**Table 2: Comparison mean and median observed and predicted yearly income** **<u>(AEU USD PPP)</u>**

|WB region|Income|Mean<br>|Median|<sup>Std.dev</sup><br>|Min|Max|N|
|---|---|---|---|---|---|---|---|
|Latin|Observed|4 850|3 014|6 491|225|76 535|848|
|America|Predicted|4 751|3 542|4 234|372|48 844|848|
|Sth Ai|Observed|1 459|1 139|1 370|129|26 156|1 094|
|ou sa|Predicted|1 439|1 321|668|296|5 013|1 094|
|East Asia|Observed|1 675|1 239|1 922|0|39 619|1 297|
||Predicted|1 678|1 535|908|215|6 371|1 297|
|Afri|Observed|1 015|526|2 585|13|100 351|4 090|
|ca|Predicted|940|683|858|63|15 731|4 090|
|Total|Observed|1 642|832|3 309|0|100 351|7 329|
||Predicted|1 586|1 060|2 023|63|48 844|7 329|

Based on the poverty categorization (Table 1) and the predicted income from the regression analysis, the distribution of households across poverty categories is presented in Table 3. Naturally, most households are in the _income & asset poor_ or _income & asset rich_ categories, with 39.1% in each. 10.9% are in each of the _income poor & asset rich_ or _income & asset rich_ categories, with the shares being slightly higher in South Asia (12.1%) and East Asia (13.4%).

**Table 3: Number of households in different poverty categories across regions**

|Region|_Income&_<br>_assetpoor_|_Income rich&_<br>_assetpoor_|_Income poor&_<br>_asset rich_|_Income&_<br>_asset rich_|N|
|---|---|---|---|---|---|
|Latin America|345|79|79|345|848|
|South Asia|414|133|133|415|1094|
|East Asia|474|174|174|414|1297|
|Africa|1630|415|415|1630|4090|
|Total|2863|801|801|2864|7329|

Notes: _Income & asset poor:_ observed and predicted income below the median. _Income rich & asset poor:_ observed income above the median, predicted income below the median. _Income poor & asset rich:_ observed income below the median, predicted income above the median. _Income & asset rich:_ observed and predicted income above the median.

The groups are significantly different from each other with respect to assets (see Table A9), not surprising since asset holdings partly formed the basis to categorize households. But there are some interesting anomalies. While the _income & asset poor_ and _income rich & asset poor_ have less agricultural land per adult equivalent and less financial assets, they have _more_ labor available within the household (a factor contributing to higher income). A notable exception from this pattern is livestock holdings. At the global level, the _income rich & asset poor_ have almost twice as much livestock as the _income & asset poor_ . Further, the household head of _income & asset poor_ and _income rich & asset poor_ are older on average and a larger share are female headed in our global sample.

# **3.4 Scope and limitations of study**

The PEN data set, reviewed in section 3, constrains our analysis in some important ways. First, we do not have a large panel data set for different scales, which would be needed to fully assess climate change over time and different stressors, pathways of impact, and thresholds. Using cross-sectional data to discuss dynamic phenomena is not uncommon, but has its limitations as elaborated further in Noack et al. (2015). We study forest-adjacent communities with different 14