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iii. Even for the

iii. Even for the relatively good cross-sectional indicators, step-wise targeting based solely on cross-sectional data is significantly more costly than perfect targeting in achieving any given impact on chronic poverty. iv. While there is some variation from year to year, there is no obvious pattern, such as between ‘good’ and ‘bad’ agricultural years. This does not bear out the finding of Lanjouw and Stern (1991) that current income in a good agricultural year is a better indicator of chronic poverty than in a bad year. The last mile reach strategies can attempt to focus the benefit of public expenditure to the poor by identifying them as direct beneficiaries, screening out unintended beneficiaries as well as by dovetailing the programmes to suit the specific needs of the targeted populations. Last mile reach strategies involve identification of the potential beneficiary and the administration of the benefit. These strategies refer to strategies for targeting, allocation of funds or benefits, and conversion of funds or benefits into outcomes aimed at achieving the MDGs. 6.1 Targeting Strategies a. Methods of Targeting A number of alternative methods of targeting can be identified, as discussed below: a1. Individual Assessment Mechanism In this mechanism, each potential beneficiary has to be examined separately to assess whether he is a bonafide applicant on the basis of various criteria, to receive the benefit of the programme. a2. Group Assessment In this case, an eligible group is decided on the basis of special characteristics, e.g., school lunch programmes that operate only in poor areas, programmes that predominantly benefit chosen states, districts, blocks, neighbourhoods based on relevant characteristics. a3. Geographic Targeting The main attraction of geographic targeting is its simplicity. Regions can be assigned priority on the basis of existing aggregate data. The complicated administrative mechanisms or means test for selecting beneficiaries individually are not required in the case of geographic targeting. Many Latin American countries have attempted geographic 128

targeting as a device to improve effects of poverty programmes. Ravallion (1992) and Datt and Ravallion (1993) have investigated the potential of geographic targeting for India (and Indonesia) through a model designed to minimize poverty. Results for both the countries indicate that the qualitative effect of reducing regional disparities in average living standards generally favours the poor. The overall maximum impact for India was equivalent to what could be achieved by a uniform, untargeted transfer of 1.5 percent of mean consumption. Based on simulation exercises, Baker and Grosh (1994) also examine geographic targeting. Their findings indicate that as compared to generalised food subsidy programme, the accuracy of geographic targeting is much better. a4. Self-targeting Mechanisms Self-targeting involves relying on the individual decisions of a potential beneficiary to participate in the programme. The programme is decided in such a way as to discourage the non-poor from using it. Self-selection as a method of targeting has been recognised as one of the best. There are two main caveats about self-targeted schemes. First, they screen participants by imposing a cost on them. Good schemes ensure that the cost is higher for the non-poor than the poor. But, the cost may be significant for the poor also including the cost of forgone income. b. Targeting Errors It is generally recognised that there are two types of errors in targeting expenditures for poverty alleviation: type I and type II. Type I error is an error of omission of the poor from the scheme, and type II error is the error of inclusion of non-poor in the scheme. Cornia and Stewart (1995) have referred to these errors as the F-mistake and the E- mistake. The F-mistake is the failure in the prime objective of intervention. The E-mistake is that of excess coverage. If the total population is N, and the target (poor) population is P, the two types of mistakes can be indicated as in Table 6.1. Table 6.1: Classification Matrix: E- and F-Mistakes Population Covered Poor Non-Poor Total Population All covered by programme P c NP c N c All not covered by programme Source: Cornia and Stewart (1995). P nc (E-mistakes) NP nc N nc (F-mistakes) P NP N 129

World Comparative Economic And Social Data
Nammakal - Tamil Nadu Police
Police Stations - Tamil Nadu Police
N u m b e r o f S c h o o l s - DISE
Census 2011 population of Latur district
PDF: 1.0MB - Population Reference Bureau