Assignment On Economic Concepts And Market Theories

Part 1: Fundamental Economic Concepts

1. We decide which one has higher risk by computing the coefficient of variation as formulated: V= σ/r; where V is the coefficient of variation, σ is the ratio of standard deviation and r is the expected value.

For project A we have: V^A= RM40000/RM50000 = 0.8

V^B= RM125000/RM250000=0.5

Since V^A>V^B, we conclude that project A has a higher risk with a higher coefficient of variation.

1. A)   The price elasticity of demand is given as:

Ed= [(Q2-Q1)/{(Q2+Q1)/2}]/ [(P2-P1)/{(P2+P1)/2}]    (Sowell, 2010)

Here: Q2=10000 and Q1=8000; P2=89.50, P1=100.

Ed=[ (10000-8000)/{(10000+8000)/2}]/[(89.50-100)/{(89.50+100)/2}]

= -2.005

If we ignore the negative value, the absolute value of elasticity of demand is 2.01 which are greater than 1, indicating high elasticity; hence, consumers are sensitive upon price changes (Mankiw, 2007).

1. B)   The income elasticity of demand is computed with the help of the following formula:

Ey= {(Q2-Q1)/ (Q2+Q1)}/ {(I2-I1)/ (I2+I1)}   (Sen, 2007)

= {(10000-8000)/ {(10000+8000)}/ {610-650/610+650}

= -3.50

Ignoring the negative sign, with an elasticity value of 3.5 which is greater than 1, we conclude it is highly elastic implying a higher income would be followed by higher demand for goods (Lipset et al, 2010).

1. C)   P=89.50 remaining same. New income is 610+40=650billion.

Q1=10000, Y2=650, Y1=610, Ey=-3.5

Ey= {(Q2-Q1)/ (Q2+Q1)}/ {(Y2-Y1)/ (Y2+Y1)}   (Samuelson et al, 2010)

-3.5= {(Q2-10000)/ (Q2+10000)}/ {(650-610)/ (650+610)}

Q2=8000

1. D) Firstly we calculate the impact of an increase in the personal income by 40 billion, which is

=> 8000-10000=-2000

The increase in price by RM20 millions results in the impact as:

Ed= [(Q2-Q1)/ {(Q2+Q1)/2}]/ [(P2-P1)/ {(P2+P1)/2}]

-2.01= [(Q2-10000)/ {(Q2+10000)/2}]/ [(109.5-89.5)/ {(109.5+89.5)/2}]

Q2=6639

Hence the impact is: 6639-10000=-3361

Thus the forecast of 2015 sales is 10000-2000-3361=4639

PART 2:

The data given to us for this part is in the table as follows for Mexico for 1955 to 1974:

YEAR	GDP (million of 1960 Peso)	Labour (thousands of people)	CAPITAL (million of 1960 Peso)
1955	114,043	8,310	182,113
1956	120,410	8,529	193,749
1957	129,187	8,738	205,192
1958	134,705	8,952	215,130
1959	139,960	9,171	225,021
1960	150,511	9,569	237,026
1961	157,897	9,527	248,897
1962	165,286	9,662	260,661
1963	178,491	10,334	275,466
1964	199,457	10,981	295,378
1965	212,323	11,746	315,715
1966	226,977	11,521	337,642
1967	241,194	11,540	363,599
1968	260,881	12,066	391,847
1969	277,498	12,297	422,382
1970	296,530	12,955	455,049
1971	306,712	13,338	484,677
1972	329,030	13,738	520,553
1973	354,057	15,924	561,531
1974	374,977	14,154	609,825

Converting the values into their logarithm forms we get the table as given below:

Year	Log of GDP	Log of Labor	Log of capital
1,955	11.64433085	9.025214888	12.11238265
1,956	11.69865786	9.0512274	12.17431879
1,957	11.76901625	9.07543661	12.23170141
1,958	11.81084248	9.09963225	12.27899778
1,959	11.84911195	9.123801611	12.32394901
1,960	11.92179145	9.166283986	12.37592512
1,961	11.9696982	9.161885152	12.42479444
1,962	12.01543259	9.175955945	12.47097599
1,963	12.09229346	9.243194709	12.52621949
1,964	12.20335395	9.303921786	12.59601117
1,965	12.26586398	9.371268036	12.66259519
1,966	12.33260397	9.351926736	12.72974144
1,967	12.39335687	9.35357454	12.80380689
1,968	12.47181964	9.398146859	12.87862674
1,969	12.533569	9.417110609	12.9536654
1,970	12.59990367	9.469237093	13.02816038
1,971	12.63366448	9.498372383	13.09123797
1,972	12.70390421	9.527920995	13.16264699
1,973	12.7772132	9.675582684	13.23842226
1,974	12.83461997	9.557752549	13.32092731

