This paper uses five rounds of Mexican and Brazilian census extracts to evaluate the accuracy of different model specifications and estimation methods that use survey and census data to generate small area estimates of poverty. Models that utilize more granular data for prediction (household- and/or village-level predictors) tend to produce more accurate estimates of poverty than models estimated only using area-level predictors. Differences in accuracy across models and methods that utilize household or village level predictors are minor. Models that omit household-level predictors tend to be more robust than unit-level models to the use of old census data and classical measurement error in survey predictors. The performance of the Fay-Herriot area-level model falls in the presence of sample selection bias and small sample sizes. Rescaling sample weights is important in Mexico, where the sample is informative within areas. Applying raw sample weights without rescaling in this case greatly reduces the accuracy of estimates from linear models and distorts methodological comparisons. Overall, no one approach dominates across all contexts, but when sample weights are rescaled there is no downside to using more granular data for prediction.

Estimates of poverty in India post - 2011/12 are subject to heated public and scholarly debate. The first official, nationally representative consumption survey after 2011/12 to be publicly released pertains to 2022/23. While this survey was also administered and fielded by India’s National Sample Survey Organization, the resulting consumption aggregate cannot be directly compared to that from the 2011/12 round due to far-reaching changes in consumption definition, questionnaire design, sampling, and survey organization. Moreover, since 2011/12 there has been no officially endorsed poverty line that updates the 2011/12 line with respect to inflation and spatial price variation. We confront these problems of non-comparability by employing survey-to-survey imputation methods in which consumption is predicted into the data for 2022/23 based on consumption models calibrated using data from 2011/12. We consider a range of model specifications and employ both the NSSO consumption surveys and Periodic Labour Force Surveys (PLFS). The latter allow us to also track poverty during the years between 2017 and 2022/23. All estimates indicate a significant slowing of poverty decline compared to the preceding decade. We report state-level alongside aggregate trends. We reveal sharp differences across states in their achievement of poverty decline. More broadly, our analysis demonstrates both the potential and the limitations of survey-to-survey imputation as a tool for tracking poverty when official data face severe comparability constraints — a challenge all too common across developing countries — and provides a detailed validation of the method against a period of known, rapid poverty decline

This paper empirically validates several survey-to-survey (S2S) imputation methods, focusing on their performance during structural shocks, using household survey data from Peru from 2010–2022. While these methods deliver high accuracy in stable periods, their predictive power declines sharply during the COVID-19 pandemic. A decomposition analysis shows that failures arise primarily from fundamental shifts in the relationship between household characteristics and welfare, rather than from changes in unobservable factors. Incorporating variables strongly associated with consumption-based welfare, such as labor income and government transfers, can partially mitigate prediction errors during crises. However, cross-country validations with data from Mongolia and Georgia reveal that the effectiveness of these predictors is highly context-dependent. These findings underscore a critical challenge: variables most predictive during crises are often unavailable in non-consumption surveys, precisely when S2S imputation is most needed. The paper highlights the importance of understanding local contexts and setting realistic expectations for model performance under structural change.

This paper presents initial and partial estimates of poverty from the 2022-23 consumption survey. In the absence of new official poverty lines, the paper updates the official Tendulkar committee poverty lines for 2022-23. It also reports alternative estimates of poverty based on updated Rangarajan committee poverty lines. Preliminary estimates suggest that while poverty has continued to fall post-2011/12, the pace of poverty reduction has slowed irrespective of the choice of poverty line and inflation adjustment. However, as serious comparability issues between the 2022-23 survey and earlier surveys remain unresolved, even this conclusion must remain tentative at best. Given the multiple comparability concerns, as well as pending decisions regarding the choice of price deflators and the normative content of the poverty line, there is a need for a newly established official committee to present recommendations for the best way forward

This paper assesses the reliability of poverty maps derived from off-the-shelf remote-sensing data. Employing data for Malawi, it first obtains small area estimates of poverty by combining household expenditure survey data with population census data. It then ignores the population census and obtains a second poverty map by combining the survey with predictors of poverty derived from remote sensing data. The two approaches reveal the same patterns in the geography of poverty. However, there are instances where the two approaches obtain markedly different estimates of poverty. Poverty maps obtained using remote sensing data may do well when the decision maker is interested in comparisons of poverty between assemblies of areas yet may be less reliable when the focus is on estimates for specific small areas.

