Descriptive statistics
Variable | Obs | Mean | Std. dev | Min | Max |
---|---|---|---|---|---|
EX | 51 | 91.8209 | 22.3377 | 13.4548 | 121.4929 |
RI | 50 | 4.2799 | 6.5624 | −9.8764 | 24.2052 |
DI | 50 | 5.6208 | 3.5140 | 0.3206 | 13.9935 |
LIR | 50 | 10.4936 | 6.7661 | 0.85265 | 29.6467 |
HCI | 51 | 0.5692 | 0.1179 | 0.32 | 0.844 |
GDPA | 51 | 4.7590 | 2.5176 | −1.5729 | 9.5509 |
GDPC | 51 | 3.5910 | 2.6561 | −2.8471 | 9.0272 |
FDI | 51 | 2.7627 | 2.6960 | −.01364 | 11.8244 |
GFCF | 51 | 25.3819 | 6.4332 | 12.5206 | 43.8613 |
DC | 51 | 0.2386 | 0.2219 | 0.01658 | 0.8806 |
CC | 51 | 0.0947 | 0.1557 | 0.00126 | 0.6838 |
BM | 51 | 77.2724 | 59.8395 | 19.7292 | 244.0197 |
Model 1 | Model 2 | Model 3 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Unemployment | Coefficient | >|z| | [95% conf. Interval] | Coefficient | >|z| | [95% conf. Interval] | Coefficient | >|z| | [95% conf. Interval] | |||
GDPA | −0.5003 | 0.324 | 0.5003 | −1.0213** | −1.0213** | 0.048 | −2.0344 | −0.0082 | ||||
GDPC | 0.5660 | 0.248 | 0.5665 | 1.0338** | 1.0338** | 0.038 | 0.05669 | 2.0109 | ||||
HCI | 0.2622 | 0.951 | 0.2622 | −8.1736** | −8.1736** | 0.036 | −15.8002 | −0.5471 | ||||
FDI | −0.0168 | 0.915 | −0.0168 | −0.09233 | −0.0923 | 0.546 | −0.3918 | 0.20721 | ||||
GFCF | 0.0350 | 0.507 | 0.0352 | 0.01883 | 0.01883 | 0.761 | −0.1026 | 0.14033 | ||||
BA | −5.512*** | 0.001 | −5.5127 | −5.9872*** | 0.000 | −8.3045 | −3.6698 | |||||
IU | 1.6017 | 0.655 | 1.6012 | 6.9136* | 0.10 | −1.4867 | 15.3138 | |||||
DP | −7.3234** | 0.046 | −7.3237 | −7.0696** | 0.019 | −12.9752 | −1.1603 | |||||
MDP | 8.8117** | 0.048 | 8.8117 | 8.8117** | 9.1124 | 9.11294 | 9.11294 | 9.1129 | ||||
MMA | −1.0811 | −1.0811 | −1.0811 | −1.0811 | ||||||||
_cons | 7.3245*** | 0.009 | 7.3245 | 7.3245*** | 10.9697*** | 0.000 | 5.678726 | 16.2607 | 4.6252 | 0.0254 | 7.6584 | 8.6258 |
-squared | 0.5389 | 0.2757 | 0.5191 | |||||||||
( -value) | 30.69 (0.000) | 9.28*** (0.08) | 34.22(0.000) |
Model 1 | Model 2 | Model 3 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
INF | Coefficient | >|z| | [95% conf. Interval] | Coefficient | >|z| | [95% conf. Interval] | Coefficient | >|z| | [95% conf. Interval] | |||
EX | −0.0398* | 0.1 | −0.08882 | −0.0588** | −0.05885** | 0.033 | −0.1129 | −0.0048 | ||||
RI | −0.3148*** | 0.000 | −0.45627 | −0.342343*** | −0.3423*** | 0.000 | −0.4935 | −0.1911 | ||||
DI | −0.2490 | 0.240 | −0.6641 | −0.4598** | −0.4598** | 0.029 | −0.8714 | −0.0482 | ||||
LIR | 0.1375 | 0.211 | −0.0778 | 0.2313314** | 0.2313** | 0.047 | 0.0032 | 0.4593 | ||||
GDPA | −0.9968* | 0.1 | −2.2292 | −1.1891* | −1.1891* | 0.082 | −2.5299 | 0.1515 | ||||
GDPC | 1.0061* | 0.10 | −0.2113 | 1.2035* | 1.2035* | 0.076 | −0.1250 | 2.5321 | ||||
BM | −0.0298*** | 0.001 | −0.0482 | −0.0426*** | −0.0426*** | 0.000 | −0.0607 | −0.0245 | ||||
