Happiness before and after an election: An analysis based on a daily survey around Japan’s 2009 election

This paper investigates whether the Japanese voters became happy and/or unhappy due to the results of the General Election in 2009. We conducted a daily web survey for seven days before and after the election, obtaining 1068 responses. Estimating a fixed effects model, we found that among those whose expectation differed from the reality, supporters of the Democratic Party of Japan (DPJ), the winner, became significantly happier, and supporters of the Liberal Democratic Party of Japan (LDP) and New Komeito, the losers, became significantly unhappier on the day following the election. However, happiness returned to the previous level in one or two days, implying people adapted to the news very quickly. On the other hand, the happiness level of those whose expectation of the election results were realized did not change. In a word, only unexpected results matter. Dividing those who support the policies of DPJ into two groups, those who expect material benefits from the victory of DPJ and those who do not, the obtained results suggested that the reason why the supporters of the winner (DPJ) felt happy was not because they obtained material benefits from the change of government.     

PITFALLS IN CROSS‐SECTION STUDIES WITH INTEGRATED REGRESSORS: A SURVEY AND NEW DEVELOPMENTS

In cross‐section studies, if the dependent variable is I(0) but the regressor is I(1), the true slope must be zero in the resulting “unbalanced regression.” A spuriously significant relationship may be found in large cross‐sections, however, if the integrated regressor is related to a stationary variable that enters the DGP but is omitted from the regression. The solution is to search for the related stationary variable, in some cases the first difference of the integrated regressor, in other cases, a categorical variable that can take on limited number of values which depend on the integrated variable. We present an extensive survey, new developments, and applications particularly in finance.     

What do professional forecasters actually predict?

This paper studies what professional forecasters predict. We use spectral analysis and state space modeling to decompose economic time series into trend, business cycle, and irregular components. We examine which components are captured by professional forecasters by regressing their forecasts on the estimated components extracted from both the spectral analysis and the state space model. For both decomposition methods, we find that, in the short run, the Survey of Professional Forecasters can predict almost all of the variation in the time series due to the trend and the business cycle, but that the forecasts contain little or no significant information about the variation in the irregular component.     

Demystifying residents' walking behaviors: Active transport in South East Queensland,Australia

This study investigates the correlates of a resident's walking behaviors and aims to shed light on mechanisms through which walking may be encouraged. The results of this investigation paint a complex and nuanced picture of the residents’ walking behaviors in South East Queensland, Australia. The results suggest that sociodemographic characteristics separately may contribute greatly to whether or not one engages in walking behaviors. Further, cumulatively these differences might be greater for some groups of residents compared to others. One of the most prominent findings of the study is that the purpose, the characteristics of the origin, and the characteristics of the destination of a trip tended to be similar in the heterogeneity they exhibit over the distribution of time spent walking. For example, pick something up, undertake work, or engage in personal business, move to or from a workplace, shop, or social place are activities that tend to be associated with walking as a main mode of transport and a higher number of walking episodes. However, these trips tended to be short.     

Forecasting GDP growth with NIPA aggregates: In search of core GDP

In addition to GDP, which is measured using expenditure data, the U.S. national income and product accounts (NIPAs) provide a variety of measures of economic activity, including gross domestic income and other aggregates that exclude one or more of the components that make up GDP. Similarly to the way in which economists have attempted to use core inflation—which excludes volatile energy and food prices—to predict headline inflation, the omission of GDP components may be useful in extracting a signal as to where GDP is going. We investigate the extent to which these NIPA aggregates constitute “core GDP.” In an out-of-sample forecasting exercise using a novel real-time dataset of NIPA aggregates, we find that consumption growth and the growth of GDP excluding inventories and trade have historically outperformed a canonical univariate benchmark for forecasting GDP growth, suggesting that these are promising measures of core GDP growth.     

Does tourism really cause stress? A natural experiment utilizing ArcGIS Survey123

ABSTRACT

Tourism related stress has recently been revealed as an issue for tourism host community residents. Prior studies have relied on directly asking residents if tourism caused them stress, resulting in possible participant priming. This study utilized the ArcGIS Survey123 app to collect data on resident stress from a community with high levels of tourism visitation and a community with low levels of tourism visitation. During the study, researchers never mentioned tourism. Half of study participants in the community with high tourism visitation discussed tourism directly as a stressor, accounting for almost 5% of all stressful experiences, while no participants in the community with low tourism visitation mentioned tourism as a stressor. Geospatial data revealed that tourism related stressors occurred outside of the ‘tourism zone’ in the community with high tourism visitation, and tourism related stressors mirrored perceived tourism impacts measured in a post experiment survey of study participants.     