Monitoring the status and evolution of Sustainable Development Goals (SDGs) is typically carried out at the national level. However, significant variation can exist within countries, and this may not be captured by aggregate statistics. Here, we develop a unique dataset representing indicators for three SDGs at a district level for Lao PDR. The indicators comprise prevalence of stunting (SDG 2, Zero hunger), poverty headcount (SDG 1, No Poverty), and share of natural area (SDG 15, Life on land) for two moments in time: 2005 and 2015. In both years, we find considerable variation among district-level outcomes for stunting and poverty. We also find that higher stunting and poverty rates are significantly correlated with higher shares of natural land in both years. This is consistent with the common perception of a trade-off between environmental outcomes and socioeconomic wellbeing. The correlation vanishes, however, when we consider changes in poverty, stunting, and natural area over the ten-year study period. This holds as well when we focus on agricultural land instead of natural areas. We observe that most regions show improvements in both stunting and poverty, albeit not always in a statistically significant sense. This points to synergistic development. Similarly, improvements in both indicators are associated with losses in natural areas in all regions, indicating a trade-off. These results suggest that both trade-offs and synergies between SDGs can arise at the district level, but that context and local conditions likely moderate the strength of these interactions. Our results highlight the importance of quantifying and monitoring sustainable development at the detailed subnational level.

This study employs a synthetic panel approach based on nationally-representative micro-level data to track poverty and income mobility in Malaysia in the period 2004–2016. On aggregate we observe large reductions in chronic poverty and increases in persistent economic security, but note that those who remain poor in 2016 are increasingly likely to be poor in a structural sense. Further, we find that poverty and income dynamics differ notably across geographic dimensions. Such disparities are most striking when we contrast affluent urban Peninsular Malaysia with poorer rural East Malaysia. Although there are important differences in welfare levels between the main ethnic groups in Malaysia, we observe that mobility trends generally point in the same direction. While our findings show that there is still scope for poverty reduction through the reduction of inter-ethnic inequalities, we underscore the importance of taking regional inequalities into greater account when it comes to ensuring a fairer distribution of socioeconomic opportunities for poor and vulnerable Malaysians. Hence, addressing chronic poverty is likely to require additional attention to less developed geographic areas, as a complement to the largely ethnicity-based policies that have historically played a dominant role.

Panel data are rarely available for developing countries. Departing from traditional pseudo-panel methods that require multiple rounds of cross-sectional data to study poverty mobility at the cohort level, we develop a procedure that works with as few as two survey rounds and produces point estimates of transitions along the welfare distribution at the more disaggregated household level. Validation using Monte Carlo simulations and real cross-sectional and actual panel survey data - from several countries, spanning different income levels and geographical regions - perform well under various deviations from model assumptions. The method could also inform investigation of other welfare outcome dynamics.

The North Indian village of Palanpur has been the subject of close study over a period of six decades from 1957/8 to 2015. Himanshu et al. ( 2018 ) document the evolution of the village economy over this period and point to two distinct drivers of growth and distribution of income. An early period of agricultural intensification associated with the green revolution saw an expansion of irrigation and the introduction of new agricultural technologies, leading to rising incomes accompanied by falling poverty and fairly stable, or even declining, income inequality. From about the mid-1970s onwards, a cumulative process of non-farm diversification took hold, and was associated with further growth and poverty decline but also a significant rise in income inequality. Such a process of structural transformation has been observed more widely in rural India. We construct a simple model of a village economy that captures several of the salient features of the Palanpur economy and society, and that is able to reproduce the distributional outcomes observed in the village. Our analysis suggests that while non-farm diversification occurred alongside rising inequality, the counterfactual of no diversification would in fact be associated with an even greater increase. We suggest therefore that non-farm diversification has in fact helped to contain growth in inequality, and has played a particularly pronounced role in reducing poverty. To the extent that other villages in India share features similar to Palanpur, our findings may also hold elsewhere.

Measuring poverty trends and dynamics are important inputs in the formulation and design of poverty reduction policies. The empirical underpinnings of such exercises are often constrained by the absence of suitable data. We provide a broad, generalist, overview of regression-based imputation methods that have seen widespread application to estimate poverty outcomes in data-scarce environments. In particular, we review two imputation methods employed in tracking poverty over time and estimating poverty dynamics. We also discuss new areas that promise of further research.

We describe a recently developed approach for constructing synthetic panels from cross-section data and we consider how it can be employed to study poverty dynamics.

This symposium issue speaks to the topic of poverty dynamics and vulnerability during an unusual, unexpected and damaging global pandemic. We provide here an introduction and overview to the symposium.