BA | −8.4047*** | 0.002 | −13.6384 | 15.3331 | 0.465 | −25.7565 | 15.3331 | |||||
MDP | 4.0848* | 0.1 | −1.6068 | 124.2891* | 0.086 | −17.6763 | 266.2545 | |||||
IU | 6.2548* | 0.087 | −0.9001 | 60.2048* | 0.096 | −10.7094 | 131.1192 | |||||
DP | −89.3933* | 0.1 | −215.8394 | 37.0526 | ||||||||
DC | 44.7651** | 0.040 | 1.97581 | 87.5544 | ||||||||
CC | −72.1584* | 0.069 | −149.866 | 5.5490 | ||||||||
MMA | −65.8895 | 0.281 | −185.8022 | 54.0231 | ||||||||
_cons | 16.1192 | 0.000 | 9.7380 | 110.5115*** | 0.000 | 94.37108 | 126.652 | |||||
Rsquared | 0.5144 | 0.4643 | 0.4091 | |||||||||
( -value) | 62,30*** (0,000) | 42.48***(0.000) | 18.04***(0.006) |
Note(s): * p < 0.1, ** p < 0.05, *** p < 0.01
Source(s): Authors’ calculations
Accenture ( 2016 ). Global fintech investment growth continues in 2016 driven by Europe and Asia . Accenture Study Finds .
Acemoglu , D. , & Restrepo , P. ( 2017 ). Robots and jobs: Evidence from US labor markets National Bureau of Economic Research (NBER) Working Paper No. 23285, NBER, Cambridge, MA . doi: 10.3386/w23285 .
AFD ( 2019 ). Numérique et innovation . Available from: https://www.afd.fr/fr/page-thematique-axe/numerique-et-innovation
Agarwal , S. ( 2014 ). Predatory lending and the subprime crisis . Journal of Financial Economics , 113 , 29 – 52 . doi: 10.1016/j.jfineco.2014.02.008 .
Aitken , A. ( 1936 ). On least-squares and linear combinations of observations . Proceedings of the Royal Society of Edinburgh , 55 , 42 – 48 . doi: 10.1017/S0370164600014346 .
Anagnostopoulos , I. ( 2018 ). Fintech and regtech: Impact on regulators and banks . Journal of Economics and Business , 100 , 7 – 25 . doi: 10.1016/j.jeconbus.2018.07.003 .
Aron , J. , Muellbauer , J. and Sebudde , R. ( 2015 ). Inflation forecasting models for Uganda: Is mobile money relevant? CSAE Working Paper. No. 17 .
Attanasio , O. , Guiso , L. , & Jappelli , T. ( 2002 ). The demand for money, financial innovation, and the welfare cost of inflation: An analysis with household data . Journal of Political Economy , 110 , 317 – 351 . doi: 10.1086/338743 .
Barjot , D. ( 2018 ). L’ascension économique de l’Asie : Quels facteurs ? Quels modèles ? Entreprises et Histoire , 90 , 6 – 24 , ISSN 1161-2770 , ISBN 9782747227865 .
Barro , R. ( 1997 ). Determinants of economic growth: A cross-country empirical study . Cambridge, Mass : MIT Press .
Belot , M. & Van Ours . J. ( 2001 ) Does the recent success of some OECD countries in lowering their unemployment rate lie in the clever design of their economic reforms? IZA Discussion Paper No. 147 .
Ben Romdhane , Y. , Loukil , S. , & Kammoun , S. ( 2020 ). Economic african development in the context of fintech . In Employing Recent Technologies for Improved Digital Governance . IGI Global . doi: 10.4018/978-1-7998-1851-9.ch014 .
