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This study examines genomic prediction within 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in gene banks. The Mexican and Iranian collections were evaluated in separate field trials, including an optimum environment for several traits, and in two separate environments (drought, D and heat, H) for the highly heritable traits, days to heading (DTH), and days to maturity (DTM). Analyses accounting and not accounting for population structure were performed. Genomic prediction models include genotype × environment interaction (G × E). Two alternative prediction strategies were studied: (1) random cross-validation of the data in 20% training (TRN) and 80% testing (TST) (TRN20-TST80) sets, and (2) two types of core sets, “diversity” and “prediction”, including 10% and 20%, respectively, of the total collections. Accounting for population structure decreased prediction accuracy by 15–20% as compared to prediction accuracy obtained when not accounting for population structure. Accounting for population structure gave prediction accuracies for traits evaluated in one environment for TRN20-TST80 that ranged from 0.407 to 0.677 for Mexican landraces, and from 0.166 to 0.662 for Iranian landraces. Prediction accuracy of the 20% diversity core set was similar to accuracies obtained for TRN20-TST80, ranging from 0.412 to 0.654 for Mexican landraces, and from 0.182 to 0.647 for Iranian landraces. The predictive core set gave similar prediction accuracy as the diversity core set for Mexican collections, but slightly lower for Iranian collections. Prediction accuracy when incorporating G × E for DTH and DTM for Mexican landraces for TRN20-TST80 was around 0.60, which is greater than without the G × E term. For Iranian landraces, accuracies were 0.55 for the G × E model with TRN20-TST80. Results show promising prediction accuracies for potential use in germplasm enhancement and rapid introgression of exotic germplasm into elite materials.

We examine the impact of policy uncertainty on trade, prices, and real income through firm entry investments in general equilibrium. We estimate and quantify the impact of trade policy on China’s export boom to the United States following its 2001 WTO accession. We find the accession reduced the US threat of a trade war, which can account for over one-third of that export growth in the period 2000–2005. Reduced policy uncertainty lowered US prices and increased its consumers’ income by the equivalent of a 13-percentage-point permanent tariff decrease. These findings provide evidence of large effects of policy uncertainty on economic activity and the importance of agreements for reducing it.

We examine the effects of trade liberalization in China on the evolution of markups and productivity of manufacturing firms. Although these dimensions of performance cannot be separately identified when firm output is measured by revenue, detailed price deflators make it possible to estimate the average effect of tariff reductions on both. Several novel findings emerge. First, cuts in output tariffs reduce markups, but raise productivity. Second, pro-competitive effects are most important among incumbents, while efficiency gains dominate for new entrants. Third, cuts in input tariffs raise both markups and productivity. We highlight mechanisms that explain these findings in the Chinese context.

This paper focuses on empirical connections between crime and immigration, studying two large waves of recent U.K. immigration (the late 1990s/early 2000s asylum seekers and the post-2004 inflow from EU accession countries). The first wave led to a modest but significant rise in property crime, while the second wave had a small negative impact. There was no effect on violent crime; arrest rates were not different, and changes in crime cannot be ascribed to crimes against immigrants. The findings are consistent with the notion that differences in labor market opportunities of different migrant groups shape their potential impact on crime.

This paper investigates the effect of the socioeconomic development on life expectancy at birth as an indicator of mortality or longevity in five EU accession candidate countries (Macedonia, Serbia, Bosnia and Herzegovina, Montenegro, and Albania). Using aggregate time series pool data on an annual level from UN and World Bank databases for the period 1990–2017 and Full Information Maximum Likelihood model, it was found that this connection between the socioeconomic conditions and life expectancy at birth is a prerequisite for longer life in all these five countries. Our dependent variable was the life expectancy at birth, and the background exploratory variables for the socioeconomic development were GDP per capita and infant mortality rate. The main results are that higher values of GDP per capita and lower values of infant mortality levels lead to higher life expectancy at birth suggesting that longevity of people in these five countries is increasing. These results are supported by our theoretical background and research framework hypotheses.