Estimating bribe payment when zero values are ...

Estimating bribe payment when zero values are frequent and economically determined: An application of zero-inflated Negative Binomial model using firmlevel data from the Productivity and Investment Climate Surveys

Suryadipta Roy*

Keyword: Corruption; bribery; size-adjusted markup; count data models; zero-inflated Negative Binomial. JEL classification : D73; C24; C26; O17. ----------------------------------------------------------------------------------------------------------* Corresponding author Dept. of Economics, High Point University, Phillips School of Business – 210 High Point, NC – 27262. Phone: 336-841-9163. Email: [email protected]

Abstract Using firm-level survey data from the Productivity and Investment Climate Surveys (PICS), the paper implements zero-inflated Negative Binomial (ZINB) model to estimate firm-level corruption across industries in different countries. Two different measures of corruption are used in the study: unofficial payments paid by firms as percent of annual sales, and gifts required to be paid as percent value of government contract. Both data are characterized by large number of zero observations and are skewed to the right. Predictions from the ZINB model are found to explain corruption data better than the Poisson model. Size-adjusted firm-level markups are found to be associated with higher expected unofficial payments. Thus, small firms that make large profits are more likely to pay larger bribes. The result remains unchanged after addressing the endogeneity of the markup term using instruments for size-adjusted markups. While size-adjusted markups are found to be associated with significantly lower probability of corruption incidence, the result is reversed when we address the endogeneity of markup.

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1. Introduction Data on behavioral studies and psychosocial research, e.g. alcohol and substance abuse, are often characterized by excessive zeroes. Given that payments related to corrupt activities, e.g. bribery are usually considered to be illegal or unethical in most societies, there is likely to be preponderance of zeroes for data on such payments. In this regard, it is to be noted that two different kinds of firms may report not paying any bribe in equilibrium and hence report paying zero bribe. One group of firms may report paying no bribes since they are in a country with low corruption level, or because they operate in an industry where corruption levels are structurally low, and hence never been asked to pay bribe. This subgroup of firms who are not at risk of predation from corrupt officials or politicians will always produce a zero outcome. Such zeroes are called structural zeroes. Another group of firms that are at risk of predation by corrupt officials may still report paying zero bribes since they avoided paying bribe due to random factors. The zero outcome for this subgroup of firms is referred to as random zeroes. Thus, although both groups report paying zero bribes, these two groups might possess very different characteristics (e.g. difference in ownership structure) and may differ in measurable performance outcomes (e.g. productivity levels) that are affected by corruption. In this paper, we estimate corruption by using survey data on bribes paid by firms obtained from the Productivity and Investment Climate Survey (PICS) database, and by implementing zero-inflated count data models that enable us to distinguish between the two kinds of firms discussed above. The regression equation includes several important firm-level and country-level determinants of corruption as explanatory variables, and separately control for country, period, and industry fixed effects. Regression results from the zero-inflated

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models also enable us to understand the differences in the effects of the major determinants of corruption on bribe incidence and the magnitude of bribes paid. The paper argues that while determining the relationship between corruption and competition, studies need to address the large number of zero observations of firm-level bribe payments and distinguish between the probability of bribe incidence and the magnitude of bribe paid by the firm. Fig. 1 shows the histogram for one of the corruption measures used in the study, i.e. “Unofficial payments to get things done as % of annual sales”. A striking feature of the data is that most of the firms report not paying any bribe. A total of 19,498 of the 32,015 firms (about 61% of the sample) that responded to the question as to what percent of annual sales are paid as unofficial payments reported making zero payments. The figure also suggests that very small number of firms pay bribes that comprise of a large fraction of their annual sales. Frequency data for the second measure of corruption used in the study, “Gifts expected as % value of government contracts” plotted in Fig. 2 reveal similar pattern. The combination of rightskewed data with the large number of zero observations have been modeled by zeroinflated negative binomial (ZINB) model in the paper. Our results from the baseline ZINB model suggest that firm-level profit measured by size-adjusted markup has opposite effect on bribe incidence, i.e. probability of paying bribe, and the expected amount of bribe paid. Higher markup is found to be positively associated with the probability of a firm to never having to pay any bribe, where bribe is either measured by unofficial payments or gifts required to obtain government contract. On the other hand, higher markup is positively associated with higher expected bribe payments, although the effect is found to be statistically insignificant when corruption is measured by gift

