metadata: authors: 0: author: Cusolito,Ana Paula
1: author: Fattal Jaef,Roberto N.
2: author: Mare,Davide Salvatore
3: author: Singh,Akshat Vikram
count: World
docna: 0: docna: The Role of Financial (Mis)allocation on
            Real (Mis)allocation: Firm-level Evidence for European Countries
docty: Policy Research Working Paper
abstracts: cdata!: This paper leverages the novel
            methodology by Whited and Zhao (2021) to identify financial
            distortions and applies it to a sample of 24 European
            countries. The analyses reveal that less developed economies
            face more severe financial misallocation. Distortions in the
            allocation of financial resources raise the relative cost of
            finance for younger, smaller, and more productive firms.
            Counterfactual analysis indicates that alleviating financial
            distortions could boost aggregate productivity by
            approximately 30-70 percent. On average, 75 percent of these
            gains across countries result from better access to finance,
            with the remainder from optimizing the debt-to-equity ratio.
            The paper also quantifies the link between financial
            misallocation and real-input allocative inefficiency,
            showing that reducing financial misallocation from the
            median to the 25th percentile of the cross-industry
            distribution can increase aggregate productivity by an
            average of 5.2 percent. The effect is larger, at 6.4
            percent, for industries heavily reliant on external finance.
display_title: The Role of Financial
            (Mis)allocation on Real (Mis)allocation: Firm-level Evidence
            for European Countries
listing_relative_url: /research/2024/06/34345166/role-financial-misallocation-real-misallocation-firm-level-evidence-european-countries
topicv3: Private Sector Development m1326060 1921,Finance and Growth m1327404 1081,Firm Dynamics and Productivity Growth
            m1504195 2864
publishtoextweb_dt: 2024-06-20T00:00:00Z
dois: 10.1596/1813-9450-10811
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 47
author_keys: 0: author_key: 1003705
1: author_key: 1253388
2: author_key: 1366794
3: author_key: 2039438
model_extractions: Policy Research Working Paper 10811

# The Role of Financial (Mis)allocation on Real (Mis)allocation

Firm-level Evidence for European Countries _Ana P. Cusolito Roberto N. Fattal-Jaef Davide S. Mare Akshat V. Singh_ Development Research Group Global Indicators Group & Europe and Central Asia Region June 2024
Policy Research Working Paper 10811

# **Abstract**

This paper leverages the novel methodology by Whited and Zhao (2021) to identify financial distortions and applies it to a sample of 24 European countries. The analyses reveal that less developed economies face more severe financial misallocation. Distortions in the allocation of financial resources raise the relative cost of finance for younger, smaller, and more productive firms. Counterfactual analysis indicates that alleviating financial distortions could boost aggregate productivity by approximately 30–70 percent. On average, 75 percent of these gains across countries result from better access to finance, with the remainder from optimizing the debt-to-equity ratio. The paper also quantifies the link between financial misallocation and real-input allocative inefficiency, showing that reducing financial misallocation from the median to the 25th percentile of the cross-industry distribution can increase aggregate productivity by an average of 5.2 percent. The effect is larger, at 6.4 percent, for industries heavily reliant on external finance.

This paper is a product of the Development Research Group, the Global Indicators Group, Development Economics and the Office of the Chief Economist, Europe and Central Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at rfattaljaef@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# The Role of Financial (Mis)allocation on Real (Mis)allocation: Firm-level Evidence for European Countries

Ana P. Cusolito<sup>a</sup> , Roberto N. Fattal-Jaef<sup>a,</sup><sup>_∗_</sup> , Davide S. Mare<sup>a,b</sup> , Akshat V. Singh<sup>c</sup>

> _aWorld Bank, Washington, D.C., US_

> _bUniversity of Edinburgh, Edinburgh, UK_

> _cUniversity of Oxford, Oxford, UK_

_JEL classification_ : D24; D61; O47 _Keywords:_ total factor productivity, financial misallocation, economic growth, frictions

> _∗_ Corresponding Author

_Email addresses:_ `acusolito@worldbank.org` (Ana P. Cusolito), `rfattaljaef@worldbank.org` (Roberto N. Fattal-Jaef), `dmare@worldbank.org` (Davide S. Mare), `akshat.singh@economics.ox.ac.uk` (Akshat V. Singh)
# **1. Introduction**

The study of financial market imperfections and their overall impact on economic development is a major focus in macroeconomics research. Despite the extensive literature on this topic, the question of how significant financial frictions are and their implications for overall income and productivity remains unanswered. This paper aims to fill this gap and offer a comprehensive characterization of the topic across a diverse set of countries. We employ the novel framework developed by Whited and Zhao (2021) to quantify the effect of financial distortions on firm and aggregate productivity. In doing so, we work with a sample of 24 European economies for which we have firm-level balance sheet information for the period 2010-2016.

The objective of this paper is threefold. First, we aim to provide a comprehensive understanding of the differences in finance misallocation across countries and its impact on overall productivity, considering the varying effects on firms of different sizes, ages and physical productivity. Second, we intend to examine the relationship between the measured misallocation of financial liabilities, as determined by Whited and Zhao (2021), and the misallocation of physical inputs, as computed by Hsieh and Klenow (2009). Specifically, we aim to understand how much of the observed variation in the marginal revenue products of physical inputs can be attributed to variation in the marginal returns to debt and equity across firms within specific industries. Third, we quantify the overall effect on total factor productivity of removing such financial Our first quantitative result indicates a strong negative correlation between financial misallocation and economic development. The aggregate productivity gains from efficiently reallocating finance across firms are more than twice as high in countries with the lowest per capita income in the sample relative to the richest counterparts. At a more disaggregated level and exploiting a rich set of country, sector, and timefixed effects, we identify a substantial discount in the shadow cost of finance for larger and older firms. A 10% increase in the size of firms measured by a firm’s total assets is associated with a 5.4% reduction in the shadow cost of finance, whereas a 1-year 2
gap in a firm’s age corresponds to a reduction in the order of 0.6%. While this result has been documented earlier in the literature in different contexts and methodologies, we arrive at this conclusion from the direct observation of the distribution of financial liabilities interpreted through the lens of an efficient theory of finance allocation.

Our second contribution is to document a strong productivity dependence on the distribution of financial distortions. Estimating regression coefficients between the logarithm of distortions against the logarithm of firm-level productivity within each country, we find that the relative cost of financing is higher for less productive firms, resulting in a misallocation of financial resources away from its more productive uses. Moreover, we find this elasticity to be decreasing with economic development, a feature that is reassuring of the subdued misallocation expected of economies with more developed capital markets. The productivity dependence of financial distortion echoes the pattern in the real misallocation literature, where productivity-dependent real distortions are a pervasive feature of the data.

Our third contribution concerns quantifying the relationship between financial misallocation and real-input allocative inefficiency. Using the setting developed in Rajan and Zingales (1998), we classify industries into high and low dependence on external sources of finance. We then regress the standard deviation of the marginal revenue products of real inputs (i.e., real inputs misallocation) within a country, industry, and year against the standard deviation of the marginal return to financial liabilities (i.e., financial misallocation) and its interaction with the external finance indicator. We find that a decrease in financial misallocation from the median to the 25<sup>_th_</sup> percentile of the cross-industry distribution induces an improvement in the allocation efficiency of real inputs that, in turn, increases aggregate productivity by 5.2% on average and by 6.4% in industries with higher dependence on external finance.

The theoretical framework providing the benchmark of an efficient allocation of real and financial resources is drawn directly from Hsieh and Klenow (2009) and Whited and Zhao (2021). At the framework’s core is the notion that, unless frictions and distortions are in place, the maximization of aggregate output requires that real 3
resources and financial liabilities are distributed across firms to equalize the marginal returns to additional units of real and financial inputs.<sup>1</sup> A more fundamental connection between the two methodologies is the assumption that the mechanism through which financial misallocation generates losses in aggregate productivity is by causing a misallocation of real inputs. Despite this strong link, the quantitative strength of this mechanism is not obvious since firms may appeal to internal funding resources to circumvent financial frictions and mitigate their disruptive effect.<sup>2</sup> Therefore, the quantitative effect of finance misallocation on the misallocation of real resources is ultimately an empirical question we address in this paper.

