Active Ageing and Shadow Economy in Romania. An Empirical Causality Analysis

The paper aims to analyze the unidirectional relationship from active ageing phenomena to the size of the Romanian shadow economy in order to see if the unofficial sector represents a social buffer for older workers who have lower labor market opportunities. In order to do that, we applied two important causality analyses, Granger and Toda-Yamamoto, based on quarterly data over the period 2000-2010. The size of the Romanian shadow economy was previously estimated using a revised version of the currency demand approach based on autoregressive distributed lag (ARDL) approach. For active ageing, the employment rate for older workers was used as proxy. The cointegration empirical results highlight the existence of a positive long-run relationship between employment rate of elderly and unofficial sector. The empirical causality results conclude that there is a unidirectional Granger causality that runs from employment rate of older workers to shadow economy both on long-run and short-run. The empirical results of Toda-Yamamoto revealed the absence of a short-run causal relationship from employment rate for older workers to the size of shadow economy. One possible explanation for the existence of a positive relationship that runs from employment rate of elderly to unofficial sector can be the low capacity of the economy to generate proper jobs, so this age group of older workers does not have qualifications that meet the needs of formal economy, and therefore shadow economy becomes an alternative to formal work and it may provide a buffer for some workers who have few alternative labor market opportunities. Another alternative could be the fact that this age group of elderly remains occupied in the formal lab our market, but with low earnings and they work in informal activities in order to supplement their income

Identifying the main determinants of retention in Jordanian hospitals. An empirical analysis based on McCloskey/Mueller Satisfaction Scale

The paper aims to identify the main determinants of job satisfaction and intention of retention in Jordanian hospitals using a sample of 325 employees from six hospitals at the level of the year 2015. In order to do that, we used McCloskey/Mueller Satisfaction Scale (MMSS), applying logistic regression models for measuring the intention of stay among health employees. The study also analyzed the differences between socio-demographic variables and the retention and satisfaction factors using t-test and ANOVA analysis. The empirical results revealed that the main job satisfaction factors that could be considered as predictors of the intention of retention are satisfaction related with recognition, satisfaction with extrinsic rewards and satisfaction with professional opportunities. A significant impact on the decision of remaining employed has also the socio-demographic variables like type of hospital, age, and graduation degree and time experience in hospitals. The findings revealed that the financial incentives are very important but also non-financial incentives are fundamental in enhancing motivation among health employees

SHADOW ECONOMY AND FOREIGN DIRECT INVESTMENTS: AN EMPIRICAL ANALYSIS FOR THE CASE OF ROMANIA

Shadow economy (SE) represents a controversial phenomenon, present more or less in all economies, whose empirical estimates should be regarded with due reserve. The main goal of this paper is to analyze the nature of the relationship between Romanian shadow economy, expressed as % of the official GDP and the foreign direct investments (FDI) using two causality analysis methods, namely the Granger causality analysis method and the Toda-Yamamoto procedure, based on quarterly data, over the period 2000-2010. The paper will also try to particularize the implications of this relationship on the sustainable development of the Romanian economy. For that purpose we will use the shadow economy series estimated in a previous article using one of the monetary approaches, the currency demand approach, based on econometrical methodology of error correction models and co-integration. The quantitative demarche of shadow economy estimation is detailed in Alexandru (Davidescu) and Dobre (2013). The empirical results highlight a unidirectional short-run causality that runs only from foreign direct investments to the shadow economy. The impulse responses function indicates a short-run negative relationship between FDI and SE

An empirical analysis using panel data gravity models and scenario forecast simulations for the Romanian exports in the context of COVID-19

The paper focuses on the trade performance of Romania, a representative country for the Central and Eastern European region, strongly connected with its European partners in global value chains and thus affected by any change in these countries’ relationships with the rest of the world in general and China in particular. Using panel data gravity models for the 2008-2019 period, we find that Romania’s exports are significantly influenced by the demand of its major trade partners in the EU, and imports from China and the rest of the world. In addition, exports are vulnerable to the effectiveness of the government in relation to the other countries, corruption control and cultural values such as collectivism. We also assess the capacity of Romanian exports to regain their ascending trend displayed before the COVID-19 pandemic by using simulation forecasting scenarios based on the shape of the economic recovery and the type of shock transmission across economies. We observe a sharp decrease in 2020 followed by an important recovery in 2021 in a V-shape scenario and uniform transmission of the pandemic shock in the internal demand and in the foreign trade, or followed by a very slow recovery in 2021 (in a U-shape scenario and non-uniform transmission type in the two previously mentioned elements), especially when the global relation with rest of the world is included

The Relationship between Shadow Economy and Unemployment Rate. A Ardl Causality Analysis for the Case Of Romania

