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Bribes, Rents and Industrial Firm Performance in Albania and Kosovo

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Abstract

Using data from a novel representative survey, we examine how corruption affects the performance of industrial-sector firms in Albania and Kosovo, two low–middle-income post-socialist economies in the Western Balkan region. Bribes are costs that firms incur to “grease the wheels” of the bureaucracy and/or to seek rents. Rents, however, may improve firm performance. Thus, to estimate the total effect of corruption, we develop and collect a set of perception-based indicators of both corruption and rents. In addition, we allow both bribery and rents to affect output growth through multiple channels—by influencing the firm’s investment and hiring decisions, by affecting total-factor productivity and by modifying the marginal product of factor additions. We find that, in Albania and Kosovo, bribes and rents have both positive and negative effects on firm performance. The net effect of corruption, however, is negative and large and is not fully offset by the beneficial effects of rents.

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Notes

  1. Around 90% of the population of Kosovo is ethnic-Albanian.

  2. See references in the next paragraph.

  3. These include concealing accounting information or limiting employee participation in firm decision-making.

  4. Here, I do not discuss the macro-literature on corruption and growth at the country level. See Aidt (2009) for a review.

  5. With the exception of Commander and Svejnar (2011).

  6. This account is largely based on Uberti (2017).

  7. In 2014, industry accounted for less than 10 per cent of GDP in Albania and manufacturing for just over 10 per cent in Kosovo.

  8. Interview No. 22 with firm manager from the metal-processing sector, March 2015, Ferizaj/Uroševac, Kosovo.

  9. Interview No. 41 with firm owner from the mining sector, July 2015, Tirana, Albania.

  10. Interview AL10 with firm owner from mining company, July 2016, Tirana, Albania.

  11. We remain agnostic about the effect of corruption on employment growth.

  12. Interview No. 40 with firm manager from the metal-processing industry, March 2015, Viti/Vitina, Kosovo.

  13. As noted by the manager of a USAID project in Kosovo, “when talking about corruption, you need to ask about the benefits. In Kosovo, the list of [formal] benefits [that the state can provide] is very short” (interview 010 with representative of donor agency, March 2015, Pristina, Kosovo).

  14. In 2015, the average trade-weighted tariff rate in Albania and Kosovo was, respectively, 3.9 per cent and 7 per cent (MTI 2015, 48). See also WTO, Tariff Download Facility, 2016.

  15. Interview with firm manager from the food and beverage industry, March 2015, Ferizaj/Urosevac (Kosovo).

  16. Interview No. 52 with firm manager from the non-metallic minerals industry, April 2015, Prizren (Kosovo).

  17. Interview AL31 with programme director, GIZ Albania, July 2015, Tirana (Albania).

  18. Interview KS07 with programme director, GIZ Kosovo, October 2014, Pristina (Kosovo).

  19. The former director was later acquitted.

  20. Interview KS09 with two GIZ consultants, April 2015, Pristina (Kosovo).

  21. Of course, some powerful businesses may be allowed to operate informally precisely by virtue of their political connections (Danielsson 2016). In this case, it is informal companies that earn a rent in the form of a waiver on rule enforcement.

  22. A good example is Kosovo’s plastics and rubber industry, whose companies have suffered declining profits due to the unrestricted entry of informal competitors (Interview No. 57 with firm manager from the plastics and rubber sector, April 2015, Han i Elezit/Djeneral Janković, Kosovo).

  23. In both countries, it is relatively easy to establish a business. Furthermore, many registered firms are “shell” entities established to apply for grants from development agencies.

  24. Since, for Albania, we do not have data on the number of companies by sector and by size, we cannot perform this calculation.

  25. Authors’ calculations.

  26. The survey questionnaire is available from the authors upon request.

  27. To obtain the average annual percentage growth rate of labour force (and, mutatis mutandis, capital stock), we use the following geometric mean formula: \(\Delta \ln L = \left( {L_{2015} /L_{2011} } \right)^{1/4} - 1\).

