1 Equity Offerings, Technology Investments, and Employee Skill Composition E. Han Kim, Heuijung Kim, Yuan Li, Yao Lu, and Xinzheng Shi † Abstract We examine whether and how equity financing affects technology investments, demand for skills, firm-level employment, and wages. Analyses of job advertisements posted online reveal that seasoned equity offerings (SEOs) are associated with higher demands for computer skills and non-routine task skills. To draw causal inferences, we construct an instrument using exogenous shocks on the eligibility to issue SEOs, treatments of which are based on past irreversible firm behavior. We find capital infusion through SEOs increases purchases of machines and equipment, innovations, and employee skill composition. SEOs lead to a net decline in firm-level employment, displacing a greater number of low-skilled workers than adding high-skilled employees. Average wages increase because of higher skill composition, but total wages remain unchanged because the higher average wage applies to a smaller number of employees. These results illustrate channels through which stock markets affect labor markets. First Draft: September 5, 2017 Revised: January 18, 2018 Keywords: Capital Skill Complementarity, Equity Issuance, Employment, Investment in Technology, Innovations, Wages. JEL Classifications: G32, J24, J31 † E. Han Kim is Everett E. Berg Professor of Finance at the University of Michigan, Ross School of Business, Ann Arbor, Michigan 48109: [email protected]. Heuijung Kim is an instructor at Sungkyunkwan University, SKK Business School, Seoul, Korea: [email protected]. Yuan Li is a doctoral student at University of Southern California: [email protected]. Yao Lu is Associate Professor of Finance at Tsinghua University School of Economics and Management, Beijing, China: [email protected]. Xinzheng Shi is Associate Professor of Economics at Tsinghua University School of Economics and Management, Beijing, China: [email protected]. We are grateful to Ben Iverson, Francine Lafontaine, Binying Liu, John McConnell, Jagadeesh Sivadasan, Stefan Zeume, and participants at 2017 Red Rock and HKUST conferences and seminars at University of Michigan, SUNY/Buffalo, University of Georgia, UNLV, and Korea University for helpful suggestions, and Zhang Peng and Yeqing Zhang for excellent research assistance. This project received generous financial support from Mitsui Life Financial Research Center at the University of Michigan. Yao Lu acknowledges support from Project 71722001 of National Natural Science Foundation of China. Xinzheng Shi acknowledges support from Project 71673155 of National Natural Science Foundation of China.
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1
Equity Offerings, Technology Investments, and Employee Skill
Composition
E. Han Kim, Heuijung Kim, Yuan Li, Yao Lu, and Xinzheng Shi†
Abstract
We examine whether and how equity financing affects technology investments, demand for skills,
firm-level employment, and wages. Analyses of job advertisements posted online reveal that seasoned
equity offerings (SEOs) are associated with higher demands for computer skills and non-routine task
skills. To draw causal inferences, we construct an instrument using exogenous shocks on the
eligibility to issue SEOs, treatments of which are based on past irreversible firm behavior. We find
capital infusion through SEOs increases purchases of machines and equipment, innovations, and
employee skill composition. SEOs lead to a net decline in firm-level employment, displacing a greater
number of low-skilled workers than adding high-skilled employees. Average wages increase because
of higher skill composition, but total wages remain unchanged because the higher average wage
applies to a smaller number of employees. These results illustrate channels through which stock
markets affect labor markets.
First Draft: September 5, 2017
Revised: January 18, 2018
Keywords: Capital Skill Complementarity, Equity Issuance, Employment, Investment in Technology,
Innovations, Wages.
JEL Classifications: G32, J24, J31
†E. Han Kim is Everett E. Berg Professor of Finance at the University of Michigan, Ross School of Business,
Ann Arbor, Michigan 48109: [email protected]. Heuijung Kim is an instructor at Sungkyunkwan University,
SKK Business School, Seoul, Korea: [email protected]. Yuan Li is a doctoral student at University of
Southern California: [email protected]. Yao Lu is Associate Professor of Finance at Tsinghua
University School of Economics and Management, Beijing, China: [email protected]. Xinzheng Shi
is Associate Professor of Economics at Tsinghua University School of Economics and Management, Beijing,
China: [email protected]. We are grateful to Ben Iverson, Francine Lafontaine, Binying Liu, John
McConnell, Jagadeesh Sivadasan, Stefan Zeume, and participants at 2017 Red Rock and HKUST conferences
and seminars at University of Michigan, SUNY/Buffalo, University of Georgia, UNLV, and Korea University
for helpful suggestions, and Zhang Peng and Yeqing Zhang for excellent research assistance. This project
received generous financial support from Mitsui Life Financial Research Center at the University of Michigan.
Yao Lu acknowledges support from Project 71722001 of National Natural Science Foundation of China.
Xinzheng Shi acknowledges support from Project 71673155 of National Natural Science Foundation of China.
2
1. INTRODUCTION
News stories abound about automation, robots, and artificial intelligence replacing workers. Under the
catchy title “Will robots displace humans as motorized vehicles ousted horses?” The Economist (April
1, 2017) cites evidence from Acemoglu and Restrepo (2017) and warns that robots might replace
humans and depress wages.1 Adopting and advancing technology, whether it involves robots, AI, or
other automation technologies, requires capital, often for large investments with uncertain outcomes.
When such investments require external funding, firms typically access capital markets. So if
technology affects employment, what role does capital markets play in that process?
To investigate the issue, we estimate the impacts that capital infusion through equity offerings
has on firm-level investments in technology, employee skill composition, employment, and wages.
Capital-skill complementarity predicts that technology embodied in capital complements non-routine
abstract tasks and substitutes for routine tasks.2 Because non-routine abstract tasks tend to require
higher skills than routine tasks, the complementary and substitution effects may add high-skilled
workers and displace low-skilled workers, increasing the relative proportion of high-skilled
workers—a higher skill-composition of employees. If these predictions prevail, what happens to
employment and wages at the firm level? Do displaced unskilled workers outnumber newly added
skilled employees? A higher skill composition implies a higher firm average wage due to the skill
premium (Card, 1999). However, if the higher average wage applies to a smaller work force, it is not
clear whether the total wage will increase or decrease.
Equity offerings are similar to other means to raise equity, such as family or venture financing;
they not only raise new equity but also help raise incremental debt by increasing the firm’s equity
base. The finance literature offers numerous studies on how equity offerings affect shareholder value,
1 A Wall Street Journal article, “Firms leave the bean counting to the robots,” (October 23, 2007) also describes
how robots are replacing workers but more even-handedly. 2 See Griliches (1969); Hamermesh (1993); Fallon and Layard (1975); Berman, Bound, and Griliches (1994);
Goldin and Katz (1996, 1998); Doms, Dunne, and Troske (1997); Autor, Katz and Krueger (1998); Machin and
Van Reenen (1998); Krusell, et al. (2000); Caroli and Van Reenen (2001); Bresnahan, Brynjolfsson, and Hitt
(2002); Autor, Levy, and Murnane (2003); Duffy, Papageorgiou, and Perez-Sebastian (2004); Lindquist (2004);
Acemoglu and Finkelstein (2008); Yasar and Paul (2008); Ben-Gad (2008); Lewis (2011); Parro (2013); and
Akerman, Gaarder and Mogstad (2015); Kasahara, Liang, and Rodrigue (2016); Hershbein and Kahn (2016);
Acemoglu and Restrepo (2016, 2017); and Autor and Salomons (2017).
3
financial policies, investments, and agency costs, but mostly from the shareholder perspective.3 This
focus on capital providers omits an important stakeholder—employees, who also can be affected by
equity offerings. Equity offerings can be either seasoned equity offerings (SEOs) or initial public
offerings (IPOs), depending on whether a publicly listed or a private firm makes the offering. We do
not consider IPOs because of the confounding effects associated with private firms becoming public
firms (Bernstein, 2015). SEOs are devoid of the confounding effects since listed firms make SEOs.
Studying how SEOs affect employment and skill composition is challenging because SEOs
are endogenous and data on employee skills are scarce. We address endogeneity issues by
constructing an instrument using the 2006 and 2008 regulatory shocks on the eligibility to issue public
SEOs in China, both of which are exogenous to individual firm decisions. The Chinese experiments
also help solve the data problem. The China Securities Regulatory Commission (the CSRC,
equivalent to the SEC in the U.S.) requires publicly listed firms to disclose yearly the composition of
the workforce by occupation and education in company filings. Since occupation and education are
related to skill, the data allow us to infer how SEOs affect the workforce skill composition.
Additionally, Chinese accounting rules require publicly listed firms to disclose payroll information in
financial statements, providing access to reliable wage data.
Our sample period is 2000 through 2012, spanning the exogenous shocks on the eligibility to
issue SEOs. Because of China’s unique political and economic system, one may be concerned with
the generalizability of results obtained from the data. Appendix 1 reviews the literature on the Chinese
labor market, which suggests that major economic reforms undertaken during the 1980s and 1990s
had transformed the labor market in the 2000s to one resembling those of capitalistic market-oriented
economies. The Chinese stock market also became the second largest in the world during our sample
period in terms of both market cap and total value of shares traded. One limitation of our sample is
that it contains only publicly listed firms. However, publicly listed firms play an important role in the
3 Studies relating equity offerings to shareholder value include Asquith and Mullins (1986); Masulis and Korwar
(1986); Korajczyk, Lucas, and McDonald (1990); Eckbo and Masulis (1995); Bayless and Chaplinsky (1996);
and Eckbo, Masulis, and Norli (2000). Studies relating equity offerings to financial policies include Pagano,
Panetta, and Zingales (1998); DeAngelo, DeAngelo, and Stulz (2010); McLean (2011); and Gustafson and Iliev
(2017). Studies relating equity offerings to corporate investments include Kim and Weisbach (2008) and
Gustafson and Iliev (2017). Studies relating equity offerings to agency costs include Jung, Kim, and Stulz (1996)
and Kim and Purnanandam (2014).
4
Chinese economy; their total outputs in 2010, for example, accounted for 43% of China’s GDP
(Bryson, Forth, and Zhou, 2014).
Our investigation begins by relating SEOs to demand for skills as manifested in job
advertisements by a major online job posting company in China. We find that job advertisements
posted by firms receiving SEO proceeds are significantly more likely to contain words related to (1)
and (4) non-routine interactive task skills. Table 1, Panel B lists the English translation of Chinese
keywords used to identify each skill.
