Department of Statistics, UCLA

The case study engages students and makes them use the tools they know to investigate a complex process. At the same time, they learn basic time-series concepts using only their intro stats tools.

Texas State University, Women Doing Math and Talk Math 2 Me Joint Statistics Seminar

Non-response in establishment surveys is a very important problem that can bias results of statistical analysis. The bias can be considerable when the survey data is used to do multivariate analysis that involve several variables with different response rates, which can reduce the effective sample size considerably. Fixing the non-response, however, could potentially cause other econometric problems. This paper uses an operational approach to analyze the sensitivity of results of multivariate analysis to multiple imputation procedures applied to the U.S. Census Bureau/NSF‘s Business Research and Development and Innovation Survey (BRDIS) to address item non-response. Multiple imputation is first applied using data from all survey units and periods for which there is data, presenting scenario 1. A scenario 2 involves separate imputation for units that have participated in the survey only once and those that repeat. Scenario 3 involves no imputation. Sensitivity analysis is done by comparing the model estimates and their standard errors, and measures of the additional uncertainty created by the imputation procedure. In all cases, unit non-response is addressed by using the adjusted weights that accompany BRDIS micro data. The results suggest that substantial benefit may be derived from multiple imputation, not only because it helps provide more accurate measures of the uncertainty due to item non-response but also because it provides alternative estimates of effect sizes and population totals.

This paper uses new business micro data from the Business Research and

Development and Innovation Survey (BRDIS) for the years 2008-2011 to relate

the discrete innovation choices made by U.S. companies to features of the com-

pany that have long been considered to be important correlates of innovation.

We use multinomial logit to model those choices. Bloch and Lopez-Bassols

(2009) used the Community Innovation Surveys (CIS) to classify companies

according dual, technological or output-based innovation constructs. We found

that for each of those constructs of innovation combinations considered, man-

ufacturing and engaging in intellectual property transfer increase the odds

of choosing innovation strategies that involve more than one type of cate-

gories (for example, both goods and services, or both tech and non-tech) and

radical innovations, controlling for rm size, productivity, time and type of

R&D. Company size and company productivity as well as time do not lean

the choices in any particular direction. These associations are robust across

the three multinomial choice models that we have considered. In contrast with

other studies, we have been able to use companies that do and companies that

do not innovate, and this has allowed to rule out to some extent selectivity

bias.

 This paper presents a novel empirical study of innovation practices of U.S. com-

panies and their relation to productivity levels using new business micro data from

the Business Research and Development and Innovation Survey (BRDIS) for the years

2008-2011. The paper follows the work of Frenz and Lambert, who use factor analysis

to reduce a set of inputs and outputs of innovation activities into four latent unob-

served innovation modes or practices for OECD countries using Community Innovation

Surveys (CIS). Patterns obtained with BRDIS data are very similar to those found by

those authors in some OECD countries. Companies are grouped according to their

scores across the four factors to see that in large, small and medium companies more

than one mode of innovation practices prevails. The next step in the analysis links dif-

ferent types of innovation practices to levels of productivity using regression analysis.

The four innovation modes have a statistically signicant positive relation with the

level of productivity, other things constant. The paper demonstrates the possibility of

taking into account the multidimensionality of innovation without the use of composite indicators.