1. On running a regression analyses using the Data Analysis tool pack in Excel we get the following output, where the dependent variable is the log of GDP and the independent variables are the log of labor and capital respectively:

Interpretation of results:

As we see from the output, the high R2 of 0.9975 indicates high goodness of fit implying the model fits the data really well with reliable results where more than 99% of the variation in GDP is explained. Next the standard error is 0.03 and the number of observations in the model is 20.  Moving on to the ANOVAs table we see that the model is in overall highly statistically significant at 1% level of significance since the p-value is 0.00.

Out of the two explanatory variables, capital is found to be highly significant at 1% LOS in explaining variation in GDP with a coefficient of 0.84 which implies 1% rise in capital explains 0.84% increase in GDP which cannot be considered statistically reliable at level of significance of 5% and 1%. The positive sign indicates an increase in the capital increases GDP. The labor variable is found to be insignificant at 1% or 5% level of significance but can be considered significant at 10% level of significance (Gujarati, 1995). The coefficient is 0.34 which indicates 1% change in labor shall affect GDP positively by 34%. Lastly the intercept is found to be significant at 5% level of significance with a negative coefficient of -1.65.

1. For the second regression we have to compute GDP/Labor and GDP/capital. We use the logarithm values of each variable as computed above and get the two terms as below:

Part 2: Production Functions and Cost minimization

Year	GDP/labor	GDP/capital
1955	1.2902	0.961357578
1956	1.292494	0.960929155
1957	1.296799	0.962173279
1958	1.297947	0.961873493
1959	1.298703	0.961470381
1960	1.300613	0.963305073
1961	1.306467	0.96337193
1962	1.309448	0.963471712
1963	1.308237	0.96535858
1964	1.311635	0.96882686
1965	1.30888	0.968669044
1966	1.318723	0.968802393
1967	1.324986	0.967943126
1968	1.327051	0.968412231
1969	1.330936	0.967569303
1970	1.330614	0.967128382
1971	1.330087	0.965047347
1972	1.333334	0.965148137
1973	1.320563	0.965161327
1974	1.342849	0.963492981

We again do a regression analysis taking GDP/labor as the dependent variable and GDP/capital as the independent variable. 

Interpretation of Results:

As obtained in the output the R2 for this regression is 0.34 which is less than 0.8. But, considering no other control variables are considered, the model fitting the data and explaining about 34% of variation in the dependent variable is not completely poor. In overall the model is found to be significant at 1% level of significance with a p-value of 0.0065 which is less than 0.01. The explanatory variable taken as the GDP/capital is found to be statistically significant at 1% Los with a coefficient of 3.28 indicating a 1 unit rise in the ratio will lead to 3.28 units rise in the dependent variable ratio. The intercept is found to be insignificant with p-value greater than 0.05.

1. To conclude whether it follows increasing, decreasing or constant returns to scale we apply the below given theory:

We have the regression equation as:

Log (GDP) = a+ b1 [log (labor)] + b2 [log (capital)]

Hence, if b1+b2>1, it is increasing returns to scale

If b1+b2<1, it is decreasing returns to scale

If b1+b2=1, it is constant returns to scale   (Gujarati, 1995)

From our regression analysis in part A we have got the coefficients as:

               B1= 0.339732, b2= 0.845997

Hence, b1+b2= 1.18573 which is greater than 1 and hence it exhibits increasing returns to scale.

1. The five forces model by Porter is an analysis tool which uses the five industry forces to determine the profitability level in an industry and the intensity of competition within it.

The five forces can be categorized into two levels:

Horizontal forces: competitive rivalry, threat of substitutes, threat of new entrants

Vertical forces: Bargaining power of buyers and customers respectively (Hacks, 2012).

