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on Transition Economics |
| By: | Dang, Hai‐Anh H.; Kilic, Talip; Abanokova, Kseniya; Carletto, Calogero |
| Abstract: | Accurate poverty measurement relies on household consumption data, but such data are often inadequate, outdated, or display inconsistencies over time in poorer countries. To address these data challenges, we employ survey-to-survey imputation to produce estimates for several poverty indicators including headcount poverty, extreme poverty, poverty gap, near-poverty rates, as well as mean consumption levels and the entire consumption distribution. Analysing 22 multi-topic household surveys conducted over the past decade in Bangladesh, Ethiopia, Malawi, Nigeria, Tanzania, and Vietnam, we find encouraging results. Adding either household utility expenditures or food expenditures to basic imputation models with household-level demographic, employment, and asset variables could improve the probability of imputation accuracy between 0.1 and 0.4. Adding predictors from geospatial data could further increase imputation accuracy. The analysis also shows that a larger time interval between surveys is associated with a lower probability of predicting some poverty indicators, and that a better imputation model goodness-of-fit (R2) does not necessarily help. The results offer cost-saving inputs into future survey design. |
| Keywords: | consumption;Ethiopia;household surveys;Malawi;Nigeria;poverty;Sub-Saharan Africa;survey-to-survey imputation;Tanzania;Vietnam |
| JEL: | C15 I32 O15 |
| Date: | 2026–07–26 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:140357 |
| By: | Parviainen, Sinikka; Samoiliuk, Maksym |
| Abstract: | Ukraine's EU accession is the most economically complex enlargement in recent EU history. It is unfolding amidst conditions of an active war, large-scale reconstruction needs, and rapid evolution of Ukraine's defence sector. This policy brief examines EU-Ukraine economic integration using Finland as a stress test for plausible outcomes in bilateral trade relations with Ukraine. The fact that Finland is an EU member state without geographic proximity or deep historical ties to Ukraine allows identification of structural, rather than geography-driven, complementarities present across key sectors. The analysis finds strong complementarities in digital services, energy, reconstruction, and defence-industrial cooperation. Notably, none of these complementarities are yet reflected in trade and investment flows, which remain limited and concentrated in low-risk activities. Thus, the challenge for policymakers is translating these identified complementarities into investment at scale under conditions of elevated risk, particularly for SME-led projects in the private sector. |
| Keywords: | Ukraine, EU-Ukraine trade, EU accession, Finland, reconstruction |
| JEL: | F15 F21 F22 F52 O52 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:bofitb:342405 |
| By: | Martin, Reiner; Monnin, Pierre |
| Abstract: | Buildings account for approximately 40% of the EU’s total energy consumption and around one-third of its energy-related greenhouse gas emissions. Therefore, if the EU’s climate strategy is to succeed, it will need to improve the energy performance of Europe’s building stock. The task is particularly urgent given that 75% of the building stock is energy-inefficient, and nearly one-fifth of the EU population live in homes that are not comfortably warm in winter. This policy brief draws on new research covering Slovakia, Latvia and Hungary to explore how borrower-based measures (BBMs) can support energy-efficient renovation. The authors argue that significant increases in renovation finance need not involve a trade-off with financial stability. |
| JEL: | N0 F3 G3 R14 J01 |
| Date: | 2026–07–21 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:140308 |
| By: | Rashid Mirzaakhmedov (The Central Bank of Uzbekistan) |
| Abstract: | This paper examines how foreign ownership of importing firms shapes exchange rate pass-through (ERPT) to import prices in Uzbekistan across three distinct monetary policy regimes. Using transactionlevel customs data matched with a firm ownership registry, I find that ERPT remained near-complete before inflation targeting but declined sharply following its formal adoption in October 2019. Foreignaffiliated importers exhibit significantly lower pass-through than domestically owned firms under inflation targeting, with the gap most pronounced for capital and intermediate goods. Rolling window estimation reveals that foreign affiliates adjusted more rapidly to the new monetary framework, suggesting that ownership structure and institutional credibility jointly shape import price dynamics. The results provide micro-level evidence that the central bank’s credibility has weakened the exchange rate channel of inflation, while the growing presence of foreign affiliates reduces the effectiveness of exchange rate depreciation as an instrument for correcting the trade balance. |
