Microestimates of wealth for all low- and middle-income countries

Many critical policy decisions, from strategic investments to the allocation of humanitarian aid, rely on data about the geographic distribution of wealth and poverty. Yet many poverty maps are out of date or exist only at very coarse levels of granularity. Here we develop microestimates of the relative wealth and poverty of the populated surface of all 135 low- and middle-income countries (LMICs) at 2.4 km resolution. The estimates are built by applying machine-learning algorithms to vast and heterogeneous data from satellites, mobile phone networks, and topographic maps, as well as aggregated and deidentified connectivity data from Facebook. We train and calibrate the estimates using nationally representative household survey data from 56 LMICs and then validate their accuracy using four independent sources of household survey data from 18 countries. We also provide confidence intervals for each microestimate to facilitate responsible downstream use. These estimates are provided free for public use in the hope that they enable targeted policy response to the COVID-19 pandemic, provide the foundation for insights into the causes and consequences of economic development and growth, and promote responsible policymaking in support of sustainable development.

Impact of food price increases on poverty in Indonesia: empirical evidence from cross-sectional data

PurposeUsing cross-sectional household survey data, this paper aims to determine the impact of food price increases on poverty in Indonesia.Design/methodology/approachThis paper uses the quadratic almost ideal demand system applied to the 2013 Indonesian household survey data. The impact of food price increase on household welfare is calculated using a welfare measure, compensating variation.FindingsThree food groups with the most outstanding price impact on poverty are rice, vegetables and fish were studied. The 20% increase in the price of each food group causes an increase in the headcount ratio by 1.360 points (rice), 0.737 points (vegetables) and 0.636 points (fish). Maintaining food price stability for these food groups is very important because the more price increases, the more impact on poverty. Food price policies in rural areas are also more critical than in urban areas because the impact of food price increases in rural areas is higher.Research limitations/implicationsThis paper does not consider the positive impact of rising food prices on food-producing households.Practical implicationsImplementing appropriate poverty alleviation policies through food policies for main food groups and social protection.Social implicationsPromoting rural development policies and agricultural growth.Originality/valueThis paper contributes to the existing literature by providing empirical results regarding the impact of domestic food prices increase on poverty in Indonesia.

Does the inverse farm size productivity hypothesis hold for perennial monocrop systems in developing countries? Evidence from Kenya

The inverse farm size and productivity relationship (IR) is a recurring theme in the literature. However, most previous studies were undertaken within a setting of mixed cropping systems. In this article, we investigate the effect of farm size on productivity within the context of a perennial monocropping system, acute competition for farmland, frequent subdivision of farms and declining yields. We apply household survey data of smallholder tea farms in western Kenya and consider both technical efficiency (TE) and the yield per hectare as indicators of productivity. The findings show that the effect of farm size on productivity is nonlinear, with TE initially declining and then rising with farm size. The findings also demonstrate that the farm size and productivity relationship is important for perennial monocrops and that the use of robust measures of productivity is important for the IR. The findings have important implications for agricultural policy in developing countries.

The Household Water Insecurity Experiences (HWISE) Scale: comparison scores from 27 sites in 22 countries

Household survey data from 27 sites in 22 countries were collected in 2017–2018 in order to construct and validate a cross-cultural household-level water insecurity scale. The resultant Household Water Insecurity Experiences (HWISE) scale presents a useful tool for monitoring and evaluating water interventions as a complement to traditional metrics used by the development community. It can also help track progress toward achievement of Sustainable Development Goal 6 ‘clean water and sanitation for all’. We present HWISE scale scores from 27 sites as comparative data for future studies using the HWISE scale in low- and middle-income contexts. Site-level mean scores for HWISE-12 (scored 0–36) ranged from 1.64 (SD 4.22) in Pune, India, to 20.90 (7.50) in Cartagena, Colombia, while site-level mean scores for HWISE-4 (scored 0–12) ranged from 0.51 (1.50) in Pune, India, to 8.21 (2.55) in Punjab, Pakistan. Scores tended to be higher in the dry season as expected. Data from this first implementation of the HWISE scale demonstrate the diversity of water insecurity within and across communities and can help to situate findings from future applications of this tool.

Lower and upper bound estimates of inequality of opportunity for emerging economies

Equality of opportunity is an important normative ideal of distributive justice. In spite of its wide acceptance and economic relevance, standard estimation approaches suffer from data limitations that can lead to both downward and upward biased estimates of inequality of opportunity. These shortcomings may be particularly pronounced for emerging economies in which comprehensive household survey data of sufficient sample size is often unavailable. In this paper, we assess the extent of upward and downward bias in inequality of opportunity estimates for a set of twelve emerging economies. Our findings suggest strongly downward biased estimates of inequality of opportunity in these countries. To the contrary, there is little scope for upward bias. By bounding inequality of opportunity from above, we address recent critiques that worry about the prevalence of downward biased estimates and the ensuing possibility to downplay the normative significance of inequality.