Person:
Abanokova, Ksenia

Author Name Variants

Abanokova, Kseniya

Last updated: April 29, 2025

Biography

Ksenia Abanokova is an Economist for the Living Standards Measurement Study (LSMS), the World Bank’s flagship household survey program housed at the Development Data Group. She is a core team member at LSMS, developing and applying data imputation methods to poverty and other development outcomes. Prior to joining LSMS in 2022, Ksenia worked as a consultant on various projects at the Development Data Group. Ksenia holds a Candidate of Science (the highest academic degree that is equivalent to a Ph.D.) from the Higher School of Economics in Russia.

Full item page

Publication Search Results

Now showing 1 - 9 of 9

Female Headship and Poverty in the Arab Region: Analysis of Trends and Dynamics Based on a New Typology
(World Bank, Washington, DC, 2024-01-23) AlAzzawi, Shireen; Dang, Hai-Ahn; Hlasny, Vladimir; Abanokova, Ksenia; Behrman, Jere
Various challenges are thought to render female-headed households (FHHs) vulnerable to poverty in the Arab region. Yet, previous studies have had mixed results and the absence of household panel survey data hinders analysis of poverty dynamics. This paper addresses these challenges by proposing a novel typology of FHHs and analyzes synthetic panels constructed from 20 rounds of repeated cross-sectional surveys spanning the past two decades from the Arab Republic of Egypt, Iraq, Jordan, Mauritania, the West Bank and Gaza, and Tunisia. The paper finds that the definition of FHHs matters for measuring poverty levels and dynamics. Most types of FHHs are less poor than non–FHHs on average, but FHHs with a major share of female adults are generally poorer. FHHs are more likely to escape poverty than households on average, but FHHs without children are the most likely to do so. While more children are generally associated with more poverty for FHHs, there is heterogeneity across countries in addition to heterogeneity across measures of FHHs. The findings provide useful inputs for social protection and employment programs aiming at reducing gender inequalities and poverty in the Arab region.
Using Survey-to-Survey Imputation to Fill Poverty Data Gaps at a Low Cost: Evidence from a Randomized Survey Experiment
(Washington, DC: World Bank, 2024-03-26) Dang, Hai-Anh; Kilic, Talip; Hlasny, Vladimir; Abanokova, Kseniya; Carletto, Calogero; Abanokova, Ksenia
Survey data on household consumption are often unavailable or incomparable over time in many low- and middle-income countries. Based on a unique randomized survey experiment implemented in Tanzania, this study offers new and rigorous evidence demonstrating that survey-to-survey imputation can fill consumption data gaps and provide low-cost and reliable poverty estimates. Basic imputation models featuring utility expenditures, together with a modest set of predictors on demographics, employment, household assets, and housing, yield accurate predictions. Imputation accuracy is robust to varying the survey questionnaire length, the choice of base surveys for estimating the imputation model, different poverty lines, and alternative (quarterly or monthly) Consumer Price Index deflators. The proposed approach to imputation also performs better than multiple imputation and a range of machine learning techniques. In the case of a target survey with modified (shortened or aggregated) food or non-food consumption modules, imputation models including food or non-food consumption as predictors do well only if the distributions of the predictors are standardized vis-à-vis the base survey. For the best-performing models to reach acceptable levels of accuracy, the minimum required sample size should be 1,000 for both the base and target surveys. The discussion expands on the implications of the findings for the design of future surveys.
Imputing Poverty Indicators without Consumption Data: An Exploratory Analysis
(Washington, DC: World Bank, 2024-08-19) Dang, Hai-Anh H.; Kilic, Talip; Abanokova, Kseniya; Carletto, Calogero; Abanokova, Ksenia
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, this paper employs 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. Analysis of 22 multi-topic household surveys conducted over the past decade in Bangladesh, Ethiopia, Malawi, Nigeria, Tanzania, and Viet Nam yields encouraging results. Adding 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 by 0.1 to 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 for future survey design.
Educational inequalities during COVID-19: Results from longitudinal surveys in Sub-Saharan Africa
(Elsevier, 2025-01-21) Dang, Hai-Anh H.; Oseni, Gbemisola; Abanokova, Kseniya; Abanokova, Ksenia
While the literature on the COVID-19 pandemic is growing, there are few studies on learning inequalities in a lower-income, multi-country context. Analyzing a rich database consisting of 34 longitudinal household and phone survey rounds from Burkina Faso, Ethiopia, Malawi, Mali, Nigeria, Tanzania, and Uganda with a rigorous linear mixed model framework, we find lower school enrolment rates during the pandemic. But countries exhibit heterogeneity. Our variance decomposition analysis suggests that policies targeting individual household members are most effective for improving learning activities, followed by those targeting households, communities, and regions. Households with higher education levels or living standards or those in urban residences are more likely to engage their children in learning activities and more diverse types of learning activities. Furthermore, we find some evidence for a strong and positive relationship between public transfers and household head employment with learning activities for almost all the countries.
Is Climate Change Slowing the Urban Escalator out of Poverty?: Evidence from Chile, Colombia, and Indonesia
(World Bank, Washington, DC, 2023-03-30) Nakamura, Shohei; Abanokova, Kseniya; Dang, Hai-Anh; Takamatsu, Shinya; Pei, Chunchen; Prospere, Dilou; Abanokova, Ksenia
