Dataset Information

Estimating the volume of dirty money in Iran.

ABSTRACT: In this study, the volume of dirty money in Iran was estimated. The data belonged to the period of 1997-2019, and was taken from the Central Bank of Iran (website: https://www.cbi.ir). Fuzzy logic was used to estimate the underground economy. Fuzzy theory can mathematically formulate many variables that are imprecise and ambiguous concepts. This theory is appropriate for reasoning, inference, control, and decision-making under uncertainty. This approach works in conditions of uncertainty. In cases in which the variables are inaccurate, this method is used. Fuzzy set theory is a generalization of the set theory. The underground economy is important in estimating the amount of dirty money and has a positive effect on this amount. The effect of the underground economy was investigated using the vector autoregressive (VAR) and vector error correction (VECM) models.•In this article applied the fuzzy logic, to estimate the underground economy.•The method presented in this article can be useful for Researchers and managers in the monetary trend of economics.•The fuzzy method is the best way to estimate the size of the underground economy because it is a measure of uncertainty.

SUBMITTER: Dowlatzadeh A

PROVIDER: S-EPMC7558215 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Estimating the volume of dirty money in Iran.

Dowlatzadeh Anita A Akbarifard Hossein H

MethodsX 20200928

In this study, the volume of dirty money in Iran was estimated. The data belonged to the period of 1997-2019, and was taken from the Central Bank of Iran (website: https://www.cbi.ir). Fuzzy logic was used to estimate the underground economy. Fuzzy theory can mathematically formulate many variables that are imprecise and ambiguous concepts. This theory is appropriate for reasoning, inference, control, and decision-making under uncertainty. This approach works in conditions of uncertainty. In cas ...[more]

PMID: 33088727

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Project description:ObjectiveThe aim of this study was to estimate the fraction of lung cancer incidence in Iran attributed to occupational exposures to the well-established lung cancer carcinogens, including silica, cadmium, nickel, arsenic, chromium, diesel fumes, beryllium, and asbestos.MethodsNationwide exposure to each of the mentioned carcinogens was estimated using workforce data from the Iranian population census of 1995, available from the International Labor Organization (ILO) website. The prevalence of exposure to carcinogens in each industry was estimated using exposure data from the CAREX (CARcinogen EXposure) database, an international occupational carcinogen information system kept and maintained by the European Union. The magnitude of the relative risk of lung cancer for each carcinogen was estimated from local and international literature. Using the Levin modified population attributable risk (incidence) fraction, lung cancer incidence (as estimated by the Tehran Population-Based Cancer Registry) attributable to workplace exposure to carcinogens was estimated.ResultsThe total workforce in Iran according to the 1995 census identified 12,488,020 men and 677,469 women. Agriculture is the largest sector with 25% of the male and 0.27% of female workforce. After applying the CAREX exposure estimate to each sector, the proportion exposed to lung carcinogens was 0.08% for male workers and 0.02% for female workers. Estimating a relative risk of 1.9 (95% CI of 1.7-2.1) for high exposure and 1.3 (95% CI 1.2-1.4) for low exposure, and employing the Levin modified formula, the fraction of lung cancer attributed to carcinogens in the workplace was 1.5% (95% CI of 1.2-1.9) for females and 12% (95% CI of 10-15) for males. These fractions correspond to an estimated incidence of 1.3 and 0.08 cases of lung cancer per 100,000 population for males and females, respectively.ConclusionThe incidence of lung cancer due to occupational exposure is low in Iran and, as in other countries, more lung cancer is due to occupational exposure among males than females.

Dataset Information

Estimating the volume of dirty money in Iran.

Publications

Estimating the volume of dirty money in Iran.

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