Debt contracts in the presence of performance manipulation

Empirical evidence suggests that firms often manipulate reported numbers to avoid debt covenant violations. We study how a firm’s ability to manipulate reports affects the terms of its debt contracts and the resulting investment and manipulation decisions that the firm implements. Our model generates novel empirical predictions regarding the use and the level of debt covenant, the interest rate, the efficiency of investment decisions, and the likelihood of covenant violations. For example, the model predicts that the optimal debt contract for firms with relatively strong (weak) corporate governance (i.e., cost of manipulation) induces overinvestment (underinvestment). Moreover, for firms with strong (weak) corporate governance, an increase in corporate governance quality leads to tighter (looser) covenant, more (less) frequent covenant violations and lower (higher) interest rate. Our model highlights that the interest rate, which is a common proxy for the cost of debt, neither accounts for the distortion of investment efficiency nor the expected manipulation costs arising under debt financing. We propose a measure of cost of debt capital that accounts for these effects.     

Internal control system,earnings quality,and the dynamics of financial reporting

Using an earnings management model in which managers manipulate information when the firm’s control system fails, I introduce a measure of earnings quality, based on the notion of integral precision, that has solid theoretical foundations. A trade‐off between the frequency and the magnitude of overstatements is shown: overstatements are larger when misreporting is less likely. Overall, the model generates a distribution of earnings announcements similar to its empirical analogue and provides a structural method to identify the likelihood and magnitude of misreporting by exploiting information from the moments of the distribution of reported earnings.     

No news is good news: voluntary disclosure in the face of litigation

We study disclosure dynamics when the firm value evolves stochastically over time. The presence of litigation risk, arising from the failure to disclose unfavorable information, crowds out positive disclosures. Litigation risk mitigates firms' tendency to use inefficient disclosure policies. From a policy perspective, we show that a stricter legal environment may be an efficient way to stimulate information transmission in capital markets, particularly when the nature of information is proprietary. We model the endogeneity of litigation risk in a dynamic setting and shed light on the empirical controversy regarding whether disclosure preempts or triggers litigation.     

Measurement of an individual entrepreneur��s social capital: a multidimensional model

The aim of this paper is twofold. First of all, we participate in the open discussion on the nature of social capital and we show that the one-dimensional approach is not enough to capture the complex nature of social capital. Second, we present, implement and validate a specific three-dimensional measurement tool that can be used to analyze the role of social capital in further entrepreneurship research. The measurement tool is implemented through a Structural Equation Model, which is estimated and validated from a database including information from 282 Argentinean entrepreneurs who answered a questionnaire specifically designed for this research. Our measurement model considers the dimensions proposed by Koka and Prescott (Strategic Management Journal, 23:795–816, 2002), i.e. relational, resources and structural.     

Forecasting aggregates and disaggregates with common features

This paper focuses on the provision of consistent forecasts for an aggregate economic indicator, such as a consumer price index and its components. The procedure developed is a disaggregated approach based on single-equation models for the components, which take into account the stable features that some components share, such as a common trend and common serial correlation. Our procedure starts by classifying a large number of components based on restrictions from common features. The result of this classification is a disaggregation map, which may also be useful in applying dynamic factors, defining intermediate aggregates or formulating models with unobserved components. We use the procedure to forecast inflation in the Euro area, the UK and the US. Our forecasts are significantly more accurate than either a direct forecast of the aggregate or various other indirect forecasts.     

The Credibility of Performance Feedback in Tournaments

This paper studies the effect of performance feedback on tournament outcomes, when a possibly dishonest principal may manipulate the agents' expectations to stimulate their effort. Under plausible circumstances, an increase in the principal's propensity to tell the truth (i.e., integrity) induces a mean preserving spread in the distribution of effort and leads to a decrease in expected profits and welfare. More generally, I identify conditions under which a lower integrity can improve the effectiveness of financial incentives in inducing the agents' effort, thus leading to higher expected profits for the principal.     

Multi-stakeholder platforms for linking small farmers to value chains: evidence from the Andes

Value chains linked to urban markets and agro-industry present new opportunities for adding value and raising rural incomes. Small farmers, who produce small volumes, struggle to enter these markets. A lack of trust among value chain actors increases transaction costs and short-circuits innovation. This paper explores how multi-stakeholder platforms have been used to address these problems in potato-based value chains in Bolivia, Peru and Ecuador. It uses the Institutional Analysis and Development (IAD) framework to understand how platforms work. Differences in characteristics of the value chains, the participating actors and institutional arrangements have led to the emergence of two types of platforms. The first type brings traders, processors, supermarkets and others together with farmer associations and research and development (R&D) organizations to foster the development of new market opportunities through commercial, institutional and technological innovation. The second type is structured around geographically delimited supply areas, meshing farmers and service providers to address market governance issues in assuring volumes, meeting quality and timeliness constraints and empowering farmers. Evidence from these cases indicates that platforms that bring stakeholders together around value chains can result in new products, processes, norms and behaviours that benefit poor farmers, which could not have been achieved otherwise.     

Fiscal Federalism and Legislative Malapportionment: Causal Evidence from Independent but Related Natural Experiments

We exploit three natural experiments in Argentina in order to study the role of legislative malapportionment on the biased federal tax sharing scheme prevalent in the country. We do not find support to attribute it to legislative malapportionment during periods when democratic governments were in place; nor did we find any evidence that the tax sharing distribution pattern became less biased under centralized military governments. We argue that these results are at least partly attributable to two of Argentina's institutional characteristics: first, the predominance of the executive branch over the legislature (main fiscal decisions are the outcome of a bargaining process among executive authorities); and, second, the lack of any significant difference in the pattern of geographic representation in the executive branch under democratic and autocratic governments. Thus, the observed biases in the distribution of tax revenues among the Argentine provinces are not caused by legislative malapportionment, but are probably the result of a more structural equilibrium that transcends the geographic distribution of legislative representation and even the nature of the political regime. Our findings illustrate the importance of informal institutions and de facto mechanisms to study fiscal federalism in developing countries.     

Mergers and Acquisitions in Latin America: Industrial Productivity and Corporate Governance

This article examines the impact of industrial productivity in the country of origin on transnationals M&As, directed from OECD countries toward Latin America in the period 1996–2010. It also analyzes the relationship between external mechanisms of corporate governance and transnational M&As. Employing a gravitational model at the industry level, we find that industry productivity in the country of origin promotes transnational M&A activity, although capital productivity affects it negatively. We also find evidence that higher standards of corporate governance in both origin and destination countries increase the likelihood of transnational M&As taking place.     

A machine learning approach to big data regression analysis of real estate prices for inferential and predictive purposes

The hedonic price regressions have mainly been used for inference. In contrast, machine learning employed on big data has a great potential for prediction. To contribute to the integration of these two strategies, this article proposes a machine learning approach to the regression analysis of big data, viz. real estate prices, for both inferential and predictive purposes. The methodology incorporates a new procedure of selecting variables, called ‘incremental sample with resampling’ (MINREM). The methodology is tested on two cases. The first is data from web advertisements selling used homes in Colombia (61,826 observations). The second considers the data (58,888 observations) from a sample of the Metropolitan American Housing Survey 2011 obtained and prepared by a reference study. The methodology consists of two stages. The first chooses the important variables under MINREM; the second focuses on the traditional training and validation procedure for machine learning, adding three activities. In both test cases, the methodology shows its value for obtaining highly parsimonious and stable models for different sample sizes, as well as taking advantage of the inferential and predictive use of the obtained regression functions. This paper contributes to an original methodology for big data regression analysis.