  28. The t tests reported in Table 1 can never reject the null that the means reported in columns (c) and (d) are equal.

  29. To the extent that the responses also reflect sector-level events, our indicator would still be capturing relevant information. Even if the respondent does not pay bribes, operating in a business environment in which bribe solicitations are widespread creates uncertainty. As a result, managers might have to devote time to anticipating and pre-empting bribe solicitations, and resources may have to be set aside as insurance. By asking firms about their perceptions of sector-level events, our indicator captures the full magnitude of the corruption effect.

  30. Conceptually, the total bribe transacted equals the frequency of bribes times the magnitude of individual bribes.

  31. The p values of the t tests for the equality of the means are, respectively, 0.043 (top-right quadrant) and 0.037 (bottom-left quadrant).

  32. Age is defined as years since the firm’s legal establishment. The logarithmic specification, which is common in the firm productivity literature (e.g. Gatti and Love 2008), allows for productivity growth to decline at a decreasing rate over time. For former SOEs, the year of establishment is taken to be the year in which the firm was privatised.

  33. Only 5 (14) per cent of Kosovo’s (Albania’s) sampled firms were run or owned by a female top manager.

  34. Within strata, companies are not sampled randomly but based on a measure of firm size (number of employees). Since firm size may be related to the dependent variable (output growth), ignoring the sampling probability may lead to sample selection bias.

  35. Note that operating in an environment with “intermediate” levels of corruption, by contrast, is not associated with a productivity dividend.

  36. Former SOEs are generally much older than the private firms established de novo in the post-transition period. It is thus to be expected that they should grow their TFP at a lower rate.

  37. This model is not reported to save space.

  38. Several firms interviewed in Kosovo report operating at 10-40 per cent of their nameplate capacity (e.g. interviews No. 7, 15, 21, 34, March–April 2015, various locations, Kosovo).

  39. Albania does not have a working system of free economic zones. In 2005–2013, Albania did try to establish such zones, but with no success.

  40. Interview KS013 with head of Department of Industry, Ministry of Trade and Industry, December 2014, Pristina, Kosovo.

  41. Some of zones, however, were inactive at the time of the survey. The zones may be established by a local municipality, which then becomes responsible for building the facility and granting access to businesses on a competitive basis.

  42. Since firms located in the former socialist-era industrial zones also benefit from round-the-clock electricity supply (which is one of the main benefits of being located in a free economic zone), we code these firms together with the firms located in the newly established zones. At the time of the survey, the 10 kV, high-tension lines, which guarantee round-the-clock supply, were only available in the free zones and in the former industrial zones.

  43. On average (when \(\Delta \ln K\) and \(\Delta \ln L\) are at their sample means), the marginal effect of \(f1\) (12.3, p value = 0.010) and \(f2\) (8.1, p value = 0.035) are again positive and statistically significant, consistent with previous results.

  44. Interview No. 16 with manager from the textile and apparel industry, May 2015, Tirana, Albania.

  45. Interview No. 51 with manager from the food and beverages industry, April 2015, Prizren, Kosovo.

  46. That said, it is very possible that, on net, the company might have benefitted from this (informally negotiated) rent allocation.

  47. In Albania and Kosovo, it is plausible to assume that workers are imperfectly mobile. Also, levels of bank penetration tend to vary sub-regionally.

  48. We stress “up to” because CORR measures all types of bribery—those intended to “grease the wheels” in the face of extortion by public officials, and those intended to buy special favours (rents) from politicians.

  49. This results, however, is contrast with previous findings from the macro-literature (e.g. Mauro 1995).

  50. The p value of a F-test of their joint significance is 0.902.

  51. Interacting the sector FE with the Albania dummy allows for the sector-specific effects (due to technological characteristics, for instance) to vary between Albania and Kosovo.

