Ridder, K., Keller, S., Dams, M., Rupp, A. K., Schlaudraff, J., Del Turco, D., Starmann, J., Macas, J., Karpova, D., Devraj, K., Depboylu, C., Landfried, B., Arnold, B., Plate, K. H., Höglinger, G., Sültmann, H., Altevogt, P., & Momma, S. (2014). Extracellular Vesicle-Mediated Transfer of Genetic Information between the Hematopoietic System and the Brain in Response to Inflammation. PLoS Biology, 12, e1001874.
https://doi.org/10.1371/journal.pbio.1001874

has been cited by the following article:

• TITLE: Economic Crime: Tax Evasion in a Developing Country

  AUTHORS: Marina Lamaj

  KEYWORDS: Tax Evasion, Transition, Informal, Developing Country

  JOURNAL NAME: Open Journal of Business and Management, Vol.11 No.3, May 8, 2023

  ABSTRACT: Tax compliance is sensitive in nature, making empirical research challenging, and reliable data on tax evasion is scarce. Large-scale data collection on tax evasion is an inherently complex task, and it becomes even more challenging in a country that is transitioning from a communist to a democratic government. This essay explores the difficulties of collecting such data in such a country and offers a case study to demonstrate how the main challenge of technology can be overcome. In recent years, there has been growing interest in the informal sector of the economy, including the many commercial businesses that operate outside of formal channels. Unfortunately, many countries, including those with mature fiscal systems and those still in the early stages of economic growth, have a culture of “cheating the government.” Albania is one such country that continues to face significant challenges from tax and customs fraud. Several studies conducted in Albania have shown that informal commercial transactions still account for 30% or more of all transactions, which is higher than the regional average. Both empirical and theoretical analyses point to the tax burden as the primary predictor of tax evasion and the shadow economy. In other words, when taxes are high, people are more likely to engage in informal economic activity to avoid paying them. We employed the crosswise model, a novel technique for sensitive data collecting, to get more reliable estimates of tax evasion (CM). Conceptually, the CM is based on the randomized response method (RRT). We used the CM (N = 163) in an experimental online survey. Our findings demonstrated that by enhancing privacy during the data collecting process, the CM was able to extract a larger percentage of self-stigmatizing allegations of tax evasion.