The supply of bureaucratic capital by the regulator

This model focuses on the financial sector, in which top agency regulators, after their term in public office, are invited to work in a company they have regulated.

During her time in office, the regulator fulfills her public responsibilities, but at the same time, she develops bureaucratic capital. As explained above, the bureaucratic capital is all the red tape and the connections within regulatory agencies that she is developing. One unit of bureaucratic capital can be understood as one piece of red tape, or one personal connection. The regulator decides the optimal amount of bureaucratic capital she wants to develop, that will cost her effort, but will permit her to get a higher income in the future.

The structure of the model is simple. During her term as a regulator for T years, each year, she acquires bureaucratic capital, which costs her effort of sizeE _i, each year. These costs are mainly in the form of investing in networks, creating red tape, and building influence, since these actions require effort.Footnote ¹⁷

The amount of bureaucratic capital created each year by E _i units of effort per year, is monotonically increasing and concave in the total amount of effort expended during that year, and is a differentiable variable, which takes the functional form of: [(1 + γ)E _i]^1/1+γ γ > 0.Footnote ¹⁸

This rate of accumulation is constant over the regulator's term T, so that, at the end of the term, the total amount of bureaucratic capital created by regulator i is:Footnote ¹⁹

(1)$$H_i(E_i) = T[(1 + \gamma )E_i]^{1/1 + \gamma }\quad \gamma \gt 0, $$

where γ represents the institutional constraints over bureaucratic capital creation during public office.

After leaving her job as regulator, the bureaucrat works for a period of length τ in the industry that she has regulated. She receives, on top of her “regular” salary, a rent related to the bureaucratic capital H _i that she has accumulated.

The regulator maximizes her lifetime income which consists of (1) earnings which are not related to the creation of bureaucratic capital, denoted by Ω, and (2) income related to the creation of bureaucratic capital, which equals the net income she gets on entering the industry, after passing through the revolving door. She will be able to sell her bureaucratic capital, H _i at price q for a number of years τ so that her total income is:

(2)$$V_i = \Omega -{\kern 1pt} {\kern 1pt} T{\kern 1pt} E_i + \tau {\kern 1pt} qH_i(E_i)\quad $$

Equation (2) can be rewritten as a function only of the level of bureaucratic capital, by substituting E _i from equation (1). We get:

(3)$$V_i = \Omega -\,{\kern 1pt} \displaystyle{{H_i^{1 + \gamma}} \over {(1 + \gamma )T^\gamma}} + \tau {\kern 1pt} qH_i\quad$$

From the point of view of the bureaucrat, there is an optimal level of bureaucratic capital $\hat{H}$ that maximizes her income – equation (3) and is:

(4)$$\hat{H}_i = T(\tau \,q)^{1/\gamma} $$

Equation (4) describes the “supply” function of bureaucratic capital by the regulator as an increasing function of the price q. The numbers of years worked in the public agency, as well as in the private sector both have a positive effect on the amount of bureaucratic capital. We now turn to discuss the behavior of the firms and their demand for bureaucratic capital.

The demand for bureaucratic capital

The financial sector is composed of financial firms producing intermediate goods and services, in a monopolistic, competitive environment.Footnote ²⁰ While Romer (Reference Romer1990) develops a model of general equilibrium for the whole economy, in this paper we focus on the financial sector. A special feature of the financial sector is that financial firms are not all equal: some are big, and therefore have a lower cost of capital than small firms, as we have explained above.

In this model, there are N financial firms, consisting of n _b big firms, and n _s small firms, respectively producing the intermediate goods x _b and x _s. These intermediate goods enter the production of a final good Y in the following form (based on Romer, Reference Romer1990):

(5)$$Y\, = \,\Delta + \;L_y^{1-\alpha} (\sum\limits_0^{n_b} {x_b^\alpha} + \sum\limits_0^{n_s} {x_s^\alpha} )\quad \quad \alpha \gt 0, $$

where Y is the total output in the economy, Δ the output of the economy not related to the financial sector; and L _y the workers in the economy who are not working in Δ; x _b and x _s are the intermediate financial goods used in the production of Y. While the intermediate services sector consists of monopolistic firms, the final good is produced in a perfect competitive environment. The firms involved in producing Y are maximizing profits:

(6)$$Max\,\;L_y^{1-\alpha} (\sum\limits_0^{n_b} {x_b^\alpha} + \sum\limits_0^{n_s} {x_s^\alpha} )-w_yL_y-\sum\limits_0^{n_b} {\,p_bx_b^{}} -\sum\limits_0^{n_s} {\,p_sx_s^{}} $$

where w _y are the wages paid for labor, p _b and p _s are the prices of the intermediate services x _b and x _s. From the profit maximization in the production sector, we get:

(7)$$p_b = \alpha {\kern 1pt} {\kern 1pt} L_y^{1-\alpha } x_b^{\alpha -1} \; {\rm for}\,{\rm all}\,b\in \,{\kern 1pt} [{\kern 1pt} 0,n_b]$$

(8)$$p_s = \alpha {\kern 1pt} L_y^{1-\alpha } x_s^{\alpha -1} \; {\rm for}\,{\rm all}\,s\in \,[0,n_s]$$

We focus on the production of the intermediate goods and services, x _b and x _s. Following Romer (Reference Romer1990) and Brezis (Reference Brezis2017), we assume that these firms are producing their goods using capital k. But physical capital is not the only factor affecting production; the amount of bureaucratic capital also affects the output of the firms, and in a positive way.

However, let us emphasize that if all similar firms have the same amount of bureaucratic capital, then bureaucratic capital is useless for the firm.Footnote ²¹ This assumption stems from the rent-seeking arguments according to which the revolving door does not lead to an increase in production due to improved productivity but due to rent seeking.Footnote ²² If bureaucratic capital was a productivity factor, it would increase firm j’s production, whatever stock of bureaucratic capital was accumulated by its competitors.

As a reminder, there are two different sizes of firms, and as explained in Appendix A (available online),Footnote ²³ the output of the firms can be described in the following way:

(9)$$x_b = [k_b + (\displaystyle{{{\bar{H}}_b} \over {{\bar{H}}_s}})^{\mathop \mu \nolimits^{}} ](\displaystyle{{H_b} \over {{\bar{H}}_b}})^{\mathop \phi \nolimits^{}} \quad \phi \gt 0,\quad \mu \gt 0 $$

(10)$$x_s = [k_s + (\displaystyle{{{\bar{H}}_s} \over {{\bar{H}}_b}})^{\mathop \mu \nolimits^{}} ](\displaystyle{{H_s} \over {{\bar{H}}_s}})^{\mathop \phi \nolimits^{}} \quad \phi \gt 0, \quad \mu \gt 0 $$

where H _b and H _s are the level of bureaucratic capital demanded by big and small firms respectively; and $\bar{H}_b$ and $\bar{H}_s$ are the average level of bureaucratic capital owned by the other big and small firms respectively. In other words, we divide the total rent-seeking effect into two elements: the first one is the amount of bureaucratic capital of the firm relative to the amount in its own group, and the second one is the relative amount of its group as compared to the other group. In what follows, we detail profit maximization by big and small firms respectively.

The small firms

All firms maximize profits. Let us analyze first the small firms producing x _s. The profit maximization for an intermediate financial firm s is:

(11)$$Max\,\,\pi _s = p_s(x_s)x_s-rk_s-qH_s, $$

where r is the cost of capital in the market; and q the cost of bureaucratic capital. qH _s is the remuneration related to bureaucratic capital obtained by the bureaucrat for being hired by the firm. Each firm maximizes profits by finding the optimal amount of factors of production. We substitute k _s, and equation (11) can be rewritten in the following way:

(12)$$Max\,\pi _s = p_s(x_s)x_s-r_{}[x_s(\displaystyle{{H_s} \over {{\bar{H}}_s}})^{-\phi} -(\displaystyle{{{\bar{H}}_s} \over {{\bar{H}}_b}})^{\mathop \mu \nolimits^{}} ]-qH_s $$

The two first-order conditions for maximizing profits are:

(13)$$p^{\prime}_s(x_s)x_s + p_s(x_s)-r(\displaystyle{{H_s} \over {{\bar{H}}_s}})^{-\phi} = 0 $$

(14)$$q\bar{H}_s = \phi {\kern 1pt} \,r_{}{\kern 1pt} x_s(\displaystyle{{H_s} \over {{\bar{H}}_s}})^{-\phi -1} $$

From equation (8), we note that the demand elasticity of p _s(x _s) is equal to α-1. Substituting into equations (13) and (14), in a symmetric equilibrium where all H _s are the same, we get:

(15)$$p_s = \displaystyle{{r} \over \alpha} \quad $$

and the demand function for H is:

(16)$$H_s = \bar{H}_s = \displaystyle{{\phi {\kern 1pt} rx_s} \over q} = D_s $$

Now we turn to analyze the big firms.

