Managerial economics. Decisions regarding the allocation of scarce resources
Managerial economics is a subfield of economics which seeks to amalgamate the basic definitions of economics such as allocation of resources and study of production, distribution, and consumptions with real-world business practices. The primary purpose of managerial economics is to provide terminology and reasoning for the improvement of managerial decision-making in an organization.We will write a custom Managerial Economics and Business Analysis specifically for you
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This implies an understanding of the microeconomic focus of entity behaviour in the economy as well as the macroeconomic environment which evaluates the state of things, both of which are vital for management. The science of managerial economics helps to rationalize economic theory and determine a logic solution which a firm may face in its operations. It includes understanding and evaluating various data regarding econometrics, statistics, analytics, and a firm’s consumers, competitors, and suppliers within the global business environment (Principles of managerial economics n.d.).
In a modern global economy, the risks of business have increased as financial crises are more frequent and growth is comparatively sluggish than in previous decades. Managerial economics focuses on understanding this competitive business environment and examines the optimal decision-making of firms (Salvatore 2015). Economics is inherently a study of the allocation of resources, and manager decisions involve the distribution of firm’s resources to meet the managerial objective. The three primary allocation decisions that managers must make are: 1) What must be produced? 2) How it should be produced? 3) For whom should it be produced? It is the responsibility of a manager to identify a correct combination of inputs and factors to achieve the set managerial goal within the budget and resource allocated by the organization, focusing on maximizing output with minimal cost (Leiblein, Chen & Posen 2017).
Supply, demand, and equilibrium price. Non-price determinants of supply and demand
Before beginning to define these concepts, it is vital to identify the context of a free market, one where prices and quantities are set by informed consumers and producers of the traded goods, without the influence of other market powers. The supply and demand model operates within the context of a free market. The demand curve shows the number of goods that consumers are willing to buy at each price point, commonly having a downward slope since the lower the price, the greater the quantity purchased. Meanwhile, the supply curve demonstrates the quantities sellers are willing to sell for at each price at the same time (Principles of macroeconomics 2016).
Equilibrium is reached when the quantity supplied equals the quantity demanded, a state where neither quantity or prices of products are changing. Achieving market equilibrium is optimal as it avoids externalities which may affect parties that are not part of the transaction. The law of market equilibrium states that a free market, if imbalanced, constantly strives towards achieving equilibrium (Law of market equilibrium n.d.).
Non-price determinants are considered to be influenced outside the traditional supply-demand curve which may result in shifts to either side of the equilibrium regardless of the pricing points. Non-price determinants of supply include changes in costs of production factors, changes in technology, the price of related or similar goods, government interventions, seasonal demands and future expectations, and economic shocks. Meanwhile, non-price determinants of demand include changes in population income, shifts in consumer preferences, prices of related goods, demographic changes, climate and season change, market size, branding, complementary goods, and future expectations as well (Goodwin et al. 2018).
Price elasticity, cross-elasticity, and income elasticity
The price elasticity of demand is the direct measurement of the quantity demanded of a product at its price ranged. It is commonly expressed by the formula:Get your
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Price elasticity is a critical indicator to economists of the health and functioning of the economy. Some goods are inherently inelastic, given that price does not significantly vary despite supply or demand, such as gas which will always be in demand to fuel transportation. Meanwhile, other goods price elastic and its supply and demand is directly influenced by cost (Price elasticity of demand and price elasticity of supply n.d.)
Cross-elasticity of demand attempts to measure the response of quantity demanded of a good when a price for another, usually its substitute or complement, changes. The cross-elasticity for substitute goods is always positive since when the price for one good increases, the demand for its cheaper substitute will increase automatically. However, that only holds if the substitutes are strongly correlated. Meanwhile, complementary goods have a negative cross elasticity of demand since the price hike for the main product will result in similar price hikes for complementary products, and therefore, decreased demand. Firms can use cross-elasticity to establish pricing points at which to sell their products based on substitutes on the market, thus helping to increase sales (Gregory 2018).
Income elasticity of demand is the measurement of the change in quantity demanded a good in comparison to the change in real income of consumers. Income elasticity is helpful in identifying whether a good is a necessity or a luxury. Therefore, when consumer income changes, the responsiveness for the demand of goods may change as well. Some goods will be bought even if real income drops, signifying it as a vital necessity. However, other luxury goods have a high-income elasticity as demand rises in proportion to positive income changes. In turn, inferior goods (lower in price and quality) become less demanded. Businesses can benefit from understanding the macroeconomics of income elasticity to predict the impact of economic cycles on sales (Lumen Learning 2018).
