Environmental income improves household-level poverty assessments and dynamics Solomon Zena Walelign a , Lindy Charlery a, ⁎, Carsten Smith-Hall a , Bir Bahadur Khanal Chhetri b , Helle Overgaard Larsen a a Department of Food and Resource Economics, University of Copenhagen, Rolighedsvej 25, 1958 Frederiksberg C, Copenhagen, Denmark b Institute of Forestry, Tribhuvan University, Pokhara, Nepal abstract article info Article history: Received 3 July 2015 Received in revised form 12 April 2016 Accepted 5 July 2016 Available online xxxx Household-level poverty assessments and analyses of poverty dynamics in developing countries typically do not include environmental income. Using household (n = 427 in 2006, 2009 and 2012) total income panel data sets, with and without environmental income, from Nepal, we analysed the importance of environmental income in household-level poverty assessments (Foster-Greer-Thorbecke indices) and dynamics (movements in the Pover- ty Transition Matrix). Random effects logit and ordered logit models were applied to estimate variables covarying with poverty categories and compared for annual household incomes with and without environmental income. Using the without environmental income data set significantly changed the number of households classified as poor, as well as rates of movements in and out of poverty. Excluding household-level environmental income also distorted estimation of covariates of poverty incidence and poverty dynamics. Poverty incidence and dynam- ics models including environmental income perform better than those without. Rural poverty studies based on welfare measures excluding environmental income may thus be inaccurate for environmental reliant communities. © 2016 Elsevier B.V. All rights reserved. Keywords: Environmental income Household income Nepal Poverty dynamics Spells approach 1. Introduction Poverty reduction in developing countries has been a global priority for more than four decades and its continued relevance is affirmed by the adoption of the Sustainable Development Goals and the UN commit- ment to pursue poverty eradication (United Nations, 2015). The success of poverty reduction strategies greatly depends on our abilities to target the poor (Krantz, 2001) and the causes of poverty (Krishna, 2007). To achieve this as fast and cheap as possible, we need to understand the na- ture of poverty and we need to be able to measure it. This paper contrib- utes to enhance the methodological quality of poverty assessments. Rural livelihoods in developing countries commonly rely on environ- mental income – mainly from consumptive use of environmental resources (e.g., Byron and Arnold, 1999; Ellis, 2000a; World Bank, 2004). A recent global study estimates that environmental income on average accounts for 28% of rural households' total subsistence and cash income (Angelsen et al., 2014). This income source is currently not captured by standard household surveys, such as the World Bank's Living Standard Measurement Survey, widely used to provide data for poverty assessments (Grosh and Glewwe, 2000). This could, e.g., lead to overestimation of rural, as compared to urban, poverty (Maltsoglou and Taniguchi, 2004; van der Ploeg, 2012) and inappropriate policy measures (Vedeld et al., 2007). Environmental in- come generally contributes relatively more to poorer households (Angelsen et al., 2014) and its inclusion in poverty assessments may therefore lead to improved understanding of rural poverty and subsequently more appropri- ate interventions. Two decades ago, poverty measures were based on data from cross- sectional household surveys, yielding static snapshots of poverty. The grow- ing availability of panel data sets in the past decade has allowed analysis of the dynamic nature of household poverty. Households move into and out of poverty, e.g. as they experience changes in demographics and access to other livelihood strategies (Baulch and Hoddinott, 2000; Baulch, 2011; Cruces and Wodon, 2003; Dartanto and Nurkholis, 2013; Dhamija and Bhide, 2011; Haddad and Ahmed, 2003; Kedir and Mckay, 2005; Lohano, 2009; May and Woolard, 2007; Muller, 2003; Nega et al., 2010; Woolard and Klasen, 2005). This was originally conceptualized in the spells of pover- ty approach (Bane and Ellwood, 1986) providing the foundation for under- standing the temporal dimension of poverty, as permanent versus transient, leading to work uncovering factors inducing or preventing pover- ty in given temporal and spatial contexts (e.g. Krishna, 2010). The aim of the present paper is to analyse the importance of including environmental income in total household income when undertaking household-level poverty assessments in developing countries. Using an Forest Policy and Economics 71 (2016) 23–35 ⁎ Corresponding author. E-mail address: lindycharlery@gmail.com (L. Charlery). http://dx.doi.org/10.1016/j.forpol.2016.07.001 1389-9341/© 2016 Elsevier B.V. All rights reserved. Contents lists available at ScienceDirect Forest Policy and Economics journal homepage: www.elsevier.com/locate/forpol