The big firms

The big firms' access to financial markets is affected by their size. As emphasized in the literature presented above, big firms face a lower cost of capital due to the “too-big-to-fail” externality. In consequence the cost of capital, r _b, is lower for these “too-big-to-fail” firms than the regular cost of capital r, and we denote that: r _b = r/β β > 1.

In consequence, the profit maximization for big financial firms b producing x _b is:

(17)$$ Max\,\pi _b = p_b(x_b)x_b-r_bk_b-qH_b $$

where r _b is the cost of capital k _b; and q the cost of bureaucratic capital. As in the previous case, the two first-order conditions for maximizing profits are:

(18)$$p^{\prime}_b(x_b)x_b + p_b(x_b)-r_b(\displaystyle{{H_b} \over {{\bar{H}}_b}})^{-\phi} = 0 $$

(19)$$q\bar{H}_b = \phi {\kern 1pt} \,r_b{\kern 1pt} x_b(\displaystyle{{H_b} \over {{\bar{H}}_b}})^{-\phi -1} $$

and we get:

(20)$$p_b = \displaystyle{1 \over \alpha} r_b\quad $$

(21)$$H_b = \bar{H}_b = \displaystyle{{\phi {\kern 1pt} r_bx_b} \over q} = D_b $$

In Appendix A,Footnote ²⁴ we show that the demand for small and big firms – equations (16) and (21) – can be rewritten in the following form:

(22)$$H_s = \bar{H}_s = \displaystyle{{\phi {\kern 1pt} r\beta ^{1/\alpha -1}\,K} \over {nq}} = D_s $$

(23)$$H_b = \bar{H}_b = \displaystyle{{\phi {\kern 1pt} rK} \over {nq\beta}} = D_b $$

where n = n _b + n _s β ^1/α−1. In consequence, the ratio of bureaucratic capital in small and big firms is:

(24)$$\displaystyle{{H_s} \over {H_b}} = \delta \; \lt 1\quad where\;\delta = \beta ^{\alpha /\alpha -1} \le 1 $$

Equations (22) and (23) represent the demand for bureaucratic capital, as a decreasing function of q, for both types of firms. The ratio of bureaucratic capital in small and big firms is given by equation (24). For big firms, the equilibrium bureaucratic capital is H ₁^*, while for small firms, the equilibrium is given by H ₂^* which is lower than H ₁^*. Moreover, substituting prices and quantities into the profits, we get:

(25)$$\pi _b = \displaystyle{{rK} \over {n\beta}} [\displaystyle{1 \over \alpha} -1-\phi ] + \displaystyle{r \over {\beta \delta ^\mu}} $$

(26)$$\pi _s = \displaystyle{{rK\beta ^{1/\alpha -1}} \over n}[\displaystyle{1 \over \alpha} -1-\phi ] + r\delta ^\mu $$

We therefore get the following proposition:

Proposition

1. 1. The amount of bureaucratic capital of small financial firms is lower than the amount for big, and especially “too-big-to-fail”, firms, and the ratio is presented in equation (24).

2. 2. Through bureaucratic capital accumulation, big firms maintain their profits at a higher level than small firms.

To conclude this section, this model of bureaucratic capital emphasized two main facets related to the revolving door. The first one is that differences between firms due to their size and hence their costs of capital lead to major differences in their behavior. Big firms can hire many revolvers and be state-connected, while small firms cannot hire many revolvers and in consequence they are less connected, if at all. These differences in the revolving door lead to differences in profits between them. In other words, “too-big-to-fail” firms maintain their profits and market power via the revolving door, and not through efficient decisions. Therefore, the revolving door process leads to distortions in the economy.

The second facet is that this model also sets a theoretical foundation for the measurement of bureaucratic capital supply at regulator level, and bureaucratic capital accumulation at firm level, which, in fine, frames the empirical analysis of bureaucratic capital allocation and of the resulting inequality of influence among financial firms, as we present in the next section.

