ISO certification, financial constraints, and firm performance in Latin American and Caribbean...

Global Finance Journal 25 (2014) 203–228

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Global Finance Journal

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ISO certification, financial constraints, and firmperformance in Latin American andCaribbean countries☆

Barkat Ullah a,⁎, Zuobao Wei b, Feixue Xie b

a Rhode Island College, United Statesb University of Texas at El Paso, United States

a r t i c l e i n f o

☆ We thank two anonymous referees and participanthe 2014 Eastern Finance Association annual meeting⁎ Corresponding author at: 600 Mount Pleasant

E-mail address: [email protected] (B. Ullah).

http://dx.doi.org/10.1016/j.gfj.2014.10.0031044-0283 © 2014 Elsevier Inc. All rights reserved.

a b s t r a c t

Available online 15 October 2014
We employ World Bank Enterprise Survey data collected in 2006–2010for 21,852 firms from 31 Latin American and Caribbean countries toinvestigate determinants of the adoption of International Organizationfor Standardization (ISO) certification, the relation between ISO certifica-tion and firm financial constraints, and the effect of ISO certification onfirm performance. We find that ISO accreditation is positively relatedto firm size and firm age. Exporters and foreign firms are more likelyto adopt ISO certification. We document that ISO-certified firms exhibitsignificantly lower level of financial constraints and higher labor pro-ductivity and lower cost of sales than non-certified firms.
© 2014 Elsevier Inc. All rights reserved.

JEL classification:G32L15L25

Keywords:ISO certificationFinancial constraintFirm performanceInternational standardLatin AmericaCaribbean countries

1. Introduction

Information asymmetry is a ubiquitous phenomenon in the business world. Business insiders, such asmanagers, know more about the internal operations and future prospects of their firm than outside stake-holders, such as investors, customers, and creditors. Information asymmetries increase transaction costs ofmarket exchanges (Williamson, 1985). For example, creditors incur costs to gather certain information relat-ed to the creditworthiness and future prospects of a firm. These costs are ultimately passed onto the firm in

ts in the 2014 Southwestern Finance Association conference in Dallas, Texas andin Pittsburgh, Pennsylvania for their helpful comments and suggestions.Avenue, Providence, RI 02908, United States. Tel.: + 1 401 456 9528.

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204 B. Ullah et al. / Global Finance Journal 25 (2014) 203–228

the form of higher financing costs. These costs are reduced if the firm can effectively signal that it possessescertain desirable characteristics. Therefore, business insiders have incentives to reduce such informationasymmetries, especially when they need access to external capital markets.

An effective mechanism to reduce information asymmetry is by way of signaling (Spence, 1973). King,Lenox, and Terlaak (2005) show that firms can signal desirable yet unobservable firm characteristics to exter-nal stakeholders by obtaining privatemanagement standards, such as the family of quality certifications fromthe International Organization for Standardization (ISO). In fact, obtaining an ISO certification has become apopular managerial and strategic tool for businesses across the globe for more than a decade. Can ISO certifi-cation serve as an effective signal to creditors/investors that certified firms possess desirable firm character-istics related to financial strength? We examine this question for the first time in the ISO and financialconstraint literature.

The ISO organization officially claims that firms benefit from ISO certification through “cost savings, en-hanced customer satisfaction, access to new markets, increased market share, and environmental benefits.”1

Most papers in the existing ISO literature are single-country studies that examine whether the aforemen-tioned benefits materialize at the firm level. The overall empirical evidence is inconclusive.2What is the effectof ISO certification on firm performance in a cross-section, multi-country setting? We examine this questionin this paper aswell. Specifically, we employ theWorld Bank Enterprise Survey (WBES) data collected in 2006–2010 for more than 21,000 firms in 31 Latin American and Caribbean (LAC, hereafter) countries to investigatethe twomain research questions mentioned above. In addition, we examine firm-level and country-level de-terminants of the adoption of ISO certification in LAC countries.

