metadata: authors: 0: author: Zaourak,Gabriel Roberto
count: Argentina
docna: 0: docna: Rent-Seeking Activities, Misallocation, and
            Innovation in Argentina
docty: Policy Research Working Paper
subtopic: International Trade and Trade Rules,Employment and Unemployment,Innovation,National Governance,Government Policies,Quality of Life & Leisure,Social Analysis,Youth and Governance,Labor Markets,Rural Labor Markets
teratopic: International Economics and Trade,Science and Technology Development,Public Sector Development,Governance,Rural Development,Social Development,Social Protections and Labor
abstracts: cdata!: What is the efficiency cost of
            rent-seeking activities in Argentina? This paper
            quantitatively shows that rent-seeking activities in the
            form of bribes have aggregate effects through two channels.
            First, they generate misallocation of resources across firms
            because they prevent resources from flowing to the most
            productive firms, reallocating resources instead to those
            that succeed at rent-seeking. Second, such activities affect
            the allocation of resources within firms because
            rent-seeking drives resources away from innovation. These
            two channels can help in understanding why Argentina has
            more misallocation across firms and less investment in
            research and development, compared with developed economies,
            explaining a sizable portion of Argentina's low productivity.
geo_reg_key: 119228,517191
repnme: repnme: Rent-Seeking Activities, Misallocation, and
            Innovation in Argentina
display_title: Rent-Seeking Activities,
            Misallocation, and Innovation in Argentina
listing_relative_url: /research/2020/06/32163102/rent-seeking-activities-misallocation-innovation-argentina
topicv3: Political Economy of Reform m1326263 836,Labor Markets m1326488 855,Trade m1326924 698,Enterprise Surveys m1504072 2762,Firm-level Innovation m1504610 2650
publishtoextweb_dt: 2020-06-23T00:00:00Z
historic_topic: International Economics and Trade,Science and Technology Development,Public Sector Development,Governance,Rural Development,Social Development,Social Protections and Labor
origu: EFI-LCR-MTI-MacroFiscal (ELCMU)
no_of_pages: 40
author_keys: 0: author_key: 1594959
model_extractions: Policy Research Working Paper 9293 Rent-Seeking Activities, Misallocation, and Innovation in Argentina _Gabriel Zaourak_ Macroeconomics, Trade and Investment Global Practice June 2020
Policy Research Working Paper 9293

# **Abstract**

What is the efficiency cost of rent-seeking activities in Argentina? This paper quantitatively shows that rent-seeking activities in the form of bribes have aggregate effects through two channels. First, they generate misallocation of resources across firms because they prevent resources from flowing to the most productive firms, reallocating resources instead to those that succeed at rent-seeking. Second, such activities affect the allocation of resources within firms because rent-seeking drives resources away from innovation. These two channels can help in understanding why Argentina has more misallocation across firms and less investment in research and development, compared with developed economies, explaining a sizable portion of Argentina’s low productivity.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. 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 author may be contacted at gzaourak@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
# **Rent-Seeking Activities, Misallocation, and Innovation in Argentina**

Gabriel Zaourak<sup>1</sup> World Bank

**JEL code:** E62, O11, O41.

**Keywords:** growth, misallocation, rent-seeking, innovation.

> 1 Chad Syverson, Ana Cusolito, Peter Siegenthaler and Xavi Cirera provided great comments to improve the paper. I thank Cecile Niang and Daniel Gomez for the opportunity to write this paper. The views expressed herein are my own and should not be attributed to the World Bank, its executive directors, or the countries they represent. All errors or omissions are my own.

Email: gzaourak@worldbank.org
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This behavior is not specific to Argentina; many studies and anecdotes detail how corruption, regulation, or direct government involvement distort the allocation of resources across firms, especially in developing countries. As described by Krueger (1974, page 2), “These restrictions give rise to rents of variety of forms, and people often compete for the rents. Sometimes, such competition is perfectly legal. In other instances, rent seeking takes other forms, such as bribery, corruption, smuggling, and black markets”.

In this paper, I show quantitatively that rent-seeking decisions in the form of bribes affect longrun total factor productivity (TFP) by reducing investment in innovation. I focus on the interaction between bribes and innovation because there is a long tradition in development literature that stresses these channels (including Acemoglu 1995; Krueger 1974; Lopez 2013; and Murphy, Shleifer, and Vishny 1991, 1993).

This paper makes three contributions. First, I provide evidence that TFP is lower in countries where bribes are pervasive and that two of the mechanisms lowering these countries’ TFPs are misallocation of resources and reduction of incentives to innovate. Second, I contribute to the literature on cross-country differences in TFP and income by quantifying a new channel—rentseeking in the form of bribes—that interacts with incentives to innovate and amplifies the effects of that source of misallocation. Third, I conduct counterfactual experiments to study the long-run implications for Argentina of rent-seeking in the form of bribes. To my knowledge, this paper is the first to try to quantify this channel of TFP reduction.

