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This study compares the distributional impacts of the main tax and social spending programs in eight countries of the former Soviet Union (Armenia, Belarus, Georgia, Kyrgyzstan, Moldova, the Russian Federation, Tajikistan, and Ukraine) by applying a state-of-the-art fiscal incidence analysis based on the Commitment to Equity methodology. The region is highly interesting due to a unique combination of strong elements of path dependency (socialist legacies) with radical liberalization and welfare state retrenchment. The study examines the actual outcomes in terms of inequality and poverty and assesses the extent to which these outcomes can be attributed to various welfare state policies in these countries. It examines the extent to which taxes and social spending are progressive (whether the average transfer declines with income) and equalizing (whether they reduce inequality). In contrast to the majority of fiscal incidence studies, which are typically limited to the assessment of the impact of direct taxes and transfers, the study estimates the cumulative impact of the tax-benefit system as a whole, including direct and indirect taxes, cash transfers, and transfers in kind such as public education and health care.

Declined fertility rate and population is a matter of serious concern, especially in the developed nations. Assisted Reproductive Technologies (ART), including in vitro fertilization (IVF), have provided great hope for infertility treatment and maintaining population growth and social structure. With the help of ART, more than 8 million babies have already been born so far. Despite the worldwide expansion of ART, there is a number of open questions on the IVF success rates. Male factors for infertility contribute equally as female factors, however, male infertility is primarily focused on the “semen quality”. Therefore, the search of new semen parameters for male fertility evaluation and the exploration of the optimal method of sperm selection in IVF have been included among the top 10 research priorities for male infertility and medically assisted reproduction. The development of imaging systems coupled with image processing by Artificial Intelligence (AI) could be the revolutionary step for semen quality analysis and sperm cell selection in IVF procedures. For this work, we applied optical nanoscopy technology for the analysis of human spermatozoa, i.e., label-based Structured Illumination Microscopy (SIM) and non-invasive Quantitative Phase Microscopy (QPM). The SIM results demonstrated a prominent contrast and resolution enhancement for subcellular structures of living sperm cells, especially for mitochondria-containing midpiece, where features around 100 nm length-scale were resolved. Further, non-labeled QPM combined with machine learning technique revealed the association between gradual progressive motility loss and the morphology changes of the sperm head after external exposure to various concentrations of hydrogen peroxide. Moreover, to recognize healthy and stress-affected sperm cells, we applied Deep Neural Networks (DNNs) to QPM images achieving an accuracy of 85.6% on a dataset of 10,163 interferometric images of sperm cells. Additionally, we summarized the evidence from published literature regarding the association between mitochondrial DNA copy numbers (mtDNAcn) and semen quality. To conclude, we set up the high-resolution imaging of living human sperm cells with a remarkable level of subcellular structural details provided by SIM. Next, the morphological changes of sperm heads resulting from peroxidation have been revealed by QPM, which may not be explored by microscopy currently used in IVF settings. Besides, the implementation of DNNs for QPM image processing appears to be a promising tool in the automated classification and selection of sperm cells during IVF procedures. Moreover, the results of our meta-analysis showed an association of mtDNAcn in human sperm cells and semen quality, which seems to be a relevant sperm parameter for routine clinical practice in male fertility assessment.

This paper presents baseline results from the latest version of EUROMOD (version I3.0+), the tax-benefit microsimulation model for the EU. First, we briefly report the process of updating EUROMOD. We then present indicators for income inequality and risk of poverty using EUROMOD and discuss the main reasons for differences between these and EU-SILC based indicators. We further compare EUROMOD distributional indicators across all EU 28 countries and over time between 2017 and 2020. Finally, we provide estimates of marginal effective tax rates (METR) for all 28 EU countries in order to explore the effect of tax and benefit systems on work incentives at the intensive margin. Throughout the paper, we highlight both the potential of EUROMOD as a tool for policy analysis and the caveats that should be borne in mind when using it and interpreting results. This paper updates the work reported in Kneeshaw (2020).

In this report - constituting Deliverable 9.3 for Work Package 9 of the InGRID-2 project - we document the development of the new EUROMOD module on parental leave benefits (PLBs). The previous versions of EUROMOD did not cover contributory maternity, paternity or parental benefits for all the EU countries because the EUROMOD input data do not contain necessary information on the employment history of respondents. We extended EUROMOD with the module that simulates these policies for potential recipients (and whenever possible for current recipients) using information about current employment of EU-SILC respondents and some common assumptions to calculate eligibility and entitlements. The main objective of the module is to provide an opportunity for the assessment of the financial consequences of a simulated childbirth on household disposable income and thereby enable an assessment of the scale and distribution of replacement incomes in a cross-country comparative framework.

This paper presents baseline results from the latest version of EUROMOD (version I2.0+), the tax-benefit microsimulation model for the EU. First, we briefly report the process of updating EUROMOD. We then present indicators for income inequality and risk of poverty using EUROMOD and discuss the main reasons for differences between these and EU-SILC based indicators. We further compare EUROMOD distributional indicators across all EU 28 countries and over time between 2016 and 2019. Finally, we provide estimates of marginal effective tax rates (METR) for all 28 EU countries in order to explore the effect of tax and benefit systems on work incentives at the intensive margin. Throughout the paper, we highlight both the potential of EUROMOD as a tool for policy analysis and the caveats that should be borne in mind when using it and interpreting results. This paper updates the work reported in Tammik (2019).

The purpose of this paper is to introduce applications of RUSMOD -- a microsimulation model for fiscal incidence analysis in the Russian Federation. RUSMOD combines household survey micro-data and fiscal policy rules to simulate the Russian tax-benefit system: the size and distribution of taxes collected and benefits paid, and the impact of the system on different population groups. Microsimulation models, such as RUSMOD, are habitually used in developed countries, and can be versatile budgetary policy tools. Using this model, the current tax-benefit system in Russia is examined. The impact of the system is measured across the income distribution, age groups, family types, localities, as well as across time. One of the applications of RUSMOD this paper aims to assess is the role of the tax-benefit system in explaining the incidence of informal employment in Russia. The paper investigates whether the existing system creates disincentives for formalization in terms of reducing disposable incomes and increasing poverty and inequality, and whether a hypothetical tax reform would be able to reduce the opportunity costs of formalization for informal workers, improve distributional outcomes, and increase fiscal revenues.