7 Stock markets in mainland China offer two types of stocks: A- and B-shares. Originally, the A-share market
was for domestic investors to trade with RMB; the B-share market was for foreign investors to trade with U.S.
dollars. The B-share market was opened to domestic investors in 2001, and qualified foreign institutional
investors were allowed to trade in the A-share market beginning in 2006. A firm can issue both A-shares and B-
shares, and both shares have identical rights. We restrict our sample to the A-share market because the total
market capitalization of the A-share market is about 122 times that of the B-share market as of the end of 2013.
In addition, most firms listed in the B-share market are also listed in the A-share market.
11
All estimations are at the job advertisement level, relating skills mentioned in each job
posting to whether the posting occurred during the year in which a company receives SEO proceeds.8
The dependent variable is either an indicator for the presence of a keyword indicating a specific skill
or the log of one plus the number of key words associated with each skill type to capture the intensity
of the skill requirement. The variable of interest is the SEO indicator, JP_SEO, turned on only in the
year SEO proceeds are received.9 All regressions control for year- and firm dummies to control for
heterogeneity in demand for skills and jobs across time and firm. We also control for location
dummies at the county level because many firms operate in multiple locations and job skill
requirements may vary across location (for example, R&D centers requiring advanced computer and
non-routine analytical skills tend to be located in metropolitan areas, while sales offices tend to be
located in both countryside and metropolitan areas.)
Table 2, Panel A reports results relating advanced computer skills to the SEO indicator.
Columns (1) and (3) show positive and significant coefficients, suggesting that firms receiving SEO
proceeds are more likely to require advanced computer skills.10 Hershbein and Kahn (2016) point out
online job postings tend to target white-collar employees more than blue-collar workers. To control
for job-related omitted variables, we add job dummies in Columns (2) and (4) using job titles
mentioned in the postings. Reestimation results continue to show significant positive coefficients on
the SEO indicator, suggesting that when firms receive SEO proceeds, they are more likely to demand
advanced computer skills for the same type of jobs. Panel B repeats the same exercise on basic
computer skills. Coefficients on the SEO indicator remain positive and significant, indicating the
probability of specifying basic computer skills is higher when firms receive SEO proceeds.
In Table 3, we relate the SEO indicator to non-routine analytical and interactive task skills.
Again, the coefficients are all positive and six of eight are significant. In sum, when firms obtain new
capital through SEOs, they seem to have greater demand for technical and non-routine task skills.
8 We cannot conduct firm level analyses because firms may advertise job openings with other job posting
companies and/or through other recruiting channels. 9 We turn on the indicator only in the year a firm receives SEO proceeds because if a firm fills newly advertised
positions in the year of the posting, the advertisement is unlikely to appear in the following year unless the
newly hired employees leave the firm and because the sample period covers only three years. 10 We lose three observations for OLS regressions because location information is unavailable.
12
These results, though informative, do not establish a causal relation, because they are about
association between two endogenous variables. Furthermore, our analysis is confined to job
advertisement level variation, which indicates only whether given a job posting the probability of
requiring specific skills changes. Because the number of job postings at the firm level may also
change, a more complete analysis requires a firm level analysis.
4. CAUSAL EFFECT ANALYSES WITH PANEL DATA
In this section, we conduct firm-level analyses using exogenous shocks on the eligibility to
issue SEOs to identify the causal effects that SEOs have on investment in technology, innovation,
skill composition, firm-level employment, and firm wages.
4.1. Regulatory Changes on the Eligibility to Issue SEOs
On May 6, 2006, the CSRC issued Decree No.30 requiring that to conduct a public SEO, a
firm’s cumulative distributed profits in cash or stocks during the most recent past three years must be
no less than 20% of the average annual distributable profits realized over the same period. Prior to this
regulation, the eligibility requirement was a positive dividend payment during the past three years.
The CSRC further tightened the requirement on October 9, 2008, when it issued Decree No.57, which
raises the threshold to 30%, counting only cash payments as distributed profits.
These shocks are exogenous to individual firm decisions. Although they limit the ability to
issue public SEOs, they do not directly affect how firms use SEO proceeds for investments and
employment. The catalyst for the 2006 regulation was the Split Share Structure Reform of 2005,
which made non-tradable controlling shares tradable in stock markets beginning 2005. The reform led
to a large increase in the supply of tradeable shares, which the CSRC deemed had an adverse effect on
stock price. The intent of the 2006 regulation was to limit the supply of newly issued shares. When
the Split Share Structure Reform began in April 2005, the CSRC suspended all public equity offerings.
Since the suspension was likely to end eventually, some market participants may have anticipated
some form of regulation on SEOs. To check the extent of public knowledge about the specifics or the
timing of the regulation before the announcement on May 6, 2006, we search for news stories in
Chinese media and find some news reports in early February 2006 that a new regulation would be
13
announced soon.11 The reports turned out to be wrong, however, as the CSRC denied it on February 9,
2006, and did not announce the regulation until three months later.
Two years after the 2006 regulation, the CSRC decided to raise the bar on the eligibility to
issue public SEOs. The intent was to reduce the supply of newly issued shares amid a stock market
crash. The Shanghai Stock Exchange Composite Index reached its peak on October 16, 2007, and
then fell precipitously, dropping more than 50% by June 2008. The CSRC issued a draft of the 2008
regulation on August 22, 2008, followed by an official announcement on October 9, 2008.
4.2. Empirical Design
We construct an instrument using the regulatory shocks. We use the IV approach, instead of a
difference-in-differences (DID) approach, because it provides direct estimates of the impacts of SEOs
on outcome variables, while the DID approach provides estimates of the impacts of the policy
changes.12 The 20% and 30% thresholds in 2006 and 2008 shocks raise the possibility of a regression
discontinuity (RD) design. However, observations in the neighborhood around the thresholds are too
few to conduct meaningful RD analyses.13
4.2.1. Construction of the Instrumental Variable
We construct the instrument for “SEO years,” the period when SEOs are most likely to affect
the outcome variables. SEOs can occur at different points in a year (e.g., February vs. November), and
it takes time for deployment of SEO proceeds to affect outcome variables, especially employment and
employee skill composition. We define SEO years as the year of receiving SEO proceeds and two
years afterward. The SEO process itself also takes time. In our sample, the average time from the
initial announcement of an SEO to the receipt of the proceeds is 337 calendar days. We allow for an
11 Chinese news coverage can be found in http://finance.people.com.cn/GB/1041/4090899.html. 12 Let y = α + β ∗ SEO + ε, where β captures effects of SEOs. We construct an IV from a regulatory shock, and
the relation between SEO and IV is SEO = γ + δ ∗ IV + v . The DID approach estimates y = α + β ∗�γ + δ ∗ IV + v� + ε = α + β ∗ γ + β ∗ δ ∗ IV + β ∗ v + ε . That is, the coefficient we get from the DID
approach is β ∗ δ, not β that we hope to estimate using the IV approach. 13 For the 2006 regulation cutoff, there are no eligible firms conducting SEOs and four ineligible firms not
conducting SEOs in the neighborhood of [19%, 21%]. For wider neighborhoods of [17%, 23%] and [15%, 25%],
there are one and four eligible firms conducting SEOs and 7 and 11 ineligible firms not conducting SEOs,
respectively. For the 2008 regulation cutoff, for the neighborhoods of [29%, 31%], [27%, 33%], and [25%,
35%], the number of eligible firms conducting SEOs is 2, 11, and 22; the number of ineligible firms not
conducting SEOs is 7, 13, and 22. For the neighborhood containing the most observations ([25%, 35%]), the
calculated power of the RD strategy for the estimated effect of SEO on the proportion of production workers by
the IV strategy in the paper (i.e., the coefficient of SEO" in Table 7, Panel A, Column 1) is only 0.052, lower than
the conventional threshold 0.8. Stata code "rdpower" is used for this calculation.
14
extra year because the shocks occurred in May and October of 2006 and 2008, respectively. Thus, if a
firm’s payout ratio over 2003 – 2005 is less than 20%, the firm is treated by the 2006 regulation, and
the instrument, SEOIneligible, is equal to one during 2008 – 2010, the SEO years. The CSRC
specifies the formula to calculate the payout ratio as (Dt-1 + Dt-2 + Dt-3) / [(It-1 + It-2+ It-3) / 3], where D
is the amount of dividends paid and I is the amount of distributable profits.
(http://www.csrc.gov.cn/zjhpublic/zjh/200804/t20080418_14487.htm.) 14 The distributable profit is
measured by net income (the parent’s net income for consolidated financial statements). 15
(http://www.csrc.gov.cn/pub/newsite/gszqjgb/fwzn/201603/t20160329_294910.html). For firms listed
for less than three years, the payout ratio is calculated for the years it has been listed (see the CSRC
internal publication, BaoJianYeWuTongXun (Investment Banking Practice Letters) 2, 2010, p.24).
We also turn on the instrument in 2009, 2010, and 2011 for firms treated by the 2006
regulation in 2007—firms with the payout ratio less than 20% over 2004 – 2006, because it may be
difficult to circumvent the regulation in 2007 by increasing dividends in 2006 alone. The results are
robust to not turning on the instrument for firms affected by the 2006 regulation in 2007 (see Section
5.3.) We assume firms are unaffected by the 2006 regulation in 2008 because firms could have
circumvented the regulation by increasing dividends in 2006 and 2007. We follow the same procedure
for firms treated by the 2008 regulation. SEOIneligible is equal to one in 2010, 2011, and 2012 for
firms with the payout ratio less than 30% over 2005 – 2007, and in 2011 and 2012 for firms with the
payout ratio less than 30% over 2006 – 2008. When a single observation satisfies these conditions
multiple times, it is equal to one only once. Appendix 2 illustrates how the instrument is constructed.
4.2.2. Validity of the Instrument
Because we construct the instrument using past payout ratios, treated and untreated firms may
differ to the extent that dividend payouts reflect firm characteristics. To help meet the exclusion
restriction that the instrument is uncorrelated with the error term in the second-stage regression, we
control for the determinants of dividend payouts offered by the dividend literature (described in the
14 Stock dividends are included in computing the payout ratios for the 2006 regulation but excluded for the 2008
regulation. 15 Some firms show negative payout ratios because the average annual distributable profit over the past three
years is negative. These firms are unaffected by the regulation because the CSRC does not approve public SEOs
for firms with negative profits.
15
next section). Another dividend-related issue is that dividends may reduce misuse of free cash flows
(Jensen, 1986), influencing outcome variables of interest.16 However, the instrument is based on past
dividend payouts, not current dividends. Dividend payouts could be serially correlated due to
persistency in corporate financial policies (Lemmon, Roberts, and Zender, 2008), but firm fixed
effects help control for the persistency. As a precautionary measure, however, we include the current
dividend payout ratio, DIV_PR, as a control variable.
