BuSuCo: Business Survey Sample Coordination

The BuSuCo package provides functions for coordinating survey samples of businesses across multiple surveys and time periods. Business survey coordination aims to control the overlap between consecutive samples of the same survey and across different surveys while managing the response burden on enterprises.

Installation

From CRAN

install.packages("BuSuCo")

Features

• Four coordination algorithms: Dutch, French, Norwegian, and Swiss approaches
• Synthetic data generation: Create realistic business data with population dynamics
• Unequal probability sampling: Support for sampling designs with unequal inclusion probabilities
• Target correlation sorting: Reorders the values of two variables until a desired correlation is reached

Quick Start

library(BuSuCo)

# Generate synthetic business data
DATA <- Gen.Data(
  N = 1000,
  periods = 5,
  D = 2,
  D.probs = c(0.8, 0.2),
  H = 3,
  borders = c(100, 500)
)

# Define survey relevance
Sur_rel <- Survey.relevance(
  DATA, periods = 5, years = c(1, 2),
  domains.survey = list(1:2, 1),
  ge.or.less = c("ge", "l"),
  TO.or.EM = c("TO", "EM"),
  size.class = c(0, 5)
)

# Create strata
strat <- Stratify(
  DATA, Survey.relevant = Sur_rel, periods = 5, years = c(1, 2),
  TO.or.EM = c("TO", "EM"), strata.TO = c(500), strata.EM = c(10)
)

# Apply Dutch BSC algorithm
result <- Dutch.BSC(
  DATA = DATA,
  period = 1,
  sf = c(0.2, 0.5),
  rf = c(0.5, 1),
  years = c(1, 2),
  chi_S = c(0.5, 2),
  survey.prio = 1:2,
  burden.periods = 5,
  seed = 123,
  Survey.relevant = Sur_rel,
  stratification = strat
)

# Access results
sampled_units <- result$in.sample
updated_data <- result$DATA

Available Algorithms

Dutch.BSC

The Modified Dutch Business Sample Coordination Algorithm achieves coordination via ordering the data according to the values of a customisable burden indicator.

French.BSC

The Modified French Business Sample Coordination Algorithm uses a theoretical sample inclusion indicator which is determined through a beta-distribution.

Norwegian.BSC

The Modified Norwegian Business Sample Coordination Algorithm achieves coordination by using two separate burden indicators, one based on the total number of draws, the other one based on the number of consecutive inclusion or pause periods.

Swiss.BSC

The Modified Swiss Business Sample Coordination Algorithm supports interval-based coordination across periods for sophisticated longitudinal sample coordination.

Documentation

For detailed documentation, see:

# Package vignette
vignette("BuSuCo")

# Function help
?Dutch.BSC
?French.BSC
?Norwegian.BSC
?Swiss.BSC
?Gen.Data

Main Functions

References

• Gros, E. (2015). Études et Simulations Relatives à la Nouvelle Méthode de Coordination des Échantillons d’Enquêtes Entreprises et Établissements Développé a l’Insee. In: Journées de Méthodologie Statistique de l’Insee, 2015, pp. 1-23.

• Guggemos, C., & Sautory, O. (2012). Sampling Coordination of Business Surveys Conducted by Insee. In: Proceedings of the Fourth International Conference of Establishment Surveys. Montréal, Canada.

• Qualité, L. (2009). Unequal probability sampling and repeated surveys (PhD Thesis). Université de Neuchâtel, Switzerland.

• Smeets, M., & Boonstra, J.H. (2018). Sampling Coordination of Business Surveys at Statistics Netherlands. In: B. Lorenc et al. (Eds.), The Unit Problem and Other Current Topics in Business Survey Methodology (pp. 1-6). Newcastle upon Tyne, UK: Cambridge Scholars Publishing.

• Straubinger, J. (2025). Methods to Coordinate Survey Samples in the Context of German Business Statistics (PhD Thesis). Universität Trier, Germany.

• Zhang, L.-C. (2009). Coordination of business surveys. In: European Establishment Statistics Workshop (EESW09). Stockholm, Sweden.

License

This package is licensed under GPL (>= 3).

Contributing

Contributions are welcome. Please feel free to submit issues.

Authors

• Johannes Straubinger
• Ralf Münnich