Our empirical strategy to conduct such an assessment involves estimating the relationship between two model-based summary measures of misallocation: the standard deviation of the logarithm of firms’ marginal revenue product of real resources relative to the industry mean, derived using the Hsieh and Klenow (2009) methodology; and the standard deviation of the logarithm of firms’ marginal revenue products of financial resources relative to the industry mean, yielded by the application of the methodology in Whited and Zhao (2021). Besides controlling for time, industry, and country fixed effects, we strengthen the identification by classifying industries into high or low external finance dependence, based on the external finance dependence of the industry being above or below the median across U.S. sectors and estimating the differential impact of finance misallocation on the highly external-finance dependent industries. We find a statistically significant decline in real-input misallocation resulting from reducing the dispersion of marginal returns to financial resources from the median to the 25<sup>_th_</sup> percentile value.

The regression described above allows us to predict the reduction in the real-input misallocation associated with a particular improvement in financial misallocation but

> 1A set of assumptions must be adopted to ensure this is indeed the efficient prescription. Among the most important ones are the imposition of a common technology across firms within a narrow industry and the frictionless reallocation of resources.

> 2This insight plays a key role in the quantitative papers in the macro-development literature assessing the aggregate effects from financial frictions, such as Buera et al. (2011), Midrigan and Xu (2014), and Moll (2014). We discuss the literature in greater detail below.

4
it is silent about the aggregate productivity gains associated with such reduction. To make this extra step, we leverage the richness of our data to estimate the effect of an industry’s real misallocation on the aggregate productivity gains associated with its reversal. Equipped with this estimate, we then find that reducing finance misallocation from the median to the 25<sup>_th_</sup> percentile of the cross-industry distribution yields an aggregate productivity gain of 5.2%, accruing through an improvement in the allocative efficiency of real inputs. The aggregate gains are 1.2 percentage points higher in industries with high external finance dependence. Interestingly, these results fall within the range of estimates reported in the macro-development literature, appealing to radically different quantification strategies.

Several studies attempt to analyze the link between financial frictions and productivity growth. Finance is needed to buy production inputs (capital, labor, research, and development) and affects firm entry and exit, all of which influence a firm’s productivity. The presence of financial frictions may generate misallocation of resources, lowering firm productivity (Buera et al., 2011; Midrigan and Xu, 2014; and Moll, 2014). Frictions can also generate misallocation across sectors because they increase barriers to entry in more productive sectors. Furthermore, capital misallocation can derive from credit constraints (Banerjee and Duflo, 2005) and inefficient contract monitoring and enforcement (Cole et al., 2016). These studies quantify the aggregate effect of financial frictions, pursuing an approach that, unlike us, dispenses from the direct observation of the distribution of financial liabilities. They postulate a specific financial friction, typically a collateral constraint, and calibrate it to match measures of aggregate credit to _GDP_ or moments from the distribution of firm dynamics across age and size. Despite the methodological differences, we find aggregate productivity gains from alleviating financial frictions within the same range identified in these macro-development studies. We interpret the similarity of the results as giving reassurance to the validity of our empirical approach.

The objective of our analysis is not to pinpoint the source of financial misallocation, but rather to elucidate how the sub-optimal allocation of financial liabilities 5
can lead to real-input allocative inefficiency. One critical channel through which this occurs is weaknesses in the banking sector. Banks with capital buffers only marginally above the regulatory minimum may engage in “zombie lending”, continuing to finance weak or insolvent borrowers. Evidence from Japan and European banks in the aftermath of the global financial crisis shows that following the bursting of an asset price bubble and widespread losses in the financial sector, banks with lower capital buffers were more likely to provide frequent rounds of loan restructuring – also known as “evergreening” (Peek and Rosengren, 2005 and Giannetti and Simonov, 2013) and to reduce lending to weak borrowers significantly less than to stronger ones (Acharya et al., 2018; Acharya et al., 2019; Schivardi et al., 2022; and Ozlem<sup> ̈</sup> Dursun-de Neef and Schandlbauer, 2021). When banks’ capital gets locked up in troubled sectors and companies, this may prevent some second-round business failures, but it also diverts funds away from more productive sectors of the economy. Inefficient firms could thus have a dominant impact on the functioning of input and output markets, generating lower economic output, investment, and employment (Caballero et al., 2008). Another important channel works through the structure and maturity of a firm liabilities. Departing from the Modigliani and Miller (1958) framework, empirical studies point to a reduction on long-term productive investment due, for example, to excessive reliance on short-term debt (Vermoesen et al., 2013). Sudden credit rationing may also affect disproportionately firms relying on debt financing affecting productivity through a reduction in investment in innovation.<sup>3</sup> We contribute to this literature by employing an identification strategy that allows us to identify the effect of finance on real misallocation. We show that mitigating capital market imperfections can lead to an average aggregate productivity gain of 5.2%. Furthemore, these productivity gains are even more pronounced in industries that are heavily dependent on external finance (an additional 1.2 percentage point gain).

The remainder of the paper is organized as follows. In section 2 we introduce and

> 3For example, Granja and Moreira (2021) show that in the aftermath of the global financial crisis, disruption in the credit market caused a decrease in product innovation.

6
summarize the data, and present the theoretical framework underlying the identification of real and financial misallocation. In section 3 we discuss the aggregate results and then in section 4 we present the heterogeneous effects based on firm age and size. Section 5 we present and implement our empirical strategy to connect financial with real misallocation. In section 6 we conclude.

# **2. Empirical approach**

In this section, we summarize the model and the data. We also provide some stylized facts about our sample.

# _2.1. Model_

Assessing the misallocation of financial and real resources requires an explicit notion of efficiency against which to compare the observed distribution of these inputs. To this end, we follow closely Hsieh and Klenow (2009)’s methodology for the identification of real-input distortions and Whited and Zhao (2021)’s methodology for the case of financial inputs. Given that both types of misallocation feature prominently in our analysis, we introduce the methodologies in parallel.

We assume there is a single final good _Y_ produced under perfect competition combining output from all industries, _Ys_ , under a Cobb-Douglas technology: We consider each industry _s_ to be populated by a large number of monopolistically competitive firms ( _Ms_ ). Each sector’s output _Ys_ is a constant elasticity of substitution (CES) aggregate of differentiated varieties, given by: 7
where _Ysv_ is the quantity produced of variety _v_ in sector _s_ and _σ_ is the elasticity of substitution.

The differentiated varieties, in turn, are produced by combining physical capital and labor input in a Cobb-Douglas production function with sector-specific factor shares: The physical productivity of the firm _i_ , also referred to as _TFPQ_ , is denoted with _Asi_ . Notice that the capital and labor factor shares are assumed to be industry-specific.

Firms need to issue debt and raise equity to finance the acquisition of the physical capital, the labor input, and the series of expenses that go into the determination of its _TFPQ_ . Rather than imposing a specific theory for why debt and equity are not perfectly substitute and for how the total amount of financing is distributed into its various applications, we postulate a direct mapping from financial liabilities into real value added that captures these unmodeled elements in reduced form. The imperfect substitutability between debt and equity is reflected in a constant elasticity of substitution specification, which we estimate from the data. The distribution of finance into capital, labor, and innovation is subsumed in a finance-based measure of productivity which we label Total Finance Benefit ( _TFB_ ), which we will also back-out from the data. Formally: where _Zsi_ denotes the Total Finance Benefit, _γ_ is the industry-specific elasticity of substitution between debt and equity, and _αs_ is the industry-specific weight of debt in real value added. Notice that, for the sake of differentiating notation with respect to the real-input representation, the real value added here is denoted with _Fsi_ .

8
Empirically, however, we shall extract information about real output from the same observable in the data, the value added of the firm.

In terms of the optimal determination of the capital and labor inputs, we assume these are chosen every period, taking the capital rental rate and the wage rate in factor markets as given. To capture frictions and policies in these markets, we introduce wedges that distort the aggregate scale of the firm and the relative price between capital and labor. These are the output wedge _τysi_ and the capital wedge _τksi_ . Importantly, the wedges are assumed to be idiosyncratic to the firm, capturing the idea that the frictions and policies may exert a heterogeneous impact on the firms’ input choices. Given the monopolistically-competitive behavior of the variety producers, each maximizes where equation 6 is the demand for variety _i_ in sector _s_ and where the firm’s output is given by equation 3. Solving the optimization problem yields Equations 7 and 8 shows the direction in which the wedges distort the decisions away from the efficient level. Under no distortions, firm size is determined by the firm’s _TFPQ_ , _Asi_ , and capital-labor ratios are equalized within industries. With distortions, both properties break down.