While urbanization has great potential to facilitate poverty reduction, climate shocks represent a looming threat to such upward mobility. This paper empirically analyzes the effects of climatic risks on the function of urban agglomerations to support poor households to escape from poverty. Combining household surveys with climatic datasets, the panel regression analysis for Chile, Colombia, and Indonesia finds that households in large metropolitan areas are more likely to escape from poverty, indicating better access to economic opportunities in those areas. However, the climate shocks offset such benefits of urban agglomerations, as extreme rainfalls and high flood risks significantly reduce the chance of upward mobility. The findings underscore the need to enhance resilience among the urban poor to allow them to fully utilize the benefits of urban agglomerations.
Impact of COVID-19 on Learning: Evidence from Six Sub-Saharan African Countries
(World Bank, Washington, DC, 2021-05) Oseni, Gbemisola; Dang, Hai-Anh; Zezza, Alberto; Abanokova, Kseniya; Abanokova, Ksenia
The COVID-19 pandemic has wreaked havoc upon global learning, with many countries facing severe school disruptions and closures. An emerging literature based on household survey data points to the pandemic as having exacerbated inequalities in education and learning in countries from Italy to Denmark, the United Kingdom, and the United States. This brief offers new analysis on the impacts of the COVID-19 pandemic on learning outcomes for six sub-Saharan African countries. The authors analyze detailed household level data from several rounds of panel phone surveys collected by the World Bank in Burkina Faso, Ethiopia, Malawi, Mali, Nigeria, and Uganda. These surveys were first implemented between late April and early June 2020, after school closures due to the pandemic. In each survey round, the surveyed households were asked a set of core questions on topics such as knowledge of COVID and mitigation measures, access to educational activities during school closures, dynamics of employment, household income and livelihood, income loss and coping strategies, and received assistance.O
Poverty Imputation in Contexts without Consumption Data: A Revisit with Further Refinements
(World Bank, Washington, DC, 2021-11) Kilic, Talip; Dang, Hai-Anh H.; Carletto, Calogero; Abanokova, Kseniya; Abanokova, Ksenia
A key challenge with poverty measurement is that household consumption data are often unavailable or infrequently collected or may be incomparable over time. In a development project setting, it is seldom feasible to collect full consumption data for estimating the poverty impacts. While survey-to-survey imputation is a cost-effective approach to address these gaps, its effective use calls for a combination of both ex-ante design choices and ex-post modeling efforts that are anchored in validated protocols. This paper refines various aspects of existing poverty imputation models using 14 multi-topic household surveys conducted over the past decade in Ethiopia, Malawi, Nigeria, Tanzania, and Vietnam. The analysis reveals that including an additional predictor that captures household utility consumption expenditures—as part of a basic imputation model with household-level demographic and employment variables—provides poverty estimates that are not statistically significantly different from the true poverty rates. In many cases, these estimates even fall within one standard error of the true poverty rates. Adding geospatial variables to the imputation model improves imputation accuracy on a cross-country basis. Bringing in additional community-level predictors (available from survey and census data in Vietnam) related to educational achievement, poverty, and asset wealth can further enhance accuracy. Yet, there is within-country spatial heterogeneity in model performance, with certain models performing well for either urban areas or rural areas only. The paper provides operationally-relevant and cost-saving inputs into the design of future surveys implemented with a poverty imputation objective and suggests directions for future research.
The Important Role of Equivalence Scales: Household Size, Composition, and Poverty Dynamics in the Russian Federation
(World Bank, Washington, DC, 2020-06) Abanokova, Kseniya; Dang, Hai-Anh H.; Lokshin, Michael M.; Abanokova, Ksenia
Hardly any literature exists on the relationship between equivalence scales and poverty dynamics for transitional countries. This paper offers a new study on the impacts of equivalence scale adjustments on poverty dynamics in the Russian Federation, using equivalence scales constructed from subjective wealth and more than 20 waves of household panel survey data from the Russia Longitudinal Monitoring Survey. The analysis suggests that the equivalence scale elasticity is sensitive to household demographic composition. The adjustments for the equivalence of scales result in lower estimates of poverty lines. The study decomposes poverty into chronic and transient components and finds that chronic poverty is positively related to the adult scale parameter. However, chronic poverty is less sensitive to the child scale factor compared with the adult scale factor. Interestingly, the direction of income mobility might change depending on the specific scale parameters that are employed. The results are robust to different measures of chronic poverty, income expectations, reference groups, functional forms, and various other specifications.
Inequality and Welfare Dynamics in the Russian Federation during 1994-2015
(World Bank, Washington, DC, 2018-10) Abanokova, Ksenia; Dang, Hai-Anh H.; Lokshin, Michael M.; Bussolo, Maurizio
The Russian Federation offers the unique example of a leading centrally planned economy swiftly transforming itself into a market-oriented economy. This paper offers a comprehensive study of inequality and mobility patterns for Russia, using multiple rounds of the Russian Longitudinal Monitoring Surveys over the past two decades spanning this transition. The findings show rising income levels and decreasing inequality, with the latter being mostly caused by pro-poor growth rather than redistribution. The poorest tercile experienced a growth rate that was more than 10 times that of the richest tercile, leading to less long-term inequality than short-term inequality. The analysis also finds that switching from a part-time job to a full-time job, from a lower-skill job to a higher-skill job, or staying in the formal sector is statistically significantly associated with reduced downward mobility and increased income growth. However, a similar transition from the private sector to the public sector is negatively associated with income growth.