Belarus - Social Assistance Policy Note : Improving Targeting Accuracy of Social Assistance Programs
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Washington, DC
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Belarus has a large and extensive social
protection system (SP) covering a significant share of the
population. Belarus has adopted a single methodology for
calculating income to target Public Targeted Social
Assistance (GASP). This methodology also is used when
testing an applicant's income/means for some of the
child benefits. To reduce the leakage of benefits to the
non-poor while expanding GASP, this note assesses the
usefulness of applying a Hybrid-Means-Test method (HMT), a
variation of the means-testing method that combines means
testing and proxy-means testing. All outcomes in this note
have been estimated on the basis of the 2008 Belarusian
Household Budget Survey (2008 HBS). The HMT model improves
estimates of 'means' by generating a predicted
value for hard-to-verify incomes, which are then added to
the observed (reported) values of easy-to-verify incomes. In
this way, the HMT model can improve predictions of per
capita households (HH) income. The note is divided in six
sections. In section one, the authors present an overview of
the current social safety net (SSN) programs in Belarus,
their design features, number of beneficiaries, and
eligibility criteria to draw the overall picture of the
types of programs delivered in Belarus and the magnitude of
their public spending. Section two reviews the targeting
accuracy of existent SP programs in Belarus. Section three
analyzes whether HMT can be an option for targeting in
Belarus. Section four presents the HMT formulae. In section
five the authors describe how HMT also can be used for
client profiling of beneficiaries. In section six, the
authors conclude by discussing the results of some
simulations about the targeting accuracy of the HMT method.
Palabras clave
ADMINISTRATIVE COSTS, AGRICULTURAL INCOMES, AGRICULTURAL PRODUCE, ANTIPOVERTY PROGRAMS, AVERAGE INCOME, BENCHMARKS, CASH TRANSFER PROGRAMS, CASH TRANSFERS, CATEGORICAL ASSISTANCE, CHILD ALLOWANCES, CITIES, COMPETENCIES, CONSUMPTION LEVELS, DISTRIBUTION OF BENEFITS, DIVIDENDS, DURABLE GOODS, ECONOMIC GROWTH, ECONOMIC SHOCKS, EMPLOYMENT OPPORTUNITIES, EMPLOYMENT STATUS, EXPENDITURES, FAMILIES, FAMILY ASSISTANCE, FAMILY INCOME, FISCAL CONSTRAINTS, FLEXIBILITY, GDP, GMI, GUARANTEED MINIMUM INCOME, HBS, HBS DATA, HEALTH CARE, HOUSEHOLD BUDGET, HOUSEHOLD BUDGET SURVEY, HOUSEHOLD PER CAPITA INCOME, HUMAN CAPITAL, ILLNESS, IMPACT ON POVERTY, INCIDENCE ANALYSIS, INCOME, INCOME COMPOSITION, INCOME DISTRIBUTION, INCOME SOURCES, INCOME SUPPORT, INSPECTORS, INSURANCE, INTEGRATION, LAND OWNERSHIP, MEANS TESTING, MEDICAL CARE, MEDICAL SERVICES, MEDICAL TREATMENT, OLD AGE, PENSIONS, PER CAPITA CONSUMPTION, PER CAPITA INCOMES, PERMANENT INCOME, POLICY AGAINST POVERTY, POOR, POOR PEOPLE, POVERTY GAP, POVERTY HEADCOUNT, POVERTY LINE, POVERTY RATES, PREGNANCY, PROBABILITY, PROGRAM BENEFICIARIES, PROGRAM COVERAGE, PUBLIC ADMINISTRATION, PUBLIC SPENDING, REDUCING POVERTY, REHABILITATION, RURAL, RURAL AREAS, SAVINGS, SERVICE DELIVERY, SIMULATIONS, SOCIAL ASSISTANCE, SOCIAL INCLUSION, SOCIAL POLICIES, SOCIAL PROGRAMS, SOCIAL SAFETY NETS, SOCIAL SECURITY, SOCIAL TRANSFERS, SSN, TARGETING, TRANSITION ECONOMIES, UNEMPLOYMENT, UNEMPLOYMENT BENEFITS, WAGES, WAR, WELLBEING