An implication of the optimality conditions in the model is that the revenue pro9
ductivity of the firm, _TFPR_ , represents a summary statistic of the mix of capital and output wedges. Through a simple rearrangement of terms, it can be shown that revenue productivity, defined as _TFPRsi_ = _L_<sup>1</sup> _siP_<sup>_−_</sup> _<u>si</u>_<sup>_αs_</sup> _<u>YKsisi</u>_<sup>_αs_,becomesproportionaltothe</sup> ratio of distortions in the following fashion: This representation of _TFPR_ turns out to be very useful for the characterization of the misallocation in an economy. Since, in the efficient allocation with no distortions, the _TFPR_ must be equalized across firms, any dispersion in revenue productivity is a sign of misallocation. Furthermore, the level of a given firm’s _TFPR_ reveals information on the direction in which the distortions are affecting the firm relative to the average in its industry. A high _TFPR_ is indicative of an inefficiently low level of labor and capital flowing to the firm, whereas the opposite is true if _TFPR_ is lower than the average.

One can arrive at an equivalent characterization of the optimal levels of debt and equity in a firm as a function of prices and distortions in capital markets. In this case, the profit maximization problem confronted by the firm is: where _r_ and _λ_ are the prices of debt and equity, and _τDsi_ and _τEsi_ are the distortions in each market. Static optimization yields the following optimality conditions: 10
As it was the case when choosing real inputs, profit maximization requires that the marginal revenue products of equity and debt are equalized to their marginal costs. Under no distortions, our assumption of price-taking in capital markets would require that these marginal returns are equalized across firms. Therefore, any dispersion in marginal returns would once again constitute evidence of misallocation, in this case of liabilities The CES structure of finance-based real value added precludes a transparent characterization of _TFPR_ as a function of distortions as it was possible for real inputs. For this reason, we define the finance-based marginal returns ( _TFPRfin_ ) as the following weighted average of the debt and equity distortions: In the empirical and quantitative analysis that follows, we characterize the logarithm of the demeaned values of the firm-level marginal returns, _log_ � _TFPRTFPRrealreal,s,si_ � and _TFPRf in,si log_ � _TFPRfin,s_ �. By demeaning the marginal returns with the industry average we are acknowledging that the only type of misallocation we are capturing is within a sector, being silent about any misallocation of real and financial inputs across industries. Lastly, following the literature, we conduct the within-sector average at the lowest level of aggregation allowed for by the data.

The last piece in the characterization of the equilibrium that feeds directly into the empirical analysis relates to the aggregation of firm-level outcomes. Since we are ultimately interested in the aggregate productivity gains that are reaped from efficiently 11
reallocating real and financial inputs from the observed to the undistorted allocation, we must therefore characterize the aggregate output of an industry under no distortions. The real-input-based aggregation in the undistorted economy is simplified by the Cobb-Douglas nature of the production function and boils down to the following expression: where _Asi_ , the firm-level _TFPQ_ , can be backed out from the observation of the firm’s value-added, the real inputs, and the CES structure for the demand system as: Notice that the aggregate productivity under the efficient allocation can be easily computed from equation 15 recalling that in such allocation, _TFPR_ is equalized across firms, so that equation 15 becomes: The aggregate productivity gain from reversing all the real-input misallocation in a given industry, then, is given by the ratio of the observed and the efficient aggregate productivity.

The CES structure of the function mapping the financial liabilities into real value added does not allow for a simple characterization of the aggregate total benefit as a function of demeaned marginal returns. Therefore, we must construct it for the undistorted and the observed allocations separately. Solving a benevolent social planner’s problem of maximizing aggregate real value added subject to a given aggregate 12
amount of debt and equity in the industry yields the following solution to the optimal debt and equity allocations: where _D_<sup>�</sup> _s_ and _E_<sup>�</sup> _s_ stand for the aggregate debt and equity holdings allocated to industry _s_ . Both expressions show the well-established result that, in an undistorted allocation, the more productive firms are assigned higher amounts of debt and equity, limited by the degree of substitutability between product varieties in the industry.

Given the definitions of efficient debt and equity holdings, the efficient real value added at the firm level is given by: which can be obtained by plugging in the efficient debt and equity levels derived in equations 17 and 18 and appealing to the finance-based measure of the firm’s _TFPQ_ , _Zsi_ , which is given by: The aggregate real value added of an industry under the efficient allocation is simply _Y_ � _s_ = _Mi_ =1 _s_<sup>_F_�</sup> _siσ−σ_ <u>1</u> _σ−σ_ 1 . As was the case for the real-input allocative gains, in the �� � quantitative analysis we shall focus on the aggregate gains from resolving financial misallocation as given by the ratio between the aggregate real value added in the undistorted economy and the one observed in the data.

13
# _2.2. Data and stylized facts_

We use firm-level information from Bureau van Dijk’s Orbis global database, the largest cross-country database containing information on firms’ financial statements, production activity, and firm ownership (Kalemli-Ozcan et al., 2019). We consider all industrial sectors, including financial companies and services (see table A.1 in Appendix A). We observe European firms over a full economic cycle between 2010 and 2016. To clean up the data for our intended purposes, we follow Cusolito and Didier (2020) and select firms for which we have information on the key variables in our study, namely total sales, compensation of employees, interest payments, taxes, paid-in shareholder equity, total debt and date of establishment. Our final sample comprises information on approximately 7.8 million observations across 24 European countries.

Table 1 reports the number of countries and firms in our sample. Of the 24 countries, according to the 2021 World Bank classification of countries into income groups, 18 are high-income countries and 6 are upper-middle-income countries. Italy is the country with the largest number of firms (25% of the sample) and Luxembourg the lowest (0.04% of the sample). On average, more than 60% of total assets are financed through debt. Liabilities to total assets range from 41% (Ukraine) to 74% (Italy). The ratio of value-added (VA) to total assets is on average around 0.9, varying between a minimum of 43% (Bosnia and Herzegovina) to a maximum of 143% (Bulgaria).

Table 2 summarizes the cost of financing through debt of firms classified according to their size. In all years, smaller firms face a higher cost of financing through debt. Moreover, not only does the average cost of finance appear to be higher for smaller firms, but also there is a higher dispersion, suggesting potentially greater misallocation. Table 3 shows similar patterns but with some distinctions. The cost of equity financing is still greater for smaller firms but variability within size clusters appears to be elevated also for larger firms.

Tables 4 and 5 summarize the cost of debt and equity by age group. We do not observe a clear pattern in the cost of funding among age groups. In general, firms 14
Table 1: Summary Statistics

|Country name|Firms|Liabilities to Assets|VA to Assets|
|---|---|---|---|
|Austria|10,014|0.62|0.95|
|Belgium|72,547|0.60|0.75|
|Bosnia and Herzegovina|28,352|0.51|0.43|
|Bulgaria|351,913|0.44|1.43|
|Croatia|182,335|0.59|0.70|
|Czech Republic|364,312|0.50|0.82|
|Estonia|92,486|0.44|1.03|
|Finland|131,778|0.58|1.36|
|France|831,697|0.61|1.21|
|Germany|116,200|0.63|1.09|
|Hungary|62,543|0.53|0.88|
|Italy|1,935,265|0.74|0.79|
|Luxembourg|2,806|0.58|0.86|
|Montenegro|7,500|0.48|0.66|
|North Macedonia|67,900|0.42|1.33|
|Norway|86,216|0.64|1.38|
|Poland|50,766|0.50|0.90|
|Portugal|551,947|0.61|0.71|
|Romania|627,369|0.55|0.91|
|Serbia|149,100|0.54|0.80|
|Slovak Republic|232,676|0.59|0.95|
|Slovenia|144,325|0.54|1.11|
|Spain|1,579,529|0.58|0.76|
|Ukraine|78,658|0.41|1.32|
|Total|7,758,234|0.61|0.91|

older than 10 years appear to have the lowest mean cost but not necessarily the lowest variability in the cost of funding. For the other age groups, there is not a monotonic relationship among the groups, both in terms of the mean cost of funding and the within-group variability of the cost of debt or equity.