Ben Romdhane Loukil , Y. , Loukil , S. , & Kammoun , S. ( 2021 ). Fintech development, digital infrastructure and institutions . In MENA Zone. Financial and economic systems transformations and new challenges (pp. 481 – 506 ). World Scientific Publishing . doi: 10.1142/9781786349507_0017 .
Bruno , M. , & Easterly , W. ( 1998 ). Inflation crises and long-run growth . Journal of Monetary Economics , 41 , 3 – 26 .
Chen , H. , Zhang , G. , Zhu , D. , & Lu , J. ( 2017 ). Topic-based technological forecasting based on patent data: A case study of Australian patents from 2000 to 2014 . Technological Forecasting and Social Change , 119 , 39 – 52 . doi: 10.1016/j.techfore.2017.03.009 .
Eftekhari Mahabadi , S. , & Kiaee , H. ( 2015 ). Determinants of inflation in selected countries . Journal of Money and Economy , 10 ( 2 ), 113 – 143 .
Erosa , A. , & Ventura , G. ( 2002 ). On inflation as a regressive consumption tax . Journal of Monetary Economics , 49 ( 4 ), 761 – 795 . doi: 10.1016/S0304-3932(02)00115-0 .
Fong , V. ( 2017 ). Fintech Malaysia report 2017. The State Of Play For FinTech. Available from Malaysia: https://FinTechnews.my/17922/editorspick/FinTech-malaysia-report2018/ .
Gonzalez Fernandez , M. , González , C. , & Fanjul-Suárez Velasc , J. L. ( 2020 ). Corruption, the shadow economy and innovation in Spanish regions . Panoeconomicus , 67 ( 4 ), 509 – 537 . doi: 10.2298/PAN170605003 .
Griffis , V. W. , & Stedinger , J. R. ( 2007 ). The use of GLS regression in regional hydrologic analyses . Journal of Hydrology , 344 ( 1 ), 82 – 95 . doi: 10.1016/j.jhydrol.2007.06.023 .
Gujarati , D. N. ( 2003 ). Basic econometrics ( 4th Ed. ). New York : McGraw-Hill .
Haddad , C. , & Hornuf , L. ( 2019 ). The emergence of the global fintech market: Economic and technological determinants . Small Business Economics , 53 ( 1 ), 81 – 105 . doi: 10.1007/s11187-018-9991-x .
IMF. ( 2018 ). Annual Report 2018 International, 700 19th Street NW Washington, DC 20431 USA .
Iwasaki , B. K. ( 2018 ). Emergence of fintech companies in Southeast Asia rising hopes of a solution to financial . Pacific Business and Industries , 18 ( 68 ), 1 – 32 .
Jagtiani , J. ( 2018 ). Fintech: The impact on consumers and regulatory responses . Journal of Economics and Business , 100 , 1 – 6 . doi: 10.1016/j.jeconbus.2018.11.002 .
Jaewoo , C. , & Woonsun , K. ( 2014 ). Themes and trends in Korean educational technologyresearch: A social network analysis of keywords . Social and Behavioral Sciences , 131 , 171 – 176 . doi: 10.1016/j.sbspro.2014.04.099 .
Kammoun , S. , Loukil , S. , & Ben Romdhane , B. ( 2020 ). The impact of FinTech on economic performance and financial stability in MENA zone . In Impact of Financial Technology (FinTech) on Islamic Finance and Financial Stability (pp. 253 – 277 ). IGI Global .
Kandil , M. , & Morsy , H. ( 2009 ). Determinants of inflation in GCC IMF Working Paper, Middle East and Central Asia Department .
Kennedy , P. ( 1992 ). A guide to econometrics . Cambridge, Massachusetts : The MIT Press , ISBN: 9780262110730 .
Loukil , S. , Ben Romdhane , Y. , Kammoun , S. and Ibenrissoul , A. ( 2019 ) Empirical determinants of fintech: Panel data analysis in the MENA zone . International Journal of Science and Engineering Invention , 5 ( 5 ), 90 - 97 . doi: 10.23958/ijsei/vol05-i05/159 .
