Our findings do not coincide with Cardon’s (2003) proposal that in small firms the integration of temporary workers may result in benefits, since these skilled workers represent a variable rather than a fixed cost and the financial constraints of small firms prevent them from introducing and taking advantage of high humancapital. This may be because this author bases her argument on the idea that temporary employees are highly qualified, which contrasts starkly with the European and Spanish employment situation, characterized by the fact that the proportion of employees with temporary contracts is highest at the lowest education level (Diaz-Mayans and Sánchez, 2004). Moreover, as Hayton (2003) explains, for smaller firms financial constraints may mean that the implementation of human resource practices related to humancapital may be limited; however, this does not mean that these practices will be less significant to firm success, and vice versa. In other words, a positive correlation between organizational size andhumancapital does not imply that small firms will benefit less from this investment than large firms. Likewise, although large firms are less likely than small firms to use temporary workers (Davis-Blaque and Uzzi, 1993), this should not encourage us to defend their greater use in small firms. The present study shows that temporary employment is more widespread in small than large firms, but notably, only large firms with low levels of humancapital seem to benefit from using this model.
The Spanish section of the European Community Household Panel (ECPH) is a survey where a sample of more than 17,000 Spanish individuals were interviewed about many demographic and economic variables. From this sample, I selected the answers in 1995 to labour-related question of 3,338 not self-employed workers, non civil servant employees that provided information about their employment relationship and employer characteristics. The reasons for excluding from the sample civil servants are quite obvious, given the special characteristics of the employment in the public sector, especially in Spain, where the employer is basically not allowed to dismiss employees. I also excluded self- employed workers, since the interest of this study is to analyze the compensation of firms to their employers as different parties. Finally, it must be noticed that the number of observation vary accross the different regression performed between 2,907 and 3,338, depending on the number of observations with valid values for all variables in each case. The relevant variables used are described in Appendix B. The survey included several questions related to the training and education pursued by the worker. Among them, I chose the only one that asked the respondent employee whether “the employer provides (free or subsidised) education or training” to him/her. This question is asked among other similar references to the provision of several benefits, including health care, housing help, children care and leisure activities, which will be also analysed here. Some other questions in the survey directly asked the worker about the actual training or education pursued in the last year, and whether it was general or specific. These questions, however, fail to include any reference to who paid or provided such training (except in the case of vocational training) and will only be used later to analyse differences in returns to training and education across sectors.
Finally, this research is very related to studies that analyze the effects of dual labor markets on productivity. Damiani and Pompei (2010) look at how employment contacts (permanent and temporary) affect cross-national and sectoral differences in multifac- tor productivity growth in sixteen European countries from 1995 to 2005. The authors show that fixed-term contracts, may discourage investment in skills and have detrimen- tal effects on multifactor productivity increases, and that employment protection reforms which slacken the rules of fixed-term contracts cause potential drawbacks in terms of low productivity gains. Dolado et al. (2012) show that duality institutions have a negative effect on TFP development at the firm level. With a simple model they show that a larger firing cost gap has negative effects on firms’ TFP, by lowering the exerted effort of temporary workers and the training they receive from employers. The authors test this implication by using a longitudinal firm-level dataset. They evaluate the impact of changes in the firing-cost gap on firms’ TFP using as natural experiments several labor market reforms entailing changes in EPL in 1994, 1997 and 2002. They found that firms with larger share of FTC workers before the reforms, show higher conversion rates and higher TFP after the reforms. This chapter looks to the same issue from a different an- gle. First, shows empirically the different content of firm specific humancapital between the two type of contract arrangements. Then, shows how this translates to lower labor productivity using a search and matching model with endogenous firm specific humancapital investment.
