This thesis examines how Europe sustains its leadership and competitiveness as a global center for foreign direct investment (FDI) and trade between 1991 and 2023. While EU membership historically functioned as the dominant determinant of inward FDI and trade integration, its relative influence has declined as new structural factors, based on trade dynamics and export-platform strategies, have emerged, together with the growing presence of Asian, especially Chinese, investors establishing production hubs in Central and Eastern Europe to serve the wider EU market. Lower trade costs within Europe have reinforced this shift, leading EU investors to focus on vertical FDI, while non-EU investors to adopt export-platform FDI patterns. Chinese investment has moved from infrastructure-focused projects to strategic-sector FDI, highlighting Europe’s exposure to evolving global industrial and geopolitical dynamics. Chapter 2 examines how traditional determinants of FDI, including EU membership, interact with emerging drivers, such as trade interdependence, export-platform strategies, and Asian influence, to shape investment patterns in Europe. It employs a gravity-based empirical framework augmented with newly developed indicators, comprising the Bilateral Trade Interdependence Index, the Export-Platform Indicator, and Belt and Road Initiative (BRI) participation, together with a functional integration approach, covering over 95% of European countries and their global partners from 2010 to 2023. The findings indicate that trade dependency with non-EU partners grew most rapidly, increasing by 55% between 2011 and 2023. Stronger bilateral trade interdependence is found to significantly predict higher FDI inflows. The BRI analysis and functional classification indicate a shift from infrastructure-focused Chinese investment to strategic sectors, including electric vehicles and semiconductors. Since 2018, export-platform strategies have expanded from Europe’s core economies into Central and Eastern Europe, forming emerging production hubs, and have subsequently moved toward the Western Balkans and Turkey, likely reflecting evolving EU regulations and broader supply-chain realignments. Chapter 3 expands the FDI analysis to cover a longer timeframe, from 1991 to 2017, focusing on the period when EU membership exerted a strong influence on FDI in Europe, transforming member countries from primarily cost-attractive destinations into global investment centers. Using an augmented gravity model covering 39 host and origin countries, the analysis finds that EU membership increased FDI inflows by 23%, with investments from core EU members expanding into new EU member states, while FDI from non-EU countries decreased. At the same time, EU membership may also be driven by trade, and EEA participation reflects non-FDI motivations. The chapter also highlights that EU accession strengthens both market-seeking (horizontal) and efficiency-seeking (vertical) FDI motives and applies methods to address negative and zero FDI values issues, ensuring robust estimation. The inclusion of lagged and lead variables shows that the EU integration process is phased over time, affecting FDI inflows with lags of up to 10–15 years after accession. Chapter 4 expands the range of FDI determinants by deriving trade cost indices as a proxy for connectivity and extending the geographic scope of the analysis. In addition to EU members, the sample includes the Western Balkans, Turkey, and new EU candidates and applicants (Moldova, Ukraine, and Georgia) over the period 2000 to 2020, covering approximately 80% of European FDI flows. Trade costs are calculated for each country in the sample with its trade partners, not only within and between European subregions but also with non-EU partners such as China, and are combined with measures of FDI restrictiveness. The results show that China remains among the EU’s top three trading partners in goods and that trade costs significantly influence FDI inflows in Europe. The analysis also highlights that declining trade costs between European countries have reduced market-seeking (horizontal) FDI, while non-European investors, especially China, increasingly pursue export-platform FDI to serve third-country markets. A sharp reduction in trade costs between the Western Balkans and the EU (-45%) and a smaller decline with China (-35%) illustrates how regional integration reduces the need for local horizontal FDI while reinforcing Europe’s role as a hub for global production. Chapter 5 shows that despite concerns about increasing outside influence, developed European countries remain the dominant source of FDI in the region. The chapter focuses on China’s role, examining FDI patterns across advanced EU members, new member states, and Western Balkan economies between 2000 and 2019, while distinguishing the effects of EU integration and BRI participation on FDI. Chinese influence has expanded primarily through the Belt and Road Initiative, particularly in accession and neighboring countries. Although BRI participation does not significantly increase FDI on its own, reflecting the dominant part of loan-financed infrastructure rather than private investment, it has strengthened physical and digital connectivity, laying the groundwork for future, longer-term FDI. The analysis also shows that intra-EU trade costs declined significantly after the 2004 and 2007 enlargements, while trade costs between the Western Balkans and China have fallen steadily since the launch of the BRI in 2013. As a result, Chinese influence is more pronounced in new EU member states and Western Balkan economies than in Western Europe. Over time, enhanced connectivity and supply-chain integration may support more diversified FDI inflows.

