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Household survey data are very useful for monitoring living conditions of citizens of any country. In developing countries, a lot of this data are collected through “traditional†? face-to-face household surveys. Due to the remote and dispersed nature of many populations in developing countries, but also because of the complex nature of many survey questionnaires, collection of timely welfare data has often proved expensive and logistically challenging. Yet, there is a need for faster, cheaper to collect, lighter, more nimble data collection methods to address data gaps between big household surveys. The recent proliferation of mobile phone networks has opened new possibilities. By combining baseline data from a traditional household survey with subsequent interviews of selected respondents using mobile phones, this facilitates welfare monitoring and opinion polling almost real time. The purpose of this handbook is to contribute to the development of the new field of mobile phone data collection in developing countries. The handbook documents how this innovative approach to data collection works, its advantages and challenges. The handbook draws primarily from the authors’ first-hand experiences with mobile phone surveys in Africa and also benefits from experiences elsewhere. It is intended to serve a diverse audience including those involved in collecting (representative) data using mobile phones, and those using data collected through this approach. For those who will be implementing a mobile phone panel survey, the different chapters guide them through every stage of the implementation process. For potential users of the data collected via mobile phone technology, the handbook presents a new approach to data collection which they can use for monitoring programs and facilitate almost real time decision-making. A further purpose of this book is to contribute to the debate regarding the advantages of the method as well as the challenges associated with it.
The first round of the Myanmar Household Welfare Survey (MHWS)–a nationwide phone panel consisting of 12,100 households–was implemented between December 2021 and February 2022. The objective of the survey was to collect data on a wide range of household and individual welfare indicators–including wealth, livelihoods, unemployment, food insecurity, diet quality, health shocks, and coping strategies–in a country exceptionally hard hit by conflict, severe economic collapse, and several damaging waves of COVID-19. The respondents interviewed in the MHWS were purposely selected from a large phone database aimed at being representative at the region/state level and urban/rural level in Myanmar. In this paper, we discuss two key steps taken to ensure that the MHWS is nationally and subnationally representative at the state/region and urban/rural level. First, we used a quota-based sampling strategy by setting survey quotas for respondents’ geography, education, farming status, gender, and rural/urban residence. This sampling strategy is used to address the well-known drawbacks of phone survey samples (e.g., the over-sampling of more educated respondents) and the survey’s particular interest in over-sampling farm households and equally sampling men and women. Second, we constructed household, population, and individual level weighting factors to further ensure that the survey generates nationally and subnationally representative statistics. To assess the effectiveness of these two strategies on achieving representativeness and consistency with previous surveys, we compare results from the MHWS to earlier nationally representative datasets, focusing on sample sizes of interviewed households for each state/region, and on education levels, farm/non-farm occupation, urban/rural residence, as well as respondents’ housing characteristics, which are unlikely to change substantially over short periods of time. We show that the phone-based MHWS has broader geographical coverage than previous national surveys, reaching 310 of Myanmar’s 330 townships. Moreover, our sampling approach was generally effective in reducing the education bias of phone surveys, except for a handful of states/regions. The MHWS is also unique in providing equal representation of male and female respondents. Additionally, the MHWS sampling and weighting strategies produce statistics on key indicators that closely mirror results from the two most recent national surveys in Myanmar. Overall, the results suggest that these strategies are successful in generating a subnationally representative phone survey that collected data on a rich array of household welfare indicators in exceptionally difficult political and economic circumstances.
The COVID-19 pandemic has spurred interest in the use of remote data collection techniques, including phone surveys, in developing country contexts. This interest has sparked new methodological work focusing on the advantages and disadvantages of different forms of remote data collection, the use of incentives to increase response rates and how to address sample representativeness. By contrast, attention given to associated response fatigue and its implications remains limited. To assess this, we designed and implemented an experiment that randomized the placement of a survey module on women’s dietary diversity in the survey instrument. We also examine potential differential vulnerabilities to fatigue across food groups and respondents. We find that delaying the timing of mothers’ food consumption module by 15 minutes leads to 8-17 percent decrease in the dietary diversity score and a 28 percent decrease in the number of mothers who consumed a minimum of four dietary groups. This is driven by underreporting of infrequently consumed foods; the experimentally induced delay in the timing of mothers’ food consumption module led to a 40 and 11 percent decrease in the reporting of consumption of animal source foods, and fruits and vegetables, respectively. Our results are robust to changes in model specification and pass falsification tests. Responses by older and less educated mothers and those from larger households are more vulnerable to measurement error due to fatigue.
