VALUE OF GEOGRAPHIC INFORMATION IN IMPROVING URBAN POVERTY ALLEVIATION PROGRAMS IN METROPOLITAN MANILA

Geographical targeting of welfare programs is common in developing countries and is often used in conjunction with additional targeting criteria to narrow the beneficiary population and thus reduce costs. The challenge for policymakers is to use the available resources to provide the greatest possible assistance to those who need it most. In the absence of reliable information on personal income, the first best solution of identifying the poor and directing all benefits only to them is not feasible. Even in industrial countries that have the necessary data, it is not possible to ascertain whether targeted programs do indeed reach all of the poor and do not leak to the non-intended beneficiaries.

In the past, since most developing countries did not have reliable information on individual income, many chose programs with universal coverage. However, governments had to drastically reduce or even terminate these programs due to growing budget constraints. Some countries replaced universal coverage with means testing. However, in the absence of reliable information on household income, means testing led to massive leakage. The absence of reliable information for identifying the poor and the mounting constraints on public resources made targeting by means of indirect indicators the only viable alternative for most developing countries.

An inherent and basic problem underscoring the design of effective poverty alleviation programs such as housing assistance is the appropriate identification of program beneficiaries, specifically, the urban poor. However, up until now, the identification of such households has not been satisfactorily achieved due to serious constraints in data availability and reliability. Survey data are difficult and very costly to obtain considering the wide range of socio-economic classes and poverty levels. As such, the number of samples is constrained such that the scale of the sampling domain is kept at a fairly aggregated level. In the case of the Philippines, the National Statistics Office (NSO) does not provide household income estimates for domains below the city and/or municipality level. In addition, some crucial data are omitted from the census questionnaire due to its sensitive nature as such is the case for household incomes and housing tenure. There is great scope in identifying the urban poor ‘on the ground’. First, it is vital in providing effective targeting mechanisms for housing and social policies and is essential in formulating new policies. Secondly, proper identification of households supports the need for a more robust poverty monitoring system.

The optimum solution in welfare programs, from a theoretical point of view, is to identify the target population and design the most effective program for this group. In most cases, however, it is not possible to identify the target population since this requires information that is not observable and thus difficult to verify. In poverty alleviation programs, the target population is the group of households with incomes below a certain minimum level necessary to provide basic needs. Household income is often difficult to observe, however, and efforts to assess its value and thus identify the target group may involve prohibitive costs. These costs consist not only of direct administrative expenses for collecting the necessary information on income, but also of indirect costs due to incentives that the program may give individuals either to modify their behavior or to falsify information on their income in order to qualify for the program’s benefits.

The difficulties and expenses involved in identifying eligible households leave two options: either to implement universal programs that cover the entire population, or to use observable indicators that are highly correlated with the relevant unobserved variables, such as income, in order to determine eligibility. Universal programs are too expensive for most developing countries, and even many industrial countries find the rising welfare costs daunting. The only viable option, therefore, is to use some form of targeting. This paper argues that the existence of household and individual microdata and spatial microsimulation approaches have great potential in providing spatially-disaggregate information which allow for more efficient targeting.

The paper presents the strategic role of geographic information in enhancing the effectiveness of urban poverty alleviation programs through the application of spatial microsimulation methods in the context of geographical targeting of in Metro Manila. Microsimulation provides a powerful and flexible framework in proving household-level data on income and housing needs.