Bridging the gap? Corruption, knowledge and foreign ownership

We argue that in addition to host corruption per se, as accounted for by the existing literature, an explanation of inter-country variation in FDI needs to account for the distance between the host and home corruption, which we call relative corruption. We use a large matched home-host firm-level panel data-set for 1998-2006 from CEE transition countries. Year-specific selectivity corrected estimates suggest that, ceteris paribus, higher relative ‘grand’ corruption lowers foreign ownership as the returns to investment tends to be lower in more corrupt environment. However, after controlling for the selectivity bias, knowledge-intensive parent firms are found to hold controlling ownership, as the difficulty of successful joint venture looms large in more corrupt environment. Results are robust to alternative specifications.

Implications of bank ownership for the credit channel of monetary policy transmission:evidence from India

Using bank-level data from India, we examine the impact of ownership on the reaction of banks to monetary policy, and also test whether the reaction of different types of banks to monetary policy changes is different in easy and tight policy regimes. Our results suggest that there are considerable differences in the reactions of different types of banks to monetary policy initiatives of the central bank, and that the bank lending channel of monetary policy is likely to be much more effective in a tight money period than in an easy money period. We also find differences in impact of monetary policy changes on less risky short-term and more risky medium-term lending. We discuss the policy implications of the findings.

Does lending behaviour of banks in emerging economies vary by ownership? Evidence from the Indian banking sector

While much has been discussed about the relationship between ownership and financial performance of banks in emerging markets, literature about cross-ownership differences in credit market behaviour of banks in emerging economies is sparse. Using a portfolio choice model and bank-level data from India for 9 years (1995–96 to 2003–04), we examine banks’ behaviour in the context of credit markets of an emerging market economy. Our results indicate that, in India, the data for the domestic banks fit well the aforementioned portfolio-choice model, especially for private banks, but the model cannot explain the behaviour of foreign banks. In general, allocation of assets between risk-free government securities and risky credit is affected by past allocation patterns, stock exchange listing (for private banks), risk averseness of banks, regulations regarding treatment of NPA, and ability of banks to recover doubtful credit. It is also evident that banks deal with changing levels of systematic risk by altering the ratio of securitized to non-securitized credit.

How important is ownership in a market with a level playing field? The Indian banking sector revisited

Private ownership of firms is often argued to lead to better firm performance than public ownership. However, the theoretical literature and the empirical evidence indicate that agency problems may affect the performance of privately owned firms. At the same time, competition and hard budget constraints can induce state-owned firms to operate efficiently. In India, banking sector reforms and deregulation were initiated in 1992, encouraging entry and establishing a level playing field for all banks. Data for the financial years 1995–1996 through 2000–2001 suggest that, by 1999–2000, ownership was no longer a significant determinant of performance. Rather, competition induced public-sector banks to eliminate the performance gap that existed between them and both domestic and foreign private-sector banks.

Ownership structure and investment finance in transition economies:a survey of evidence from large firms in Hungary and Poland

Using survey data on 157 large private Hungarian and Polish companies this paper investigates links between ownership structures and CEOs’ expectations with regard to sources of finance for investment. The Bayesian estimation is used to deal with the small sample restrictions, while classical methods provide robustness checks. We found a hump-shaped relationship between ownership concentration and expectations of relying on public equity. The latter is most likely for firms where the largest investor owns between 25 percent and 49 percent of shares, just below the legal control threshold. More profitable firms rely on retained earnings for their investment finance, consistent with the ‘pecking order’ theory of financing. Finally, firms for which the largest shareholder is a domestic institutional investor are more likely to borrow from domestic banks.