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payment. The quantitative effect of markup on expected unofficial payments is found to be significantly larger when we address the potential endogeneity of the markup term in the bribe equation by using suitable instruments for firm-level profits. Thus, previous studies that have investigated the relationship between economic competition and corruption by using log-linear models might have conflated the effect of competition between the extensive and the intensive margin of bribe payments, and hence obtained insignificant results on the effect of competition and corruption. The rest of the paper is organized as follows. Section 2 provides a brief literature review. Section 3 provides the analytical framework that motivates the variables used in the study. Section 4 describes the empirical strategy adopted in the paper as well as data sources and summary statistics for the major variables of interest. Section 5 presents the results and Section 6 concludes the paper. 2. Literature review There exist large number of studies on the determinants of corruption using crosscountry as well as cross-country time-series data. These studies usually measure corruption based on indexes of “corruption perceptions” assimilated by various international institutions or the World Bank.1 Recently, scholars have moved away from the perceptions-based measures of corruption to survey data on actual bribes paid by firms in different countries to measure corruption.2 For example, Svensson (2003) used survey data on bribe payments by firms in Uganda to estimate the probability of the incidence of corruption, and the magnitude of bribe payments. Rand and Tarp (2012)

1

A review of the literature on the cross-country determinants of corruption can be found in Treisman (2000, 2007), Serra (2006). 2 For a review of this literature on the findings of the determinants of bribe payments and fight against corruption using firm-level data, see e.g. Reinikka and Svensson (2002), Olken and Pande (2012).

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used the analytical model introduced in Svensson (2003) to explain the incidence of bribe payments and the magnitude of bribes paid by a panel of small- and mid-sized firms in Vietnam. Using individual level data on bribe payments from 30 countries available from the International Crime Victimization Survey, Mocan (2008) found that both countrylevel and individual-level variables to be important in determining corruption. These papers suggest that both firm-level and country-level factors affect the bribes paid by firms in different countries. The current paper contributes to the literature on the determinants of firm-level corruption by investigating the effect of firm-level and country-level determinants of corruption on bribes paid by firms across industries in different countries. An important contribution of the paper is that it implements zero inflated count data models, viz. the ZINB model to account for the large number of zero observations of bribe payments. To our knowledge, ours is the first study to implement zero-inflated count data regression models to separately identify the effect of the explanatory variables on the probability of bribe incidence and the magnitude of bribe paid. An important difference in the results on the determinants of corruption between those using the perceptions-based indices and those with survey-based data has been with regard to the effect of competition (or lack thereof) on corruption. For example, Ades and Di Tella (1999) used country-level measures of corruption based on the perceptions indexes, and found that higher levels of product market competition were associated with lower corruption levels for a group of developing and developed countries. On the other hand, using firm-level survey data from the World Bank’s Productivity and the Investment Climate Private Enterprise Survey (PICS) database, Alexeev and Song (2013)

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found that collusive corruption measured by firm-level bribe payments are positively associated with various measures of product market competition. Diaby and Sylwester (2015) used data from the Business Environment and Enterprise Performance Surveys (BEEPS) for a group of post-Communist countries to investigate the relationship between bribe payments and competition measures, and found greater competition led to increases in bribe payments. Alexeev and Song (2013) have suggested that the difference in the results can be reconciled if firm-level data refer to cost-reducing (“collusive”) corruption while country-level corruption perception indices refer to rent extraction (“coercive”) corruption. However, based on the definition of corruption used as the dependent variable used in their study (i.e. “Unofficial payments to get things done as % of annual sales”), it is not immediately clear if this is of the cost-reducing type or the coercive form of corruption. By studying the effect of the profits made by firms on firm-level bribe payments, the current paper contributes to this debate on the relationship between rent seeking corruption and product market competition between firms. Like Alexeev and Song (2013) or Diaby and Sylwester (2015), we use unofficial payments made by firms as one of the dependent variables. We also use gifts expected to be paid by firms to receive government contracts as a second measure of corruption. Our regressions control for several explanatory variables based on the analytical model introduced in Svensson (2003) and later implemented by Rand and Tarp (2012). The regression models also address unobserved heterogeneity by incorporating country, industry, and time fixed effects in the regressions. The current paper is however distinct from Svensson (2003) and Rand and Tarp (2012) in that while these papers study corruption experiences of firms in a single country, we extend the analysis to a large cross-section of firms mostly