This thesis presents four contributions in the domains of schema/ontology alignment and query processing. First, we present a novel alignment approach, denoted as FiLiPo (Finding Linkage Points), to align the schema of RDF knowledge bases with the response schema of RESTful Web APIs. FiLiPo only requires knowledge about a knowledge base (e.g., class names) but no prior knowledge about the Web APIs’ data structure. It uses fifteen different string similarity metrics to find an alignment between the schema of a knowledge base and that of aWeb API. Next, a benchmark system named ETARA (Evaluation Toolkit for API and RDF Alignment) is introduced that was created with the goal to simulate RESTful Web APIs and is able to cover all important characteristics of Web APIs, i.e., latency, timeouts, rate limits and, furthermore, provides configurable response structures (e.g., JSON or XML). Additionally, it was designed to support researchers during the development of alignment systems. Afterward, the alignments determined by FiLiPo are used to create a hybrid and federated query processor named TunA (Tunable Query Optimizer forWeb APIs and User Preferences), which allows SPARQL queries combining knowledge bases and RESTful Web APIs and is tunable towards user preferences, i.e., coverage, reliability and execution time. The primary goal of TunA is to return a query result that satisfies the user’s preferences in terms of data quality, even when using unreliable data sources by performing a majority vote over multiple sources. Lastly, we present a federated query processor, denoted as ORAQL (Overlap and Reliability Aware Query Processing Layer), which uses overlap information to reduce the number of selected sources that are available in a federation. The goal is to reduce redundant data and, hence, improve the query execution speed. Therefore, ORAQL uses a profile feature that provides information about the overlap between all data sources of a federation. Furthermore, we extend the quality estimation of TunA to cover Triple Pattern Fragment interfaces to ensure a user-provided reliability goal.

Price indices play a vital role in economic measurement as they reflect price levels and measure price fluctuations. Price level measures are used with macroeconomic indicators to express them in real terms. These measures are also used to index wages, rents, and pensions. Furthermore, they are used as a reference for monetary policy conducted by central banks. Therefore, the provision of accurate price indices is one of the most important goals of National Statistical Institutes (NSIs), and numerous studies have been devoted to this goal. This cumulative dissertation also contributes to this goal. It contains four chapters, each of which represents a separate research. The first two studies are devoted to the treatment of seasonal products by using different price index methods. The first research is co-authored with Ken van Loon. The third research is dedicated to finding the most accurate method to make price predictions for missing products. The fourth research is focused on the treatment of products by using different price index methods when products’ quality characteristics are available.

