6.2 Income Test under the Hybrid Means Testing Approach

How to Harness the Power of Data and Inference | 369

income, the ranking of households based on verifiable income provides a good approximation of the distribution of the full income. In this case, means testing is recommended as the risk of inclusion errors is relatively low, as in the case of Bulgaria. However, when the share of hard-to-verify income is high and not positively correlated with income, the risk of inclusion errors (or rank reversal) using means testing is higher, as in the case of the Kyrgyz Republic, and HMT can then be recommended.

Figure 6.2 illustrates graphically how accounting for hard-to-verify income would reduce the inclusion error in an income-targeted program, compared with the situation where such income is not considered or is mandated by the program but not reported. If all income could be observed, the number of eligible applicants would be the area 0A in the figure and inclusion and exclusion errors would be zero. Not considering the hard-toverify income increases the number of accepted applications from 0A to 0B. The segment AB represents inclusion error. Including the estimated hardto-verify (presumptive) income reduces the number of accepted applications to 0C, thus eliminating some of the inclusion error (the population segment CB).

Figure 6.2 Income Test under the Hybrid Means Testing Approach

Per capita income

Estimated (presumptive) income

Hard-to-verify income

Verifiable income

0 A C B

Source: Tesliuc, Leite, and Petrina 2009.

Population ranked by per capita income

Threshold

370 | Revisiting Targeting in Social Assistance

There is no single method for estimating the hard-to verify income. The context of each country largely determines the choice of the method. The administration could use labor market surveys, individual interviews, or the subjective evaluation of experts to determine the level of income to be attributed to each informal activity. The imputation method or values imputed can also vary from one region to another to account for local variation.

Imputation of expected income from hard-to-verify sources is at the heart of HMT. Those designing HMT tend to focus on some of the largest informal employment pools or branches of activity and develop simple rules for estimating that income. In the Europe and Central Asia region, most informal employment occurs in the agriculture and construction sectors. In this case, social programs can develop simple imputation rules for income in these sectors, rather than using regression models. An analyst may use some of the simple methods to impute informal income to add to formal income measures; more complex approaches may be useful where informal employment is spread across many sectors and thus more difficult to estimate.

There are significant inherent difficulties in measuring the agricultural income of small farmers whose households are dual production and consumption units. • Small farmers often operate as dual production and consumption units, without keeping separate accounts for what is used in the production process and what is consumed. A proper accounting of the value added generated by the farming household will separate the production account and estimate the value added produced as the sum of the (often implicit) labor earnings, imputed rent from the land owned, and residual profits. However, this is difficult and rarely done in practice. Not all the revenues or expenditures on inputs (for example, labor, equipment, and fertilizers) are monetized because a part of the production is consumed by the household, and some inputs are produced or supplied by the household (for example, fodder and some labor). Small farmers, often at risk of poverty, consume a larger share of their production themselves, bypassing markets and making the valuation of their outputs and inputs a complex task. This portion of farm income appears in the “consumption out of own production” estimates in household surveys. • Farm income can be measured only at the end of an agricultural season, which is often over a calendar year and thus much longer than in other economic branches where such estimates could be generated monthly.

In the Europe and Central Asia region, the agricultural season for crop production is typically a year and for livestock production several months, depending on the type of livestock. Over the agricultural season, expenses are incurred during the planting or breeding time, whereas

How to Harness the Power of Data and Inference | 371

revenues are collected only after harvest. Only then can farmers determine the net revenues after costs and calculate their profit. In contrast, day laborers or formal employees would know their income (wage) at the end of the day or month, respectively.

Owner-operated small enterprises in several nonagricultural branches of activity also have joint production and consumption accounts, which again makes income measurement hard. A taxi driver may similarly mix accounts—using the vehicle for family as well as client trips, taking repair money from savings, and taking gas or lunch money from daily earnings before bringing them home as earnings.

Acknowledging that it is difficult to measure incomes from small farming, many social programs in Eastern and Central Europe or the former Soviet Union have developed simple, practical approaches to estimation based on asset ownership and their estimated returns. For verification purposes, and to mitigate the risk that applicants do not report asset ownership, the programs rely on land and/or livestock registries. The registries are also used to validate changes in asset ownership, use, quantity, or quality. The quality of the income imputation depends on the quality of the information in the registry. Among the factors that would improve the precision of the imputation are the availability of information on the quantity and quality of the asset, timely information on the owner and user, and the frequency with which the information is updated.

A simple imputation approach has been and, in some cases, still is used in Albania, Armenia, the Kyrgyz Republic, Lithuania, and Romania. In the case of farm income, imputed income is estimated based on the type, location, and quality of the land. Income from livestock production is estimated based on simple farm models, with the income coefficients often estimated in consultation with agricultural agencies, research institutes, or ministries. These institutions also have a role in validating the estimates. Until 2018, Albania’s Ndhima Ekonomike30 offered a textbook example of the imputation of agricultural income. Imputation for land was done based on revenue coefficients that vary according to the type of land and geographic characteristics (table 6.1). Presumed income from livestock production was differentiated by the type of livestock and three geographical zones. A similar approach applies in the Kyrgyz Republic, where individual revenue coefficients vary by location (more than 480 locations) and type of land (personal plot versus agricultural land, irrigated versus nonirrigated) (Government of Kyrgyz Republic 2018; OECD 2018). The possession of livestock works as an exclusion filter, whereby each animal is converted to a number of notional units and all such units are then summed up and divided by the number of household members. Program eligibility requires that the number of such units per capita does not exceed a predefined program threshold.