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from the developing countries. Thus our results are more general and are likely to be helpful to policy practitioners dealing with corruption problems in other developing countries. Use of cross-country data also helps us to include relevant country-level control variables in the regression. 3. Analytical framework The analytical framework used to study the incidence and level of bribe payments for firms is the Shleifer and Vishny (1993, 1994) framework that was used by Svensson (2003) and adopted by Rand and Tarp (2012). Moreover, we incorporate the findings on country-level determinants of firm-level bribe payments by introducing country-level variables in our regressions as suggested by Mocan (2008). Following the existing literature, we define corruption as the use of public office for private gain. The possibility of bribe payment arises when a profit-maximizing firm faces a public official, who extracts bribes that need to be paid for running business operations. Firms that typically have more interaction with public officials in order to obtain the necessary documents, licenses or infrastructure services, and in sectors where the public officials have greater control over firms due to regulations are more likely to pay bribes. Thus, according to the “control rights hypothesis”, the probability of paying bribe ( p i ) can be expressed as pi  P( Payingbribe / X )  X  i  e1i

(1a)

where X is the vector of the following variables that determine the probability of bribe incidence: firm size, firm ownership, location of the firm, if the firm uses any imported intermediate input, and the time spent by the firm in dealing with government regulations. It is likely that larger firms are more visible than smaller firms and hence are more likely to pay bribe. On the other hand, dishonest officials might be less inclined to

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ask for bribes from larger firms since the latter might possess greater control due to connections with higher-level officials or politicians. Government-owned public sector firms might experience preferential treatment from public officials, and might need to pay smaller bribes in comparison to the private-owned foreign firms or domestic firms. Firms located in capital cities of a country have greater proximity to politicians in comparison to firms that are located further away from the capital cities, and hence might have greater information as to which palms to grease to get things done. Firms that need to import intermediate inputs for their production purposes are likely to require a variety of documents from the public authorities, and hence the latter are likely to have greater control over these firms. Thus importers of intermediate inputs are more likely to pay bribes. Finally, firms that need to spend more time dealing with government regulations are more likely to have greater interaction with dishonest officials and hence are more likely to pay bribes. According to Svenson (2003), the amount of bribe paid by firms is determined by their “ability to pay”, and their refusal power, i.e. the cost likely to be incurred by the firms if they refuse to pay bribe. A firm’s “ability to pay” is usually measured by its profits since firms with greater profits are more likely to have the capacity to pay larger bribes. The cost that a firm is likely to incur if it refuses to pay bribe is viewed to depend on the sunk costs of its operations. Firms with substantial sunk costs are less likely to exit the industry, and hence public officials are likely to ask for larger bribe payments from these firms. The theory suggests that the size of the bribe will depend positively on firmlevel profit and sunk cost. Thus the bargaining hypothesis can be expressed as: Yi  Z  i  e2i

(1b)

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where Z is the vector of the firm’s profit margins and sunk costs. Corruption experiences of firms in different countries are likely to depend on the nature and quality of institutions of the country that these belong to. Thus, probability of bribe incidence and magnitude of bribes are expected to depend on country-level determinants of corruption. Prior literature on the cross-country determinants of corruption has suggested that institutional quality is positively related to economic development. Moreover, the quality of bureaucracy of a country is likely to affect the level of corruption since countries where government officials possess greater independence from political influences might be less corrupt. On the other hand, greater discretion for government officials in the face of weak accountability can contribute to greater corruption. Since it is possible that factors that determine the likelihood of bribe payment also affect the magnitude of bribes and vice versa, we include the complete set of controls in the regression equation to estimate the probability of bribe payment and the magnitude of bribe paid. Combining (1a) and (b), the probability that a randomly chosen firm i must pay bribe can be reformulated as a binary equation: pi  P(Yi  1 / X , Z , W )  ( X  i  Z  i  W i  error )

(2a)

Similarly, the amount of bribe paid is estimated as: Yi  X  i  Z  i  W i  error (2b) where Yi denotes the magnitude of bribes paid by firm i, and X and Z denote the vector of explanatory variables defined above, and W denotes the vector of the country-level explanatory variables.