Bilevel problems are optimization problems for which parts of the variables are constrained to be an optimal solution to another nested optimization problem. This structure renders bilevel problems particularly well-suited for modeling hierarchical decision-making processes. They are widely applicable in areas such as energy markets, transportation systems, security planning, and pricing. However, the hierarchical nature of these problems also makes them inherently challenging to solve, both in theory and in practice. In this thesis, we study different nonlinear problem settings for the nested optimization problem. First, we focus on nonlinear but convex bilevel problems with purely integer variables. We propose a solution algorithm that uses a branch-and-cut framework with tailored cutting planes. We prove correctness and finite termination of the method under suitable assumptions and put it into context of existing literature. Moreover, we provide an extensive numerical study to showcase the applicability of our method and we compare it to the state-of-the-art approach for a less general setting on suitable instances from the literature. Furthermore, we discuss challenges that arise when we try to generalize our approach to the mixed-integer setting. Next, we study mixed-integer bilevel problems for which the nested problem has a nonconvex and quadratic objective function, linear constraints, and continuous variables. We state and prove a complexity-theoretical hardness result for this problem class and develop a lower and upper bounding scheme to solve these problems. We prove correctness and finite termination of the proposed method under suitable assumptions and test its applicability in a numerical study. Finally, we consider bilevel problems with continuous variables, where the nested problem has a convex-quadratic objective function and linear constraints. We reformulate them as single-level optimization problems using necessary and sufficient optimality conditions for the nested problem. Then, we explore the family of so-called P-split reformulations for this single-level problem and test their applicability in a preliminary numerical study.

Der ökologische Zustand der Erde befindet sich infolge von Umweltverschmutzung, Abfallaufkommen und CO₂-bedingtem Klimawandel in einem kritischen Zustand. Mit rund 40 % trägt der Bau- und Gebäudesektor erheblich zu den globalen Treibhausgasemissionen bei. Holz gilt als klimafreundliche Alternative zu Beton und Stahl, bedarf jedoch ebenfalls einer nachhaltigen Nutzung. Die Kreislaufwirtschaft bietet mit der Wiederverwendung ein zukunftsweisendes Konzept: So sind etwa 45% des beim Rückbau von Gebäuden anfallenden Holzes potenziell als Rohstoff nutzbar. Dadurch werden alternative Rohstoffquellen erschlossen und das Abfallaufkommen reduziert. Trotz dieses Potenzials liegt der Zirkularitätsgrad der Weltwirtschaft derzeit nur bei 7,2 %. Vor diesem Hintergrund untersucht die Dissertation, welche Wettbewerbsstrategien und welche organisationalen Fähigkeiten die Entwicklung zirkulärer Geschäftsmodelle fördern. Der Fokus liegt auf der Holzindustrie der DACH-Region, die historisch durch forstwirtschaftliche Nachhaltigkeit geprägt ist, jedoch bislang überwiegend linearen Strukturen folgt. Die Arbeit kombiniert theoretische Fundierung, eine vierjährige Literaturrecherche, Experteninterviews sowie im Zentrum eine quantitative Unternehmensbefragung (n = 200). Daraus wurde eine aktivitätsorientierte Skala zur Bewertung der Zirkularität eines Geschäftsmodells entwickelt. Analysiert wurden drei Perspektiven: Fähigkeiten, Strategien und Stakeholder. Im Kontext der Fähigkeitsperspektive wurde ermittelt, dass die dynamischen Fähigkeiten positive Implikationen auf die Umsetzung von Zirkularität haben. Im Forschungsfeld der Strategieperspektive wurde deutlich, dass die Innovationsführerschaft positive Effekte auf die Umsetzung der Kreislaufwirtschaft besitzt. Zudem weisen sowohl die Innovationsführerschaft als auch die Qualitätsführerschaft einen positiven indirekten Effekt über die dynamischen Fähigkeiten auf die Entwicklung zirkulärer Geschäftsmodelle auf. Im Rahmen der Stakeholderperspektive wurde eruiert, dass der Stakeholder-Druck im Zusammenwirken mit einem grünen Unternehmensimage eine Katalysator-Wirkung besitzt. Der Einfluss der Interessengruppen führt dazu, dass die Unternehmen ein grünes Image in eine substanzielle Umsetzungsphase überführen. Darüber hinaus wurde ersichtlich, dass der Stakeholder-Druck als zentraler Veränderungsfaktor wirkt. Während die direkten Auswirkungen der dynamischen Fähigkeiten durch den Druck zurückgehen, nehmen die indirekten Effekte auf die Erreichung von Zirkularität zu. Abschließend werden Handlungsempfehlungen für Unternehmen sowie wissenschaftliche Implikationen und zukünftige Forschungsmöglichkeiten abgeleitet.

