Dang, Hai-Anh H.

Person:
Dang, Hai-Anh H.

Senior Economist, Development Data Group, Development Economics

Author Name Variants

Dang, Hai-Anh, Dang, Hai-Anh Hoang, Dang, Hai-Anh H.

Fields of Specialization

development economics, poverty analysis, synthetic panels

External Links

WB/Dang

Last updated: June 24, 2025

Biography

Hai-Anh H. Dang is a Senior Economist for the Living Standards Measurement Study (LSMS), the World Bank’s flagship household survey program housed at the Development Data Group. He has contributed to around 70 World Bank projects and flagship reports, covering different countries around the world. His main research focus is on international development, poverty, inequality, human development topics, and methodology to construct synthetic (pseudo) panel data from cross sections. He has published in various journals, including Journal of Development Economics, Journal of Environmental Economics and Management, Oxford Bulletin of Economics and Statistics, World Bank Economic Review, World Development, and chapters with books published by leading academic publishers. Hai-Anh is a Research Fellow with Institute of Labor Economics (IZA), Global Labor Organization (GLO), and Indiana University’s O'Neill School of Public and Environmental Affairs, and a visiting/non-resident Senior Research Fellow with International School, Vietnam National University, Hanoi and Vietnam’s Academy of Social Sciences. He also serves as a co-editor of Review of Development Economics, on the editorial boards of other journals, and as a referee for around 50 academic journals. His research is widely disseminated, including being among the top paper downloads on IZA and Research Papers in Economics (RePEc). Hai-Anh received his Ph.D. in Applied Economics from the University of Minnesota, Twin Cities.

