Skills Data Science Designing Cross-Country Empirical Studies

Designing Cross-Country Empirical Studies

v20260724
jibs-methods
A specialized guide for structuring rigorous empirical studies, particularly for journals like JIBS. It helps researchers map their research phenomenon to the correct cross-country, multilevel, or panel design. Critically, it mandates planning for advanced econometric issues—including common-method variance, measurement equivalence, and dynamic endogeneity—at the initial design stage to ensure methodological rigor.
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Overview

Cross-Country & Multilevel Research Design (jibs-methods)

When to trigger

  • You are choosing a design for a cross-border or multi-country question
  • Your data are nested (individuals in firms in countries) and you need the right structure
  • You anticipate reviewers probing measurement equivalence, CMV, or endogeneity
  • A reviewer says "the design cannot support a cross-country inference"

JIBS is method-pluralistic; rigor is the gatekeeper

JIBS is paradigmatically and methodologically pluralistic by policy — quantitative, qualitative, mixed, and pure-theory work are all in scope. There is no single mandated method. But its practical center of gravity is theory-driven, hypothesis-testing empirical work on cross-country or multilevel data, held to demanding standards. Pick the design that fits the phenomenon, then engineer in the three rigor demands JIBS reviewers apply most insistently.

Match the design to the claim

Phenomenon / claim Design
Country/culture shapes a firm or individual outcome Multilevel (firm/individual nested in country), with country-level predictors
Internationalization as a path-dependent process Longitudinal/dynamic panel of firms over time (FDI, entry, commitment)
Cross-cultural attitudes/behavior Multi-country survey with translated, back-translated instruments
Entry-mode / location / governance choice Discrete-choice archival design on cross-border deals (SDC, fDi Markets)
Deep contextual MNE puzzle Comparative / multi-country case study (qualitative)

Build in JIBS's three rigor demands at the design stage

  1. Cross-national measurement equivalence. Because constructs are studied across countries and cultures, plan multi-group measurement from the outset: identical (translated/back-translated) instruments, and a plan to test configural, metric, and scalar invariance. Equivalence is a first-order concern at JIBS, not an afterthought.
  2. Common-method variance (CMV). JIBS actively gatekeeps CMV: survey/same-respondent designs that appear to suffer from CMV are routinely asked to run validity checks and resubmit, per a dedicated "From the Editors" CMV editorial. Design procedural remedies now — temporal/source separation, different respondents for predictor and outcome, archival anchors, a marker variable.
  3. Endogeneity / "dynamic endogeneity." For internationalization-as-process designs, JIBS names dynamic endogeneity as a recurring demand: past internationalization, performance, and unobserved firm heterogeneity co-evolve. Plan identification now — instruments, dynamic-panel GMM, lag structure, or a natural experiment — rather than bolting it on later.

Consult the methods-editorial canon

The AIB Research Methods SIG curates ~28 JIBS "From the Editors" methods editorials (2008–2022) on endogeneity, multilevel models, interaction effects, QCA, replication/reproducibility, CMV, and p-values. Reviewers treat these as de facto standards — align your design with the relevant editorial and cite it.

Execution bridge (StatsPAI / Stata MCP)

For the empirical / causal lane, estimate and audit rather than only specify. Full map: execution-with-mcp. JIBS is international business — cross-country panels with confounded institutions; emphasize fixed effects, clustering, and endogeneity of location / entry choices.

  • detect_designrecommend → fit with as_handle=trueaudit_result to enumerate the checks the design owes.
  • Panel / staggered DiD: callaway_santanna / sun_abraham + bacon_decomposition
    • honest_did_from_result. IV: effective_f_test + anderson_rubin_ci. RDD: rdrobust + mccrary_test.
  • Experiments: randomization-based inference and romano_wolf for the many-outcome family-wise correction reviewers expect.

Match the toolchain to the reviewer pool, and report the effect size the venue wants. A run end-to-end (synthetic data, real returns) is in the JF execution walkthrough.

Output format

【Design】multilevel / dynamic-panel / multi-country survey / archival choice / case ...
【Levels & nesting】 ... ; SE-clustering/aggregation plan
【Measurement equivalence plan】translation + configural/metric/scalar test ...
【CMV plan】procedural separations designed ...
【Endogeneity/dynamic-endogeneity plan】identification strategy ...
【FTE editorial alignment】which editorial(s) the design answers to ...
【Next step】jibs-data-analysis

Anti-patterns

  • Pooling countries with no plan to test measurement invariance.
  • A single-source, single-wave, same-respondent cross-country survey with no CMV remedy.
  • Internationalization-process design with no endogeneity strategy.
  • Choosing a method by habit rather than fit to the phenomenon.
Info
Category Data Science
Name jibs-methods
Version v20260724
Size 5.58KB
Updated At 2026-07-28
Language