The paper aims to investigate the nature of the relationship between the shadow economy (SE) and unemployment rates (both registered and ILO) for the case of Romania using Pesaran et al.(2001) bounds tests approach for cointegration. The study uses quarterly data covering the period 2000-2010. The size of Romanian shadow economy is estimated using the currency demand approach based on VECM models, stating that its size is decreasing over the analyzed period, from 36.5% at the end of 2000 to about 31.5% of real GDP at the middle of 2010. To investigate the long-run causal linkages and short-run dynamics between shadow economy and unemployment rate, ARDL cointegration approach is applied. Cointegration test results shows that in short-run both ILO and registered unemployment rate has a negative and statistically significant effect on the size of the shadow economy, while in the long-run the unemployment rates have a positive effect on shadow economy. The ARDL causality results revealed the existence of a long-run unidirectional causality that runs from unemployment rates (registered or ILO) to shadow economy. In addition, the CUSUM and CUSUMSQ tests confirm the stability of the both causal relationships

Comparative Analysis of Different Univariate Forecasting Methods in Modelling and Predicting the Romanian Unemployment Rate for the Period 2021–2022

Unemployment has risen as the economy has shrunk. The coronavirus crisis has affected many sectors in Romania, some companies diminishing or even ceasing their activity. Making forecasts of the unemployment rate has a fundamental impact and importance on future social policy strategies. The aim of the paper is to comparatively analyze the forecast performances of different univariate time series methods with the purpose of providing future predictions of unemployment rate. In order to do that, several forecasting models (seasonal model autoregressive integrated moving average (SARIMA), self-exciting threshold autoregressive (SETAR), Holt–Winters, ETS (error, trend, seasonal), and NNAR (neural network autoregression)) have been applied, and their forecast performances have been evaluated on both the in-sample data covering the period January 2000–December 2017 used for the model identification and estimation and the out-of-sample data covering the last three years, 2018–2020. The forecast of unemployment rate relies on the next two years, 2021–2022. Based on the in-sample forecast assessment of different methods, the forecast measures root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percent error (MAPE) suggested that the multiplicative Holt–Winters model outperforms the other models. For the out-of-sample forecasting performance of models, RMSE and MAE values revealed that the NNAR model has better forecasting performance, while according to MAPE, the SARIMA model registers higher forecast accuracy. The empirical results of the Diebold–Mariano test at one forecast horizon for out-of-sample methods revealed differences in the forecasting performance between SARIMA and NNAR, of which the best model of modeling and forecasting unemployment rate was considered to be the NNAR model

Nature of the Relationship between Minimum Wage and the Shadow Economy Size: An Empirical Analysis for the Case of Romania

The recent increase in the minimum wage in Romania in early May 2016 represented a popular topic at the national level, which indicated that aggressive increases in the minimum wage could create a competitiveness problem in the context of a relatively high level of informal economic activities. The objective of this paper is to analyse the nature of the relationship between the minimum wage and the size of the Romanian shadow economy using quarterly data for the period 2000-2015. The MIMIC model has been used to estimate the dimension of the shadow economy, and the empirical results revealed that unemployment, self-employment, indirect taxation and a lack of trust in the government are considered the main causes of Romanian informality. The results also indicated that the Romanian shadow economy decreased until 2008 to a value of approximately 27.8% of the official GDP. During the economic crisis, a slow increase in the shadow economy occurred, whereas in recent quarters, a slow decrease was observed. The potential effect of an increase in the minimum wage on the size of the shadow economy has been analysed using the Granger causality approach with vector error correction models. The empirical results indicated that an increase in the minimum wage can be considered a long-term supporting factor for the shadow economy because it increases informal economic activities, as firms will seek alternative methods of circumventing authorities. However, the empirical results do not support any effects of an increase in the minimum wage in the short run

Quantitative Approaches for Exploring the Influence of Education as Positional Good for Economic Outcomes

Education is one of the most important drivers for development and wellbeing both at the level of the society and at an individual level. Recognising the key role of education for social development, economic growth and individual wellbeing, education expansion has become an important objective for educational systems across the world. Education influences distribution of economic outcomes, making people pursue more education in order to obtain higher rewards. While expansion of education accelerates, new theories treat education as a positional good. From this perspective, due to its positional character, returns to education are affected in situations of skills imbalances characterised by a supply of graduates that surpasses the demand of the labour market. This paper employs this new perspective and explores the influence of education on economic outcomes in Romania. The authors present and discuss the use of traditional and new quantitative methods in order to shed light on the positional character of education. Our findings show that, in the case of Romania, the expansion of education did not reach the point at which education can be considered a positional good. The application of such methods is useful to inform a data-driven governance system targeting a better match between the supply and demand for education and skills