The demand effect in WTP elicitation

2021 ◽  
Author(s):  
Yu Gao
Keyword(s):  
Demand Effect

Collecting High-Quality Data

2018 ◽  
Author(s):  
Mary Kay Gugerty ◽  
Dean Karlan
Keyword(s):  
Data Collection ◽  
Social Desirability ◽  
Measurement Errors ◽  
Monitoring And Evaluation ◽  
Quality Data ◽  
Social Desirability Bias ◽  
High Quality ◽  
High Quality Data ◽  
Evaluation Systems ◽  
Demand Effect

Without high-quality data, even the best-designed monitoring and evaluation systems will collapse. Chapter 7 introduces some the basics of collecting high-quality data and discusses how to address challenges that frequently arise. High-quality data must be clearly defined and have an indicator that validly and reliably measures the intended concept. The chapter then explains how to avoid common biases and measurement errors like anchoring, social desirability bias, the experimenter demand effect, unclear wording, long recall periods, and translation context. It then guides organizations on how to find indicators, test data collection instruments, manage surveys, and train staff appropriately for data collection and entry.

Segmentation of markets in Central and Eastern Europe with the use of the CMS method

2019 ◽  
Vol 64 (2) ◽  
pp. 5-16
Author(s):  
Marcin Salamaga
Keyword(s):  
Eastern European ◽  
Commodity Trade ◽  
Multivariate Statistical ◽  
Statistical Analysis Method ◽  
Detailed Assessment ◽  
Trade Competitiveness ◽  
Constant Market Share ◽  
Eastern European Countries ◽  
Selected Effects ◽  
Demand Effect

The paper aims at making a comparative analysis of the Central and Eastern European countries in the scope of effects accompanying changes in their export. The Eurostat’s data for 2016 were used in the study. The effects of changes in export of individual countries were separated based on the Constant Market Share (CMS) model developed by Leamer and Stern. The calculated effectssuch as: demand effect, market distribution effect, commodity composition effect and competitiveness effect enabled a detailed assessment of the sources of changes occurring in export of individual countries. They allowed, in particular, for answeringthe following question: to what extent may changes in export be explained by the economic situation in the world commodity trade of individual clustersand to what extent do they result from the competitiveness of these countries? The application of the multivariate statistical analysis method for the selected effects allowed for the identification of clusters of countries with the most similar position in the spatial and commodity arrangement, including countries of similar trade competitiveness.

Experimenter Demand Effect in the Lab

2020 ◽  
Author(s):  
Priyoma Mustafi ◽  
Alistair Wilson
Keyword(s):  
Demand Effect

scholarly journals Research on driving factors of China’s carbon emissions based on structural decomposition

2021 ◽  
Vol 248 ◽  
pp. 02026
Author(s):  
Hua Gao ◽  
Zhoujie Huang
Keyword(s):  
Carbon Emissions ◽  
Energy Intensity ◽  
Final Demand ◽  
Driving Factors ◽  
Structure Effect ◽  
Influence Coefficient ◽  
Intensity Effect ◽  
Demand Structure ◽  
Demand Effect ◽  
Main Factor

After further processing the input-output tables of 2007, 2012 and 2017, the carbon emissions are decomposed into four driving factors: energy intensity effect, Leontief technology effect, final demand structure effect and final total demand effect through IO-SDA model. The results show that the energy intensity effect has a significant negative effect, which is the main factor to promote the reduction of carbon emissions. The Leontief technical effect and the final total demand effect are positive effects, and the total final demand effect is the main factor leading to the increase in carbon emissions, and the effect of the final demand structure effect is not significant. In addition, the results of the influence coefficient and the inductance coefficient show that: metal smelting and rolling manufacturing, petroleum processing and coking and nuclear fuel processing, coal mining and processing, and oil and gas mining and processing industries are high-energy-consuming industries, but the status of the basic industry makes it possible to formulate energy-saving policies only in terms of technological progress.

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