This paper emphasizes that rent-seeking activities in the form of bribes have aggregate effects through two channels. First, they generate misallocation of resources across firms, because they prevent resources from flowing to the most productive firms, reallocating them instead to those that succeed at rent-seeking. Second, rent-seeking activities affect the allocation of resources within firms, because they drive resources away from innovation activities. These two channels help explain why Argentina has both more misallocation across firms and less investment in research and development than developed economies, which explains a sizable portion of Argentina’s underdevelopment. Compared to an economy without bribing, I find that the extent of bribing measured for Argentina in the WBES reduces output and TFP by 4.4 percent and 3 percent, respectively.

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As the World Bank Enterprise Surveys (WBES) reveal, bribing and corruption occur frequently (though less commonly in the Western world), they are obstacles to doing business, and they carry a large cost for businesses. In Argentina, almost 9.5 percent of establishments in the economy participated reported bribing of a government official, almost 1 percent more than the average in Latin America and the Caribbean but below the worldwide average. In addition, bribes in Argentina were sizable, averaging 6.5 percent of the establishments’ annual sales.

Motivated by this evidence, this paper aims to quantify the efficiency cost of rent-seeking activities in the form of bribes in Argentina. To this end, I introduce bribing into a general equilibrium model of firm dynamics as in Atkeson and Bursteinn (2010). In this model, firms are heterogeneous with respect to their levels of productivity and behave in an environment with imperfect competition.<sup>4</sup> Productivity in this model evolves stochastically, but the probability of increasing the level of productivity depends on research and development (R&D) expenditure. In in the spirit of Restuccia and Rogerson (2008) and Hsieh and Klenow (2009), firms also face idiosyncratic subsidies and taxes that affect their size, which generate static misallocation in the model. However, unlike the static subsidies and taxes presented in those papers, these subsidies and taxes also evolve stochastically, conditional on the rent-seeking efforts (bribes) that each firm chooses to undertake. As a result of these choices, some firms will invest in rent-seeking activities and others will invest in improving the quality of their production process (by improving physical productivity, management practices, or intangible capital).<sup>5</sup> By diverting talent and resources away from production and innovation activities, rent-seeking activities can have sizable negative effects on aggregate efficiency.

For the quantitative analysis, I calibrate an undistorted version of the model to match key features related to the size distribution of firms in the U.S. economy, as it is usual in the literature. Next, to evaluate the quantitative implications of the model, I use data from the WBES, which is one of the few surveys that provide establishment-level evidence related to bribing. I use this micro-level evidence of bribing in Argentina to calibrate the distorted version of the model. In particular, I use the frequency of bribing, the average cost of bribing in terms of sales, bribing expenditures across the size distribution of firms, and statistics from the firm-level productivity

> 4 In the model, productivity captures physical productivity as well as management practices and intangible capital. 5 In another background paper, Castro et al. (2019) find evidence supporting the fact that firms that pay bribes tend to have worse management outcomes, which would be reflected in productivity. This evidence supports the modeling strategy of this paper and the paper’s general interpretation of productivity.

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distribution to pin down key parameters in the model.<sup>6</sup> In the distorted version of the model, aggregate output and TFP fall by 4.4 percent and 3 percent, respectively, compared to the undistorted economy. These effects are substantial, because bribes affect less than 10 percent of firms in the model. The model is consistent with the fact that R&D spending and average firm productivity are lower in economies where bribing is more pervasive. Finally, the model is used to explore the results of increasing the incentives for bribing by reducing the cost of this activity. As expected, TFP and output losses increase as bribing becomes easier.

Two channels misallocate resources across firms and generate these aggregate effects. First, as is well established in the literature, the idiosyncratic distortions that change the size of firms. However, in the model these distortions are partially endogenous when bribing is introduced to a model: each firm can choose to spend resources on bribes to affect the probability of the firm’s revenue increasing. However, the final outcome is still stochastic, and therefore some firms are taxed (and thus contract) and others are subsidized (and thus expand). Second, bribing changes incentives to innovate, affecting the allocation of resources within firms. Firms respond to the presence of bribing by reducing innovation spending, which increases the share of unproductive firms in the economy and amplifies TFP losses from misallocation.

To understand how much the innovation channel contributes to the decline in TFP and output observed in the model, I study the effects of the increase in distortions coming only from rentseeking. To do this, I allow bribing but keep firm-level innovation at the undistorted level (the first best) and I compare that model’s results with those of a version in which innovation is affected by rent-seeking incentives. The quantitative results show that TFP losses are 9.6 percent larger than otherwise when the incentives to innovate are affected by bribing. The economy exhibits higher dispersion in revenue productivity, more unproductive firms, and a reduction in the average productivity of firms of 19.5 percent.

This paper relates to many strands of the development literature. First, it relates to the strand that studies the misallocation of resources as an important determinant of aggregate productivity. Restuccia and Rogerson (2008) and Hsieh and Klenow (2009), for example, focus on distortions in the form of wedges that act as implicit taxes or subsidies that distort the allocation of capital and labor across firms. Such papers, which take what is typically known as the ”indirect” approach, are often silent about the underlying channels through which misallocation takes place and

> 6 This firm-level productivity distribution is stated in terms of the revenue productivity measure, TFPR.

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consider only the overall effect of all potential sources of distortion on the economy.<sup>7</sup> In contrast, other papers take what is referred to as the ”direct” approach, picking a particular policy or institution and using quantitative models to assess its contribution to the misallocation of resources and its impact on aggregate TFP.<sup>8</sup> This paper contributes to the latter line of research by focusing on the static and dynamic effects of one specific mechanism, bribing, that generates misallocation.

