The Impact of Status:  Loan Distribution Discrepancies among Middle-Status Firm

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Abstract

This paper investigates the impact of status on loan distribution discrepancies within the banking sector, addressing a gap in understanding the interplay between market rationality and status-driven behaviors. It mainly focuses on middle-status firms, hypothesizing that their lending decisions are heavily influenced by status-related concerns, especially the fear of status leakage and the aspiration for status gain. The study asserts that these firms, when experiencing an increase in status, are less inclined to lend to low-status companies to prevent status loss while showing a tendency to lend to high-status firms to bolster their standing. Furthermore, the paper argues that visible transactions amplify these status-driven tendencies. The research employs a multi-dimensional methodological framework for suggestions on methodology to verify these hypotheses. It includes analyzing comprehensive financial data, utilizing social perception metrics to assess banking status, and eigenvector centrality to evaluate company status. The study also examines the impact of transaction visibility on lending behavior, differentiating between high-visibility transactions and lower-visibility activities. This detailed approach aims to provide nuanced insights into the role of social dynamics in financial decision-making processes. The findings are expected to provide nuanced insights into the role of social dynamics in financial decision-making processes, offering a more comprehensive understanding of lending behaviors in the banking industry. The study highlights the importance of considering social status in regulatory frameworks to ensure equality in financial actions and suggests avenues for future research to explore the implications of status in the banking sector further.

Keywords: High-status firms, Middle-status firms, Low-status firms, Loan distribution, Status leakage, Transaction visibility.

Introduction

Status is power. The market is not made of rational actors but of calculated movements in search of status. Status is closely tied to the idea of hierarchy, for an individual’s status is determined by the degree to which they possess a generally desired attribute within their environment1. Thus, in the context of banking, status refers to the perceived prestige and influence of a financial institution within the industry. Status serves as an important indicator of an entity’s quality and, consequently, an indicator of success2. Another aspect of status is that it is made through connections. In this paper, I reference two channels: firms and companies. Firms, in this context, account for all types of banking institutions, such as commercial banks, investment banks, and other financial institutions that offer banking services like lending, deposit accounts, and investment management. On the firm channel, the bigger it is, as noted by Hochberg, Ljungqvist, and Lu (2007), the more connections and relationships it has3. The connections and relationships, then, are built upon by Song and Bitektine (2018), who claim that relationships and connections between firms also determine the status level of a firm4. In simpler terms, a firm’s status is shaped by the scale of its networks and is gauged by the number of its partnerships with better-networked, and subsequently higher status, companies5.

Since a firm’s size is correlated to the number of connections and networks it has, size has everything to do with status6. Larger firms are in a much more advantageous position in the banking scene. Larger firms have access to more resources and services, enabling them to operate on a global scale, a presence that often leads to increased efficiency in their operations. The capacity of resources available exclusively to these firms allows for more diversified and risk-managed portfolios, and their global reach provides them with access to a wider range of markets and opportunities7. Furthermore, larger firms can acquire more modern technologies8, which significantly enhances their operational capabilities and competitiveness and enables them to optimize production and logic processes. To reiterate, these benefits stem from the number of connections the firms have, which not only serves as a social asset but also as a means to mitigate risks associated with status fluctuations9. These connections can be particularly valuable during times of economic stress when firms strive to maintain or enhance their status through strategic associations6. Aside from large firms’ benefits regarding their status level, their behavior deviates significantly from small firms: large firms, to their customers, typically offer superior liquidity services but lower deposit rates10. Their customers also exhibit lower demand elasticities concerning deposit rate spreads. Due to their status, large firms are known to interact more impersonally with their borrowers, establish shorter connections, and are less successful in alleviating restrictions related to borrowing11. Additionally, larger firms tend to interact more with established companies that have better accounting records12. Smaller firms, on the other hand, see higher deposit rates13. These firms, commonly met with a narrower range of financial products and services, focus more on smaller businesses; inversely to larger companies, smaller companies prioritize smaller firms, as the smaller firms are better able to meet their demands14. Despite their disadvantages, small firms offer fewer and/or lower fees, with more competitive rates on deposit accounts and loans, as well as providing more personalized support.

