Load Data

# Load your merged IPO dataset
ipo_data <- read.csv("../data/merged_IPO_2014.csv")

# Ensure year and firm ID are treated as factors
#ipo_data$Year <- as.factor(ipo_data$Year)
ipo_data$ID <- as.factor(ipo_data$ID)

All together

model_hb <- plm(W_TobinsQ ~ 
                  Founder_is_CEO+ 
                # log_Revenue+ 
                # Leverage_2+ 
                 log_Assets_Total+
                 CEO_Change_at_IPO+
                 #GDP+
D_Profitability_pior_IPO+
#PCE+
#Personal_Income+
#RealPersonalIncome_Millions+
#SP500+
          Leverage +
                 #  CEO_Chairman_Duality +
                     CEO_Board_Member + 
                   Audit_Expertise +
  Audit_Expertise +
                  log_Market_Cap +
                  W_ESG_Score  +
                  log_Capex_Total+
                  log_Workforce_Score+
  Board_skills_percent + 
log_Debt_Total,
                 # CEO_Change_after_IPO,
                 data = ipo_data, index = c("ID", "Year"), model = "within", effect= "individual")
 
 coeftest(model_hb, vcov = vcovHC(model_hb, type = "HC1", cluster = "group"))
## 
## t test of coefficients:
## 
##                        Estimate Std. Error t value  Pr(>|t|)    
## Founder_is_CEO       -0.1085423  0.2208612 -0.4915  0.623950    
## log_Assets_Total     -2.8377374  0.3703222 -7.6629 3.953e-12 ***
## Leverage              1.5214174  0.4831192  3.1492  0.002038 ** 
## CEO_Board_Member     -2.6985884  0.2773051 -9.7315 < 2.2e-16 ***
## Audit_Expertise       0.6719393  0.7044862  0.9538  0.341982    
## log_Market_Cap        2.1226363  0.3192955  6.6479 7.760e-10 ***
## W_ESG_Score           0.0172734  0.0060388  2.8604  0.004943 ** 
## log_Capex_Total       0.1735659  0.1014695  1.7105  0.089592 .  
## log_Workforce_Score  -0.3759687  0.1974832 -1.9038  0.059181 .  
## Board_skills_percent -0.0045819  0.0045124 -1.0154  0.311830    
## log_Debt_Total       -0.0427636  0.0149164 -2.8669  0.004849 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Introduction

This study explores how newly public firms perform in the critical years following their initial public offering (IPO), with a particular focus on the role of corporate governance, executive leadership, and sustainability orientation.

While the IPO marks a pivotal shift—granting access to capital and market visibility—it also introduces increased accountability and strategic complexity. Drawing on upper echelons theory, we examine how characteristics of the top management team and board—such as board size, independence, skill composition, meeting frequency, and attendance—shape post-IPO outcomes.

Furthermore, we incorporate insights from agency theory, emphasizing the mechanisms firms use to align managerial actions with long-term value creation. This includes linking executive compensation to performance metrics like total shareholder return (TSR) and sustainability goals.

By integrating ESG scores, compensation structure, and governance practices, this study asks:

How do leadership composition, governance design, and sustainability commitment influence firm performance in the years following an IPO?

Motivation

Recent evidence suggests that many firms experience a sharp decline in performance in the years following their IPO, a phenomenon observed consistently across sectors and market cycles. In our own analysis of Tobin’s Q—an established proxy for firm value—this pattern is striking: for a substantial number of firms, the ratio between market value and assets drops significantly from the first to the fifth year post-IPO. This widespread depreciation raises a critical question: what internal organizational factors account for firms’ ability (or inability) to sustain value after going public?

While investors and policymakers often focus on market conditions and industry dynamics to explain post-IPO volatility, much less attention has been paid to the role of internal decision-making structures—specifically governance, leadership incentives, and sustainability orientation. This is a significant omission, especially in an era where firms are expected not only to grow financially but also to demonstrate legitimacy, transparency, and environmental and social responsibility.

This study is motivated by the need to unpack this underexplored terrain. It seeks to understand why some firms are better equipped to navigate the post-IPO transition, while others struggle despite similar market opportunities. By focusing on variables such as board configuration, executive pay design, and ESG performance, the research aims to identify internal levers that may help preserve or even enhance firm value after the IPO event.

TobinsQ visualization

This graph shows the change in Tobin’s Q by calculating the difference between the first year and the last year:

Tobin’s Q (first year) – Tobin’s Q (last year)

  • A negative value indicates that Tobin’s Q has decreased over time.
  • For example, if a company had a Tobin’s Q of 10 in its first year and 5 in its last year, the graph would display -5.