This paper is relevant to Argentina because the country’s weak institutional capacity, high perception of corruption and repeated high-profile corruption scandals in recent years have caused a loss of faith in institutions, which this paper shows to play a key role in enabling and perpetuating idiosyncratic distortions across establishments. More generally, because difficulties in doing business not only affect investment decisions but also generate incentives to obtain private rents, simplifying the regulations around starting and running businesses in order to reduce those incentives is of first-order importance.

In the following section, I present the facts on bribing and development that motivated this research. In section 3, I present the model that will try to account for these facts. In sections 4 and 5, respectively, I present the strategy for calibrating the model and the model’s quantitative results. Section 6 concludes and gives some policy recommendations.

# **2. Data**

The establishment-level data I use to calibrate this model are from the WBES. The data were collected through interviews, typically with managers, aimed at understanding the major obstacles establishments face in their day-to-day operations. A convenient feature of the surveys is that they were administered in a similar form within continents, which allows for cross-country comparisons. The initial rollout of the survey was conducted in 2003, with subsequent and more complete rollouts conducted within a three-to-four-year span. For this paper, I use the data set from 2006–2018 provided by the World Bank. This data set contains firm-level observations for 135 countries, and it includes sections on corruption and regulations, among many other variables traditionally used in firm-level analysis.<sup>9</sup> In particular, establishments are asked whether they have

> 7 The names of these approaches were first introduced by Restuccia and Rogerson (2013).

> 8 For a more exhaustive description of the resource misallocation literature, see Hopenhayn (2014), Jones (2011), Restuccia and Rogerson (2013), and references therein.

> 9 The WBES data on corruption and bribery have reliability problems, but they are the only source of such data available, to my knowledge. Due to their illegal nature, it is difficult to obtain data on corruption and bribery. Data on lobbying activity (a “legal

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faced episodes of bribing in the past year (bribery incidence), the percentage of public transactions in which a gift or informal payment was requested of them (bribery depth), whether the firm was expected to give gifts to public officials "to get things done," what percentage of their sales was paid out in these bribes to “get things done,” and the extent to which corruption is an obstacle to business operations (the potential responses are “not a problem,” “a minor problem,” “a moderate problem,” and “a major problem” and are coded on a scale of 1 to 4).

Table 1 shows evidence of the incidence of bribes and corruption in selected countries.<sup>10</sup> There are three main facts to highlight: bribing and corruption occur frequently, they are obstacles to doing business in an economy, and bribing in particular carries a large cost for businesses.<sup>11</sup> In Argentina, almost 9.5 percent of establishments experienced a situation of bribing, almost 1 percent more than the average in Latin America and the Caribbean but below the worldwide average.

Though bribing is undoubtedly present in many countries, how much of a constraint is it to doing business? The third column in table 1 provides proxy evidence by reporting the percentage of establishments that indicate that corruption is a major obstacle to the operation of their business. In Argentina, 50 percent of establishments report that this is the case. To put this figure in context, only 3 percent of establishments in Sweden say the same.

The fifth column in table 1 reports the average bribe payment as a percentage of sales by country, conditional on a business having faced a situation in which an informal payment was requested. The evidence shows that Argentina is close to Brazil and Peru in terms of the size of the bribes requested, and these bribes are 6 times larger than those requested in Germany, indicating a large deviation of resources to unproductive activities.

> source of bribes”) have been used recently to study various areas in the case of the United States. See Richter, Samphantharak, and Timmons (2009); Zaourak (2018); and references therein. Unfortunately, this kind of data is not available for Argentina. 10 These patterns also emerge in other countries, it is not specific to the countries selected.

> 11 In addition, there is evidence of a large heterogeneity in the amount of bribes businesses have to pay for a given firm size.

7
# **Table 1. Bribes in selected countries**

||Faced|Reported|Bribery|Firms|Bribes as|CPI|
|---|---|---|---|---|---|---|
||bribery|corruption|depth (%|expected to|a share of||
||(% of<br>firms)|as a major<br>constraint (%<br>of firms)|of firms)|give bribes “to<br>get things<br>done” (%)|sales (%)||
|Sweden|1.9|2.7|1.5|-|-|84|
|Poland|5.1|18.6|4.5|14.5|-|60|
|Guatemala|7.3|64.6|4.2|12.9|12.4|28|
|Argentina|9.3|50.0|6.2|14.7|6.5|39|
|Brazil|11.7|68.8|8.4|12.5|6.6|37|
|Peru|16.5|51.2|15.9|15.5|7.2|37|
|Germany|-|3.8|-|-|1.1|81|
|All countries|18.0|32.7|14.1|22.5|-|43|
|LAC|8.7|36.3|6.3|10.0|-|43|

_Source:_ WBES and BEEPS 2005.

_Note_ : The CPI is measured from 0 to 100, and a higher number means a lower perception of corruption. BEEPS =The Business Environment and Enterprise Performance Survey; CPI = Corruption Perception Index; WBES = World Bank Enterprise Surveys.