Mawejje , J. , & Lakuma , P. C. ( 2017 ). MacroeconomicEffects of mobile money in Uganda (Vol. 135 , pp. 1 – 34 ). Economic Policy Research Centre . doi: 10.22004/ag.econ.260017 .
Mediero , L. , & Kjeldsen , T. R. ( 2014 ). Regional flood hydrology in a semi-arid catchment using a GLS regression model . Journal of Hydrology , 514 , 158 – 171 . doi: 10.1016/j.jhydrol.2014.04.007 .
Mohamed Sheikh , A. T. , Oyagi , B. , & Tirimba , O. I. ( 2015 ). Assessment of non-financial motivation on employee productivity: Case of ministry of finance headquarters in hargeisa somaliland . International Journal of Business Management and Economic Research , 6 ( 6 ), 400 – 416 , ISSN: 2229- 6247 .
Mohanty , M. S. , & Klau , M. ( 2001 ). What determines inflation in emerging market economies? BIS Papers No 8 .
Morikawa , M. ( 2014 ). Towards community-based integrated care: trends and issues in Japan’s long-term care policy . International Journal of Integrated Care , 14 . doi: 10.5334/ijic.1066 .
Mulligan , X. , & Sala-i-Martin , C. ( 2000 ). Extensive margins and the demand for money at low interest rates . Journal of Political Economy , 108 ( 5 ), 961 – 991 . doi: 10.1086/317676 .
Mumtaz , M. , & Zachary , S. ( 2020 ). Empirical examination of the role of fintech in monetary policy . Pacific Economic Review , 25 ( 5 ), 620 – 640 . doi: 10.1007/s12599-017-0464-6 .
Narayan , S. , & Sahminan , S. ( 2018 ). Has fintech influenced Indonesia’s exchange rate and inflation? Bulletin of Monetary Economics and Banking , 21 ( 2 ), 1 – 14 . doi: 10.21098/bemp.v21i2.966 .
Nampewo , D. , & Opolot , J. ( 2016 ). Financial innovations and money velocity in Uganda.bank of Uganda Working Paper, 5. Bank of Uganda .
Nepote-Cit , M. , Ruberti , S. , & Tran , V. ( 2018 ). Perspectives économiques régionales: Afrique subsaharienne . Washington, DC : International Monetary Fund , ISBN: 9781498304139 .
Nickell , S. ( 1997 ). Unemployment and labor market rigidities: Europe versus North America . Journal of Economic Perspectives, Summer , 11 ( 3 ), 55 – 74 .
Nicoletti , G. , & Scarpetta , S. ( 2002 ). Interactions between product and labour market regulations: Do they affect employment? Evidence from OECD countries . Paris : OECD Economics Department .
Nigusse , T. , Tadesse , T. , & Melaku , T. ( 2019 ). Supply and demand side determinants of inflation in Ethiopia auto-regressive distributed lag model (ARDL) . International Journal of Commerce and Finance , 5 ( 2 ), 8 – 21 .
Pollin , R. , & Zhu , A. ( 2005 ). Inflation and economic growth: A cross- country non-linear analysis Working Papers Series, N°109, Political Economy Research Institute, University of Massachusetts, Amherst .
Rezaeian , M. , Hamid , M. , & Roel , L. ( 2017 ). Science foresight using life-cycle analysis, text mining and clustering: A case study on natural ventilation . Technological Forecasting and Social Change , 117 , 270 – 280 . doi: 10.1016/j.techfore.2017.02.027 .
Simpasa , A. , & Gurara , D. ( 2012 ). Inflation Dynamics in selected East African countries: Ethiopia, Kenya,Tanzania and Uganda , AFDB Brief 2012 . African Development Bank .
Taherdoost , H. ( 2018 ). Understanding of e-service security dimensions and its effect on quality and intention to use . Information & Computer Security , 25 , 535 – 559 .
Thiel , P. , & Masters , B. ( 2014 ). Zero to one: Notes on startups, or how to build the future . New York Times Bestseller .
