Corporate Social Responsibility and Firm Performance


Corporate Social Responsibility and Firm Performance

Type of document Research Paper

Number of pages/words 8 Pages Double Spaced (approx 275 words per page)

Subject area Finance

Academic Level Undergraduate

Style Harvard

Number of sources/references 10

Order description:

topic: Corporate Social Responsibility and Firm Performance

The Model

The model and the data are the starting points of an econometric project. The first step in formulating a model is to select a topic of interest and to consider the model’s scope and purpose. In particular thought should be given to the objectives of the study, what boundaries to place on the topic, what hypotheses might be tested, what variables might be predicted, and what policies might be evaluated. Close attention must be paid, however, to the availability of adequate data. In particular the model must involve causal relations among measurable variables.

The Data

Data form an essential ingredient in any econometric study, and obtaining an adequate and relevant set of data is an important and often critical part of the econometric project. Data must be available for all the variables in the model.

National Statistical Abstracts, Statistical Yearbooks, or Statistical Handbooks, published annually by most major countries provide both summary statistics and references to primary sources. For Australia, the best source would be the ABS and the Reserve Bank. If you are planning to use a US

data the for the United States, the best starting point for the acquisition of relevant data is the Statistical Abstract of the United States which is published annually.

Likewise, the appendix to the annual Economic Report of the President contains information on fewer variables than the Statistical Abstract, but has a longer times series for these variables. It includes series on income, employment and production. The U.S. Department of Commerce, Bureau of Economic Analysis publishes the Survey of Current Business each month. Business Statistics, the biennial supplement to the Survey provides historical data and methodological notes for approximately 2,100 series. Depending on the series, the data are published on a monthly, quarterly, and or annual basis. Some series are seasonally adjusted. Numerous private agencies also collect economic data. Citibank has a database covering over 5,000 key economic time series called Citibase. The Conference Board collects data on several economic variables, as does the Institute of Social Research at the University of Michigan.

For financial data, there are several primary sources. In Australia, try the Australian Stock Exchange (ASX), CIRCA , DX database and the International Financial Statistics (IFS). The Center for Research in Securities Prices (CRSP) dataset contains data on market prices and quarterly dividends for every firm listed on the New York Stock Exchange (NYSE) since 1926. The ILS dataset, produced by Interactive Data Corporation (IDC), contains daily stock-trading volume, prices, quarterly dividends, and earnings for all NYSE and AMEX securities, and some OTC securities. The Compustat dataset, produced by Investors Management Sciences, Inc. (IMSI), contain over 20 years of annual data for more than 3,500 stocks. For international data, the United Nations Statistical Yearbook provides a wealth of data on member countries, as do statistical yearbooks of other international organizations like the OECD. The Federal Reserve Bank of St. Louis puts out International Economic Conditions which gives comparative data for Canada, France, Germany, Italy, Japan, Netherlands, Switzerland, United Kingdom, and the U.S. Various almanacs, sources on the WWW like www.census.gov, and other reference works also abound in statistics. Take a look at the course homepage and the economics department homepage. All of these sources contain data on so many topics that they may suggest a topic for the econometric project. You should also talk to librarians and other professors and just keep your eyes open.

Data can be either time-series or cross-section. For this project it is probably best not to pool data of the two types. Also it is best to avoid data sets which are too small, say less than forty observations. The data should be examined, and if necessary, refined to make them suitable for the purposes of the model. For time-series data it may be necessary to use seasonal adjustments or perhaps to eliminate certain trends. For both time-series and cross-section consideration should be given to whether to divide the data into separate samples or perhaps exclude certain observations. Thus in time-series data it may (or may not) be appropriate to exclude war years or years of a recession. In a cross-section of nations it may be inappropriate to include all countries that are UN members. The developed countries might be treated as one group and the developing countries as another group. Dividing the data this way into sub samples not only leads to more homogenous data sets but also facilitates the study by allowing comparative analyses.

The EstimationAfter both the model and data have been developed, the next step is to utilise econometric techniques to estimate the model. Your final paper is expected to use multiple regression analysis to estimate your multivariate model and test relevant hypotheses. You recommended using STATA 12 to run your model as this is the statistical package that will be used in the unit. For this project it is best if the dependent variable is a quantitative variable. Do make sure that you have enough observations for all the variables and that the dependent and independent variables show some variation over the observations. You should not be estimating any identities, or using the dependent variable on the right hand side of the equation unless it is lagged.
The Write-Up

Title Page

Abstract

Table of Contents

Introduction
Review of Previous Literature
Specification of the Model
Data Description
Empirical Results (or Analysis)
Conclusions & Recommendations
List of References Appendices

Introduction
Discuss the nature and objectives of the topic, provide a general description of the scope of the model, and the hypotheses to be tested and/or policies to be evaluated. Here you should motivate your paper by explaining why the issues you are studying are important.

Review of Previous Literature
Discuss the approaches and results of previous studies of this topic or related topics. Explain why your paper is better than the previous literature.

Specification of the Model
Define and discuss the specification of your model. What variables are included in the model? Explain why you chose those variables and the role they play in the model. Have you included all the important variables in the model? What are the expected signs of all the coefficients? Explain the stochastic and other assumptions being made in the model.

Data Description
Provide complete descriptions of all the data, their sources, refinements used, and their possible biases or other possible weaknesses.

Empirical Results
Present the estimates of the model and its related statistics such as standard errors, t statistics and the R2 . Discuss which coefficients are significant at the 5% and 1% levels. If relevant, a discussion of possible serial correlation and its correction; a discussion of possible heteroscedasticity and its correction; and a discussion of possible multicollinearity and its correction. Estimate alternative models to test the robustness of the results. Discuss the signs and magnitudes of the estimated coefficients and their comparisons to predicted or theoretical signs and magnitudes. What have we learned? Consider how the model might be reformulated in future studies, and implications for future econometric research. Please see the suggested format of Summary Table of Results.

Conclusions and Recommendations
Sum up the major results of your study. Also, discuss the policy implications of the study.

Reference List

Include complete citations of all items referred to in the paper.

Appendices (Data & Computer Output)

If reasonable, provide a table of all the data used. Also provide the summary statistics for the data. All computer output should be attached or if too big, a disk should also be submitted. Also, see the suggested Table to be included as an appendix.

https://corporate-sustainability.org/wp-content/uploads/91-Flammer.Apr_.13pdf.pdf

Research topic: Corporate social responsibility (CSR) and firm performance

PURPOSE:

The concept of corporate social responsibility (CSR) has emerged over the past 30 years to occupy a significant role in certain aspect of the organizational theory. The purpose of this research is to examine the impact of CRS and firm’s operational competitive performance the concept of social responsibility of corporation has ingenerated considerable interest in Australia in recent years. While previous research on the relationship between corporate social responsibility and financial performance has largely been based on international data, this report will examine the relationship between the adoption of corporate social responsibility and the financial performance of companies within Australia.

Specification of the model

Variables:

Shareholders responsibility
Employees responsibility
Supply chain (suppliers and customers) responsibility
Environment responsibility
Public responsibility
Model:

This study is designed to examine the relationship between CSR and corporate financial performance (CFP).it appears that the regression model is used to investigate the association between social and financial performance .regression analysis is a statistic process for estimating the relationship among variables.

Following equation provides a further study of the relationship between unitary CSR and firm performance. All of the CSR sub-criteria are explanatory variables in regression equation where S, EM,EN, P,C Stand for shareholders responsibility ,employees responsibility ,environment responsibility, public responsibility and supplier, customer and consumer responsibility ,respectively.

Reference:

www.jstor.org/stable/256342

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