An additional measure to consider is Transparency International’s Corruption Perception Index (CPI). This index ranks 176 countries on a scale from 0 (“highly corrupt”) to 100 (“very clean”) as determined by expert assessments and opinion surveys. In 2017, Denmark and New Zealand were perceived as the least corrupt countries in the world, while the country perceived as the most corrupt in the world was Somalia. The last column of table 1 displays the index values for the selected countries. We can see that Argentina ranks slightly below the Latin American and Caribbean average, and it ranks more than 40 points below developed economies like Sweden and Germany. Using this alternative measure, we can also conclude that corruption is an important problem for developing economies.

Figure 2 shows the correlation between the CPI and bribery incidence. As we can see, countries with a low CPI (high perceived corruption) tend to have a high percentage of firms facing bribery. The figure also shows that Argentina is below the linear predicted relationship between these two variables. This implies that the incidence of bribery in Argentina is lower than what a linear model 8
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depends on parameters and aggregate variables and is common to all firms.

From equation 3, there are important aspects of this model to keep in mind. First, at any given point in time, _τ_<sup>_ρ_</sup> _exp_ ( _z_ ) is a summary statistic of the size of a firm. This observation will help solve for the general equilibrium. Second, that expression makes clear the role of the firm-specific wedge _τ_ : for values of _τ_ > 1 (a subsidy), the firm will be making profits higher than for the frictionless benchmark _τ_ = 1, while for values of _τ_ < 1 (a tax), the firm will generate lower profits than for the benchmark. In addition, conditional on their levels of productivity, firms with subsidies will be relatively larger than firms with firm-specific taxes.

# **3.3. Entry, Exit, and Dynamics**

In this model’s economy, there are exogenous and endogenous sources of firm exit. At the beginning of each period, every firm is exposed to an exogenous death shock with probability _δ_ and survives with probability 1 − _δ_ . Surviving firms can either choose to exit or continue to operate and pay a fixed cost of production _f_ . The size of a firm over time depends on idiosyncratic shocks and on two endogenous mechanisms: innovation and bribing. A firm that decides to keep operating can choose to spend resources (innovate) to improve its productivity or to engage in rent-seeking behavior through bribes to try to improve its firm-specific revenue tax or subsidy. In the following subsections, I describe these mechanisms individually.

## **3.3.1. Innovation**

Productivity in this model evolves as in Atkeson and Bursteinn (2010). The productivity of a firm evolves depending on the firm’s investment in R&D and on firm-specific shocks. A firm with state ( _z_ , _τ_ ) that decides to keep producing in period _t_ can invest _exp_ ( _z_ ) _cφ_ ( _φ_ ) (in labor units) in R&D in order to increase the likelihood of improving its productivity in period _t_ + 1. In particular, if a firm spends _exp_ ( _z_ ) _cφ_ ( _φ_ ), it has a probability _φ_ of increasing its productivity to _z_ + Δ _z_ and a probability 1 − φ of decreasing its productivity to _z_ − Δ _z_ , where Δ _z_ is the step size of the productivity support. I assume that the function _cφ_ ( _φ_ ) is convex in _φ_ .

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# **3.3.2. Rent-seeking**

The evolution of firm-specific taxes or subsidies is partially determined by bribing, as discussed in Krueger (1974). According to her, competition for rents (in her case, import licenses) can occur through firms allocating resources to influence their probability of obtaining the rents. On the basis of this interpretation, each firm can choose to spend resources on bribes that will affect the probability of increasing the firm’s revenue. However, whether or not there is an opportunity in the firm’s specific tax or subsidy is exogenous.<sup>14</sup> With probability _ψ_ , the firm keeps its level of idiosyncratic distortion, and with probability (1 − _ψ_ ), the firm gets a draw from a probability distribution that depends on the bribe _μ_ . The higher the firm’s bribe, the higher the probability of getting an improvement in its specific tax or subsidy.<sup>15</sup> In particular, I assume that the probability distribution follows a bounded Pareto distribution, where _τmax_ > _τmin_ > 0.

By choosing _μ_ , firms can alter the distribution of their future firm-specific tax or subsidy. Figure 7 shows the distributions for two different values of _μ_ . The way to determine _μ_ will be described below. When taking the model to the data, I discretize the support of the distribution of _τ_ with linear spacing given by Δ _τ_ .

## **Figure 7. Conditional firm-specific tax or subsidy probability distribution function**

> 14 This aspect of the model reflects the fact that even if a firm bribes a government official, it will not always be successful in obtaining rents. There are “windows of opportunity” that cannot be influenced at the firm level.

> 15 A high _μ_ means that the distribution dominates in first order the distributions with low _μ_ ; that is, a firm with higher bribes will on average draw a better firm-specific tax or subsidy.

16
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## **3.6. Competitive Equilibrium**

An equilibrium in this economy is as follows:

- a collection of sequences of aggregate prices, wages, capital rental rates and prices

- for intermediate goods;

- a collection of sequences of intermediate firms’ value functions, profits, exit rules,

- and innovation and rent-seeking decisions; and

- distributions of operating firms and measures of entering firms such that

- households maximize utility subject to the budget constraint,

- intermediate firms and final-good producers maximize profits, and

- feasibility constraints (such as market clearing for all markets) are satisfied

and the distribution of operating firms evolves endogenously.