| Keywords: | Exchange Rate Pass-Through; Import Prices; Foreign Ownership; Inflation Targeting; Transaction-level Customs Data; Uzbekistan |
| JEL: | F31 F14 E31 F23 E52 |
| Date: | 2026–08–07 |
| URL: | https://d.repec.org/n?u=RePEc:gii:giihei:heidwp22-2026 |
| By: | Amponsem, Maxwell Peprah; Guney, Selin; Osei, Edward; Yu, Mark |
| Abstract: | This paper examines the effects of the Russia-Ukraine war on global wheat trade using monthly bilateral trade data from 2010-2024. We estimate gravity-based difference-indifferences, event-study, and exposure-based triple-difference specifications to identify the trade and food-security consequences of the invasion. The results reveal sharply asymmetric responses across the two belligerent exporters. Ukraine’s wheat exports collapsed immediately following the invasion and remained substantially below pre-war levels, while Russia’s exports remained comparatively resilient and expanded in several destination markets. The disruption was concentrated among importers dependent on Black Sea maritime shipping, particularly in the Middle East, North Africa, and Sub-Saharan Africa. Delivery-route heterogeneity test indicate that disruption of Black Sea shipping infrastructure, rather than sanctions or generalized demand contraction, was the primary transmission mechanism behind Ukraine’s export collapse. Exposure-based triple-difference estimates further show that importers with greater pre-war dependence on Ukrainian wheat experienced significantly larger post-invasion trade declines. Translating the estimated disruptions into importer-level exposure measures reveals substantial short-run food-security exposure among several import-dependent economies, althoughreplacementdynamicsindicatethat muchoftheinitial shortfall was offset within several months through trade reallocation toward alternative suppliers. |
| Keywords: | International Development |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea26:404641 |
| By: | Arias, Omar (World Bank); Cesar, Andres (CEDLAS-UNLP); Fukuzawa, Daisuke (World Bank); Le, Duong (World Bank) |
| Abstract: | We examine the labor market effects of industrial robot adoption in Viet Nam. Using differences between districts in their initial industrial structure and exposure to global robotization trends, we find that robot adoption between 2014 and 2020 increased employment, wages, and the inflow of skilled workers. More exposed districts also experienced faster growth in value added, labor productivity, and trade flows. Employment and wage gains were concentrated among middle and high-skilled workers, while low-skilled workers were driven into informal employment or out-migration. We also find positive spillovers to neighboring districts, consistent with production linkages and higher demand for non-tradable goods. |
| Keywords: | Industrial Robots, Local Labor Markets, Wages, Informality, Migration, Export-led Growth, Viet Nam |
| JEL: | J23 J24 J31 O12 O14 O17 O33 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18845 |
| By: | Tamkin Nuriyev (Central Bank of the Republic of Azerbaijan); Aygun Garayeva (Central Bank of the Republic of Azerbaijan); Gulzar Tahirova (Central Bank of the Republic of Azerbaijan) |
| Abstract: | Using 800, 000 transaction-level customs records from January 2018 to February 2026, the paper constructs a trade-weighted Imported Food Price Index (IFPI), covering 34 items from the consumer basket with significant import dependence. The index is developed using the Fisher ideal methodology to provide a timely measure of external food price pressures. The results indicate that the IFPI leads official food Consumer Price Index (CPI) by approximately two months, with a maximum correlation of 0.81, highlighting its potential usefulness as an early indicator of domestic food inflation. Building on this, the paper develops a forecasting framework for the IFPI by combining non-parametric Binary Segmentation and Hidden Markov Models with a regularized machine-learning ensemble. The model employs an ensemble approach that combines Histogram-based Gradient Boosting Regression Tree, Random Forest, and Extreme Gradient Boosting, alongside rigorous time-series crossvalidation. The optimized ensemble achieves a 58% out-of-sample R² relative to a random walk benchmark, vastly outperforming traditional linear Autoregressive Distributed Lag (ARDL) (13.60%) and Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) (0.18%) baselines. The forecast results are intended to be incorporated into broader inflation forecasting models to improve short-term projections. |