The existing literature related to financial constraints and ISO certification offers little empirical evidencefrom the LAC countries. We are the first to explore the related issues in this region. LAC countries as a grouphave been experiencing steady economic growth over the past decade or so, making this region an importantpart of the global economy. In fact, taken as a whole, LAC would rank as the world's fourth largest economy,after the European Union, the United States, and China, and above Japan.3 In addition, there are wide cross-country variations in the fraction of firms that are ISO certified in LAC countries, ranging from 1% inDominica to 36% in Grenada and Bahamas. Clougherty and Grajek (2008) find that ISO-rich countries benefitmore from the ISO certification at themacro-level, measured in international trades and foreign direct invest-ments (FDI). Do the effects of ISO certification on firm-level financial constraints and performance differ inISO-rich vs ISO-poor countries? Furthermore, the levels of economic and institutional development alsovary widely among the LAC countries (Table 1). Per capita GDP ranges from US$ 820 in Nicaragua to US$20,750 in Bahamas. Financial market development, proxied by private credits to GDP (Priv), ranges from4.6% in El Salvador to 109.4% in St. Lucia. The single-country studies in the extant literature providemixed re-sults on the effect of ISO certification on firm performance. Are these firm-level mixed results attributable tothe country-level variations in macroeconomic variables? In other words, is the effect of ISO certification onfirm performance also a function of a country's economic and institutional development? LAC countriesallow us to examine these related questions.

Our results from both the univariate tests andmultivariate regressions show that ISO accreditation is pos-itively related to firm size and age. Exporters and firms with foreign ownership stakes are also more likely toadopt ISO certification than their respective counterparts. Furthermore, we find that ISO-certified firms expe-rience significantly lower level of financial constraints than non-certified firms, indicating that ISO certifica-tion conveys certain favorable characteristics from creditors' point of view. Finally, our multi-country studyprovides concrete evidence that ISO-certified firms exhibit significantly higher labor productivity and lowercost of sales than non-certified firms. Our results are robust after controlling for relevant firm characteristics,country-level macroeconomic variables, country fixed effects, and industry and year effects. As robustnesschecks, we test for potential endogeneity of ISO certification to both financial constraints and firm perfor-mance using the propensity score matching method. Our overall findings remain robust after controllingfor potential endogeneity caused by selection bias.

1 See the official ISO website www.iso.org.2 See, for instance, Sharma (2005), Heras, Dick, and Casadesus (2002), Lafuente et al. (2009), Terziovski et al. (1997), Singels et al.

(2001), Corbett et al. (2002), and Simmons and White (1999).3 Based on the World Bank's GDP ranking.

http://www.iso.org

Table 1Summary statistics.

Variable Obs Mean Median Std Min Max

ISO 21852 0.212 0 0.409 0 1Financing 21599 1.630 2 1.301 0 4Productivity1 21847 13.285 12.494 2.874 8.112 20.367Productivity2 11176 12.107 11.450 2.914 5.821 18.895COGS 11774 0.627 0.638 0.286 0.019 2.004Firm Age 21220 24.009 19 19.465 3 340Firm Size 21320 122.630 25 550.456 5 21,955Exporter 21829 0.274 0 0.446 0 1Ownership 18206 69.908 70 27.279 0 100Government 20854 0.004 0 0.063 0 1Foreign 20846 0.121 0 0.327 0 1Priv 31 32.227 23.897 21.645 4.623 109.480GDP 31 24.743 25.130 1.909 19.824 27.425GDP per capita 31 4507.685 4289.061 2800.186 820.783 20750.780GDP growth 31 0.038 0.040 0.025 −0.061 0.090Inflation 31 0.063 0.051 0.040 0.013 0.271