# **4. Calibration**

In this section, I calibrate the model to the data. First, I need to provide a functional form for the costs of innovation, _cz_ ( _φ_ ). I assume that the costs are given by which is strictly convex in _φ_ , with _hz_ zz _z_ > 0 and zz _bzz_ > 0. For the cost of bribing, zz _cττ_ ( _φ_ ), I assume the mm (φφ) = h exp(bb φφ), which is strictly convex in _φ_ , with _hz bzz cττ_ ( _φ_ ), I assume the same functional form, given by which is strictly convex in _μ_ , with _h_ zz _τ_ > 0 and zz _bτ_ > 0. zz mm (μμ) = h exp(bb μμ), For the sake of clarity, I will classify the model’s parameters into four groups: (a) { _γ_ , _ρ_ , _α_ , Ω} are the standard parameters, (b) { _δ_ , _fe_ , _f_ } are technological parameters, (c) {Δ _z_ , _hz_ , _bz_ } are parameters related to innovation, and (d) {Δ _τ_ , _ψ_ , _τmin_ , _τmax_ , _hτ_ , _bτ_ } are parameters related to rentseeking in the form of bribing.

The strategy I used to calibrate the parameters in the steady state has three parts. First, I set some of the parameters according to existing microeconomic and macroeconomic evidence in the literature. Second, I calibrated the technological parameters to an undistorted economy where _τ_ = 0 for all firms. In order to map this economy to the data, I followed the literature and used the U.S.

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economy as a benchmark, given that the United States has a relatively low intensity of bribing.<sup>16</sup> Third, to evaluate the quantitative effects of rent-seeking in the form of bribes, I calibrated the rest of the parameters to match relevant features of bribing in Argentina. All data moments and targets are based on the WBES, unless otherwise stated. One year in the model is equivalent to one year in the data.

# **4.1. Moment Selection and Calibration**

**Standard parameters** . Values for the model’s standard parameters can be found in table 2. The discount factor _β_ is calibrated to match a real interest rate _r_ of 4 percent. This rate implies a calibrated impatience rate of 0.05. The depreciation rate Ω is set to imply an average investment to capital ratio of approximately 6 percent, which corresponds to the average value for the private capital stock in the United States. Following the literature, I set the share of capital in value added at _α_ = 1 ⁄ 3. Finally, I set the elasticity of substitution at _ρ_ = 3, as in Hsieh and Klenow (2014).<sup>17</sup>

**Table 2. Standard parameters**

|**Parameter**|**Value**|**Target/source**|
|---|---|---|
|Share of income to capital|_α_= 0.33|Literature|
|Elasticity of substitution|_ρ_= 3|Broda and Weinstein (2006)|
|Depreciation rate|Ω = 0.06|Literature|
|Discount factor|_β_= 0.96|Interest rate of 4%|

**Technological and innovation parameters** . Six parameters need to be jointly calibrated to match six relevant moments for the U.S. economy. For technical details of the computational procedure used to calibrate them, see appendix B.4. The moments and parameters are described in table 3.

> 16 What matters for this analysis is that the U.S. economy is relatively undistorted by bribing behavior. There could be other ways by which firms in the United States try to influence their profits, like lobbying, but those ways are outside the scope of this paper, which focuses on illegal activities.

> 17  This number is consistent with estimates found in the trade and industrial organization literature. See, for example, Broda and Weinstein (2006), which provides estimations of elasticity of substitution for U.S. imports at a disaggregated level.

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# **Table 3. Technological parameters and innovation**

|**Parameter**|**Value**|**Target/source**|
|---|---|---|
|Cost of entry|_fe_= 1||
|Fixed cost of production|_f_= 0.2|Exit rate of 6%|
|Exogenous exit rate|_δ_= 0.02|Exit rate of large firms of 0.55%|
|Step size of productivity|Δ_z_= 0.2|Standard deviation of employment<br>growth of large firms|
|Scale parameter of|_hz_= 0.001|R&D ⁄ GDP = 2.7%<br><br><br><br>|
|innovation cost|||
|Curvature parameter of<br>innovation cost<br>_Note_: GDP = gross domestic product|_bz_= 13.54<br>R&D = research and|φ500≤l≤1000<br>ma<br>/φ1000≤l≤5000<br>ma<br>= 38.1%<br>development.|

_Note_ : GDP = gross domestic product; R&D = research and development.

I chose Δ _z_ so that the standard deviation of the growth rate of employment of large firms in the model would be 25 percent (annualized), following Atkeson and Bursteinn (2010), which is based on data in line with estimates from Davis et al. (2007).

I chose the exogenous exit rate _δ_ so that the model’s annual employment-weighted exit rate of large firms would be 0.55 percent, which is consistent with the exit rate for large firms in the United States, according to the Statistics of U.S Businesses database.<sup>18</sup> Note that in the model, large firms do not exit because of lack of profitability; they exit because of this exogenous shock. Hence, _δ_ directly determines the annual exit rate of these firms. Next, I parameterize the distribution _G_ of productivity draws so that all firms enter with the same level of productivity, _z_ = 0. In the full model, firms will also enter with _τ_ = 0.

