امنیت شغلی و گریز از تکنولوژی بالا
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|18030||2006||18 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Review of Economic Dynamics, Volume 9, Issue 2, April 2006, Pages 224–241
Do institutional firing costs slow the diffusion of information and communications technology (ICT)? The paper develops a model in which, as the technology at a given plant drops behind the best practice, it optimally reduces its workforce. As a result, firing costs are particularly detrimental to profits in industries in which the rate of technical change is rapid—such as ICT—and countries with high firing costs specialize in industries in which technical change is sluggish. The paper suggests that industry composition is a new channel through which labor market regulation might impact macroeconomic aggregates.
It is widely known that the prevalence of information and communications technologies (ICT) varies across countries. This phenomenon can be observed not only between “developed” and “developing” economies, but across industrialized countries also. For example, Pilat and Lee (2001) report that, among OECD economies, the number of personal computers (PCs) per 100 inhabitants in 1999 ranged from 65 in the United States down to 10 in Spain and Portugal. This paper argues that a factor behind these differences in ICT diffusion could be differ- ences across countries in employment protection legislation (EPL). Countries vary substantially in terms of EPL and, as discussed below, there is a strong correlation between the presence of these policies and the slow diffusion of ICT.The theory developed in this paper works as follows. Suppose that industries differ among each other in terms of the rate of technical change . In an industry in which technical change is rapid, plants will tend to fall behind the frontier technology faster than in industries in which technical change is slow. As a consequence, the optimal plant size will decline at a pace that is linked to the rate of technical change—indeed, Mitchell (2002) finds that cross-industry US data is consistent with such a link. This implies that a given job will be destroyed sooner in industries in which technical change is rapid: the effects of firing costs imposed by EPL should then be most severe in such industries. Several studies show that the rate of technical change does in fact vary significantly across industries—see Gordon (1990), Bartelsman and Gray (1996), Cummins and Violante (2002), and Wilson (2002). In particular, this rate appears to be most rapid in industries that employ ICT intensively—such as communications, computers and electronics. If the above intuition regarding the relationship between the rate of technical change and EPL is correct, it implies that, ceteris paribus, ICT should be less prevalent in countries in which dismissal costs are high. The paper has two main contributions. First, although an extensive literature addresses the impact of EPL upon labor markets and aggregate income, the potential cross-industry effects of EPL have not been addressed. Second, the broader implication of the results is that the equilib- rium industry composition constitutes a new and potentially important channel through which EPL and possibly other forms of regulation might affect macroeconomic aggregates. Oliner and Sichel (2000) attribute a large part of the resurgence in US economic growth in the late 1990s to the diffusion of ICT, while Colecchia and Schreyer (2002) find that this phenom- enon does not extend to all industrialized countries. This suggests that the observed differences in ICT diffusion may have significant macroeconomic consequences. 1 This paper finds that em- ployment protection policies could be a factor behind these differences. I conclude the introduction with some evidence of a link between EPL and the slow diffusion of ICT. Nicoletti et al. (2000) construct an index of EPL. The main components of the index are mandated severance pay and advance notice requirements, each of which can be shown to act as firing costs under simple assumptions—whence the attention devoted to them in the literature. 2 Results are reported for four indicators of ICT diffusion. Some direct indicators are available, such as the number of personal computers per capita, and the share of ICT in aggregate spending. In addition, one use of ICT capital for which there are no direct substitutes is e-commerce: hence, the paper also uses the log number of internet hosts and the log number of secure servers relative to the population as measures of the prevalence of e-commerce infrastructure. 3 Figure 1 reveals a striking negative relationship between EPL and measures of ICT diffusion. The correlations are all negative and significant, ranging from − 60% for PCs and hosts to about 80% for secure servers and ICT spending. 4 Spain, Italy and Portugal have the highest levels of EPL and the lowest diffusion of ICT, whereas the US has the lowest level of EPL and the highest diffusion of ICT by most measures. Section 2 introduces dismissal costs into an industry model in which the optimal scale of production and the rate of technical change are related. Section 3 shows how this relationship can imply cross-country differences in industry composition. Section 4 extends the model to allow for learning over the life cycle, and Section 5 provides an illustration of the quantitative implications of the results. Section 6 concludes
نتیجه گیری انگلیسی
The paper shows that industry composition constitutes a channel through which EPL, and perhaps other policies, might affect aggregates. A fairly standard industry model of technical change is found to be consistent with the existence of a negative relationship between EPL and ICT. As plants fall away from the technological frontier for their industry they optimally begin to contract, and the rate at which this occurs depends on the rate of technical change. Hence, EPL should be most costly in industries in which technical change is rapid—such as ICT. The model predicts that the industry composition of a country with strong job security policies will be skewed away from ICT, and skewed towards industries in which technical change is slower. This prediction is consistent with the empirical relationship between ICT and EPL. The model may be usefully extended in several ways. For example, plants could be allowed to respond to the fact that their technology is determined by vintage by updating their technology periodically. To the extent that updating is a factor of plant dynamics, firms may be able to reduce firing by updating more frequently. This would weaken the results of the paper, and it would be interesting to extend the model by introducing this feature. At the same time, the empirical pattern of plant-level investment suggests that a large proportion of plants do not significantly change their technology over their lifetimes, and that any updating occurs at widely-spaced intervals. 9 Another interesting extension would be to allow firms to operate multiple plants: in this case, they might avoid firing costs by shifting workers between plants as they fail. This extension might require addressing the question of why some firms integrate whereas others do not. 10 Finally, the analysis of the paper takes place in a partial equilibrium environment. The effect of firing costs on the time path of prices and wages could be addressed by incorporating the model into a general equilibrium framework. The results beg several questions. First, might industry composition be a new and important channel through which EPL could affect macroeconomic and labor market performance? Nu- merical experiments suggest that this may be the case, although it naturally hinges upon the ease with which entrepreneurial resources can shift between sectors. Second, might variations in the rate of embodied technical change at the aggregate level interact with EPL to produce changes in employment over time ? Greenwood et al. (1996) find that, since the mid-1970s, the rate of em- bodied technical change appears to have accelerated significantly. If this is the case, the model suggests that differences in employment between countries with different levels of EPL might be exacerbated, providing a novel account of the European Unemployment dilemma documented by Ljungqvist and Sargent (1998), inter alia. Samaniego (2005) addresses this question in a gen- eral equilibrium context. Third, if the rate of technical change is a microeconomic determinant of industry location and comparative advantage, then countries might be destined to different medium- or long-run growth rates based on the effect that their labor market and other institu- tions have upon sectoral composition. The literature on barriers to growth related to for example Parente and Prescott (2000) tends to focus on the cost of importing capital or monopoly as factors behind low growth. This paper suggests that labor market regulation could be another factor