Descriptive evidence on bureaucratic capital destination and origin

As Zingales (Reference Zingales2015) points out, the political power of modern corporations results from the interaction of concentrated economic power with politics. One way to grasp the extent of the regulatory power of financial firms engaged in the revolving door would hence consists of focusing on the concentration of bureaucratic capital among the biggest financial firms. To this purpose, we gathered data on the career path of regulators, i.e. revolvers that undertook public-to-private or private-to-public sector movements between the top 20 US “diversified banks” and US regulatory agencies. We put the emphasis on the diversified banking sector rather than the whole financial industry, because different types of financial firms – e.g. diversified banks, investment banks, or insurance companies – may be subject to different regulations and incentives, and hence might not fully compete with each other.Footnote ²⁵ Appendix B onlineFootnote ²⁶ provides information on the data collection and treatment processes. Building on this information, we constructed four proxies of the stock of bureaucratic capital accumulated by banks:

1. (1) Proxy 1, “revolvers”: the number of revolvers that have been hired by banks.

2. (2) Proxy 2, “movements”: the cumulated number of public-to-private sector and private-to-public sector revolving door movements undertaken by revolvers hired by banks.

3. (3) Proxy 3, “prominence”: the number of revolvers who obtained a prominent position in a public agency (see Appendix B online for the list of prominent positions considered).

4. (4) Proxy 4, “experience”: the total number of years of experience in the public sector accumulated by revolvers hired by banks.

Looking at the revolving door in the 20 biggest US diversified banks, we identified 304 revolvers, among which 155 are considered as prominent. These revolvers have undertaken 384 revolving door movements between public and private positions and vice versa, mostly undertaken between 1960 and 2015, corresponding to a total of 2,256 years of experience in public office. Appendix CFootnote ²⁷ provides information on the distribution of revolvers among firms by regulatory agencies.

Figure 1 represents the distributions of the four bureaucratic capital proxies in the various banks. In general, the data show that more than three-quarters of the bureaucratic capital stock is concentrated in the five biggest banks (Goldman Sachs, JP Morgan, Citigroup, Bank of America, and Wells Fargo), including 79% of the total number of revolvers (proxy 1), 79% of revolving door movements (proxy 2), 86% of the total number of prominent revolvers (proxy 3), and 82% of the revolver's years of public sector experience (proxy 4). Among these five banks, Goldman Sachs takes the lion's share of the bureaucratic capital allocation.

Figure 1.

Sector distribution of bureaucratic capital among the top 20 diversified banks

Among the 304 revolvers identified, 187 revolvers worked in the nine key regulatory agencies directly related to the regulation of the financial sector or with strong influence over it.Footnote ²⁸ The remaining 117 revolvers worked in agencies not directly related to financial matters such as congressmen's offices (most of them), the Internal Revenue Service, the Department of Defense, the Department of State, embassies, governorates, and so on.

Figure 2 represents the distribution of bureaucratic capital according to its agency of origin, and shows that the Fed, the Treasury, and the White House represent around 40–50% of bureaucratic capital origin: that is, 39% of the total number of revolvers, 44% of revolving door movements, 47% of prominent revolvers, and 42% of public sector experience (in years).

Figure 2.

Distribution of bureaucratic capital among public agencies

To get a more striking view of bureaucratic capital concentration among diversified banks, in Table 1 we report two key synthetic indexes. First, we report the Ratio Index (RI), which is the share of bureaucratic capital cumulated by the top five banksFootnote ²⁹ in the total stock of bureaucratic capital cumulated by the top 20 diversified banks, using the four proxies of bureaucratic capital. Second, we report a second index, denoted as Concentration Index (CI), derived from a normalized Hirschman–Herfindahl index formula and computed using the four proxies of bureaucratic capital.Footnote ³⁰ The RI and CI both measure the inequality of influence, since the higher the index, the stronger the concentration of bureaucratic capital, the greater the inequality of influence in the financial sector. These indices do not measure all the possible negative effects of the revolving door; they specifically focus on the differences between firms hiring many former regulators, and those without revolvers. In other words, these metrics focus on the disparity between connected and less connected firms stemming from the revolving door phenomenon.

Table 1.

The inequality of influence in the US banking sector.

The RI shows that Goldman Sachs, JPMorgan, Citigroup, Bank of America, and (to a lesser extent) Wells Fargo comprise together 79% of revolvers and total revolving door movements, 86% of prominent revolvers and two-sided movements (back-and-forth movements between the same agencies or the same banks), and 82% of the total stock of years of public office experience (see Table 1).