Using data from the Business Dynamics Statistics, Atkeson and Bursteinn (2010) find that the size of the median firm in the U.S. employment-based firm size distribution is 500 employees. In the model, firm size in terms of number of employees is a normalization. I set the level of employment _L_ so that entering firms’ employment will be 6 percent of the median employment in the economy, as reported by Luttmer (2007). Finally, I normalize _fe_ = 1 and set _f_ = 0.2, which implies an exit rate of 6 percent.<sup>19</sup>

> 18 These statistics can be found at <u>www.sba.gov.</u>

> 19 Notice that the exit rate is determined by the fixed cost of producing _f_ , the entry cost _fe_ that determines the minimum productivity required to stay in the market, and the stochastic process. In a model with rent-seeking, the stochastic process for the firm-specific revenue tax or subsidy will also play a key role in determining survival. In that case, firms with low productivity could stay in the market if they had subsidies large enough.

22
To calibrate _hz_ and _bz_ , I use firm-specific R&D data as a proxy for the innovation effort. As documented in Aw, Roberts, and Xu (2011) **,** firms that invest in R&D on average increase their productivity over time. Boothby, Lau, and Songsakul (2008) and Ranasinghe (2014) documented the patterns of R&D expenditure for U.S. firms based on a data set provided by the National Science Foundation, and they found that R&D (when measured as employment) is increasing. Following Ranasinghe’s (2014) strategy, I calibrate the curvature parameter _bz_ of the innovation cost to target average R&D spending by firms that have between 500 and 1,000 employees relative to average R&D spending by firms that have between 1,000 and 5,000 employees. The scale parameter _hz_ of the cost of R&D is closely related to the total spending on innovation. Following the literature, I target the ratio of R&D to GDP in the United States, which for 2016 was equal to 2.7 percent.

**Bribing parameters** . Based on the data provided by the WBES, total bribes increase with firm size (measured as employment), with large differences across firms. For example, the average total bribe paid by firms with 50 to 100 employees is 40 percent of the average total bribe paid by those with 100 to 500 employees.<sup>20</sup> In contrast, firms with 100 to 500 employees spend on average 80 percent as much on bribes as do firms with 500 to 1,000 employees. Based on these features of the data, I calibrate the curvature parameter _bz_ of the cost of rent-seeking to target average bribes for firms that have between 500 and 1,000 employees relative to average bribes for firms that have between 1,000 and 5,000 employees. I calibrate the probability of keeping a firm’s distortion, _ψ_ , on the basis of the percentage of firms that have faced bribes in Argentina. Intuitively, the higher the value of _ψ_ , the lower the incentives to engage in bribing in the model. A list of parameters and targets is given in table 4.

> 20 See figure in appendix A.

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**Table 4. Bribing parameters**

|**Parameter**|**Value**|**Target/source**|
|---|---|---|
|Step size of firm-specific<br>tax/subsidy|Δ_τ_= 0.15|_Avg_[_ln_(_TFPRi_)] = 0|
|Probability of keeping firm<br>distortion|_ψ_= 0.43|Percentage of firms bribing =<br>9.3%|
|Lower-bound wedge distribution|_τmin_= 0.01|_Min_[_ln_(_TFPRi_)] = 0.1<br><br>|
|Upper-bound wedge distribution|_τmax_= 2|_max_[_ln_(_TFPRi_)] = 2<br><br><br><br>|
|Scale parameter of bribing cost|_hτ_= 0.002|aa�<br>brmbe<br>smles<br>~~�~~= 6.9%<br><br><br><br>|
|Curvature parameter of bribing<br>cost<br>_Note_: TFPR =|_bτ_= 5<br>total factor revenue p|μ500≤l≤1000<br>ma<br>/<br>μ1000≤l≤5000<br>ma<br>= 64.9%<br>roductivity.|

Table 5 reports target moments from the model and the data. Despite solving a rather complicated multidimensional mapping, the model targets all moments quite closely. The only two moments that the model finds difficult to match are the employment-weighted exit rate for large firms and the percentage of firms engaging in bribery, but the differences between the model and these moments are not dramatic.

**Table 5. Calibration: data and model targets**

|**Moment targeted**|**Data**|**Model**|
|---|---|---|
|**_U.S.-based targets_**|||
|Exit rate|0.006|0.006|
|Employment-weighted exit rate for large firms|0.0055|0.0045|
|Standard deviation of employment growth of large firms<br><br><br><br>|0.25|0.245|
|R&D as share of GDP<br><br><br><br>|0.027|0.028|
|φ500≤l≤1000<br>ma<br>/φ1000≤l≤5000<br>ma<br>= 38.1%|0.38|0.37|
|**_Argentina-based targets_**|||
|_Avg_[_ln_(_TFPRi_)] = 0|0|0.05|
|Percentage of firms bribing|0.093|0.097|
|_Min_[_ln_(_TFPRi_)]<br>|0.1|0.1|
|_max_[_ln_(_TFPRi_)]<br><br>|2|2|
|a~~�~~<sup>brbb</sup><br><br>�<br><br><br><br>|0.069|0.067|
|a<br>salbs<br><br><br><br><br>|||
|μ500≤l≤1000<br>ma<br>/μ1000≤l≤5000<br>ma<br>= 64.9%|0.649|0.641|