Tobin’s Q is calculated as:

Tobin’s Q = Market Value / Total Assets
(Only when total assets are not zero)

knitr::include_graphics("C:/workspace/IPO/Fig/TobinsQ.png")

Descriptive Statisitcs

# Define the variables used in your models
vars <- c(  "W_TobinsQ", "log_TobinsQ", "W_Leverage", "Leverage", "Leverage_2",
  "log_Net_Income", "log_Revenue", "log_Assets_Total", "log_Debt_Total",
  "log_Market_Cap", "log_Capex_Total", "log_Total_Liabilities", "log_Net_Cash_Flow_Opera",
  "ROA_Actual", "Profitability_prior_IPO", "D_Profitability_pior_IPO",
  "CEO_Comp_Link_TSR", "Comp_LT_Objectives", "log_Executives_Compensation",
  "CEO_Chairman_Duality", "CEO_Chairman_Duality_Current", "CEO_Board_Member",
  "CEO_Change_after_IPO", "CEO_Change_at_IPO", "Founder_is_CEO",
  "Independent_Board", "Board_Size", "log_Board_Size", "Board_skills_percent",
  "Num_Board_Meetings", "Audit_Expertise", "Board_Committee",
  "ESG_Score", "W_ESG_Score", "Governance_Score", "Comp_Controversies_Score",
  "log_ESG_Score", "log_Governance_Score", "log_Workforce_Score",
  "GDP", "RealGDP_Millions", "PerCapitaPCE", "PerCapitaIncome", "PCE",
  "Personal_Income", "RealPersonalIncome_Millions", "Total_Employment",
  "SP500", "ThreeMonthTBill", "USTenYearBond", "BaaCorpBond",
  "RealEstate", "RealEstate_scaled_robust", "Gold"
)


# Filter to available columns
vars <- intersect(vars, names(ipo_data))

# Select data and drop rows with missing values
desc_data <- ipo_data %>%
  select(all_of(vars)) %>%
  na.omit()

# Compute descriptive stats and round to 2 decimals
desc_stats <- desc_data %>%
  summarise(across(everything(), list(
    Mean = ~round(mean(.), 2),
    SD   = ~round(sd(.), 2),
    Min  = ~round(min(.), 2),
    Max  = ~round(max(.), 2)
  ), .names = "{.col}_{.fn}")) %>%
  t() %>%
  as.data.frame()

# Add readable variable names as a column
colnames(desc_stats) <- "Value"
desc_stats$Variable <- rownames(desc_stats)
rownames(desc_stats) <- NULL

# Reorder columns
desc_stats <- desc_stats %>%
  tidyr::separate(Variable, into = c("Variable", "Statistic"), sep = "_(?=[^_]+$)") %>%
  tidyr::pivot_wider(names_from = Statistic, values_from = Value)



#kable(desc_stats, digits = 2, caption = "Descriptive Statistics of Model Variables")

