Tedds Word

[Twyla Plack]

This program combines the same great calculations from Tedds with all the capabilities of Microsoft Word. You'll learn how to correctly add calculations, copy similar designs and communicate information between modules. For more information, visit our website at -started-tekla-tedds.

Tekla is a software product family that consists of programs for analysis and design, detailing and project communication. Tekla software is produced by Trimble, the publicly listed US-based technology company.[3]

Between 1966 and 2015[citation needed], Tekla Corporation was a software engineering company specialised in model-based software products for building, construction and infrastructure management.[4] The company was listed on the Helsinki Stock Exchange from May 2000 until February 2012.[5]

The name Tekla is a given name, used in the Nordic countries, in Poland and in Georgia. However, in this case it is an abbreviation of the Finnish words Teknillinen laskenta, which means technical computation.[6]

In May 2011, California-based business technology specialist Trimble Navigation announced a public tender offer to acquire Tekla for $450 million.[2] The acquisition was completed in February 2012.[7]

In November 2013, Trimble acquired CSC, a UK-based engineering software company and Tekla's former business partner.[8] This added Fastrack, Orion, and Tedds to their software portfolio. In November 2013, CSC was formally merged with Tekla.[9]

Tekla Structures is 3D building information modeling (BIM) software used in the building and construction industries for steel and concrete detailing, precast and cast in-situ. The software enables users to create and manage 3D structural models in concrete or steel, and guides them through the process from concept to fabrication.[12] The process of shop drawing creation is automated. Along with the creation of CNC-files, files for controlling reinforcement bending machines, controlling precast concrete manufacturing, importing in PLM-systems etc. Tekla Structures is available in different configurations and localised environments to suit different segment- and culture-specific needs.[13]

Tekla BIMsight is a software application for building information model-based construction project collaboration. It can import models from other BIM applications using the Industry Foundation Classes (IFC) format, also DWG and DGN. With Tekla BIMsight, users can perform spatial co-ordination (clash or conflict checking) to avoid design and constructability issues, and communicate with others in their construction project by sharing models and notes.[15]

NOE includes various economic activities which are difficult to measure, because they are shadow, illegal, informal or they are households producing goods or there is a deficiency in the basic data collection system (OECD, 2002).

However, independent experts estimate NOE size higher than statistical services. They make their calculations using indirect methods based on macroeconomic indicators. They often give rather high estimations, i.e. they characterize the top level of NOE, that is the reason for possible criticism towards incomplete estimation of this phenomena by the state statistical services. But still the indirect methods are actively used by many specialists.

The important merit of models developed during last decades in the group of indirect methods (MIMIC and DGE) is the fact that they can estimate NOE size as a % of GDP for the long period of time and for many countries.

The critics of this method (Breusch (2005) point out that the estimation obtained using this model is a digital accident having little to do with the data indicators. So, he made a conclusion that this method lacks objective conceptions about variables used in the model (Breusch, 2005, p. 367-391).

Elgin and Schneider compared panel dataset for 38 OCED countries calculated by Elgin and Oztunali (2012) and Buehn and Schneider (2012) using both the DGE and the MIMIC approaches. Besides the authors analyzed and compared relative impact of casual variables on the size and development of the NOE economies using two datasets.

Their analysis showed that there were similar levels of the NOE in different countries: world average level of the NOE was 20,6% according to the DGE model and 20,2% according to the MIMIC model. Both approaches illustrate a declining trend of NOE size for the period of analysis. This fact confirms the viability of MIMIC model.

But there are some differences with respect to the impact of casual variables of NOE. Particularly, the estimates obtained by using the MIMIC model showed that all seven driving forces of NOE examined for the period from 1999 to 2010have similar effect on the size of NOE (from 13,8% personal income tax to 14,6% unemployment and self-employment).

Such a big difference in the estimation of variables indicates that policy recommendations in both approaches are also different.But they came to conclusion that the estimation of driving forces has effect on the non-observed economy and characterizes a certain aspect of the development of the economy andmarkets. These factors are called the determinants of the NOE. They are very valuable for the characteristics of the informal economy.

If we refer to the Resolution of the 15th Conference of specialist in labor statistics we will find such concepts as an informal sector, informal employment and employment in the informal sector. Determining the borders of the informal sector the experts proceed from the features of operating production units in the economy. So, the informal sector is the number of uncorporated household enterprises, operating in production in the borders of production sphere (ILO, 1983). This approach is famous as a production approach.

Difficulties in using this approach resulted in introducing a new wider concept of informal employment according to which people engaged in formal or informal employment are characterized by the nature of labor relations (ILO, 2003). This approach is called a legalistic one.

Since employment in the informal sector is included in the estimation of the NOE we suppose that the estimation of the effect of those factors on the informal employment will help us to estimate the driving forces of the NOE in Russia as well.

You can find a lot of researches of NOE about Russia along and in comparison with other countries in domestic and foreign publications, where specialists concentrate on estimation of the informal sector in Russia. But there are few works which consider regional differences within Russia.

The questions to be answered by the authors are: what economic factors determine the level of the employment in the informal sector and in NOE as well, and whether these factors work in the same way in all the regions of Russian Federation.

The data of Federal State Statistic Service of Russian Federation (FSSS) between 2009 and 2015 are used (for the period of 7 years). This period was chosen based on comparability of the methodology for collecting the required indicators.

Russia has 85 regions united in 8 districts. But complete data are available only for 77 regions, that gives 539 observations. To use an unbalanced dataset panel of NOE size as % of GDP for all regions is supposed to be incorrect due to the lack of observations.

As a depended variable, we used the index Employed in the informal sector in % to total number of employed population in Russian regions. The data were taken from the results of the Population survey on employment issues. It is a monthly selective survey of households made by RSSS in all the regions of Russia according to ILO methodology. Annual selective number includes approximately 800000 people and accounts for 0,75% of all the population at the age of economic activity (15-72 years old). A various share of selection was used in different regions, it depended on the total number of population and relative variation of the index unemployment level (Russian Federation Federal State Statistic, 2016). Although the survey of labor force was made in every region separately, official RSSS documents present only the number of people employed in the informal sector according to their type of employment in absolute persons and in % of total employed population.

All above things considered, the Russian national definition of persons engaged in the informal sector is consistent with that given in SNA 2008, and the estimation of the size of the informal sector is included in the institutional sector of household as a part of NOE. But it is important to note that the definition given by ILO is wider.

That is why Russian data cannot be fully compared with the data about informal employment given in foreign surveys. However, these data fully comply with requirements of our study. According to RSSS data for 2015 14,8 million people were engaged in the informal sector in Russia and it accounted for 20,5% of the total number of employed people. Thus, the index of employed in the informal sector in % to the total number of employed people shows the size of the informal sector. Dynamics of the size of the informal sector in Russia and in the federal districts is shown in Figure 1.

The main reason for an essential decrease of the informal sector size in 2010 and its increase in 2015 was the fact that during those years FSSS made continuous survey of small and medium-sized enterprises activity. During such surveys, some enterprises stopped administratively registered activity or misreported. So, the number of individual entrepreneurs in Russia decreased to 1,9 million people by the end of 2010 as compared to 2,7 million people at the beginning of the year.

Descriptive statistics of the dependent variable given in Table 1 allows to follow the changes of variation according to years: the largest size of inter-group variation was in 2010, that confirms the above reason named by the authors.

When choosing explanatory variables, we proceeded from the list of driving forces of NOE used by Elgin and Schneider and drivers of informal activities formulated by ILO. Preference was given to economic drivers, but not to social ones.

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