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<!-- Start of picture text --> 1<br>=5 OsO08 a-<br>Zong Pa<br>= e Data<br>5 = sees ode<br>O06 |S<br>O5<br>O5 6-10 11-20 21-100 101-500<br>Firm Size<br><!-- End of picture text -->
**Table 6. Moments not targeted in calibration**

|<br>**Moments not targeted**<br><br><br><br><br><br><br><br>|<br>**Data**|<br>**Model**|
|---|---|---|
|φ5≤l≤50<br>~~ma~~<br>/φ50≤l≤100<br>~~ma~~<br><br><br><br><br><br><br><br>|0.285|0.301|
|φ50≤l≤100<br>ma<br>/φ100≤l≤1000<br>ma<br><br><br><br><br><br><br><br>|0.365|0.418|
|μ5≤l≤50<br>ma<br>/μ50≤l≤100<br>ma<br><br><br><br>|0.051|0.113|
|μ50≤l≤100<br>ma<br>/μ100≤l≤1000<br>ma|0.404|0.431|
|_std_[_ln_(_TFPRi_)]|1.868|1.71|

# **4.3. Innovation with and without Rent-Seeking**

Having described the model’s equilibrium, I now focus on the key channel emphasized in the model: the role that rent-seeking in the form of bribes plays in innovation and how it thereby shapes the productivity distribution in the long run, and on how rent-seeking generates misallocation of resources in a static sense. I begin by considering a rent-seeking free environment in which all firm-specific distortions are set to 0 to highlight the effects of innovation on future productivity.

In figure 9, we see how the policy function for innovation behaves when there is no rentseeking in the form of bribes (the light blue line). Because the only way that firms can increase profits in the future is by improving their level of productivity, firms optimally decide to spend a considerable amount of resources on R&D. However, when rent-seeking is introduced, we see that a firm with a given level of productivity on average spends less resources on innovation. As hypothesized, this change affects the aggregate TFP of the economy in steady state. In the following section, distortions are introduced into the model to explore their consequences on aggregate outcomes and on firm-level characteristics.

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<!-- Start of picture text --> 0.35<br>=———— Subsidy<br>=——— No distortion<br>03 esm= No| OXrent seeking;<br>0.25<br>0.2<br>0.15<br>0.4<br>0.05<br>0 ee<br>-10 -5 0 5 10 15 20<br>productivity<br><!-- End of picture text -->

<!-- Start of picture text --> 0.1840.2 i ———“=== No Subsidydistortion<br>ms 7X<br>0.16<br>0.14<br>0.12<br>0.1<br>0.08<br>0.06<br>0.04<br>0.02<br>0<br>-10 -5 0 5 10 15 20<br>productivity<br><!-- End of picture text -->
# **5. Aggregate Effects of Bribing**

This section compares the effects of rent-seeking in the form of bribes relative to an economy without bribing (the benchmark economy). Table 7 shows selected statistics on aggregate variables (such as aggregate output, TFP, and aggregate innovation), firms (such as average productivity per firm and output per firm), and bribing behavior. All variables, except for those in the last two rows, are displayed relative to the benchmark economy, in which _τ_ = 0 for all firms.

**Table 7. Quantitative effects of bribing**

||**Bribing**<br>**economy**<br>**(****_hτ_ = 0.002)**|**Bribing**<br>**economy**<br>**(****_hτ_ = 0.001)**|**Bribing**<br>**economy**<br>**(****_hτ_ = 0.005)**|
|---|---|---|---|
|**_Aggregate_**||||
|**_statistics_**||||
|Output|95.60|90.42|97.41|
|TFP|96.99|91.34|98.89|
|Consumption|94.05|88.78|96.03|
|Aggregate|71.3|65.21|80.28|
|innovation||||
|**_Firm-level_**||||
|**_statistics_**||||
|Average|97.28|93.14|98.25|
|productivity||||
|Average size|98.12|92.59|98.93|
|Output per firm|88.81|84.71|93.21|
|Number of firms|111.43|119.21|104.72|
|Std (TFPR)|104.13|108.36|102.77|
|_Bribing statistics_||||
|Percentage of<br>|10|22|6|
|firms bribing<br><br>||||
|<sup>brbb</sup><br><br>|0.07|0.13|0.02|
|aa~~�~~<br>salbs<br>�<br>_Note:_All results are relat<br>productivity; TFPR = total facto|ive to the benchmark econ<br>r revenue productivity.|omy in which there is no|bribing. TFP = total factor|

_Note:_ All results are relative to the benchmark economy in which there is no bribing. TFP = total factor productivity; TFPR = total factor revenue productivity.