Notice that the job creation and destruction rates are just two summary statis- tics of the underlying distribution of establishment-level employment growth rates. A closer examination of this distribution using data from the BED shows that it has become more compressed over time. Specifically, Figure 1.1c depicts the evolution of the share of establishments with no employment change from the previous quarter (i.e. the inaction rate). During the 1990s, the share was around 44 percent and it has increased over time, reaching an average close to 50 percent in mid-2012. The inaction rate provides additional information not contained in the job flow measures analyzed so far, as those establishment with unchanged em- ployment contribute to neither job creation nor job destruction. The counterpart of the increasing number of inactive firms is a decline in the share of firms that ad- just, visible in nearly all categories by size of change (see Figure A.2 in Appendix A.2). Similar results for the employment-weighted distribution are provided by Davis et al. (2012) and Hyatt and Spletzer (2013), using confidential microdata from the BED and LEHD, respectively. 5 Thus, during the last two decades there has been a narrowing distribution of establishment growth, with more employment in establishments with no change.
We should add that members of the so-called ‘creative class’, i.e. individuals endowed with high levels of humancapital are no doubt especially unlikely to shift location in the absence of relevant employment opportunities (which is not the same as saying that they are relatively immobile). These are individuals who have by definition invested considerable resources and time in acquiring know-how, skills and qualifications, and they are presumably unwilling to dissipate their investments in this respect by moving to places where their personal assets are systematically at risk or undervalued in the local job market. Such individuals typically choose to locate on the basis of some sort of structured match between their talents and the forms of economic specialization and labor demand to be found in the places where they eventually settle. By the same token, there can be no reason to suppose that Silicon Valley, Hollywood, or the City of London came into being because massive numbers of the creative class located there in advance of clusters of firms in semiconductors, film-making or finance, respectively. There were certainly a few small innovative firms and their employees in Silicon Valley in the 1950s and 1960s, just as there was a small cluster of temporary studios and associated workers in Los Angeles some time between 1905 and 1915 (Scott, 2005). In neither place was there anything that could be identified as a future labor force of computer engineers or movie-makers seeking, say, tolerance or even interaction with other individuals like them. Additional semiconductor engineers subsequently gravitated to Silicon Valley, would-be actors and directors to Hollywood, and financial analysts to London because that is where their talents could be effectively deployed and rewarded in growing specialized clusters. Moreover, in all these cases, much humancapital has also no doubt been endogenously created via the acquisition of agglomeration-specific experience (learning by doing, career ladders), as well as by education and training programs that themselves evolve in response to the demands of local production systems.
An instrumental variables estimator (IV) was used to avoid the bias of the traditional estimates due to the likely endogeneity of education. Suitable instruments should capture exogenous factors that affect the choice of the individuals’ degree of education but not their current wages. Immediate information on variables of this kind (such as family background and ability) is not readily available from surveys like the one used in this study. So we follow the suggestion made in the recent related literature and use as instruments variables that reflect whether the education of the individual was affected by profound changes in the educational system and by extraordinary historical events such as a war (see for instance Harmon and Walker, 1995; Ichino and Winter-Ebmer, 1999 and 2004; Arrazola et al, 2003). Specifically, a dummy variable was defined to account for the effect of the change in the regulation of the Spanish educational system brought in by the 1970 General Education Act, which established free, compulsory education for children between 6 and 14 years old. The instrument is a dummy variable that takes a value of 1 for individuals aged 6 or under in 1971, that is, members of the sample whose period of schooling was affected by the reform. An instrument related to the Spanish Civil War (1936 to 1939) was also defined to capture its effects on individuals who were of school age during that period; in this case the corresponding dummy variable takes a value of 1 for individuals born in or before 1945. In addition, following the suggestion in Wooldridge (2002), the variables in Z, that is, the ones that affect the probability of employment, were included in the list of potential instruments for education in the wage equation. 12
Targeted support policies are related to academic recognition procedures, targeted work- related training, specific bridging/work placement programmes andemployment mentors/coaches for newcomers. This index goes from 0 to 100, with lower values indicating more unfavourable policy frameworks for immigrants. As shown in Table 1, according to these data, between 2007 and 2010 nearly all new EU member states (EU-13) had unfavourable policies, while the old EU member states (EU-15) formed two clear groups: one made up of Austria, Belgium, Greece, Ireland, Italy, Luxemburg and the United Kingdom with less favourable policies, and the other made up of Germany, Denmark, Spain, Finland, France, Netherlands, Portugal and Sweden where policies were more favourable. Table 2 shows that between 2007 and 2010 the situation was fairly stable. In fact, only Austria changed from less favourable to more favourable policies in that period. The fact that these policies remained unchanged makes it possible to use this classification for analysis.