Note: ISO is a dummy variable equal to 1 if a firm is ISO certified, and 0 otherwise. Financing is survey response as specified in the surveyquestionnaire. It takes values between 0 and 4, where 0 indicates no financing obstacle and 4 a very severe financing obstacle.Productivity1 is log [sales(t − 1)/employees(t − 1)], where t is the year the survey was conducted. Productivity2 is log[{sales(t − 1) − COGS(t − 1)}/employees(t − 1)]. COGS is a firm's annual cost of goods sold scaled by sales at year (t − 1). Firm Ageis the log of a firm's age. Firm Size is the log of the number of permanent, full-time employees at year (t − 1). Exporter is a dummyvariable equal to 1 if firm exports, and 0 otherwise. Ownership is the percent of the firm owned by the largest owner. Government is adummy variable equal to 1 if firm is owned by government/state, and 0 otherwise. Foreign is a dummy variable equal to 1 if a firm hasforeign ownership stakes, and 0 otherwise. Priv, the measure of country-level financial development, is the ratio of domestic bankingcredit to the private sector divided by GDP at year (t − 1). GDP is the log of GDP in current millions of U.S. dollars. GDP per capita isthe real GDP per capita in U.S. dollars. GDP growth is the growth rate of GDP. Inflation is log difference of consumer price indices. Thecountry level variables are the average over year (t − 3), (t − 2), and (t − 1). Detailed variable definitions and sources are given inTable A.2 in Appendix A.

205B. Ullah et al. / Global Finance Journal 25 (2014) 203–228

This study is the first in the literature that examines the relation between ISO certification and firm-levelfinancial constraints. Our results suggest that an ISO certification plays the role of a “guarantee” and serves as asignal to external stakeholders including creditors that certified firms possess certain desirable characteristicsrelated to quality management, environmental policy, and/or corporate social responsibility. In addition, ourpaper provides concrete evidence in a cross-section, cross-country setting that ISO certification is significantlyand positively related to firm performance.

The paper is organized as follows. Section 2 reviews relevant literature and develops the hypotheses,whileSection 3 describes data and summary statistics. Section 4 discusses themethodologies and presents the em-pirical results. Section 5 discusses the robustness checks while Section 6 concludes the study.

2. Literature review and hypothesis development

2.1. Adoption of ISO certification: who adopts ISO certification?

Many researchers have studied and identified important firm-level determinants of ISO adoption.4Most ofthese studies provide evidence that firm-level factors are oftenmore important for the ISO adoption decisionthan regulatory requirements.

Firm size has been positively linked to the adoption of ISO certification. Even thoughmaintaining quality isessential for ensuring customer satisfaction and competitive positioning for any firm regardless of its size, re-source constraints related to managerial time, training funds, and quality “know-how”may result in a quality

4 See, for example, Anderson et al. (1999), Adams (1999), Tsekouras, Dimara, and Skuras (2002), Wu, Chu, and Liu (2007), Lafuenteet al. (2009), Pekovic (2010), and Hudson and Orviska (2013).


disadvantage for smaller firms (Grolleau,Mzoughi, & Pekovic, 2007). Large firmsmay benefit from economiesof scale by spreading the certification costs over a large revenue stream (Dobbin & Sutton, 1998), and mayraise the cost for their smaller competitors by influencing the design of standards (Grolleau et al., 2007).Large firms often have a larger network of business contacts and specialized auditors and accountants whomay act as facilitators of quality initiatives (Ghobadian & Gallear, 1996).

Similar to larger firms, older firms often have less resource constraints,more complex business operations,andmore contracting incentive problems, compared to younger firms. Hudson and Orviska (2013) show thatthe probability of ISO certification increases with firm age.

Pekovic (2010) shows that exporters are more likely to seek international quality standards than non-exporters. The information asymmetry is higher between the firm and international customers than betweenthe firm and domestic customers (Hudson & Orviska, 2013). Therefore, the signaling effect of ISO certificationshould be stronger for exporters than non-exporters. Moreover, as argued byMontiel and Husted (2009), ex-porters have more likelihood to be exposed to new ideas of doing business which can increase their propen-sity to adoptmanagement standards. Christmann and Taylor (2001) and Cushing, McGray, and Lu (2005) findthat exporting to the developed countries is a key factor of firms in developing countries to adopt ISO 14001certification. Lafuente, Bayo‐Moriones, andGarcía‐Cestona (2009) also show that the presence in internation-al market can trigger firms to seek ISO 9000 certification. Corbett (2006) finds that exports drive earlycertifications.