When bribing is introduced (column 2), output and TFP decrease by 4.4 percent and 3 percent, respectively (both relative to the benchmark economy with no bribing). This decrease is due to 28
three related effects. First, resources are reallocated from “taxed” firms to “subsidized” firms, that is, from productive firms to unproductive firms. In other words, bribing prevents resources from flowing to the most productive firms, diverting them instead to the firms that succeed in their rentseeking efforts. Second, rent-seeking activities affect the allocation of resources within firms, because they drive resources away from innovation activities. This shift translates into a reduction in firm-level and aggregate innovation, implying that firms will on average have lower productivity, which also reduces aggregate TFP. Third, the existence of firm-specific subsidies reduces the minimum level of productivity required to operate in the economy. Therefore, firms that in an economy without bribing would have exited the market instead stay in it (the exit rate decreases).<sup>21</sup> Also, due to this “lack of selection” effect, the number of firms increases (entry is now easier), and firms’ average size, output, and productivity decrease.

As discussed, the quantitative effects of bribing on economies with high incentives to bribe are sizable. What are the quantitative effects in economies with higher incentives to bribe? To assess this, I decrease the cost of bribing (decreasing _hτ_ ) while keeping the rest of the parameters in the economy unchanged. The results of this exercise are shown in column 3 of table 7. A pattern emerges: as the cost of bribing falls, the losses to TFP, output, consumption, and innovation grow. In addition, as we increase the incentives to bribe by lowering the cost of doing so, the model also delivers features related to underdevelopment, namely lower average firm productivity, a higher number of small businesses with low scale, and larger TFPR dispersion. Finally, as the cost of bribing increases, the economy gets closer to the undistorted economy (see column 4).

Next, I evaluate how much of the effects of bribery comes from the misallocation channel (effects one and three) and how much comes from the endogenous response of innovation to bribing (effect two). In order to perform this comparison, I keep firm-level innovation fixed at the “undistorted economy” steady state and recompute the aggregate statistics. The difference between the results obtained in this exercise and the results obtained when firms can adjust innovation “optimally” is the aggregate effect of bribing on innovation and long-run TFP. Table 8 compares the economy in which the firm-specific tax or subsidy evolves endogenously while firms adjust innovation (column 2) and the economy in which innovation is fixed at the undistorted level but endogenous bribing is allowed (column 3).

> 21 In other words, in this scenario there is a larger concentration of firms in the left tail of the productivity distribution.

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The main result obtained from this counterfactual simulation is that TFP losses are 9.6 percent larger when innovation is allowed to adjust optimally to the introduction of bribing. The economy exhibits higher dispersion in revenue productivity, more firms that are less productive, and a reduction in average productivity of 19.5 percent. On the basis of these results, I conclude that the dynamic effect of bribing on innovation amplifies the misallocation that comes from bribing.

**Table 8. Decomposition of effects**

||**Bribing economy**|**Bribing, keeping innovation constant**|
|---|---|---|
|**_Aggregate statistics_**|||
|Output|95.60|96.53|
|TFP|96.99|97.28|
|Consumption|94.05|95.01|
|**_Firm-level statistics_**|||
|Average size|98.12|98.98|
|Output per firm|88.81|95.2|
|Average productivity|97.28|97.81|
|Number of firms|111.43|102.21|

_Note:_ All results are relative to the benchmark economy in which there is no bribing. TFP = total factor productivity.

# **6. Conclusions and Policy Recommendations**

Since Bils, Klenow, and Ruane (2018); Hsieh and Klenow (2009); and Restuccia and Rogerson (2008), the misallocation of resources across firms has been given a major role in the macro development agenda while keeping the distribution of productivity given exogenously. In Hsieh and Klenow (2014), the authors tried to understand why average productivity is lower in poor countries, using a framework similar to that of Hsieh and Klenow (2009) but in a dynamic environment, and therefore making the distribution of productivity endogenous. This paper contributes to this strand of the literature by presenting a general equilibrium model of bribing to highlight bribing’s effects on the economy through misallocation (a static effect) and on the distribution of firm-level productivity through reductions in innovation (a dynamic effect). The paper’s quantitative model is guided by establishment-level data on bribing and captures 30
reasonably well the cross-country features from the data, namely that countries with higher bribery incidence have lower TFP and lower R&D spending.

The model’s results show that rent-seeking and corruption not only are very detrimental to governance but also have a large impact on productivity. A clear policy implication of this paper is that it is important to increase the cost of engaging in bribes both for firms and for government officials. In principle, this could be done by increasing legal punishments if firms or government officials are caught in bribing schemes. However, a second important policy implication of this paper is that bribery incidence tends to be higher in countries where the costs of doing business are high. This result suggests that simplifying and increasing the predictability of the regulatory and bureaucratic frameworks for businesses could have a dual positive impact, increasing investment by reducing the cost of doing business and increasing productivity by reducing rentseeking behavior. The potential gains from minimizing rent-seeking are large.

In terms of a data agenda, it would be important to generate data on prices and quantities produced at the firm level in order to estimate the distribution of TFPQ (physical productivity). This information would allow researchers to compare the distribution of physical productivity that comes endogenously from the model to its data counterpart.

Future research could also consider the model’s sectoral implications, for example, whether some sectors tend to engage more in bribing than others because they face more extensive regulation. According to the model, one should observe more variability in subsidies and taxes across firms in sectors that face more complex regulation. In order to test this implication, more data are needed on the ease of doing business in various sectors and on lobbying and bribing at the firm level in those sectors. Finally, it would be interesting to extend this model to allow firms to lobby or bribe to produce differential entry costs. By blocking potential competition, firms could generate additional rents.

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