Index of environmental awareness through the MIMIC approach

Khakimova, Dilya; Lösch, Stefanie; Wende, Danny; Wiesmeth, Hans; Okhrin, Ostap

doi:10.1111/pirs.12420

Cited by 5 publications

(17 citation statements)

References 31 publications

(39 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…2. Lösch, Okhrin, and Wiesmeth (2018a) This paper is basically an extension of the previous paper Khakimova et al (2019). The temporal and spatial development of EA is investigated.…”

Section: Published and Submitted Articlesmentioning

confidence: 95%

“…Teile der Dissertation wurden vom Proof Reading Service (https://www.proof-reading-service.com) lektoriert. Khakimova et al (2019). .…”

Section: Danksagungunclassified

“…1. Khakimova et al (2019): Index of environmental awareness through the MIMIC approach. Papers in Regional Science, 98, 3, 1419 -1441. https: //doi.org/10.1111/pirs.12420…”

Section: Published and Submitted Articlesmentioning

confidence: 99%

“…The research area is again the 81 Russian regions during the time period between January 2014 and April 2016. In contrast to Khakimova et al (2019), more data are available and the EA index development can be analysed for each of the 28 months. We find seasonal and spatial variations of the EA, which are strong correlated with the regional temperature.…”

Section: Published and Submitted Articlesmentioning

confidence: 99%

“…This chapter contains parts, figures and tables, which are published in Khakimova et al (2019), Lösch, Okhrin, andWiesmeth (2018a), Lösch, Okhrin, and Wiesmeth (2018b) and Lösch, Okhrin, and Wiesmeth (2017). The citations is unified and the format is adjusted.…”

Section: Quantifying Regional Environmental Awarenessmentioning

confidence: 99%

See 4 more Smart Citations

Awareness of climate change: differences among Russian regions

Lösch

Okhrin

Wiesmeth

2018

Area Development and Policy

Self Cite

View full text Add to dashboard Cite

This thesis reveals methods for estimating individual perception at the regional level, such as environmental awareness or wage effects due to labour market tightness. Environmental awareness belongs to individual attitudes, which is driven by socialization, culture and education. Consequently, it is difficult to compare environmental awareness between regions. Labour market tightness might be reflected in individual wages, but the latter is also triggered by a lot of exogenous variables. Given that a simple linear regression model fails in both cases, existing alternative approaches from the econometrics like Multiple Indicator Multiple Cause model and Least Absolute Shrinkage and Selection Operators are used.First, a Multiple-Indicator Multiple-Causes model is introduced, which allows us to estimate a not-directly observable individual attitude, environmental awareness, for different regions and to rank them. The method is cost-effective and less time-consuming, it also allows for comparisons between regions. The study area serves 81 regions in Russia. The model is constructed in such a way that Internet queries from the search engine c Yandex are assumed to be indicators, which are affected by the regional environmental awareness index. In addition, regional characteristics, such as Gross Regional Product per capita, the proportion of employees in specific industry sectors, and also the environmental situation within the regions are potential cause variables. The regional environmental awareness index is estimated for each of the Russian regions from January 2014 until April 2016. Furthermore, the findings shows a rather non-linear positive relationship between the regional environmental awareness and regional wealth, as well as a strong negative correlation with the temperature. The colder the region and the observed month, the higher the interest of the population in environmental topics. Furthermore, the regions can be grouped into four environmental awareness clusters by using k-means clustering algorithm. It seems that the environmental awareness index shrinks from the Eastern to the Western part of Russia. The highest values can be estimated in Chukotka, Kamchatka, and Magadan. The lowest values are found in the rather poor and warm Caucasus area.Second, another issue concerns the estimation of an effect of an observable regional variable, such as labour supply shortage, on individual wages. This thesis investigates the ten year wage development of employees who first enter the labour market from 1995 until 2004 and looks for positive wage effects of labour market tightness in different occupational groups. Due to incomplete vacancy data, labour market tightness is measured as the number of unemployed people divided by the number of employees within an occupational field and region. Mean and quantile regression methods are applied. Because the number of right-hand side variables could lead to incorrect detected statistical significant coefficients, different Least Absolute Shrinkage and Selection Operato...

show abstract

“…2. Lösch, Okhrin, and Wiesmeth (2018a) This paper is basically an extension of the previous paper Khakimova et al (2019). The temporal and spatial development of EA is investigated.…”

Section: Published and Submitted Articlesmentioning

confidence: 95%

“…Teile der Dissertation wurden vom Proof Reading Service (https://www.proof-reading-service.com) lektoriert. Khakimova et al (2019). .…”

Section: Danksagungunclassified

“…1. Khakimova et al (2019): Index of environmental awareness through the MIMIC approach. Papers in Regional Science, 98, 3, 1419 -1441. https: //doi.org/10.1111/pirs.12420…”

Section: Published and Submitted Articlesmentioning

confidence: 99%

Section: Published and Submitted Articlesmentioning

confidence: 99%