The gender wage gap in labor market outcomes has been intensively investigated for decades, yet it remains a relevant and innovative research topic in labor economics. Chapter 2 of this dissertation explores the pressing issue of gender wage disparity in Ethiopia. By applying various empirical methodologies and measures of occupational segregation, this chapter aims to analyze the role of female occupational segregation in explaining the gender wage gap across the pay distribution. The findings reveal a significant difference in monthly wages, with women consistently earning lower wages across the wage distribution. Importantly, the result indicates a negative association between female occupational segregation and the average earnings of both men and women. Furthermore, the estimation result shows that female occupational segregation partially explains the gender wage gap at the bottom of the wage distribution. I find that the magnitude of the gender wage gap in the private sector is higher than in the public sector. In Chapter 3, the Ethiopian Demography and Health Survey data are leveraged to explore the causal relationship between female labor force participation and domestic violence. Domestic violence against women is a pervasive public health concern, particularly in Africa, including Ethiopia, where a significant proportion of women endure various forms of domestic violence perpetrated by intimate partners. Economic empowerment of women through increased participation in the labor market can be one of the mechanisms for mitigating the risk of domestic violence. This study seeks to provide empirical evidence supporting this hypothesis. Using the employment rate of women at the community level as an instrumental variable, the finding suggests that employment significantly reduces the risk of domestic violence against women. More precisely, the result shows that women’s employment status significantly reduces domestic violence by about 15 percentage points. This finding is robust for different dimensions of domestic violence, such as physical, sexual, and emotional violence. By examining the employment outcomes of immigrants in the labor market, Chapter 4 extends the dissertation's inquiry to the dynamics of immigrant economic integration into the destination country. Drawing on data from the German Socio-Economic Panel, the chapter scrutinizes the employment gap between native-born individuals and two distinct groups of first-generation immigrants: refugees and other migrants. Through rigorous analysis, Chapter 4 aims to identify the factors contributing to disparities in employment outcomes among these groups. In this chapter, I aim to disentangle the heterogeneity characteristic of refugees and other immigrants in the labor market, thereby contributing to a deeper understanding of immigrant labor market integration in Germany. The results show that refugees and other migrants are less likely to find employment than comparable natives. The refugee-native employment gap is much wider than other migrant-native employment gap. Moreover, the findings vary by gender and migration categories. While other migrant men do not differ from native men in the probability of being employed, refugee women are the most disadvantaged group compared to other migrant women and native women in the probability of being employed. The study suggests that German language proficiency and permanent resident permits partially explain the lower employment probability of refugees in the German labor market. Chapter 5 (co-authored with Uwe Jirjahn) utilizes the same dataset to explore the immigrant-native trade union membership gap, focusing on the role of integration in the workplace and into society. The integration of immigrants into society and the workplace is vital not only to improve migrant's performance in the labor market but also to actively participate in institutions such as trade unions. In this study, we argue that the incomplete integration of immigrants into the workplace and society implies that immigrants are less likely to be union members than natives. Our findings show that first-generation immigrants are less likely to be trade union members than natives. Notably, the analysis shows that the immigrant-native gap in union membership depends on immigrants’ integration into the workplace and society. The gap is smaller for immigrants working in firms with a works council and having social contacts with Germans. Moreover, the results reveal that the immigrant-native union membership gap is decreasing in the year since arrival in Germany.