Ownership characteristics and access to finance:evidence from a survey of large privatised companies in Hungary and Poland

We examine financial constraints and forms of finance used for investment, by analysing survey data on 157 large privatised companies in Hungary and Poland for the period 1998 - 2000. The Bayesian analysis using Gibbs sampling is carried out to obtain inferences about the sample companies' access to finance from a model for categorical outcome. By applying alternative measures of financial constraints we find that foreign companies, companies that are part of domestic industrial groups and enterprises with concentrated ownership are all less constrained in their access to finance. Moreover, we identify alternative modes of finance since different corporate control and past performance characteristics influence the sample firms' choice of finance source. In particular, while being industry-specific, the access to domestic credit is positively associated with company size and past profitability. Industrial group members tend to favour bond issues as well as sells-offs of assets as appropriate types of finance for their investment programmes. Preferences for raising finance in the form of equity are associated with share concentration in a non-monotonic way, being most prevalent in those companies where the dominant owner holds 25%-49% of shares. Close links with a leading bank not only increase the possibility of bond issues but also appear to facilitate access to non-banking sources of funds, in particular, to finance supplied by industrial partners. Finally, reliance on state finance is less likely for the companies whose profiles resemble the case of unconstrained finance, namely, for companies with foreign partners, companies that are part of domestic industrial groups and companies with a strategic investor. Model implications also include that the use of state funds is less likely for Polish than for Hungarian companies.

Endogenous ownership structure:factors affecting the post-privatisation equity in largest Hungarian firms

Using a data set for the 162 largest Hungarian firms during the period of 1994-1999, this paper explores the determinants of equity shares held by both foreign investors and Hungarian corporations. Evidence is found for a post-privatisation evolution towards more homogeneous equity structures, where dominant categories of Hungarian and foreign owners aim at achieving controlling stakes. In addition, focusing on firm-level characteristics we find that exporting firms attract foreign owners who acquire controlling equity stakes. Similarly, firm-size measurements are positively associated with the presence of foreign investors. However, they are negatively associated with 100% foreign ownership, possibly because the marginal costs of acquiring additional equity are growing with the size of the assets. The results are interpreted within the framework of the existing theory. In particular, following Demsetz and Lehn (1985) and Demsetz and Villalonga (2001) we argue that equity should not be treated as an exogenous variable. As for specific determinants of equity levels, we focus on informational asymmetries and (unobserved) ownership-specific characteristics of foreign investors and Hungarian investors.

The effects of ownership and stock liquidity on the timing of repurchase transactions

We analyze detailed monthly data on U.S. open market stock repurchases (OMRs) that recently became available following stricter disclosure requirements. We find evidence that OMRs are timed to benefit non-selling shareholders. We present evidence that the profits to companies from timing repurchases are significantly related to ownership structure. Institutional ownership reduces companies' opportunities to repurchase stock at bargain prices. At low levels, insider ownership increases timing profits and at high levels it reduces them. Stock liquidity increases profits from timing OMRs.

Does ownership concentration improve M&A outcomes in emerging markets?: Evidence from India

Using firm level data from India, we examine the impact of ownership concentration on post-M&A performance of firms. Our analysis has implications for both the M&A literature, which emphasises the role of agency conflict between managers and owners of widely held companies as a key reason for M&A failures, and the corporate governance literature, especially in the context of emerging market economies. A cautious interpretation of our results suggests that while ownership concentration may reduce the manager–owner agency conflict, it may nevertheless precipitate other forms of agency conflict such that ownership concentration may not necessarily improve post-M&A performance. In particular, our results have implications for the literature on the agency conflict between large (or majority) shareholders and small (or minority) shareholders of a company, especially in contexts such as emerging market economies where corporate governance quality is weak.

Ownership identity, strategy and performance:business group affiliates versus independent firms in India

We consider whether the impact of entrepreneurial orientation on business performance is moderated by the company affiliation with business groups. Within business groups, we explore the trade-off between inter-firm insurance that enables risk-taking, and inefficient resource allocation. Risk-taking in group affiliated firms leads to higher performance, compared to independent firms, but the impact of proactivity is attenuated. Utilizing Indian data, we show that risk-taking may undermine rather than improve business performance, but this effect is not present in business groups. Proactivity enhances performance, but less so in business groups. Firms can also enhance performance by technological knowledge acquisition, but these effects are not significantly different for various ownership categories.

