دانلود مقاله عوامل تعیین گر تفاوت های ثروت در اتحادیه اروپا: تحقیق حسابداری
چکیده
مقدمه
حسابداری توسعه، مطالعات در رشد و حسابداری توسعه
چارچوب حسابداری توسعه و منابع آماری
کاربردهای عددی برای شناسایی و نسبت دادن توصیفی
شناسایی و تخصیص قدرت توصیفی هر عامل
شناسایی و تخصیص قدرت توصیفی هر عامل
خلاصه نتایج
نتیجه گیری
منابع
Abstract
Introduction
Development Accounting, Literature in Growth and Development Accounting
Development Accounting Framework and Statistical Sources
Numerical Applications for Identifying and Attributing Explanatory
Identifcation and Attribution of the Explanatory Powers of Each Factor
Identifcation and Attribution of the Explanatory Powers of Each Factor
Summary of the Results
Conclusion
References
چکیده
این مقاله یک چارچوب حسابداری توسعه را به منظور تعیین کمیت عوامل تعیین کننده نابرابری در تولید ناخالص داخلی به ازای هر ساعت کار در اتحادیه اروپا در سال 2016 ارائه می دهد. اصالت آن تا آنجا که از یک سو، از یک سو، به لحاظ نظری چارچوب موجود را از 2 عامل به n گسترش می دهد. عوامل توضیحی و از سوی دیگر، به صورت عددی همین چارچوب را در مواردی که n=3 عامل نشان می دهد، نشان می دهد. این تصویر به صورت کلان اقتصادی بین 19 کشور اتحادیه اروپا - که 90 درصد از کل تولید ناخالص داخلی آن را نشان می دهد - و به صورت بخشی بین بخش های بازار، دولتی و مختلط آنها ساخته شده است. داده های کالیبراسیون از آخرین نسخه های EU-KLEMS و PWT می آیند. بررسی نتایج با تجزیه نزدیکی شدید انحرافات استاندارد کلان اقتصادی (74/13 دلار در ساعت) و حوزه های بازار (38/13) و غیر بازاری (34/12) را نشان می دهد. تفاوت‌های بین کشورها اساساً (حدود 90٪ با توجه به هر یک از سه حوزه) با تفاوت در کیفیت کار (و حدود 10٪ با تفاوت در تعمیق سرمایه) توضیح داده می شود. با این حال، این مشخصات در فعالیت‌های املاک و مستغلات (بخش مختلط) که تولید ناخالص داخلی آن در هر ساعت انحراف استاندارد به 570.28 دلار در ساعت می‌رسد به هیچ وجه یکسان نیست و کاملاً با تفاوت در تعمیق سرمایه توضیح داده می‌شود.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
Abstract
This paper presents a development accounting framework in order to quantify the determinants of disparities in GDP per hour worked within the EU in 2016. Its originality is twofold insofar as, on the one hand, it theoretically extends the existing framework from 2 factors up to ? explanatory factors and on the other, it numerically illustrates this same framework in case where ?=3 factors. This illustration is made macro-economically between 19 EU countries—representing 90% of its aggregate GDP—and sectorally between their market, state, and mixed sectors. The calibration data come from the latest EU-KLEMS and PWT versions. Examination of the results by decomposition shows a strong proximity of macroeconomic standard deviations ($ 13.74/h) and the market (13.38) and non-market (12.34) spheres. The differences between countries are fundamentally (around 90% according to each of the three spheres) explained by the disparities in labor quality (and around 10% by the disparities in capital deepening). The profile, however, is not at all the same in real estate activities (mixed sector) whose GDP per hour’ standard deviation reaches $ 570.28/h and is completely explained by the disparities in capital deepening.
Introduction
International development accounting works involve decomposing the variance of a variable of interest. They open up interesting perspectives in the identification and quantification of the sources of divergence in living standards between nations. Their application here in the GDP per hour worked (GDP/h) disparities within the framework of the theory of production is not moreover the only perspective. The application to many other relevant variables, to assess the sources of inequalities between countries, regions, sectors ... as indicators of the World Bank (2019), is also possible. More generally, it is the same for indicators other than GDP (including non-economic issues) with the condition, like here, that their variances can be decomposed.
Conclusion
The subject studied consisted of quantifying the contributions of production factors to disparities in living standards for 19 countries representing 9/10 of the EU wealth in 2016. The processing framework is that of development accounting, precisely by decomposition of the GDP/hour’s variance. The results are methodological and empirical. Methodologically, we note three advances:
We have generalized the development accounting framework which is currently 2-factors in the literature toward a ?-factors one. It also becomes possible to count for all the interactions between factors explaining the disparities in GDP/h. Four numerical illustrations were displayed;
A second contribution was to show how to carry out the statistical breakdown of these economies into three sectoral levels (SM, SNM and Sm) from the most recent statistical sources;
A third relates to the accounting decomposition of these economies—that is to say, the application of the development accounting framework and our strategy of redistribution of covariances—both macro-economically (between the 19 EG) and between their sectoral levels (SM, SNM and Sm).
Finally, it is worth mentioning that this approach would obviously extend to the variance decomposition of growth as by replacing variables in level with the same variables in rate of change.