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4. Data and estimation strategy All firm-level variables used in the study have been obtained from the PICS database.3 The database consists of responses by a cross-section of more than 40,000 firms, mostly from developing and transition countries, covering the manufacturing, services, agribusiness, and construction industry between 2001 and 2005. The sampling methodology for the PICS is stratified random sampling where the strata are firm size, business sector, and geographic region within a country. Stratification by firm size divides the population of firms into three strata: small firms (5-19 employees), medium firms (20-99 employees), and large firms (100 or more employees). The survey covers a broad range of business environment topics including access to finance, competition, corruption, crime, gender, infrastructure, and performance measures. While some of the countries have been included in more than one round of the survey, this is not a panel dataset. The first measure of corruption is the percentage of annual sales that firms pay as unofficial payments “to get things done” (Bribe). Of the 32,015 firms that responded to the question “On average, what percent of annual sales value would such expenses cost a typical firm like yours?”, only 12,517 firms (i.e. approx. 40%) report paying bribes, so that the proportion of zeros is larger than 60%. The average amount of bribe paid (1.44% of the sales) is larger than the median payment which suggests a positively skewed distribution for bribe payments. Approx. 95% of the firms pay bribes comprising less than or equal to 10% of their total sales, and the maximum bribe paid is 100% of the total sales (for 11 firms). The second measure of corruption is the percentage of contract value that firms are typically expected to pay as gifts or informal payments to secure 3

Michael Alexeev kindly shared the dataset.

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government contracts (Gift). Over 70% firms (= (20662/28,662)*100) responding to this question reported paying zero gift to obtain government contract. As reported in Table 1, both measures are characterized by significant overdispersion with the conditional variance being greater than the conditional mean. The dataset identifies with firms with foreign ownership. Following Kokko (1994), we define domestic firms with government ownership of more than 49% as stateowned firms. Based on this classification, majority of firms belong to domestic private sector (80%), followed by foreign firms (13%), and the government sector (7%). Following Alexeev and Song (2013), firm-level markup has been calculated as the ratio of the difference between total market value of production and production costs (i.e. raw materials, energy, manpower, interest and financial fees, overhead expenses, and “other” costs) to the market value of production. When observations have missing data on the market value of production, we have used data on the volume of total sales instead. The variable is a measure of the amount of economic rent available for capture by rentseeking public officials. Firm’s refusal power to not pay a bribe (i.e. outside option) has been proxied by the firm’s spending on design or research & development (R&D) since these expenses are partly sunk.4 Following Svensson (2003), both the markup measure and the expenditure on design and R&D have been rescaled by employment size so as to ensure that the results are not driven by spurious correlation (since both variables are correlated with firm size). If the business is subject to scale economies, then larger firms are likely to have higher profits. On the other hand, returns to investment are likely to be larger for smaller firms experiencing faster growth and lower for larger firms. The PICS 4

For comparison purposes, value of production and R&D expenditure have been converted from nominal to real numbers first by dividing them with official US-dollar exchange rate and then by US GDP deflator (base year=2010).

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data have been appended by data on real per capita income of countries obtained from the World Development Indicators (WDI), and a measure of bureaucratic quality obtained from the International Country Risk Guide (ICRG) Ratings provided by the PRS Group. The bureaucratic quality measure takes high values for countries where the bureaucracy is mostly immune to political pressures and there exists established mechanisms for recruitment and training. Variables used in the study together with the summary statistics are reported in Table 1. According to Greene (1994), it is important to separate the problem of overdispersion from that of excess zeros in the data, and that two different processes drive the occurrence of zeros and that of positive outcomes in the data. Thus, while some firms do not require paying bribe and hence report paying zero bribe (e.g. if these firms do not need to interact with public officials or belong to a country with very low corruption level), a different group of firms have probably evaded paying bribe due to random factors. In this paper, we have implemented the ZINB model to model the data. Zeroinflated count data model, introduced by Lambert (1992), change the mean structure to allow zeroes to be generated by two distinct processes, compared with one process generating zeroes in the standalone count data models. While the Poisson or the Negative Binomial (NB) model will assume that every firm has a positive probability of being subject to corruption, it is possible that some firms experience no corruption since they are in a country with very low corruption levels, or operate in an industry that involve minimal contact with government officials. Zero-inflated models allow for this possibility, thereby increasing the conditional variance and the probability mass of zero observations. Figs. 3a and 3b plot the predicted values of the dependent variable for the

