Separating aggregate discouraged and added worker effects: the case of a former transition country

Research background: We analyse the added worker effect (AWE) and the discouraged worker effect (DWE) from an aggregate perspective. The first effect refers to an increase in labour force participation in response to a decrease in the wage rate. The second effect refers to the decision by workers who have been unsuccessful in their job search to leave the labour market or to decrease their labour force participation. For our analysis, we use the case of Poland, a country with a persistently low labour force participation rate. Purpose of the article: While previous studies focused on the net of the two effects, we aim to analyse the two effects both separately and simultaneously. We propose a new approach for analysing the two effects. We generalise and model them as resulting from different shocks: (i) the AWE as the result of a negative wage income shock, and (ii) the DWE as the result of a positive job search time shock. The underlying assumption is that both shocks have at least a transitory effect on the labour force participation rate. However, we also track the potential long-lasting effects of these shocks, and we analyse the reactions of gender and age groups to them. While this approach demonstrates the robustness of our results, it also provides the range of the sensitivity, as it shows that there are large differences in the magnitude of the AWE and the DWE for different labour market cohorts. Methods: We use the multivariate unobserved component model to extract the AWE and the DWE, and we then use VAR models, applying sign and exclusion restrictions to model the underlying shocks. We use quarterly data for Poland in 1995?2019. Most of these data come from the Labour Force Survey, while the rest come from Statistics Poland. Findings & value added: In contrast to previous literature, which analysed only the net effect of the two effects, we model the AWE and the DWE separately. Contrary to the findings of previous research, our approach seems to confirm that both effects are simultaneously present in the labour market, and both effects influence the labour force participation rate. Thus, we find that both effects are significant. Specifically, we show that the AWE is stronger, but transitory; while the DWE is weaker, but long-lasting.

Political Instability Equals the Collapse of Tourism in Ukraine?

The purpose of this study was to determine how political instability influences inbound tourist flows in Ukrainian cities, performance of tourism-related businesses, and tourism-based profits in general. This study allows us to present the impact of various events on the tourism economy in Ukraine; however, the available secondary data with the unobserved component model procedure detection give only a general overview of the situation. Thus, interviews were conducted with experts, including managers of accommodation facilities, employees of municipal tourism development departments, and researchers investigating tourism. Interviews with experts revealed opportunities, threats, and future scenarios of tourism in Ukraine in the face of five years of political instability. The results support previous findings that political instability reduces tourist traffic over the short term. On the other hand, the interviews with experts representing major province cities have shown different results for the long-term perspective. Cities with developed tourism sectors in areas away from the place of conflict are beneficiaries of political instability. Disadvantaged are cities that had their tourist flows based on the citizens of the aggressor’s country—the Russian Federation. Cities that are underdeveloped in terms of tourism did not experience a significant impact of the political instability in eastern Ukraine.

The fiscal policy and the dynamic of the economic cycle

PurposeThis study seeks to determine in some detail whether the state of the economic cycle matters in considering the effects of fiscal policy shocks on output.Design/methodology/approachThis issue leads us to two primary objectives: to define the economic cycle measuring the gap with the unobserved component model with a smoother trend, which can be used efficiently to generate gap measures for use in real-time decision-making and avoids the criticisms of measures based on contentious structural models; and to look empirically at the fiscal policy stance over the phases of the cycle, bearing in mind the short time variation and smooth change between the cycle regimes.FindingsThis paper provides evidence that the fiscal policy rule seems to operate with varied coefficients depending on whether the transition variable is below or above the estimated threshold value.Originality/valueThe asymmetric response gives policymakers the impetus to reconsider the fiscal policy framework because of specific circumstances, such as shocks that can dramatically affect the nominal features of the business cycle. Put differently, stable and moderate fiscal policies would at least not contribute to cyclical fluctuations, and therefore would be better than what we have typically experienced. There would, therefore, seem to be a distinct need to address the properties of economic cycles under different fiscal policy rules.