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Publication Search Results

Now showing 1 - 10 of 55

Did Program Support for the Poorest Areas Work?: Evidence from Rural Viet Nam
(Washington, DC: World Bank, 2025-01-10) Dang, Hai-Anh H.; Deininger, Klaus; Nguyen, Cuong Vjet
This paper investigates the impact of a large-scale poverty alleviation program targeted at the 62 poorest districts in Viet Nam. The analysis of multiple data sets spanning the past 20 years uses a regression discontinuity design with district fixed effects. The findings do not reveal significant program effects on household welfare (as measured by per capita income and poverty) or local economic development (as measured by nighttime light intensity and establishment of new firms). However, the findings show that the program facilitates a shift from farm to nonfarm employment and significantly increases the share of nonfarm income for rural households. A possible explanation for the positive effects on nonfarm employment is the improved access to credit that the program provides to participating households. The findings also show that the program increases household access to electricity, public transfers, educational subsidies for students residing in the program districts, and health care utilization, possibly through improving the availability of commune health care centers.
Rapid Economic Growth but Rising Poverty Segregation: Will Viet Nam Meet the SDGs for Equitable Development?
(Washington, DC: World Bank, 2025-02-20) Dang, Hai-Anh H.; Dhongde, Shatakshee; Do, Minh; Nguyen, Cuong Viet; Pimhidzai, Obert
Viet Nam is widely regarded as a success story for its impressive economic growth and poverty reduction in the last few decades. Yet, recent evidence indicates that the country’s economic growth has not been uniform. Compiling and analyzing new, extensive province-level data from the Vietnam Household Living Standards Surveys spanning 2002 to 2020 and other data sources, this paper finds within-province inequality to be much larger than between-province inequality. Furthermore, this inequality gap has been rising over time. Despite the country’s fast poverty reduction, the poor were increasingly segregated in certain provinces, particularly those with a larger ethnic minority population. The analysis finds a beneficial impact of economic growth on poverty reduction, but this can depend on inequality levels. It also finds that greater inequality has had negative effects on economic growth but varying negative effects on different poverty indicators. The paper provides supportive evidence of the beneficial impact of economic transitions from agriculture to non-agriculture. The results suggest that policy makers in Viet Nam should focus on reducing spatial disparities and income inequality to attain sustainable economic development.
The Impacts of COVID-19 on Female Labor Force Participation in the Islamic Republic of Iran
(Washington, DC: World Bank, 2024-06-12) Dang, Hai-Anh H.; Salehi-Isfahani, Djavad; Do, Minh N. N.
Although female labor force participation in the Islamic Republic of Iran is among the lowest in the world, there is a lack of studies on the effects of the COVID-19 pandemic on the country’s female labor force participation. This paper finds that female labor force participation decreased during the pandemic years by around 1 percentage point in 2021 and 2022. When controlling for excess mortality rates, the declines increase by as much as 3.9 and 8.7 percentage points in late 2021 and early 2022, respectively. Compared to the modest, pre-pandemic female labor force participation rates, these figures translate into 5 percent and 18-40 percent decreases, respectively. There is heterogeneity, with more educated individuals being more likely to work. Compared to married individuals, divorcees were more likely to work, and those who were widowed or never married were less likely to work. The results offer relevant inputs for labor policies, particularly those aimed at reducing gender inequalities.
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.
On the Construction of the World Bank’s Subnational Poverty and Inequality Databases: Documentation
(World Bank, Washington, DC, 2023-09-26) Nguyen, Minh Cong; Yang, Judy; Dang, Hai-Anh; Sabatino, Carlos
In many countries, large differences in poverty persist at the subnational level. In addition, global challenges such as climate change, fragility, economic crises, and food insecurity are often trans-border issues that pose significant risks for poverty reduction both across and within countries. Traditional poverty measures are generally presented at the national level, potentially obscuring local and regional variations of poverty and inequality. To overcome these challenges, this note describes the construction of two databases designed to provide a more granular perspective on poverty. The Subnational Poverty and Inequality Database (SPID) presents direct survey estimates of poverty and inequality from nationally representative household surveys over time. The Global Subnational Atlas of Poverty (GSAP) presents poverty estimates of survey-representative administrative areas projected to a common year. Both databases use the same underlying household survey data used by the World Bank to monitor global poverty.
Does Global Warming Worsen Poverty and Inequality? An Updated Review
(Washington, DC: World Bank, 2024-02-08) Trinh, Trong-Anh; Dang, Hai-Anh H.; Hallegatte, Stéphane
This paper offers an updated and comprehensive review of recent studies on the impact of climate change, particularly global warming, on poverty and inequality, paying special attention to data sources as well as empirical methods. While studies consistently find negative impacts of higher temperature on poverty across different geographical regions, with higher vulnerability especially in poorer Sub-Saharan Africa, there is inconclusive evidence on climate change impacts on inequality. Further analysis of a recently constructed global database at the subnational unit level derived from official national household income and consumption surveys shows that temperature change has larger impacts in the short term and more impacts on chronic poverty than transient poverty. The results are robust to different model specifications and measures of chronic poverty and are more pronounced for poorer countries. The findings offer relevant inputs into current efforts to fight climate change.
Reviewing Assessment Tools for Measuring Country Statistical Capacity
(Washington, DC: World Bank, 2024-03-11) Pullinger, John; Serajuddin, Umar; Stacy, Brian; Dang, Hai-Anh H.
Country statistical capacity is increasingly recognized as crucial for development, but no academic study exists that reviews the available assessment tools. This paper offers the first review study that fills this gap, paying particular attention to data and practical measurement challenges. It compares the World Bank’s recently developed Statistical Performance Indicators and Index with other widely used indexes, such as the Open Data Inventory index, the Global Data Barometer index, and other regional and self-assessment tools. The findings show that each index brings advantages in data sources, number of indicators, measurement focus, coverage of countries and time periods, and correlation with common development indexes. The Open Data Inventory index covers the most countries, the Global Data Barometer index collects data through its surveys, and the Statistical Performance Indicators and Index offer a broader framework for assessing statistical systems. The paper offers further thoughts on the potential mechanisms through which these tools can bring positive impacts on economic activities and some political economy concerns, as well as future directions for development.
Does Hotter Temperature Increase Poverty and Inequality?: Global Evidence from Subnational Data Analysis
(World Bank, Washington, DC, 2023-06-24) Dang, Hai-Anh H.; Trinh, Trong-Anh
Despite a vast literature documenting the harmful effects of climate change on various socio-economic outcomes, little evidence exists on the global impacts of hotter temperature on poverty and inequality. Analysis of a new global panel dataset of subnational poverty in 134 countries finds that a one-degree Celsius increase in temperature leads to a 9.1 percent increase in poverty, using the US$1.90 daily poverty threshold. A similar increase in temperature causes a 0.8 percent increase in the Gini inequality index. The paper also finds negative effects of colder temperature on poverty and inequality. Yet, while poorer countries—particularly those in South Asia and Sub-Saharan Africa—are more affected by climate change, household adaptation could have mitigated some adverse effects in the long run. The findings provide relevant and timely inputs for the global fight against climate change as well as the current policy debate on the responsibilities of richer countries versus poorer countries.
Educational inequalities during COVID-19: Results from longitudinal surveys in Sub-Saharan Africa
(Elsevier, 2025-01-21) Dang, Hai-Anh H.; Oseni, Gbemisola; 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.
Improving Estimates of Mean Welfare and Uncertainty in Developing Countries
(World Bank, Washington, DC, 2023-03) Merfeld, Joshua D.; Dang, Hai-Anh H.; Newhouse, David
Reliable small-area estimates of economic welfare significantly inform the design and evaluation of development policies. This paper compares the accuracy of wealth estimates obtained from the empirical best predictor (EBP) of a linear nested error model, Cubist regression, extreme gradient boosting, and boosted regression forests. The evaluation draws two-stage samples from unit-level household census data in seven developing countries, combines them with publicly available geospatial indicators to generate small area estimates of assets for all seven countries and poverty for two, and evaluates these estimates against census-derived benchmarks. Extreme gradient boosting and Cubist regression generally produce more accurate predictions than traditional EBP models. A proposed two-stage residual bootstrap procedure slightly underestimates confidence intervals, but leads to higher coverage rates than the parametric bootstrap approach used for EBP predictions. These results demonstrate that, given a sufficiently large sample of enumeration areas, predictions from extreme gradient boosting or Cubist regression with a two-stage residual block bootstrap generally provide more accurate point and uncertainty estimates for generating small-area welfare estimates.