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This study focuses on four Asia Pacific countries ¡V Australia, Japan, Korea, and Taiwan ¡V and analyses how the other three influence one¡¦s domestic manufacturing sectors through international trade and FDI activities. To reveal the truth that different industrial structures and absorption capacity may affect each country's efficiency to assimilate spillovers, the index of capture parameter is built in the dynamic adjustment model. By applying the unbalanced panel data from 1990-2003, it is found that both trade and FDI serve as important channels of international technology diffusion. With all sectors are considered, Korea is the country that benefits the most from international trade and FDI activities. For Australia and Japan, FDI spillovers bring more sectors with positive effects than what trade spillovers do. Taiwan, on the other hand, receives beneficial spillovers from both trade and FDI in two sectors. While the positive spillovers effect occurs in every manufacturing sector, negative relationship between domestic sectors¡¦ productivity and international spillovers can be found in some sectors as well. This phenomenon can be accounted for the lack of technology capture ability and the occurrence of market-stealing effect or asymmetric bargaining power.

Advanced technology plays a more and more important role in economic growth. With increasing international transactions, technology spillover between countries is becoming more important for especially developing countries. The main objective of this essay is to investigate the relationship between labor productivity and technological spillovers measured by Foreign Direct Investments (FDI), import and Research and Development expenditure (R&D). We use data covering 41 developing countries for the time period 2005 to 2008 to assess the extent to which technological spillovers from US influence labor productivity in the selected developing countries. Our results show that the relationship between technological spillovers and labor productivity in developing countries are highly sensitive to model specification and estimation techniques. Simple pooled data estimations revels a clear relation between technological spillover an labor productivity while more complex models such as  dynamic panel data models fails in this task.

<p>Developing countries often hoard the largest number of species, but also experience very high poverty levels. This dissertation reviews the evidence of the performance common conservation interventions. I find that despite the billions of dollars channeled towards conservation efforts annually, there is still very limited evidence whether or not conservation policies work. The evidence has been limited to exceptional countries like Costa Rica and Thailand and outlines like deforestation, without considering ecosystem function and ecosystem services. Furthermore, I find that the conservation impact evaluation literature has currently not highlighted the channels through which conservation policies effect change and how the effectiveness varies with the baseline characteristics of the area. </p><p>This dissertation aims to address some of the gaps in current conservation literature. Focusing on Indonesia between 2000 and 2006, I evaluate the performance of protected areas in terms of stalling deforestation as well as providing a wide range of ecosystem services and benefits (Chapter 2). In Chapter 3 I examine the role of context in which protected areas operate and show significant heterogeneity in their performance. In Chapter 4 I develop a static spatially explicit model of household fuelwood extraction that allows me to predict the location and magnitude of spillovers when a protected area is introduced. I find that depending on the characteristics of the areas, it may be optimal for households to buy fuelwood than collect it.</p> / Dissertation

The focus here is on the log-normal version of the spatial interaction model. In this context, we consider spatial econometric specifications that can be used to accommodate two types of dependence scenarios, one involving endogenous interaction and the other exogenous interaction. These model specifications replace the conventional assumption of independence between origin-destination-flows with formal approaches that allow for two different types of spatial dependence in flow magnitudes. Endogenous interaction reflects situations where there is reaction to feedback regarding flow magnitudes from regions neighboring origin and destination regions. This type of interaction can be modeled using specifications proposed by LeSage and Pace (2008) who use spatial lags of the dependent variable to quantify the magnitude and extent of feedback effects, hence the term endogenous interaction. Exogenous interaction represents a situation where spillover arise from nearby (or perhaps even distant) regions, and these need to be taken into account when modeling observed variation in flows across the network of regions. In contrast to endogenous interaction, these contextual effects do not generate reaction to the spillovers, leading to a model specification that can be interpreted without considering changes in the long-run equilibrium state of the system of flows. We discuss issues pertaining to interpretation of estimates from these two types of model specification, and provide an empirical illustration. (authors' abstract)

This thesis examines the spillover effects of armed conflicts on trade in neighboring countries. The empirical results, obtained by using a rich dataset on trade and conflict for 168 countries during the 1950-2011 period, and thus including the onset of the Arab Spring, show that conflict disrupts the trade of neighboring countries, even though they are not directly involved in any conflict. These spillovers are strongest one year after the onset of the conflict, thus suggesting that the negative effects of regional war on trade are lagged rather than contemporaneous, while they also increase the more violent the conflict is. When conflict in secondary neighbors, defined as countries that are not directly contiguous yet closer than 250km to any country in the trading-pair, is introduced the results are unclear as a majority of the estimates are insignificant and not robust to different model specifications.