Income inequality is a topic of longstanding interest in India. Historically, attention has tended to focus on the lower tail of the welfare distribution—on poverty— rather than on overall income inequality.1 This would seem appropriate given the very high levels of absolute poverty that have long prevailed in India. Recently, however, as economic growth in India has accelerated, and as absolute poverty rates have started to fall fairly rapidly, there has been a turn also to questions about the broader distributional impact of India’s growth trajectory. There are deep concerns about the possible consequences of rising inequality for social stability. An important dimension of inequality in India pertains to widespread horizontal inequalities. India’s complex caste structure translates into significantly different opportunities and aspirations across population segments. Religious, gender, and even spatial differences also play a role in shaping wellbeing. It is important to accommodate these horizontal inequalities into any analysis of the evolution of India’s overall income distribution. This chapter reports on a recently completed research project that seeks to inform the debate on inequality in India by offering a bird’s-eye view of inequality trends and dynamics at the all-India level over three decades up to 2011/12, and contrasting this with similar evidence at the level of the Indian village or the urban block. We explore dynamics by reporting ‘snapshots’ of inequality at different time periods, but also by tracing the movement of people within the income

India has been hard-hit by the Covid-19 pandemic. The virus has exacted a heavy toll in terms of lives lost and deteriorating health outcomes. The economic consequences of the pandemic have been similarly grim. In this paper we attempt an initial, interim, assessment of the impacts of the crisis on poverty. We review the growing literature that considers emerging poverty impacts, noting that there remain significant knowledge gaps due to limited evidence on current welfare outcomes. We analyze pre-Covid survey data to examine the incidence of chronic poverty and downward mobility during a period of rapid economic growth and declining poverty. A profile of poverty during such a period might offer a plausible, partial, window on population groups currently at risk. We suggest that, notwithstanding the severe initial impacts of the crisis on poverty, there are grounds for expecting further consequences going forward. As the virus has spread out of the relatively affluent cities, and as economic stagnation persists, rural areas, with historically higher rates of chronic poverty and vulnerability, may see particularly sharp increases in poverty. While recent vaccination developments offer some grounds for optimism, there remains an urgent need to identify, implement and amplify effective policy alleviation measures.

Measuring poverty trends and dynamics is an important undertaking for poverty reduction policies, which is further highlighted by the SDG goal 1 on eradicating poverty by 2030. We provide a broad overview of the pros and cons of poverty imputation in data-scarce environments, update recent review papers, and point to the latest research on the topics. We briefly review two common uses of poverty imputation methods that aim at tracking poverty over time and estimating poverty dynamics. We also discuss new areas for imputation.

This chapter examines income mobility in developing countries. We start by synthesizing findings from the available evidence on relative mobility and poverty dynamics. We then describe evidence on economic mobility obtained via synthetic panels constructed from cross-section data. We echo earlier literature in pointing to substantial movement across income classes by households over time—poverty is not inevitably a chronic condition. However, less clear are the factors driving the observed ‘churning’. In an attempt to make headway, we consider the story of economic mobility in one village in northern India over seven decades. We describe patterns of poverty dynamics and economic mobility in the village, and we highlight some of the processes that have been important in driving these patterns. While this in-depth study does not permit inferences to broader populations, it may provide a reference point against which findings from studies elsewhere can be compared.

In this paper, we employ recently completed “ poverty maps ” for three countries as tools for an ex ante evaluation of the distributional incidence of geographic targeting of public resources. We simulate the impact on poverty of transferring an exogenously given budget to geographically defined sub-groups of the population according to their relative poverty status. We find large gains from targeting smaller administrative units, such as districts or villages. However, these gains are still far from the poverty reduction that would be possible had the planners had access to information on household level income or consumption. Our results indicate that a useful way forward might be to combine fine geographic targeting using a poverty map with within-community targeting mechanisms.

This paper examines income mobility in developing countries. We start by synthesizing findings from the available evidence on relative mobility and poverty dynamics. We then describe evidence on economic mobility obtained via synthetic panels constructed from cross-section data. We echo earlier literature in pointing to substantial movement across income classes by households over time: poverty is not inevitably a chronic condition. However, less clear are the factors driving the observed ‘churning’. In an attempt to make headway, we consider the story of economic mobility in one village in northern India over seven decades. We describe patterns of poverty dynamics and economic mobility in the village, and we highlight some of the processes that have been important in driving these patterns. While this in-depth case study does not permit inferences to broader populations, it may provide a reference point against which findings from studies elsewhere can be compared.