The database also asked two extra questions to respondents who answered yes to this question to identify people who had in mind being involved in activities of self-employmentand those who were already involved in these activities, which are as follows: “Over the past twelve months, have you done anything to help start a new business, such as looking for equipment or a location, organizing a start-up team, working on a business plan, beginning to save money, or any other activity that would help launch a business?” and “Will you personally own all, part, or none of this business?”. In the study, only respondents who answered “yes” to the first question and to the second question “all” or “part” were considered nascent entrepreneurs. This dependent variable is a binary variable that is classified in the following way: “1 = Yes”, “0 = No”, attempting to identify whether the individual was a nascent entrepreneur when the survey was conducted.
Companies immersed in exporting activities keep seeking production factors that can strengthen the generation of lasting competitive advantages over time. In Ecuador, shrimp exports appear as one of the fastest-growing economic activities, generating employmentand dynamism in related activities (Banco Central del Ecuador, 2015; Cámara Nacional de Acuacultura, 2015). El Oro Province ranks second in shrimp production at a national level, since its exporting companies stay in international markets fulfilling quality-related regulations and requirements. Hence the central concern in this paper: exploring the reality of companies in order to check if the latter are managing intellectual capital by means of organizational routines that make it possible to generate innovation ‒ the key to maintain business competitiveness.
To build an efficient intellectual asset management should develop the ability to extract value from the intellectual point of view of intangible or intellectual assets. As a multi-functional competency, the skills required for success include a wide range of capabilities including technical expertise in IP, financial assessment experience, business strategy and practical knowledge. This involves developing a vision and strategy, implementing better processes, and establishing a growth-focused organization. The implementation of a well-constructed intangible asset management system plays a crucial role in developing an effective and efficient system. Such a system supports processes and decisions necessary to create, manage and use assets to their fullest potential. Without a system that provides systematic electronic asset management, a series of activities for IP management, and tools for strategic analysis of intellectual property data, the development of public competencies is difficult, if not impossible. According to (Raissa ,2015: 10), (Martin, et.al., 2011: 6) identifying the size and measurement of intangible assets helps organizations to:
In spite of what appears as consensus on this hypothesis, the empirical evidence in its support is often only anecdotal. While a few studies have presented some evidence for certain countriesÐsee, for instance, Dasgupta, Mody, and Sinha (1996) on China, India, Indonesia, Malaysia, Philippines, Thailand and Vietnam; Iyanda and Bello (1976) on Nigeria; Kumar (1990) on India; Nataranjan and Miang (1992) on Southeast Asia; Sibunruang and Brimble (1988) on Thailand; Yong (1988) on Malay- siaÐstudies covering a large sample of devel- oping countries are rare and their ®ndings are by and large inconclusive. Three major cross- country studies that consider humancapital as a possible determinant of FDI in developing countries are Root and Ahmed (1979), Schne- ider and Frey (1985) and Narula (1996).
in R&D and duplication externalities— are neglected in the AFS model, which assumes that in- novation depends exclusively and linearly on humancapital devoted to R&D. Additionally, G´omez (2011b) has recently examined the ability of the simplest AFS model to describe the development process and concluded that it can hardly be reconciled with data. First, G´omez (2011b) notes that previously reported simulations with the AFS model made by Funke and Strulik (2000), G´omez (2005) and Iacopetta (2010) feature three main problems, namely, instability of the steady state, too fast convergence, and unrealistic highly oscillatory dynamics which are at odds with data. Thereafter, G´omez (2011b) performs a detailed sensitivity analysis of the (two) stable roots of the fully industrialized economy which shows that numerical simulations with the AFS model could hardly yield realistic transition paths for plausible parameter values.