# **3. Aggregate Results**

Equipped with a theoretical framework and a suitable global database, we begin the presentation of the aggregate results obtained using our empirical framework. At the aggregate level, the headline object of interest is the aggregate productivity gain that would be obtained from the reversal of the misallocation of financial liabilities. Following Whited and Zhao (2021), the thought experiment underlying this result is the computation of the aggregate _TFP_ under the hypothetical efficient allocation 15
Table 2: Cost of Debt (by Firm Size)

||0-|5|5-1|5|15-|30|30-|50|50-|70|70-|85|85-|95|95-1|00|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Mean|S.D.|Mean|S.D.|Mean|S.D.|Mean|S.D.|Mean|S.D.|Mean|S.D.|Mean|S.D.|Mean|S.D.|
|2010|1.20|3.25|0.78|1.35|0.61|2.29|0.49|0.90|0.41|0.81|0.35|0.74|0.30|0.38|0.23|0.32|
|2011|1.27|15.97|0.80|2.67|0.61|1.41|0.49|1.51|0.40|1.25|0.34|0.64|0.29|0.47|0.23|0.29|
|2012|1.13|4.72|0.79|5.51|0.60|2.14|0.47|1.10|0.39|0.77|0.34|0.90|0.29|0.46|0.24|0.36|
|2013|1.19|9.84|0.79|2.73|0.61|1.98|0.49|2.79|0.40|1.31|0.35|0.82|0.31|0.97|0.26|2.54|
|2014|1.17|7.28|0.80|3.12|0.61|1.41|0.49|1.22|0.41|1.12|0.36|0.71|0.31|0.59|0.25|0.34|
|2015|1.18|3.18|0.83|1.73|0.67|4.26|0.54|2.02|0.45|2.56|0.37|0.72|0.35|8.56|0.26|0.64|
|2016|1.40|13.41|0.94|13.83|0.69|2.69|0.55|1.92|0.44|0.96|0.38|0.66|0.34|1.84|0.27|0.48|

Note: This table reports the mean and standard deviation in each year of the cost of debt of firms classified according to their size, from the smallest size (up to the 5th percentile) to the largest size (95th percentile to 100th percentile).

Table 3: Cost of Equity (by Firm Size)

||0-|5|5-1|5|15-|30|30-|50|50-|70|70-|85|85-|95|95-|100|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Mean|S.D.|Mean|S.D.|Mean|S.D.|Mean|S.D.|Mean|S.D.|Mean|S.D.|Mean|S.D.|Mean|S.D.|
|2010|1.28|3.24|0.98|2.86|0.84|6.93|0.69|4.17|0.57|2.54|0.54|19.58|0.42|4.04|0.41|10.68|
|2011|1.25|3.94|1.00|3.38|0.87|3.76|0.76|5.52|0.68|14.81|0.53|3.58|0.43|2.31|0.44|10.85|
|2012|1.31|5.21|1.03|3.34|0.90|5.95|0.76|4.48|0.71|23.97|0.54|4.34|0.44|2.21|0.37|5.34|
|2013|1.30|3.54|1.11|6.51|0.91|6.97|0.76|3.07|0.67|14.36|0.53|2.38|0.44|2.04|0.40|5.07|
|2014|1.40|7.40|1.10|4.94|0.91|2.93|0.78|5.02|0.67|6.43|0.55|3.27|0.46|2.71|0.42|7.92|
|2015|1.46|4.28|1.16|4.88|1.01|12.26|0.83|9.80|0.66|3.09|0.64|36.06|0.67|55.72|0.40|7.41|
|2016|1.43|3.22|1.17|4.53|0.96|5.03|0.80|4.13|0.66|3.96|0.56|5.59|0.44|2.44|0.37|2.15|

Note: This table reports the mean and standard deviation in each year of the cost of equity of firms classified according to their size, from the smallest size (up to the 5th percentile) to the largest size (95th percentile to 100th percentile).

Table 4: Cost of Debt (by Firm Age)

||0-|2|2-|5|5-1|0|10|+|
|---|---|---|---|---|---|---|---|---|
||Mean|S.D.|Mean|S.D.|Mean|S.D.|Mean|S.D.|
|2010|0.44|0.59|0.53|0.68|0.58|1.29|0.48|1.38|
|2011|0.50|0.79|0.71|23.44|0.59|3.23|0.48|1.50|
|2012|0.60|2.60|0.59|9.06|0.60|2.90|0.46|1.13|
|2013|0.60|1.23|0.61|1.61|0.63|5.56|0.47|1.85|
|2014|0.58|1.85|0.63|2.28|0.60|3.05|0.47|1.91|
|2015|0.55|1.23|0.68|1.62|0.62|3.44|0.50|3.97|
|2016|0.73|1.68|0.69|8.07|0.62|2.45|0.50|5.11|

Note: This table reports the mean and standard deviation in each year of the cost of debt of firms classified according their age, from the youngest (up to 2 years) to the oldest firms (greater than 10 years).

16
Table 5: Cost of Equity (by Firm Age)

||0-|2|2-|5|5-|10|10|+|
|---|---|---|---|---|---|---|---|---|
||Mean|S.D.|Mean|S.D.|Mean|S.D.|Mean|S.D.|
|2010|0.99|3.04|1.24|3.69|1.08|5.51|0.59|9.44|
|2011|1.52|10.13|1.41|5.84|1.08|5.62|0.62|8.40|
|2012|1.48|7.95|1.62|46.17|1.11|6.77|0.60|7.97|
|2013|1.50|5.61|1.51|12.13|1.03|4.40|0.60|7.81|
|2014|1.54|4.02|1.49|10.40|0.98|4.69|0.59|3.78|
|2015|1.46|9.45|1.39|5.22|0.99|13.33|0.65|27.88|
|2016|1.70|8.72|1.25|5.59|0.92|5.74|0.58|3.18|

Note: This table reports the mean and standard deviation in each year of the cost of equity of firms classified according their age, from the youngest (up to 2 years) to the oldest (greater than 10 years) firms.

and compare it with the _TFP_ under the observed distribution of debt and equity. In Figure 1, we plot the aggregate gains against the log of GDP per-capita, which we take as proxy for the degree of financial development across countries.

Figure 1 shows a strong negative relationship between finance misallocation and economic development. At the bottom of the income distribution, the conditional _TFP_ gains of efficiently reallocating financial liabilities (dotted line in 1) reach approximately 75%, almost doubling the conditional gains to be reaped by the richer countries. As a benchmark, Whited and Zhao (2021) find gains in the order of 11% to 12% in the U.S. and 70% to 80% in China. These numbers reinforce the plausibility of our findings and imply that most of the countries in our sample fall between the polar cases of China and that of the U.S.

The correlation between firms’ productivity and financial distortions has an important bearing on the aggregate effects of financial distortions. Are financial frictions relatively more stringent for productive firms? There are various mechanisms through which distortions in credit markets may favor less productive firms at the expense of more productive ones. Government policy often subsidizes credit to micro and small enterprises, which, in turn, are relatively less productive than their larger counterparts. Moreover, capable yet young entrepreneurs may also confront higher costs of credit due to limited commitment and informational asymmetries. Such policy and 17
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misallocation.

# **4. Heterogeneous Effects: The Role of Age and Size**

A long-lasting hypothesis in the macro-finance literature is that the size and the age of the firm constitute important determinants of a firm’s access to finance (Beck et al., 2008). Larger firms are better able to pledge collateral in contexts where the relevant financial frictions is a limited commitment problem. Similarly, moral hazard may be attenuated when the borrower is a large firm. A similar reasoning applies to older firms where the accumulation of capital over time and the availability of information reduce financial frictions. In this section, we leverage the richness of the data to explore these hypotheses.

We begin by exploring the differences in the average cost of finance across firms of different sizes. Within an industry in a particular country, we classify firms into small and large based on their asset holdings being above or below the median in the sector-country. Then, we take the model-implied average cost of finance among small and large firms and average it across countries to construct a global measure of the average cost of finance by firm size within each sector. The results are plotted in Figure 6.

