We show analytically and empirically that non-classical measurement errors in the two key variables in a hypothesized relationship can bias the estimated relationship between them in any direction. Furthermore, if these measurement errors are correlated, correcting for either one alone can aggravate bias in the parameter estimate of interest relative to ignoring mismeasurement in both variables, a ‘second best’ result with implications for a broad class of economic phenomena of policy interest. We illustrate these results empirically by demonstrating the implications of mismeasured agricultural output and plot size for the long-debated (inverse) relationship between size and productivity.

Today’s unprecedented growth of data and their ubiquity in our lives are signs that the data revolution is transforming the world. And yet much of the value of data remains untapped. Data collected for one purpose have the potential to generate economic and social value in applications far beyond those originally anticipated. But many barriers stand in the way, ranging from misaligned incentives and incompatible data systems to a fundamental lack of trust. World Development Report 2021: Data for Better Lives explores the tremendous potential of the changing data landscape to improve the lives of poor people, while also acknowledging its potential to open back doors that can harm individuals, businesses, and societies. To address this tension between the helpful and harmful potential of data, this Report calls for a new social contract that enables the use and reuse of data to create economic and social value, ensures equitable access to that value, and fosters trust that data will not be misused in harmful ways. This Report begins by assessing how better use and reuse of data can enhance the design of public policies, programs, and service delivery, as well as improve market efficiency and job creation through private sector growth. Because better data governance is key to realizing this value, the Report then looks at how infrastructure policy, data regulation, economic policies, and institutional capabilities enable the sharing of data for their economic and social benefits, while safeguarding against harmful outcomes. The Report concludes by pulling together the pieces and offering an aspirational vision of an integrated national data system that would deliver on the promise of producing high-quality data and making them accessible in a way that promotes their safe use and reuse. By examining these opportunities and challenges, the Report shows how data can benefit the lives of all people, particularly poor people in low- and middle-income countries. .

Dynamic monitoring of crop phenotypic traits (e.g., LAI, plant height, biomass, nitrogen, yield et al.) is essential for exploring crop growth patterns, breeding new varieties, and determining optimized strategies for crop management. Traditional methods for determining crop phenotypic traits are mainly based on field sampling, handheld instrument measurement, and mechanized high-throughput platforms, which are time-consuming, and have low efficiency and incomplete spatial coverage. The development of crop science requires more rapid and accurate access to field-based crop phenotypes. Remote sensing provides a novel solution to quantify crop structural and functional traits in a timely, rapid, non-invasive and efficient manner. With the development of burgeoning remote sensing sensors and diversified algorithms, a range of crop phenotypic traits have been determined, including morphological parameters, spectral and textural characteristics, physiological traits, and responses to abiotic/biotic stresses in different environments. In addition, research advances in varying disciplines beyond agricultural sciences, such as engineering, computer science, molecular biology, and bioinformatics, have brought new opportunities for further development of remote sensing-based methods and technologies to gain more quantitative information on crop structure and function in complex environments

Increasing population pressure and population density in many African countries are inducing land scarcity and land constraints. These increasing land constraints are expected to trigger various responses and adaptation strategies, including agricultural intensification induced by land scarcity, as postulated by the Boserup hypothesis. However, most empirical evaluations of the Boserup hypothesis come from rainfed agriculture and mostly from Sub-Saharan Africa (SSA), where application of improved agricultural inputs remains historically low. Agricultural intensification practices as well as the relevance of the Boserup hypothesis in irrigated agriculture and in contexts where application of improved inputs is high remains unexplored. Furthermore, while much of the debate on the topic in Africa has focused on how to boost agricultural intensification, there is scant evidence on whether evolving agricultural intensification practices in some parts of Africa are sustainable, yield-enhancing, and optimal. In this paper we investigate the implication of land scarcity on agricultural intensification and the relevance of the Boserup hypothesis in the context of Egypt, where agriculture is dominated by irrigation and input application rates are much higher than SSA. We also examine whether evolving agricultural intensification practices induced by land scarcity are agronomically appropriate and yield-enhancing. We find that land scarcity induces higher application of agricultural inputs, mainly nitrogen fertilizers, sometimes beyond the level that is agronomically recommended. More importantly, land scarcity increases overapplication of nitrogen fertilizer relative to crop-specific agronomic recommendations. This implies that land constraints remain as important challenges for sustainable agricultural intensification. Finally, we find suggestive evidence that such overapplication of nitrogen fertilizers is not yield-enhancing, but, rather, yield-reducing. We also document that land scarcity impedes mechanization of agriculture. Our findings have important implications to inform appropriate farm management and sustainable intensification practices. Furthermore, our results can inform long-term policy responses to land scarcity.

Smallholder agriculture in sub-Saharan Africa is commonly characterized by high levels of technical inefficiency. However, much of this characterization relies on self-reported input and production data, which are prone to systematic measurement error. We theoretically show that non-classical measurement error introduces multiple identification challenges and sources of bias in estimating smallholders’ technical inefficiency. We then empirically examine the implications of measurement error for the estimation of technical inefficiency using smallholder farm survey data from Ethiopia, Malawi, Nigeria, and Tanzania. We find that measurement error in agricultural input and production data leads to a substantial upward bias in technical inefficiency estimates (by up to 85 percent for some farmers). Our results suggest that existing estimates of technical efficiency in sub-Saharan Africa may be severe underestimates of smallholders’ actual efficiency and what is commonly attributed to farmer inefficiency may be an artifact of mismeasurement in agricultural data. Our results raise questions about the received wisdom on African smallholders’ production efficiency and prior estimates of the productivity of agricultural inputs. Improving the measurement of agricultural data can improve our understanding of smallholders’ production efficiencies and improve the targeting of productivity-enhancing technologies.

Recall biases in retrospective survey data are widely considered to be pervasive and have important implications for effective agricultural research. In this paper, we leverage the survey design literature and test three strategies to attenuate mental anchoring in retrospective data collection: question order effects, retrieval cues, and aggregate (community) anchoring. We embed a survey design experiment in a longitudinal survey of smallholder farmers in Malawi and focus on anchoring bias in maize production and happiness exploiting differences between recalled and concurrent responses. We find that asking for retrospective data before concurrent data reduces recall bias by approximately 34% for maize production, a meaningful improvement with no increase in survey data collection costs. Retrieval cues are less successful in reducing the bias for maize reports and involve more data collection time, while community anchors can exacerbate the bias. Reversing the order of questions and retrieval cues do not help to ease the bias for happiness reports.