Person:

Yildirim, Muhammed

Novel protein-coding genes can arise either through re-organization of pre-existing genes or de novo. Processes involving re-organization of pre-existing genes, notably following gene duplication, have been extensively described. In contrast, de novo gene birth remains poorly understood, mainly because translation of sequences devoid of genes, or “non-genic” sequences, is expected to produce insignificant polypeptides rather than proteins with specific biological functions. Here, we formalize an evolutionary model according to which functional genes evolve de novo through transitory proto-genes generated by widespread translational activity in non-genic sequences. Testing this model at genome-scale in Saccharomyces cerevisiae, we detect translation of hundreds of short species-specific open reading frames (ORFs) located in non-genic sequences. These translation events appear to provide adaptive potential, as suggested by their differential regulation upon stress and by signatures of retention by natural selection. In line with our model, we establish that S. cerevisiae ORFs can be placed within an evolutionary continuum ranging from non-genic sequences to genes. We identify ~1,900 candidate proto-genes among S. cerevisiae ORFs and find that de novo gene birth from such a reservoir may be more prevalent than sporadic gene duplication. Our work illustrates that evolution exploits seemingly dispensable sequences to generate adaptive functional innovation.

The comparative advantage of a location shapes its industrial structure. Current theoretical models based on this principle do not take a stance on how comparative advantages in different industries or locations are related with each other, or what such patterns of relatedness might imply about the underlying process that governs the evolution of comparative advantage. We build a simple Ricardian-inspired model and show this hidden information on inter-industry and inter-location relatedness can be captured by simple correlations between the observed patterns of industries across locations or locations across industries. Using the information from related industries or related locations, we calculate the implied comparative advantage and show that this measure explains much of the location’s current industrial structure. We give evidence that these patterns are present in a wide variety of contexts, namely the export of goods (internationally) and the employment, payroll and number of establishments across the industries of subnational regions (in the US, Chile and India). The deviations between the observed and implied comparative advantage measures tend to be highly predictive of future industry growth, especially at horizons of a decade or more; this explanatory power holds at both the intensive as well as the extensive margin. These results suggest that a component of the long-term evolution of comparative advantage is already implied in today’s patterns of production.

Venezuela has seen an unprecedented exodus of people in recent months. In response to a dramatic economic downturn in which inflation is soaring, oil production tanking, and a humanitarian catastrophe unfolding, many Venezuelans are seeking refuge in neighboring countries. However, the lack of official numbers on emigration from the Venezuelan government, and receiving countries largely refusing to acknowledge a refugee status for affected people, it has been difficult to quantify the magnitude of this crisis. In this note we document how we use data from the social media service Twitter to measure the emigration of people from Venezuela. Using a simple statistical model that allows us to correct for a sampling bias in the data, we estimate that up to 2.9 million Venezuelans have left the country in the past year.

Recent geopolitical challenges have revived the implementation of industrial and innovation policies. Ongoing discussions focus on supporting cutting-edge industries and strategic technologies but hardly pay attention to their impact on economic growth. In light of this, we discuss the design of innovation policies to address current development challenges while considering the complex nature of productive activities. Our approach conceives economic development and technological progress as a process of accumulation and diversification of knowledge. This process is limited by the tacit nature of knowledge and by countries’ binding constraints to growth. Consequently, effective innovation policies should be place-based and multidimensional, leveraging countries’ existing capabilities and addressing countries’ current problems. This contrasts policies that lead to economic efficiencies, such as copying other countries’ solutions to problems that countries do not currently have.

Technological know-how in a country shapes its growth potential and competitive- ness. Scientific publications, patents, and international trade data offer complementary insights into how ideas from science, technology, and production evolve, combine, and are transformed into capabilities. Analyzing their trajectories enables a more comprehen- sive and multifaceted understanding of the whole innovation process, from generating ideas to internationally commercializing products. We analyze the production patterns in these three domains, documenting the differences between advanced and emerging mar- ket economies. We find that future income, patenting, and publishing growth correlate with the economic complexity indices calculated from these domains. Capabilities em- bedded in the country also shape future diversification opportunities and make the inno- vation process path dependent. Lastly, we also show that diversification opportunities can be inferred across innovation domains.

We analyze the effects of bans on exports at the level of 5000 products and show how our results can inform economic sanctions against Russia after its invasion of Ukraine. We begin with characterizing export restrictions imposed by the EU and the US until mid May 2022. We then propose a theoretically-grounded criterion for targeting export bans at the 6-digit HS level. Our results show that the cost to Russia are highly convex in the market share of the sanctioning parties, i.e., there are large benefits from coordinating export bans among a broad coalition of countries. Applying our results to Russia, we find that sanctions imposed by the EU and the US are not systematically related to our arguments once we condition on Russia’s total imports of a product from participating countries. Quantitative evaluations of the export bans show (i) that they are very effective with the costs to Russia typically being by a factor of ∼100 larger than the costs to the sanctioners. (ii) Improved coordination of the sanctions and targeting sanctions based on our criterion allows to increase the costs to Russia by about 60% with little to no extra cost to the sanctioners. (iii) There is scope for increasing the cost to Russia further by expanding the set of sanctioned products.

Predicting gene functions by integrating large-scale biological data remains a challenge for systems biology. Here we present a resource for Drosophila melanogaster gene function predictions. We trained function-specific classifiers to optimize the influence of different biological datasets for each functional category. Our model predicted GO terms and KEGG pathway memberships for Drosophila melanogaster genes with high accuracy, as affirmed by cross-validation, supporting literature evidence, and large-scale RNAi screens. The resulting resource of prioritized associations between Drosophila genes and their potential functions offers a guide for experimental investigations.

Assignment models in trade predict that countries with higher productivity levels are assortatively matched to industries that make better use of these higher levels. Here, we assume that the driver of productivity differences is the differential distribution of factors among countries. Utilizing such a structure, we define and estimate the average factor level (AFL) for countries and products using only the information about the production patterns. Interestingly, our estimates coincide with the complexity variables of (Hidalgo and Hausmann, 2009), providing an underlying economic rationale. We show that AFL is highly correlated with country-level characteristics and predictive of future economic growth.

Production is shaped by capability requirements of products and availability of these capabilities across locations. We propose a capabilities based production model and an empirical strategy to measure product sophistication and location’s production ability. We apply our framework to international trade data, and employment data in the US, recovering measures of production ability for countries and cities, and sophistication of products and industries. We show that both country and city level measures have a strong correlation with income, and economic growth at different time horizons. Product sophistication is positively correlated with measures like education and training needed in the industry. Our model-based estimations also predict the diversification patterns through the extensive margin.

Geo-political tensions and disruptions to global value chains have led policymakers to reevaluate their approach to globalisation. Many countries are considering regionalisation and friend-shoring – trading primarily with countries sharing similar values – as a way of minimising exposure to weaponisation of trade and securing access to critical inputs. If followed through, this process has the potential to reverse global economic integration of recent decades. This paper estimates the economic costs of friend-shoring using a quantitative model incorporating inter-country inter-industry linkages. The results suggest that friend-shoring may lead to real GDP losses of up to 4.6% of global GDP. Thus, although friend-shoring may provide insurance against extreme disruptions and increase the security of supply of vital inputs, it would come at a significant cost.