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on Transition Economics |
| By: | Alena Pavlova (Institute of Economic Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic) |
| Abstract: | This article explores the relationship between labor costs and price inflation under two conditions. Firstly, with linear assumption and classical techniques. Secondly, without assuming linearity, by a novel non-parametric machine learning method, namely gradient boosting. With quarterly data from 1996 to 2022 for V4 countries, we find linear and non-linear dependency between labor cost and price inflation. However, the magnitude of the connection is country-specific and changes over time. Our findings indicate that a significant linear relationship between considered variables does not lead to the higher predictability power of labor cost in a non-parametric model, which predicts inflation. Even opposed, the Czech Republic, the country with the highest correlation between unit labor cost(ULC) and deflator, shows better prediction in a case when the ULC is not in the set of independent variables. This fact highlights the importance of non-linearity for the inflation model. |
| Keywords: | inflation, labor cost, non-linear model, V4 countries |
| JEL: | E24 E31 E37 |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:fau:wpaper:wp2022_25&r= |
| By: | Artem Vdovychenko (National Bank of Ukraine) |
| Abstract: | In this study, we apply the Kalman filter to estimate the set of reduced-form Phillips curves for different types of inflation in Ukraine. Based on the estimated models, we derive a number of series of non-accelerating inflation rate of unemployment (NAIRU) that provide information about the general trajectory and last tendencies of trend unemployment. To better identify the unemployment trend, we include indicators of long-term unemployment and the Beveridge curve shifts as exogenous variables in the NAIRU equation. Both variables demonstrate a significant impact on NAIRU dynamics. Our estimates show that the Phillips curve slope in Ukraine lies in a standard range of -0.3 to -0.5, with high statistical significance. The median value of estimated NAIRUs was at its lowest at 7.2% at the end of 2008, after which it gradually increased to 9.4% by the end of 2021. |
| Keywords: | Phillips curve; unemployment; NAIRU; Kalman filter; Beveridge curve |
| JEL: | E24 E31 |
| Date: | 2022–04–10 |
| URL: | http://d.repec.org/n?u=RePEc:gii:giihei:heidwp21-2022&r= |
| By: | Aleksandra Posarac; Elena Andreeva; Dmitry Bychkov; Aleksandr Spivak; Olesya Feoktistova; Maria Nagernyak |
| Keywords: | Poverty Reduction - Access of Poor to Social Services Social Protections and Labor - Social Protections & Assistance Public Sector Development - Public Sector Expenditure Policy |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:wbk:wboper:35622&r= |
| By: | World Bank |
| Keywords: | Health, Nutrition and Population - Health Service Management and Delivery Health, Nutrition and Population - Health Systems Development & Reform Industry - Health Care Services Industry |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:wbk:wboper:35931&r= |
| By: | Elinder, Mikael (Department of Economics, Uppsala University); Erixson, Oscar (Department of Economics, Uppsala University); Hammar, Olle (Research Institute of Industrial Economics (IFN)) |
| Abstract: | We present estimates of the number of refugees expected to flee Ukraine and to which countries they are expected to migrate based on migration preferences data from the Gallup World Poll. This is important in terms of both immediate refugee assistance efforts and long-term integration policies. Our key finding is that as many as twelve million people may want to leave Ukraine permanently and that refugee policies in potential destination countries are likely to have a substantial impact on the distribution of Ukrainian refugees between different countries. More specifically, international solidarity in response to the migration crisis would significantly reduce the refugee flows to EU countries, incur a limited burden on non-EU countries, and, at the same time, better take the preferences of the Ukrainians into account. |
| Keywords: | Ukraine; Refugees; Migration preferences |
| JEL: | F22 J15 O15 |
| Date: | 2022–09–21 |
| URL: | http://d.repec.org/n?u=RePEc:hhs:iuiwop:1440&r= |
| By: | Gentilini,Ugo; Almenfi,Mohamed Bubaker Alsafi; Tirumala Madabushi Matam I,Hrishikesh; Okamura,Yuko; Urteaga,Emilio Raul; Valleriani,Giorgia; Muhindo,Jimmy Vulembera; Aziz,Sheraz |
| Abstract: | This note provides an update of social protection and related measures in Ukraine and fordisplaced Ukrainian populations in a variety of countries. Previous versions of the note were published on March 10(v.1), March 18 (v.2), and April 8 (v.3). Data is preliminary and meant to elicit comments, additions,integration, and revisions to be incorporated in next living paper versions. Measures include typical social protectionprograms (social assistance, insurance, and active labor market measures), while select services in the realm ofhousing, health, education, and other human development dimensions are also reported. This is because such measuresare often part of an integrated package of interventions for refugees, asylum seekers and other displaced populations.Specifically, the note tracks four broad measures, namely cash transfers; in-kind transfers; labor markets; and “otherselect measures.” The latter includes five subcategories, i.e., education, health, housing, transportation, and otherservices. For Ukraine only, we also include budget support as a core measure. Because of the nature of displacementsupport, humanitarian assistance in the form of programs similar to governmentsupported social protection (e.g., cashtransfers, food assistance) is also recorded. Details are still preliminary and incomplete. More information onspecific measures will be provided as data becomes available, although this version 4 already includes asubstantial number of sources (about a thousand, see endnotes). To this effect, continuous monitoring ofinstitutional and government websites and announcements, as well as scanning of news outlets and programmatic materialsby humanitarian organizations is ongoing. Data sources for reported measures are provided as weblinks. Suggestions onmaterials and measures to be included in future updates are welcome and could be signaled to the team directly. |
| Date: | 2022–06–09 |
| URL: | http://d.repec.org/n?u=RePEc:wbk:hdnspu:173060&r= |
| By: | World Bank |
| Keywords: | Transport - Roads & Highways Health, Nutrition and Population - Health and Poverty Health, Nutrition and Population - Public Health Promotion |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:wbk:wboper:35986&r= |
| By: | Hasan Dudu; Adanna Chukwuma; Armineh Manookian; Anastas Aghazaryan; Muhammad Zeshan |
| Keywords: | Health, Nutrition and Population - Health Economics & Finance Health, Nutrition and Population - Health Indicators Macroeconomics and Economic Growth - Economic Growth Macroeconomics and Economic Growth - Economic Modeling and Statistics |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:wbk:wboper:35688&r= |
| By: | Nicole Fraser; Adanna Chukwuma; Marianna Koshkakaryan; Lusine Yengibaryan; Xiaohui Hou; Tommy Wilkinson |
| Keywords: | Health, Nutrition and Population - Disease Control & Prevention Health, Nutrition and Population - Health Economics & Finance Health, Nutrition and Population - Health Insurance Health, Nutrition and Population - Health Service Management and Delivery Health, Nutrition and Population - Health Systems Development & Reform Industry - Health Care Services Industry |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:wbk:wboper:35347&r= |