desc_stats %>%
  kable(digits = 2, caption = "Descriptive Statistics of Model Variables") %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = FALSE) %>%
  scroll_box(height = "400px")
Descriptive Statistics of Model Variables
Variable Mean SD Min Max
W_TobinsQ 2.34 2.63 0.00000e+00 9.47
log_TobinsQ 0.97 0.75 0.00000e+00 3.37
W_Leverage 0.27 0.25 0.00000e+00 1.04
Leverage 0.27 0.25 0.00000e+00 1.04
Leverage_2 1.21 3.92 -2.32900e+01 17.89
log_Net_Income 11.74 9.13 0.00000e+00 21.31
log_Revenue 19.79 3.83 0.00000e+00 22.93
log_Assets_Total 21.42 1.62 1.80100e+01 25.92
log_Debt_Total 16.37 8.16 0.00000e+00 25.03
log_Market_Cap 21.49 1.13 1.81600e+01 23.75
log_Capex_Total 17.12 1.89 1.11600e+01 22.27
log_Total_Liabilities 20.89 1.92 1.65100e+01 25.84
log_Net_Cash_Flow_Opera 14.36 8.44 0.00000e+00 23.65
ROA_Actual -0.02 0.23 -1.60000e+00 0.35
Profitability_prior_IPO 26742692.59 135658089.68 -2.18900e+08 869000000.00
D_Profitability_pior_IPO 0.62 0.49 0.00000e+00 1.00
CEO_Comp_Link_TSR 0.18 0.39 0.00000e+00 1.00
Comp_LT_Objectives 0.18 0.39 0.00000e+00 1.00
log_Executives_Compensation 16.15 0.88 1.16300e+01 17.97
CEO_Chairman_Duality 0.54 0.50 0.00000e+00 1.00
CEO_Chairman_Duality_Current 0.50 0.50 0.00000e+00 1.00
CEO_Board_Member 0.98 0.15 0.00000e+00 1.00
CEO_Change_after_IPO 0.27 0.51 0.00000e+00 2.00
CEO_Change_at_IPO 0.04 0.21 0.00000e+00 1.00
Founder_is_CEO 0.29 0.46 0.00000e+00 1.00
Independent_Board 76.51 14.08 3.33300e+01 93.33
Board_Size 8.44 2.07 5.00000e+00 14.00
log_Board_Size 2.22 0.22 1.79000e+00 2.71
Board_skills_percent 61.62 21.85 1.66700e+01 100.00
Num_Board_Meetings 7.41 3.45 3.00000e+00 23.00
Audit_Expertise 0.97 0.17 0.00000e+00 1.00
Board_Committee 0.97 0.17 0.00000e+00 1.00
ESG_Score 28.55 11.17 9.94000e+00 64.23
W_ESG_Score 28.55 11.17 9.94000e+00 64.23
Governance_Score 31.05 18.62 1.14000e+00 81.20
Comp_Controversies_Score 51.62 0.21 5.14500e+01 52.70
log_ESG_Score 3.32 0.36 2.39000e+00 4.18
log_Governance_Score 3.25 0.73 7.60000e-01 4.41
log_Workforce_Score 3.50 0.69 7.60000e-01 4.48
GDP 1347444.36 1046024.96 5.66040e+04 3068629.70
RealGDP_Millions 1325198.59 1027919.41 5.83879e+04 2969609.00
PerCapitaPCE 43971.88 5351.59 3.20050e+04 54501.00
PerCapitaIncome 56613.10 8425.36 3.87120e+04 74176.00
PCE 862511.30 653198.33 4.20561e+04 1941141.00
Personal_Income 1120108.48 859772.55 5.17752e+04 2539747.40
RealPersonalIncome_Millions 1037548.26 751965.71 5.21665e+04 2211989.00
Total_Employment 11746495.89 8475541.84 6.26983e+05 24224461.00
SP500 15.48 14.52 -4.23000e+00 31.21
ThreeMonthTBill 1.50 0.68 5.00000e-02 2.06
USTenYearBond 3.91 4.15 -2.00000e-02 9.64
BaaCorpBond 7.62 7.65 -3.27000e+00 15.25
RealEstate 4.75 0.96 3.69000e+00 6.21
RealEstate_scaled_robust -0.12 0.12 -2.50000e-01 0.07
Gold 9.66 8.67 -1.21100e+01 19.08 
selected_data <- ipo_data[, c("W_TobinsQ",
  "Founder_is_CEO",
  "CEO_Change_after_IPO",
  "CEO_Change_at_IPO",
  "CEO_Chairman_Duality",
  "CEO_Board_Member",
  "Audit_Expertise",
  "Leverage",
  "Leverage_2",
  "W_Leverage",
  "Board_skills_percent",
  "CEO_Comp_Link_TSR",
  "Comp_LT_Objectives",
  "D_Profitability_pior_IPO",
  "log_Revenue",
  "log_Assets_Total",
  "log_Executives_Compensation",
  "log_Net_Cash_Flow_Opera",
  "log_Total_Liabilities",
  "GDP",
  "PCE",
  "PerCapitaPCE",
  "PerCapitaIncome",
  "Personal_Income",
  "RealPersonalIncome_Millions",
  "RealGDP_Millions",
  "SP500",
  "BaaCorpBond")]

# All variables 
# "Leverage", "Leverage_2",
#   "Net_Income", "Revenue", "Assets_Total", "Debt_Total",
#   "Market_Cap", "Capex_Total", "Total_Liabilities", "Net_Cash_Flow_Opera",
#   "ROA_Actual", "Profitability_prior_IPO",
#   "CEO_Comp_Link_TSR", "Comp_LT_Objectives", "Executives_Compensation",
#   "CEO_Chairman_Duality", "CEO_Chairman_Duality_Current", "CEO_Board_Member",
#   "CEO_Change_after_IPO", "CEO_Change_at_IPO", "Founder_is_CEO",
#   "Independent_Board", "Board_Size", "Board_skills_percent",
#   "Num_Board_Meetings", "Audit_Expertise", "Board_Committee",
#   "ESG_Score", "Governance_Score", "Comp_Controversies_Score",
#   "Workforce_Score"


# Controls
# "GDP", "RealGDP_Millions", "PerCapitaPCE",
#   "PerCapitaIncome", "PCE", "Personal_Income", "RealPersonalIncome_Millions",
#   "Total_Employment", "SP500", "ThreeMonthTBill", "USTenYearBond",
#   "BaaCorpBond", "RealEstate", "RealEstate_scaled_robust", "Gold"