Truong , O. ( 2016 ). How fintech industry is changing the world B.Sc. Thesis. Centria University of Applied Sciences. Degree Programme in Business Management .
Walker ( 2016 ). Customers 2020: The future of B-to-B customer experience . Available from: https://www.walkerinfo.com/Portals/0/Documents/Knowledge,%20Center/Featured%20Reports/WALKER-Customers20 20.pdf
Xiang , D. , Huang , Z. , & Cheng , X. ( 2019 ). FinTech and sustainable development: Evidence from China based on P2P data . Sustainability , 11 ( 22 ), 1 – 20 . doi: 10.3390/su11226434 .
Related articles, all feedback is valuable.
Please share your general feedback
Contact Customer Support
As you were browsing something about your browser made us think you were a bot. There are a few reasons this might happen:
To regain access, please make sure that cookies and JavaScript are enabled before reloading the page.
Pssst… we can write an original essay just for you..
Any subject. Any type of essay. We’ll even meet a 3-hour deadline.
writers online
Case Study: Macroeconomic Challenges: Unemployment and Inflation
This assignment is broken in to two three parts.
Assignment Description
In this assignment, you need to identify two main issues related to unemployment and inflation. Then you are going to study their past trends, provide an overview of their current status, and provide solutions to overcome them. You will use data, articles, experts’ opinions, and government reports to draw a clear picture of the current unemployment and inflation issues.
Some areas you might consider are (you could also choose different topics) the following.
Your research needs to be structured with consistent and clear thoughts. It also needs to be supported by facts and data. Your results need to be based on solid facts. Your conclusion and recommended solution need to be thorough and based on your findings and understanding of macroeconomic challenges and macroeconomic policies
Important Note:
The case study itself is a one assignment that is broken into two parts over two weeks. Part I is due in Week 5 (06/09/19) and Part II (including Part I from Week 5) is due in Week 6 (06/14/19).
The final and complete case study submission that is due in Week 6 must contain the portion of the assignment that was due in Week 5. In other words, the final assignment that is due in Week 6 must contain the following sections: (1) Introduction (2) Data (3) Analysis (4) Reflection (5) Solution (6) References.
The References should include a minimum of five sources in the final Week 6 submission. As an example, 3 sources could have been used in the Week 5: Part I assignment and 2 or more additional sources could have been used for the Week 6: Part II assignment, equaling 5 or more total professional sources in the final combined References page. Note that all references used for Week 5: Part I should be included in your Week 5 submission and in your Week 6 submission.
Writing Style and Page Number Requirements
Font Type : Times New Roman or Arial
Font Size : 12
Spacing : Double
Number of Pages : The final combined Part I and Part II case study should be at least six (6) pages total, not counting the Title page and References page
Structure and Requirements
Part ii: due in week 6.
Number of Pages : Six (6) to eight (8) pages that include Part I and Part II combined. Note that this 6-8 number of pages is not counting the Title page and References page that are also required.
Save time and get your custom paper from our expert writers.
In case you can’t find a sample example, our professional writers are ready to help you with writing your own paper. All you need to do is fill out a short form and submit an order
Unemployment and inflation.
A high unemployment rate in any economy is a symptom of dysfunctionality in the economy. People need income to be able to consume, and consumption leads to production and production leads to a higher demand for labor (employment), so unemployment is a crucial macroeconomic issue that confronts all modern economies. A lower unemployment rate, however, is not the end of the problem.
The type of employment and the price of labor (wages) must be sufficient in order for consumption (which is the major component of GDP) in the economy to be healthy. If wages are low or if the prices in the economy are high, consumption would be low and thus, production and demand for labor would be low, too. High unemployment rate, high part-time employment rate for economic reasons, low wages, and high inflation could have negative effects on economic growth.
Assignment Description
In this assignment, you need to identify only two specific issues related to unemployment. You will study their past trends, provide an overview of their current status, and provide solutions to overcome them. You will use data, articles, experts’ opinions, and government reports to draw a clear picture of the current unemployment issues.
