Quantitative Methods For Social Sciences Lionel Nesta Observatoire Français des Conjonctures Economiques CERAM February-March-April 2008

Objective of The Course  The objective of the class is to provide students with a set of techniques to analyze quantitative data. It concerns the application of quantitative and statistical approaches as developed in the social sciences, for future decision makers, policy markers, stake holders, managers, etc.  All courses are computer-based classes using the SPSS statistical package. The objective is to reach levels of competence which provide the student with skills to both read and understand the work of others and to carry out one's own research.  Class Password: stmarec123

Examples  Rise in biotechnology  Should the EU fund fundamental research in biotechnology?  Has biotechnology increased the productivity of firm-level R&D?  Did it increase the speed of discovery in pharmaceutical R&D?  Increasing university-industry collaborations  Does it facilitate innovation by firms?  Does it increase the production of new knowledge by academics?  Does it modify the fundamental/applied nature of research?

Examples  Economic (productivity) Growth  Does it come mainly from new firms or improving existing firms?  Is market selection operating correctly?  Why do good firms exit the market?  How does the organisation of knowledge impact on performance?  How do knowledge stock and specialisation impact on productivity?  How do firms enter into new technological fields?  Do firms diversify in new technologies/businesses purposively?

Structure of the Class  Class 1 : Descriptive Statistics  Class 2 : Statistical Inference  Class 3 : Relationship Between Variables  Class 4 : Ordinary Least Squares (OLS)  Class 5 : Extension to OLS  Class 6 : Qualitative Dependent variables

Structure of the Class  Class 1 : Descriptive Statistics  Mean, variance, standard deviation  Data management  Class 2 : Statistical Inference  Class 3 : Relationship Between Variables  Class 4 : Ordinary Least Squares (OLS)  Class 5 : Extension to OLS  Class 6 : Qualitative Dependent variables

Structure of the Class  Class 1 : Descriptive Statistics  Class 2 : Statistical Inference  Distributions  Comparison of means  Class 3 : Relationship Between Variables  Class 4 : Ordinary Least Squares (OLS)  Class 5 : Extension to OLS  Class 6 : Qualitative Dependent variables

Structure of the Class  Class 1 : Descriptive Statistics  Class 2 : Statistical Inference  Class 3 : Relationship Between Variables  ANOVA, Chi-Square  Correlation  Class 4 : Ordinary Least Squares (OLS)  Class 5 : Extension to OLS  Class 6 : Qualitative Dependent variables

Structure of the Class  Class 1 : Descriptive Statistics  Class 2 : Statistical Inference  Class 3 : Relationship Between Variables  Class 4 : Ordinary Least Squares (OLS)  Correlation coefficient, simple regression  Multiple regression  Class 5 : Extension to OLS  Class 6 : Qualitative Dependent variables

Structure of the Class  Class 1 : Descriptive Statistics  Class 2 : Statistical Inference  Class 3 : Relationship Between Variables  Class 4 : Ordinary Least Squares (OLS)  Class 5 : Extension to OLS  Regressions diagnostics  Qualitative explanatory variables  Class 6 : Qualitative Dependent variables

Structure of the Class  Class 1 : Descriptive Statistics  Class 2 : Statistical Inference  Class 3 : Relationship Between Variables  Class 4 : Ordinary Least Squares (OLS)  Class 5 : Extension to OLS  Class 6 : Qualitative Dependent variables  Linear probability model  Maximum likelihood (logit, probit)

Class 1 Descriptive Statistics

Types of Data Descriptive statistics is the branch of statistics which gathers all techniques used to describe and summarize quantitative and qualitative data. Quantitative data  Continuous  Measured on a scale (value its the range)  The size of the number reflect the amount of the variable  Age; wage, sales; height, weight; GDP Qualitative data  Discrete, categorical  The number reflect the category of the variable  Type of work; gender; nationality

Descriptive Statistics All means are good to summarize data in a synthetic way: graphs; charts; tables. Quantitative data  Graphs: scatter plots; line plots; histograms  Central tendency  Dispersion Qualitative data  Graphs: pie graphs; histograms  Tables, frequency, percentage, cumulative percentage  Cross tables

Central Tendency and Dispersion  A distribution is an ordered set of numbers showing how many times each occurred, from the lowest to the highest number or the reverse  Central tendency: measures of the degree to which scores are clustered around the mean of a distribution  Dispersion: measures the fluctuations around the characteristics of central tendency  In other words, the characteristics of central tendency produce stylized facts, when the characteristics of dispersion look at the representativeness of a given stylized fact.

Central Tendency  The mode  The most frequent score in distribution is called the mode.  The median  The middle value of all observed values, when 50% of observed value are higher and 50% of observed value are lower than the median  The mean  The sum of all of the values divided by the number of value The mode, the mean and the median ore equal if and only of the distribution is symmetrical and unimodal.

Dispersion  The range  Difference between the maximum and minimum values  The variance  Average of the squared differences between data points and the mean (average) quadratic deviation  The standard deviation  Square root of variance, therefore measures the spread of data about the mean, measured in the same units as the data

Dispersion  The range  Difference between the maximum and minimum values  The variance  Average of the squared differences between data points and the mean (average) quadratic deviation  The standard deviation  Square root of variance, therefore measures the spread of data about the mean, measured in the same units as the data

Research Productivity in the Bio-pharmaceutical Industry EU Framework Programme 7

Stylised Facts about Modern Biotech 1. Innovations emerge from uncertain, complex processes involving knowledge and markets: Roles of networks. 2. Economic value is created in many ways – globally and in geographical agglomerations 3. Various linkages exist among diverse actors (LDFs, DBFs, Univ, Venture Capital) in innovation processes, but the firm plays a particularly important role. 4. Regulations, social structures and institutions affect on- going innovation processes as well as their impacts on society: Importance of IPR.

SPSS Statistical Package for the Social Sciences

The SPSS software  Statistical Package for the Social Sciences (1968)  Among the most widely used programs for statistical analysis in social sciences.  Market researchers, health researchers, survey companies, government, education researchers, and others.  Data management (case selection, file reshaping, creating derived data)  Features of SPSS are accessible via pull-down menus  The pull-down menu interface generates command syntax.

SPSS : Opening SPSS

SPSS : Importing data

 Settings in the “import text” dialogue box  No predefine format (1)  Delimited (2)  First lines contains the variable names (2)  One observation per line // all observations (3)  Tab delimited only (4)  Finish (6)

SPSS windows  SPSS has opens automatically windows  The datasheet window  Observe, manage, modify, create, data  The results window  Everything you do will be stored there  The syntax window can be opened

SPSS : Data sheet (1)

SPSS : Data sheet (2)

SPSS : Result / Journal

SPSS : Saving data

SPSS : working, at last!

Recoding Variables  Changing existing values to new values (biotechnologie → DBF, pharmaceutique → LDF) 1 2 3

Computing New Variables  Taking logarithm (normalization of continuous variables) 1 2

Creating Dummy Variables  Taking logarithm (normalization of continuous variables) 1 2 3

Computation of Descriptive Statistics 1 2 3

Descriptive Statistics

Splitting Database 12

Descriptive Statistics (by type)

Assignments  Compute logarithm for all quantitative variables patent, assets, rd, and name them lnpatent, lnassets and lnrd, respectively.  Compute descriptive statistics for both LDFs and DBFs.  Draw conclusion by comparing means.

Logarithm  Normalization  Taking the logarithm is a transformation which usually normalize distribution.  Elasticities  A change in log of x is a relative change of x itself.  Cobb-Douglas production function