One presumption for the validity of the IV is that if there were no shock, affected and
unaffected firms would have no different time trends in the outcome variables. We examine this issue
in Section 5.1 and find no different pre-trends in outcome variables between treated firms and control
firms prior to the first shock in 2006.
Another source of violation of the exclusion condition is some firms circumventing
regulations by increasing payout ratios prior to the shocks. That is, the instrument could correlate with
the error term in the second-stage regression because firms in greater need of capital for investments
in technology are more likely to manipulate the payout ratios. However, circumventing the regulations
is difficult because otherwise low payout-firms have to anticipate the regulatory changes and increase
payouts to meet the thresholds prior to the regulations. Anticipation is subject to uncertainty, which
makes the benefits from dividend maneuvers uncertain, reducing the present value of the benefits. The
uncertainty is not only about future regulations; there is also the approval uncertainty. SEOs in China
and the size of an SEO require the CSRC’s approval, which adds uncertainty over whether and how
much an SEO can raise capital. The cost of maneuvering dividends in anticipation of the 2008
regulation is likely to be economically significant because it requires paying higher cash dividends,
then grossing up the size of the SEO to make up for the cash used to pay the higher dividends prior to
the SEO. Such maneuvers are costly. Firms wishing to issue SEOs tend to be cash constrained
(DeAngelo et al., 2010). Paying out extra cash dividends may lead to foregoing value-enhancing
16 The regulators tied the eligibility to dividend payouts because the CSRC believed firms paying out more free
cash flows are less likely to waste them and thereby better serve investors. See the press conference on the 2006
regulation (http://www.csrc.gov.cn/pub/newsite/hdjl/zxft/lsonlyft/200710/t20071021_95210.html). For more
details, see Regulation for Issuing Stocks, 2006, China’s Securities Regulatory Commission.
16
investments. If the firm takes on more borrowing to meet the cash needs, financial leverage will
exceed the optimal level.
Costs of maneuvering dividend payouts in anticipation of the 2006 regulation is likely to be
lower because it counts stock dividends towards meeting the dividend requirement. If low payout-
firms anticipated this aspect of the forthcoming regulation, they could have satisfied the dividend
requirement by issuing sufficient stock dividends during 2003 - 2005. Data show otherwise. Stock
dividends were relatively rare in China during that period. Among 600 dividend cases in 2005, for
example, only 41 included stock dividends. Over the 2003-2005 period, 94% of all the dividend cases
did not include any stock dividends.
If, in spite of these considerations, firms somehow manipulated payout ratios prior to the
shocks to meet the eligibility requirements, the past payout ratios are likely to be just above 20% for
2006 and 30% for 2008. They are unlikely to exceed the thresholds by much because the maneuver
forces the firm to payout more than it would have done otherwise. Thus, we use the method proposed
in McCrary (2008) to detect a discontinuity in the past three-year payout ratios at 20% for 2006 and at
30% for 2008. None of the discontinuity estimates is significant.17 Although the McCrary test is only
about the necessary condition, the results support the validity of our instrument. 18
4.2.3. Baseline Specifications
We rely on two specifications throughout the paper to crosscheck robustness of the estimation
results. In the first specification, we control only for year- and firm fixed effects, and four
predetermined variables—firm age and three legal variables. Year-fixed effects control for economy-
wide shocks affecting all firms in the same year, such as a greater supply of university graduates
following a policy change to increase college enrollments in 1999 and the stock market crash
preceding the 2008 regulation, while firm-fixed effects control for time-invariant firm characteristics.
Firm age is proxied by the log of the number of years a firm has been listed, ln(NYEAR_LISTED).
Legal variables include: (1) The minimum wage required in the province or provincial-level city of a
17 We use Stata command “DCdensity,” which automatically chooses bin size and bandwidth. The discontinuity
estimate for 2006 is 0.724, with standard error and P-value of 0.708 and 0.306, respectively. For 2008, the
discontinuity estimate is 0.179, with standard error and P-value of 0.274 and 0.514. 18 Some firms treated by the shocks may raise funds through means other than an SEO, which will bias the
results downward.
17
firm’s headquarters location, ln(MIN_WAGE).19 Minimum wages may affect the skill composition of
employees by imposing a lower limit on what firms can pay unskilled workers. (2) The 2008 Labor
Law of People’s Republic of China on employment and wages. The law is likely to have greater
effects on firms with greater labor intensity. Because the law became effective on January 1, 2008, we
measure the law’s effect, Labor_Law_Effect, by interaction of the industry average ratio of the total
number of employees to all fixed assets in 2007 with an indicator equal to one for 2008 through 2012.
We use industry classifications as defined by the CSRC. (3) Local legal environment, LAWSCORE. A
higher score indicates the firm is located in a region with more developed legal institutions and
stronger law enforcement.20 We include this variable because the law and finance literature (e.g., La
Porta et al., 1998) suggests firms located in countries with stronger investor protection tend to have
better corporate governance and suffer from fewer agency problems, which may affect firms’
investment decisions and labor policies.
The second specification controls for more time-varying firm characteristics. As noted, we
add the determinants of dividend payouts offered by the dividend literature, which began with the
Miller and Modigliani irrelevancy theorem (1961). After much, sometimes-heated, debate over half a
century, a consensus has emerged, at least on which factors are possible determinants of dividend
payouts. A hypothesis lately receiving much empirical support is a life-cycle theory (DeAngelo and
DeAngelo, 2006): In early years, firms’ investment opportunities exceed internally generated capital,
so they retain more earnings and pay few dividends. In later years, internally generated cash exceeds
investment opportunities and firms pay out the excess funds to prevent misuse of the free cash flows.
Empirically, DeAngelo, DeAngelo, and Stulz (2006) report the propensity to pay dividends among
U.S. firms is higher when retained earnings comprise a larger fraction of total equity. Denis and
Osobov (2008) find the same pattern for five other developed economies. Following these studies, we
use the ratio of retained earnings to total equity, RE/TotalEq, as a determinant of dividend payouts.
19 Provinces and provincial-level cities adjust minimum wages every two or three years. In China, there are four
provincial-level cities: Beijing, Shanghai, Tianjin, and Chongqing. 20 The National Economic Research Institute (NERI) constructs the index for each province or provincial-level
region. The index changes over time, reflecting changes in the number of lawyers as a percentage of the
population, the efficiency of the local courts, and the protection of property rights. For a more in-depth
description, see Wang, Wong, and Xia (2008).
18
Other determinants of dividends offered in the literature include: (1) Size and profitability
(e.g., Denis and Osobov, 2008). We proxy firm size by the log of sales, ln(SALES);21 and profitability
by return on assets, ROA. (2) Dividend tax clienteles (e.g., Eades, Hess, and Kim, 1984; Fenn and
Liang, 2001; Allen and Michaely, 2003; and Crane, Michenaud, and Weston, 2016). In China,
individual investors pay a flat 20% tax on dividends (until 2013) and institutional investors pay no tax
on dividends. Tax clientele is therefore proxied by the fraction of shares held by institutional investors,
Inst_OWN. (3) Dividend signaling (e.g., Bhattachaya, 1979; Allen and Michaely, 2003). Demand for
signaling may be greater for firms with greater information asymmetry. We proxy information
asymmetry by outsiders’ costs of acquiring information as measured by stock return volatility,
Tot_Volatility. (4) Executive share ownership (e.g., Brown, Liang, and Weisbenner, 2007). Since the
rationale for this determinant is control and self-interest, we proxy it by the percentage of shares
owned by the largest shareholder, %_LARGST_SH.
We also control for six types of variables related to the outcome variables: (1) Strength of
corporate governance. Governance may affect investment decisions (Jensen, 1986). It may also
influence labor policies, affecting employment and wages (Bertrand and Mullainathan, 2003;
Atanassov and Kim, 2009; Cronqvist et al., 2009; Kim and Ouimet, 2014). Proxies for corporate
governance include LAWSCORE included in the first specification; %_LARGST_SH mentioned above;
the percentage of independent directors on the board, %_IND_DIR; and the percentage of shares held
by local and/or central government, %_STATE_OWN. State share ownership varies substantially over
time and across firms. (2) Sales growth rate, SALES_GR, because growth opportunities affect
employment (Hanka, 1998). (3) An indicator for private equity offerings, D_PRIVATE_PLACE, and
an indicator for overseas SEOs issued by dual-listed firms, Overseas_SEOs. These indicators help
control for the effects of capital infusion through other equity offerings unaffected by the shocks. (4)
Asset tangibility, as measured by property, plants, and equipment over total assets, PPE/TA. High tech
firms tend to have fewer fixed assets and fewer production workers. (5) Financial leverage, Leverage,
21 We measure firm size by sales instead of the number of employees or total assets, because the number of
employees is one of the outcome variables of main interest and total assets automatically increase when firms
receive SEO proceeds. SEOs might also affect sales, but the impacts take time and the control variable is the
concurrent sales revenue.
19
to partial out the leverage channel through which SEOs may affect wages. SEOs reduce leverage
(Pagano, Panetta, and Zingales, 1998; Eckbo, Masulis, and Norli, 2000; Gustafson and Iliev, 2017),
and as mentioned earlier, a number of studies argue leverage affects wages. (6) Percentage of non-
tradable shares, %_NONTRD_SH, to control for the potential confounding effects of the Split Share
Structure Reform.
4.3. Data and Summary Statistics
4.3.1. Sample Construction and Data Sources
The sample period covers 2000 through 2012 to span the regulatory shocks. Underwritten
offerings in China were first allowed in 2000, and some corporate governance variables, such as board
information, are available only after 2000. As before, we construct the sample using all A-share firms
listed on the Shanghai and Shenzhen Stock Exchanges. We exclude financial firms as defined by the
CSRC (e.g., banks, insurance firms, and brokerage firms), firms with total employment less than 100,
and ST (special treatment) and *ST firms, which have had two (ST) or three (*ST) consecutive years
of negative net profit.
The main data source for labor, financial and corporate governance variables is Resset
(http://www.resset.cn/en/). It is similar to Compustat but unlike Compustat, it provides reliable data
on wages and employment. It also provides firm-level panel data on employee occupation and
education. The CSRC does not require a specific format for reporting employee composition, but all
firms report in company filings the number of employees by occupation or job type, and most firms
report the number of employees by education. Resset collects the information and constructs firm-
level data on the number of employees by occupation and education. It also provides verbal
descriptions of each job type coded from the company filings for each firm-year.
We manually clean the occupation data using the verbal descriptions. Firms vary in how they
define occupations due to differences in the nature of business, operation, and organizational structure.
