Reinforcing Regulatory Regimes: How States, Civil Society, and Codes of Conduct Promote Adherence to Global Labor Standards

In response to pressure from various stakeholders, many transnational businesses have developed codes of conduct and monitoring systems to ensure that working conditions in their supply chain factories meet global labor standards. Many observers have questioned whether these codes of conduct have any impact on working conditions or are merely a marketing tool to deflect criticism of valuable global brands. Using a proprietary dataset from one of the world’s largest social auditors, containing audit-level data for 31,915 audits of 14,922 establishments in 43 countries on behalf of 689 clients in 33 countries, we conduct one of the first large-scale comparative studies of adherence to labor codes of conduct to determine what combination of institutional conditions promotes compliance with the global labor standards embodied in codes. We find that these private transnational governance tools are most effective when they are embedded in states that have made binding domestic and international legal commitments to protect workers’ rights and that have high levels of press freedom and nongovernmental organization activity. Taken together, these findings suggest the importance of multiple, robust, overlapping, and reinforcing governance regimes to meaningful transnational regulation

CUSUM charts for the Koel Common name queries.

<p>Red dots indicate a deviation of the number of tweets, in particular the upper part of the chart points to an increase in tweets. UDB: Upper Decision Boundary; LDB: Lower Decision Boundary.</p

CUSUM charts for the Bogong moth queries.

<p>Red dots indicate a deviation of the number of tweets, in particular the upper part of the chart points to an increase in tweets. UDB: Upper Decision Boundary; LDB: Lower Decision Boundary.</p

Number of hits from 1 July 2013 to 2 July 2014 for different queries as outlined in Table 1.

<p>The relevant hits were counted after manual processing of the tweet content (where feasible). The signal-to-noise ratio (SNR) represents the ratio of the number of relevant tweets over the number of total hits for each query.</p

Examples of Twitter conversation on the Bogong moth (left) and the Koel (right).

<p>Left figure reprinted from Twitter under a CC BY license, with permission from Tim the Yowie Man, original copyright 2014. Right figure reprinted from Twitter under a CC BY license, with permission from Kym Druitt, original copyright 2015.</p

Validation of Koel Twitter data against historical monthly sightings.

<p>The Twitter data are represented as time series of the weekly counts, while the historical data are shown as monthly bars that are replicated for each migration season. The grey shaded area delimits the time period in which tweets couldn’t be reliably captured.</p

CUSUM charts for the Koel symptomatic queries.

<p>Red dots indicate a deviation of the number of tweets, in particular the upper part of the chart points to an increase in tweets. UDB: Upper Decision Boundary; LDB: Lower Decision Boundary.</p

Overview of the queries used in this study.

<p>Three types of queries are distinguished: (1) a taxonomic query using the taxonomic classification, (2) a common name query using the common name of the species, and (3) a symptomatic query that searches for tweets that indicate the presence of the species without mentioning either the taxonomic or common name.</p

Lag parameters used to fit AR models for the Twitter time series.

<p>Lag parameters used to fit AR models for the Twitter time series.</p

Validation of Bogong moth Twitter data against ground truth data collected in surveys.

<p>The Twitter data are represented as time series of the weekly counts, while the survey data are shown as bar charts. The grey shaded area delimits the time period in which tweets couldn’t be reliably captured.</p