Forecasting techniques in business
Business forecasting is the practice of predicting and estimating future developments in a business environment such as expenses, sales, and profits. Considering that these factors and other fluctuations in economic activity can a significant effect on the business operation and profit margins, it is vital to incorporate business forecasting into corporate planning and provide the necessary tools for managers to make decisions based on anticipated economic trends (Hyndman & Athanasopoulos 2018). Business forecasting can be split into two areas, qualitative and quantitative.
Qualitative techniques include market research and the Delphi method. Market research consists of conducting large-scale surveys of the population to determine opinions or perceptions of a company or a particular good or service to determine its success once developed and launched. Meanwhile, the Delphi method is gathering field expert opinion regarding the business environment and how a product would fare in it and compiling these responses into a comprehensible forecast (The Delphi method n.d.).
Meanwhile, quantitative methods are extensive and vary significantly. There is the time-series (historical) forecasting technique which measures data over time to determine potential trends. The empirical method attempts to determine business sequences based on previous patterns using indexes to anticipate trends. Scientific forecasting focuses on using the scientific method to determine causal relationships. The direct or bottom-up method attempts to collect information from internal operations such as production and sales to compile data on a company as a whole and use for aggregated data prognosis. Finally, the indirect or top-down approach collects data from the industry and narrows it down to a specific case of a particular product (Dhaval n.d.).We will write a custom
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Game theory in management decisions
Game theory is a science of mathematical models and strategic decision-making which seeks to determine the best outcome in a wide array of scenarios or “games.” Game theory was developed to create various forms of conflict and choice for the players, where participants could either compete and cooperate. Game theory incorporates disciplines of mathematics, psychology, and philosophy, but its principles are commonly applied in the complexity of other fields including economics, finance, and business to enhance reasoning or identifying decision-making skills in a multifactorial environment (Dixit & Nalebuff n.d.).
Game theory plays an important role in a business helping develop business models and decision-making in a variety of contexts such as supply chains. The application of game theory reflects on the thought process of firms in their market strategy and interaction with other market entities. Various components of game theory are meant to simulate the business environment and then mathematical models can be used to calculate the best outcome from an empirical perspective (Budler & Trkman 2017).
As mentioned above, game theory offers valuable insight into the strategic behaviour of stakeholders in a business environment. Through game theory, the strategic behaviour of cooperation, risk attitude, access to information and uncertainty. The aspects of the game theory that studies the psychology of behaviour and fulfilment of personal interests for individuals can be vital for managers when applied to bargaining, negotiations, voting, or managing personnel (Madani et al. 2015).
Since managers often operate in contexts of uncertainty in a business environment, game theory is a critical strategic tool. The erratic global economy can cause radical shifts in demand, industrial capacity, market prices. Game theory offers guidance in difficult or unprecedented situations by helping develop various outcomes of reasonable actors and present the advantages or disadvantages of each choice (Lindstädt & Müller 2009).
Qualitative and quantitative techniques of investigation
Qualitative research is a non-empirical, primarily exploratory type of investigation which seeks to observe and understand the reasons, opinions, and motivations behind the selected topic of research. The strength of qualitative research as it provides a “human” side to any issue by providing complex textual descriptions of experiences and opinions. Furthermore, qualitative research helps to identify the influence of intangible factors such as social norms, gender, ethnicity, and others.
Common qualitative research techniques include participant observation by collecting data on naturally occurring behaviours. Also, in-depth interviews are practised by collecting comprehensive knowledge regarding individuals, their histories, and motivations to behaviours, a technique particularly viable for sensitive topics. Finally, focus groups are an effective method, helping to elicit data from a small group on issues such as cultural norms or trends in a more private setting than observing natural behaviours in context (Teherani et al. 2015).
Quantitative research is based on empirical and numerical data, using mathematical analysis, statistics, and other direct data to draw its conclusions. Quantitative research is more popular and commonly considered to be more valid, particularly in business as it allows for more predictability using analytics and a range of data points collected from a business enterprise can be useful in decision-making.Not sure if you can write
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Some quantitative methods include survey research using either cross-sectional or longitudinal surveys that allows for various types of data collection, correlational research which establishes a link between closely related aspects, and causal-comparative research that establishes a causal relationship. However, the distinguishing aspect of quantitative research is data analysis which allows to implement various models and statistical tests with the data in order to establish patterns (Saunders, Lewis & Thornhill 2016).
Budler, M & Trkman, P 2017, ‘The role of game theory in the development of business models in supply chains’, in 2017 IEEE Technology & Engineering Management Conference (TEMSCON), IEEE, Santa Clara, CA, pp. 1-5.
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