The loan relationship between large firms, large companies, small firms, and small companies is evident, where larger firms will tend to prioritize companies that have better records and become distant from those who are “less important15,” while smaller firms do the opposite14. However, that relationship is merely proven by research from quantifiable metrics. This paper serves to add new reasoning that status has an important role in loan discrepancy and that the market isn’t made of actions from purely rational actors whose economic actions and decisions are not solely driven by rational calculations of profit and loss, but instead, of complex, intricately calculated movements.  Understanding the role of status in loan distribution is crucial, as it influences the allocation of financial resources, affects the competitive dynamics within the banking sector, and has implications for economic inequality. By examining how status shapes lending decisions, the underlying social mechanisms that drive financial decision-making become evident, and their impact on both the stability of the financial system and the broader economy is thus revealed. While most analyses of loan discrepancies are of technicality, with many’s assumptions that banks just focus on their customers, I hypothesize that social construct is a driver of behavior, that sometimes banks just want to “show off.” Identifying these tendencies is crucial, as when undisturbed, they can lead to suboptimal allocation of financial resources, worsening systemic risks.

In this paper, the term status scale serves as a determinant of a firm’s social status within the industry. An important term repeated throughout the paper is status leakage. This term suggests a potential loss or erosion of a firm’s standing, recognition, or reputation in the eyes of stakeholders (e.g., customers, investors, competitors, or even internal employees).Within the hierarchy of status, on one end, there are firms with a low status. These are firms that, for various reasons such as size, market influence, or industry recognition, are seen as having lesser standing in the banking community9. Thus, they are classified as low-end status. Intriguingly, note that because low-end status firms have so little status, they no longer care about status leakage16. Above the low-end status category, the fine line represents the threshold beyond which a firm is considered to be more than just a low-status entity but not yet at the level of a high-status firm. The fine line position is interesting as it shows an actor that is in the middle of both high and low status; thus, these actors are cautious about losing their current status while at the same time having a relatively modest standing that mitigates the fear of loss, as they perceive that they have less to lose in the grand scheme.

To establish a theoretical framework for the later analysis of status presented in this paper, I discuss here some popular, long-established theories on organizational behavior and social psychology. These include the Expectation States Theory, which establishes that individuals or groups with higher status are often expected to perform better and are thus given more opportunities to make decisions17, and in this case, may receive preferential treatment in lending decisions; the Exchange Theory, which argues that social behavior is a result of an exchange process aimed at maximizing benefits and minimizing costs18; and the Resource Dependence Theory, which states that the key to organizational success is the ability to acquire and maintain resources (status being one of them)19, and in this context, explicates how banks leverage their power in lending decisions–offering less favorable terms to those who depend heavily on them (typically low-status and some middle-status firms) and more favorable terms to high-status firms.

Since loans serve as the backbone of the later analysis, it is important to understand loans. The loan process consists of four steps: application, credit evaluation, review, and repayment performance, and is a critical aspect of banking operations20. It serves as a lens through which we can examine how banks perceive and interact with firms based on their status. Due to the nature of lending, a long-term relationship between the bank and the company, status plays a major crucial role. Banks, when considering lending, assess the financial viability of a firm and its reputation and standing in the business community–an important note, as companies that receive loans from more dominant banks are likely to be viewed as higher quality21.

I aim to propose status-based reasons for the discrepancy in loan distribution, evaluating the role a middle-status firm’s fear of status leakage and hope of status gain plays in its decisions on both loan reduction and increase. The results of this study offer insight into the behavioral aspects of financial decision-making among firms and the social dynamics that influence their lending policies, especially during periods of economic uncertainty, where the discrepancy between small and large firms, as per the hypotheses, increases at an alarming rate. I propose three hypotheses. The first suggests that when middle-status firms experience status growth, they are less inclined to provide loans to low-status companies, primarily due to the fear of status leakage. The second posits that these firms are more likely to extend loans to high-status companies, seeking to enhance their status and reduce risk. Finally, the third hypothesis, divided into two parts (3A and 3B), explores the effect of visibility in business transactions: 3A emphasizes the worsened negative relationship with low-status companies, while 3B highlights the bolstered positive relationship with high-status entities. I end with suggestions for future research and discuss future steps and missed areas of coverage.

Hypothesis

When considering the status scale and the position of lower-status small firms and higher-status large firms22, one often overlooks the role of firms that are on the fine line. These middle-status firms are often faced with an intriguing decision, as described by Song and Bitektine (2018):

Middle-status actors enjoy a certain level of status and strive to maintain or improve it. These are actors that value the social acceptance by their group, but they are insecure in their current position (Kelley and Shapiro 1954; Pettit et al. 2010). The insecurity and risk of losing their fragile status elicits high levels of social conformance from these actors (Festinger et al. 1963; Phillips and Zuckerman 2001) and prompts them to exert substantial efforts to maintain and/or (hopefully) enhance their status position (Pettit et al. 2010).23.