| Keywords: | Import price index; Fisher Ideal index; Food price inflation; Machine learning forecasting; Hidden Markov models |
| JEL: | C43 C53 C55 E31 F14 |
| Date: | 2026–08–03 |
| URL: | https://d.repec.org/n?u=RePEc:gii:giihei:heidwp20-2026 |
| By: | Abdukakhkhor Abdurakhmonov (Central Bank of Uzbekistan) |
| Abstract: | This paper provides the first systematic assessment of machine learning methods for macroeconomic forecasting in Uzbekistan. Using a comprehensive dataset of more than 170 indicators, we forecast CPI inflation and GDP growth with nine machine learning models and compare them against three traditional benchmarks (ARIMA, VAR, and BVAR). For both targets, the relative performance of machine learning improves as the forecast horizon increases. For inflation, machine learning provides clear and growing gains as the horizon increases, and a simple equal-weighted ensemble of the machine learning models is the most accurate approach overall, achieving the lowest forecast error at nearly every horizon. For GDP growth, by contrast, the traditional benchmarks (ARIMA in particular) remain the most accurate across most horizons, although regularized linear and dimension-reduction machine learning methods are competitive at short horizons. Tree-based models struggle to forecast GDP when growth exceeds the range observed during training because they cannot extrapolate beyond the training data. This limitation is particularly relevant in Uzbekistan's rapidly changing economy, where rapid economic growth in 2024-2025 pushed the level of GDP beyond the range observed in the training sample. We show that forecasting stationary transformations of the target largely removes this weakness. Overall, the findings suggest that machine learning is best used to complement rather than replace the existing forecasting toolkit. It improves the accuracy of medium-term inflation forecasts, whereas traditional models remain more accurate for forecasting GDP. |
| Keywords: | Machine Learning; Macroeconomic Forecasting; Ination; GDP Growth; Model Evaluation and Selection; Uzbekistan |
| JEL: | C22 C45 C53 E31 E37 E52 |
| Date: | 2026–08–03 |
| URL: | https://d.repec.org/n?u=RePEc:gii:giihei:heidwp19-2026 |
| By: | Mikula, Stepan (Masaryk University); Sabatini, Fabio (Sapienza University of Rome) |
| Abstract: | We study the local economic effects of Ukrainian long-range strikes on Russian oil refineries, combining a verified event-level strike record with quality-screened daily satellite radiance from NASA Black Marble. The analysis covers 29 large Russian refineries, 22 of which sustain verified direct hits over June 2022-May 2026. Using a monthly staggered difference-in-differences design, we find that nighttime radiance falls immediately and persistently after a refinery enters the strike campaign: by roughly 30 percent in the innermost measured ring and by 15-18 percent within five kilometers, with the effect attenuating until it becomes small at twenty-five kilometers. A complementary daily instrumental-variables design uses directional wind alignment to shift strike incidence and trace the strike-day dynamic. Radiance rises at short horizons, consistent with a fire-related light signature corroborated by NASA FIRMS detections, and turns negative at a six-month horizon, with weak-instrument-robust inference. Applying the same satellite product and empirical specification to 106 non-refinery deep-strike targets produces no comparable contraction, weighing against a generic-war-disruption interpretation. |
| Keywords: | conflict economics, industrial destruction, drone warfare, nighttime lights, staggered difference-in-differences, instrumental variables, Russia-Ukraine war |
| JEL: | D74 F51 H56 L71 O13 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18772 |
| By: | Rashidghalam, Masoomeh (University of Tabriz, Tabriz, Iran); Heshmati-Kim, Jieun (Seoul National University, Seoul, South Korea); Heshmati, Almas (University of Economics Ho Chi Minh City) |
| Abstract: | AI is becoming a driving force in Vietnam’s economic transformation. The country is moving beyond a growth model based on low-cost labour and export-led industries. Adoptions of the widespread generative AI and AI-powered assistants have accelerated the transformation. AI, by reshaping the nature and future of work, redefines the rules and productivity. This research overviews the recent research investigating how AI is transforming work, demand for digital skills, and productivity gains in AI-adopting Vietnamese industries. An optimal blend of AI and human collaboration positively impacts its productivity. Application of AI enables the use of its potential, but it also has significant challenges and risks of skill gaps, training costs, trust, job quality, and distribution of its effects. Focusing on adaptability, lifelong learning, and integration of AI ensures a positive future of work. This study identifies factors determining the adoption of AI and heterogeneity in its productivity and future of work impacts. |
