In 2013, the Hungarian government cut the school leaving age from 18 to 16. We study the impact of this unique reform on the country's sizeable Roma minority using census data on the universe of 17-year-olds in 2011 and a 10 percent random sample in 2016. School attendance fell by more than 20 percentage points among Roma youth as opposed to less than 6 points with their non-Roma counterparts. Roma's post-reform drawbacks in school enrolment were predominantly explained by their family background, neighborhood characteristics, and, much less importantly, below-average school performance. Changes in local employment prospects had no remarkable impact on the post-reform ethnic gap. More stringent selection and self-selection by social status and school performance (rather than ethnicity) nevertheless affected the Roma minority disproportionally, with close to 30 percent of their 17-year-old children being out of education, training, and employment three years after the reform.

Research on the effect of in utero shocks on health at birth may be influenced by in utero selection. This study outlines a conceptual framework and shows that the results of the standard empirical approach are biased if (i) the exposure changes the probability of fetal death and (ii) health differences exist between deceased and surviving fetuses. Furthermore, an empirical example is provided to illustrate, the potential importance of fetal selection. Examining the impact of heat on birth weight, I find that accounting for fetal selection substantially increases the heat effect compared to the standard approach. These results suggest that incorporating the distorting effect of fetal losses into the estimations may be critical in some cases to provide more informed guidance for public policy.

This paper provides a summary of the latest advancements in generative artificial intelligence using large language models over the past six months. The impact of this breakthrough remains uncertain, but it is evident that GPT is a General-Purpose Technology (GPT) that will significantly alter various aspects of our economy and society in ways that are yet to be fully comprehended. While it is essential for the government to regulate GPT technology, it is inevitable that the technology will continue to expand and evolve at a rapid pace. There is no doubt that every corner of the new world if it exists at all, will be covered by millions of forms of artificial intelligence. The taming of AIs and successful social and personal cooperation with domesticated AIs could ensure our survival and prosperity in that world. Whether or not AIs are capable and willing to cooperate will populate the new world is neither an individual nor a national matter. But how a country and its people fare in the new world is more so.

This paper analyzes the dynamics of sectoral Real Gross Value Added (RGVA) around sudden stops in foreign capital inflows. We identify sudden stop episodes statistically from changes in gross capital inflows from the financial account, and use an event study methodology to compare RGVA before and after the start of sudden stops. In the baseline specification, we estimate changes in the growth rate of sectoral RGVA during sudden stops and in the few quarters following them. In an additional exercise, we analyze deviations from the sectors' long-run growth path. Our findings indicate that: (i) tradable sectors, especially manufacturing, face larger damages during sudden stops than nontradable sectors, (ii) but they also lead the recovery after recessions that accompany sudden stops on impact, partly due to the fact that they benefit from the depreciation of the domestic currency that occurs during sudden stops, (iii) construction and professional services are the most seriously hurt nontradable sectors during sudden stops, while information and communication, and financial services grow slower even in the aftermath of the events than before their onset. However, this slowdown only constitutes a return to their long-run sectoral growth paths. Overall, our results suggest a prolonged reallocation of economic activity away from service sectors, towards the production of goods. This is consistent with a traditional view of the role of tradable and nontradable sectors in a sudden stop episode.

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1. fejezet - online melléklet
2. fejezet - online melléklet
3. fejezet - online melléklet
4. fejezet - online melléklet

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This paper explores the spatial patterns and underlying determinants of repeated inventor collaboration across European NUTS 3 regions. It is found that only a small fraction of co-inventor linkages across regions are repeated, while community detection reveals that these collaborations are clustered in geographical space more intensively compared with collaboration in general. Additional results from gravity modelling indicate that links in the inter-regional co-patenting network emerge mainly through the triadic collaboration of regions, while geographical proximity becomes the most influential factor for repeating co-inventor ties. In addition to that, the combination of technological similarity and shared third partner regions offer a premium for the likelihood of repeating collaboration, but only when geographical proximity is present as an enabler.

Social networks amplify inequalities by fundamental mechanisms of social tie formation such as homophily and triadic closure. These forces sharpen social segregation, which is reflected in fragmented social network structure. Geographical impediments such as distance and physical or administrative boundaries also reinforce social segregation. Yet, less is known about the joint relationships between social network structure, urban geography, and inequality. In this paper we analyze an online social network and find that the fragmentation of social networks is significantly higher in towns in which residential neighborhoods are divided by physical barriers such as rivers and railroads. Towns in which neighborhoods are relatively distant from the center of town and amenities are spatially concentrated are also more socially segregated. Using a two-stage model, we show that these urban geography features have significant relationships with income inequality via social network fragmentation. In other words, the geographic features of a place can compound economic inequalities via social networks.

We scrutinize the systemic consequences of state intervention triggered by external shocks in the transforming Chinese economy before and after the global crisis. We interpret investment dynamics using a comparative party-state model concept framework. We identify the overinvestment as an outcome of the dynamics of party-state power formed by relations of dependence and interest promotion between party, state and economic decision-makers and of emerging structural motivations inside of this network. Due to the structural and operational characteristics of the party-state network, which are self-similar in time, space and at various aggregation levels, overinvestment and economic overheating can also be detected on the provincial level. This local phenomena is intensified by the specific decentralized pattern of power distribution of the Chinese party-state system. Thus, local intensity of overheating is further increased by major state interventions reacting to external shocks. Overheating is further amplified during economic transformation by market actors adapting to network priorities. Investment swings in both heating and cooling periods hide different forms of behavior in enterprises with different ownership types.

Social connections that reach distant places are advantageous for individuals and firms by providing access to new skills and knowledge. However, systematic evidence on how firms work up global knowledge access is still missing. In this paper, we analyse how global work connections relate to differences in the skill composition of employees within companies. We gather survey data from 10% of workers in a local industry in Sweden and complement this with digital trace data to map co-worker networks and skill composition. This unique combination of data and features allows us to quantify global connections of employees and measure the degree of skill-similarity and skill-relatedness to co-workers. We find that the workers with extensive local networks typically have related skills to others in the region and to their co-workers. Workers with more global ties typically bring in less related skills to the region. These results provide new insights to the composition of skills within knowledge intensive firms by connecting the geography of networks contacts to the diversity of skills accessible through them.

We allocate objects to agents as exemplified primarily by school choice. Welfare judgments of the object-allocating agency are encoded as edge weights in the acceptability graph.  The welfare of an allocation is the sum of its edge weights. We introduce the constrained welfare-maximizing solution, which is the allocation of highest welfare among the Pareto-efficient allocations. We identify conditions under which this solution is easily determined from a computational point of view. For the unrestricted case, we formulate an integer program and find this to be viable in practice as it quickly solves a real-world instance of kindergarten allocation and large-scale simulated instances. Incentives to report preferences truthfully are discussed briefly.

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