Figure 6 reveals a clear pattern: in all industries, the average cost of finance is higher for smaller than for larger firms. A higher cost of finance in our model is reflective of a high marginal return to additional units of finance and is thus suggestive of a scarcity of funds relative to the optimal level. Therefore, our results dictate that in response to a hypothetical reform that liberalizes capital and credit markets, we should expect finance to flow from the larger to the smaller firms. Indeed, this prediction is in accordance with the findings in the recent literature studying the firm-level response to financial liberalization episodes,<sup>5</sup> although in these papers the inference is based on the observation of the reallocation of real inputs.

> 5See, for instance, the work of Larrain and Stumpner (2017), and Bau and Matray (2020).

23
<!-- Start of picture text --> N- Administrativeand support service activities<br>R-Arts, entertzinmentand recreation<br>H- Transportation and storage<br>J- Information and communication<br>S - Other service activities<br>M- Professional, scientificand technical activities<br>| -Accommodationand food service activities<br>F - Construction<br>E - Water supply: sewerage. waste management and remediation activities<br>(C- Manufacturing<br>G - Wholesale and retail trade; repair of motor vehicles and motorcycles<br>B - Miningand quarrying<br>K - Financial and insuranceactivities<br>D - Electncity, gas, steam and air conditioning supply<br>A-Agriculture, forestry and fishing<br>L- Real estate activities<br>aaa|<br>02 4 6 8<br>Sector-wise Average Cost of Finance<br>G Above Median Size {Below Median Size<br><!-- End of picture text -->
<!-- Start of picture text --> N- Administrativeand support service activities<br>S - Other service activities<br>R- Arts, entertainmentand recreation<br>J- Information and communication<br>H- Transportation and storage<br>M- Professional, scientificand technical activities<br>| -Accommodationand food service activities<br>F - Construction<br>E - Water supply: sewerage. waste management and remediation activities<br>G - Wholesale and retail trade; repair of motor vehicles and motorcycles<br>(C- Manufacturing<br>K - Financial and insuranceactivities<br>B - Miningand quarrying<br>L- Real estate activities<br>A-Agriculture, forestry and fishing<br>D - Electricity, gas, steam and air conditioning supply<br>aa|<br>02 4 6 8<br>Sector-wise Average Cost of Finance<br>GD Age >=5 years lM Age <5 years<br><!-- End of picture text -->
financial distortion in the model though it is a feature that is also part of the social planner’s assessment of the economic environment (David et al., 2020).

In addition to isolating the effect of a firm’s age and size on its average cost of finance from other factors, the regression specifications also seek to assess productivity dependence in the distribution of financial wedges. This is motivated by the real misallocation literature, where it is a well-established feature of resource misallocation that distortions tend to tax productive firms more heavily than less productive ones. As shown in the literature, idiosyncratic distortions carry larger aggregate effects when increasing with the firms’ physical productivity.<sup>6</sup> By including _log_ ( _TFPQi,s,t/TFPQs,t_ ), in contemporaneous or lagged fashion, we seek to establish if such a relationship manifests as well in the context of financial distortions.

Table 6: Cost of Finance: The Role of Firm Age and Size

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Age of the frm|0.0114<sup>_∗∗∗_</sup>|0.0097<sup>_∗∗∗_</sup>|-0.0057<sup>_∗∗_</sup>|-0.0242<sup>_∗∗∗_</sup>|
||(0.0033)|(0.0033)|(0.0027)|(0.0083)|
|lnasset|-0.5315<sup>_∗∗∗_</sup>|-0.5384<sup>_∗∗∗_</sup>|-0.5460<sup>_∗∗∗_</sup>||
||(0.0002)|(0.0002)|(0.0002)||
|log<br>~~T~~FPQ|0.3970<sup>_∗∗∗_</sup>|0.3995<sup>_∗∗∗_</sup>|0.4160<sup>_∗∗∗_</sup>||
||(0.0001)|(0.0001)|(0.0001)||
|lnasset_t−_1||||-0.1715<sup>_∗∗∗_</sup><br>(0.0009)|
|log<br>~~T~~FPQ_t−_1||||0.0680<sup>_∗∗∗_</sup><br>(0.0004)|
|Observations|7758234|7758234|7758234|5263441|
|Time fxed efects|Y|N|N|N|
|Firm fxed efects|Y|Y|Y|Y|
|Industry-time fxed efects|N|Y|Y|Y|
|Country-time fxed efects|N|N|Y|Y|

Note: Standard errors in parentheses. This table presents the results obtained using equation 22.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

> 6See, for instance, Hsieh and Klenow (2014) and Bento and Restuccia (2017).

26
Table 6 confirms that older and larger firms confront a relatively lower cost of financing relative to their younger and smaller peers in the same industry and country. Recall that the cost of finance is measured according to the theory and reflects the cost of acquiring an extra unit of debt and equity that rationalizes marginal returns exhibited by the firm in the data. Using the most conservative estimates reported in Column 3 Table 6, a 10% increase in the size of a firm is associated with a 5.4% reduction in the shadow cost of finance, whereas a 1-year increase in firm age corresponds to a reduction in the order of 0.6%. Column 4 shows that these results are robust to using lagged measures of firm size and TFPQ.

The productivity dependence of idiosyncratic financial distortions shows prominently in table 6. Comparable to estimates in the real misallocation literature, financial frictions are significantly and strongly increasing in the underlying level of physical productivity of the firm. Even when projecting distortions on lagged values of physical productivity and controlling for a wider range of firm and other types of fixed effects, the positive relationship between productivity and financial distortions is weakened but remains statistically significant.

To summarize, in this section, we provided empirical validation for the widely believed and convincingly proven idea that firm age and size are important determinants of the ease of access to financing opportunities by firms. We investigated this hypothesis in the context of a novel inference strategy of financial misallocation that relies on the direct observation of the distribution of financial liabilities across firms, applied to a rich firm-level database with rich cross-country and time-series coverage. We found that larger and older firms do face significantly lower costs of external financing. We also show evidence that the most productive firms are more financially distorted than they should optimally be.

# **5. Establishing the Link between Financial and Real (Mis)allocation**

We now turn to address the second contribution of the paper, namely, answering the question we postulated in the introduction: how much of the observed misalloca27
tion of real inputs can be attributed to a misallocation of finance? As stated earlier in the text, the core of the measurement of finance misallocation in our paper lies in the assumption that disruptions in the allocation of finance across firms cause a misallocation of real inputs, which in turn translates into productive inefficiency. While the aggregate evidence presented in section 3 is suggestive of such a relationship being at play, in this section we propose a richer empirical strategy aimed at identifying a causal link between and real misallocation.

Our strategy involves estimating the relationship between two model-based summary measures of misallocation: the standard deviation of the logarithm of the demeaned marginal revenue product of real inputs within an industry, yielded by the application of Hsieh and Klenow (2009)’s methodology, and the standard deviation of the logarithm of the demeaned marginal revenue products of financial resources, yielded by the application of the methodology in Whited and Zhao (2021). Financial market imperfections may affect industries in a different fashion, hence the first channel to be explored is whether industries with higher measured finance misallocation are associated with a higher real misallocation, controlling for country, year, and industry fixed effects. Moreover, to get one step closer to identifying a causal relationship, we follow Rajan and Zingales (1998) in splitting industries into high or low external finance dependence, based on a classification applied to industries in the United States. Equipped with this taxonomy, we then also consider the interaction between the high/low external finance indicator and the finance misallocation of an industry as another explanatory variable for the degree of real misallocation. The idea here is that industries that rely more heavily on external borrowing should exhibit a higher degree of real misallocation, for any given degree of finance misallocation. Formally, our estimating equation is given by: 28
where _s_ denotes industry, _c_ denotes country, _t_ denotes time, _EFD_ denotes external finance dependence of an industry, and _sd_ ( _log_ _~~T~~ FPR_ ) _s,c,t_ is the standard deviation of log(TFPR/ _TFPR_ ) within an industry-country. _real_ and _fin_ denotes real and financial distortions, respectively. To attenuate concerns about reverse causality and endogeneity, we consider specifications of the estimating equation where the explanatory variable of interest, the standard deviation of the log of financial distortions, is lagged to the previous period’s value.

The external finance dependence within an industry is computed as an average of the following firm-level indicator: That is, following Rajan and Zingales (1998), we define the external finance dependence of the firm _i_ in sector _s_ as the fraction of its capital expenditures that could not be financed with internal sources of funding. The total cash flow, in turn, is computed as an aggregate of total funds from operations, plus increases in accounts payable, decreases in inventories, and decreases in receivables. Importantly, these cash flows are independent of the internal equity of the firm, which we treat as a source of external finance in the measurement of finance misallocation. The sector-wide external finance is the average across firms in the industry. As said, for identification, we apply this indicator to the U.S. economy based on Compustat data. Finally, we classify an industry as highly dependent on external finance if its indicator is above the median across all industries.