The Government Analytics Handbook presents frontier evidence and practitioner insights on how to leverage data to strengthen public administration. Covering a range of microdata sources—such as administrative data and public servant surveys—as well as tools and resources for undertaking the analytics, it transforms the ability of governments to take a data-informed approach to diagnose and improve how public organizations work. Readers can order the book as a single volume in print or digital formats, or visit worldbank.org/governmentanalytics for modular access and additional hands-on tools. The Handbook is a must-have for practitioners, policy makers, academics, and government agencies. “Governments have long been assessed using aggregate governance indicators, giving us little insight into their diversity and how they can practically be improved. This pioneering handbook shows how microdata can be used to give scholars and practitioners granular and real insights into how states work, and practical guidance on the process of state-building.†? —Francis Fukuyama, Stanford University, author of State-Building: Governance and World Order in the 21st Century “The Government Analytics Handbook is the most comprehensive work on practically building government administration I have ever seen, helping practitioners to change public administration for the better.†? —Francisco Gaetani, Special Secretary for State Transformation, Government of Brazil “The machinery of the state is central to a country’s prosperity. This handbook provides insights and methodological tools for creating a better shared understanding of the realities of a state, to support the redesign of institutions, and improve the quality of public administration.†? —James Robinson, University of Chicago, coauthor of Why Nations Fail
‘This open access book addresses an urgent issue on which little organized information exists. It reflects experience in Africa but is highly relevant to other fragile states as well.’ —Constantine Michalopoulos, John Hopkins University, USA and former Director of Economic Policy and Co-ordination at the World Bank Fragile countries face a triple data challenge. Up-to-date information is needed to deal with rapidly changing circumstances and to design adequate responses. Yet, fragile countries are among the most data deprived, while collecting new information in such circumstances is very challenging. This open access book presents innovations in data collection developed with decision makers in fragile countries in mind. Looking at innovations in Africa from mobile phone surveys monitoring the Ebola crisis, to tracking displaced people in Mali, this collection highlights the challenges in data collection researchers face and how they can be overcome.
The chief economist for the World Bank's Africa region, Shanta Devarajan, delivered a devastating assessment of the capacity of African states to measure development in his 2013 article "Africa's Statistical Tragedy". Is there a "statistical tragedy" unfolding in Africa now? If so then examining the roots of the problem of provision of statistics in poor economies is certainly of great importance. This book on measuring African development in the past and in the present draws on the historical experience of colonial French West Africa, Ghana, Sudan, Mauritania and Tanzania and the more contemporary experiences of Ethiopia and the Democratic Republic of Congo. The authors each reflect on the changing ways statistics represent African economies and how they are used to govern them. This bookw as published as a special issue of the Canadian Journal of Development Studies.
In early June 2020, we called by telephone a representative sample of nearly 600 households in Addis Ababa, Ethiopia to assess income changes and household food and nutrition security status during the COVID-19 pandemic (survey period covering May). This was the second administration of a COVID-19 related survey to these households, following an initial survey conducted in early May 2020 covering the situation of the survey households in April. More than two-third of the households indicated in the second survey that their incomes were lower than expected (up from 58 percent in April) and 45 percent reported that they are extremely stressed about the situation (up from 35 percent in April). Using a pre-pandemic wealth index, we find that less-wealthy households were considerably more likely to report income losses and high stress levels than were wealthier households. Compared to a period just before the pandemic (January and February 2020), indicators measuring food security have significantly worsened but have remained the same since April. During the pandemic, households are less and less frequently consuming relatively more expensive but nutritionally richer foods, such as fruit and dairy products. However, overall food security status in Addis Ababa is not yet alarming, possibly because many households have been able to use their savings to buffer food consumption. As the pandemic is still in an early stage in Ethiopia, it is likely that these savings will not last throughout the pandemic, calling for a rapid scale-up of existing support programs.
At the beginning of 2020, as the COVID-19 pandemic swept across the US in multiple waves, health systems had to rapidly develop systems for tracking various aspects related to managing the pandemic. This included not just overall trends in incidence, hospitalizations, and outcomes; but also metrics related to the response. COVID-19 was the first pandemic in the United States since the widespread adoption of electronic health records incentivized by the Meaningful Use program. As a result, the availability of health information was much broader than in any previous pandemic. The widespread impact of COVID-19 also meant that every healthcare institution was affected, and was tracking data related to the pandemic in some form. There has been more focused activity with data and analytics regarding COVID-19 than we have ever had with any other disease, including important advances as well as technical and regulatory obstacles.
As in most low and middle-income countries, the paucity of timely economic data in Ethiopia makes it difficult to understand the economic impacts of the COVID-19 pandemic. To mitigate this, several organizations have launched phone surveys to gather more information about the crisis. This research report reviews the available phone survey evidence as of mid-August 2020 and identifies knowledge gaps. First, the available evidence suggest that the pandemic has not led to unusually large increases in food prices. However, a case study in the vegetable sector suggests that price dynamics are highly context and crop specific, calling for more comprehensive price monitoring to identify food value chains and areas where food price increases may have been unusually rapid. Second, employment losses have concentrated on informal sector workers while redundancies in the formal sector have been less significant. Third, there is considerable uncertainty about the income, poverty, and food security implications of this crisis. While most households report income losses, the qualitative and subjective nature of these questions meanthat the magnitudes of these losses are unknown. In Addis Ababa, less subjective food security measures indicate only small negative changes in household food and nutrition security. Finally, due to limited access to mobile phones in rural areas, we have imperfect and incomplete information on how this crisis is affecting rural households.
This collection brings together a diverse group of scholars from throughout the world who have grappled with and investigated the impact of the COVID-19 crisis on the lives of young children. Profound changes have occurred in all facets of early childhood education and care (ECEC). Young children and their families, college students enrolled in teacher preparation programs, inservice teachers/caregivers, and postsecondary faculty have endured prolonged periods of quarantine, disruption, stress, and grief precipitated by the pandemic. These consequences have been even more challenging for individuals and groups who were already struggling or marginalized prior to the advent of the coronavirus. Collectively, the chapter authors draw upon findings from their research and insights gleaned from professional experiences to recommend ways of providing high-quality programs despite persistent global health threats.​