This thesis deals with consumption-investment allocation problems with Epstein-Zin recursive utility, building upon the dualization procedure introduced by [Matoussi and Xing, 2018]. While their work exclusively focuses on truly recursive utility, we extend their procedure to include time-additive utility using results from general convex analysis. The dual problem is expressed in terms of a backward stochastic differential equation (BSDE), for which existence and uniqueness results are established. In this regard, we close a gap left open in previous works, by extending results restricted to specific subsets of parameters to cover all parameter constellations within our duality setting. Using duality theory, we analyze the utility loss of an investor with recursive preferences, that is, her difference in utility between acting suboptimally in a given market, compared to her best possible (optimal) consumption-investment behaviour. In particular, we derive universal power utility bounds, presenting a novel and tractable approximation of the investors’ optimal utility and her welfare loss associated to specific investment-consumption choices. To address quantitative shortcomings of those power utility bounds, we additionally introduce one-sided variational bounds that offer a more effective approximation for recursive utilities. The theoretical value of our power utility bounds is demonstrated through their application in a new existence and uniqueness result for the BSDE characterizing the dual problem. Moreover, we propose two approximation approaches for consumption-investment optimization problems with Epstein-Zin recursive preferences. The first approach directly formalizes the classical concept of least favorable completion, providing an analytic approximation fully characterized by a system of ordinary differential equations. In the special case of power utility, this approach can be interpreted as a variation of the well-known Campbell-Shiller approximation, improving some of its qualitative shortcomings with respect to state dependence of the resulting approximate strategies. The second approach introduces a PDE-iteration scheme, by reinterpreting artificial completion as a dynamic game, where the investor and a dual opponent interact until reaching an equilibrium that corresponds to an approximate solution of the investors optimization problem. Despite the need for additional approximations within each iteration, this scheme is shown to be quantitatively and qualitatively accurate. Moreover, it is capable of approximating high dimensional optimization problems, essentially avoiding the curse of dimensionality and providing analytical results.

In bilevel optimization, some of the variables of an optimization problem have to be an optimal solution to another nested optimization problem. This specific structure renders bilevel optimization a powerful tool for modeling hierarchical decision-making processes, which arise in various real-world applications such as in critical infrastructure defense, transportation, or energy. Due to their nested structure, however, bilevel problems are also inherently hard to solve—both in theory and in practice. Further challenges arise if, e.g., bilevel problems under uncertainty are considered. In this dissertation, we address different types of uncertainties in bilevel optimization using techniques from robust optimization. We study mixed-integer linear bilevel problems with lower-level objective uncertainty, which we tackle using the notion of Gamma-robustness. We present two exact branch-and-cut approaches to solve these Gamma-robust bilevel problems, along with cuts tailored to the important class of monotone interdiction problems. Given the overall hardness of the considered problems, we additionally propose heuristic approaches for mixed-integer, linear, and Gamma-robust bilevel problems. The latter rely on solving a linear number of deterministic bilevel problems so that no problem-specific tailoring is required. We assess the performance of both the exact and the heuristic approaches through extensive computational studies. In addition, we study the problem of determining optimal tolls in a traffic network in which the network users hedge against uncertain travel costs in a robust way. The overall toll-setting problem can be seen as a single-leader multi-follower problem with multiple robustified followers. We model this setting as a mathematical problem with equilibrium constraints, for which we present a mixed-integer, nonlinear, and nonconvex reformulation that can be tackled using state-of-the-art general-purpose solvers. We further illustrate the impact of considering robustified followers on the toll-setting policies through a case study. Finally, we highlight that the sources of uncertainty in bilevel optimization are much richer compared to single-level optimization. To this end, we study two aspects related to so-called decision uncertainty. First, we propose a strictly robust approach in which the follower hedges against erroneous observations of the leader's decision. Second, we consider an exemplary bilevel problem with a continuous but nonconvex lower level in which algorithmic necessities prevent the follower from making a globally optimal decision in an exact sense. The example illustrates that even very small deviations in the follower's decision may lead to arbitrarily large discrepancies between exact and computationally obtained bilevel solutions.

