There is limited knowledge of the mechanisms that can inspire people's concern and engagement in the protection of unpopular and unappealing species. We analyzed Polish people's interest in themed internet memes featuring the proboscis monkey (Nasalis larvatus) and the consequences of this interest for conservation marketing. We examined Google Trends data, used Google Search, and searched popular media materials to estimate interest in the proboscis monkey in Poland. Photos of the proboscis monkey when presented with humor in internet memes attracted as much interest as usually more popular species (e.g., koala, panda, and orangutan) used in marketing by nongovernmental organizations. Amusing internet memes spread by social media positively correlated with increasing interest in the unappealing species, such as proboscis monkey. Interest in amusing internet memes positively correlated with individuals' decisions to donate to 6 crowdfunding actions. Thus, conservation marketing that includes amusing memes and social media may provide a worthwhile complement to traditional campaigns and are likely to influence individuals who are unaffected by the usual means.

N a t i o n a l B a n k o f P o l a n d

The main objective of the paper is to investigate properties of business cycles in the Polish economy before and after the recent crisis. The essential issue addressed here is whether there is statistical evidence that the recent crisis has affected the properties of the business cycle fluctuations. In order to improve robustness of the results, we do not confine ourselves to any single inference method, but instead use different groups of statistical tools, including non-parametric methods based on subsampling and parametric Bayesian methods. We examine monthly series of industrial production (from January 1995 till December 2014), considering the properties of cycles in growth rates and in deviations from long-run trend. Empirical analysis is based on the sequence of expanding-window samples, with the shortest sample ending in December 2006. The main finding is that the two frequencies driving business cycle fluctuations in Poland correspond to cycles with periods of 2 and 3.5 years, and (perhaps surprisingly) the result holds both before and after the crisis. We, therefore, find no support for the claim that features (in particular frequencies) that characterize Polish business cycle fluctuations have changed after the recent crisis. The conclusion is unanimously supported by various statistical methods that are used in the paper, however, it is based on relatively short series of the data currently available.

Research background: The probabilistic setup and focus on evaluation of uncertainties and risks has become more widespread in modern empirical macroeconomics, including the analysis of business cycle fluctuations. Therefore, forecast-based indicators of future economic conditions should be constructed using density forecasts rather than point forecasts, as the former provide description of forecast uncertainty. Purpose of the article: We discuss model-based probabilistic inference on business cycle fluctuations in Poland. In particular, we consider model comparison for probabilistic prediction of growth rates of the Polish industrial production. We also develop a class of indicators of future economic conditions constructed using probabilistic information on the rates (that make use of joint predictive distribution over several forecast horizons). Methods: We use Bayesian methods (in order to capture the estimation uncertainty) and consider two groups of models. The first group consists of Dynamic Conditional Score models with the generalized t conditional distribution (with conditional heteroskedasticity and heavy tails, being important for modelling of extreme observations). Another group of models relies on deterministic cycle modelling using Flexible Fourier Form. Ex-post density forecasting performance of the models is compared using the criteria for probabilistic pre-diction: Log-Predictive Score (LPS) and Continuous Ranked Probability Score (CRPS). Findings & value added: The pre-2013 data support the deterministic cycle models whereas more recent observations can be explained by a simple mean-reverting Gaussian AR(4) process. The results indicate a structural change affecting Polish business cycle fluctuations after 2013. Hence, forecast pooling strategies are recommended as a tool for further research. We find rather limited support in favor of the first group of models. The probabilistic indicator of future economic conditions considered here leads actual phases of the growth cycle quite well, though the effect is less obvious after 2013.

The threat of the negative consequences of global warming makes the discussion about the relationship between economic growth, productivity, and increasing renewable energy involvement an important topic. Hence, the aim of this study is to analyze the impact of renewable energy and energy supply on economic growth and productivity at the national level using stochastic frontier analysis and the aggregate production function framework. In doing so, we analyzed a panel of annual data from 133 countries from 2008 to 2014. We apply a generalized stochastic frontier model, which allows us to differentiate between persistent and transient inefficiency, as well as individual effects. Our results indicate a threshold level in terms of a country’s development that needs to be obtained to benefit from increasing renewable energy involvement over time. However, if this threshold level is obtained, productivity gains are evident. We also found that the role of the energy supply in aggregate production is nontrivial. That is, its inclusion changes the relationship between key input factors (capital and labor) by decreasing their overall elasticities and increasing the observed economies of scale.