1. Impact of Job Loss on Household Income during the COVID-19 Pandemic : Results of a Quasi-Experimental Analysisin the Republic of the CongoTed Cléophane Ngassa, 2026, izvirni znanstveni članek Opis: This study evaluates the average monthly income loss associated with COVID-19-related job losses in households in the Republic of the Congo (ROC). Firstly, the propensity score matching (PSM) method is used for the estimates. Secondly, endogenous switching regression accounts for theunobserved effect. The data used are from the survey on the impact of the COVID-19 pandemic on the living conditions of Congolese households conducted in 2020 by the National Institute of Statistics of the ROC. The results show, on the one hand, that the PSM method underestimates the assessed impact and, on the other hand, that the increase in household income loss attributed to job loss due to COVID-19 is 232,432.9 XAF on average per month. These results imply several measures (reducing the costs imposed on employers, subsidies, etc.) and the adaptation of labour market regulations to the new realities.
Ključne besede: job loss, household income loss, COVID-19, propensity score matching, endogenous switching regression, Republic of the Congo
Objavljeno v RUP: 10.04.2026; Ogledov: 514; Prenosov: 17
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2. Agricultural-Induced Environmental Kuznets Curve for South Africa : A Threshold Regression and ARIMA Forecasting ApproachAndrew Phiri, Rasaq Raimi, 2025, izvirni znanstveni članek Opis: The purpose of the paper is to examine the impact of the agricultural sector on agricultural emissions in South Africa. To this end, we estimate an agricultural-induced environmental Kuznets curve (EKC) for South Africa between 1990 and 2022 using conventional and threshold regression frameworks. Our regression estimates reveal a ‘humped-shaped’ relationship between agricultural production and agricultural emissions, whereby agricultural production produces lower agricultural emissions above threshold estimates of 4,876 and 6,100 metric tons of CO2 emissions. Further investigations show that the South African economy has consistently remained above these thresholds since 2010. Moreover, a forecast analysis of the time series using ARIMA models shows that agricultural productionis (emissions are) on an upward (a downward) trajectory. However, the forecasting analysis also shows that the South African agricultural sector is not scheduled to reach the net-zero emissions target by 2050. Altogether, these findings imply that whilst South Africa had followed a trajectory of sustainable development prior to the COVID-19 pandemic, the current trajectory may not be sufficient to attain the 2050 Sustainable Development Goals.
Ključne besede: agricultural sector, agricultural emissions, environmental Kuznets curve (EKC), threshold regression model, ARIMA forecasting, South Africa
Objavljeno v RUP: 16.01.2026; Ogledov: 817; Prenosov: 4
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3. Opportunities for Indian Women in Gig Jobs without Using Digital Platforms : The Importance of Vocational TrainingSayantani Santra, Amit Kundu, 2025, izvirni znanstveni članek Opis: Flexible working hours can provide a better option for Indian women entering the workforce. This can be accomplished by engaging in gig jobs without depending on a digital platform, as many individuals have limited access to technology. Casual labourers and self-employed workers are considered gig workers who can perform their jobs without utilising digital platforms. The Probit model identifies the factors that can enhance the likelihood of such gig jobs occurring without the use of a digital platform for Indian women. By employing a Bivariate Probit regression model based on Periodic Labour Force Survey data for 2022–23 and addressing endogeneity, the paper demonstrates that both formal and informal vocational training positively influence women’s participation in gig jobs without relying on any digital platform. However, the impact of informal training is more pronounced.
Ključne besede: gig job, own-account worker, casual wage labourer, unpaid household job, formal vocational training, bivariate probit regression
Objavljeno v RUP: 18.12.2025; Ogledov: 1059; Prenosov: 7
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4. Does Financial Development Drive Entrepreneurship in Africa? A Panel Data AnalysisAfees Oluwashina Noah, David Oladipo Olalekan, 2025, izvirni znanstveni članek Opis: Entrepreneurship in Africa faces a multitude of challenges, with financial issues being prominently discussed in scholarly literature. Thus, this study explores how financial development plays a crucial role in encouraging entrepreneurship in Africa, analysing both short- and long-term impacts alongside the direction of causality within the continent. The study utilises panel data regression techniques to analyse data from 28 African countries, spanning from 2006 to 2020. The analysis reveals that financial development, alongside the growth of financial institutions and markets, consistently boosts entrepreneurship development in both time frames. Even though this is more pronounced in the long run, this suggests that the influence of financial development and its components is uniformly positive, with no significant differential impacts observed in either the short or long run. Causality results establish unidirectional causality between entrepreneurship, financial development, and its components, flowing from financial development and its components to entrepreneurship development. Given these insights, the study underscores the necessity for policymakers to focus on sustainable financial development strategies that enhance stability and inclusivity within financial markets.
Ključne besede: Africa, entrepreneurship, financial development, panel regression
Objavljeno v RUP: 18.12.2025; Ogledov: 618; Prenosov: 4
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5. TF-IDF-based classification of Uzbek educational textsKhabibulla Madatov, Sapura Sattarova, Jernej Vičič, 2025, izvirni znanstveni članek Opis: This paper presents a baseline study on automatic Uzbek text classification. Uzbek is a morphologically rich and low-resource language, which makes reliable preprocessing and evaluation challenging. The approach integrates Term Frequency–Inverse Document Frequency (TF–IDF) representation with three conventional methods: linear regression (LR), k-Nearest Neighbors (k-NN), and cosine similarity (CS, implemented as a 1-NN retrieval model). The objective is to categorize school learning materials by grade level (grades 5–11) to support improved alignment between curricular texts and students’ intellectual development. A balanced dataset of Uzbek school textbooks across different subjects was constructed, preprocessed with standard NLP tools, and converted into TF–IDF vectors. Experimental results on the internal test set of 70 files show that LR achieved 92.9% accuracy (precision = 0.94, recall = 0.93, F1 = 0.93), while CS performed comparably with 91.4% accuracy (precision = 0.92, recall = 0.91, F1 = 0.92). In contrast, k-NN obtained only 28.6% accuracy, confirming its weakness in high-dimensional sparse feature spaces. External evaluation on seven Uzbek literary works further demonstrated that LR and CS yielded consistent and interpretable grade-level mappings, whereas k-NN results were unstable. Overall, the findings establish reliable baselines for Uzbek educational text classification and highlight the potential of extending beyond lexical overlap toward semantically richer models in future work.
Ključne besede: Uzbek language, text classification, low-resource languages, TF-IDF, cosine similarity, linear regression, k-Nearest Neighbors
Objavljeno v RUP: 17.10.2025; Ogledov: 1072; Prenosov: 10
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