 The forces are explained as follows:

• Competitive Rivalry:  This implies the rivalry degree between the companies existing in the market. Competitive pressure shall drive prices, profits and strategies. A company may eventually result in having little or may be no power in its own industry provided there is availability of various products of good quality offered in the market in direct competition with it. Competitive rivalry is high under certain circumstances. Such are when companies of similar size operate in one market with similar strategies, the products offered have same features and same utilities, barriers to exit are high and entry are low and industry growth is slow (Emmerich, 2014).
• Threat of new entrants: Potential new entrants in the market may also be a threat to a company. A profitable industry or attractive in the long term in a strategically manner, may attract many new entrants if barriers to entry in the market is not there. The scenario in an industry for accompany is considered to really attractive if there are low barriers to exit and high barriers to entry. These barriers to entry are high when there exists some sort of patents or proprietary knowledge, there is access to specialized infrastructure and technology, initial investment is needed to be high, there exists economies of scale or obstacles which are government driven, consumers especially loyal ones face high switching costs from one company to another and there exists a difficulty to access raw materials and distribution channels.
• Substitutes’ threat: products which can be used as substitutes for some other product to satisfy the e same needs are said to be substitutes. The more the number of substitutes for the product the larger would be the competitive environment of the industry and potential for profit would be low.
• Bargaining power of buyers: Another import factor is when buyers have the power to affect prices.  If buyers are important to the company they tend to affect prices, where buyers may buy in bulk or switch to some other supplier.
• Suppliers’ bargaining power: Suppliers may also have the power to dictate terms or availability timelines or even set prices depending upon the dynamics of the market, which in turn may cause in increasing costs (Jurevicious, 2013).

1. A) It is given that the total demand for polyglue is Qt=Qf+QL, which is represented by the function:

P = 10,000 – 10Q_T

Also, Alchem’s marginal cost function for manufacturing and selling polyglue is:

MCL = 100 + 3Q_L

Alchem’s profit maximizing output occurs where MRl =MCl  (Varian, 2010).

MRl= d(TRl)/dQl

TRL=P*QL

= QL(10000-10QT)

We know QL= QT-QF

We can solve for QT having, P=10000-10QT

QT=1000-0.1P

To get QF, the firm lets its follower firms sell as much polyglue as they want to at the given market price. Hence, the firms face a horizontal demand function and thus:

 MRF=P (Cowell, 2004)

For maximizing profits the follower firm will operate whereMRF= SMCF

i.e., P= 50+2QF

Solving for QF, we get QF= 0.5P-25

Substituting QT and QF in the expression for QL we get:

=(1000-0.1P)- (0.5P-25)=1000-0.1P-0.5P+25

QL=1025-0.6P

Thus P= 1708.3-1.666QL

Hence we get TRL= (1708.3-1.666QL) QL

MRL= 1708.3-3.33QL

Setting MRL =MCL we get:

1708.3-3.33QL=100 + 3Q_L

6.33QL=1608.3

QL*= 1608.3/6.33=254.076

P*=1708.3-423.3=1285

1. B) P = 10,000 – 10Q_T

P=1285

QT= (10000-1285)/10=871.5

The followers supply QF= QT-QL= 871.5-254.076=617.424

1. In a cartel, producers cooperate in setting output levels and prices. Uner a perfect cartel type, output and price determination of the whole industry and also each member firm is determined by the administrative authority in common which enables achieving maximum joint profits for the member firms. The profits are distributed in a manner which is already decided between them. The share from the overall profits of each individual firm maybe not in proportion to the supply output quota and the cost incurred on it. This output quota for each firm is decided by the central administrative authority in a way that the total costs to the total output produced is the least. To find this output where the cost is minimized with various firms in the cartel producing different outputs such that their marginal costs are equal. If this marginal cost is not equal then the marginal units are possible to be produced at lower cost by the firms with lower marginal cost compared to the ones with higher marginal costs (Pindyck et al, 2009).

Part 3: Competitiveness and Strategies

We assume that two firmsenter into an agreement and form a cartel which aims at the joint profit maximization for the member firms. Firstly the cartel would estimate the industry product’s demand curve which will be the aggregate demand curve of the consumers of the product, sloping dowanwards as shown in the figure by DD. The marginal revenue curve lies below the demand curve, whereas the marginal cost or the MCc curve of the cartel is an horizontal addition of the MC curves of both the firms, Mca and MCb respectively.

The cartel MC curve indicates the least cost of producng each industry output and the output for each industry is distributed in such a way between te two firms that their marginal costs are equal (Shapiro,1989). The cartel maximizes it’s profits by fixing output where MR is equal to MC(point R). This output oQ determines price QL or OP. Now on deciding this total output, the cartel allots output quota to each firm such that their marginal costs are same. As seen from the diagram, when A firm produces OQ1 and  same firm B produces OQ2, their marginal cost is equal. The total output OQ is the sum of OQ1 and OQ2. We see in part a of the figure that OQ and price OP A makes profit equal to PFTK and with OQ and OP B’s profit is equal to PEGH. The sum of these profits would be maximum (Hall et al, 2010).