#major variables only
# c('W_Leverage',
#                   'log_Total_Liabilities',
#                   'log_Net_Cash_Flow_Opera',
#                   'log_Revenue',
#                   'ROA_Actual',
#                   'CEO_Comp_Link_TSR',
#                   'CEO_Chairman_Duality',
#                   'Independent_Board',
#                   'CEO_Board_Member',
#                   'Num_Board_Meetings',
#                   'Founder_is_CEO',
#                   'CEO_Change_after_IPO',
#                   'Comp_LT_Objectives',
#                   'ESG_Score',
#                   'Governance_Score',
#                   'Comp_Controversies_Score',
#                   'Audit_Expertise')

# Check if all columns are numeric

# Convert all columns to numeric, handling conversion issues and NAN
selected_data[] <- lapply(selected_data, function(x) {
  x <- as.numeric(as.character(x))
  x[is.infinite(x)] <- NA
  x
})

# Recompute the correlation matrix handling NA values
correlation_matrix <- cor(selected_data, use = "pairwise.complete.obs")

if (all(is.finite(correlation_matrix))) {
  corrplot(correlation_matrix,
         method = "number",
         type = "upper",
         order = "hclust",
         tl.cex = 0.6,            # smaller text labels
         tl.col = "black",
         tl.srt = 45,
         number.cex = 0.5,        # smaller numbers so everything fits
         addCoef.col = "black",
         cl.cex = 0.6,
         mar = c(0, 0, 2, 0))     # minimize margin
}

Management

Hypotheses Derived from Post-IPO Performance Analysis

This section develops testable hypotheses based on the empirical results from a fixed-effects panel regression model using Tobin’s Q as a proxy for firm value. These hypotheses are supported by relevant theoretical frameworks and academic literature.

Hypothesis 1: Founder Leadership and Firm Value

H1: Firms where the founder remains CEO after IPO are associated with higher Tobin’s Q.

Rationale: Founder-CEOs often retain deep organizational knowledge, long-term strategic vision, and stronger alignment with firm culture. Stewardship theory posits that such leaders act in the best interest of the firm, which is particularly valuable during the vulnerable post-IPO phase.

“The persistence of founder influence has a long-lasting impact on strategic direction and performance.”
— Nelson (2003), Strategic Management Journal

Hypothesis 2: CEO Turnover Signals Strategic Adaptation

H2: Firms that change CEOs after IPO experience higher Tobin’s Q.

Rationale: Post-IPO CEO turnover can reflect strategic adaptation or governance accountability. It may signal that the board is actively aligning leadership with public investor expectations, which can enhance legitimacy and market valuation.

“Leadership changes are often precursors to corporate restructuring and improved market reception.”
— Wiersema (1995), Human Resource Management

Each hypothesis provides a path for empirical testing and theoretical contribution in the field of post-IPO corporate performance. These hypotheses also offer actionable insights for investors, policymakers, and founders navigating the transition to public ownership.

model_hb <- plm(W_TobinsQ ~ Founder_is_CEO+ 
                 log_Revenue+ 
                 Leverage_2+ 
                 log_Assets_Total+
                 CEO_Change_at_IPO+
                 GDP+
#PerCapitaPCE+
##PerCapitaIncome+
PCE+
Personal_Income+
#RealGDP_Millions+
RealPersonalIncome_Millions+
#Total_Employment+
SP500+
#ThreeMonthTBill+
##USTenYearBond+
#BaaCorpBond+
#RealEstate+
#Gold+
                  CEO_Change_after_IPO,
                 #CEO_Chairman_Duality+
                 #CEO_Chairman_Duality_Current,
                 #Independent_Board,
                 data = ipo_data, index = c("ID", "Year"), model = "within", effect= "individual")
 
 coeftest(model_hb, vcov = vcovHC(model_hb, type = "HC1", cluster = "group"))
## 
## t test of coefficients:
## 
##                                Estimate  Std. Error t value  Pr(>|t|)    
## Founder_is_CEO               1.0617e+00  2.7892e-01  3.8065 0.0001561 ***
## log_Revenue                 -3.9174e-02  2.3347e-02 -1.6779 0.0939077 .  
## Leverage_2                  -4.4114e-03  1.5565e-03 -2.8342 0.0047562 ** 
## log_Assets_Total            -5.0656e-01  1.8226e-01 -2.7794 0.0056251 ** 
## GDP                          1.3224e-05  6.1718e-06  2.1427 0.0325551 *  
## PCE                          1.5068e-05  6.5657e-06  2.2949 0.0220993 *  
## Personal_Income             -3.9025e-05  1.6095e-05 -2.4247 0.0156306 *  
## RealPersonalIncome_Millions  1.9237e-05  9.7593e-06  1.9711 0.0491923 *  
## SP500                        2.7247e-03  4.0762e-03  0.6684 0.5041252    
## CEO_Change_after_IPO         4.5919e-01  2.1933e-01  2.0936 0.0367357 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 #Simple version also works
 
 model_hb <- plm(W_TobinsQ ~ Founder_is_CEO+ 
                 log_Revenue+ 
                 Leverage_2+ 
                 log_Assets_Total+
                 CEO_Change_at_IPO+
                CEO_Change_after_IPO,

