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.
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