Ownership control of foreign affiliates:a property rights theory perspective

This paper applies property rights theory to explain changes in foreign affiliates’ ownership. Post-entry ownership change is driven by both firm-level characteristics and by the differences in the institutional environments in host countries. We distinguish between financial market development and the level of corruption as two different institutional dimensions, such that changes along these dimensions impact upon ownership change in different ways. Furthermore, we argue that changes in ownership are affected by the foreign affiliate’s relatedness with its parent’s sector, as well as by the affiliate’s maturity. We use firm level data across 125 host countries to test our hypotheses.

Personal learning artefacts: persistence, ownership and privacy

Traditional classrooms have been often regarded as closed spaces within which experimentation, discussion and exploration of ideas occur. Professors have been used to being able to express ideas frankly, and occasionally rashly while discussions are ephemeral and conventional student work is submitted, graded and often shredded. However, digital tools have transformed the nature of privacy. As we move towards the creation of life-long archives of our personal learning, we collect material created in various 'classrooms'. Some of these are public, and open, but others were created within 'circles of trust' with expectations of privacy and anonymity by learners. Taking the Creative Commons license as a starting point, this paper looks at what rights and expectations of privacy exist in learning environments? What methods might we use to define a 'privacy license' for learning? How should the privacy rights of learners be balanced with the need to encourage open learning and with the creation of eportfolios as evidence of learning? How might we define different learning spaces and the privacy rights associated with them? Which class activities are 'private' and closed to the class, which are open and what lies between? A limited set of set of metrics or zones is proposed, along the axes of private-public, anonymous-attributable and non-commercial-commercial to define learning spaces and the digital footprints created within them. The application of these not only to the artefacts which reflect learning, but to the learning spaces, and indeed to digital media more broadly are explored. The possibility that these might inform not only teaching practice but also grading rubrics in disciplines where public engagement is required will also be explored, along with the need for consideration by educational institutions of the data rights of students.

BUILDING EFFICIENT AND COST-EFFECTIVE CLOUD-BASED BIG DATA MANAGEMENT SYSTEMS

In today’s big data world, data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the Cloud due the several advantages provided by the Cloud such as scalability, elasticity, availability, low cost of ownership and the overall economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go Cloud environment, the cost of analytics can grow linearly with the time and resources required. Reducing the cost of data analytics in the Cloud thus remains a primary challenge. In my dissertation research, I have focused on building efficient and cost-effective cloud-based data management systems for different application domains that are predominant in cloud computing environments. In the first part of my dissertation, I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the Cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions that incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime. In the second part of my dissertation, I focus on sampling-based progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides the data scientists with user control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the Cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred during such analytics. Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph-structured data in the Cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that are of interest to an analysis task and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at the level of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over largescale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of graph data analytics in the Cloud. The results of our extensive experimental evaluation of these prototypes with several real-world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the Cloud.

The Influence of Age and Size on Family-Owned Firms’ Financing Decisions: Empirical Evidence Using Panel Data

Based on four samples of Portuguese family-owned firmsdi) 185 young, low-sized family-owned firms; ii) 167 young, high-sized familyowned firms; iii) 301 old, low-sized family-owned firms; and iv) 353 old, high-sized family-owned firms d we show that age and size are fundamental characteristics in family-owned firms’ financing decisions. The multiple empirical evidence obtained allows us to conclude that the financing decisions of young, low-sized family-owned firms are quite close to the assumptions of Pecking Order Theory, whereas those of old, high-sized family-owned firms are quite close to what is forecast by Trade-Off Theory. The lesser information asymmetry associated with greater age, the lesser likelihood of bankruptcy associated with greater size, as well as the lesser concentration of ownership and management consequence of greater age and size, may be especially important in the financing decisions of family-owned firms. In addition, we find that GDP, interest rate and periods of crisis have a greater effect on the debt of young, low-sized family-owned firms than on that of family-owned firms of the remainder research samples.