                 data = ipo_data, index = c("ID", "Year"), model = "within", effect= "individual")
 
 coeftest(model_hb, vcov = vcovHC(model_hb, type = "HC1", cluster = "group"))
## 
## t test of coefficients:
## 
##                        Estimate Std. Error t value  Pr(>|t|)    
## Founder_is_CEO        0.9512839  0.2527246  3.7641 0.0001842 ***
## log_Revenue          -0.0399446  0.0228247 -1.7501 0.0806383 .  
## Leverage_2           -0.0047327  0.0015560 -3.0415 0.0024604 ** 
## log_Assets_Total     -0.5101600  0.1760503 -2.8978 0.0039000 ** 
## CEO_Change_after_IPO  0.4893498  0.2217026  2.2072 0.0276891 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Additional Hypothesis that can be derived

Hypothesis A: Leverage and Performance Penalty

H(a): Firms with higher leverage ratios post-IPO will have lower Tobin’s Q.

Rationale: According to trade-off and agency theories, high debt increases financial risk and can lead to underinvestment or operational constraints. This impairs investor confidence, especially when firms are under increased scrutiny after going public.

“Capital structure decisions significantly influence firm value, especially when leverage is excessive.”
— Frank & Goyal (2009), Financial Management

Hypothesis B: Asset Intensity and Market Valuation

H(b): Firms with greater total assets are associated with lower Tobin’s Q post-IPO.

Rationale: Asset-heavy firms are often seen as less agile and less capable of rapid innovation. In modern capital markets, scalability and intangible value (like innovation or branding) often command higher premiums than traditional asset bases.

“Investors reward firms that signal growth and flexibility, especially through intangible assets.”
— Lev & Zarowin (1999), Journal of Accounting Research

Hypothesis C: Macroeconomic Moderators

H(c): Macroeconomic indicators (GDP, PCE, personal income) are positively associated with Tobin’s Q post-IPO.

Rationale: Favorable macroeconomic conditions improve firm prospects and investor sentiment. Higher GDP and income levels generally correlate with stronger demand, revenue potential, and risk-adjusted valuations—amplifying firm value in public markets.

“IPO performance is sensitive to macroeconomic cycles and broader economic signals.”
— Ritter (1991), The Journal of Finance

Hypotheses from Governance and Economic Drivers of Post-IPO Firm Value

Following the same structure as before this section presents testable hypotheses investigating how internal governance features and macroeconomic conditions influence firm performance in the years following an IPO. Each hypothesis is grounded in theory and directly supported by statistically significant regression findings.

Hypothesis 3: Board Control and Strategic Rigidity

H3: Firms where the CEO is also a board member exhibit lower Tobin’s Q after IPO.

Rationale: While board membership may enhance executive insight, it can also signal reduced oversight and increased entrenchment. Agency theory warns that dual roles may weaken governance accountability, particularly in newly public firms.

“Excessive executive influence on boards undermines independent oversight.”
— Fama & Jensen (1983), Journal of Law and Economics

  • Supported by:
    CEO_Board_Member coefficient = –5.264, p < 0.001

Additional Hypothesis that can be derived

Hypothesis D: Leverage and Post-IPO Risk Discount

H(d): Higher financial leverage is associated with lower Tobin’s Q after IPO.

Rationale: High debt can constrain post-IPO flexibility and amplify investor-perceived risk. This aligns with the trade-off theory, which predicts valuation discounts for firms with excessive leverage.

“Financial leverage increases the risk of underinvestment and distress, especially after IPO.”
— Myers (1984), The Journal of Finance

  • Supported by:
    Leverage coefficient = –2.968, p = 0.006
Hypothesis E: Market Confidence Reflected in Stock Index Performance

H(e): Higher S&P 500 levels are positively associated with post-IPO firm value.

Rationale: A rising stock market enhances investor sentiment and valuation multiples across the board. Strong index performance serves as a signal of economic optimism and increased market liquidity.

“Market conditions exert a significant influence on the valuation of newly public firms.”
— Loughran & Ritter (1995), The Journal of Finance

  • Supported by:
    SP500 coefficient = +0.0551, p = 0.0023
Hypothesis F: Credit Market Tightness and Valuation Penalty

H(f): Higher corporate bond yields (Baa) are negatively associated with Tobin’s Q.

Rationale: Rising Baa bond yields reflect tighter credit markets and elevated investor risk aversion, particularly impacting firms with uncertain or volatile post-IPO cash flows.

“Bond yields are a key signal of credit conditions, which affect equity risk premiums.”
— Chen, Roll & Ross (1986), Journal of Business

  • Supported by:
    BaaCorpBond coefficient = –0.0979, p = 0.0039
Hypothesis G: Consumer Demand Indicators and Firm Valuation

H(g): Higher per capita consumption expenditures (PerCapitaPCE) are associated with lower Tobin’s Q.