On the concentration of bureaucratic capital, we find that the index is equal to 0.23 when using the number of revolvers (proxy 1), 0.24 for the number of revolving door movements (proxy 2), and 0.26 for the number of years worked in the agency (proxy 4).Footnote ³¹

Among these 20 firms, Goldman Sachs appears as the prime beneficiary of bureaucratic capital accumulation, concentrating 27% of the total stock of revolvers (proxy 1) and revolving door movements (proxy 2), 32% of prominent revolvers (proxy 3), and 31% of the cumulated years of public office experience (proxy 4).

Next, in Table 2, we measure the concentration of revolvers among firms according to their public agency of origin. Focusing on key regulatory agencies, as defined above, we found a strong concentration among the “big five” of bureaucratic capital created at the White House. Indeed, the CI rises dramatically to 0.39 and the RI indicates that 92% of the White House's revolvers have worked for the big five. The concentration of bureaucratic capital created at the Treasury is also strong, with a CI equal to 0.36 and a RI of 88% for the big five (43% for Goldman Sachs alone).

Table 2.

The inequality of influence in the US banking sector, by key regulatory agency.

By contrast, the Federal Reserve System, which provided the greatest number of revolvers, is associated with the lowest concentration of scores (CI of 0.24, and the RI is “only” 74%). Indeed, it is worth noting that regarding the revolving door from the Fed, there is more “equality” in the system, and a greater number of small firms can also hire revolvers from this agency.

On the other hand, we observe very high concentration patterns at the SEC, intelligence agencies, and the CFTC – agencies with great regulatory powers (CI of 0.43, 0.34, and 0.67 respectively). Therefore, while bureaucratic capital from these agencies is scarce, it seems highly valuable since almost all revolvers from these agencies end up in the big five.

To conclude this part, this statistical description has permitted us to measure the inequality of influence among firms, resulting from the concentration of revolving door movements. Most of the bureaucratic capital accrues to the five biggest diversified banks and a small residual amount of bureaucratic capital is spread among the remaining 15 banks. Among these five firms, Goldman Sachs is the main beneficiary of bureaucratic capital concentration, by accumulating around 30% of the bureaucratic capital stock, which corresponds, in terms of regulatory experience, to 698 years among a total of 2,256 cumulated years of public office experience.

Figure 3 presents graphically the partial correlation between the four proxies of bureaucratic capital and bank size, measured by their total revenue in 2015. We find a positive and significant correlation between bureaucratic capital accumulation by firms and their size. Comparing the explanatory power of a univariate OLS regression of the bank size over the four proxies of bureaucratic capital, we find that this positive relationship gets stronger, as evidenced by the 0.63 and 0.69 R-squared, when bureaucratic capital is measured in terms of prominence in public office (proxy 3) or public office experience (proxy 4), respectively. Therefore, beyond the total number of revolver or revolving door movements, it is the prominence and experience of regulators that seems to matter particularly for big financial firms.

Figure 3.

Bureaucratic capital and bank's total revenue

Notes: data from Fortune 500. Top 5 banks are in red.

To conclude, this descriptive statistical analysis, conducted at the firm level, strongly supports the proposition of our model according to which big firms, with lower costs of capital, accumulate a greater stock of bureaucratic capital. We now present a multivariate analysis of bureaucratic capital allocation among the big five, which focuses on revolvers' public-to-private sector movements.

Multivariate analysis of bureaucratic capital allocation

One important prediction of our model is that too-big-too-fail firms are the principal destination of bureaucratic capital allocation, as in equation (24). In consequence, regulators completing their term in public agencies with lengthy experience in the public sector, or leaving a prominent position in public office, are more likely to be hired by big firms. Therefore, a higher amount of bureaucratic capital created in public office should increase the likelihood for the revolver of being hired by the biggest banks.

Econometric specification

To test our model, we first conduct a multivariate analysis of the effect of bureaucratic capital supplied by revolvers on the conditional probability of working in the top five banks after serving in a public office. First, we apply a logistic model to cross-section data on 219 public-to-private revolving door movements from public agencies toward the top-20 US diversified banks, with the dependent binary variable:

$$h_i = \left\{ {\matrix{ 1 &{{\rm \;\ if\;\ the\;} i^{{\rm th}}{\rm \;\ revolver\;\ has\;\ been\;\ hired\;\ by\;\ a\;\ top\;} 5{\rm \;\ bank}} \cr 0 &{{\rm \;\ otherwise}} \cr}} \right.$$

The random variable h _i takes the value one or zero, associated with the probability p _i and (1 – p_i) respectively. We assume that the logit of the probability p _i is:

(27)$$Logit(\,p_i) = X_i^{\prime} \beta ,$$

where β is a vector of regression coefficients and X_i is a vector of covariates, which includes proxies of bureaucratic capital and other determinants of firm size.