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two measures of corruption (Bribe and Gift) based on the baseline specification from four different count data models: Poisson, NB, zero-inflated Poisson (ZIP), and the ZINB.5 For both variables, expected frequency of zero observations from the Poisson model is found to underpredict the actual distribution of zeros. On the other hand, the NB, and the zero-inflated models are found to better predict the data. The zero-inflated models separately estimate the probability of the dependent variable taking the value of zero, besides estimating the count probability when the dependent variable takes all possible values including zero (Wooldridge (2010)). The sample is divided into two latent groups: (a) the Always Zero group for which the dependent variable takes the value of zero with a probability of one, and (b) the Not Always Zero group in which the dependent variable can take the value of zero, but there is a nonzero probability that the dependent variable takes a positive count. The probability of belonging to the Always Zero group has been modeled using the logit model. The probability of the dependent variable belonging to the Not Always Zero group has been modeled using the NB model, since the data are found to be highly skewed and the assumption of conditional mean to be equal to the conditional variance is found to be violated as reported in Table 1. Following equations (2a) and (2b), the same set of explanatory variables are assumed to determine membership in the two groups. 5. Results As discussed before, effect of firm-level profits on bribe payments is a topic of debate among economists in that some of the previous studies using firm-level data found profits not to have any statistically significant effect on bribes, or to be negatively

5

While the Poisson and the NB models estimate eqn. (2b), the ZIP and the ZINB models estimate eqn. (2a) and (2b) combined.

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associated with bribe payments. Results from the ZINB model provide a potential explanation to this conundrum. According to the logit model, higher markups are found to be positively associated with the probability of a firm to never pay any bribe. As reported in col. (a) of Table 2, one standard deviation increase in size-adjusted markup is associated with higher probability of never having to pay Bribe by a factor of 1.3 (=exp(2.353*0.103)6, holding other variables unchanged. Similarly, for an average firm, one standard deviation increase in size-adjusted markup is found to be associated with greater likelihood to be among firms that never pay Gift to obtain government contract by a factor of 1.07 (=exp(0.985*0.101)7 (col. (c) of Table 2). The effect is found to be statistically significant in each case. On the other hand, higher markups are found to have statistically significant and positive effect on expected bribe payments based on results from the count data model. As reported in col. (b) of Table 2, expected bribe payments are found to increase by approx. 23 percent points (=(exp(1.991*0.103)-1)*100%) for unit standard deviation increase in size-adjusted markup, everything else unchanged. The results suggest that markups have different (or opposite) effects on bribe incidence and the amount of bribe paid, and studies on the effect of firm-level profit on corruption need to pay attention to this difference. One possible explanation is that size-adjusted markup is an indicator of labor productivity, and more productive and profitable firms are from the relatively high-income countries with low levels of corruption and better quality of institutions. Thus, these firms face very little corruption problems. On the other hand,

6

After including all explanatory variables, regression for unofficial payments as dependent variable is based on 8,798 observations. Standard deviation of size-adjusted markup = 0.103 for the included observations. 7 Standard deviation of size-adjusted markup = 0.101 for the included observations with Gift as dependent variable.

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highly profitable firms from countries with high levels of corruption that make substantially large profits compared to their size will need to pay large parts of their profits as bribes. We also find evidence that economic development measured by per capita income is significantly associated with the probability of a firm to be in the Always zero group, while expected bribe payments are observed to increase with per capita incomes. By separating firms that are observed to not pay bribes into two groups, we thus provide an explanation as to why some of the previous studies had observed higher markups to be associated with lower bribe payments. Our results suggest that once we take into consideration only the firms that are likely to pay bribes, higher markups are associated with higher expected bribe payments measured by unofficial payments. Markups are found to have positive but not statistically significant effect on expected gift payments. While the explanatory variables mostly have similar effects for both Bribe and Gift, one major difference relates to the effect of bureaucratic quality. While better bureaucratic quality in a country is associated with significantly higher probability for a firm to be among those that never needs to pay gifts to receive government contracts, the effect is found to be opposite for unofficial payments. This suggests that gift expected for government

contract

more

specifically

captures

bureaucratic

corruption,

and

improvement in bureaucratic quality increases the likelihood for firms to evade paying gifts to obtain government contracts. We also observe that higher sunk costs measured by (size-adjusted) R&D expenditure is negatively associated with the probability to be in the Always zero group, i.e. higher sunk costs significantly increase the probability of bribe incidence and gift payment. On the other hand, greater (size-adjusted) R&D expenditure is found to be