Consequently, the occupation data in Resset show some inconsistencies between occupation variable
names and verbal descriptions of occupation or job type. We also find some jobs classified as “others”
by Resset can be classified into a specific occupation group using the verbal descriptions.
20
The first occupation category, Production, is production workers. It includes mainly blue-
collar workers performing assembly line work, sorting, moving, and other routine physical tasks.
Most firms report this category quite clearly. Many high-tech and non-manufacturing firms have no
employees in this category.
The second category, Staff, stands for support staff. This category is not as clear-cut as the
production worker category. Some firms report the number of employees with a finer breakdown,
such as office support staff and HR staff, while others aggregate them into one category of staff,
which may include both office staff (receptionists, secretaries, customer service providers, and office
administrators) and non-office staff (employees for warehouse maintenance, security, and logistics
support). Some firms report office and non-office staff separately, while others lump them together.
To make the data comparable across firms, we manually check verbal descriptions for each firm-year
and sum the number of employees in all staff positions. Most employees in this group perform routine
clerical or low-skill tasks. However, some (e.g., drivers and janitors) perform non-routine low-skill
manual tasks and some are in administrative positions within the support group (e.g., HR manager,
logistics supervisor, office managers), which require non-routine interactive skills.
The third category, Tech_R&D, includes technicians and R&D employees. Technicians
consist of engineers and IT staff. R&D employees include scientists, researchers, and designers
working on creative tasks, and employees working on developing new products. We group
technicians and R&D staff into one category because only about 20% of our sample firms have a
separate category for R&D employees.
The fourth and fifth categories, S&M and Finance, are the sales and marketing force and
finance staff. These categories are rather straightforward. The sales and marketing force includes
employees in sales, marketing, advertising, and brand management. Finance staff includes
accountants and finance staff involved in investment and asset management.
The last category, Others, includes those reported as “others” by sample firms and job
categories which cannot be put into one of the above five categories. Job descriptions such as
“operating” are ambiguous, making it difficult to put into a specific category, so they are counted as
Others. Some firms report employees in distinctly different occupations, such as sales and technicians
21
or financial accountants and sales, as one category. Since we cannot separate them, we treat them as
Others. We do the same when some firms report the number of managers. We do not make a separate
category for managers because only about 25% of sample firms report the number of managers, which
cannot mean the rest of sample firms do not have managers.
To separate employees by education, we construct two high-education groups: holders of
post-graduate degrees, Grad, and holders of four-year university Bachelor’s degrees and above, BA.
Grad includes all masters and doctorate degrees (e.g., MS, MA, MBA, EMBA, PhD, MD, and JD).
About 50% of sample firms separately report the number of employees with post-graduate degrees.
Others lump four-year university Bachelor’s degrees and above in one category. When firms report
post-graduate degree holders separately from four-year Bachelor’s degree holders, we combine them
to construct BA. Some firms report degree holders from three-year or lower level colleges together
with four-year university degree holders as one group. We do not include them in BA.
For data on SEOs we rely on CSMAR (http://www.gtarsc.com/), because it provides more
detailed SEO information than Resset. We hand-collect minimum wages from provincial government
webpages. To mitigate outlier problems, we winsorize all financial variables at 1% and 99% level and
replace them with the value at 1% or 99%. We normalize all monetary variables to 2000 RMB.
Table 4 lists the sample distribution by year. The sample contains 17,838 firm-year
observations associated with 2,341 unique firms. In total, our sample contains 557 public SEOs. We
do not include privately placed equity offerings because the regulations did not apply to private
offerings. The table shows a surge of public SEOs when underwritten offerings were first allowed in
2000. The small number of SEOs in 2005 and 2006 are due to the suspension of all public equity
offerings during the Split Share Structure Reform. (The suspension began in April 2005 and ended in
May 2006.) SEO activities recovered in 2007 and increased in 2008, but the 2008 regulation seems to
have succeeded in limiting the supply of newly issued shares; the number of SEOs dropped in 2009
and remained relatively low until the end of the sample period.
4.3.2. Descriptive Statistics
Table 5 provides summary statistics for all key variables. Appendix 3 provides variable
definitions and data sources. The SEO indicator, SEO, shows 9% of firm-year observations are in
22
SEO years. The instrument, SEOIneligible, indicates 16% of observations are treated by the
regulatory shocks. The average fractions of production workers, support staff, technicians and R&D
staff, the sales and marketing force, finance staff, and others are 48%, 9%, 17%, 13%, 3%, and 18%,
respectively. 22 The very high percentage of production workers is due to the dominance of the
manufacturing sector among domestic-listed firms and the exclusion of the financial services sector.
About 20% of employees have Bachelor’s degrees and above, and 3% have post-graduate degrees.
The average number of employees is 4,585. The average past three-year payout ratio, P3_PR, is about
three times the average annual dividend payout ratio, DIV_PR, 23 because the denominator in
calculating P3_PR is the average annual distributable profit over the past three years. (See the formula
in Section 4.2.1.) The average wage for all employees, AWAGE, is slightly lower than the average
wage for all non-executive employees, AWAGE_NonExe, which is calculated over 2001-2012 because
firms did not separately disclose payroll information for executives until 2001.
4.4. Skill Composition and Employment
Our job posting data analyses show a positive association between SEOs and demand for
skills. If SEOs increase demand for skills and the demand is met, SEOs will lead to an increase in the
proportion of skilled employees in the work force. In this section, we estimate how SEOs change
employee occupation- and education-composition and level of employment.
The first-stage is estimated by the firm-level conditional (fixed-effects) logistic regression
because the endogenous variable is an indicator. Under the assumption that the instrument has
predictive power over the endogenous variable, IV estimators using the logit model in the first-stage
are asymptotically efficient; i.e., coefficients of the model can be more precisely estimated
(Wooldridge, 2010, p.939). Standard errors of the first-stage regression are clustered at the firm level,
and those of the second-stage regression are corrected by bootstrapping.
Table 6 reports first-stage results. The coefficients on SEOIneligible are negative and highly
significant for both specifications, indicating that the instrument has strong predictive power over the
endogenous variable. F-statistics are not reported because the first-stage regression is conditional logit,
22 The percentages do not sum to 100% because of missing observations. 23 The minimum DIV_PR is zero because no firm in our sample paid dividends in a year of negative profits.
23
a non-linear estimation. When we estimate the first-stage using the OLS with the full set of control
variables, F-statistic is 15.56.
Table 7 reports second-stage results for occupation- and education-composition. Both
specifications show that SEOs significantly increase the proportions of technicians and R&D
employees, and the sales and marketing force. The fractions of employees with Bachelor’s degrees
and above and with post-graduate degrees also significantly increase when the specification contains
the full set of control variables.24 Technicians and R&D employees tend to possess technical and
analytical skills required for non-routine abstract tasks. Sales and marketing forces tend to possess
communication skills required for non-routine interactive tasks. Employees with Bachelor’s and post-
graduate degrees possess higher skills in general.
In contrast, SEOs significantly decrease the fraction of production workers that mostly perform
routine physical tasks. The fraction also declines significantly for support staff, most of whom
perform either routine clerical tasks requiring low skills or non-routine manual tasks requiring low
skills and less education. From these results, we infer that SEOs increase (decrease) the relative
proportion of skilled (unskilled) employees.
Coefficients on control variables are largely consistent with intuition. The proportion of
technicians and R&D staff is positively related to sales growth rate, accumulated earnings, board
independence, and institutional ownership, but is negatively related to asset tangibility and firm size.
The same variables show mostly opposite signs for the proportion of production workers, which is
negatively related to sales growth rate, firm profitability, industry exposure to the labor law, board
independence, and financial leverage, but is positively related to asset tangibility and firm size. The
fraction of employees with Bachelor’s degrees and above is positively related to the minimum wage,
accumulated earnings, ownership concentration, and financial leverage, but is negatively related to
firm size, asset tangibility, and industry exposure to the labor law.
Table 8 reports second-stage results for the level of employment. Both specifications show
SEOs significantly reduce firm-level employment by about 7 - 8%. The remaining columns break
24 Post-graduate degree holder regressions in Tables 7 and 8 contain substantially fewer observations than other
regressions because many firms do not separately report the number of employees with post-graduate degrees.
24
down the number of employees by occupation and education, where the dependent variable is the log
of one plus the number of employees (some firm-years show no employees in some occupation and
education categories.) Both specifications show that the number of technicians and R&D employees
significantly increases and the number of production workers and support staff significantly decreases.
Estimations with the full set of control variables indicate the number of production workers and
support staff decline by 24% and 44%, whereas technicians and R&D staff, the sales and marketing
force, and post-graduate degree holders increase by 10%, 13%, and 10%, respectively.
In sum, capital infusion through SEOs decreases the proportion of employees performing tasks
requiring low skills and increases the proportion of employees in occupations requiring high-skills,
resulting in a higher skill composition. Displaced low-skilled workers outnumber newly added high-
skilled workers, resulting in a net decline in employment.
4.5. Investments in Technology and Innovations
We argue these changes in the skill composition and employment level are the results of
SEOs leading to more investments in technologies. Investments to adopt new technologies require
purchases of new machinery and equipment, while investments to advance technology require
innovative activities, the outcome of which we proxy by the number of patents.
The dependent variable in the first two columns of Table 9 is acquisition costs of newly added
machines and equipment. These data are available from 2003 when the CSRC first required listed
firms to breakdown acquisition costs of newly added fixed assets by type. The estimated coefficient
with the full set of control variables implies that SEOs lead to a 19% increase in investments in
machines and equipment. The last column confirms previous studies on other countries (e.g., Kim and
Weisbach, 2008; Gustafson and Iliev, 2017): Chinese SEOs also increase capital expenditures.
Coefficients of control variables suggest purchases of machines and equipment are positively
related to sales growth rate, accumulated earnings, privately placed equity offerings, 25 firm size,
tangibility of assets, and financial leverage; and negatively related to firm age, industry exposure to
the labor law, and the strength of legal institutions.
25 Investments in machines and equipment are unrelated to equity offerings in foreign stock exchanges, perhaps
because firms deploy the proceeds for foreign subsidiaries’ overseas operations.
25
Investments in innovative activities are proxied by one of their outcomes; the log of the
number of patents granted in year t + 3. 26 The three-year lag allows time to invest SEO proceeds, for
investment to yield innovations, and for innovations to become patents. Although many patents could
turn out to be useless, the number of newly granted patents reflects the effort and resources devoted to
advance technology. Baiten (http://www.baiten.cn/) is the source of our patent data. When we count
the number of patents, we omit patents withdrawn in later years by the State Intellectual Property
Office (SIPO) (http://www.sipo.gov.cn/), the Chinese government agency in charge of patent
administration. It classifies patents into three types: invention patents, utility model patents, and
design patents. According to the Guidelines for Patent Examination 2010 on the SIPO website,
invention (design) patents are considered most (least) innovative among the three, requiring the
longest (shortest) evaluation period with the longest (shortest) protection period.