In machine learning, classification is the task of predicting a label for each point within a data set. When the class of each point in the labeled subset is already known, this information is used to recognize patterns and make predictions about the points in the remainder of the set, referred to as the unlabeled set. This scenario falls in the field of supervised learning. However, the number of labeled points can be restricted, because, e.g., it is expensive to obtain this information. Besides, this subset may be biased, such as in the case of self-selection in a survey. Consequently, the classification performance for unlabeled points may be limited. To improve the reliability of the results, semi-supervised learning tackles the setting of labeled and unlabeled data. Moreover, in many cases, additional information about the size of each class can be available from undisclosed sources. This cumulative thesis presents different studies to combine this external cardinality constraint information within three important algorithms for binary classification in the supervised context: support vector machines (SVM), classification trees, and random forests. From a mathematical point of view, we focus on mixed-integer programming (MIP) models for semi-supervised approaches that consider a cardinality constraint for each class for each algorithm. Furthermore, since the proposed MIP models are computationally challenging, we also present techniques that simplify the process of solving these problems. In the SVM setting, we introduce a re-clustering method and further computational techniques to reduce the computational cost. In the context of classification trees, we provide correct values for certain bounds that play a crucial role for the solver performance. For the random forest model, we develop preprocessing techniques and an intuitive branching rule to reduce the solution time. For all three methods, our numerical results show that our approaches have better statistical performances for biased samples than the standard approach.

This thesis consists of four highly related chapters examining China’s rise in the aluminium industry. The first chapter addresses the conditions that allowed China, which first entered the market in the 1950s, to rise to world leadership in aluminium production. Although China was a latecomer, its re-entry into the market after the oil crises in the 1970s was a success and led to its ascent as the world’s largest aluminium producer by 2001. With an estimated production of 40.4 million tonnes in 2022, China represented almost 60% of the global output. Chapter 1 examines the factors underlying this success, such as the decline of international aluminium cartels, the introduction of innovative technology, the US granting China the MFN tariff status, Chinese-specific factors, and supportive government policies. Chapter 2 develops a mathematical model to analyze firms’ decisions in the short term. It examines how an incumbent with outdated technology and a new entrant with access to a new type of technology make strategic decisions, including the incumbent’s decision whether to deter entry, the production choice of firms, the optimal technology adoption rate of the newcomer, and cartel formation. Chapter 3 focuses on the adoption of new technology by firms upon market entry in four scenarios: firstly, a free market Cournot competition; secondly, a situation in which the government determines technology adoption rates; thirdly, a scenario in which the government controls both technology and production; and finally, a scenario where the government dictates technology adoption rates, production levels, and also the number of market participants. Chapter 4 applies the Spencer and Brander (1983) framework to examine strategic industrial policy. The model assumes that there are two exporting firms in two different countries that sell a product to a third country. We examine how the domestic firm is influenced by government intervention, such as the provision of a fixed-cost subsidy to improve its competitiveness relative to the foreign company. Chapter 4 initially investigates a scenario where only one government offers a fixed-cost subsidy, followed by an analysis of the case when both governments simultaneously provide financial help. Taken together, these chapters provide a comprehensive analysis of the strategic, technological, and political factors contributing to China’s leadership in the global aluminium industry. Chapter 1: The Rise of China as a Latecomer in the Global Aluminium Industry This chapter examines China’s remarkable transformation into a global leader in the aluminium industry, a sector in which the country accounted for approximately 58.9% of worldwide production in 2022. We examine how China, a latecomer to the aluminium industry that started off with labor-intensive technology in 1953, grew into the largest aluminium producer with some of the most advanced smelters in the world. This analysis identifies and discusses several opportunities that Chinese aluminium producers took advantage of. The first set of opportunities happened during the 1970s oil crises, which softened international competition and allowed China to acquire innovative smelting technology from Japan. The second set of opportunities started at about the same time when China opened its economy in 