| Keywords: | AI application, nature of work, future of work, economic transformation, skill requirements, AI productivity impacts, Vietnam |
| JEL: | D24 E24 F63 J24 L52 O33 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18825 |
| By: | Alrababah, Ala; De Vries, Catherine Eunice (Bocconi University); Myrick, Rachel (Duke University) |
| Abstract: | Do external security threats reduce support for far-right parties? Existing scholarship suggests such threats should weaken extreme parties by generating rally effects, shifting attention to foreign policy, and increasing demand for experienced leadership. We develop a theoretical framework distinguishing these channels and test them in the context of Russian aggression in Europe. In Study 1, leveraging an unexpected event during survey fieldwork in the European Social Survey, we find no evidence that Russia's invasion of Ukraine reduced support for far-right parties. In Study 2, a pre-registered survey experiment in Germany, we expose respondents to a scenario of US withdrawal from NATO and a Russian invasion of an EU member state. Threats increase demand for competent leadership and boost incumbent support by about 4 points, but do not reduce far-right support, which proves remarkably sticky. Together, these findings show that security shocks can reshape mainstream competition without eroding far-right support. |
| Date: | 2026–07–22 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:9m4jf_v1 |
| By: | Mdhlalose, Dickson |
| Abstract: | This paper critically evaluates the economic effects of unauthorised immigration on host economies through a cross-national comparative lens, drawing on empirical evidence published between 2020 and 2026. The analysis examines twelve major host economies across four continents: the European Union (with detailed evidence from Germany, Italy, Spain, France, and Poland), the United Kingdom, the Russian Federation, Türkiye, the Gulf Cooperation Council states, South Africa, Malaysia, Thailand, India, Brazil, the United States, and Canada. Five questions structure the inquiry: the global magnitude and distribution of unauthorised migrant populations; fiscal contributions and costs across diverse institutional contexts; labour market impacts on native workers; sectoral concentrations; and macroeconomic effects, including projected consequences of large-scale removal. Synthesis of authoritative sources indicates that approximately 5.8 million unauthorised migrants reside in EU-27 member states, 2.9 million in the Russian Federation, 2.2 million in South Africa, 2.7 million in Malaysia, 2.4 million in Thailand, and 3.6 million Syrians remain under Temporary Protection in Türkiye (International Organization for Migration [IOM], 2025; Eurostat, 2025; United Nations High Commissioner for Refugees [UNHCR], 2025). The Organisation for Economic Cooperation and Development (OECD, 2025) estimates that the 2019-2024 immigration surge raised hostcountry potential gross domestic product (GDP) by an average of 1.4 percent across destination economies. The International Monetary Fund (Allen et al., 2024) projects that mass-removal scenarios in major host economies would reduce GDP by 1.8-6.2 percent over five years, with disproportionate impacts in agriculture, construction, and care sectors. Türkiye Statistical Institute (TÜİK, 2024) data indicate that Syrian-owned enterprises generated approximately US$2.3 billion in annual turnover by 2023. The paper argues that aggregate net economic benefits coexist with genuinely localised fiscal costs and distributional pressures on competing low-skill workers, and that policy responses should be calibrated accordingly. Recommendations include regularisation pathways, intensified employer-side labour-standards enforcement, targeted fiscal transfers to high-impact localities, and sectoral guest-worker reforms. |
| Keywords: | unauthorised immigration, irregular migration, fiscal impact, labour market, gross domestic product, mass deportation, regularisation, comparative migration policy |
| JEL: | J61 H20 F22 E62 J11 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:341746 |
| By: | Gondauri, Davit; Guramishvili, Guram |
