Effects of Energy and Food Supply on Domestic Water Demand - Elasticity Estimation Using Panel Data Model -

2018 ◽  
Vol 14 (4) ◽  
pp. 41-53
Author(s):  
Hee Kyun Oh ◽  
◽  
Hee Chan Lee ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 1414
Author(s):  
Mónica Madonado-Devis ◽  
Vicent Almenar-Llongo

In urban water provisioning, prices can improve efficiency, contributing to the achievement of the environmental objective. However, household responses to price changes differ widely based on the household characteristics. Analyses performed at the aggregate level ignore the implications of water demand incentives at the individual household level. A large data sample at the household level enables estimation of econometric models of water demand, capturing the heterogeneity in domestic consumption. This study estimated the domestic water demand in the city of Valencia and its elasticity, along with the demands of its different districts and neighbourhoods (intra-urban scale analysis). Water price structure in Valencia is completely different from that of other Spanish cities: it is a price structure of increasing volume (increasing rate tariffs, IRT). For this estimation, from a microdata panel at the household level, the demand function with average prices for the period 2008–2011 was estimated using panel data techniques including a fixed effect for each neighbourhood. The domestic water demand elasticity at the average price in Valencia was estimated at −0.88 (which is higher than that estimated for other Spanish cities). This value indicates an inelastic demand at the average price of the previous period, which can cause consumers to overestimate the price and react more strongly to changes.


2021 ◽  
pp. 1-25
Author(s):  
Yu-Chin Hsu ◽  
Ji-Liang Shiu

Under a Mundlak-type correlated random effect (CRE) specification, we first show that the average likelihood of a parametric nonlinear panel data model is the convolution of the conditional distribution of the model and the distribution of the unobserved heterogeneity. Hence, the distribution of the unobserved heterogeneity can be recovered by means of a Fourier transformation without imposing a distributional assumption on the CRE specification. We subsequently construct a semiparametric family of average likelihood functions of observables by combining the conditional distribution of the model and the recovered distribution of the unobserved heterogeneity, and show that the parameters in the nonlinear panel data model and in the CRE specification are identifiable. Based on the identification result, we propose a sieve maximum likelihood estimator. Compared with the conventional parametric CRE approaches, the advantage of our method is that it is not subject to misspecification on the distribution of the CRE. Furthermore, we show that the average partial effects are identifiable and extend our results to dynamic nonlinear panel data models.


2021 ◽  
Vol 40 (7) ◽  
pp. 688-707
Author(s):  
Yan Meng ◽  
Jiti Gao ◽  
Xibin Zhang ◽  
Xueyan Zhao

Sign in / Sign up

Export Citation Format

Share Document