(1970 − 1972) is 0.49. For the last three years (2003 − 2005) it is on average 0.92. This implies that the predicted increase of the alignment coefficient between the sectoral capital intensity and the average wage during this period is (0.92 − 0.49) ∗ 0.218 = 0.09, conditional on everything else fixed. The observed average increase in the alignment coefficient is some 0.17. This implies that financial development is able to explain a substantial fraction of the average increase in alignment between capital intensity and average wage over the period covered. Still, there is quite some heterogeneity across countries, which is to be expected given the heterogeneity in countries’ characteristics. 42 Structural Change. The regression results for the alignment between the employ- ment shares and sectoral capital intensity are in Table (6). In the first column, I regress the estimated β coefficients on the country and year fixed effects together with the finan- cial development indicator (again, controlling for the country clustered standard errors). I find a negative sign that indicates that higher levels of financial development lead to employment shifts to more labor-intensive sectors. Yet, the coefficient is not significant at the conventional significance levels. In the second column, I include the economic openness indicator and interact it with financial development. Now, the financial devel- opment indicator becomes significant. This is in line with the prediction that in a closed economy the employment shares increase in the labor-intensive sectors with financial development. Furthermore, the interaction term has the expected sign, higher levels of economic openness imply that financial development leads to less structural change to the labor-intensive sectors. The coefficient on financial development remains significant if I add the other candidate explanations for structural change and the control variables. The observed average change in the alignment coefficient between the sectoral capital intensity and the employment shares is −0.35 over the time period. The observed change in financial development predicts an average change of (0.92 − 0.49) ∗ 0.33 = −.14. Thus, the relative explanatory power of financial development for structural change seems to be smaller compared to sorting reversals. 43 Again, note that there is a lot of heterogeneity across countries, which is to be expected given the heterogeneity in countries’ characteristics and our theoretical predictions regarding the effects of economic openness on the coefficient of financial development.
One of the main problems that arises when estimating humancapital ex- ternalities is that a regression of aggregate average wages against an aggregate measure of humancapital is very likely to imply a problem of endogeneity in all possible specifications. Humancapital is not distributed randomly at the regional level. Cities and regions with higher productivity, and therefore with higher average aggregate wages, will attract more skilled workers simply because of higher standards of living or a larger supply of amenities. Firms may also choose to locate in cities and regions where there is a higher average level of humancapital in order to reduce search costs or trying to appropriate externalities generated by a more specialized labor market. Therefore it is reasonable to assume that these two variables are both endogenous. As a consequence, the direction of causality between wages and aggregate humancapital level has to be identified using appropriate instruments. Furthermore the use of different proxies for the average humancapital intensity at the regional level may imply important measurement errors which may exacer- bate the correlation of the explanatory variables and the random terms of our equations. We are going to use as instruments the population structure in terms of the weight of young and old groups prior to 1980. This is prede- termined for the period that we consider, but the change in humancapital may be correlated with the weight of young and old people in the population at the beginning of the sample period.
This paper focuses on student’s decisions and preferences; in particular, preferences for her ranking in test scores distribution and for the total humancapital accumulated by herself, as well as her decisions on how much effort to exert and the level of cooperation between them. Specifically, it analyzes how cooperation and competition can alter students’ decisions and cognitive outcomes. On the one hand, it is important to wonder for the presence of positive technological externalities in the educational production functions. Under the presence of these externalities, cooperation decisions of student i can positively affect the educational outcomes of student j. On the other hand, cooperative environments can diminish group educational outcomes if students perceive individual and group effort levels as substitutes. Under this perception, more cooperative environments produce free-riding effects in which each individual expects other students to help her and, as a consequence, reduces her effort. This would result in a lower overall group effort which would have a negative impact on educational performance. Even though there is little evidence on cooperative or competitive attitudes of a group and their effect on cognitive outcomes, the topic is related to a vast economic literature. For exam- ple, it has been suggested that there are positive externalities in humancapital accumulation. Specifically, Lucas (1988) introduced average humancapital in the society as an argument in the production function. This element captures the existence of positive technological external- ities because it shows how the construction and spreading of ideas constitute social processes in which interactions among individuals are essential. Then, if relations among students also have these positive externalities, cooperative attitudes would increase their accumulated humancapital, and this would translate into higher academic test scores.