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Acknowledgements

The research that led to the preparation of this article was generously supported by the Regional Research Promotion Programme—Western Balkans under Grant No. KS_205 (2014–2017), as well as by the University of Otago (New Zealand), where the author did his Ph.D. We would like to thank the participants of the workshop on “The Causes and Consequences of Trust and Bribery in Society”, organised by the University of the West of England (March 2019), the participants of the LSEE workshop on the “Economics of the Western Balkans” (London School of Economics and Political Science, June 2019), Elodie Douarin (UCL), Randolph Bruno (UCL), and the journal’s anonymous referees, for their insightful comments on earlier versions of this paper. I would also like to thank the enumerators that worked tirelessly to collect the survey data: Dorela Lazaj, Ergent Pire, Fitnete Ballçaj, Donjeta Murati, Nora Koka, Verka Jovanović, Dardan Zhegrova, Shkëlqim Selmani, and Shëngjyl Osmani. Lastly, I am indebted to Drini Imami for his dynamic coordinating role and to Geoffrey Pugh for invaluable comments on survey design. Part of this research was also conducted while the author was a Visiting Fellow at the Centre for Political Courage, University of Prishtina (Kosovo) in 2014–2015.

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Appendices

Appendix 1: Survey Design Characteristics

The survey interviews were conducted face to face by nine enumerators, who were trained by the authors. All enumerators were native speakers of Albanian, expect for one Kosovar enumerator, a native Serbian speaker, who was responsible for conducting interviews in the Serb-majority municipalities of northern Kosovo. The respondent was always either the company’s owner or a top manager. To ensure consistency and data quality, the authors supervised the firm selection process and audited some 100 interviews across the two countries.

The survey employed a two-stage sampling design. Using information from official business registries, the total population of industrial firms in Albania and Kosovo was first partitioned into 22 country sectors, or “strata”. Next, individual firms were selected independently within each stratum. Stratification ensures that firms from more successful sectors, which are naturally more numerous, are not overrepresented in the sample (Levy and Lemeshow 2008, p. 123).

Furthermore, the sampling procedure was such that larger firms (measured by the number of full-time employees) were more likely to be selected into the sample than smaller firms from the same stratum; firms of equal size were equally likely to be sampled. To implement this scheme, the stratum population was ranked by firm size. The interviewers were instructed to first telephone and arrange interviews with larger companies, working their way down in descending order. The randomness of selection arises from the possibility that a firm may not answer the phone call or may decline to be interviewed.

Table 8 provides descriptive information on the 22 country sectors, including the sample (\(n_{h}\)) and population (\(N_{h}\)) size for stratum h, the sampling rate (\(f_{h} = n_{h} /N_{h}\)) and the share of the sample (\(n_{h} /\sum n_{h}\)) and population (\(N_{h} /\sum N_{h}\)) pertaining to each stratum.

Table 8 Sample and population characteristics.

Appendix 2: Factor Analysis of the Rent Dummies

The factor analysis estimates the following system of equations, written in matrix form:

$$\overrightarrow {Rent} = \underline{A} \vec{f} + \vec{u}$$

where \(\overrightarrow {Rent}\) is the vector of observed rent dummies, \(\vec{f}\) is a vector of factor scores, \(\underline{A}\) is the pattern matrix and \(\vec{u}\) is the uniqueness vector. The elements \(a_{mn}\) of the pattern matrix (\(\underline{A}\)) are known as “factor loadings”, since they measure the partial correlation between each factor and the observed variables. Estimating \(\underline{A}\) requires solving the following eigenvalue problem: \(\underline{\varSigma } \vec{a}_{mi} = \gamma_{i} \vec{a}_{mi}\), where \(\underline{\varSigma }\) is the correlation matrix of the observed rent variables, \(\vec{a}_{mi}\) is the vector of partial correlations between \(fi\) and the four rent variables, and \(\gamma_{i}\) is the eigenvalue.

A critical assumption of factor analysis is that the observed variables are continuous. Our \(Rent\) variables, however, are binary. A factor analysis of the Pearson correlation matrix for a set of dummies can be severely misleading. The standard solution is to factor-analyse the matrix of tetrachoric correlations between the observed dummies (StataCorp 2014, p. 2679). A tetrachoric correlation “estimates the Pearson correlation of the latent continuous variables” (ibid.). The assumption is that the respondents’ agreement/disagreement with questions 1.1–1.4 (Table 2) is just a coarse measurement of an underlying variable—the degree to which firms in a given sector receive rents.