Rationale: While consumer spending usually signals economic strength, in this context it may reflect inflationary pressures or saturated demand, reducing growth expectations for post-IPO firms.

“Macroeconomic indicators may be interpreted differently depending on market sentiment and timing.”
— Ritter (1991), The Journal of Finance

  • Supported by:
    PerCapitaPCE coefficient = –0.0009, p = 0.0422

These hypotheses reflect how governance structure and macroeconomic conditions shape firm valuation trajectories after going public. The findings reinforce the importance of external context and internal accountability in sustaining firm value post-IPO.

# model_hc <- plm(W_TobinsQ ~ 
#                    #CEO_Comp_Link_TSR + 
#                    Comp_LT_Objectives +
#                    log_Revenue + 
#                    Leverage_2 +
#                      #CEO_Change_after_IPO + 
#                   #CEO_Chairman_Duality +
#                      #CEO_Board_Member + 
#                    Audit_Expertise +
#                      Founder_is_CEO + 
#                    #Num_Board_Meetings +
#                      log_Executives_Compensation,
#                  data = ipo_data, index = c("ID", "Year"), model = "within", effect= "individual")
# summary(model_hc)

model_hc <- plm(W_TobinsQ ~ 
                  #CEO_Comp_Link_TSR + 
                   #Comp_LT_Objectives +
                  log_Revenue + 
                   Leverage +
                    # CEO_Change_after_IPO + 
                   CEO_Chairman_Duality +
                     CEO_Board_Member + 
                   Audit_Expertise +
                     Founder_is_CEO + 
                   #Num_Board_Meetings +
                  GDP+
PerCapitaPCE+
PerCapitaIncome+
PCE+
Personal_Income+
RealGDP_Millions+
#RealPersonalIncome_Millions+
#Total_Employment+
SP500+
#ThreeMonthTBill+
#USTenYearBond+
BaaCorpBond+
#RealEstate+
#Gold+
                     log_Executives_Compensation,
                 data = ipo_data, index = c("ID", "Year"), model = "within", effect= "individual")
 #summary(model_hc)
coeftest(model_hc, vcov = vcovHC(model_hc, type = "HC1", cluster = "group"))
## 
## t test of coefficients:
## 
##                                Estimate  Std. Error  t value  Pr(>|t|)    
## log_Revenue                 -1.1282e-01  9.1351e-02  -1.2350  0.218912    
## Leverage                    -2.9676e+00  1.0658e+00  -2.7844  0.006110 ** 
## CEO_Chairman_Duality         3.9265e-01  3.8026e-01   1.0326  0.303595    
## CEO_Board_Member            -5.2638e+00  3.1716e-01 -16.5968 < 2.2e-16 ***
## Audit_Expertise             -1.1202e+00  6.4949e-01  -1.7247  0.086800 .  
## Founder_is_CEO               2.1809e-01  5.4077e-01   0.4033  0.687354    
## GDP                          8.2002e-06  7.6333e-06   1.0743  0.284560    
## PerCapitaPCE                -9.0518e-04  4.4151e-04  -2.0502  0.042224 *  
## PerCapitaIncome              5.0699e-04  2.8739e-04   1.7641  0.079907 .  
## PCE                          4.2466e-05  2.2232e-05   1.9101  0.058174 .  
## Personal_Income             -2.3222e-05  1.3101e-05  -1.7725  0.078500 .  
## RealGDP_Millions            -2.0572e-05  1.2015e-05  -1.7122  0.089082 .  
## SP500                        5.5112e-02  1.7713e-02   3.1114  0.002261 ** 
## BaaCorpBond                 -9.7893e-02  3.3322e-02  -2.9378  0.003871 ** 
## log_Executives_Compensation -2.6047e-01  1.6740e-01  -1.5560  0.121982    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Hypothesis 4: ESG Performance and Market Penalty

Legitimacy theory (Suchman, 1995) and signaling theory (Connelly et al., 2011) suggest that formal CSR structures signal commitment to broader stakeholder goals, environmental responsibility, and social ethics. These structures may be viewed favorably by investors seeking sustainable, forward-thinking firms.

H4:
Higher ESG performance scores are negatively associated with Tobin’s Q in the post-IPO period.

Rationale:
Contrary to many ESG narratives, this model shows a negative association between W_ESG_Score and firm value. This may suggest investor skepticism about the near-term financial return of ESG efforts, or that ESG-oriented firms are trading off profitability for stakeholder legitimacy in the early public years.