In this thesis, we analyze volatility spillovers between crude oil and food commodities. The principal hypothesis assumes crude oil to behave as a production factor of the agricultural food commodities, thence we are looking for appropriate price effects. We mainly employ wavelet coherence and partial wavelet coherence, which provide us with valuable insight into the commodities nexus, without any strict restraints and assumptions levied on our data. Secondly, we build a DCC-GARCH model in order to model the presumed volatility spillovers. We also perform several simple benchmark analyses, in particular we test for Granger causality and we compute the Pearson correlation coefficients. Our data sample, including 10 commodities and 2 indices, covers the latest decade, significantly widening the existing contextual literature. Our results are mostly compliant with related literature, especially regarding the crude oil-fuels bundle and food commodities bundle, respectively. Considering the main research question of volatility spillovers between food commodities and crude oil, our results are indicating reasonably strong relationships with crude oil for soybeans and corn, leaving cotton and wheat rather on the verge of strong relationship and finding cattle to be completely unrelated. Main merits of the thesis...

The first chapter provides an introduction to my investigation of the impact of state-level educational investments in public K-12 education on future labor markets, specifically earnings and employment. In Chapter 2, the current literature supporting this investigation is examined while I offer a hole in the literature that I intend to fill. Then, in Chapter 3 I present a two-period, balanced-budget theoretical model in which I relate educational investments, mobility, and future earnings. This theoretical model is then implemented in Chapter 4 using state-level data and again in Chapter 5 using individual-level data. Chapter 4 examines the impact of state-level educational investments in public education on aggregate state labor markets, specifically earnings and employment. Using data on K-12 educational spending, 8th grade cognitive test scores, and educational demographics of a state’s labor force, I observe the impact these state-level investments have on employment and earnings growth. Taking interstate migration into account, I separate the benefits from educational investment into benefits due to in-state investment and benefits due to out-of-state investment. By doing so I am able to identify whether or not educational investment spillovers exist between states. Results indicate that the earnings benefits associated with public K-12 educational spending spill over into other states, 8th grade NAEP test scores do not spill over into other states, and neither has a significant impact on other states’ employment growth. Chapter 5 examines the impact of educational investments in public education on earnings of individuals. I extend my analysis from Chapter 4 by employing micro-data (on individuals) from the American Community Survey (ACS) instead of using state-level data. Using micro-data allows me to more accurately measure the investments used in the education of an area and to incorporate where education was attained and where it was employed. Using individual-level data also allows me to narrow my focus to younger participants in the labor force, providing a stronger link between lagged educational spending and earnings. Results indicate that K-12 educational spending does spill over in the form of positive earnings benefits, which helps to support the results of Chapter 4.

Thesis advisor: Theodore Papageorgiou / This dissertation addresses questions in the labor market with a focus on firms. In the first chapter, I examine different learning opportunities across firms by distinguishing learning from coworkers and firms. The second chapter studies firm organizational spillovers. In the third chapter, I investigate how exporting affects firms' hiring decisions in the entry-level labor market. Chapter 1: This chapter examines and separately identifies two types of learning at the workplace: learning from coworkers and learning from firms. I consider a structural model of idea flows in a competitive market where workers' compensation consists of learning, amenities, and wages. Workers accumulate human capital by interacting with their coworkers and directly from their firm. Using German employee-employer matched data, I exploit a clustering method to classify firms into learning and amenity groups. Then I allow learning functions to differ across groups and separately estimate firm learning and coworker learning parameters. Amenity value is estimated from switchers by relying on features of the model. I find that both types of learning are significant, consistent with previous studies examining each learning type separately. There is significant heterogeneity across firms of different types: some firms provide workers with more firm learning, while in others, workers' learning mostly comes from their coworkers. The relationship between two non-wage compensation also varies across workers. I explore the implication of the findings for inequality. Chapter 2: In this chapter, Div Bhagia and I study whether the organizational decisions of new entrants in a market are influenced by the hierarchical structure of their incumbent peers. Using matched employer-employee data from Brazil, we classify establishments into one to four-layer entities and examine how a new entrant’s decision to add an organizational layer varies with the average number of layers of other establishments in their industry and location. To address the potential endogeneity of peers’ layers, we construct an instrument based on layers of other establishments in peers’ firms that operate in different markets. We find that new entrants are twice as likely to add a layer within five years if their average peer has one more layer at the time of entry. Our results suggest that organizational structure spillovers can provide a new source of agglomeration advantages. We also find that the influence of peers is stronger in more similar industries. Additionally, we show that new entrants with high-layer peers hire more workers from within the market in the newly created layers, indicating personnel exchanges as a mechanism for organizational spillovers. Chapter 3: I investigate the impact of exporting on hiring decisions in the entry-level labor market. Firms face higher opportunity costs of foregone output when they hire inexperienced workers, who require more training than experienced workers. Using Korean establishment-level data, where I distinctively observe experienced and inexperienced new hires, I show that exporting firms hire fewer inexperienced workers but more experienced workers than non-exporting firms. Moreover, foreign market opportunities further induce exporters to favor experienced workers. This finding suggests that high export opportunities, which increase the opportunity costs of training, may increase barriers to better jobs in the entry-level labor market for young workers. / Thesis (PhD) — Boston College, 2023. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.

