and Proxy Means Testing Models in Algeria

376 | Revisiting Targeting in Social Assistance

• For the segment of the population that derives income from both formal and informal sources, the method could approximate quite well the true welfare level on average, because formal incomes are on average higher than informal ones.

The overall performance of the method depends on the relative shares of the three types of households. Accuracy will be higher if most of the households are in the first and third categories. The method will not work well if households are broadly separated into the first and second categories and the share of the second category is large. In this case, HMT would not be appropriate, although PMT may be more so. To select between HMT and PMT, a simulation of the two targeting methods based on household survey data is recommended. The example of Algeria in box 6.3 illustrates the value added of this approach.

BOX 6.3

Simulating the Accuracy of the Hybrid Means Testing and Proxy Means Testing Models in Algeria

In Algeria, formal employment represents half of total employment and formal incomes are about two-thirds of total household income. Algeria carried out a simulation to compare the performance of a hybrid means testing (HMT) model and a proxy means testing (PMT) model in 2019. For the HMT, the simulation assumed observability of formal wages and social protection transfers. The main sources of informal income were imputed based on two simple scenarios. Under the first scenario, the median earnings by branch of activity and region were imputed to all potential applicants. Under the second scenario, 75 percent of the median value was taken instead to reduce exclusion error and encourage reporting. The program definition of income was the sum of the two components. The PMT model estimated household consumption using typical covariates (household size and composition, education, employment and sector of activity of the adults, endowment of durables, dwelling characteristics, and region).

The assessment suggested that the HMT targeting model would more accurately identify low-income households (for several thresholds, such as the 10, 20, or 30 percent poorest) than a PMT model. The simulations led the government of Algeria to decide to develop a national database of formal incomes and assets, as well as implement other incremental reforms to improve the quality, timeliness, and availability of administrative data.

How to Harness the Power of Data and Inference | 377

The HMT would work well in countries where (1) most informal income is concentrated in a few economic sectors; (2) the variation in earning rates per worker is relatively low; and (3) there is a relatively simple way to estimate the average level of informal earnings per sector, as the average returns of a productive asset (for example, land, livestock, and equipment) per month, or as an average per occupation type (for example, average taxi earnings in a given town). Among the key advantages of this approach are transparency and simplicity. Beneficiaries can easily understand the program eligibility criteria and determine on their own if they are eligible or not for a program and what level of benefit they might receive. This increases the political acceptability of the program among beneficiaries, social assistance staff, and the population at large.

In considering whether to use HMT, the program should also test the quality of the administrative data systems that would support it. This assessment will tell program administrators whether what was simulated based on household survey data is supported by the data infrastructure of the country, and whether HMT could work in practice.

The ideal situation for HMT to work well is one in which formal earnings and social protection transfers (including pensions) are reported regularly, in full (in terms of coverage of the income recipient and income level), with this information kept in databases that are interoperable and can exchange the relevant information on an as-needed basis. Apart from the technical prerequisites, access to information is also regulated by the data privacy regulations in force. For example, in many countries, information on savings and interest income cannot be accessed due to privacy regulations. Some countries have laws that restrict the use of tax record data. Moreover, the frequency of income reporting also matters for the ability of HMT to respond rapidly to changing circumstances. Countries where the social security agency collects earnings information annually could implement social protection programs using HMT that are less adaptive than those in countries where earnings are reported monthly.

A few factors can reduce the accuracy of a would-be HMT system. In some countries, certain categories of formal employees do not have individual earnings records in the social security system (for example, for all public sector employees or those working in national security, the army, police, or magistrates/judiciary). Social security records sometimes cover only the base wage and do not record elements such as premia for hardship, merit, and so forth. To encourage contributions to social security among higher-income earners, some legislation allows capping the earnings they report, with a similar cap on the maximum pension and related benefits. This will diminish the accuracy of the income information available in the social security database. At the other end of the income reporting spectrum, some social security administrations use minimum imputed