Asset data collection is relatively easy and cost-effective. Many household surveys, including DHSs and MICS, collect asset and housing conditions through appropriate modules. Using assets minimizes measurement problems associated with income and consumption-based indicators, such as recall bias and seasonality.<sup>16</sup> Estimation of a wealth index using the PCA method is rather straightforward, and there are several ready-to-use commands in statistical and econometric software to implement the method.

Despite its simplicity, important limitations in using asset indexes to measure and track well-being over time should be considered. Several papers have demonstrated that correlation between asset indexes and consumption poverty is low (for instance, Filmer and Scot, 2008). The discrepancy may be stronger in the poorest areas where households spend larger shares of their budgets on food. Nevertheless, many still consider asset indexes useful for welfare analysis when consumption data are not available.

Using asset indexes across heterogenous settings and over time has concerns (Harttgen et al., 2013). First, preferences for some assets, such as mobile phones and flat-screen TVs, may increase over time and become part of “normal” living conditions; this may cause incomparability between asset indexes over time. Second, asset indexes do not consider age or quality of assets; some households may accumulate old assets, therefore overestimating a rise in assets. Third, in many countries, access to some assets, such as electricity or clean water, is a result of public policies and does not represent a reliable proxy measure for actual household consumption.

Constructing asset indexes using MICS or DHSs could represent an important alternative to monetary poverty in MENA countries where existing consumption surveys are very old. However, as shown in the previous section, countries do not collect MICS or DHSs frequently in the region and the problem of outdated data remains. In addition, correlation between assets and monetary poverty could be poor in the region, especially in conflict-affected countries where asset ownership may not highly correlate with poverty.

_Food security and subjective well-being_ This section focuses on two alternatives to non-monetary measures of well-being: food security and subjective well-being. Phone and public opinion surveys often collect these measures and each type of survey is discussed below.

# **Phone surveys**

# <u>Method</u>

Remote forms of data collection, such as mobile and fixed-line phone surveys, are particularly effective in two contexts: i) settings where it is not safe or possible to perform face-to-face surveys, and ii) settings needing high-frequency data collection (Hoogeveen et al., 2014). Both settings are prevalent in MENA. Nearly 30 percent of the region’s population is exposed to either conflict or persistent violence, and the entire region is particularly vulnerable to political, climate, and other types of shocks where welfare monitoring at higher frequency could better inform policy making. During the COVID-19 pandemic, many

> 16 Households may have difficulties in remembering all expenditures they made in the past leading to recall bias. Some expenditures/consumption are also seasonal fluctuating across time. Therefore, data collection should ideally last the whole year to properly capture the seasonal variation.

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