Issue 2 (June)

Economic growth rests on entrepreneurship and capitalism. Entrepreneurship is important because entrepreneurs work unceasingly to create, use, and disseminate new products and productive techniques to raise living standards. Capitalism is characterized by private property rights, a free market, and the pursuit of profit, which are driving forces for innovation and productivity and in turn economic growth. Using six proxies for economic growth, our results show that capitalism, measured by the Economic Freedom of the World Index, and entrepreneurship, measured by the Global Entrepreneurship Monitor data set, fostered economic growth in member states of the European Union between 2000 and 2021.

Investors are keenly interested in the risk of informed trading, as it can have an immediate impact on transaction costs imposed by liquidity providers. This paper examines microblogging-based informed trading as a systematic risk for liquidity at both market and firm levels. Assets at firm level were categorized into financial and non-financial perspective. In this context, the study constructed a bank index and non-financial firms (NFF) index within the broader market. In a relative market, the liquidity was priced pessimistically and a higher probability for appearance of spread was noted during pessimism environments. The bank index liquidity was significantly responsive towards systematic bearish and bullish sentiments. In addition, the posterior probability of systematic sentiment risk was considerably higher for bank assets’ liquidity. The NFF index liquidity was not exposed to the systematic bearish and bullish sentiments. Meantime, the posterior probability of systematic sentiment risk was considerably lower for non-financial assets’ liquidity. The relative market’s liquidity was not influenced by changes in past series of bearish and bullish sentiments. Similarly, the sentiments’ lags were not strong enough to impact the firm index liquidity in the short or long run.

This study investigates the impact of institutions and public agricultural Research and Development (R&D) on agricultural productivity in low- and middle-income countries. The paper employs panel data with fixed-effects models, spanning 12 years and including 49 low-and middle-income countries. Agricultural productivity is measured by cereal yield, while institutional factors are assessed through four key indicators: judicial independence, property rights protection, contract enforcement, and impartial public administration. Public agricultural R&D investments serve as the measure for agricultural R&D. The findings show that property rights protection positively influences agricultural productivity, whereas legal contract enforcement has a negative impact across these countries. Additionally, public agricultural R&D significantly and positively affect productivity in Asia and the LAC regions, but not in sub-Saharan Africa.

This paper quantifies the impact of horizontal mergers on the prices of drugs. We study all mergers between two publicly traded pharmaceutical companies working in the US between 2010 and 2015 and their effect on their drugs prices. Using a differences-in-differences approach, we estimate that drugs marketed by the merging firms experience an AWP increase of about between 8% and 15% during the five years following the merger. These price increases are present even in markets with more than five competitors.

This paper presents a model of bank runs and evaluates relevant policy tools. The model is founded on the historical pattern of banking panics, involving an economic boom, an adverse shock, prominent bank failures, and runs on both insolvent and solvent banks. The model analyzes various ways in which solvency information affects the likelihood of systemwide bank runs. An interesting result is that partial bank-specific information can be worse than no bank-specific information. The model can also explain runs driven by liquidity concern based on incomplete solvency information. The main policy implication derived from the model and the evaluation of policy tools is that policy actions to contain a financial crisis should incorporate weeding out insolvent institutions and assuring the solvency of remaining institutions.

The goal of this paper is to work out a poststratification method for estimating in-time indicators for international end-to-end delivery processes when

• collected data cannot cover all the strata making up the logistics universe to be surveyed,
• the true weights of the strata - needed to correct biases in representativeness caused by disproportionate sampling and incomplete coverage - are unknown but can be inferred from marginal subtotals related to stratification criteria considered separately, rather than jointly, and conditional on each end of the delivery journey: outbound- versus inbound-specific. Within this perspective, poststratification is used here to mean a statistical correction of measurements derived from incomplete stratified samples, an ex-post calibration aimed at yielding more accurate estimates based on an analysis of the data. Thus, we tackle instances where ex-ante assignment to strata is not a problem, but when surveying all strata is out of the question.

For that purpose, an econometric model is designed

• to link the discrete transport lead times, counted in days, of tested items to the specifics of their material characteristics (e.g. size/weight), as well as those of the routes they take through the distribution network (e.g. origin and destination zones),
• and provide performance predictions for each of the strata, covered as well as non-covered.

Benchmarking the multinomial cumulative logit regression against the negative binomial one reveals that delivery time had better be treated as an ordinal categorical system’s response, rather than as a ratio-scaled count.

The model-based fitted and extrapolated estimates are then used as inputs to the ex-post weighting stage, which produces robust point- and interval-estimates of aggregate key performance indicators (KPIs) through bootstrapping. Simple linear programs provide two extreme weighting sets, one per country-to-country path: the first minimizes the KPIs’ values, while the second maximizes them. 

Probabilities of delivery within deadlines summarize distributions of delivery times better than their means and standard deviations, because logistical efforts to cut transit by one day must be enhanced more and more as it gets shortened. Three types of graphs are proposed to help visualize this exponential increase in the service quality required. The applicability of the methodology developed is demonstrated on the 2023 database of the International Post Corporation. In this case, the imprecision of the KPI estimates depends much more on the uncertainty caused by disturbances occurring during the first- and last-miles, than on the imperfection of the information about the real weights of the strata. 

This research paper investigates the policy impact of tariff, interest rate, and political stability on balance of payments among three income groups classified by the World Bank. The global panel data is taken from the World Bank from 2002 to 2022 and divided into three groups based on the classification on per capita income into High Income, Upper middle income, and Lower middle-income countries. This study explores how these variables impact balance of payments in different economic conditions and if their impact is different. The study employed in this paper is the Panel Least Squares, Fixed Effects Regression, GMM and Dumitrescu panel granger causality tests. The findings do confirm that these variables indeed have unlike impacts in the selected groups and the importance of tariff and interest rate as a policy tool cannot be sidelined. This research contributes to a deeper understanding of the complex relationship between macroeconomic variables within each income group also highlighting the importance of political stability for economic outlook.