Towards a More Inclusive Zanzibar Economy
Bibliographic record
Abstract
This report assesses recent progress in poverty reduction in Zanzibar.It is based on Zanzibar's last three household budget surveys and considers the period between 2009 and 2019, with a focus on the last four years of this decade: 2015-2019.Poverty -based on household consumption -fell by 9 percentage points over the decade before the COVID-19 pandemic: it dropped from 34.9 to 25.7 percent.However, the pace of poverty reduction was slow relative to population growth and as such, the number of poor dropped by only 27,000.The drop was fastest in urban areas and because poverty levels were already lower than in rural areas, the gap between rural and urban poverty widened, driven by differences between the islands of Unguja and Pemba.Simulations suggest that the COVID-19 pandemic increased urban poverty increased by 1.8 percentage points in 2020-21 while rural poverty dropped by 0.8 percentage points.Substantial progress was also made across a range of non-monetary poverty indicators, notably in improved access to basic services including electricity and education.During the period 2009-2019, access to the electricity network increased from 38 to 57 percent, while education indicators also improved considerably.For example, between 2015 and 2019, lower secondary gross enrolment went up from 68 to 90 percent.Despite progress, gaps remain especially among the poor living in rural areas, notably in Pemba.Results from a multi-dimensional poverty index (MPI) calculated based on 3 dimensions and 13 indicators using the HBS 2019-20 indicate that 36.6 percent of Zanzibaris were multi-dimensionally poor, that is, they were deprived in at least a third of the MPI indicators used.The relationship between economic growth and poverty reduction was weak as during 2009-19 growth did not sufficiently translate into improved well-being of the poorest.Although during the period 2014 to 2020-21 Zanzibar witnessed a large shift of people out of low-productivity agriculture into services, particularly of women (a 10-percentage point shift according to labor force survey data), 'decomposition analysis' shows that population shifts to other sectors of work barely contributed to poverty reduction.Many likely adopted low-productivity work in the services sector.In fact, the creation of quality jobs was limited, and informality increased during this period.To accelerate poverty reduction in Zanzibar, a combination of policies are required to (i) make tourism, the main growth engine of the economy, more inclusive, for example through the diversification of tourism products; (ii) improve labor market outcomes for women and youth through better skills training and internship programs; (iii) improve the distribution of public spending in education and health to make it more pro-poor; and (iv) improve the business operating and regulatory environment of SMEs and better connect farm smallholders to high-value markets to enhance value addition, job creation and poverty reduction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".