The first two columns of Table 10 show SEOs increase the number of newly granted patents.
The estimated coefficient with the full set of control variables implies that, on average, SEOs increase
the total number of patents by 13%. The last three columns show the impact is significant only for
patents considered more innovative, invention and utility model patents, with no increase for the least
innovative type, design patents. It appears a significant portion of SEO proceeds is invested to
advance technology, leading to more innovations.
Control variables reveal interesting correlations. In China, firms seem to generate more
patents as they become more established – older and larger with more accumulated earnings.
Institutional ownership is negatively associated with the number of patents, perhaps because
management is less willing to take the risk inherent in innovative activities when their performance
faces greater scrutiny from institutional investors.
4.6. Firm Wages
The higher skill composition following SEOs should increase firm average wages because
skilled workers are paid more, which is also true in China. The China Urban Household Survey shows
26 Another possible proxy for technology-advancing investments is research and development expenditures.
However, we do not have sufficient data on R&D because the Chinese accounting rule did not require disclosure
of R&D expenditures until 2007.
26
Chinese workers with more education are paid more, and technicians are paid substantially more than
production, staff and service, or agricultural workers (see Appendix 4). Table 11 reports the second-
stage results on firm average wages. Unsurprisingly, average wages for all employees increase
significantly following SEOs. The last two columns separate employees into non-executive
employees and executives. Non-executive employees, whose wages increase by 10%, drive the
increase in average wages. Average wages of executives (classified as such in financial statements)
are unaffected by the capital infusion. New capital infusion improves the skill composition of non-
executive employees, increasing their average wages. However, average executive wages do not
increase, suggesting that the effects that SEOs have on skill composition are limited to non-executive
employees.27
Coefficients on control variables are largely consistent with intuition. Average wages are higher
when the minimum wage is higher, when firms are larger and more profitable with more accumulated
earnings, when state share ownership is greater, when ownership is more concentrated, and when
assets consist of more intangible assets.
How do the changes in skill composition and employment affect total wages? Because the total
number of employees declines, the higher average wage does not necessarily imply a higher total
wage. Table 12 reports second-stage results for total wages. Regardless of which specification is used
and how we stratify employee groups, SEOs have no significant impact on total wages.
5. ROBUSTNESS
In this section, we examine (1) pre-trends prior to the first shock; (2) whether past dividend
payout ratios can explain our results; and (3) whether our results are robust to alternative ways to
construct the instrument and to excluding small SEOs.
27 The executive wage results do not reflect the value of equity incentives, which are an important component of
executive compensation in the U.S. In China, wages constitute most of executive compensation, with executive
stock options playing no, or a minor role in the compensation. Brison et al. (2014) reports, “Fewer than 1% of
top executives were granted options in any given year between 2006 and 2010 and, for these few cases, at the
median they were worth 30% of CEO cash compensation and 21% of non-CEO top executive cash
compensation.” Chinese firms were unable to offer stock options until 2006, when equity incentives were
formally introduced in the form of employee stock options and discounted share purchase programs. Stock
options are granted and vested shortly after shareholder approval. They are exercisable according to a fixed
schedule tied to certain performance targets. Discounted share purchase programs allow stock purchases at a
discount but they cannot be sold until a performance target is achieved. These equity incentives are issued to
both non-executive employees and executives.
27
5.1. Pre-Trends
We construct the instrument based on the variation in the impacts of the shocks on the
eligibility to issue SEOs. One presumption for its validity is that if there were no shock, affected and
unaffected firms would have no different time trends in the outcome variables. To test whether this
presumption holds, we conduct a placebo test using the 2000-2005 samples prior to the regulatory
shock in 2006 to simulate the situation with no shock. We do not use post-2006 shock samples
because of the presence of the second shock in 2008. We construct an indicator for firms affected by
the 2006 regulation, Affected. Then we test whether there is any differnce between the outcome
varables of shock-affected and shock-unaffected firms during the years prior to 2006 using 2000 as
the base year. We define five placebo shock indicators, Year01,…, Year05, which are equal to one for
years 2001 through 2005. We then estimate the baseline regression with the full set of control
variables for all key outcome variables with the interactions of Affected and placebo indicators as
variables of interest.
Table 13 reports coefficients on the interaction terms. None of the coefficients is statistically
significant for any of the outcome variables, implying that the outcome variables of affected and
unaffected firms prior to the 2006 shock are not different.28 These results suggest no different time
trends in the outcome variables between affected and unaffected firms had there been no shock.
5.2. Can Past Payout Ratios Explain our Results?
Firms may pay out more of their earnings when management anticipates positive shocks to
cash flows in the future. As the anticipated positive shocks realize, firms make more investments in
technology, increasing demand for skills, which, in turn, leads to higher skill composition and other
results we document. To investigate whether this scenario can explain our results, we add the most
recent past three-year payout ratio, P3_PR, as an explanatory variable and re-estimate regressions.
Some firm-years show negative P3_PR because of negative average annual distributable profit over
the past three years, in which case we replace a negative P3_PR by one and add a dummy for the
negative ratio, P3_PR_D.
28 Placebo shock indicators for investments in machinery and equipment cover only 2004 and 2005 with 2003 as
the base year because data on purchases of new machines and equipment are available only from 2003.
28
Table 14 reports the second-stage results for key outcome variables with the full set of control
variables. (Appendix 5, Column 1 reports the first-stage result.) The coefficients on the predicted SEO
hardly change. The coefficient on P3_PR is positive for the fraction of production workers, the
opposite to the coefficient on the predicted SEO, and is insignificant for most of the other outcome
variables of interest. The alternative scenario cannot explain our results.
5.3. Alternative Ways to Construct the Instrument
Since the key to our identification is the instrument, we re-estimate the baseline regressions
using virtually all conceivable alternative ways to construct it. First, there was a short-lived regulatory
shock on December 7, 2004, when the CSRC issued a set of regulations aiming to standardize the
corporate governance practice of public firms. Included in the regulations was a provision that if a
listed firm did not pay any cash dividend during the past three years, it would not be allowed to issue
a public SEO. (Prior to this requirement, publicly listed firms were eligible to issue SEOs as long as
profits were positive during the past three years.) This provision soon became void when the CSRC
embarked on the Split Share Structure Reform in April 2005 and suspended all public equity offerings
until May 2006 (at which time the CSRC issued the 2006 regulation on the eligibility to issue SEOs.)
Excluding this short-lived shock may bias our estimates if SEOs approved during December 2004 -
April 2005 have different impacts on outcome variables from those of SEOs in the later years. To
check whether our estimates are subject to such bias, we include the 2004 shock in constructing the
instrument. Specifically, we also set SEOIneligible equal to one in 2006 if a firm did not pay any
dividend during 2001 – 2003, and in 2007 if a firm did not pay any dividend during 2001 – 2003 or
during 2002 – 2004, and in 2008 if a firm did not pay any dividend during 2001 – 2003 or during
2002 – 2004. Second, some firms may circumvent the 2006 and 2008 regulations in 2007 and 2009,
respectively, by increasing dividends in 2006 and 2008. To guard against such possibilities, we turn
on the instrument only for firms affected by the 2006 regulation in 2006 and firms affected by the
2008 regulation in 2008. Third, we shorten the time elapsed from the announcement to the receipt of
SEO proceeds from two years to one year. Fourth, we rely only on the 2006 shock because some
firms may have anticipated the 2008 shock. All second-stage results, reported in Table 15, Columns
(2) – (5), are robust.
29
As a final robustness check, we exclude small SEOs in the bottom decile in the size of the
proceeds. Firms conducting these small SEOs are typically small cap firms with highly volatile
performance. The last column in Table 15 shows robust results. Appendix 5 reports all first-stage
results for tests conducted in this section.
6. SUMMARY AND IMPLICATIONS
This paper studies how capital infusion through SEOs affects investment in technology, skill
composition, firm-level employment, and wages. We begin by analyzing job advertisements posted
online, which suggest demand for computer and non-routine task skills increase when firms receive
SEO proceeds. To identify causal effects, we rely on external shocks that cut off access to public
SEOs as a means to raise external capital. We find capital infusion through SEOs increase
investments in technology, innovations, and the proportion of high-skilled employees relative to low-
workers, resulting in a net reduction in employment. The higher-skill composition increases average
wages because higher-skilled workers are paid more, but total wages remain unchanged due to the
reduction in total employment. On average, SEOs enable firms to upgrade the skill composition of
employees without increasing total wages.
These findings shed light on how stock markets affect labor markets by altering demand for
high- vs. low-skilled workers. Easier access to capital may not only increase demand for high-skilled
workers but also stimulate their supply, as the demand for and the supply of skills are endogenous to
each other and dynamically move together. If the supply of high-skilled workers increases in response
to increased demand, it may induce greater development of skill complementary technologies, which
may enhance economic growth.
The highly developed, sophisticated, and global financial markets of recent years have
allowed less costly access to external capital, which we show leads to displacement of low skilled and
less-educated workers. Retraining to upgrade skills to meet changing demands requires financial
resources, time, and effort; thus, many low-skilled workers may not be able to leave the shrinking
market for their services, at least not in the short run. The ensuing imbalance between the supply of
and demand for low-skilled and less-educated workers is likely to keep their income low. High-skilled
30
and highly-educated employees, on the other hand, will enjoy increasing demand for their services as
frictions to accessing external capital decline and capital skill complementarity kicks in. The result
might be further widening income inequality in the short-run.
In the end, however, positive spillovers of technology advances to the tertiary sector might
more than offset the negative employment effect on low-skilled workers (Autor and Salomons, 2017).
If and when enough low-skilled workers are properly retrained to perform tasks for tertiary services,
the aggregate employment opportunities might grow as capital markets facilitate development of
complementary technologies and processes, which are necessary to harness the recent technological
advances to yield their full economic benefits.
31
APPENDICES
Appendix 1: Institutional backgrounds on Chinese labor and capital markets
1. Labor Markets and Economic Reforms
China’s labor market has undergone several major changes. In the early years of Communist
China (1952-1978), the state sector dominated employment in the urban area and management did not
have the authority to hire or fire workers without government approval (Lin, Cai, and Li, 1996). Firms
set wages according to a grid determined by the government; wages hardly reflected differences in
productivity (Cai, Park, and Zhao, 2008).