Some areas you might consider are the following (based on gender, education, race, age):
Your research needs to be structured with consistent and clear thoughts. It also needs to be supported by actual data. Your results need to be based on solid facts. Your conclusion and recommended solution need to be thorough and based on your findings and understanding of macroeconomic challenges and macroeconomic policies.
Writing Style (APA) and Page Number Requirements
Document Type: Word
Font Type : Times New Roman or Arial
Font Size : 12
Spacing : Double
Number of Pages : Four pages, not counting the separate Title page and the separate References page
Structure and Requirements
There are 5 sections to this case study (Introduction, Data, Analysis, Reflection, Solution). You must have 5 section/headers in your paper since each section has its own marks/points (see Case Study Rubric document).
Your introduction needs to include the following.
Obtain data from at least 3 credible sources (not Wikipidia). Use tables, graphs, and figures to support your argument. You can find the latest unemployment and inflation data at www.bls.gov (Bureau of Labor Statistics).
It is important to obtain your unemployment data from the Bureau of Labor Statistics (BLS) website. Follow the “ Guide to using the BLS website Data †provided on this assignment page.
You need 5 data pieces in the data section (3 General data pieces: GDP Growth Rate, Unemployment Rate, Inflation Rate. Variable data pieces: 2 specific unemployment rates based on your variable (gender, race, education, etc.). All pieces of data need the same 10-year period you have chosen.
Data (graphs or tables) that have to be provided in this section:
Get Optical Scanner Assignment Here!!
This section needs to contain the following discussions based on the data that was gathered and your understanding of unemployment, inflation, and GDP.
Connect all the dots together by relating your above analysis to other areas in the macroeconomy. Basically, you will link changes in overall unemployment, the two unemployment related issues you selected, inflation, and GDP to one another and how they impacted each other during periods of economic decline (recessions) and periods of economic growth (expansion).
What would you do to solve the macroeconomic issues you addressed if you were in charge of the U.S. economy? State why.
Use at least three professional sources to support your argument. The references need to be in APA format.
To learn more about APA format, click (and hold your CTRL key) on the link below or copy/paste the link into your browser address bar.
APA Resources: http://libguides.devry.edu/c.php?g=181472&p=1194156
Try it now!
How it works?
Follow these simple steps to get your paper done
Place your order
Fill in the order form and provide all details of your assignment.
Proceed with the payment
Choose the payment system that suits you most.
Receive the final file
Once your paper is ready, we will email it to you.
Our Services
Help in Schoolwork has stood as the world’s leading custom essay writing services providers. Once you enter all the details in the order form under the place order button, the rest is up to us.
At Help in Schoolwork, we prioritize on all aspects that bring about a good grade such as impeccable grammar, proper structure, zero-plagiarism and conformance to guidelines. Our experienced team of writers will help you completed your essays and other assignments.
Admission and Business Papers
Be assured that you’ll definitely get accepted to the Master’s level program at any university once you enter all the details in the order form. We won’t leave you here; we will also help you secure a good position in your aspired workplace by creating an outstanding resume or portfolio once you place an order.
Editing and Proofreading
Our skilled editing and writing team will help you restructure you paper, paraphrase, correct grammar and replace plagiarized sections on your paper just on time. The service is geared toward eliminating any mistakes and rather enhancing better quality.
Technical papers
We have writers in almost all fields including the most technical fields. You don’t have to worry about the complexity of your paper. Simply enter as much details as possible in the place order section.
IMAGES
VIDEO
COMMENTS
Unemployment and Inflation: Both unemployment and inflation rates have experienced significant changes during the last ten years. The Bureau of Labor Statistics (BLS) reports that the unemployment rate ranged from 4% in May 2011 to 3% in May 2019. However, the COVID-19 pandemic's economic effects caused the jobless rate to rise to 6% in May 2020.
5 overall in the ten-year span, men and women have almost the same rate. Men and women both had a very low unemployment rate in 2007 and grew to its highest unemployment rate in 2009. Since 2009 the unemployment rate steadily went down over into 2017 with it at about the same rate it was in 2007. Reflection and Critical Thinking The unemployment rate, GDP and inflation holds true to in the ten ...