Ownership of firms can also be an important determinant for the adoption of ISO certification. Similar toexporters, firmswith foreign ownership stakesmay have better access to resources andmanagerial expertise(Gourlay& Pentecost, 2002). Pekovic (2010)finds thatfirmswith foreign ownership stakes tend to face great-er internal pressure to obtain ISO certification. Hudson and Orviska (2013) find that firms with foreign own-ership aremore likely to become ISO certified. In addition,we investigatewhether ownership concentration isrelated to ISO adoption decision for LAC firms. Lastly, we examinewhether country-levelmacroeconomic var-iables are important determinants of ISO adoption.

2.2. The ISO certification and firm financial constraints

Since Fazzari, Hubbard, and Petersen (1988), a substantial body of empirical literature has emerged to ex-amine financial constraints of firms.5 This line of research relies on the assumption that financing from exter-nal sources is more costly than internal financing because of asymmetric information and agencyproblems. Beck, Demirgüç-Kunt, Laeven, andMaksimovic (2006) suggest that a firm is financially constrained“…if a windfall increase in the supply of internal funds results in a higher level of investment spending”.

Information asymmetry constrains a firm's ability to access external financing and as a result, limits itsability to pursue positive NPV projects (Demirgüç‐Kunt & Maksimovic, 1998). Myers and Majluf (1984) andFazzari et al. (1988) show that informational asymmetry raises the cost of external financing that may forcefirms to forgo potentially positive NPV projects. They argue that in the presence of information asymmetry,a firm's growth will be constrained to its internal resources. Thus, reducing information asymmetry helps afirm to improve its access to external financing and attain higher growth.

Williamson (1985) broadly argues that information asymmetry leads to higher transaction costs ofmarketexchanges. The specific types of cost borne by information asymmetry depend on the market exchanges in-volved. For creditors, the costs involved include cost of gathering and verifying firm characteristics relatedto its creditworthiness and the cost of contract design and enforcement. These costs are ultimately passedon to the firm. Therefore, insiders have incentives to reduce the information asymmetry. Spence (1973) sug-gests that it is less costly for firms that choose to signal themarket of their possession of certain desirable butunobservable characteristics than firms that do not.

Akerlof (1970) shows that a “guarantee” by suppliers can reduce the quality uncertainty of their productsand services, hence lead to lower transaction costs. ISO certification can play the role of this “guarantee” andserve as a credible signal to external stakeholders, including creditors (King et al., 2005). To obtain ISO

5 See the surveys by Schiantarelli (1995) and Hubbard (1998).


certification, a firm has to incur implementation and certification costs. Specifically, a firmneeds to incur costsof creating and updating required procedures and documents, costs of organizational restructuring and per-sonnel training, and third-party audit fees.6 Taking into account these costs, Anderson, Daly, and Johnson(1999) and Delmas and Montiel (2009) show that ISO certification schemes act as informative and crediblesignals in the product markets.

Can an ISO certification serve as a credible and informative signal in the external capitalmarket?We inves-tigate this question in our study and hypothesize the answer to be affirmative. We conjecture that creditors/investors view ISO certified firms more favorably than non-certified when providing financing. Specifically,we expect that ISO-certified firms experience lower degree of financial constraints and higher level of finan-cial strength than comparable non-certified firms.

2.3. ISO certification and firm performance: an unsettled issue

In the extant literature, two complementary theories emerge in examining thepossible sources offinancialbenefits resulted from ISO certification: the Internal Improvement Theory and theExternal Improvement The-ory. The internal improvement theory suggests that firms benefit from ISO certification through greater qual-ity awareness among employees (BSI, 2000), improvement of product and service quality (Quazi, Hong, &Meng, 2002), and improved productivity and efficiency (BSI, 2000; Buttle, 1997; RAB, 2000; Reed, Lemak, &Montgomery, 1996). The external improvement theory posits that by adopting ISO certification, firms striveto gain competitive advantage over their competitors (Carr, Mak, & Needham, 1997).