China embarked on economic reforms in 1978, leading to a new, floating wage system by the
mid-1980s. The reforms allowed an enterprise’s total payroll to reflect its performance in the previous
three years. (Prior to this reform, central and local planners had determined the total payroll for each
enterprise (Yueh, 2004)). At the same time, the State Council formally introduced the concept of labor
contracts, giving management the flexibility to adjust employment in response to market competition
(Meng, 2000). However, the labor contract system gave firms the freedom to hire suitable workers,
but the dismissal of workers remained under the government’s tight control.
In 1992 state-owned enterprises (SOEs) were given more autonomy, enabling them to link the
total payroll more closely to firm performance and set their internal wage structures (Li and Zhao,
2003; Yueh, 2004). More reforms followed in 1994-1995, allowing listed SOEs to set their own
wages and encouraging enterprises to consider skills and productivity in addition to occupation and
rank in determining wages (Yueh, 2004). Some SOEs began to lay off workers, as the labor law
issued in 1994 permitted no-fault dismissal of workers in response to changing economic conditions
(Ho, 2006). A major state-sector restructuring followed, closing down or privatizing more than 80%
of SOEs (Hsieh and Song, 2015). When restructuring-affected employees left SOEs, they faced a
more market-driven re-employment process, and the previously inflexible labor market became one in
which supply and demand affected employment and wages. By the mid-2000s, China’s labor market
had become similar to those of other countries based on capitalism; labor is mobile, and enterprises
32
consider market conditions in making employment decisions and in setting wages (Cai, Park, and
Zhao, 2008).
During our sample period, China had well-established legal provisions on hours of work,
payment of wages, and employment. The standard workweek is 40 hours (eight hours per day, five
days per week). Overtime must be paid for any work exceeding standard working hours and cannot
exceed three hours a day or 36 hours per month (Labor Law Article 41). Wages are paid on a monthly
basis, and may not be delayed without reason (Labor Law Article 50). Employees can be fired in the
middle of two fixed-term contracts (or ten years of employment),29 after which contracts must be
made open-ended. Open-ended contracts can be terminated only for cause (Gallagher et al., 2015).
A consequence of these reforms particularly relevant to our study is the increase in returns to
education. Li, et al. (2012) show that the return to an additional year of schooling increased from 2.3
percent in 1988 to about 9 percent in 2000, and the return to college education increased from 7.4
percent in 1988 to 49.2 percent in 2009. These dramatic increases in returns to education are
attributable to the labor reforms and the fast-growing demand for skills (Zhang et al., 2005).
2. Capital Markets and Chinese SEOs
The modernization of Chinese capital markets began when former Premier Rongji Zhu, who
led China to join the World Trade Organization (WTO), spearheaded a series of reforms during his
tenure as vice premier and premier in 1993 – 2003. The reforms included restructuring of state-owned
enterprises (SOEs) and the banking industry.30 A major theme of the reforms was to modernize capital
markets and corporate governance practices of SOEs. The modernization process sped up in 2001
when China officially joined the WTO. In January 2004, the State Council issued a document,
“Opinions on Promoting the Reform, Opening and Steady Growth of Capital Markets,” which sets the
importance of developing capital markets as a high-priority national strategy.31 In response to the
guiding principles from the State Council, the CSRC has implemented a number of new regulations to
29 Contracts are subject to negotiation after the first term. 30 Economist, March 6th, 2003. http://www.economist.com/node/1623179 31 OECD report: Corporate Governance of Listed Companies in China.
modernize stock markets and improve corporate governance.32 According to the World Bank, the
modernization of stock markets, together with the rapid growth of the Chinese economy, have helped
stock markets in mainland China to become the second largest in the world in both market cap and
total value of shares traded in 2009.33
In China, the stock market has been a more important source of external financing than its
corporate bond market, which has been growing at a much slower pace than the stock market.
Although a regulated bond market for enterprises began in 1996, only very large and stable companies
can issue bonds because of the strict approval process required for issuing bonds. Over the period
2010 through 2012, for example, Chinese listed firms raised 2,147.5 billion RMB through stock
markets (via SEOs and IPOs), while bond markets helped raise only 429.5 billion RMB. Over the
same period, adjusted for differences in stock market capitalization, non-financial Chinese firms
issued SEOs more than three times their U.S. counterparts.34
The Chinese stock market is well suited to study SEOs. The types of SEOs available and the
underwriting procedures in China are similar to those in the U.S. There are three types of SEOs: rights
offerings, underwritten offerings, and private placements to no more than ten qualified investors. As
in the U.S., there are two types of underwriting contracts, best efforts and firm commitments.
In comparison to U.S. SEOs, Chinese SEOs provide a cleaner sample to study how the
proceeds from SEOs affect firm investments, employment, and wages because virtually all Chinese
SEOs are primary shares.35 SEOs in the U.S. often include secondary offerings, sale of shares held by
32 In a Q&A session with media, officials from CSRC explain the background of the regulations in details. See
http://www.china.com.cn/chinese/FI-c/723240.htm (in Chinese). 33http://data.worldbank.org/indicator/CM.MKT.LCAP.CD?end=2016&locations=CN-JP-US-HK-FR-GB-
DE&name_desc=false&page=5&start=2003&view=chart and
DE&name_desc=false&page=5&start=2003&view=chart. 34 Over the period 2010 through 2012, the average total Chinese stock market capitalization is 3,949.77 billion
USD and non-financial Chinese listed firms raised 86.09 billion USD through SEOs, 2.18% of total market
capitalization. This is more than three times the ratio for US counterparts. During the same period, the average
market capitalization of US stock market is 17,149.34 billion USD and non-financial US listed firms raised
102.75 billion USD through SEOs, 0.6% of the total market cap. Total stock market capitalization excludes
financial firms. Capital raised through SEOs is taken from SDC Platinum. The market capitalization data are
taken from data on the World Bank website (http://data.worldbank.org/). Capital raised through SEOs includes
only proceeds from primary offerings. 35 There were only three mixed offerings containing secondary offerings of state-owned shares, all of which
occurred in 2001. At that time, the CSRC required that if a firm plans to issue N new shares through an
34
insiders and block holders. Proceeds of secondary offerings do not go to the firm and hence cannot
affect investment and employment decisions. Thus, if one studies the effects of deploying U.S. SEO
proceeds without carefully screening out secondary offerings, the results will contain much noise and
confounding effects.
underwritten offering and has state-owned shares, then the offering must contain 10% of N state-owned shares.
The regulation lasted for only four months, and there have been no mixed offerings since 2001.
35
Appendix 2: Construction of the instrumental variable.
This table illustrates how the instrument, SEOIneligible, is constructed. “Conditions” specify the
past three-year period during which the minimum payout ratio applies to make a firm ineligible
to issue a public SEO. For example, 2003 – 2005 < 20% means that if the payout ratio over 2003
– 2005 is less than 20%, the firm is ineligible to issue a public SEO in 2006. In this table, we
assume it takes two years to complete an SEO. Since SEO years include the year of receiving the
proceeds and two years afterward, we turn on the instrument in 2008, 2009, and 2010 for firms
affected by the 2006 regulation in 2006. We follow the same procedure for firms affected by the
2006 regulation in 2007, and for firms affected by the 2008 regulation in 2008 and 2009.
Year SEOIneligible Conditions
2000 0 NA
2001 0 NA
2002 0 NA
2003 0 NA
2004 0 NA
2005 0 NA
2006 0 NA
2007 0 NA
2008 1 If 2003 – 2005 < 20%
2009 1 If 2004 – 2006 < 20% or 2003 – 2005 < 20%
2010 1 If 2005 – 2007 < 30%, 2004 – 2006 < 20%, or 2003 – 2005 < 20%
2011 1 If 2006 – 2008 < 30%, 2005 – 2007 < 30%, or 2004 – 2006 < 20%
2012 1 If 2006 – 2008 < 30% or 2005 – 2007 < 30%
36
Appendix 3: Variable Definitions and Data Sources.
Variables
Definition
Data
Sources
SEO-related Variables
JP_SEO An indicator equal to one in the year in which a firm
receives SEO (public or private placement) proceeds, and
zero otherwise.
CSMAR
SEO An indicator equal to one in SEO years (the year in
which SEO proceeds are received and two years after),
and zero otherwise. It applies to only public offerings.
CSMAR
SEOIneligible Instrument for SEO years. Appendix 2 illustrates how it
is constructed. Wind
Outcome Variables
Adv_Computer_Dum An indicator for the presence of words indicating
advanced computer skills in a job advertisement. Lagou.com
Ln(Adv_Computer) Log of one plus the number of words indicating
advanced computer skills in a job advertisement. Lagou.com
Basic_Computer_Dum An indicator for the presence of words indicating basic
computer skills in a job advertisement. Lagou.com
Ln(Basic_Computer) Log of one plus the number of words indicating
advanced computer skills in a job advertisement. Lagou.com
Non-routine Analytical
Task Skill_Dum
An indicator for the presence of words indicating non-
routine analytical task skills in a job advertisement. Lagou.com
Ln(Non-routine Analytical
Task Skills)
Log of one plus the number of words indicating non-
routine analytical task skills in a job advertisement. Lagou.com
Non-routine Interactive
Task Skill_Dum
An indicator for the presence of words indicating non-
routine interactive task skills in a job advertisement. Lagou.com
Ln(Non-routine Interactive
Task Skills)
Log of one plus the number of words indicating non-
routine interactive task skills in a job advertisement. Lagou.com
EMP Total number of employees at the firm-level. Unit:100. Resset
Production Number of production workers. Resset
Staff Number of support staff. Resset
Tech_R&D Number of technicians (including engineers and IT staff)
and R&D employees. Resset
S&M Number of employees in sales and marketing. Resset
Finance Number of accounting and finance staff. Resset
Others Number of employees with unidentified occupation. Resset
BA Number of employees with four-year university
Bachelor’s degrees and above. Resset
Grad Number of employees with post-graduate degrees. Resset
AWAGE Average annual cash salary and bonuses to all employees
in 2000 RMB. Unit: 10,000. Resset
AWAGE_NonEXE Average annual cash salary and bonuses to all non-
executive employees in 2000 RMB. Unit: 10,000. Resset
AEXEPAY Average annual cash salary and bonuses to all executives
in 2000 RMB. Unit: 10,000. Resset
Ln(Payroll) Log of total annual cash salary and bonuses to all
employees in 2000 RMB. Unit: 10,000. Resset
Ln(Payroll_NonExe) Log of total annual cash salary and bonuses to all non-
executive employees in 2000 RMB. Unit: 10,000. Resset
Ln(Payroll_Exe) Log of total annual cash salary and bonuses to all
executives in 2000 RMB. Unit: 1,000,000. Resset
37
Outcome Variables
Definition
Data
Sources
Ln(Capx) Log of total capital expenditures in 2000 RMB. Unit:
10,000. Resset
Ln(Inv_Tech_Assets) Log of one plus the cost of newly acquired machines and
equipment in 2000 RMB. Unit: 10,000. CSMAR
Ln(Total_Patent) Log of one plus the total number of patents granted. Baiten
Ln(Invention) Log of one plus the number of invention patents granted. Baiten
Ln(Utility_Model) Log of one plus the number of utility model patents
granted. Baiten
Ln(Design) Log of one plus the number of design patents granted. Baiten
Control Variables
NYEAR_LISTED Number of years a firm has been listed since its IPO. Resset
SALES Total sales in 2000 RMB. Unit: 1,000,000. Resset
Leverage Total liability divided by total assets. Resset
ROA Return on assets: Net income divided by total assets. Resset
PPE/TA Property, plants, and equipment divided by total assets. Resset
SALES_GR Sales growth rate from year t-1 to year t. Resset
%_IND_DIR Percentage of independent directors on the board. Resset
%_STATE_OWN Percentage of shares held by the government. Resset
%_LARGEST_SH Percentage of shares held by the largest shareholder. Resset
%_NONTRD_SH Percentage of non-tradable shares. Resset
Inst_OWN Fraction of shares owned by institutional investors. Resset
Overseas_SEOs
An indicator equal to one if a firm issues an equity
offering in foreign exchanges including the Hong Kong
Stock Exchange.