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significantly associated with lower expected bribe payments and gift payments. These results provide mixed support for the “ability to pay” hypothesis. The results are distinct from Svensson (2003) who did not find any evidence of any effect of the firm’s profitability or alternative return on capital (measured by its sunk costs) on the probability of bribe payment by Ugandan firms. Thus, the zero-inflated models highlight an important empirical finding, i.e. firms that reports paying zero unofficial payments or gift payments are significantly different from the ones that report paying positive amounts, and based on their profit levels and sunk costs, firms making larger profits compared to the size of their workforce are more likely to pay larger bribes. The results provide support for the “control rights” hypothesis since firms that import their intermediate inputs face significantly higher probability of corruption. The intermediate input importer dummy variable is found to be not significant in the count regressions, which suggests that this variable affects only the extensive margin of corruption not the intensive margin, i.e. the volume of payments. Firms whose managers spend more time dealing with government regulations are found to have both higher probability of corruption incidence, as well making higher expected payments.8 The overdispersion parameter (alpha) is found to be significantly different from zero, thereby suggesting that the NB model fits the data better compared to the Poisson model. 5a. Addressing markup endogeneity Under both the rent-seeking model and the regulatory capture model of corruption, greater firm-level profits should lead to higher corruption. According to the rent seeking model, bureaucrats and politicians compete for rents associated with bribes 8

There is possibility that bribes in turn can cause firms to spend greater time circumventing government regulations. However, we did not find a suitable instrument for this variable.

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and kickbacks by selling government favors that perpetuate excess profits. According to the regulatory approach, regulations that benefit firms and preserve their economic profits are obtained by industries via bribes. Thus there exists potential reverse causality in the relationship between corruption and competition. In order to address this issue, we instrument for size-adjusted markup by using two dummy variables: the age of the firm, and if the firm has received ISO certification. It may be hypothesized that older firms will have older technology and possibly suffer from problems related to diseconomies of scale and hence have lower markups. Firms with ISO certifications might potentially have better management and hence make larger profits. On the other hand, these firms might potentially compete in more contested markets and hence need to satisfy stringent regulations that adversely affect their profit margins. Data for both variables have been obtained from the PICS. Based on the results for the first-stage regression reported in Table A1 in the Appendix, the estimated coefficients of the excluded variables are statistically significant and make economic sense. Older firms are associated with lower markups, and firms with ISO certifications are found to have lower profits margins. Overall, the excluded exogenous variables together with the included variables and the country, industry, and time dummies together explain about 50% of the variability in observations of the suspected endogenous variable. The endogeneity of size-adjusted markup in the zero-inflated count data model has been addressed by using a two-step approach laid out in Cameron and Trivedi (2010). In the first step, size-adjusted markup is regressed on all explanatory variables, including the excluded instruments. In the second step, predicted residuals from the first-stage

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regression are included in the zero-inflated regression. Results for the two-step IV regression are reported in Table 3. Controlling for endogeneity, we observe that higher markup leads to both higher probability of bribe incidence, as well as greater expected bribe payments. The effect of size-adjusted markup on Bribe remains statistically significant and is found to be quantitatively larger in the two-step regression compared to the baseline regression. As reported in col. (e) of Table 3, one standard deviation increase in size-adjusted markup leads to approx. 200 percent points (=exp(10.249*0.108)-1)*100%) increase in expected unofficial payments. Moreover, greater markups are now found to significantly increase the probability of bribe payment. The markup term is however found to be statistically not significant when corruption is measured as gifts required for government contracts. Referring to the residual term from the logit model from col. (e), we observe that it is positive and statistically significant. This suggests that any unobserved factor that increases the firm’s profits increases the likelihood for the firm to be in the group that never pays bribe. The estimated residual is found to be negative and significant in the count regression for the ZINB model (col. (f)). This suggests that any unobserved factor that increases the firm’s profits lowers the expected bribe payments. Thus it is possible that profit-making firms which are older are able develop networks and relationships that enable them to evade graft or pay lower bribes. For the other explanatory variables, the results from the IV regressions are mostly like that from the baseline regressions. For instance, time spent in bureaucratic regulations has particularly serious implications on corruption experiences by firms. Irrespective of its nature, greater time spent leads to both higher expected bribe payments and gift payments by firms, and significantly

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increases the probability of bribe incidence. Similarly, the status of an intermediate input importer leads to significant increase in the likelihood of a firm to be a bribe payer. In contrast, bureaucratic quality is found to be statistically insignificant in the equation for gift payments. 6. Conclusions We study the determinants of corruption faced by a cross-section of firms, giving special attention to the large number of zero observations on bribery. This is done by implementing zero-inflated NB regression models and using two separate measures of corruption: unofficial payments as % of annual sales, and gifts required to paid as % value of government contract. Apart from being able to explain the larger number of zero observations, the zero-inflated NB specification helps us to explain the highly skewed nature of data that characterizes payments for corrupt activities. Based on the baseline ZINB specification, firms with larger profit margins compared to their size are found to make higher expected unofficial payments as bribes. This result remains unchanged after correcting for the endogeneity of size-adjusted markups. While higher markups are found to be associated with lower probability of bribe incidence in the baseline ZINB specification, this result is reversed when we have addressed the endogeneity of the markup term. In contrast, size-adjusted markup is found to have positive but statistically insignificant on the expected volume of gifts given by firms in both the baseline and the IV specifications. The results suggest that firm-level profits might have different effects on the incidence of bribery faced by firms, and their expected bribe payments, and the effects are likely to be different based on the definition of corruption adopted. The results

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highlight the importance of giving special attention to the zero observations of bribe payments by firms.

