Open Research Repository
The Open Research Repository (ORR) is the official institutional repository of the Central European University. The repository provides access to the research output of the CEU community by collecting open access versions of scholarly works authored or co-authored by CEU faculty and students.
For more information, please contact us at: scholcom@ceu.edu
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Item Dance Around a “Sacred Cow” : Women’s Night Work and the Gender Politics of the Mass Worker in State-Socialist Hungary and Internationally(Palgrave Macmillan, 2023)This chapter explores the struggle over the prohibition of women’s night work in industry that took place in state-socialist Hungary and between Hungary and the International Labour Organization ILO during the 1960s and 1970s. In Hungary, dedicated women trade union functionaries advocated for a gendered policy scheme that called for far-reaching special labor protections to be granted to women workers on social grounds and simultaneously ensured that such protections would not translate into gendered disadvantages. In the context of the Hungarian New Economic Mechanism, this feminist-laborist policy vision was overruled by the politics of transforming the woman worker into an economic being who did not deserve special protection, yet continued to suffer from economic discrimination as compared with men workers. This Hungarian development was part of the broader abandonment of (most) restrictions on women’s night work in state-socialist Central and Eastern Europe. Internationally and at the ILO, this development served as a forerunner to and an indicator of a larger global trend reversal. The old laborist and laborist-feminist dream that woman-specific restrictions would be superseded by equally strict restrictions for both sexes died in the context of European-wide economic liberalization.Item Importance value of landscapes, flora and fauna to Tsonga communities in the rural areas of Limpopo province, South Africa(2007)Many parts of the former homeland areas of South Africa are believed to be experiencing environmental scarcity, and are increasingly vulnerable to resource over-exploitation. Frequently, these areas are adjacent to formally protected areas and present unique challenges in integrating biodiversity conservation and sustaining livelihoods, especially for resource-dependent rural communities. Although studies have been undertaken on the use of various plants by Tsonga communities, and the economic value of specific taxa, no investigation on the relative importance value that considers both wild flora and fauna, together with landscapes, has been carried out previously in the former Gazankulu homeland. We used a weighted ranking exercise for nine focus groups within three rural villages bordering the Kruger National Park, which are largely dependent on wild resources, to assess the relative importance of landscape units and species-level biodiversity. Landscape units, particularly forest/bush and river/stream, were found to be extensively used in meeting community needs, across a range of resource use categories including maintaining socio-cultural norms. Moreover, landscape units vary among villages and age/gender regarding how they contribute to sustaining livelihoods. In total, 162 taxa were identified, with two taxa (Sclerocarya birrea subsp. caffra; Ficus spp.) exploited in up to seven use categories. Sclerocarya birrea, Combretum imberbe and Colophospermum mopane were the most highly valued species among those surveyed, contributing 22% to the overall value of wild flora and fauna in the area. Of those identified, 28 faunal (60%) and 10 floral (8.7%) taxa are listed in either IUCN, national or provincial protected species schedules. Based on combined Local Users Value scores, over 20% of all biodiversity value for local communities comes from protected tree species. Similarly, faunal taxa with enhanced protection constitute almost 12% of all local biodiversity value. In developing strategies for resource conservation, it is necessary to recognize this widespread use of the natural environment and the wild products, including those under formal protection, exploited by local people.Item The Impact of Emergencies on Corruption Risks : Italian Natural Disasters and Public Procurement(2025-01-25)Theory and case studies suggest that emergencies and disasters increase corruption, especially in public procurement, hampering relief and reconstruction efforts. Despite a growing interest in the topic, including in research, there is still little systematic evidence about these effects, their structure and trajectories. We set out to investigate the medium-term impact of disasters on corruption risks, using large-scale administrative data on public tenders in Italy from 2007 to 2020, combined with data on 5 natural disasters. We employ logistic regression, coarsened exact matching and difference-in-differences estimators. We find that disasters increase corruption risks in the medium-term (3 or more years after the disaster), even more than on the short term (1 year after the disaster). In the matched and diff-in-diff analyses, we find 3%–10% points more non-open procedures used, 19%–21% points fewer call for tenders published, 19%–29% points more tenders with short advertisement period and 14%–17% points more single bidding tenders. Our findings highlight the importance of ring-fencing corruption risks associated with disaster response, especially in the medium to long term.Item The political economy of open contracting reforms in low- and middle-income countries(2024-09-04)Transparency reforms make government contracting more open and amenable to public scrutiny, helping to improve public spending efficiency. But they are also politically sensitive, complex and highly technical, which makes them especially difficult to implement if state capacity is weak. Our research on nine low- and middle-income countries in Africa and Asia systematically assesses progress in improving the legal framework for procurement transparency and implementing systems that allow open access to data, between 2008 and 2019. Through interviews with key informants, we explore the reasons for progress or its absence, finding that success relies on strong leadership commitment, broad coalitions of state and non-state actors, and sufficient technical capacity. Leadership commitment ensures that implementing bodies have the appropriate mandate and resources, while broad coalitions sustain commitment and harness external technical assistance. Both factors are best achieved by framing the reforms as a way of improving efficiency rather than fighting corruption.Item Predicting pharmaceutical prices. Advances based on purchase-level data and machine learning(2024-07-15)Background: Increased costs in the health sector have put considerable strain on the public budgets allocated to pharmaceutical purchases. Faced with such pressures amplified by financial crises and pandemics, national purchasing authorities are presented with a puzzle: how to procure pharmaceuticals of the highest quality for the lowest price. The literature explored a range of impactful factors using data on producer and reference prices, but largely foregone the use of data on individual purchases by diverse public buyers. Methods: Leveraging the availability of open data in public procurement from official government portals, the article examines the relationship between unit prices and a host of predictors that account for policies that can be amended nationally or locally. The study uses traditional linear regression (OLS) and a machine learning model, random forest, to identify the best models for predicting pharmaceutical unit prices. To explore the association between a wide variety of predictors and unit prices, the study relies on more than 200,000 purchases in more than 800 standardized pharmaceutical product categories from 10 countries and territories. Results: The results show significant price variation of standardized products between and within countries. Although both models present substantial potential for predicting unit prices, the random forest model, which can incorporate non-linear relationships, leads to higher explained variance (R2 = 0.85) and lower prediction error (RMSE = 0.81). Conclusions: The results demonstrate the potential of i) tapping into large quantities of purchase-level data in the health care sector and ii) using machine learning models for explaining and predicting pharmaceutical prices. The explanatory models identify data-driven policy interventions for decision-makers seeking to improve value for money.
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