Next, we also see how income diversity differ across the poverty categories. High income diversity is generally assumed to make households less vulnerable to shocks, as they have not “put all the eggs in one basket”. We summarize this in the following research question: _Do the income and/or asset poor households have higher environmental reliance and higher income diversity?_ The overall pattern found in the literature is that the income poor are more reliant on environmental income in a relative sense, but the non-poor are the most reliant in an absolute sense. This is confirmed by Table 7 (income shares) and Table A7 (absolute income). The _income & asset poor_ have the highest environmental income share; 29% of their income is derived from natural environments compared to 25% for the _income & asset rich_ .<sup>18</sup> As also noted in another analysis of the PEN data, the differences in income shares across income levels is less pronounced compared to what several earlier studies suggested (Angelsen et al. 2014).

Most of the environmental income is derived from forested areas (20%) rather than nonforested areas (7%). Among forest income, the three dominant categories are food (30.3%), woodfuel (35.2%) and structural and fiber products (24.9%). For the non-forest environmental incomes, the share is higher for food (48.9%) and lower for woodfuel (20.6%) and structural and fiber products (9.9%).

Crop income is the single most important income source in all groups (30-31%).<sup>19</sup> The livestock income share is lower for the _income & asset poor_ (11%) compared to the other groups (1316%), while this group rely more on wage income (17%) than the other groups (12%).

There are marked regional differences in the income composition (Table A7). While the sampled households in Latin America earn the highest share from environmental income, crops are the most important source of income in the other regions.<sup>20</sup> The income composition across poverty categories also varies more when we look at the regional income composition, as shown in Figure 2.

In the Latin American sites, the _income & asset poor_ have the lowest environmental income shares, probably explained by the existence of a few valuable commercial forest products that generates high income and is able to lift some groups above the poverty line. In both Latin America and East Asia, the _income rich & asset poor_ have the highest environmental reliance. In the case of East Asia, the _income poor & asset rich_ have almost the same absolute environmental income as the _income & asset rich_ (USD 587 vs. USD 611). The sub-samples in South Asia and Africa follow the global pattern, with the highest environmental income shares found among the two income poor groups.

> 18 These are the average of the household level income shares, and are different from the shares obtained when first calculating average absolute income (top panel of table) and then calculate income shares.

> 19 The income composition in the PEN sample could be compared with larger surveys of rural income in developing countries. Davis et al. (2014) uses data from 41 national household surveys in 22 countries. Agricultural crop income dominates in the African sub-sample with 55% as the simple mean (35% in our sample for Africa). Livestock makes up 9% (12 %), wage income 13% (9%) and non-farm self-employment 15%. The latter include a range of activities, including business and extraction of resources from the wild. Overall, compared with our PEN sample, our data have lower crop and higher environmental income shares. The difference is likely to be due to two factors, which we do not know the relative importance of: (1) the PEN sample is representative for rural areas with some access to natural forests, but not for rural areas in general; (2) the other household surveys have not sufficiently captured the environmental income, and/or it is classified as agricultural income. The income shares in the non-African (less representative) sample are markedly different: crop income has 25%, while wage employment makes up 34%.

> 20 This should be interpreted with care, as the sample from Latin America is considered the least representative of its region. Several sites with high-valued forest products are included in the Latin American sample (e.g., Duchelle et al. 2014).

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