5 resultados para division of labor

em University of Connecticut - USA


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In a marvelous but somewhat neglected paper, 'The Corporation: Will It Be Managed by Machines?' Herbert Simon articulated from the perspective of 1960 his vision of what we now call the New Economy the machine-aided system of production and management of the late twentieth century. Simon's analysis sprang from what I term the principle of cognitive comparative advantage: one has to understand the quite different cognitive structures of humans and machines (including computers) in order to explain and predict the tasks to which each will be most suited. Perhaps unlike Simon's better-known predictions about progress in artificial intelligence research, the predictions of this 1960 article hold up remarkably well and continue to offer important insights. In what follows I attempt to tell a coherent story about the evolution of machines and the division of labor between humans and machines. Although inspired by Simon's 1960 paper, I weave many other strands into the tapestry, from classical discussions of the division of labor to present-day evolutionary psychology. The basic conclusion is that, with growth in the extent of the market, we should see humans 'crowded into' tasks that call for the kinds of cognition for which humans have been equipped by biological evolution. These human cognitive abilities range from the exercise of judgment in situations of ambiguity and surprise to more mundane abilities in spatio-temporal perception and locomotion. Conversely, we should see machines 'crowded into' tasks with a well-defined structure. This conclusion is not based (merely) on a claim that machines, including computers, are specialized idiots-savants today because of the limits (whether temporary or permanent) of artificial intelligence; rather, it rests on a claim that, for what are broadly 'economic' reasons, it will continue to make economic sense to create machines that are idiots-savants.

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Transaction costs, one often hears, are the economic equivalent of friction in physical systems. Like physicists, economists can sometimes neglect friction in formulating theories; but like engineers, they can never neglect friction in studying how the system actually does let alone should work. Interestingly, however, the present-day economics of organization also ignores friction. That is, almost single-mindedly, the literature analyzes transactions from the point of view of misaligned incentives and (especially) transaction-specific assets. The costs involved are certainly costs of running the economic system in some sense, but they are not obviously frictions. Stories about frictions in trade are not nearly as intriguing as stories about guileful trading partners and expensive assets placed at risk. But I will argue that these seemingly dull categories of cost what Baldwin and Clark (2003) call mundane transaction costs actually have a secret life. They are at least as important as, and quite probably far more important than, the more glamorous costs of asset specificity in explaining the partition between firm and market. These costs also have a secret life in another sense: they have a secret life cycle. I will argue that these mundane transaction costs provide much better material for helping us understanding how the boundaries among firms, markets, and hybrid forms change over time.

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In this survey, we examine the operations of innovation processes within industrial districts by exploring the ways in which differentiation, specialization, and integration affect the generation, diffusion, and use of new knowledge in such districts. We begin with an analysis of the importance of the division of labor and then investigate the effects of social embeddedness on innovation. We also consider the effect of forms of organization within industrial districts at various stages of product and process life, and we examine the negative aspects of embeddedness for innovation. We conclude with a discussion of the possible consequences of new information and communications technologies on innovation in industrial districts.

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Input congestion occurs at a given input bundle when the assumption of free disposability of inputs does not hold and an increase in input leads to a decline in output. In this paper we employ the nonparametric method of Data Envelopment Analysis (DEA) to examine the question on input congestion with respect to labor, using state level data from the Annual Survey of Industries for the period 1986-87 through 1999-2000. When the standard assumption of strong disposability is relaxed for the labor inputs, the nonparametric analysis of state-level data from Indian manufacturing shows considerable measure of labor input congestion. While in selected states congestion comes from non-production workers as well, the principal source of labor congestion is production labor. There is no evidence that the problem of labor congestion has become less severe during the post-Reform years. It appears that market forces without any major institutional changes in enforcement of labor discipline cannot eliminate congestion.

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We use a novel dataset and research design to empirically detect the effect of social interactions among neighbors on labor market outcomes. Specifically, using Census data that characterize residential and employment locations down to the city block, we examine whether individuals residing in the same block are more likely to work together than individuals in nearby but not identical blocks. We find significant evidence of social interactions operating at the block level: residing on the same versus nearby blocks increases the probability of working together by over 33 percent. The results also indicate that this referral effect is stronger when individuals are similar in sociodemographic characteristics (e.g., both have children of similar ages) and when at least one individual is well attached to the labor market. These findings are robust across various specifications intended to address concerns related to sorting and reverse causation. Further, having determined the characteristics of a pair of individuals that lead to an especially strong referral effect, we provide evidence that the increased availability of neighborhood referrals has a significant impact on a wide range of labor market outcomes including employment and wages.