Firms’ research and development (R&D) efforts are known to generate spillover effects on other firms’ outcomes, e.g., innovation and productivity. Policy recommendations that ignore spillover effects may not be optimal from a social perspective whence the importance of accounting for spillover effects. Quantifying R&D spillover effects typically requires a spatial matrix that characterises the structure of interaction between firms. In practice, the spatial matrix is often unknown due to factors that include multiplicity of forms of connectivity and unclear guidance from economic theory. Estimates can be biased if the spatial matrix is misspecified, and they can also be sensitive to the choice of spatial matrix. This dissertation develops robust techniques that estimate the spatial matrix alongside other parameters from data using a two-pronged approach: (1) model elements of the spatial matrix using spatial covariates (e.g., geographic and product market proximity) and a parameter vector of finite length and (2) estimate the spatial matrix as a set of parameters from panel data. Approaches (1) and (2) address two identification challenges - uncertainty over relevant forms of connectivity and high-dimensionality of the design matrix - in single-index models. In this three-chapter dissertation, the first approach is applied in the first and third chapters, while the second approach is applied in the third chapter. Chapter 1 proposes a parsimonious approach to estimating the spatial matrix and parameters from panel data when the spatial matrix is partly or fully unknown. By controlling for several forms of connectivity between firms, the approach is made robust to misspecification of the spatial matrix. Also, the flexibility of the approach allows data to determine the degrees of sparsity and asymmetry of the spatial matrix. The chapter establishes consistency and asymptotic normality of the MLE under conditional independence and conditional strong-mixing assumptions on the outcome variable. The empirical results confirm positive spillover and private effects of R&D on firm innovation. There is evidence of time-variation and asymmetry in the interaction structure between firms. Geographic proximity and product market proximity are confirmed as relevant forms of connectivity between firms. Moreover, connectivity between firms is not limited to often-assumed notions of proximity; it is also linked to past R&D and patenting behaviour of firms. Single-index models suffer non-identification due to rank deficiency when the design matrix is high-dimensional. Chapter 2 proposes an estimator that projects a high-dimensional parameter vector into a reduced consistently estimable one. This estimator generalises the assumption of sparsity which is required for shrinkage methods such as the Lasso, and it applies even if the high-dimensional parameter vector’s support is bounded away from zero. Monte Carlo simulations demonstrate high approximating ability, improved precision, and reduced bias of the estimator. The estimator is used to estimate the network structure between firms in order to quantify the spillover effects of R&D on productivity using panel data. The empirical results show that firms on average generate positive R&D spillovers on firm productivity. The spatial autoregressive (SAR) model has wide applicability in economics and social networks. It is used to estimate, for example, equilibrium and peer effects models. The SAR model, like other spatial econometric models, is not immune to challenges associated with misspecification or uncertainty over the spatial matrix. Chapter 3 applies the approach developed in Chapter 1 to estimate the spatial matrix in the SAR model with autoregressive disturbances in a parsimonious yet flexible way using GMM. The asymptotic properties of the GMM estimator are established, and Monte Carlo simulations show good small sample performance. / Economics