These two theories suggest that adopting and implementing ISO certification bring better internal controlsand external market discipline to the firm (Corbett, Montes-Sancho, & Kirsch, 2005). Certified firms are re-quired to implement continuous quality monitoring and measurement following sufficiently well-definedand documented procedures to ensure that appropriate corrective action is taken whenever defects occur.Such defects are also identified earlier should they occur, leading to lower cost of corrections. Corbett et al.(2005) further argue that discipline resulted from ISO certification helps firms identify ongoing practicesthat are obsolete or counterproductive. Furthermore, firms with well-defined and well-documented proce-dures can improve worker productivity through better training programs.

Despite these arguments promulgated by the aforementioned theories, extant empirical research on therelation between ISO certification and firm performance is inconclusive. A large number of empirical studieshave established a strong positive effect of ISO certification on thefinancial performance of a firm (e.g. Corbettet al., 2005; Häversjö, 2000; King & Lenox, 2001; Lafuente et al., 2009; Naveh &Marcus, 2005; Sharma, 2005;Starke, Eunni, Fouto, & de Angelo, 2012; and Terlaak & King, 2006a, Terlaak & King, 2006b). Others indicatethat the relation is weak (e.g. Beattie & Sohal, 1999; Lima, Resende, & Hasenclever, 2000; Shams-ur, 2001;Singels J., Ruel G., & Van de Water H., 2001; Terziovski, Samson, & Dow, 1997; and Wayhan, Kirche, &Khumawala, 2002).

Most of the empirical papers cited above are single country studies. The effectiveness of ISO certificationmay also depend on a country's overall institutional environment and economic development. The cross-country difference in economic and institutional developments allows us to examine the relation betweenISO certification and firm performance while controlling for observable macroeconomic variables (such asGDP), or unobservable characteristics unique to a country (country fixed effect analysis).

3. Data and variables

In this study, we employ the World Bank Enterprise Survey (WBES) data for 21,852 firms in 31 LAC coun-tries.7 Most of the countries in the sample have two surveys: one in 2006 and the other in 2010. Several

6 ISO develops different international standards, such as ISO 9001, ISO 14001 and ISO 31000. However, ISO is not involved in the cer-tification to any of the developed standards. ISO certification is performed by external certification bodies or third party auditors (e.g.IRAM for Argentina and IBNORCA for Bolivia). The auditor conducts the audit following an audit plan and generates an audit report iden-tifying any non-conformances (NCR) to the ISO standard. The audit documentation is then reviewed by an expert for approval. Once thereviewer approves the closure of NCRs, the certificate of registration is issued and sent to the firm.

7 We use the May 2014 version of the WBES dataset.


countries have only one survey. Table A.1 (Panel A) in Appendix A presents the number of ISO and non-ISOfirms surveyed by country and survey year(s).

In the enterprise surveys, the World Bank uses standardized survey instruments in collecting firm-leveldata on the business environment from business owners and top managers. These standardized instrumentsallow for cross-country comparisons and analysis. The main focus of the survey is to assess the critical obsta-cles in the business environment that hinder firm growth around the world. Main topics of the surveys en-compass access to finance, corruption, political, infrastructure, crime, competition, labor market, and legalobstacles. The survey also contains information on ownership concentration and foreign ownership, exportstatus, and limited data on firm performance.

We start with all observations in the WBES LAC (Latin American and Caribbean) database and proceed todelete firms that do not have either “Yes (1)” or “No (2)” answer to the ISO certification question (WBES orig-inal data item: ‘b8’).We exclude firms of less than 3 years in business and less than 5 employees at year (t− 1)from our final sample, where t is the year the surveywas conducted.We do not delete firms for the lack of anyother variable. We include all the LAC countries available in the WBES LAC database. After these filtering, ourfinal sample is of 21,852 unique firms in 31 LAC countries over the period between 2006 and 2010.8 Amongthese, 4632 sample firms (more than 20% of the total sample firms) have at least one type of ISO certification(ISO 9000, 9002, or 14000). Our dataset includes firms from 14 industries. Table A.1 (Panel B) in Appendix Apresents the number of ISO and non-ISO firms surveyed by industry. The relevant key variables are describedbelow. The detailed variable definitions and original sources are given in Table A.2 in Appendix A.

