THE RELATIONSHIP BETWEEN GDP AND TAX REVENUES FROM THE MARKET OF GAMBLING AND LOTTERIES IN LITHUANIA
Bibliographic record
Abstract
More than two decades after the legalisation of gambling and almost three decades after the beginning of the organisation of lotteries in Lithuania (the Gaming Law of the Republic of Lithuania came into force in 2001 and the Law on Lotteries in 2004), it is already possible to discern the trends in the development of this business and the impact of this business on the individual and on society. Gambling and lotteries are seen ambiguously both in the work of researchers and in society: some see the activity as a fun pastime or a form of leisure, while others argue that it is an addiction with negative psychological, social and economic consequences for the individual, the family and society. In Lithuania, there has been very little research on the impact of gambling and lotteries on individuals and society, compared to other countries such as Italy, the United States, Australia, New Zealand and Canada. In these countries, gambling and lotteries have a very long and deep tradition, are a very important area of the economy (business) and a popular way of spending leisure time. This article examines the development trends of the gambling and lotteries market in Lithuania. The study made use of quarterly time series data including from 2004Q1 to 2021Q4. During the research we established, that every year, the income from land-based gambling decreases and the gross income from online gambling increases. In 2021, 53 percent of the gross gambling and lottery revenue structure was from online gambling. Additionally, according to the of Autoregressive Distributed Lag (ARDL) model, the paper estimated the relationship between the gross domestic product of Lithuania and the tax receipts of gambling and lotteries to the Lithuanian state budget. According to the study, GDP growth influences gambling and lottery tax revenues directly, without postponed effect, but at the same time, there is a fairly strong inertia in budget revenues from gambling and lottery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".