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References: Ades, Alberto, and Rafael Di Tella. 1999. “Rents, competition, and corruption.” American Economic Review 89, no. 4 :982-993. Alexeev, Michael, and Yunah Song. 2013. “Corruption and product market competition: an empirical investigation.” Journal of Development Economics 103, no. C:154-166. Cameron A. Colin, and Pravin Trivedi. 2010. Microeconomterics using Stata. Revised edition, Stata Press. Diaby Aboubacar, and Kevin Sylwester. 2015. “Corruption and Market Competition: Evidence from Post-Communist Countries.” World Development 66 (February):487-499. Greene, William. H. 1994. “Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models.” Working Paper No. EC-94-10, Department of Economics, Stern School of Business, New York University. Kokko, Ari. 1994. “Technology, market characteristics, and spillovers.” Journal of Development Economics. 43 (April):279- 293. Lambert, Diane. 1992. “Zero-inflated Poisson regression, with an application to defects in manufacturing.” Technometrics. 34 no.1:1-14. Mocan, Naci. 2008. “What determines corruption: international evidence from microdata.” Economic Inquiry 46 (October):493-510. Olken, Benjamin, and Rohini Pande. 2012. “Corruption in Developing Countries.” Annual Review of Economics 4 (September):479-509. Rand, John, and Finn Tarp. 2012. “Firm-level corruption in Vietnam.” Economic Development and Cultural Change 60 no. 3:571 - 595.

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Reinikka, Ritva, and Jakob Svensson. 2002. “Survey Techniques to Measure and Explain Corruption.” World Bank Policy Research Working Paper Series # 3071. Serra, Danila. 2006. “Empirical determinants of corruption: A sensitivity analysis.” Public Choice 126 (January):225–256. Shleifer, Andrei and Robert Vishny. 1993. “Corruption.” The Quarterly Journal of Economics 108 no. 3:599-617. Shleifer, Andrei and Robert Vishny. 1994. “Politicians and Firms.” The Quarterly Journal of Economics 109 no. 4:995-1025. Svensson, Jakob. 2003. “Who must pay bribes and how much? evidence from a cross section of firms.” The Quarterly Journal of Economics 118 no. 1:207-230. Treisman, Daniel. 2000. “The causes of corruption: a cross-national study.” Journal of Public Economics 76 no. 3:399-457. Treisman, Daniel. 2007. “What Have We Learned About the Causes of Corruption from Ten Years of Cross-National Empirical Research?” Annual Review of Political Science, 10 (June): 211-244. Wooldridge, Jeffrey. 2010. “Econometric Analysis of Cross Section and Panel Data.” MIT Press.

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Variable

Table 1: Summary statistics Definition Source Mean Standard deviation

Min.

Max.

N

Bribe

Unofficial payments to get things done (% of annual sales)

PICS

1.441

4.525

0

100

32,015

Gift

Gifts expected as % value of government contract

PICS

2.359

6.266

0

100

28,662

Government/State ownership

Dummy variable = 1 if firm owned by government

PICS

0.070

.256

0

1

47,432

Foreign ownership

Dummy variable = 1 if firm belongs foreign private sector

PICS

0.128

.334

0

1

47,432

Domestic ownership

Dummy variable = 1 if firm belongs to domestic private sector

PICS

0.802

.399

0

1

47,432

Capital city

Location of establishment = 1 if firm is capita city, = 0, otherwise

PICS

0.290

.454

0

1

43,845

Small size

Dummy variable = 1 if 5-19 workers

PICS

0.403

.491

0

1

46,667

Medium size

Dummy variable = 1 if 20-99 workers

PICS

0.333

.471

0

1

46,667

Large size

Dummy variable = 1 if 100+ workers

PICS

0.264

.441

0

1

46,667

Intermediate input importer

Dummy variable = 1 if firm imports inputs and supplies, directly or indirectly

PICS

0.512

0.499

0

1

35,032

Timereg

Time spent in dealing with bureaucratic regulations by senior management Real markup/# of employees

