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Xianhua Wu, Zhe Xu, Hui Liu, Ji Guo, and Lei Zhou

cyclones on the quantity of labor employed and employee remuneration, this paper puts forward several research hypotheses after analyzing the mechanism of tropical cyclones’ impact on employment. Then, meta-regression analysis is adopted to study the sample data from four aspects, including industry dimension, time dimension, income dimension, and tropical cyclone intensity. The conclusions provide some suggestions for the medium- and long-term management of tropical cyclones. The differences between

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Danielle E. Nagele and Joseph E. Trainor

experience) and 1 (at least one experience). All variables were coded using Statistical Package for the Social Sciences (SPSS). SPSS was also used for the data analysis. A multinomial logistic regression was performed to determine the relationship between the dependent variables and the independent variable. 8. Results Table 2 shows a correlation matrix of all independent and dependent variables. This matrix provides support for many but not all of our hypothesized relationships, as detailed below

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Chukwuma Otum Ume, Ogochukwu Onah, Kehinde Paul Adeosun, Onyekwe Chris Nnamdi, Nice Nneoma Ihedioha, Chukwuemeka Onyia, and Ezinne Orie Idika

social structures such as internal practices within a particular field, and individual experiences. The objective of understanding the underlying drivers and households’ perception of climate change adaptation fall within this scope of analysis and as such justifies its usage in the study. (A sample of the interview transcript can be found in appendix A , and a detailed result of the regression analysis using the StataCorp Stata, release 14 (Stata 14), software program can be found in appendix B

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George B. Frisvold and Anand Murugesan

measures ( Bishop et al. 2010 ; Chouinard et al. 2008 ; Sheeder and Lynne 2011 ). They test this theory through discrete choice regression analysis, where the adoption regression depends on a reduced form specification of utility V = V ( x , ω ). The papers find that many variables equivalent to ω above are individually, statistically significant and that the predictive power of the models improve when these behavioral variables are included. Equation (1) further acknowledges that farm

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Matthew Potoski, R. Urbatsch, and Cindy Yu

weather-based effects on attitudes toward global warming and presidential approval. This implies that accurately measuring public opinion requires adjusting standard survey results with conditioning strategies, such as weights, propensity score matching, or regression-based controls relating to weather. 2. Temperature, public opinion, and surveys Though many components of weather (e.g., precipitation or windiness) can affect behavior, we focus on deviations from normal temperature. People have

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Julie L. Demuth, Jeffrey K. Lazo, and Rebecca E. Morss

. Measuring where individuals fall along factor scales also provides information on respondent heterogeneity that we explore further in the regression analysis ( section 4 ). We performed 1 the factor analysis using the principle axis factoring extraction method with an orthogonal varimax rotation. To select criteria for the factor analysis, we followed guidance from Hatcher (2007) and Garson (2009a) with the goals of our study in mind. We retained factors that accounted for at least 10% of the

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Xianhua Wu, Lei Zhou, Ji Guo, and Hui Liu

variables, and regression analysis is carried out based on the establishment of panel model. Specific models are shown below: In the models, Q it represents the unit employment 9 of region i in the period t ; PW it the per capita remuneration of region i in the period t ; TC it the dummy variable of typhoon, whose value is 1 when it is in experimental group where region i in the period t is hit by typhoons and 0 when it is in control group free from typhoons; GDP it the gross product of

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Leon S. Robertson

opportunity to estimate the correlation of these factors with CO 2 emissions. A least squares regression model was used to estimate the effect of differences in the hypothesized predictive factors on CO 2 emissions among the 48 contiguous U.S. states during the years 2000–14. Alaska and Hawaii were excluded because the data on temperatures in those states were not available. Weather stations are concentrated in more highly populated areas ( National Oceanic and Atmospheric Administration 2017b ). The

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Benjamin M. Miller

tornadoes and hence may be less informative about the impact of NWR transmissions more generally. Both methods use Poisson regression analysis to estimate the causal impact of NWR access on tornado injuries and fatalities. 12 The independent variable of interest is coverage , which is a represents the percentage of counties on a tornado’s path that receive broadcasts from at least one NWR transmitter. 13 Coverage takes values ranging from 0 to 1, with higher values reflecting cases where a larger

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Christoph Toeglhofer, Roland Mestel, and Franz Prettenthaler

(nights t ). On the one hand, we use regression models to estimate sensitivity for both the full sample period and for the more recent time periods. On the other hand, we use the so-called analog approach in order to study the impact of the recent snow-poor winter season 2006/07 in more detail. We then compare results from this approach with the regression results. When carrying out regression analysis, we make use of an autoregressive distributed lag (ADL) model, which in its most general form

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