from those of job or industry mobility. In fact, Kambourov and Manovskii (2004c) found a very substantial increase in occupational mobility in the US over the 1968-1993 period. This has implications for a number of actively researched issues. It has been documented that, since the late 1960s, there was a considerable increase in (within-group) wage dispersion, a decline in wage stability, and a pronounced flattening of the life-cycle profiles of earnings for the cohorts of workers entering the labor market later in the period. Kambourov and Manovskii (2004a,b) argue that the increase in occupational mobility coupled with occupational specificity of humancapital provides a natural explanation for those facts. Relatedly, a number of researchers, e.g., Bertola and Ichino (1995) and Ljungqvist and Sargent (1998), have described the 1970s and 1980s as a period of increased “economic turbulence.” The term “turbulence” is typically defined as an unobservable increase in the rate of skill depreciation upon a job switch during the two decades. Our results suggest that a potentially more useful definition may be an observable increase in occupational mobility over the period. Finally, anecdotal evidence and surveys of worker perceptions suggest that job stability and job security have declined in the 1980s and 1990s. It turned out to be difficult, however, to find a substantial increase in job (employer) mobility in the United States over the last three decades (see Journal of Labor Economics (1999) special issue). In light of the occupational specificity of humancapital, it may be appropriate to reinterpret workers’ feeling of insecurity as a realization that they are now more likely to switch occupations.
Firm board capital in the context of this study is expected to have moderating effects on weak external corporate governance regimes. Despite the fact that our study sample covers six equity markets in Latin-America with common commercial (i.e., trade agreements such as the Pacific Alliance, Andean Pact, Mercosur) and cultural ties (i.e., language, religion, legal regime) within organizations across the region, there are persisting and important differences regarding country institutional quality of investor protection such as rule of law, regulatory quality and corruption control. Clearly, there are broad national differences across governance regimes in the sample countries. An assessment of the effects of board capital may be shaped by a country's macro conditions such as the quality of its institutions and regulatory control, all of which affects overall levels of investor protection and might vary across governance regimes. For instance, with regard the rule of law (World Bank governance indicators) that reflects perceptions of the extent to which agents have confidence in the quality of contract enforcement, property rights, and courts, Argentina obtained the lowest score for the years 2001-2012 with a mean estimate of -0.69. Chile by contrast obtained the highest estimate score of 1.29, that is 2.8 times higher. These arguments lead us to state the next hypothesis:
Moreover, as stressed by Coleman (1988), there are more parallels that can be established between social capitaland the other types of assets. Like physical andhumancapital, social capital is also productive (IV), making feasible the achievement of goals that would not be possible otherwise. Also, similarly to these assets, it is not completely fungible but it might be specific to a certain category of economic activities: A component of social capital that is very valuable in enhancing a given type of action can be useless or even harmful if applied in a different economic context. As an example, consider the connection in a network of a given population A: If we are referring to the industrial organization of a set of firms, a network structure can be very helpful in stimulating the efficiency of the productive process – for instance by reducing transaction costs, or by facilitating logistics and distribution. Conversely, if we apply the same concept to criminal organizations, the network can provide with more intense illegal activity, with the consequent negative spillovers on the civil society. Thus, to properly define social capital is not enough to consider the mere structure of A (in our example not all networks equally generate social capital) and we should focus on those items that, at least potentially, generate positive influence on output.
Los resultados de la estimación de la función de producción se presentan en los Cuadros 3 y 4 para mínimos cuadrados ordinarios y efectos fijos por firma respectivamente. Se probaron los valores corrientes y rezagados de las variables de transferencia internacional de tecnología. Como mencionamos anteriormente incluimos las variables rezagadas por dos razones, por un lado es más probable que los derrames y los efectos de aprendizaje tomen tiempo en verificarse, y por otra parte usar las variables rezagadas ayuda a mitigar el problema de endogeneidad que puede estar presente. En el caso de las regresiones con efectos fijos no podemos testear el efecto de la propiedad extranjera del capital dado que esta variable tiene baja variación temporal. Por otra parte, tanto el test F como el de Hausman indican que los modelos de efectos fijos serían más apropiados que el de mínimos cuadrados. 14 También chequeamos el modelo de efectos fijos en relación al modelo de efectos aleatorios a través del test de Hausman, encontrando que el modelo de efectos fijos sería el más adecuado.