Having estimated the pattern matrix \(\underline{A}\) using a principal component method, we performed a rotation of the matrix elements, a standard procedure intended to simplify factor structure and aid interpretation of the factor scores (Hamilton 2013, p. 318). We employed an oblique promax rotation, which “simplifies factor patterns while permitting some degree of correlation between the factor [scores]. Correlated factors will be statistically less parsimonious […], but [potentially] more realistic” (Hamilton 2013, p. 340). The results, however, are very similar if we choose an orthogonal varimax rotation instead. We only retain factors whose eigenvalue is greater than 1, implying that they explain more than the equivalent of one observed variable’s variance (Hamilton 2013, p. 316).

Appendix 3: Sampling Design and OLS Estimators

Under our sampling scheme, a stratum is defined as the firm’s country-sector. Each stratum \(h\) consists of a finite population of firms (\(N_{h} < \infty\)). To make a finite-population correction, we define the “sampling rate” \(f_{h}\) for stratum \(h\) as the ratio of sampled individuals to the size of the population in \(h\): \(f_{h} = n_{h} /N_{h}\).

Next, we weighted our observations by their probability of being selected into the sample. The “sampling weight” \(w_{ih}\) for observation \(i\) in stratum \(h\) is proportional to the inverse of the probability of i’s being selected into the sample. Under our sampling scheme, the probability of selection depends on the stratum’s population size, but also on the size of the company, measured in terms of the number \(L_{i}\) of full-time employees. Thus, following Levy and Lemeshow (2008, p. 350), we define the sampling weight as follows:

$$w_{ih} = \frac{C}{{P_{ih} }} = C\left( {\frac{{N_{h} }}{{n_{h} }}} \right)\left( {\frac{1}{{L_{i} }}} \right)$$

where \(P_{ih}\) is the probability of observation i being selected and \(C\) is a constant that we set equal to 1. To perform the estimation, we use Stata’s svy suite of commands for survey data (StataCorp 2013).

Appendix 4: Output Growth Models: Additional Specifications

Here, we further investigate the robustness of the results presented in column 4, Table 5. In that model, the fact that the coefficients on \(f2\), \(\left( {f2 \cdot \Delta \ln K} \right)\) and \(\left( {f2 \cdot \Delta \ln L} \right)\) are all insignificant may result from multicollinearity. Thus, model 1 in Table 9 omits the two interaction termsFootnote 50 and recovers the positive and significant coefficient on \(f2\) estimated in models 2 and 3 (Table 5). Although the rents captured by \(f2\) (import protection and protection from unfair competition) do not exert a moderating influence on the marginal product of labour and capital, they do appear to stimulate TFP growth.

Table 9 Output growth models: additional specifications

Lastly, model 2 in Table 9 tests the robustness of our results to an alternative fixed-effects specification. The ability of firms to generate sales revenue may depend on sector—(e.g. prices) and/or location-specific (e.g. institutional) effects. Omitting these determinants of firm performance may bias the parameter estimates. For instance, an exogenous change in market prices may allow firms to increase revenues without improving product quality or increasing the scale of production; and this effect may be spuriously picked up by our rents or corruption variables. Alternatively, governance quality may vary across regions, and these differences may influence both the performance of local firms and the incidence of corruption (e.g. in the municipal bureaucracy). To address these possibilities, model 2 in Table 9 investigates the sensitivity of our results to replacing the country fixed-effects dummy (Albania) with a full set of country-sector and location (city) fixed effects.Footnote 51 The location fixed effects (but not the country-sector fixed effects) are jointly significant. Even so, our results are qualitatively unaltered (although, of course, the OLS standard errors are generally larger in this much more extensively specified model).

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Uberti, L.J. Bribes, Rents and Industrial Firm Performance in Albania and Kosovo. Comp Econ Stud 62, 263–302 (2020). https://doi.org/10.1057/s41294-020-00112-5

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