Liking what others “Like”: using Facebook to identify determinants of conformity

In this paper we explore the micro-level determinants of conformity. Members of the social networking service Facebook express positive support to content on the website by clicking a Like button. We set up a natural field experiment to test whether users are more prone to support content if someone else has done so before. To find out to what extent conformity depends on group size and social ties we use three different treatment conditions: (1) one stranger has Liked the content, (2) three strangers have Liked the content, and (3) a friend has Liked the content. The results show that one Like from a single stranger had no impact. However, increasing the size of the influencing group doubled the probability that subjects expressed positive support. Friendship ties were also decisive. People were, on average, four times more likely to press the Like button if a friend, rather than a stranger, had done so before them. The existence of threshold effects in our experiment clearly shows that both group size and social proximity matters when opinions are shaped.     

Machine learning for financial transaction classification across companies using character-level word embeddings of text fields

An important initial step in accounting is mapping financial transfers to the corresponding accounts. We devised machine-learning-based systems that automate this process. They use word embeddings with character-level features to process transaction texts. When considering 473 companies independently, our approach achieved an average top-1 accuracy of 80.50%, outperforming baselines that exclude the transaction texts or rely on a lexical bag-of-words text representation. We extended the approach to generalizes across companies and even across different corporate sectors. After standardization of the account structures and careful feature engineering, a single classifier trained on 44 companies from 28 sectors achieved a test accuracy of more than 80%. When trained on 43 companies and tested on the remaining one, the system achieved an average performance of 64.62%. This rate increased to nearly 70% when considering only the largest sector.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

ICT Statistics at the New Millennium—Developing Official Statistics—Measuring the Diffusion of ICT and its Impact

The paper gives a short and very rough overview of the ongoing work in the field of statistics relating to the development of information and communication technology (ICT) and its impacts on the economies and on the society as a whole. It introduces three slightly different approaches with different emphasis on describing the emergence and diffusion of ICT and the respective economic and social change. These are termed the indicators approach, the new economy approach and the intellectual capital approach. The paper also discusses the basic requirements for the establishment of a new statistical system, as well as the present obstacles and problems of this work. Finally, some remarks are presented regarding further statistical co-operation in this field.     

FDI facilitated by agglomeration economies: evidence from manufacturing and services joint ventures in China

The open door policy of China’s economic reform since the 1980s has attracted heavy foreign direct investment (FDI) flows into China and especially to Guangdong (particularly the Pearl River Delta region, PRD) and induced significant economic growth during the past two decades. While there exist various classical theories of FDI in attempting to identify the determinants of FDI inflow and to explain the behavior of FDI flows, limited attention has been given from the perspective of agglomeration effects generated by a core-periphery (CP) relation.This paper intends to study the impacts of agglomerations on FDI inflows in the context of Krugman’s CP relation (1991) by investigating (1) the formation of a CP relation via gravity model analysis; (2) whether different types of industry FDI flows will respond differently in the CP-system, given agglomeration effects; and (3) whether FDI origin and firm scale matter in affecting FDI flows.A database consisting of a population frame of 37,742 firm-level manufacturing and services joint ventures investing in Guangdong in 1998 was used. Empirical results show that the agglomerations of the CP relation have affected FDI flow patterns. While both manufacturing and services FDI and sources of investment responded differently to the impacts, smaller firms were found more responsive to the CP-agglomeration settings regardless of FDI by industry type and by source. The significance and implications of the CP-system to further facilitate FDI in the region are discussed.     

Changes in food provision in Russian households experiencing perestroika

The purpose of this article is to describe how households in Novgorod the Great, Russia, deal with food provision in everyday life. The study focuses on changes experienced in food provision and consumption in Russian society, in order to illustrate how households respond to the transformation towards a market economy. The study reflects women's perspective on food provision. Students from Novgorod the Great visited 105 households and asked the women in the household to answer a questionnaire. Results from the study show that in order to cope with changes in society related to economic reforms, Russian households had changed both their food consumption and food production patterns. There was no big difference between urban and rural households. Nearly all of the households were self‐sufficient in the provision of vegetables and potatoes. Many households had a ‘dacha’ (plot), where they produced most of what they needed. Among the changes experienced during recent years (i.e. during the end of the 1990s), a decade after perestroika was initiated, households mentioned the rise in food prices and the decrease of income. Households reported that they consumed less fruit and/or meat. Some households also mentioned that the quality of nourishment had decreased, thereby indicating lower general quality, lower nutrition value, or less healthy foodstuffs.     

Carbon tax simulations using a household demand model

The main objective of this paper is to analyse consumer response and welfare effects due to changes in energy or environmental policy. To achieve this objective we formulate and estimate an econometric model for non-durable consumer demand in Sweden that utilises micro- and macro-data. In the simulations, we consider two revenue neutral scenarios that both imply a doubling of the CO₂ tax; one that returns the revenues in the form of a lower VAT and one that subsidise public transport. One conclusion from the simulations is that the CO₂ tax has regional distribution effects, in the sense that household living in sparsely populated areas carry a larger share of the tax burden.     

Do interventions in foreign exchange markets modify investors' expectations? The experience of Japan between 1992 and 2004

The purpose of this paper is to analyze the impact of the Bank of Japan's official interventions on the JPY/USD parity during the period 1992–2004. The novelty of our approach is to combine two recent advances of the empirical literature on foreign exchange interventions: (i) drawing on over-the-counter option prices to characterize more precisely the distribution of market expectations; (ii) redefining interventions in terms of events as they tend to come in clusters. Moreover, in order to deal with the features of the data (small sample size, non-standard distribution), we use bootstrap tests.We show that interventions have a significant impact on the mean expectation (the forward rate). The results are more ambiguous for variance. Additionally, we find that the effect of interventions on skewness is significant, robust to different definitions of skewness, and consistent with the direction of interventions. On the contrary, our results clearly show that kurtosis is not affected by interventions. We finally show that: (i) coordination increases effectiveness of interventions; (ii) results are not altered when controlling for other economic and political news.