Results

The main results are reported in Table 3. First, all models put in evidence a positive and significant effect of bureaucratic capital supply on the probability of being hired by the big five. Moreover, including the firms' determinants of total revenue (number of employees and financial performance) as controls affects the strength, but not the sign or the significance of bureaucratic capital proxies in the regressions (columns (2), (5), and (8)). In the same way, relationships are also robust to the inclusion of bureaucratic capital depreciation control (columns (3), (6), and (9)).

Table 3.

Empirical evidence from public-to-private revolving door movements, logit estimates (1/2).

Notes: Marginal effects reported, constant not reported. Standard errors are presented in parentheses: * significant at 10%, ** significant at 5%, *** significant at 1%. Standard errors are robust to heteroscedasticity

Second, estimates in column (3) stress that being a prominent revolvers increase by 11.4% the probability of being hired by one of the top five banks, while estimates in column (6) show that one additional year of public office experience increases this probability by 0.7%. Taking the two proxies together, estimates in column (9) support prominence having an effect independently of the effect of experience. Both effects are at least 10% significant, but the effect of prominence is found to be more significant than the effect of experience in public office.

Third, it is also worth noting that the time gap between public and private offices erodes bureaucratic capital accumulation. This gap is found to significantly reduce the probability of being hired by a top-five bank in models A₁ (column (3)), B₁ (column (6)), and C₁ (column (9)), thereby giving an empirical justification to regulations on bureaucratic capital allocation such as cooling-off periods after leaving public office. Concretely, estimates support that adding one year to a cooling-off period after leaving public office would reduce the probability of being hired by the big five by 0.4%.

In a last step, we provide additional evidence on the effect of bureaucratic capital (i.e. prominence and/or experience in public office) by exploiting information on the revolvers who worked in a key regulatory agency. The probability of being hired therefore regresses over proxies 3 and 4, introduced separately (models A₂ and B₂) or together (model C₂).

Results are presented in Table 4 and provide additional insights into how the revolving door from key regulatory agencies generates inequality of influence among banks, compared to the revolving door from “other” agencies. First, estimates of model A₂ (column (1)) show that getting a prominent position in the public sector increases significantly the probability of being hired by one of the big five only if the revolver has worked in a key regulatory agency. Second, model B₂ (column (2)) shows that, while experience is useful whatever the importance of the agency, it has a stronger and more significant effect when the revolver has worked in a key regulatory agency. Last, model C₂ (column (3)) highlights that, including prominence and experience proxies together in the regression, the effect of the prominence of revolvers from key regulatory agencies prevails over the effect of experience from key regulatory agencies, and the effect of experience holds (in a 10% confidence level) in “other” public agencies only.

Table 4.

Empirical evidence from revolving door movements, logit estimates (2/2).

Notes: Marginal effects reported, constant not reported. Standard errors are presented in parentheses: * significant at 10%, ** significant at 5%, *** significant at 1%. Standard errors are robust to heteroscedasticity.

These results suggest that, in agencies with strong regulatory power over the financial sector, the prominence of revolvers matters more than their experience for being “bankable.” However, experience still has a positive but smaller effect on the probability of being hired by the top five when the revolver has worked in an “other” public agency.

To summarize, this section has provided evidence on the following facts: (1) around 80% of the bureaucratic capital stock is concentrated among the top five banks; (2) the top five banks are more likely to hire revolvers supplying greater amounts of bureaucratic capital – whether measured in terms of years of public sector experience or in terms of prominence in public office; and (3) the top five banks are mostly looking for prominent revolvers from key regulatory agencies, and in a lower extent, for experienced revolvers from “other” public agencies.

In conclusion, the data corroborates Proposition 1 of the theoretical model, as well as Zingales' (Reference Zingales2015) message that power and influence are not equal among financial firms. We also find that, as stated in Proposition 2 and shown in Figure 4, the profits of big and small firms are not equal. In brief, to paraphrase George Orwell, this empirical analysis has stressed that US financial firms “are equal but some firms are more equal than others.”

Figure 4.

Bureaucratic capital and bank's profits

Notes: data from Fortune 500. Top 5 banks are in red.