In the effort to enhance position, as described above, the middle status-based position seeks to either strengthen ties with high-status firms or gain favors in the eyes of high-status companies; one method is through the distribution of loans, as it is a visible action. Furthermore, to middle-status firms, lower-status firms are seen as higher-risk, due to their egocentric and altercentric uncertainty, and thus, engaging in exchange relations with entities of significantly lower status can have negative perceptual consequences for middle-status firms24. Thus, the fear of status leakage is associated with interaction with lower-status entities, presenting the following hypothesis:

Hypothesis 1

When middle-status firms are growing in status, they are less likely to give loans to low-status companies.

Middle-status firms, through the use of status-enhancing, to maintain, during economic stress, or increase their status rank, as mentioned above24, will avoid low-status firms as per hypothesis one. Furthermore, while they shift loans away from low-status companies, they will shift their loan favors toward high-status companies, as high-status companies often have lower levels of both egocentric and altercentric uncertainties24 which makes them more reliable and less risky9.

Having more connections and networks with high-status companies, especially in times of economic stress, has benefits: networks and connections with higher-status entities allow actors to have a strong defense against liquidity shocks. Supporting research by Mian and Khwaja (2006) shows that “having connections to a large conglomerate has independent advantages of its own in terms of not suffering the negative consequences of bank liquidity shocks25.” Thus, the allure of better connections and the advantages of association with high-status companies lead to the following hypothesis:

Hypothesis 2

When middle-status banks are growing in status, they are more likely to give loans to high-status companies.

As shown in hypotheses one and two, when a middle-status firm sees status growth, it directs its loan path towards high-status companies and away from low-status companies. There are many transactions, such as banknotes and commercial loans, that allow middle-status firms to interact with low-status companies. However, most firms tend to shift towards gaining status by performing visible transactions26           

In that effort to gain networks and status by interacting with high-status companies, transactions between middle-status firms and high-status firms will likely be formed in a way that is highly visible. Examples such as lines of credit, larger term loans, participation in large syndicated loans, and private capital market-issued bonds (later discussed in section 3.4a) not only showcase the strategic alignment with a high-status player but also position the middle-status firm as an innovative and significant contributor in the field, subsequently decreasing their altercentric and egocentric uncertainty24.

The effect of these magnifies the difference between middle and lower-status levels, exaggerating the negative relationship between middle-status firms and low-status companies;  the gap in perceived credibility, resources, and opportunities thus widens between them. Furthermore, low visible transactions between these levels will have drastic implications for low-status companies, including reduced access to essential resources, limited opportunities for strategic partnerships, and a decreased ability to leverage quality2. Hypothesis 3A encapsulates the negative relationship:

Hypothesis 3A

If middle-status banks are increasing, and their business transactions are very visible, the negative relationship between banks and low-status companies (shown in H1) is more evident.

The negative relationship between middle-status firms and low-status companies has a counter, positive relationship between middle-status firms and high-status companies. Visible transactions, coupled with status-enhancing strategies, allow middle-status firms’ status to grow substantially, enhancing the relationship between middle-status firms and high-status companies. Hypothesis 3B shows the opposite, positive relationship:

Hypothesis 3B

If middle-status banks are increasing, and their business transactions are very visible, the positive relationship between high-status companies and banks (shown in H2) is more evident.

Method

This section will propose the ideal design to validate the hypotheses, suggesting analytic strategies, databases, and methodologies for future research. In section 3.1, I cover the ideal database; in section 3.2, I cover the method to measure status on both the banking and company levels; in section 3.3, I cover strategies for determining lending behavior; and finally, in section 3.4, I cover visibility.

Data

The State Bank of Pakistan (SBP), a liberal representation of emerging markets, will provide data on all loans issued by lending entities to borrowing companies.  The data includes the historical records of each loan with information such as the outstanding loan amount, loan type, and default amounts27. As for the size of the SBP data set, records of more than 4 million borrowers from about 100 member financial institutions are supplied. The advantage of using this database is that it has a voluminous amount of bank loan transactions and that each transaction is transparent. Additionally, secondary databases include other government-operated databases, including Federal Reserve Economic Data (FRED), Bank for International Settlements (BIS), and any that is voluminous in bank actions. A combination of secondary data analysis and document review will be used. Furthermore, it is important to triangulate findings with other data sources to ensure the comprehensiveness of the data. Secondary data analysis extracts and analyzes relevant data from the database. Document review focuses on examining official reports, policy documents, and financial statements published by the SBP. Next, it is important to acknowledge potential biases and limitations in the SBP data, especially concerning coverage and accuracy. The data may only cover some types of lending transactions, like informal financial institutions, and there could be discrepancies in reporting practices. Moreover, time lags, the lack of standardization, missing data, and confidentiality concerns may impact the accuracy of the dataset.