CSRC
Website
D_PRIVATE_PLACE An indicator equal to one if a firm issues an equity
offering through private placement. CSMAR
RE/TotalEq Retained earnings divided by the book value of equity. CSMAR
DIV_PR Dividend payout ratio, equal to total dividend paid over
net income. Resset
Tot_Volatility Standard deviation of weekly stock returns. CSMAR
MIN_WAGE The minimum monthly wage in the province or
provincial city of the firm’s headquarters location in
2000 Yuan.
Government
Websites
LAWSCORE An index for the strength of legal environment described
in Section 4.2.2. It is updated by the National Economic
Research Institute up to 2009. For years after 2009, we
use the 2009 index.
National
Economic
Research
Institute
Labor_Law_Effect The degree to which the 2008 Labor Law of People’s
Republic of China affects a firm. See Section 4.2.2. CSMAR
Affected An indicator for firms affected by the 2006 regulation. Resset
P3_PR
The payout ratio during the most-recent past three years
as defined by the CSRC. See Section 4.2.1. If it is
negative, we replace it by one.
Resset
P3_PR_D
Indicator equal to one if the payout ratio during the most-
recent past three years as defined by the CSRC is
negative, zero otherwise.
Resset
38
Appendix 4: Average Annual Wages in China by Education and Occupation.
This table reports average annual wage in China by education and occupation. The data is from China Urban Household Survey (2000-2009), which
provides access to nine provinces; Beijing, Liaoning, Zhejiang, Anhui, Hubei, Guangdong, Sichuan, Shaanxi, and Gansu. Annual wage is deflated using
provincial CPI with 2000 as the base year and the unit is Chinese RMB.
Education
Occupation
Year College or
above High School
Middle School or
below Technician
Production
Workers
Staff or Service
Workers
Agricultural
Workers Others
2000 11084.013 8944.776 5139.363
15239.261 9258.860 11053.963 8566.029 7946.278
2001 11976.958 9554.838 5438.288
16852.991 9864.254 11841.001 9827.922 8882.542
2002 15822.367 10409.411 5757.975
18404.414 10912.095 13807.288 9452.208 9661.626
2003 17728.367 11346.542 5975.318
20489.257 12303.120 15216.043 10937.459 11118.318
2004 19451.303 12139.160 6495.877
23086.913 13622.273 16191.782 12360.412 12257.059
2005 21261.428 13013.126 7123.790
25598.902 14743.270 18072.238 15012.060 14361.187
2006 23030.351 14092.422 7931.302
27949.907 16697.195 19682.444 16756.711 15198.924
2007 24665.948 15261.617 8603.666
29624.443 17833.485 21563.516 18206.153 17030.791
2008 27924.529 16415.125 9329.643
32551.162 20094.639 23523.721 19247.500 20093.954
2009 30928.259 18155.407 10323.152
35799.283 22402.561 26124.442 23231.018 20988.433
39
Appendix 5: First-stage Regressions for Robustness Tests.
This table reports first-stage results estimated using conditional logistic regressions at the firm level. Column (1) reports
the first-stage result for Table 14; Columns (2) - (6) for Table 15. Robust standard errors, clustered at the firm level, are
in parentheses. Coefficients marked with *, **, and *** are significant at 10%, 5%, and 1%, respectively.
This table reports the number of firms in the total sample and in the seasoned equity offering sample for the
panel data analyses. The sample includes Chinese firms listed on Shanghai and Shenzhen Stock Exchanges
from 2000 to 2012. Financial firms, firms with fewer than 100 employees, ST (special treatment), and *ST
firms are excluded. Firms are classified as ST or *ST if they have two (ST) or three (*ST) consecutive years
of negative net profits. Column (1) shows the number of firms in the full sample by year. Column (2) shows
the number of public offerings (underwritten offerings and rights offerings) by offering year.
Year Full Number of SEOs
(1) (2)
2000 885 154
2001 951 131
2002 1,002 44
2003 1,059 38
2004 1,153 32
2005 1,172 7
2006 1,204 7
2007 1,323 28
2008 1,395 43
2009 1,485 18
2010 1,830 20
2011 2,120 23
2012 2,259 12
Total 17,838 557
48
Table 5: Summary Statistics of Variables Used in the Panel Data Analyses.
This table reports summary statistics for variables used in the panel data regressions. Appendix 3 provides
variable definitions and data sources.
Mean Std. Dev. Min Max
Key Variables (1) (2) (3) (4)
SEO 0.088 0.283 0.000 1.000
SEOIneligible 0.155 0.362 0.000 1.000
%_Production 0.478 0.284 0.000 0.995
%_Staff 0.092 0.108 0.000 0.998
%_Tech_R&D 0.172 0.156 0.000 0.987
%_S&M 0.128 0.160 0.000 0.917
%_Finance 0.033 0.034 0.000 0.672
%_Others 0.183 0.266 0.000 1.000
%_Grad 0.031 0.043 0.000 0.237
%_BA 0.203 0.181 0.000 0.959
Ln(EMP) 2.899 1.205 0.000 8.618
Ln(Production) 6.253 1.916 0.000 12.728
Ln(Staff) 4.414 1.592 0.000 11.353
Lu(Tech_R&D) 5.478 1.229 0.000 12.204
Ln(S&M) 4.876 1.411 0.000 11.456
Ln(Finance) 3.802 1.017 0.000 9.578
Ln(Others) 3.789 2.957 0.000 12.330
Ln(Grad) 3.337 1.401 0.000 10.112
Ln(BA) 5.531 1.320 0.000 11.937
AWAGE 6.799 10.351 0.013 285.293
AWAGE_NonExe 6.933 11.333 0.011 493.721
AEXEPAY 20.192 20.009 0.360 506.227
Payroll 296.153 1908.561 0.039 108031.000
Payroll_NonExe 306.816 1964.043 0.019 108015.900
Payroll_Exe 2.876 3.534 0.022 111.370
Ln(Inv_Tech_Assets) 2.278 2.119 0.000 11.422
Ln(Capx) 4.199 1.884 -6.896 12.420
Ln(Total_Patent) 0.518 1.081 0.000 4.394
Ln(Invention) 0.315 0.767 0.000 3.497
Ln(Utility_Model) 0.306 0.793 0.000 3.664
Ln(Design) 0.122 0.497 0.000 2.944
NYEAR_LISTED 7.011 5.013 0.000 22.000
SALES 4517.473 39862.920 0.003 2085363.000
ROA 0.038 0.088 -1.752 2.933
LEVERAGE 0.456 0.201 0.047 0.889
49
Table 5: Summary Statistics of Variables Used in the Panel Data Analyses. (Continued)
Other Variables Mean Std.Dev. Min Max
PPE/TA 0.320 0.201 0.000 0.975
SALES_GR 0.228 0.497 -0.609 3.379
RE/TotalEq 0.058 0.602 -3.922 1.829
Inst_OWN 0.128 0.170 0.000 0.995
Ln(Tot_Volatility) -2.871 0.319 -6.039 -1.408
%_IND_DIR 0.305 0.127 0.000 0.833
%_STATE_OWN 0.215 0.252 0.000 0.886
%_LARGEST_SH 0.390 0.163 0.022 0.894
%_NONTRD_SH 0.212 0.296 0.000 0.913
D_PRIVATE_PLACE 0.046 0.210 0.000 1.000
Overseas_SEOs 0.001 0.029 0.000 1.000
DIV_PR 0.259 0.306 0.000 1.500
P3_PR 0.767 0.827 0.000 4.085
P3_PR_D 0.027 0.161 0.000 1.000
Ln(MIN_WAGE) 6.404 0.351 5.340 6.990
LAWSCORE 7.784 3.916 0.000 16.610
Labor_Law_Effect 3.689 3.850 0.000 13.312
50
Table 6: First-stage Regressions for Main Results. This table reports first-stage estimation results using conditional logistic regressions at the firm level. Column (1) reports the first stage result for Panel A of Tables 7 and 8, Column (1) of Tables 9, 10, 11, and 12. Column (2) reports the first stage result for Panel B of Tables 7 and 8, Columns (2)-(3) of Table 9, Columns (2)-(5) of Table 10, Columns (2)-(4) of Table 11, and Columns (2)-(4) of Table 12. Robust standard errors clustered at the firm level are in parentheses. Coefficients marked with *, **, and *** are significant at 10%, 5%, and 1%, respectively.
SEO
VARIABLES (1) (2)
SEOIneligible -1.036*** -1.621***
(0.318) (0.416)
ln(NYEAR_LISTED) 4.471*** 4.414***
(0.431) (0.471) ln(MIN_WAGE) 1.011 1.581**
(0.633) (0.692) LAWSCORE 0.036 0.041
(0.058) (0.070) Labor_Law_Effect 0.101 0.125
(0.069) (0.086) ln(SALES) 1.110***
(0.200) RE/TotalEq 0.452
(0.451) ROA -8.213***
(1.904) Inst_OWN 1.768***
(0.504) Ln(Tot_Volatility) -0.202
(0.258) %_LARGEST_SH -1.994
(1.259) SALES_GR -0.145
(0.127) DIV_PR 0.090
(0.075) %_STATE_OWN 0.272
(0.533) %_IND_DIR -0.712
(0.644) %_NONTRD_SH -1.955***
(0.615) D_PRIVATE_PLACE -1.357***
(0.315) Overseas_SEOs -13.594***
(0.708) Leverage -6.304***
(0.973) PPE/TA 1.000
(0.981) Year Dummies Y Y Observations 5,438 5,261
Pseudo R2 0.355 0.432 Wald 553.4 1004
51
Table 7: SEO Impact on Employee Composition by Occupation and Education.