| Abstract: | This study develops and applies the Black Sea–Middle Corridor Integration Index (BSMCII), a composite measurement framework for evaluating the integration readiness of the Middle Corridor and the Black Sea basin as a multidimensional multimodal logistics system. Rather than treating the Middle Corridor as a rail-only route or the Black Sea as a peripheral maritime appendix, the study measures corridor readiness through the interaction of port performance, feeder connectivity, transit cost, documentation continuity, reliability, resilience, hinterland integration and green performance. The empirical design uses a calculation-ready and proxy-aware port/route-year dataset covering 19 observations and 11 entities. Eleven Level-I components—DEA, MPI, CPPI, Connectivity, TCDI, CAI, Reliability, Resilience, Hinterland, TradeCost and Green—are aggregated into the BSMCII composite index, quality-adjusted scores and competitive readiness rankings. The framework is then connected to an econometric evidence battery including pooled OLS, WLS, fixed/random-effects diagnostics, gravity-style controls, nonlinear and quantile checks, mediation logic, alternative weights and leave-one-component-out robustness tests. The results show that the Black Sea segment can be measured as a corridor multiplier when feeder services, port productivity, document continuity and hinterland functions are institutionally integrated. Connectivity emerges as the strongest bottleneck, with the lowest component mean, while TradeCost is the second binding constraint. Constanta remains the leading EU Black Sea benchmark, while Varna, Burgas, Poti and TITR form a measurable medium-integration cluster. Poti and Batumi show improvement over the observed period, although Georgian ports still require stronger connectivity, CPPI-type performance, cost predictability, and hinterland integration. Across the strongest model families, BSMCII is positively associated with container/transit flow outcomes, but the study interprets this as association evidence rather than definitive causal identification. The strategic implication is that BTK remains relevant but insufficient as a stand-alone rail logic; the stronger corridor strategy is a Caspian–Black Sea–EU feeder-and-hinterland integration model in which Anaklia becomes transformative only if it improves multiple readiness components simultaneously. |
| Keywords: | Middle Corridor, Black Sea logistics, BSMCII, Multimodal transport corridors, Port competitiveness, Corridor readiness index, Transport econometrics |
| JEL: | C23 C43 F14 F15 L91 L92 O18 R41 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:341861 |
| By: | Nicoli, Francesco; Biten Butorac, Merve (Universitat Autònoma de Barcelona) |
| Abstract: | Crises are often treated as engines of European integration, although the public-opinion mechanism behind crisis-led integration remains underspecified. This article argues that support for EU crisis powers depends on the configuration of the crisis: its symmetry, intensity, field of impact, direct national exposure, and attribution of responsibility. The argument is tested through a crisis-profile conjoint experiment fielded in France, Germany, Italy, Poland, Spain, and the United Kingdom between 20 April and 1 May 2025. Respondents evaluated crisis profiles and indicated whether the EU should receive fewer powers, no change in powers, or more powers to address the crisis. The results indicate that military crises, high-intensity crises, symmetric crises, and crises directly affecting the respondent's country create more favourable public-opinion conditions for EU competence expansion. Fiscal and economic crises generate less support than military crises and natural-disaster crises. Attribution of responsibility mainly affects opposition to integration: when EU policies are not blamed for the crisis, respondents become less likely to prefer fewer EU powers. These findings identify demand-side conditions under which stylised crisis descriptions make European action more acceptable as protection against a common shock. |
| Date: | 2026–07–21 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:audc8_v1 |
| By: | Davide Contu; Chiara Sotis; Giles Atkinson; Vincent Chung; Damien Dussaux; Stavros Georgiou; Susana Mourato; Irène Hu |