1978. The substantial demand for aluminium in China is influenced by both external and internal factors. Externally, the US granted China’s MFN tariff status in 1980 and China entered the World Trade Organization (WTO) in 2001. Both events contributed to a surge in Chinese aluminium consumption. Internally, China’s investment-led growth model boosted further its aluminium demand. Additional factors specific to China, such as low labor costs and the abundance of coal as an energy source, offer Chinese firms competitive advantages against international players. Furthermore, another window of opportunity is due to Chinese governmental policies, including phasing out old technology, providing subsidies, and gradually opening the economy to enhance domestic competition before expanding globally. By describing these elements, the study provides insights into the dynamic interplay of external circumstances and internal strategies that contributed to the success of the Chinese aluminium industry. Chapter 2: Technological Change and Strategic Choices for Incumbent and New Entrant This chapter introduces an oligopoly model that includes two actors: an incumbent and a potential entrant, that compete in the same market. We assume that two participants are located in different parts of the market: the incumbent is situated in area 1, whereas the potential entrant may venture into the other region, area 2. The incumbent exists in stage zero, where it can decide whether to deter the newcomer’s entry. A new type of technology exists in period one, when the newcomer may enter the market. In the short term, the incumbent is trapped with the outdated technology, while the new entrant may choose to partially or completely adopt the latest technology. Our results suggest the following: Firstly, the incumbent only tries to deter the new entrant if a condition for entry cost is met. Secondly, the new entrant is only interested in forming a cartel with the incumbent if a function of the ratio of the variable to new technology’s fixed-cost parameters is sufficiently high. Thirdly, if the newcomer asks to form a cartel, the incumbent will always accept this request. Finally, we can obtain the optimal new technology adoption rate for the newcomer. Chapter 3: Technological Adoption and Welfare in Cournot Oligopoly This study examines the difference between the optimal technology adoption rates chosen by firms in a homogeneous Cournot oligopoly and that preferred by a benevolent government upon firms’ market entry. To address the question of whether the technology choices of firms and government are similar, we analyze several different scenarios, which differ in the extent of government intervention in the market. Our results suggest a relationship between the number of firms in the market and the impact of government intervention on technology adoption rates. Especially in situations with a low number of firms that are interested in entering the market, greater government influence tends to lead to higher technology adoption rates of firms. Conversely, in scenarios with a higher number of firms and a government that lacks control over the number of market players, the technology adoption rate of firms will be highest when the government plays no role. Chapter 4: International Technological Innovation and Industrial Strategies Supporting domestic firms when they first enter the market may be seen as a favorable policy choice by governments around the world thanks to their ability to enhance the competitive advantage of domestic firms in non-cooperative competition against foreign enterprises (infant industry protection argument). This advantage may allow domestic firms to increase their market share and generate higher profits, thereby improving domestic welfare. This chapter utilizes the Spencer and Brander (1983) framework as a theoretical foundation to elucidate the effects of fixed-cost subsidies on firms’ production levels, technological innovations, and social welfare. The analysis examines two firms in different countries, each producing a homogeneous product that is sold in a third, separate country. We first examine the Cournot-Nash equilibrium in the absence of government intervention, followed by analyzing a scenario where just one government provides a financial subsidy for its domestic firm, and finally, we consider a situation where both governments simultaneously provide financial assistance for their respective firms. Our results suggest that governments aim to maximize social welfare by providing fixed-cost subsidies to their respective firms, finding themselves in a Chicken game scenario. Regarding technology innovation, subsidies lead to an increased technological adoption rate for recipient firms, regardless of whether one or both firms in a market receive support, compared to the situation without subsidies. The technology adoption rate of the recipient firm is higher than of its rival when only the recipient firm benefits from the fixed-cost subsidy. The lowest technology adoption rate of a firm occurs when the firm does not receive a fixed-cost subsidy, but its competitor does. Furthermore, global welfare will benefit the most in case when both exporting countries grant fixed-cost subsidies, and this welfare level is higher when only one country subsidizes than when no subsidies are provided by any country.