GDP per capita is by far the most popular measure of international levels of development. It is fairly well understood and widely available across countries and time. But it is also recognized that GDP per capita is an imperfect proxy for important factors such as health, education and well-being. An alternative approach has been to work directly with the variables of concern, as in the UNDP Human Development Index (HDI). The HDI combines GDP per capita with life expectancy and schooling into a single composite index. But, the HDI is difficult to compile. Moreover, because it is an index, it cannot tell us about the absolute standard of living of the underlying population: it can only provide rankings of nations at any moment in time and changes in these rankings over time. It turns out that the rankings produced by the GDP per capita and the HDI are quite highly correlated. Given that GDP per capita also provides an absolute measure of income; it is understandable that it remains so popular. Both the GDP per capita and the HDI measures suffer from that fact that ?they are averages that conceal wide disparities in the overall population? (Kelley, 1991). As a result, it becomes necessary to either supplement these measures with information on distributional inequality as in the Gini coefficient, or to directly adjust GDP per capita and other variables for distributional variations. Sen (1976) derives (1-Gini) as the appropriate adjustment factor for real income. Since a higher

The purpose of this paper is to investigate the connection between countries' openness to international trade and economic development, i.e. the standard of living of итс citizens. Given that this is a frequently researched question among economists with still open conclusions, research on this topic always sheds some new light and contributes to new conclusions on that aspect. The methodology used in the paper is a correlation analysis, where we compared different measures of openness with economic development measured by GDP per capita expressed in purchasing power parity. The sample on which we performed the analysis is 16 countries, some of which have completed the transition process like the countries of so-called New Europe, and some are still stuck in transition, such as our countries of our region. Each of these countries continues to be subject of an analysis of transition progress in the EBRD’s Transition Report. The results clearly show a strong and positive correlation between openness to international trade flows and economic development, with this relationship being stronger if we look at export flows than import flows. The conclusion is that openness has no alternative, and that the countries of the region must strengthen their export base and export performance in order to reduce the gap in relation to the countries of New Europe, for which we can easily say that have successfully completed the transition process.

How should progress be measured? Today, economists and economic historians have available a rich array of data for a large number of countries on which to base their response to this important question. The need for alternative measures of the standard of living is particularly important for economic historians exploring the distant past where conventional estimates cannot be calculated. In this paper John Komlos and Brian Snowdon review several alternative measures of ‘progress’, both orthodox and unorthodox, including recent findings from ‘anthropometric’ history. The field of Anthropometrics blends history, economics, biology, medical science and physical anthropology and is now well established having helped to clarify ‘several questions important to economic historians’ including those related to slavery, mortality, inequality, and living standards during industrialisation. While malnutrition is the scourge of poor countries, obesity has become a major problem in many developed countries, particularly during the last quarter century. Research into the economics of obesity is now a burgeoning research area and the authors briefly review some of the major findings. Finally, Komlos and Snowdon comment on the recent literature on ‘happiness’. The achievement of a higher GDP per capita is, after all, not an end in itself, but a means to an end, that is, human happiness.

Some countries are naturally endowed with abundant natural resources, which serve as a significant source of government revenue for national development. Conversely, other countries lacking such resources rely on alternative means to generate income for their developmental efforts. The disparity in development and living standards among nations, however, cannot be attributed solely to the unequal distribution of natural resources but rather to the effectiveness and efficiency with which resource revenues are utilized. This study examines the effect of oil rents on living standards in Nigeria using the Autoregressive Distributed Lag (ARDL) model. The findings reveal a positive long-run relationship between Living Standard (LS), Oil Rent (OR), and Gross Domestic Product (GDP), while a negative relationship is observed with Oil Price (OP) and Exchange Rate (ER). However, these relationships are found to be statistically insignificant. In the short run, the results show a negative and statistically significant relationship between living standards and the variables of Oil Rent, Oil Price, GDP, and Exchange Rate. These findings highlight the complex dynamics between oil rents and living standards, particularly in the context of short-run economic fluctuations. In light of these results, the study recommends that the government prioritize making all refineries operational to meet domestic fuel demand and reduce the costs associated with fuel importation, which consumes a substantia

High-resolution income data is crucial for informing policy decisions as it allows policymakers to better understand the distribution of wealth and poverty. However, obtaining this information is often cost-prohibitive, especially in developing countries. We evaluate the potential of using high-resolution satellite imagery and machine learning techniques to create income maps with a high level of geographic detail. We train a neural network with satellite images from the Metropolitan Area of Buenos Aires (Argentina) and 2010 census data to estimate per capita income at a 50x50 meter resolution for 2013, 2018 and 2022. The model, based on the EfficientNetV2 architecture, demonstrates strong predictive accuracy for household incomes (R2 = 0.878), achieving a spatial resolution over 20 times finer than existing methods in the literature. The model also allows estimating income maps for arbitrary images, and can therefore be applied at any point in time. Our approach opens up new possibilities for generating highly detailed data, which can be used to assess public policies at a local level, target social programs more effectively, and address information gaps in areas where traditional data collection methods are lacking.