| Abstract: | Exposure to chemicals has been shown to cause skin sensitisation, leading to chronic conditions such as allergic contact dermatitis. Beyond physical symptoms, skin sensitisation can also reduce one’s quality of life, impair productivity and cause psychological distress. Despite its prevalence, information on the value that the public places on avoiding chemical-induced skin sensitisation remains limited. To fill this gap, the present paper details a stated preference survey estimating individuals’ willingness-to-pay to avoid the frequency, severity and visibility of skin flare-ups in nine countries (Canada, Denmark, Japan, the Netherlands, Poland, Spain, Switzerland, the United Kingdom and the United States). It also explores how other socio-economic factors such as age, gender and income affect the valuation of skin sensitisation. This work is part of a series of large-scale studies resulting from the Surveys on Willingness-to-Pay to Avoid Negative Chemicals-Related Health Effects project that intends to establish internationally comparable values for the willingness-to-pay to avoid negative health effects due to exposure to chemicals. The results from this paper are expected to be widely applicable in analyses assessing the value of reduced skin sensitisation in a range of chemical and environmental contexts. |
| Keywords: | chemicals regulation, economic valuation, health risk, health valuation, monetised benefits, morbidity valuation, non-market valuation, skin sensitisation, stated preferences, surveys, value of a statistical case, willingness-to-pay |
| JEL: | D61 I18 J17 K32 Q51 Q53 Q58 |
| Date: | 2026–08–14 |
| URL: | https://d.repec.org/n?u=RePEc:oec:envaaa:277-en |
| By: | Fisher, Ian |
| Abstract: | This paper investigates how uncertainty arising from land tenure insecurity shapes household income diversification strategies in rural Vietnam. Although a substantial body of literature examines the effects of insecure property rights on agricultural productivity and food security, relatively little attention has been given to its influence on income-based livelihood diversification. This study addresses that gap by analyzing Vietnam’s 2013 Land Law, which extended household agricultural land use rights by an additional 50 years. Using nationally representative household panel data, the analysis leverages variation in exposure to land tenure insecurity generated by the reform across households cultivating different crops. Event study regressions are used to assess how households adjust their livelihood strategies in response to land tenure uncertainty, as well as how these responses differ across household characteristics. The results indicate some evidence of income diversification prior to the reform that is mainly driven by an increased share of household labor allocated to off-farm wage labor, though the estimated effects are generally modest in magnitude. Heterogeneity analysis further shows that factors such as household distance to nearest road, member size, and farm size influence the the degree of adaptation to land tenure insecurity. Overall, the findings suggest that households respond to tenure-related uncertainty primarily by diversifying into off-farm income sources, rather than solely through on-farm intensification. |
| Keywords: | International Development |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea26:404659 |
| By: | Kartakis, Stelios; Thiermann, Insa; Cingiz, Kutay; Wesseler, Justus |
| Abstract: | Plant products issued a plant passport upon risk-based border inspections can circulate freely within the European Union. Border control inspections are imperfect, and pests or pathogens may remain undetected. Post-border detection efforts can reduce the risk of further spread and partly rely on voluntary actions by actors involved in plant trade, such as nurseries. Nurseries have a direct interest in healthy plant material, yet investments in early detection technologies remain driven by private incentives. We investigate nursery preferences for volatile organic compound (VOC) “e-nose” sensors using a discrete choice experiment among 343 nursery operators in Italy, Romania, Germany, and France. Respondents evaluated VOC sensor alternatives against the status quo of existing inspection practices. Choice data were analyzed using mixed logit and latent class models. The predicted probability of choosing a VOC sensor alternative was 76%. Adoption decisions were primarily shaped by cost considerations and performance-related attributes, particularly detection speed and reliability. Certification potential and ownership structure of the technology were considered less important. Cross-country differences indicate that nurseries place varying importance on technology attributes, suggesting that technology design and commercialization strategies should account for heterogeneous end-user preferences. Adoption likelihood is higher among nurseries that are members of professional associations, pointing to the role of networks in fostering technology diffusion. |
| Keywords: | Environmental Economics and Policy |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea26:404459 |