Measuring Status

Status of Banks

To measure the status of banks, I suggest the methodology of categorizing top banks according to sources that, unlike official government or scientific categorization, are based on the perception within the business and banking community. This method allows the status level of a bank to be present, as its status level is easily determined by its perceived role among other banks. I will use The Banker’s “Top 500 Banks of 2023” list, where the correlation between banks and their status is most prominent. Part of their top 500 calculation relies on the Brand Strength Index (BSI) rankings. BSI, developed by Brand Finance, is measured by scoring key metrics within a firm’s three pillars: Brand Inputs, Brand Equity, and Brand Performance. These metrics include factors such as marketing spend, awareness, reputation, and market share, among others. By evaluating a bank’s performance in these areas, the BSI offers insight into a bank’s status. The BSI boasts many attributes that contribute to its reliability, ensuring an accurate measurement of status. It provides a transparent methodology that allows for validation by external parties, an empirical foundation rooted in empirical evidence and academic theory, and is overseen by a leading consultancy in brand valuation and strategy. For the classification of status, the trend of the BSI rankings sections the top 400 banks as high status and the following 100 as middle status.

Status of Companies

Since status-identifying information for companies is rarely disclosed, I use eigenvector centrality to measure status. This is beneficial as it measures not only the quantity of connections within a network but also the quality of those connections282930. In its use, the status of a company–again, a rarely disclosed topic–isn’t needed, but its calculation is still possible after evaluating network quality. I start by identifying direct connections between a company and different banks. Then, I assess the importance of each bank within the broader network using eigenvector centrality, defined by Hochberg, Ljungqvist, and Lu (2007); its formula follows:

    \[ev_i = \sum{_jpij} \, ev_j\]

A higher eigenvector centrality values equate to more status at the company level. Finally, company-level status is then calculated by summing up the weighted scores of all its connections.To ensure objectivity and accuracy in measuring company status using eigenvector centrality, it is important to ensure the existence of a diverse data set to reduce bias, standardize the data to ensure that connections are evaluated uniformly, and define clear criteria for determining a “quality” connection, such as the duration of the relationship, the financial value of transactions, or the strategic importance of the partnership.

Statistical Analysis

Following the measurement of bank and company status, the study employs a range of statistical tests suited to the nature of the data and the specific hypotheses being tested. Given the structured nature of the loan data from the State Bank of Pakistan (SBP), the following statistical tests are used. Linear regression models are employed to identify the impact of a firm’s status on loan amounts and terms. These models help to quantify the strength and direction of the relationships between firm status (independent variable) and loan conditions (dependent variables such as loan amount, interest rates, and repayment terms). Furthermore, to measure the probabilities of different outcomes based on the status level of firms, logistic regression is used, as it is best fitted regarding the hypotheses involving categorical outcomes. And, to compare the loan terms offered to firms across different status categories (low, middle, high), ANOVA (Analysis of Variance) tests are used to determine if there are statistically significant differences in the mean terms offered across these groups31. Each of these tests will be employed using appropriate statistical tools, especially the SPSS, developed by IBM to ensure accuracy. Additionally, to ensure accuracy and quality, the data recalled must undergo rigorous cleaning to handle missing values, detect outliers, correct inaccuracies, and account for assumptions such as normality, independence, and homoscedasticity. To isolate status as the major impact on the lending discrepancy and reduce confounders, it is important to implement ample control variables, including macroeconomic indicators, firm-specific financial health metrics, industry effects, and market conditions. Ensuring that these factors are accounted for will help isolate the effect of status from these other influences. Furthermore, to account for the lag effects during times of economic uncertainty, time series analysis will be employed. Through this approach, the impact of status on lending decisions’s evolution over different time periods and economic cycles is unveiled. By incorporating time lags, the temporal dynamics and delayed effects of status changes on lending practices can be understood. This method is critical in discerning not only the immediate effects but also the more prolonged influences that might only manifest under specific economic conditions.

