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Foreground or background my social responsibility: impact of the trade war on the readability of corporate social responsibility disclosures

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Abstract

This study empirically investigates the impact of the U.S.-China trade war on corporate social responsibility (CSR) disclosure readability. We first propose a measure of CSR disclosure readability with the latest word embedding model of Word2Vec. Using the U.S.-China trade war as an exogenous shock, we conduct a difference-in-differences analysis and find that firms release more readable CSR disclosures in response to the trade war. We interpret this finding to indicate that CSR disclosure can be a strategic tool to enhance the firms’ reputations when they suffer a negative shock. Moreover, such an effect is more pronounced for firms with tighter internal governance or external supervision. Our results are robust to confining our sample with a Coarsened Exact Matching algorithm and a battery of robust checks. Finally, we find that the trade war leads to increased charitable donations, indicating that our CSR readability measure is more likely to reflect actual CSR activities.

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Notes

  1. We define CSR disclosure as the nonfinancial reports containing information on the practical social responsibility activities firms have engaged in, which refers to firms disclosing their real CSR activities [5,6,7].

  2. The WinGo Textual Analytics Database (www.wingodata.com) is one of the leading Artificial Intelligence-based textual databases building on natural language processing and machine learning techniques of corporate documents released by publicly listed firms in both China and the U.S. market.

  3. For robust, we also use alternative method to calculate SIZE and find similar results (the logarithm of the market value of equity at the end of the fiscal year). We do not report them for conciseness and the full set of results is available upon request.

  4. For robust, we also control for firm’s leverage and growth opportunity with CEM. We still obtain similar results and not report them for conciseness. The full set of results is available upon request.

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Correspondence to Ruixi Long.

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Appendices

Appendix A1: Variable definitions

Variables

Definitions

Panel A: Main variables

Readability

Readability in CSR reports based on Word2Vec model and the order of words

Treat

A dummy variable equal to 1 if there is at least 1 politically connected director who was an official of the central government, a local government, or the military before the trade war, and 0 otherwise

Post

A dummy variable equal to 1 if a firm-year observation after 2017, and 0 otherwise

Earn_vol

Operating earnings volatility, measured as the standard deviation of the operating earnings during the prior five fiscal years

Ret_vol

Stock return volatility, measured as the standard deviation of the monthly stock returns in the prior year

Size

Firm size, the natural logarithm of total assets (in RMB) in year t

Age

Firm age, the natural logarithm of 1 plus the number of years since a firm has been publicly listed

mboard

Board type, a dummy variable equal to 1 if the firm is listed on the main board, and 0 otherwise

MA

Mergers and acquisitions, a dummy variable equal to 1 if the firm has undergone mergers and acquisitions in year t, otherwise 0

MB

Market-to-book ratio, the ratio of market value of equity to book value of equity in year t

IO

Institutional ownership, the proportion of firm shares owned by institutional investors

HHI

Herfindahl–Hirschman index, the sum of squared market shares for all firms in the same industry, where the market share of an individual firm is the proportion of the firm's sales to the entire industry's sales

SOE

State ownership, a dummy variable equal to 1 if the firm is a state-owned enterprise, and 0 otherwise

Panel B: Other variables

Donation

Charitable donations, defined as \(\mathrm{log}\left(1+\mathrm{\$donation}/\mathrm{operating income}\times 100\right)\) as the CSR performance measure

Log(# of words)

The natural logarithm of the word count from the CSR report

Log(# of words without figures)

The natural logarithm of the word count excluding the figure count from the CSR report

Log(# of phrases)

The natural logarithm of the phrase count from the CSR report

Log(# of phrases without figures)

The natural logarithm of the phrase count excluding the figure count from the CSR report

LEV(Leverage)

Total liabilities divided by total assets

GROWTH

The change in year-to-year total sales over last year's value

Appendix A2: Validations of our readability measure

In the following sections, we test the content validity and convergent validity of our readability measure.

  1. (1)

    Content validity

    Content validity is the degree to which a measure expresses the full domain of a specific construct [87]. Following Short et al. [88], first, we assess the content validity of our readability by reading the actual contents of CSR disclosures with high and low readability scores. We provide the related samples in Table

    Table 8 Samples of CSR reports with extreme high/low readability

    8 and show that the preliminary validity of our Readability measure indeed encapsulates the readability of CSR report. Second, we ask experts to read a random sample of CSR reports in terms of readability. Then we assess the content validity of our readability measure by measuring the Spearman correlation between the value of our Readability measure (W2V CSR Readability rank) and the readability of the CSR disclosure as assessed by experts (Expert CSR readability rank). The result in Table

    Table 9 Spearman rank order correlation between CSR readability rated by expert and that estimated by our algorithm

    9 displays a high Spearman rank-order correlation (0.952) between W2V CSR Readability rank and Expert CSR readability rank.

  2. (2)

    Convergent validity

Consistent with [89], we assess the convergent validity of readability by examining the correlations between our Readability measure and other readability indexes used in previous English literature such as the Flesch-Kincaid grade level (FK), Gunning Fog index (FI), Läsbarhetsindex (LIX), Automated Readability Index (ARI), SMOG index (SMOG), Rate Index (RIX) [32, 33, 90,91,92]. For these indexes, the higher the score, the harder the text is to comprehend (Table

Table 10 Other readability indexes formulas

10).

The One Billion Word Language Modeling Benchmark dataset was selected to calculate our proposed order complexity and a series of previously developed readability measures. The correlations were calculated. Table

Table 11 Correlation between readability with other indexes

11 reports the correlations results. Notice that our Readability has the expected relations with the alternative readability measures. That is, our Readability is significantly negative linked with the Flesch-Kincaid grade level (FK), Gunning fog index (FI), Läsbarhetsindex (LIX), Automated readability index (ARI), SMOG index (SMOG) and Rate Index (RIX). This proves the construct validity of the order-complexity-based readability measure we build in an Chinese corpus which can be extended to English corpus

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Long, R., Jia, S. Foreground or background my social responsibility: impact of the trade war on the readability of corporate social responsibility disclosures. Inf Technol Manag 24, 79–97 (2023). https://doi.org/10.1007/s10799-022-00384-6

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