An effcient exact Bayesian method For state space models with stochastic volatility

This paper introduces a Bayesian MCMC method, referred to as a marginalized mixture sampler, for state space models whose disturbances follow stochastic volatility processes. The marginalized mixture sampler is based on a mixture-normal approximation of the log-χ2 distribution, but it is implemented without the need to simulate the mixture indicator variable. The key innovation is to use the filter ing scheme developed by Kim (Kim C.-J. 1994. “Dynamic Linear Models with Markov-Switching.” Journal of Econometrics 60: 1–22.) and the forward-filtering backward-sampling algorithm to generate a proposal series of the latent stochastic volatility process. The proposal series is then accepted according to the Metropolis-Hastings acceptance probability. The new sampler is examined within an unobserved component model and a time-varying parameter vector autoregressive model, and it reduces substantially the correlations between MCMC draws.

Assessing Robustness of Asian Countries Ranking

The term ‘digital divide' refers to the gap between individuals, households, businesses and geographic areas with regard to both their opportunities to access Information and Communication Technologies (ICT) and to their use of Internet. Composite indicators are regularly used for measuring the divide and in benchmarking the country's performance. But often it creates controversies regarding the subjectivity that is connected with their construction methodology more specifically the weighting and aggregation issues. The paper attempts to assess the robustness of the ranks generated by the composite digital divide index using different weighting and aggregation schemes in case of Asian countries. Here four weighting techniques Iyengar-Sudarshan Method, Benefit of Doubt Method, Principal Component Analysis and Unobserved Component Model and three techniques of aggregation viz. Linear Aggregation, Geometric Aggregation and Weighted Displaced Ideal Method are used for mutual comparison.

Unobserved Components Model for Forecasting Sugarcane Yield in Haryana

Unlike classical regression analysis, the state space models have time-dependent parameters and provide a flexible class of dynamic and structural time series models. The unobserved component model (UCM) is a special type of state space models widely used to analyze and forecast time series. The present investigation has been carried out to study the trend of sugarcane(gur) yield in five districts (Ambala, Karnal, Panipat, Yamunanagar and Kurukshetra) of Haryana state using the unobserved component models with level, trend and irregular components. For this purpose, the time series data on sugarcane yield from 1966-67 to 2016-17 of Ambala and Karnal, 1971-72 to 2016-17 of Kurukshetra and 1980-81 to 2016-17 of Panipat and Yamunanagar districts have been used. For all the districts, the irregular component was found to be highly significant (p=0.01) while both level and trend component variances were observed non-significant. Significance analysis of the individual component(s) has also been performed for possible dropping of the level and trend components by setting their variances equal to zero. The state space models may be effectively used pertaining to Indian agriculture data, as it takes into account the time dependency of the underlying parameters which may further enhance the predictive accuracy of the most popularly used ARIMA models with parameter constancy. Moreover, the unobserved component model is capable of handling both stationary as well as non-stationary time series and thus found more suitable for sugarcane yield modeling which is a trended yield (i.e. non-stationary in nature).

An unobserved component analysis of Malaysia’s house prices

Purpose This paper aims to investigate Malaysia’s house prices behaviour by decomposing trend, cycle and stochastic component. Design/methodology/approach The authors perform an unobserved component model of a structural time series and Markov switching model that covers the period 1999Q1 to 2015Q4. Findings The results reveal that the variation in house price in Malaysia is best explained by its trend level, with a small role played by the cycle component; this implies the potential for gaining returns on investments in property by investors and households. The results also show that Malaysia’s HPI cycle is between 8 and 17 years which, in relative terms, is twice the length of the growth cycle and the business cycle in the economy. Meanwhile, the overall movement of HPI is forecast to have a marginal price increase up to 2028Q2. Originality/value As house prices remained elevated during the year, the house price dynamic is pivotal for understanding the source of changes in house price. With major findings centred on the relationship between house prices and macroeconomic as well as policy variables, little attention has been paid to composing the trend, cycle and seasonal pattern from the house price index, thus understanding the behaviour of house prices’ unobserved components.