Table 7 presents the results from estimating equation 23. We report a positive and statistically significant coefficient for the effect of finance-based on real-based _TFPR_ dispersion. Moreover, we also document a positive and statistically significant coefficient on the interaction between financial misallocation and the high external finance dependence indicator. In industries with a higher reliance on external credit, 29
Table 7: The link between financial and real misallocation

||(1)|(2)|
|---|---|---|
|sd(log<br>TFPR<br>~~f~~n)|0.2737<sup>_∗∗∗_</sup><br>(0.0101)||
|sd(log<br>TFPR<br>~~f~~n) _×_ 1_{EFDs,USA}_|0.0336<sup>_∗∗_</sup><br>(0.0134)||
|sd(log<br>TFPR<br>~~f~~n)_t−_1||0.1674<sup>_∗∗∗_</sup><br>(0.0101)|
|sd(log<br>TFPR<br>~~f~~n)_t−_1 _×_ 1_{EFDs,USA}_||0.0405<sup>_∗∗∗_</sup><br>(0.0138)|
|Observations|50,064|42,100|
|Country fxed efects|Y|Y|
|Time fxed efects|Y|Y|
|Industry fxed efects|Y|Y|

Note: Standard errors in parentheses. This table presents the results obtained using equation 23. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 the misallocation of real resources that are created by the misallocation of finance is about 4 percentage points higher than the average.

Our goal next is to provide an economic interpretation of the regression coefficients reported in table 7. What are the _TFP_ consequences of the real misallocation that results from financial frictions? The question cannot be answered directly from the estimates in the table. The missing piece is a conversion factor that translates changes in the standard deviation of real marginal returns into changes in aggregate productivity. Such a conversion factor can be gauged by running the following regression: which projects a given industry-country’s _TFP_ gain from reversing misallocation, _TFPgains,c,t_ , against the given industry-country’s degree of real-input misallocation as captured by the standard deviation of the marginal return to real factors, _SDTFPRreal,s,c,t_ .

30
Table 8: _TFP_ Effects of Dispersion in Marginal Returns to Real Inputs

|100*_{_(_YEffective/YActual_)-1_}_|
|---|
|sd(log<br>~~T~~FPR<br>~~r~~eal)<br>192.7697<sup>_∗∗∗_</sup>|
|(9.5454)|
|Observations<br>60,815|
|Country fxed efects<br>Y|
|Time fxed efects<br>Y|
|Industry fxed efects<br>Y|

Note: Standard errors in parentheses. _∗ ∗∗ ∗∗∗ p <_ 0 _._ 1, _p <_ 0 _._ 05, _p <_ 0 _._ 01 As expected, table 8 reports a strong positive coefficient for the relationship between dispersion in marginal returns and counterfactual gains in aggregate productivity. For instance, reducing _SDTFPRreal_ from its median value (0.559) to the 25<sup>_th_</sup> percentile (0.399) would imply an aggregate productivity gain of 31%.<sup>7</sup> We are now ready to answer the question of what is the economic significance of the of misallocation on real misallocation.<sup>8</sup> Consider a reduction in the dispersion of marginal returns to financial inputs from the median to the 25<sup>_th_</sup> percentile of the cross-industry distribution of standard deviations, i.e. a reduction of 0.160.<sup>9</sup> According to our estimates in table 7, such a decline reduces the standard deviation of marginal returns to real inputs by 0.027 in the average industry, and by 0.033 in industries with high external finance depen-

> 7The aggregate productivity gain is computed as follows: (0.559-0.399)*192.7697 = 30.84%.

> 8As a reminder, every time we refer to financial misallocation, we mean the within-industrycountry dispersion in the log of marginal returns to financial liabilities, demeaned by the industrycountry average, i.e. log( _TFPRf in_ <u>/</u> _TFPRfin_ ). Likewise, with real-input misallocation we mean the dispersion in log( _TFPRreal_ / _TFPRreal_ ).

> 9The reduction in the dispersion of marginal returns is computed as follows: 0.559-0.399 = 0.160.

31
dence.<sup>10</sup> Then, feeding these effects on the coefficient mapping real misallocation to aggregate productivity (table 8), we find that the proposed improvement in finance misallocation ends up increasing aggregate productivity by 5.2% on average and by 6.4% in industries with high external finance dependence.<sup>11</sup> To put the quantitative implications of our identification strategy in perspective, we contrast our results with those found in the macro-development literature. In Buera et al. (2011), Midrigan and Xu (2014), and Moll (2014), salient examples in the literature, the quantification is based on the postulation of specific financial friction (a collateral constraint) in the context of a general equilibrium model of firm dynamics and the quantification of the productivity gains resulting from the alleviation of the friction. Our results fall in the ballpark of their findings. Midrigan and Xu (2014), for instance, document productivity losses in the order of 5% to 10% arising from the misallocation of labor and capital across firms within a sector, including the 5.2% that we estimate in our study.<sup>12</sup> We interpret the proximity of our results to those emerging from alternative quantification strategies as providing reassurance to the validity of our empirical specification.

In short, in this section we constructed a bridge between the finance misallocation methodology proposed in Whited and Zhao (2021) and the real misallocation counterpart pioneered in Hsieh and Klenow (2009). We proposed an identification strategy to tease out the association of finance with real misallocation and ultimately aggregate productivity. We found that a plausible improvement in the degree of cap-

> 10The reduction in the standard deviation of marginal returns to real inputs in the average industry is computed using equation 26 as follows: 0.160*0.1674 = 0.0268. That is the computed change in the finance misallocation measure (0.160) multiplied by the estimated _β_<sup>ˆ</sup> 1 from equation 23 (0.1674; estimate in Table 7, Column 2). Similarly, the value for industries with high external finance dependence is computed using the coefficients _β_<sup>ˆ</sup> 1 and _β_<sup>ˆ</sup> 2 estimated through equation 23: (0.160*0.1674)+(0.160*0.0405)=0.0333.

> 110.0268*192.7697 = 5.2; 0.0333*192.7697 = 6.4.

> 12Midrigan and Xu (2014) and Buera et al. (2011) find larger productivity gains from reversing financial frictions when accounting for compounding forces, such as the effect of these frictions on limiting the adoption of better and more modern technologies. However, since our methodology is designed to capture the allocative channel only, it is not comparable to the magnitudes generated by these magnifying mechanisms.

32
ital market imperfections that brings finance misallocation down from the median to the 25<sup>_th_</sup> percentile of the cross-industry distribution of financial misallocation would trigger aggregate productivity gain of 5.2% on average, and an extra 1.2% gain in industries with a higher dependence on external borrowing. We found reassurance for the plausibility of these findings in that they fall in the ballpark of the gains found in macro-development studies under very different quantification strategies.

# **6. Conclusion**

Financial frictions play an important role in determining total factor productivity. In this study, we advance the extant literature by providing new evidence on the global properties of financial misallocation and its ability to account for the misallocation of real resources that is observed in the data.

In light of a promising new methodology to measure finance misallocation and its narrow application to few countries, an important goal of the paper is to assess whether such methodology would deliver reasonable implications for the distribution of finance misallocation across countries with different degrees of financial development. Such an assessment is challenged by the data requirements: one must be able to count with firm-level databases offering a wide international and sectoral coverage, as well as rich information on the firm’s balance sheet. In this paper, we took this challenge confronting the new methodology with the ORBIS database, arguably the only suitable database for the task.

We confirm that the novel measure of finance misallocation proposed in Whited and Zhao (2021) varies systematically with a country’s economic development in the direction that one would expect: finance misallocation is twice as severe in the poorest than in the richest countries in our sample.