  • Tested Variable: W_ESG_Score
  • Result: Significant negative effect (p ≈ 0.015)
  • Theory Base: Legitimacy theory, signaling theory (with nuance)
#Dummy TobinsQ did not work much, but was ok.

model_h4 <- plm(W_TobinsQ ~
                  #Board_Committee +
                  Audit_Expertise +
                  #Comp_LT_Objectives +
                  #CEO_Comp_Link_TSR +
                  log_Revenue+
                  Leverage_2+
                  log_Market_Cap +
                  #log_Governance_Score +
                  #CEO_Chairman_Duality+
                  W_ESG_Score  +
                  #CEO_Chairman_Duality+
                  log_Capex_Total+
                  log_Workforce_Score,
                data = ipo_data, index = c("ID", "Year"), model = "within")
coeftest(model_h4, vcov = vcovHC(model_h4, type = "HC1", cluster = "group"))
## 
## t test of coefficients:
## 
##                       Estimate Std. Error t value  Pr(>|t|)    
## Audit_Expertise     -0.1385611  0.4197814 -0.3301  0.741706    
## log_Revenue         -0.1198825  0.0564103 -2.1252  0.034870 *  
## Leverage_2          -0.0263357  0.0234037 -1.1253  0.261897    
## log_Market_Cap       1.2403580  0.2790779  4.4445 1.499e-05 ***
## W_ESG_Score         -0.0247172  0.0082157 -3.0085  0.002983 ** 
## log_Capex_Total      0.0423087  0.0732959  0.5772  0.564470    
## log_Workforce_Score  0.2006540  0.1751287  1.1458  0.253347    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Hypothesis 5: Board Expertise and Strategic Decision Quality

According to upper echelons theory (Hambrick & Mason, 1984), the background and competencies of board members shape strategic outcomes. Boards with more domain-specific expertise can enhance firm decision-making and adaptability, thereby improving market valuation.

H5:
A higher proportion of directors with industry or financial expertise is positively associated with Tobin’s Q post-IPO.

model_h5 <- plm(W_TobinsQ ~ Board_skills_percent + log_Board_Size + 
                  #log_Revenue +
                  #log_Capex_Total + 
                  #Leverage_2 + 
#Total_Employment+
SP500+
Gold+
                  log_Debt_Total,
                data = ipo_data, index = c("ID", "Year"), model = "within")
coeftest(model_h5, vcov = vcovHC(model_h5, type = "HC1", cluster = "group"))
## 
## t test of coefficients:
## 
##                        Estimate Std. Error t value Pr(>|t|)  
## Board_skills_percent -0.0094942  0.0043648 -2.1752  0.03078 *
## log_Board_Size       -0.5053627  0.8569678 -0.5897  0.55605  
## SP500                 0.0311937  0.0148408  2.1019  0.03681 *
## Gold                 -0.0507615  0.0250516 -2.0263  0.04406 *
## log_Debt_Total       -0.0492915  0.0269662 -1.8279  0.06905 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Using LASSO to select the best model

I believe that our main analysis should focus on the variables that were selected using Lasso this would give us a good justification of what are the main influences for a Tobin’s Q in a firm in the long run.

# Full data
lasso_data <- ipo_data %>%
  select(W_TobinsQ, W_Leverage, Audit_Expertise, BaaCorpBond, Board_Size, Board_skills_percent, 
CEO_Board_Member, CEO_Chairman_Duality, CEO_Change_after_IPO, CEO_Comp_Link_TSR, 
Comp_Controversies_Score, Comp_LT_Objectives, D_Profitability_pior_IPO, ESG_Score, 
Founder_is_CEO, GDP, Governance_Score, Independent_Board, Num_Board_Meetings, 
PCE, PerCapitaIncome, PerCapitaPCE, Personal_Income, Profitability_prior_IPO, 
ROA_Actual, RealEstate, RealEstate_scaled_robust, RealGDP_Millions, 
RealPersonalIncome_Millions, SP500, ThreeMonthTBill, Total_Employment, 
USTenYearBond, log_Capex_Total, log_Net_Cash_Flow_Opera, log_Revenue, 
log_Total_Liabilities, Gold
) %>%
  na.omit()

# STEP 1: Select relevant variables and remove missing values
# lasso_data <- ipo_data %>%
#   select(W_TobinsQ, W_Leverage, log_Total_Liabilities, log_Net_Cash_Flow_Opera,
#          log_Revenue, ROA_Actual, CEO_Comp_Link_TSR, CEO_Chairman_Duality,D_Profitability_pior_IPO,
#          CEO_Board_Member, Founder_is_CEO, CEO_Change_after_IPO, Comp_LT_Objectives,
#          Board_Size, Board_skills_percent, Independent_Board, Num_Board_Meetings,
#          ESG_Score, Governance_Score, Comp_Controversies_Score, Audit_Expertise) %>%
#   na.omit()