This study addresses the relationship between inflation and unemployment known as Phillips Curve, for the seven countries that emerged from former Yugoslavia. This phenomenon is investigated with ...
Phillips curve theory e stablished the relationship between rate of. inflation and rate of unemployment. It shows inverse relationship between rate of inflation and rate of unemployment. Therefore ...
Macroeconomics is the study of the behavior and performance of the economy as a whole. It focuses on national output, unemployment, and inflation, and how governments use monetary and fiscal ...
1 The archetypal example is the experiment conducted in Chile by the Chicago Boys in the mid-1970s. With a large reduction in the fiscal deficit and monetary issuance, the economy averaged unemployment levels of 13% between 1974 and 1978. During the Chilean stabilization in the 1990s, unemployment stood at 6.9%.
The mean (standard deviation) % values of inflation and unemployment for this term are 3.51 ( 2.93) and 5.76 ( 1.64), respectively. The correlation coefficient between unemployment and inflation for the whole data depicted in Figure 1 is less than ∼0.05, which is not statistically meaningful. Figure 1.
The main objective of this empirical study to examine the impact of corruption, unemployment and inflation on economic growth for seventy nine (79) developing countries of the world for the period from 2002 to 2018. This study uses Panel unit root tests (PUT), Pooled Mean Group (PMG), Fully modified ordinary least square (FMOLS), and Dynamic least square (DOLS), for the data estimation. The ...
Economics document from DeVry University, Keller Graduate School of Management, 8 pages, Running head: YOUTH UNEMPLOYMENT Week 5 Case Study - Macroeconomic Challenges: Unemployment and Inflation Christina Song [email protected] ECON545- Business Economic Professor: Barbara Son October 6,2019 1 YOUTH UNEMPLOYMENT 2 Unemployment among
This study attempts to explain the impact of Fintech on the Asian economies through two main indicators, inflation and unemployment over the period 2011-2014-2017.,This study uses panel data regression models to explain the relationship between Fintech, inflation as an indicator of currency circulation and unemployment since Fintech has ...
Measurement of flash indicator of macroeconomic inflation. Dr. Alexey Ponomarenko Higher School of Economics, Russia. There is a long experience of monthly GDP estimates in post - USSR countries. Russia has started to estimate monthly GDP under the IMF pressure in early 1990 because it was a condition for borrowing from the Fund.
Question. Week 6 Case Study: Macroeconomic Analysis: Unemployment and Inflation. Overview. A high unemployment rate in any economy is a symptom of dysfunctionality in the economy. People need income to be. able to consume, and consumption leads to production and production leads to higher demand for labor (employment),
Case Study: Macroeconomic Challenges: Unemployment and Inflation . This assignment is broken in to two three parts. Part I (Intro and Data): Due in Week 5 (06/09/19) Part II (Analysis and Solution): Due in Week 6 (06/14/19) Part III (Presentation) Overview. A high unemployment rate in any economy is a symptom of dysfunctionality in the economy.
Macroeconomic Analysis: Unemployment and Inflation - Week 6 Case Study Solution. The type of employment and the price of labor (wages) must be sufficient in order for consumption (which is the major component of GDP) in the economy to be healthy. If wages are low or if the prices in the economy are high, consumption would be low and thus ...
Unemployment and Inflation "Misery Index": Indicator of Socio-Economic Situation (ICSS Analytical Series, 2009) •Evaluation of "Misery Index"on the base of which Russia belongs to countries with the highest levels of inflation and unemployment Estimation of Inflation for Different Income Groups in Moscow (Analytical Report, 2008)
suggest that the high level and persistence of fear of unemployment in Russia may be caused by non-economic factors. JEL Codes: J28, J64, P23, P36 Keywords: fear of unemployment, job insecurity, Russia. 1 Director, Centre for Labour Market Studies, Higher School of Economics, Moscow, e-mail: [email protected]
unemployment growth according to the above-mentioned local indices look quite well. This is the Yaroslavl region (dynamics of the unemployment growth index - 129%, the Komi Republic-127%, the Samara region-123%). In these subjects, the deterioration of the economic situation is characterized by the influence of rather short-term factors.