Five dimensions of the uncertainty–disagreement linkage

We analyze the relationship between forecaster disagreement and macroeconomic uncertainty in the Euro area using data from the European Central Bank’s Survey of Professional Forecasters for the period 1999Q1–2018Q4 and find that disagreement is generally a poor proxy for uncertainty. However, the strength of this link varies with the dispersion statistic employed, the choice of either the point forecasts or the histogram means for calculating disagreement, the outcome variable considered and the forecast horizon. In contrast, distributional assumptions do not appear to be very influential. The relationship is weaker in subsamples before and after the outbreak of the Great Recession. Accounting for the forecasters’ entry to and exit from the survey has little impact on the results. We also show that survey-based uncertainty is associated with overall policy uncertainty, whereas forecaster disagreement is related more closely to the expected fluctuations on financial markets.     

When do B2B brands influence the decision making of organizational buyers? An examination of the relationship between purchase risk and brand sensitivity

The dominant perspective on organizational buying behavior suggests that buyers tend to rely on objective criteria when making product choice decisions and that the potential influence of subjective cues, such as brands, on buyer decision making decreases with increasing risk. An alternative perspective, confirmed in this study by in-depth interviews with various managers, suggests that brands serve as a risk-reduction heuristic, whereby the influence of brands on decision making increases as a function of risk. Building on risk and information processing theories, this research builds on these complementary perspectives to propose that risk and brand sensitivity relate in a U-shaped manner, where brand sensitivity is highest in relatively low- or high-risk situations. The results of scenario- and survey-based field studies—involving 206 and 180 members of buying centers, respectively—suggest that both perspectives have merit and support the proposed nonlinear relationship. Moreover, the findings reveal that the risk-brand sensitivity relationship is moderated by competitive intensity, such that the linear (negative) and quadratic (positive) effects are stronger when competitive intensity is low.     

面对反倾销调查:企业会计准备好了吗?——以美国为例的分析报告

反倾销调查赋予了企业会计新的内容和方法,本文在分析反倾销问卷调查有关会计内容的基础上,提出了企业会计在特殊时期应承担的具体任务:会计资料的重新归类、生产成本的合理计算、正常价格的适当选择、会计方法有目的进行调整等,按照会计信息决策有用的目标为反倾销调查取证提供依据。     

Mobile diaries – Benchmark against metered measurements: An empirical investigation

Researchers seeking to study the relationships between consumers' communications, attitudes, and behaviors could benefit from monitoring consumers over time, across multiple locations and channels, and in a way that reflects consumers' subjective perceptions. Diaries on smartphones (mobile diaries) can be used as a research tool for such purposes. A mobile diary is a self-report instrument whereby people use their mobile handset to repeatedly report experiences of interest. Mobile diaries are increasingly used in psychology, geography, medicine, and commercial marketing. Yet they have rarely been used for quantitative marketing research, and were not benchmarked against best-practice metrics in marketing.In this study, we aim to set the ground for using mobile diaries in quantitative marketing research. We first lay out the theoretical infrastructure for the usage of mobile diaries, and describe possible respondent reporting concerns, including concerns related to non-reporting, reporting over time, and concerns stemming from individual-level heterogeneity.We demonstrate the potential of mobile diaries, as well as the importance of the various concerns, using a benchmark test case in the context of primetime TV viewing. Our benchmark uses a sample of respondents with both mobile diary viewing reports and Nielsen People Meter (NPM) records. Our analysis reveals that averaging across all conditions, 47.4%–64.7% of the NPM records are reported by the diary. The major sources for mismatch are random time periods without alarms, short viewings, and periodic reporting inactivity (pulsing). Concerns such as a decrease in reporting rates over time (e.g., fatigue), smartphone ownership, and demographic variation across individuals have relatively small effects on reporting likelihood. Analyzing the cases in which diary reports do not have a matching NPM record, we find many of them can be attributed to out-of-home viewing and viewing on non-metered devices. This finding demonstrates how mobile diaries can complement metered measurements. Overall, aggregate diary-based ratings have a 0.90 correlation with NPM ratings.We discuss implications for designing and using mobile diary studies in marketing.