We also validate a number of well-rooted conjectures in the literature concerning the distribution of financing costs across firms of different ages and sizes. Based on the theoretical implication that younger and smaller firms are more likely to be financially constrained, we show that this is actually the case under our measure of theory-based 33
financing costs: small and young firms confront a significantly higher cost of accessing external Perhaps the most novel of our contributions was the ability to establish an empirical connection between the novel measure of finance misallocation and the most standard measure of real-input misallocation that has been widely adopted in the literature since the work of Hsieh and Klenow (2009). Establishing such a connection is essential for the validity of one of the central assumptions behind the approach to measuring financial misallocation: that by disrupting the costs of accessing debt and equity markets, firms are in effect unable to acquire the efficient levels of productive factors. We close this gap in this paper and show that there is a strong positive correlation between financial misallocation in an industry and its degree of misallocation of real resources. Our estimates yield significant productivity gains from reducing finance misallocation. Mediated by its implied improvement in real allocative efficiency, we find that reducing finance misallocation generates aggregate gains in Total Factor Productivity in the order of 5% to 6%, in the ballpark of what has been found earlier in the literature.

While the paper builds upon a novel approach to measuring financial misallocation, it remains silent about the potential causes of such misallocation. In future research, we expect to leverage the rich time and cross-sectional dimensions of our database to identify plausible exogenous natural experiments that can provide us with a clean identification of the effect of particular types of financial reform or friction on financial misallocation and, ultimately, aggregate productivity.

# **Acknowledgements**

The views in this paper are those of the authors and do not necessarily represent those of the World Bank, their Executive Directors, or the countries they represent. We thank Tatiana Didier, Leonardo Iacovone, Ha Nguyen, Jean Pesme and the participants to a World Bank internal workshop for constructive comments.

34
# **Appendix A. Supplementary tables**

Table A.1: Classification of firms into industries

|**Code Va**<br>01|**lue**<br>**Description**<br>Agricultural Production - Crops|**Industry**<br>A. Agriculture, Forestry, & Fishing|
|---|---|---|
|02|Agricultural Production - Livestock and Animal Specialties|A. Agriculture, Forestry, & Fishing|
|07|Agricultural Services|A. Agriculture, Forestry, & Fishing|
|08|Forestry|A. Agriculture, Forestry, & Fishing|
|09|Fishing, Hunting and Trapping|A. Agriculture, Forestry, & Fishing|
|10|Metal Mining|B. Mining|
|12|Coal Mining|B. Mining|
|13|Oil and Gas Extraction|B. Mining|
|14|Mining and Quarrying of Nonmetallic Minerals, Except Fuels|B. Mining|
|15|Construction - General Contractors & Operative Builders|C. Construction|
|16|Heamy Construction, Except Building Construction, Contractor|C. Construction|
|17|Construction - Special Trade Contractors|C. Construction|
|20|Food and Kindred Products|D. Manufacturing|
|21|Tobacco Products|D. Manufacturing|
|22|Textile Mill Products|D. Manufacturing|
|23|Apparel, Finished Products from Fabrics & Similar Materials|D. Manufacturing|
|24|Lumber and Wood Products, Except Furniture|D. Manufacturing|
|25|Furniture and Fixtures|D. Manufacturing|
|26|Paper and Allied Products|D. Manufacturing|
|27|Printing, Publishing and Allied Industries|D. Manufacturing|
|28<br>|Chemicals and Allied Products<br>|D. Manufacturing<br>|
|29|Petroleum Refning and Related Industries|D. Manufacturing|
|30|Rubber and Miscellaneous Plastic Products|D. Manufacturing|
|31|Leather and Leather Products|D. Manufacturing|
|32|Stone, Clay, Glass, and Concrete Products|D. Manufacturing|
|33|Primary Metal Industries|D. Manufacturing|
|34|Fabricated Metal Products|D. Manufacturing|
|35|Industrial and Commercial Machinery and Computer Equipment|D. Manufacturing|
|36|Electronic & Other Electrical Equipment & Components|D. Manufacturing|
|37|Transportation Equipment|D. Manufacturing|
|38|Measuring, Photographic, Medical, & Optical Goods, & Clocks|D. Manufacturing|
|39|Miscellaneous Manufacturing Industries|D. Manufacturing|
|40|Railroad Transportation|E. Transportation & Public Utilities|
|41|Local & Suburban Transit & Interurban Highway Transportation|E. Transportation & Public Utilities|
|42|Motor Freight Transportation|E. Transportation & Public Utilities|
|43|United States Postal Service|E. Transportation & Public Utilities|
|44|Water Transportation|E. Transportation & Public Utilities|
|45|Transportation by Air|E. Transportation & Public Utilities|
|46|Pipelines, Except Natural Gas|E. Transportation & Public Utilities|
|47|Transportation Services|E. Transportation & Public Utilities|
|48|Communications|E. Transportation & Public Utilities|
|49|Electric, Gas and Sanitary Services|E. Transportation & Public Utilities|
|50|Wholesale Trade - Durable Goods|F. Wholesale Trade|
|51|Wholesale Trade - Nondurable Goods|F. Wholesale Trade|
|52|Building Materials, Hardware, Garden Supplies & Mobile Homes|G. Retail Trade|
|53|General Merchandise Stores|G. Retail Trade|
|54|Food Stores|G. Retail Trade|
|55|Automotive Dealers and Gasoline Service Stations|G. Retail Trade|
|56|Apparel and Accessory Stores|G. Retail Trade|
|57|Home Furniture, Furnishings and Equipment Stores|G. Retail Trade|
|58|Eating and Drinking Places|G. Retail Trade|
|59|Miscellaneous Retail|G. Retail Trade|
|60|Depository Institutions|H. Finance, Insurance, & Real Estate|
|||Continued on next page|

35
**Table A.1 – continued from previous page**

|**Code Value**|**Description**|**Industry**|
|---|---|---|
|61|Nondepository Credit Institutions|H. Finance, Insurance, & Real Estate|
|62|Security & Commodity Brokers, Dealers, Exchanges & Services|H. Finance, Insurance, & Real Estate|
|63|Insurance Carriers|H. Finance, Insurance, & Real Estate|
|64|Insurance Agents, Brokers and Service|H. Finance, Insurance, & Real Estate|
|65|Real Estate|H. Finance, Insurance, & Real Estate|
|67|Holding and Other Investment Ofces|H. Finance, Insurance, & Real Estate|
|70|Hotels, Rooming Houses, Camps, and Other Lodging Places|I. Services|
|72|Personal Services|I. Services|
|73|Business Services|I. Services|
|75|Automotive Repair, Services and Parking|I. Services|
|76|Miscellaneous Repair Services|I. Services|
|78|Motion Pictures|I. Services|
|79|Amusement and Recreation Services|I. Services|
|80|Health Services|I. Services|
|81|Legal Services|I. Services|
|82|Educational Services|I. Services|
|83|Social Services|I. Services|
|84|Museums, Art Galleries and Botanical and Zoological Gardens|I. Services|
|86|Membership Organizations|I. Services|
|87|Engineering, Accounting, Research, and Management Services|I. Services|
|88|Private Households|I. Services|
|89|Services, Not Elsewhere Classifed|I. Services|
|91|Executive, Legislative & General Government, Except Finance|J. Public Administration|
|92|Justice, Public Order and Safety|J. Public Administration|
|93|Public Finance, Taxation and Monetary Policy|J. Public Administration|
|94|Administration of Human Resource Programs|J. Public Administration|
|95|Administration of Environmental Quality and Housing Programs|J. Public Administration|
|96|Administration of Economic Programs|J. Public Administration|
|97|National Security and International Afairs|J. Public Administration|
|99|Nonclassifable Establishments|K. Nonclassifable Establishments|

36
# **Appendix B. Data Cleaning**

Following Bureau van Dijk (2011), Kalemli-Ozcan et al. (2015), Cusolito and Didier (2020), and Kalemli-Ozcan et al. (2023), we document the steps we apply to clean the information.

1. _Fill time-invariant data gaps_ : for a given BvD.ID-year combination, with BvD.ID standing for firm unique identifier, replace missing highly-likely time-invariant information with information available for previous years (e.g., US SIC code, NAICS, NACE, NACE main sector, company name, city, region, postal code, legal form, incorporation date, thicker, isin). To perform this step, the team first worked with auxiliary raw tables, which collect legal and sectoral information of the firm, and collapsed the time-invariant variables at the BvD.ID level.

2. _Harmonize timeframe_ : convert variable closedate from string to numeric format. Then create a new variable, name it year, and assign a year to the observation according to the following rule. If closing month corresponding to the observation is June or any other month after June, then make Year take the year reported in closedate. Otherwise, make Year the year reported in closedate minus 1.