Lending Behavior

Using the data from previous section, I advocate for the creation of a granular database, which would include not only basic loan characteristics like amounts, interest rates, and maturities but also borrower demographics, loan purpose, and repayment histories. Next, according to the data, we can analyze it for any correlations in lending practices between large (high status), medium (middle status), and small (low status) banks. Furthermore, to track changes over time, especially in response to economic fluctuations and policy shifts, I would suggest a longitudinal study design and, if possible, collaboration with high-ranking banking individuals, financial experts, economists, and policymakers to help the dataset. Thus, with these foundations set, the lending behaviors of different status-ranking banks will be easier to unfold.

Visibility

When analyzing hypotheses 3A and 3B, an integral aspect is visibility within transactions. The basis of that importance is that if the visibility of different transactions between firms and companies is analyzed, the extent to which highly visible transactions to high-status companies enlarge the negative relationship between middle-status firms and low-status companies surfaces. This section aims to explain the different observable transactions and their differences in visibility. To operationalize and quantify transaction visibility, we focus on the frequency and breadth of disclosures related to the transactions, including their reporting in public financial statements, press releases, or mandatory regulatory filings such as SEC filings. This section discusses the most prominent visible transactions and non-visible transactions that are used to support hypotheses 3A and 3B.

Visible Transactions

As per hypothesis 3A, to maintain or even increase their status, middle-status firms will make more visible transactions to high-status companies, subsequently enlarging the negative relationship between middle-status firms and low-status companies; thus, many different types of transactions (visible) are used to their advantage32.

Lines of credit

Lines of credit, also known as revolving credit facilities or loan commitments, are highly visible33. Furthermore, as required by Regulation S-K, public firms must detail their lines of credit in annual 10-K Securities and Exchange Commission (SEC) filings.

Larger term loans

Term loans are loans with a set repayment schedule and maturity date. The details of term loans, especially larger ones, are often disclosed in company financial statements or regulatory filings under regulatory bodies such as the SEC.

Participation in large syndicated loans

A syndicated loan is an organized group lending practice that falls into two categories: lead arrangers and participant lenders. Syndicate loans, under regulatory bodies such as the SEC, are required to be disclosed within regulatory filings, financial reports, and press releases. Firms that lead a syndicate, lead arrangers, have the most arm-length connections34.

Private capital market-issued bonds

Contrary to what its title suggests, private capital market-issued bonds are very visible; they are usually publicly listed securities, offering transparency under regulatory bodies35.

Non-visible Transactions

A firm can choose to make many loan and debt selections that are hidden from the firm community. These transactions, unlike visible transactions, have no general effect on the status of a firm, as they are generally invisible to social perceptions36.

Commercial loans

Commercial bank debt, different from its counterpart capital market-issued bonds, according to research, “are private in nature…because they are an agreement that only extends to the parties involved in the loan and involves no market disclosure37.”

Privately issued banknotes

Privately placed banked notes typically have fewer disclosure requirements and thus are less visible to the public. The details of such transactions usually are only disclosed to the involved parties.

Discussion

Empirical evidence of the negative association between middle-status firms and their low-status companies as middle-status firms are experiencing growth in status (measured through 3.2a) would support hypothesis 1, while evidence of the contrary relationship between middle-status firms and high-status companies as middle-status firms are growing, in status, would support hypothesis 2. Additionally, findings of a greater negative relationship between middle-status firms and low-status companies with middle-status firms that use more visible transactions (shown in 3.4a) support hypothesis 3A, while a positive relationship between middle-status firms and high-status companies supports hypothesis 3B. However, if an aggregate indifference in the relationship between the channels is present, accounting for status leakage fears and aspirations, the hypothesis will be invalidated.

While the hypotheses presented above show status as the main driver for lending discrepancies, it is also crucial to consider other factors that may influence these decisions. Changes in market conditions, such as fluctuations in the financial markets or shifts in consumer confidence, can compel banks to alter their lending practices. Moreover, changes in regulatory measures, including updates to banking compliance standards or adjustments in financial policy by governing bodies, could drive banks to reassess their lending criteria. The everchanging economy, marked by periods of recession or growth, also plays a critical role, directly affecting banks’ risk tolerance and liquidity strategies. These factors must also be taken into account as additional influences.