This table reports the second-stage estimation of the impact that SEOs have on the employee composition by occupation and education. The dependent variable is the percentage of production workers in Column (1), support staff in Column (2), technicians and R&D employees in Column (3), sales and marketing forces in Column (4), finance staff in Column (5), employees in uncategorized occupations in Column (6), employees with post-graduate degrees in Column (7), and employees with four-year university Bachelor’s degrees and above in Column (8). All regressions include firm- and year fixed effects. Regressions in Panel A control for only firm age and legal variables. Regressions in Panel B include the full set of time-varying firm characteristic variables. Appendix 3 provides variable definitions and data sources. The sample period covers 2000 – 2012. Bootstrapped standard errors are reported in parentheses. Coefficients marked with *, **, and *** are significant at 10%, 5%, and 1%, respectively.
Table 8: SEO Impact on Firm-Level Employment by Occupation and Education. This table reports the second-stage estimation of the impact that SEOs have on the number of employees at the firm level. The dependent variable is the number of all employees in Column (1), production workers in Column (2), support staff in Column (3), technicians and R&D employees in Column (4), sales and marketing forces in Column (5), finance staff in Column (6), employees in uncategorized occupations in Column (7), employees with post-graduate degrees in Column (8), and employees with Bachelor’s degrees and above in Column (9). All dependent variables are logged, and all regressions include firm- and year fixed effects. Regressions in Panel A control for only firm age and legal variables. Regressions in Panel B include the full set of time-varying firm characteristic variables. Appendix 3 provides variable definitions and data sources. The sample period covers 2000 – 2012. Bootstrapped standard errors are reported in parentheses. Coefficients marked with *, **, and *** are significant at 10%, 5%, and 1%, respectively. Panel A
Table 9: SEO Impact on Investments in Technology and Capital Expenditures. This table reports the second-stage estimation of the impact that SEOs have on investments in technology and total capital expenditures. The dependent variable is the log of one plus the value of newly purchased machines and equipment in Columns (1) and (2), and the log of capital expenditures in Column (3). Appendix 3 provides variable definitions and data sources. The sample period covers 2003 – 2012 for Columns (1) and (2), and 2000-2012 for Column (3). All regressions include firm- and year fixed effects. Bootstrapped standard errors are reported in parentheses. Coefficients marked with *, **, and *** are significant at 10%, 5%, and 1%, respectively.
Ln(Inv_Tech_Assets) Ln(Capx)
VARIABLES (1) (2) (3)
SEO� 0.512*** 0.188** 0.238***
(0.128) (0.094) (0.091)
Ln(NYEAR_LISTED) 0.026 -0.163*** -0.417***
(0.066) (0.059) (0.040)
Ln(MIN_WAGE) 0.196 0.005 0.028
(0.180) (0.165) (0.090)
LAWSCORE -0.064*** -0.035** -0.007
(0.018) (0.016) (0.008)
Labor_Law_Effect -0.031*** -0.030*** -0.044***
(0.010) (0.009) (0.008)
Ln(SALES) 0.482*** 0.653***
(0.034) (0.019)
RE/TotalEq 0.156*** 0.451***
(0.033) (0.036)
ROA -0.221 1.085***
(0.185) (0.335)
Inst_OWN 0.142 0.389***
(0.094) (0.063)
Ln(Tot_Volatility) -0.012 -0.157***
(0.057) (0.037)
%_LARGEST_SH 0.012 0.147
(0.229) (0.149)
SALES_GR 0.102*** -0.055**
(0.029) (0.023)
DIV_PR 0.002 0.009
(0.033) (0.021)
%_STATE_OWN 0.086 0.085*
(0.086) (0.050)
%_IND_DIR -0.072 0.197*
(0.159) (0.102)
%_NONTRD_SH -0.104 -0.540***
(0.138) (0.089)
D_PRIVATE_PLACE 0.188*** 0.372***
(0.053) (0.036)
Overseas_SEOs -0.035 -0.034
(0.287) (0.148)
Leverage 0.869*** 0.878***
(0.124) (0.125)
PPE/TA 2.252*** 3.005***
(0.133) (0.111)
Constant 1.205 -1.769* -1.768***
(1.096) (1.002) (0.546)
Firm & Year FE Y Y Y
Observations 14,834 14,469 17,114
56
Table 10: SEO Impact on Innovations. This table reports the second-stage estimation of the impact of SEOs on patents granted. The dependent variable is the log of one plus the total number of all patents granted in t+3 in Columns (1) and (2), invention patents, utility model patents, and design patents granted in t+3 in Columns (3) - (5), respectively. Appendix 3 provides variable definitions and data sources. The sample period covers 2000 – 2012. All regressions include firm- and year fixed effects. Bootstrapped standard errors are reported in parentheses. Coefficients marked with *, **, and *** are significant at 10%, 5%, and 1%, respectively.
Table 11: SEO Impact on Firm Average Wages. This table reports the second-stage estimation of the impact of SEOs on firm average wages. The dependent variable is the log of average wage of all employees in Columns (1) and (2); the log of average wage of non-executive employees in Column (3); the log of average wage of executives in Column (4). Appendix 3 provides variable definitions and data sources. The sample period covers 2000 – 2012 for Columns (1) – (2) and 2001 – 2012 for Columns (3) – (4). All regressions include firm- and year fixed effects. Bootstrapped standard errors are reported in parentheses. Coefficients marked with *, **, and *** are significant at 10%, 5%, and 1%, respectively.
Ln(AWAGE) Ln(AWAGE_NonExe) Ln(AEXEPAY)
VARIABLES (1) (2) (3) (4)
SEO� 0.092** 0.078** 0.099*** 0.003
(0.041) (0.039) (0.035) (0.040)
Ln(NYEAR_LISTED) -0.009 -0.007 -0.015 -0.052***
(0.016) (0.019) (0.016) (0.019)
Ln(MIN_WAGE) 0.332*** 0.294*** 0.281*** 0.164***
(0.045) (0.048) (0.047) (0.048)
LAWSCORE -0.010* -0.009* -0.012** -0.029***
(0.005) (0.005) (0.005) (0.005)
Labor_Law_Effect 0.007** 0.001 0.000 0.013***
(0.003) (0.003) (0.003) (0.003)
Ln(SALES) 0.099*** 0.104*** 0.163***
(0.010) (0.011) (0.008)
RE/TotalEq 0.045*** 0.036*** 0.102***
(0.015) (0.013) (0.012)
ROA 0.219*** 0.185* 0.546***
(0.083) (0.101) (0.135)
Inst_OWN 0.008 0.007 0.168***
(0.027) (0.027) (0.022)
Ln(Tot_Volatility) 0.040* 0.040*** -0.019
(0.021) (0.015) (0.018)
%_LARGEST_SH 0.175*** 0.199*** -0.052
(0.062) (0.065) (0.061)
SALES_GR -0.002 -0.001 -0.038***
(0.010) (0.010) (0.009)
DIV_PR 0.001 0.001 0.005
(0.012) (0.013) (0.005)
%_STATE_OWN 0.095*** 0.085*** -0.011
(0.024) (0.029) (0.026)
%_IND_DIR 0.002 -0.014 -0.036
(0.049) (0.050) (0.045)
%_NONTRD_SH -0.014 0.009 -0.021
(0.037) (0.038) (0.041)
D_PRIVATE_PLACE -0.020 -0.016 -0.011
(0.016) (0.017) (0.016)
Overseas_SEOs -0.103 -0.092 0.119
(0.063) (0.078) (0.082)
Leverage 0.005 -0.009 0.062
(0.046) (0.041) (0.039)
PPE/TA -0.111** -0.078 -0.153***
(0.045) (0.048) (0.047)
Constant -1.283*** -1.633*** -1.464*** -0.002
(0.263) (0.296) (0.315) (0.269)
Firm & Year FE Y Y Y Y
Observations 17,437 17,003 16,071 16,159
58
Table 12: SEO Impact on Total Wages. This table reports the second-stage estimation of the impact of SEOs on total wages. The dependent variable is the log of total wages to all employees in Columns (1) and (2); the log of total wages to all non-executive employees in Column (3); the log of total wages to all executives in Column (4). Appendix 3 provides variable definitions and data sources. The sample period covers 2000 – 2012 for Columns (1) – (2) and 2001 – 2012 for Columns (3) – (4). All regressions include firm- and year fixed effects. Bootstrapped standard errors are reported in parentheses. Coefficients marked with *, **, and *** are significant at 10%, 5%, and 1%, respectively.
Table 13: Pre-Trends. This table reports the results of placebo tests for pre-trends with the full set of control variables. Appendix 3 provides variable definitions and data sources. Robust standard errors clustered at the firm level are reported in parentheses. Coefficients marked with *, **, and *** are significant at 10%, 5%, and 1%, respectively.
Table 14: Can Past Payout Ratios Explain Our Results? This table reports reestimation results while controlling for the most-recent past three years’ payout ratio with the full set of control variables. Appendix 3 provides variable definitions and data sources. All regressions include firm- and year fixed effects. Bootstrapped standard errors are reported in parentheses. Coefficients marked with *, **, and *** are significant at 10%, 5%, and 1%, respectively.
Table 15: Other Robustness Checks. This table reports the second-stage estimation results using alternative instruments and definitions of SEO. Column (1) lists dependentvariables. Only coefficients on the predicted SEO, standard errors, and sample size are reported for each robustness test. Column (2) includes the short-lived 2004 regulation in constructing the instrument. Column (3) turns on the instrument only for firms affected by the 2006 regulation in 2006 and firms affected by the 2008 regulation in 2008. Column (4) uses one-year lag between the beginning of an SEO process and the availability of SEO proceeds. Column (5) relies only on the 2006 regulation to construct the instrument. Column (6) excludes small SEOs with proceeds in the bottom decile. Appendix 3 provides variable definitions and data sources. Appendix 5 reports first-stage estimation results. Bootstrapped standard errors are reported in parentheses. Coefficients marked with *, **, and *** are significant at 10%, 5%, and 1%, respectively.