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Taxes and benefits

Research articles · Apr 30, 2019

Policy transparency in the public sector: The case of social benefits in Tanzania

Gemma C Wright, Vincent Leyaro, Elineema Kisanga, Christine Byaruhanga

Southern African Social Policy Research Insights, United Kingdom; Department of Economics, University of Dar es Salaam, Tanzania; Southern African Social Policy Research Institute NPC, South Africa

Volume 12(1) Spring 2019

Cite as: G. Wright, V. Leyaro, E. Kisanga, C. Byaruhanga; 2019; Policy transparency in the public sector: The case of social benefits in Tanzania; International Journal of Microsimulation; 12(1); 83–104. doi: 10.34196/ijm.00195

Abstract

In this paper we explore the eligibility criteria for the social benefits that form part of the Productive Social Safety Net programme in Tanzania — the Basic Cash Transfer and Variable Cash Transfer. An account is given of how simplified variants of these criteria were applied within TAZMOD, a static tax-benefit microsimulation model for Tanzania, in order to estimate take-up rates for these benefits. Confronted by the opaque and discretionary elements of the current benefits, we then simulate several categorical benefit alternatives which overcome many of the challenges of the current design, including a revenue neutral option that is more effective at reducing poverty and inequality. It is argued that by shifting to categorical benefits and removing the community targeting, proxy means-test, and conditionality elements, transparency would be enhanced. This would not only enable more accurate monitoring of access to and take-up of the benefits but would also enable citizens to determine their eligibility which is currently not possible.

This article contains 1 figure and 2 tables.

Figures

Figure 1. PSSN eligibility decision tree.

Tables

Table 1. Basic cash transfer and variable cash transfer amounts payable.
Transfer type Transfer name ‘Co-responsibility’ Benefit (TZS) Monthly cap (TZS) Annual max (TZS)
Fixed Basic Transfer Extreme poverty 10,000 10,000 120,000
Fixed Household child benefit HH with children under 18 4.000 4,000 48,000
Variable Infant benefit Infants 0–5 health compliance 4,000 4,000 48,000
Variable Individual primary benefit Child in primary education compliance 2,000 8,000 96,000
Variable Individual lower secondary benefit Child in lower secondary education compliance 4,000 12,000a 144,000
Variable Individual upper secondary benefit Child in upper secondary education compliance 6,000

Source: World Bank et al. (2016, p. 16).

The two bottom rows in combination may not total more than TZS 12,000 per month.

Table 2. Basic cash transfer and variable cash transfer amounts payable.
System (2017) Scenario Description Net cost (compared to baseline) TZS Million Poverty (Basic Needs) % Poverty (Food Poverty) % Inequality (Gini)
2017 Baseline (includes PSSN) 0 29.1 6.4 0.390
A No PSSN −194,198 29.1 10.9 0.397
B1 u5s Universal Child Benefit for u5s (TZS15,000 pcmb), no PSSN. 1,135,303 24.5 7.2 0.382
B2 u4s Universal Child Benefit for u4s (TZS15,000 pcm), no PSSN. 814,023 25.6 8.0 0.385
C Universal Old Age Benefit for those aged 70+ (TZS15,000 pcm), no PSSN. 22,168 28.6 10.3 0.395
D Universal Disability Benefit (TZS15,000 pcm), no PSSN. 18,290 28.5 10.5 0.395
E1 u4s All three Universal Benefits (TZS15,000 pcm), no PSSN. 1,242,878 24.5 7.2 0.382
E2 u4s All three Universal Benefits (TZS15,000 pcm), no PSSN. PIT rates increased. −96,431 25.2 7.7 0.375
F Basic Income Grant (TZS 10,000 pcm), no PSSN. 4,878,217 12.5 1.6 0.349

Source: Own analysis using TAZMOD V1.7.

Notes: In scenario E2, all tax thresholds are the same as for the Baseline, as are the rules for turnover tax and the first band of Personal Income Tax (PIT). The tax rates for other bands were increased as follows: Band 2 raised from 9 per cent to 12 per cent; Band 3 raised from 20 per cent to 25 per cent; Band 4 raised from 25 per cent to 30 per cent; and Band 5 (the top band) raised from 30 per cent to 37 per cent. This scenario is included as an example only. Further work is underway on the income data in the dataset, which will improve the robustness of estimates that use employment income data.

pcm (per calendar month)