3. _Drop duplicates_ : the raw database presents a large number of duplicates at the BvD.ID-year level. The team noticed that the information was the same, except in the SIC primary code variable. Thus, we collapsed all the SIC primary codes reported by the same BvD.ID-year in one variable, using semicolons to list all the SIC primary codes, and eliminated duplicates.

4. _Drop firms with missing relevant information_ : drop all the firms with no information for the following set of variables: US SIC code, NAICS, NACE core code, NACE main sector.

5. _Drop observations with missing information for the currency code_ : eliminate observations with missing information for the currency code.

37
6. _Drop observations with missing information for variable closedate_ : eliminate observations with missing information for the close date of the financial statement.

7. _Drop observations with relevant missing information_ eliminate observations that at the BvD.ID-year level have missing information in all the following variables: operating revenue (turnover), sales, employment, total assets.

8. _Drop duplicates and keep most updated information_ : keep observations with the most recent closing date if there are duplicates at the BvD.ID-year-first letter of consolidation code (e.g., C, U) level.

9. _Drop duplicates and keep information from annual reports_ : keep observations with annual report in _Use FillingType_ variable if there are still duplicates and keep the standardized information. Using annual reports (IFRS preferred, instead of local reports) guarantees standardization of reporting protocol at international level.

10. _Eliminate firms with noisy data_ : drop all the observations corresponding to a specific BvD.ID if any of the following variables has a negative value in a specific year – total fixed assets, tangible fixed assets, intangible fixed assets, other fixed assets, current assets, sales, and employment.

11. _Deflate values_ : use country GDP deflators from the World Bank database to deflate nominal variables and set year 2005 as the base year.<sup>13</sup> 12. _Harmonize currencies_ : convert values in local currency to USD dollars, using the average of the monthly exchange rate for year 2005.

> 13 https://data.worldbank.org/indicator/NY.GDP.DEFL.ZS.

38
# **Appendix C. Validation of Final Database**

We validate the representatives of the final database by calculating the ratio of the sum of employment and gross output in the database to their corresponding aggregates, in the same manner as Gopinath et al. (2017). Aggregates for employment and gross output are obtained from Eurostat’s Structural Business Statistics Database (SBS). Tables C.1 and C.2 show the coverage of our sample by country, separately for non-manufacturing and manufacturing sectors.

Table C.1: Coverage of Final Database Relative to Eurostat (SBS) - Non-Manufacturing

|**Country**|||**E**|**mployme**|**nt**||||||**Turnove**|**r**|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|
|Austria|19%|21%|28%|32%|26%|31%|26%|16%|18%|34%|42%|36%|39%|36%|
|Belgium|44%|41%|43%|45%|44%|44%|43%|55%|56%|55%|55%|55%|53%|52%|
|Bosnia and Herzegovina||72%|34%|66%|65%|53%|60%||73%|40%|70%|63%|59%|62%|
|Bulgaria|47%|58%|73%|70%|72%|76%|71%|53%|58%|61%|62%|62%|63%|62%|
|Croatia|53%|57%|59%|60%|60%|61%|63%|66%|67%|74%|76%|72%|72%|75%|
|Czechia|64%|70%|68%|69%|66%|68%|63%|48%|48%|49%|51%|52%|54%|51%|
|Estonia|41%|42%|42%|43%|46%|45%|47%|38%|39%|41%|39%|43%|42%|44%|
|Finland|42%|45%|45%|44%|44%|41%|39%|52%|60%|56%|61%|60%|60%|57%|
|France|25%|25%|24%|28%|32%|32%|29%|30%|31%|26%|34%|39%|37%|35%|
|Germany|26%|28%|29%|29%|30%|28%|27%|47%|51%|49%|50%|49%|45%|45%|
|Hungary|38%|39%|41%|43%|44%|39%|36%|73%|77%|82%|84%|85%|79%|72%|
|Italy|39%|54%|55%|54%|56%|59%|56%|48%|58%|55%|54%|55%|55%|56%|
|Luxembourg|39%|41%|41%|45%|125%|147%|128%|57%|64%|70%|69%|71%|89%|83%|
|Montenegro|||||||||||||||
|North Macedonia|||58%|63%|66%|67%|71%|||77%|83%|87%|90%|84%|
|Norway|10%|10%|9%|7%|7%|69%|69%|9%|9%|8%|7%|11%|59%|65%|
|Poland|19%|18%|13%|7%|6%|4%|12%|21%|20%|14%|7%|7%|6%|15%|
|Portugal|42%|46%|44%|44%|46%|46%|43%|50%|56%|55%|53%|57%|56%|51%|
|Romania|43%|47%|49%|51%|54%|58%|58%|56%|63%|69%|71%|72%|81%|82%|
|Serbia|||||||59%|||||||102%|
|Slovak Republic|52%|60%|59%|65%|65%|59%|58%|54%|71%|68%|68%|66%|64%|61%|
|Slovenia|64%|65%|69%|69%|68%|66%|67%|70%|76%|78%|79%|78%|76%|74%|
|Spain|42%|44%|43%|44%|45%|45%|42%|50%|52%|52%|53%|53%|54%|49%|

Note: Blanks correspond to country-year pairs for which the Eurostat’s SBS Database does not report information. Montenegro and Ukraine are excluded from this table as the SBS Database does not include information for any of the years in our sample.
Table C.2: Coverage of Final Database Relative to Eurostat (SBS) - Manufacturing

|**Country**|||**E**|**mployme**|**nt**||||||**Turnove**|**r**|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|
|Austria|14%|21%|34%|41%|44%|45%|47%|12%|17%|35%|51%|54%|58%|58%|
|Belgium|63%|64%|64%|65%|67%|67%|66%|78%|76%|76%|72%|74%|79%|76%|
|Bosnia and Herzegovina||64%|44%|67%|60%|62%|59%||62%|49%|68%|62%|66%|62%|
|Bulgaria|54%|65%|73%|71%|70%|74%|70%|45%|50%|41%|40%|42%|45%|45%|
|Croatia|61%|65%|65%|62%|65%|67%|65%|68%|76%|79%|75%|73%|71%|65%|
|Czechia|74%|77%|76%|77%|75%|79%|72%|50%|52%|53%|55%|53%|62%|60%|
|Estonia|39%|41%|41%|43%|44%|43%|44%|33%|33%|33%|35%|35%|36%|38%|
|Finland|47%|52%|53%|54%|49%|48%|48%|45%|50%|52%|49%|46%|46%|49%|
|France|30%|28%|23%|31%|37%|38%|40%|27%|25%|19%|30%|35%|37%|37%|
|Germany|32%|34%|34%|34%|35%|34%|33%|46%|49%|48%|48%|50%|50%|49%|
|Hungary|57%|61%|62%|62%|64%|63%|60%|75%|78%|79%|83%|89%|89%|90%|
|Italy|47%|59%|59%|62%|63%|65%|64%|54%|61%|59%|62%|61%|61%|62%|
|Luxembourg|60%|67%|64%|62%|62%|55%|53%|80%|100%|96%|77%|77%|67%|62%|
|North Macedonia|||61%|65%|66%|60%|61%|||56%|63%|47%|43%||
|Norway|32%|36%|40%|28%|25%|68%|75%|31%|35%|31%|27%|26%|64%|73%|
|Poland|26%|22%|16%|10%|7%|7%|16%|31%|25%|20%|12%|10%|10%|23%|
|Portugal|61%|65%|66%|68%|68%|69%|68%|62%|64%|65%|67%|56%|59%|56%|
|Romania|50%|53%|55%|57%|59%|60%|59%|45%|48%|57%|57%|60%|59%|58%|
|Serbia|||||||65%|||||||87%|
|Slovak Republic|61%|59%|63%|63%|61%|57%|52%|44%|48%|57%|59%|53%|43%|42%|
|Slovenia|71%|74%|75%|71%|72%|74%|66%|71%|74%|75%|72%|70%|73%|65%|
|Spain|52%|54%|54%|57%|59%|59%|56%|46%|46%|43%|49%|47%|49%|47%|

Note: Blanks correspond to country-year pairs for which the Eurostat’s SBS Database does not report information. Montenegro and Ukraine are excluded from this table as the SBS Database does not include information for any of the years in our sample.