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The purpose of this study is to reconstruct the historical representation of the highest state power in today's Russia (President's level) by considering the texts of presidential messages to the Federal Assembly. Content analysis, both semantic and quantitative, has been applied as a key research method. There were investigated the fragments of messages containing an appeal to events, phenomena, personalities of the past and the historical process as a whole. The results of the study make it possible to confirm the fact that there has been a paradigm shift in the perception of history at the highest state level of Russia, which consists in the transition from a liberal version of the modernization theory to a nationally conservative approach close to the theory of civilizations. Three Presidents of the Russian Federation - B.N. Yeltsin, D.A. Medvedev and V.V. Putin, despite the coinciding positions on a number of issues on coverage of the past, presented diverse visions to the historical process in the texts of their messages. The integration of the current policy into the general outline of history distinguished all the messages, making it possible to talk about preserving the tradition of historiosophical reflection on state activity in the Russian Federation, despite different versions of historiosophy. The transformation of historical policy in Russia is an indicator of the ideological inversion of the Russian state as a whole, the transition from a liberal to a nationally conservative pattern. The attitude of the state officials towards the history reveals the potential for the use of value-and-semantic guidelines of current policy as a means of reconstruction.

The paper analyses the effect of location-based tax incentives on rural non-farm small business performance in Armenia. It uses administrative panel data for the population of the registered taxpayers in the areas concerned, and the vast tax exemptions are granted for businesses operating in the selected set of border communities. Using an 'event study'-style difference-in-differences fixed effects estimator we initially find a negative effect of tax exemptions on the reported gross income of the affected enterprises and a positive effect on employment, mainly driven by a smaller subset of non-trade firms. To uncover these inconsistent results, we consider a possible channel for decreased reported revenues – changes in audit probabilities for the exempt businesses. Indeed, introducing controls for predicted audit probability eliminates the negative effects on gross revenues, while the positive effect on the number of employees persists, with even increased magnitudes. Gross income under-reporting incentives could emerge in the supply chain where the suppliers might be inclined not to report their sales thus inducing the exempted businesses to under-report their incomes as well. Given the short pre-treatment data, the long-term development common trend of treated and control communities is verified using satellite data on night-time illumination. [ABSTRACT FROM AUTHOR]

The paper studies the effects of abolishing fees in the public childcare facilities on the maternal labor supply and provides evidence from a post‐Soviet developing country, Armenia, characterized with low female labor force participation and high unemployment. The reform unexpectedly introduced only in the capital city created a natural experiment which we investigate using difference‐in‐differences methodology. The statistically significant increase of the childcare service utilization is shown to be affecting only marginally the maternal labor supply as measured by intention‐to‐treat effects. The positive results are more robust when only women actually utilizing the services are considered, but all the effects obtained fade out already in the second year after the reform. The robustness of our findings is tested by a series of placebo regressions. [ABSTRACT FROM AUTHOR]

Using the universe of Armenian business tax payers operating under a standard tax regime, we develop a fraud prediction model based on machine learning tools, with gradient boosting as the primary choice. Having to deal with broadly defined fraud and heterogeneous taxpayers, as well as a relatively small sample, we successfully derive important features from tax returns with a minimum of additional information. Among the important fraud predictors, we obtain historical fraud and audit, share of administrative costs, and external economic activity. We see two main contributions with generalizable practical implications for auditing authorities. First, by focusing on the lift score of the top decile, we demonstrate that even moderately accurate models can improve upon existing accuracy of rule-based approaches. Second, and more importantly, we demonstrate that the information contained in the supplier and buyer network of the taxpayer can be used whenever important predictors of fraud such as historical audits and fraud are not available. This is particularly important for situations with newly established companies, who would otherwise be under-rated in terms of fraud probability. [ABSTRACT FROM AUTHOR]