# STEP 2: Prepare predictor matrix (X) and response vector (y)
x <- model.matrix(W_TobinsQ ~ ., data = lasso_data)[, -1]  # remove intercept column
y <- lasso_data$W_TobinsQ

# STEP 3: Run cross-validated LASSO
set.seed(123)
cv_lasso <- cv.glmnet(x, y, alpha = 1, nfolds = 10)  # alpha = 1 for LASSO

# Plot CV errors and selected lambda
plot(cv_lasso)
abline(v = log(cv_lasso$lambda.min), col = "red", lty = 2)
title("LASSO Cross-Validation", line = 2.5)

# STEP 4: Fit LASSO with best lambda
best_lambda <- cv_lasso$lambda.min
lasso_model <- glmnet(x, y, alpha = 1, lambda = best_lambda)

# STEP 5: Show coefficients retained by LASSO
selected_coefs <- coef(lasso_model)
nonzero_coefs <- selected_coefs[which(selected_coefs != 0), , drop = FALSE]
print(nonzero_coefs)
## 11 x 1 sparse Matrix of class "dgCMatrix"
##                                     s0
## (Intercept)               1.279482e+01
## W_Leverage               -1.618076e+00
## Audit_Expertise           5.427508e-01
## Board_skills_percent      6.655831e-04
## CEO_Comp_Link_TSR        -3.475635e-01
## Comp_LT_Objectives        3.646776e-01
## D_Profitability_pior_IPO -9.733008e-01
## PerCapitaIncome           1.181997e-05
## Personal_Income           1.641557e-07
## log_Net_Cash_Flow_Opera   2.361724e-02
## log_Total_Liabilities    -5.345901e-01
# STEP 6: Refit a standard linear model using selected variables
# Get variable names of selected predictors (excluding intercept)
selected_vars <- rownames(nonzero_coefs)[-1]

# Build formula dynamically
lasso_formula <- as.formula(paste("W_TobinsQ ~", paste(selected_vars, collapse = " + ")))

# Refit model on original data (you can optionally standardize predictors here)
final_model <- lm(lasso_formula, data = lasso_data)

# STEP 7: Summary of the final model
summary(final_model)
## 
## Call:
## lm(formula = lasso_formula, data = lasso_data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4942 -1.1700 -0.3277  0.8431  5.9727 
## 
## Coefficients:
##                            Estimate Std. Error t value Pr(>|t|)    
## (Intercept)               1.247e+01  2.855e+00   4.368 2.49e-05 ***
## W_Leverage               -2.116e+00  7.110e-01  -2.976 0.003461 ** 
## Audit_Expertise           1.477e+00  9.900e-01   1.492 0.138161    
## Board_skills_percent      1.086e-02  8.310e-03   1.307 0.193537    
## CEO_Comp_Link_TSR        -8.230e-01  4.104e-01  -2.005 0.046956 *  
## Comp_LT_Objectives        1.045e+00  4.034e-01   2.589 0.010683 *  
## D_Profitability_pior_IPO -1.281e+00  3.464e-01  -3.699 0.000315 ***
## PerCapitaIncome           2.248e-05  2.327e-05   0.966 0.335622    
## Personal_Income           3.428e-07  2.073e-07   1.653 0.100581    
## log_Net_Cash_Flow_Opera   7.500e-02  2.205e-02   3.402 0.000882 ***
## log_Total_Liabilities    -6.535e-01  1.036e-01  -6.308 3.81e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.867 on 134 degrees of freedom
## Multiple R-squared:  0.5135, Adjusted R-squared:  0.4772 
## F-statistic: 14.14 on 10 and 134 DF,  p-value: < 2.2e-16

References

  • Brauer, M. F. (2013). The effects of short-term and long-term oriented managerial behavior on medium-term financial performance: Longitudinal evidence from Europe. Journal of Business Economics and Management, 14(2), 386–402. https://doi.org/10.3846/16111699.2012.703965

  • Connelly, B. L., Certo, S. T., Ireland, R. D., & Reutzel, C. R. (2011). Signaling theory: A review and assessment. Journal of Management, 37(1), 39–67. https://doi.org/10.1177/0149206310388419

  • Eccles, R. G., Ioannou, I., & Serafeim, G. (2014). The impact of corporate sustainability on organizational processes and performance. Management Science, 60(11), 2835–2857. https://doi.org/10.1287/mnsc.2014.1984

  • Fama, E. F., & Jensen, M. C. (1998). Separation of ownership and control. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.94034

  • Hambrick, D. C., & Mason, P. A. (1984). Upper echelons: The organization as a reflection of its top managers. The Academy of Management Review, 9(2), 193–206. https://doi.org/10.2307/258434

  • Suchman, M. C. (1995). Managing legitimacy: Strategic and institutional approaches. The Academy of Management Review, 20(3), 571–610. https://doi.org/10.2307/258788

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📚 References