PICS

1.489

1.216

0

4.615

40,577

PICS

0.043

0.084

0.128

1.657

31,271

Sunkcost

Real R&D expenditure/# of employees

PICS

0.113

0.618

0

14.32

26,900

Firm age

Year of establishment - 2005

PICS

18.295

19.352

0

1005

45,251

Markup

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ISO certification

Dummy variable = 1 if firm has received ISO certification

PICS

0.167

0.373

0

1

41,162

Log(GDPpc)

Natural logarithm of GDP per capita

WDI

7.439

1.183

4.869

10.318

46,249

Bureaucracy

Standardized measure of bureaucratic quality

ICRG

0

1

2.738

2.349

43,308

List of industries included in baseline regression

Textiles, Leather, Garments, Agro-industry, Food, Beverages, Metals and machinery, Electronics, Chemicals and pharmaceutics, Construction, Wood and furniture, Nonmetallic and plastic materials, Paper, Sport goods, IT services, Other manufacturing, Accounting and finance, Advertising and marketing, Other services, Retail and wholesale trade, Hotels and restaurants, Transport, Real estate and rental services, Mining and quarrying

List of countries included in baseline regression

Albania Armenia Bangladesh Belarus Bulgaria Chile Costa Rica Croatia Czech Republic Ecuador El Salvador Estonia Guatemala Guyana Honduras Hungary Ireland Kazakhstan Latvia Lithuania Madagascar Malawi Mali Moldova Nicaragua Philippines Poland Russian Federation Slovak Republic Slovenia South Africa Spain Sri Lanka Tanzania Turkey Ukraine Vietnam Zambia

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Table 2: Zero-inflated NB model results Dependent variableDependent variable – Gift Unofficial payments (% of expected (% value of annual sales) government contracts) (a) (b) (c) (d) Logit model Count model Logit model Count model (Always zero (Always zero group) group) Markup 2.353* 1.991*** 0.985** 0.484 (1.325) (0.645) (0.483) (0.321) [1.274] [22.8] [1.105] [5.0] Foreign -1.009 0.213 -0.318 -0.102 (0.745) (0.287) (0.234) (0.091) [0.365] [23.7] [0.728] [-9.7] Domestic -0.703 0.421* -0.515** 0.042 (0.756) (0.249) (0.224) (0.081) [0.495] [52.3] [0.597] [4.3] Capital city -0.107 0.293** -0.435*** 0.069 (0.214) (0.134) (0.139) (0.054) [0.898] [34.0] [0.647] [7.1] Medium size -0.345 -0.113 0.166* -0.026 (0.310) (0.109) (0.089) (0.050) [0.708] [-10.7] [1.180] [-2.6] Large size -0.109 -0.337* 0.369*** -0.173* (0.399) (0.178) (0.103) (0.096) [0.897] [-28.6] [1.446] [-15.9] Intermediate -0.730*** 0.010 -0.346*** -0.022 input importer (0.220) (0.089) (0.084) (0.022) [0.482] [1.0] [0.708] [-2.2] Timereg -0.037** 0.013*** -0.012*** 0.005** (0.015) (0.005) (0.003) (0.003) [0.655] [16.1] [0.877] [5.9] Sunkcost -116.601*** -1.183*** -187.764*** -74.951*** (20.937) (0.246) (12.533) (28.094) [0.128] [-2.1] [0.040] [-72.4] log(GDPpc) 5.156*** 1.014*** 7.863*** -0.242** (0.700) (0.164) (0.172) (0.105) [724.211] [264.9] [1.5e+04] [-25.6] Bureaucracy -2.346*** -0.344** 3.206*** 0.767*** (0.686) (0.167) (0.175) (0.180) [0.096] [-29.1] [24.686] [115.3] Constant -36.997*** -6.610*** -38.862*** 4.575*** (5.386) (0.987) (0.910) (0.638) Observations 8,798 9,289 Non-zero obs. 3,082 2,907 Zero obs. 5,716 6,382 alpha 2.092 0.513 (0.743) (0.084) Ln(alpha) 0.738 -0.667 (0.355) (0.163) Country FE Yes Yes Industry FE Yes Yes Year FE Yes Yes Log-likelihood -10161 -12860 Note: Standard errors robust to country-level clustering in parentheses;

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% changes of explanatory variables for count model and factor changes for logit model in brackets; *** p