Acumulação de competências tecnológicas e os processos subjacentes de apredizagem: o caso da Invensis Appliances Controls Unidade Vacaria - RS

Esta dissertação enfoca o relacionamento entre acumulação de competências tecnológicas e os processos subjacentes de aprendizagem. Este relacionamento é examinado na empresa Invensys Appliance Controls Ltda. - Unidade de Vacaria/RS, durante o período de 1981 a 2000. Trata-se de uma empresa metal-mecânica, fornecedora da indústria de linha branca. Em outras palavras, a dissertação examina as implicações dos processos subjacentes de aprendizagem tecnológica para a acumulação de competências para três funções tecnológicas: processo e organização da produção, produtos e equipamentos. Os processos de aprendizagem são examinados à luz de quatro características: variedade, intensidade, funcionamento e interação, a partir do uso de estruturas de análise existente na literatura. Baseado em estudo de caso individual, este estudo encontrou que a maneira e a velocidade de acumulação de competências tecnológicas na empresa estudada estão associadas aos diversos processos usados para adquirir conhecimento tecnológico e convertê-lo em organizacional. Ademais, a simples incidência desses processos na empresa não garantiu na empresa uma implicação positiva para a acumulação de competências tecnológicas. Através do uso de estrutura existente na literatura, porém aplicada a uma indústria diferente de estudos anteriores, esta dissertação sugere que deve existir um esforço organizado, contínuo e integrado para geração e disseminação de conhecimento em toda a empresa a fim de que a acumulação de capacitação tecnológica seja acelerada na empresa.

Do capital controls boost resilence to crises?

Os controles de capitais estão novamente em voga em razão dos países emergentes reintroduzirem essas medidas nos últimos anos face a abundante entrada de capital internacional. As autoridades argumentam que tais medidas protegem as economias no caso de uma “parada abrupta” desses fluxos. Será demonstrado que os controles de capitais parecem fazer com que as economias emergentes (EMEs) fiquem mais resistentes diante de uma crise financeira (por exemplo, uma queda na atividade econômica seguida de uma crise é menor quando o controle é maior). No entanto, os controles de capitais parecem deixar as economias emergentes (EMEs) também mais propícias a uma crise. Deste modo, as autoridades devem ser cautelosas na avaliação quanto aos riscos e benefícios relativos a aplicação das medidas dos controles de capitais.

Do capital controls boost EME´s resilience to financial crises?

Capital controls are again in vogue as a number of emerging markets have reintroduced these measures in recent years in response to a “flood” of international capital. Policymakers use these tools to buttress their economies against the “sudden stop” risk that accompanies international capital flows. Using a panel VAR model, we show that capital controls appear to make emerging market economies (EMEs) more resistant to financial crises by showing that lower post-crisis output loss is correlated with stronger capital controls. However, EMEs that employ capital controls seem to be more crisis-prone. Thus, policymakers should carefully evaluate whether the benefits of capital controls outweigh their costs.

Optimal provision of implicit and explicit incentives in asset management contracts

This paper investigates the importance of the fiow of funds as an implicit incetive provided by investors to portfolio managers in a two-period relationship. We show that the fiow of funds is a powerful incentive in an asset management contract. We build a binomial moral hazard model to explain the main trade-ofIs in the relationship between fiow, fees and performance. The main assumption is that efIort depend" on the combination of implicit and explicit incentives while the probability distrioutioll function of returns depends on efIort. In the case of full commitment, the investor's relevant trade-ofI is to give up expected return in the second period vis-à-vis to induce efIort in the first período The more concerned the investor is with today's payoff. the more willing he will be to give up expected return in the following periods. That is. in the second period, the investor penalizes observed low returns by withdrawing resources from non-performing portfolio managers. Besides, he pays performance fee when the observed excess return is positive. When commitment is not a plausible hypothesis, we consider that the investor also learns some symmetríc and imperfect information about the ability of the manager to generate positive excess returno In this case, observed returns reveal ability as well as efIort choices exerted by the portfolio manager. We show that implicit incentives can explain the fiow-performance relationship and, conversely, endogenous expected return determines incentives provision and define their optimal leveIs. We provide a numerical solution in Matlab that characterize these results.

On the construction of explicit exponential based schemes for Stiff SDEs

Trabalho apresentado Numerical Solution of Differential and Differential-Algebraic Equations (NUMDIFF-14), Halle, 7-11 Sep 2015

Cherry picking with synthetic controls

The synthetic control (SC) method has been recently proposed as an alternative method to estimate treatment e ects in comparative case studies. Abadie et al. [2010] and Abadie et al. [2015] argue that one of the advantages of the SC method is that it imposes a data-driven process to select the comparison units, providing more transparency and less discretionary power to the researcher. However, an important limitation of the SC method is that it does not provide clear guidance on the choice of predictor variables used to estimate the SC weights. We show that such lack of speci c guidances provides signi cant opportunities for the researcher to search for speci cations with statistically signi cant results, undermining one of the main advantages of the method. Considering six alternative speci cations commonly used in SC applications, we calculate in Monte Carlo simulations the probability of nding a statistically signi cant result at 5% in at least one speci cation. We nd that this probability can be as high as 13% (23% for a 10% signi cance test) when there are 12 pre-intervention periods and decay slowly with the number of pre-intervention periods. With 230 pre-intervention periods, this probability is still around 10% (18% for a 10% signi cance test). We show that the speci cation that uses the average pre-treatment outcome values to estimate the weights performed particularly bad in our simulations. However, the speci cation-searching problem remains relevant even when we do not consider this speci cation. We also show that this speci cation-searching problem is relevant in simulations with real datasets looking at placebo interventions in the Current Population Survey (CPS). In order to mitigate this problem, we propose a criterion to select among SC di erent speci cations based on the prediction error of each speci cations in placebo estimations