Forecasting the Effect of Migrants’ Remittances on Household Expenditure: COVID-19 Impact
Bibliographic record
Abstract
The unexpected pandemic has provoked changes in all economic sectors worldwide. COVID-19 has had a direct and indirect effect on countries’ development. Thus, the pandemic limits the movements of labour forces among countries, restricting migrants’ remittances. In addition, it provokes the reorientation of consumer behaviour and changes in household expenditure. For developing countries, migrant remittances are one of the core drivers for improving household wellbeing. Therefore, the paper aims to analyse how the COVID-19 pandemic has affected household expenditure in Ukraine, as being representative of a developing country. For this purpose, the data series were compiled for 2010 to the second quarter of 2021. The data sources were as follows: Ministry of Finance of Ukraine, The World Bank, and the State Statistics Service of Ukraine. The core variables were as follows: migrants’ remittances and expenditure of households by the types. The following methods were applied to achieve the paper’s aims: the Dickey–Fuller Test Unit Root and the ARIMA model. The findings confirmed that COVID-19 has changed the structure of household expenditure in Ukraine. Considering the forecast of household expenditure until 2026, it was shown that due to changes in migrants’ remittances, household expenditure in all categories tends to increase. The forecasted findings concluded that household expenditure on transport had the most significant growth due to changing migrants’ remittances.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".