of OPEN. Government Information Quarterly, 26(1), 42-50

Kankanhalli, A., Charalabidis, Y., & Mellouli, S. (2019). IoT and AI for smart government: A

research agenda.

Lee-Geiller, S., & Lee, T. D. (2019). Using government websites to enhance democratic E-

governance: A conceptual model for evaluation. Government Information

Quarterly, 36(2), 208-225.

Levin, A., Lin, C. F., & Chu, C. S. J. (2002). Unit root tests in panel data: asymptotic and finite-

sample properties. Journal of econometrics, 108(1), 1-24.

Lewis, B. D., & Hendrawan, A. (2019). The impact of majority coalitions on local government

spending, service delivery, and corruption in Indonesia. European Journal of Political

Economy, 58, 178-191.

Lindgren, I., Madsen, C. Ø., Hofmann, S., & Melin, U. (2019). Close encounters of the digital

kind: A research agenda for the digitalization of public services. Government

Information Quarterly.

Lio, M. C., Liu, M. C., & Ou, Y. P. (2011). Can the internet reduce corruption? A cross-

country study based on dynamic panel data models. Government Information

Quarterly, 28(1), 47-53.

Madariaga, L., Nussbaum, M., Marañón, F., Alarcón, C., & Naranjo, M. A. (2019). User

experience of government documents: A framework for informing design

decisions. Government Information Quarterly, 36(2), 179-195.

Montes, G. C., Bastos, J. C. A., & de Oliveira, A. J. (2019). Fiscal transparency, government

effectiveness and government spending efficiency: Some international evidence based

on panel data approach. Economic Modelling, 79, 211-225.

Policardo, L., Carrera, E. J. S., & Risso, W. A. (2019). Causality between income inequality and

corruption in OECD countries. World Development Perspectives, 100102.

Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple

regressors. Oxford Bulletin of Economics and statistics, 61(S1), 653-670.

Pedroni, P. (2001). Fully modified OLS for heterogeneous cointegrated panels. In Nonstationary

panels, panel cointegration, and dynamic panels. Emerald Group Publishing Limited.

(págs. 93-130). Emerald Group Publishing Limited.

Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series

regression. Biometrika, 75(2), 335-346.

Policardo, L., Carrera, E. J. S., & Risso, W. A. (2019). Causality between income inequality and

corruption in OECD countries. World Development Perspectives, 100102.

Sabic-El-Rayess, A., & Mansur, N. N. (2016). Favor reciprocation theory in education: New

corruption typology. International Journal of Educational Development, 50, 20-32.

Schneider, P. H. (2005). International trade, economic growth and intellectual property rights: A

panel data study of developed and developing countries. Journal of Development

Economics, 78(2), 529-547.

Shrivastava, U., & Bhattacherjee, A. (2014). ICT development and corruption: an empirical

study

Tang, Z., Chen, L., Zhou, Z., Warkentin, M., & Gillenson, M. L. (2019). The effects of social

media use on control of corruption and moderating role of cultural tightness-

looseness. Government Information Quarterly.

Tanzi, V., & Davoodi, H. (1998). Corruption, public investment, and growth. In The welfare

state, public investment, and growth(pp. 41-60). Springer, Tokyo.

Vargas, G. y Guerrero-Riofrío, P. (2019). ¿Puede la tecnología disminuir la desigualdad?

Evidencia empírica usando técnicas de datos de panel en 61 países durante 2000-

ReVista Económica, 7(6), 45-52.

Westerlund, J. (2007). Testing for error correction in panel data. Oxford Bulletin of Economics

and statistics, 69(6), 709-748.

Wooldridge, J. M. 2002. Econometric Analysis of Cross Section and Panel Data. Cambridge,

Massachusetts:The MIT Press.

Wu, S., Li, B., Nie, Q., & Chen, C. (2017). Government expenditure, corruption and total factor

productivity. Journal of cleaner production, 168, 279-289.

Zhang, H., Song, Y., Tan, S., Xia, S., Zhang, H., Jiang, C., & Lv, Y. (2019). Anti-corruption

efforts, public perception of corruption, and government credibility in the field of real

estate: An empirical analysis based on twelve provinces in China. Cities, 90, 64-73.

Zhang, H., An, R., & Zhong, Q. (2019). Anti-corruption, government subsidies, and investment

efficiency. China Journal of Accounting Research, 12(1), 113-133.

Zuazu, I. (2019). The growth effect of democracy and technology: An industry disaggregated

approach. European Journal of Political Economy, 56, 115-131.

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Publicado

2020-11-09