# **5. Environmental risks and consumption differences**

This section investigates how environmental risks relate to consumption differences between households and consumption changes over time, applying a set of regression models.

## **5.1 Methods**

Two sets of regression models are fitted to estimate risk impacts on consumption differences between households in the ‘Pooled’ cross‐section and on consumption changes over time for the ‘Panel’ data set. A particularity of the VHLSS data is that, for some households, information is available for two or three survey years. These households form the _Panel_ data set to explain consumption changes over time. Other households were only observed in one of the three years. Using these cross‐section data, treating all observations as independent observations provides a _Pooled_ data set to explain consumption differences between households.

Using Ordinary Least Squares (OLS) estimators the following equation is fitted in the _Pooled_ and _Panel_ models: where Y denotes per‐capita consumption observed for household _i_ in commune _j_ in year _t (i.e._ 2010, 2012, 2014 _)_ in the _Pooled_ model and the change in per‐capita consumption for household _i_ between years _t_ (i.e. 2010‐2012 and 2012‐2014) in the _Panel_ model. _R_ measures the environmental risk profile in commune _j_ , which is time‐invariant, as described in Section 2.2. _β_ is the parameter of interest that indicates the consumption effect of environmental risks. _Z_ includes commune‐specific geo‐spatial controls that do not change over time, such as proximity to cities and roads and the long‐term average of rainfall and temperature. _W_ includes a set of time‐variant commune variables measured by rainfall and temperature levels in the survey years 2010, 2012 and 2014 for the _Pooled_ model and by the change for 2010‐12 and 2012‐14 for the _Panel_ model. _X_ is a set of household‐specific controls that vary over time, such the levels of education, labor and land endowments in survey years for the _Pooled_ model and the difference between years for the _Panel_ model. _X_ also includes time‐ invariant demographic characteristics, such ethnicity and gender.<sup>9</sup> _T_ measures time‐fixed effects to neutralize common trends over time. _U_ is a random, idiosyncratic error term. The descriptive statistics of these variables are provided in Table A.1.

A main concern when fitting models to estimate weather impacts on economic outcomes is endogeneity bias. Problems of reverse causation (e.g. high consumption growth in one place leading to environmental depletion) are minimized by taking the historic risk profile and not measuring actual environmental conditions. Yet the model is likely to suffer from omitted variable bias caused by the potential correlation of risk variables with other commune characteristics that determine living standards. Such omitted variable bias could be minimized by fitting a fixed‐effects linear model using a within‐regression estimator based on the Panel data set as in Narloch (2016). Yet such a model does not allow to disentangle the impact of time‐invariant risk factors so it cannot be applied for the purpose of this study. To minimize omitted variable bias in this study, a set of

> 9 The data sets allow to control for land and labor inputs of some activities. These are, however, not included in the regression analyses due to potential endogeneity biases, as higher risks also determines land and labor inputs.

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