My study contributes to the highly researched topic of loans and serves as an addition to the issues of discrepancy. Recognizing the role of social status helps inform more effective regulatory frameworks aimed at ensuring equality in financial actions. For policymakers, it’s crucial to enforce enhanced reporting and transparency by requiring regular disclosure of lending data by banks, broken down into the categories of status and loan type, to monitor status-related biases. Additionally, banks should be required to publicly disclose the rationale behind significant lending decisions and ensure that these decisions are made with utmost fairness and transparency—rather than for the benefit of the actor status-wise. Furthermore, implementing stricter oversight mechanisms to ensure compliance with fair lending practices is essential—especially for the volatile middle-status companies. Establishing penalties for foul play regarding status could help curb these discrepancies. Moreover, developing programs to support low-status firms, such as government-backed loans, to increase credit and encouraging the development of alternative lending platforms that utilize objective metrics to assess creditworthiness could substantially curb the impact of status. For banking institutions, it is important to introduce internal policies that ensure fair lending practices. Banks should implement internal controls to mitigate status bias in lending decisions and conduct regular audits of lending practices to ensure adherence to fairness and transparency standards. To support middle and lower-status firms, banks should offer competitive rates and terms and utilize technology and data analytics to assess credit risk accurately. Increasing the visibility of transactions involving low-status firms by highlighting successful case studies and regularly reporting to stakeholders about the bank’s lending portfolio’s diversity and the steps to ensure fair lending practices would further promote transparency. Finally, long-term strategic initiatives should also include working with fintech companies that are developing new models for credit assessment, including a wide set of data points, which would diminish the role of status. Collaboration with non-profit organizations and community associations would go a long way in understanding and meeting the needs of low-status firms better. Furthermore, investment in research to understand the implications of status in financial decision-making, development of strategies to counteract the negative effects of status, trying out new programs, which will be aimed at leveling the playing field for all statuses of firms, monitoring the effectiveness of the programs, all would be beneficial. These recommendations, if implemented, could create a totally different landscape for lending—one that’s more level and less at the mercy of bias-ridden status.

While this paper acknowledges and reviews the role of status on loan discrepancies across the banking channel, it is important to acknowledge that other influences could alter loan distribution. Economic conditions, especially macroeconomic factors like economic downturns or blooms; regulatory policies, like changes in capital requirements, reserve ratios, or lending standards; and market competitiveness among banks all can significantly affect banks’ willingness to lend. These factors interact with status in complex ways, and future research should aim to analyze these interactions to provide a more comprehensive understanding of loan distribution in the banking sector.

Since my paper is more focused on its existence and its application, many questions are still unanswered. One of the most prominent questions is how much, as, according to the hypotheses presented, status leakage is a prominent issue, but to what extent is it? Thus, in future research, with more time and resources, many routes are possible to enrich the research presented in this paper further. I would aim to access or collect a more diverse range of data, including those of different cultural and economic environments, to enhance the mean aggregate trend. A longitudinal study would also serve to bolster my hypothesis, again supporting the aggregate trend. Experimental designs, with simulations and/or controlled experiments, could provide an environment to test the specific hypotheses and gain more exact values; instead of if it exists, we can better understand how much it affects others.

Conclusion

While ample research has focused on the multitude of quantifiable financial metrics that influence a firm’s decision to reduce or increase its loan distribution, the realm of social status is often overlooked. This study addresses this important question; I evaluate the extent to which a firm’s fear of status leakage and hope of status gain play on its decisions on both loan distribution. The results of the hypothesis proposed offer an additional reason for loan discrepancy and, consequently, hierarchical discrepancy between status-differing firms and companies. I provided the rationale for actions done by middle-status firms according to the unalluring aspects of the status scale’s low-end status level and the attracting factors around the high-end status level.

The hypotheses proposed in this study, if validated, reveal a strategic approach to lending that is heavily influenced by concerns over status preservation and enhancement. The tendency of these firms to prefer lending to higher-status companies while shunning lower-status ones speaks volumes about the social dynamics governing the industry. This behavior underscores a fear of status leakage and a desire to associate with high-status entities, especially through visible and high-profile transactions. The research design laid out in the paper includes databases, data measuring methodologies, and visibility indicators (to support H3); this framework hopes to inspire future research in this domain and to stress the ubiquity of egotism in all channels of the economic community. I split the classification of status between two channels, firms, and companies, and employ the method of social perception, according to the Brand Strength Index and eigenvector centrality, to capture relationship qualities, respectively.

Acknowledgements

I am deeply thankful to my mentor, Andrew Foley, Lumiere Education, and Loyola High School of Los Angeles for supporting me throughout this paper.

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