1. It is given that the demand function is as:

P=1,000 – Q_S – Q_T

Where QS and QT are the quantities sold by the respective firms and P is the (market) selling price. The total cost functions of manufacturing and selling the component for the respective firms are:

TC_s = 70,000 + 5Q_S+ 0.25Q²_S

TC_{t =} 110,000 + 5Q_T + 0.15Q²_T

The Marginal costs for each firm would be:

MCs= d(TCs)/dQs= 5+0.5Qs

MCt= d(TCt)/dQt= 5+0.3Qt

Total profit of S is:

Ps= PQs-TCs= (1000-Qs-Qt)Qs – 70,000 + 5Q_S+ 0.25Q²_S

=-70000+995Qs-QtQs-1.25Qs²

Now this firm’s total profit also depends on the quantity produced and sold by firm T.

 Taking partial derivative of the above equation with respect to Qs yields (Sikder, 2006):

dPs/dQs= 995-Qt-2.50Qs………………………………… (1)

Similarly we get firm T’s total profit as:

Pt= PQt-TCt= (1000-Qs-Qt) Qt – 110,000 + 5Q_T + 0.15Q²_T

=-110000+995Qt-QsQt-1.15Qt²

Taking partial derivatives with respect to Qt we obtain:

dPt/dQt= 995-Qs-2.3Qt…………………………………………(2)

Setting both equation 1 and 2 to zero yields:

2.50Qs+Qt=995

Qs+2.30Qt=995

Solving the above two equations gives us Qs*=272.32 units and Qt*=314.21 units. By substituting these two values in the demand equation we get the optimal selling price of P*=$413.47 per unit and the profits obtained are: Ps=$ 22695 and Pt=$3536.17.

1. b) Now if both the firms decide to form a cartel then total industry profits would be:

PR= Ps+Pt

=PQs-TCs+PQt-TCt

PR= (1000-Qs-Qt) Qs – 70,000 + 5Q_S+ 0.25Q²_S + (1000-Qs-Qt) Qt – 110,000 + 5Q_T + 0.15Q²_T

= 180000+995Qs-1.25Qs²+995Qt-1.15Qt²-2QsQt

To maximize this total profit we take partial derivative with respect Qs and Qt respectively:

dPR/dQs= 995-2.50Qs-2Qt

dPR/dQt=995-2.30Qt-2Qs

Setting these equal to zero we obtain:

995-2.50Qs-2Qt=0

995-2.30Qt-2Qs=0

.On solving we get Qs*=170.57units and Qt*=284.39units. Substituting these in the price functions and profit function gives P*=$545.14 per unit and PR*=$46291.43

The marginal cost function as obtained before, on substituting values we get: MCs*=MCt*=$90.29

References

Pindyck, R. Rubinfeld, D. & Mehta, P. (2009). Microeconomics. South Asia: Pearson

Varian, H. (2010). Intermediate microeconomics. New Delhi:Affiliated East-West Press

Samuelson, P. & Nordhaus, W. (2010). Economics. New Delhi: Tata McGraw Hill

Mankiw, G. (2007). Economics: principles and applications. New Delhi: Cengage learning

Lipsey, R. & Chrystal,  A. (2011). Economics.  New Delhi : Oxford

Sen, A., (2007). Microeconomics. New Delhi:  Oxford

Sowell, T.,( 2010). Basic economics. USA: Basic books

Hall, R., & Lieberman, M., ( 2010). Economics: Principles and applications, USA: CengagE learning

Sikdar,S.,( 2006). Principles of Macroeconomics. New Delhi: Oxford.

Jurevicius, O. (2013) Porter’s five forces. Available at: https://www.strategicmanagementinsight.com/tools/porters-five-forces.html . Accessed [7 March 2017]

Emmerich, G. (2014). Porter’s Five Forces Model. Available at: https://www.cleverism.com/porters-five-forces-model-strategy-framework/ . [Accessed 7 March 2017]

Hacks, A. (2012). The Strategic Management Framework. Available at: https://ocw.mit.edu/courses/sloan-school-of-management/15-902-strategic-management-i-fall-2006/lecture-notes/strmgtfr.pdf [Accessed 7 March 2017]

Cowell, F. (2004). Microeconomics. Available at: https://www.railassociation.ir/Download/Article/Books/MicroEconomics-%20Principles%20and%20Analysis.pdf [Accessed 7 March 2017]

Shapiro, C. (1989). Theories of Oligopoly behavior. Available at: https://www.sciencedirect.com/science/article/pii/S1573448X89010095 [Accessed 7 March 2017]

Gujarati, D. (2011). Basic Econometrics. New Delhi: McGraw Hill.