3.1. Dependent variables

3.1.1. Financial constraintsIn this study, we use a firm's direct answer to the survey question related to its financial constraints

(Financing). This avoids having to imperfectly infer financial constraints from financial statements of firmsas in Fazzari et al. (1988) and Kaplan and Zingales (1997).9 The financial constraint related survey questionin the WBES is as follows:

8 Eve9 Kap

ments obeen cuof liquid

“Is access to Financing, which includes availability and cost [interest rates, fees and collateral requirements],No Obstacle, a Minor Obstacle, a Moderate Obstacle, a Major Obstacle, or a Very Severe Obstacle to the cur-rent operations of this establishment?”

The WBES scores the financing obstacles on the following scale: No Obstacle = 0, Minor Obstacle = 1,Moderate Obstacle= 2,Major Obstacle= 3, and Very Severe Obstacle= 4. Besides Financing, we use anothervariable representingfirm-level financial constraints, Financing Dummy, which is a dummyvariable equal to 1if financing obstacles equal to 2 (Moderate Obstacle), 3 (Major Obstacle), or 4 (Very Severe Obstacle), and 0otherwise.

3.1.2. Firm performanceWe employ three firm performance measures in our analysis: two alternative measures for labor produc-

tivity (Productivity1 and Productivity2) following Lafuente et al. (2009), and cost of goods sold (COGS) scaledby sales following Corbett et al. (2005). These measures are calculated as follows:

Productivity1 ¼ log salest−1=employeest−1ð Þ ð1Þ

Productivity2 ¼ log salest−1−costof goodssoldt−1ð Þ=employeest−1½ � ð2Þ

COGS ¼ costof goodssoldt−1=salest−1: ð3Þ

ry firm has a unique identifier code, the “idstd” code.lan and Zingales (1997) categorize firms into “not financially constrained” to “financially constrained” based upon financial state-f annual reports; they classify firms as being severely financially constrained if these firms are in violation of debt covenants, havet out of their usual source of credit, are renegotiating debt payments, or declare that they are forced to reduce investments becauseity problems.


A common criticism of using survey data to conduct research related to firm performance is that surveydata are self-reported and therefore, the findings may suffer from self-reporting bias (Corbett et al., 2005).However, Beck, Demirgüç-Kunt, and Maksimovic (2005) argue that accounting data are more likely to be bi-ased than survey data as the incentives to distort data are likely to be higher in financial statements becausemany firm-level decisions, such as tax, financing, and managerial compensations, are in part based on finan-cial statement variables. Beck et al. (2005) also posit that the self-reporting nature of the WBES data is notlikely to be a significant source of bias. With intimate working knowledge of the WBES, they point out thatthe survey aims to evaluate the business environment instead of firm performance. Even though some firmperformance related questions were asked, the survey was specifically designed to ask those questions atthe end of the interview. This reduces the respondents' need to justify their own performance when answer-ing the earlier business environment related questions.

3.2. Explanatory variables

3.2.1. ISOOur main explanatory variable, ISO, is an indicator variable that equals one if a firm is ISO certified (WBES

original data item: ‘b8’), and zero otherwise. As shown in Table 1, ISO has a mean of 0.21 i.e. about 21% of thesurveyed firms in LAC countries are ISO-certified.

3.2.2. Firm ageHudson and Orviska (2013) find that the probability of ISO certification increases with firm age. Evans

(1987) and Dunne, Roberts, and Samuelson (1988) find that younger firms grow significantly faster thanolder firms. Anderson and Eshima (2011) find that younger firms can make up their lack of established rou-tines and processes with being more flexible and reactive in the market places than older firms. Beck et al.(2006) find that older firms experience less financing obstacles. Therefore, we expect that older, establishedLAC firms experience lower level of financial constraints than new, younger firms. We control for firm age(Firm Age) in our study and measure firm age by subtracting the firm's founding year (WBES original dataitem: ‘b5’) from the survey year. In our sample, the average firm has been in business for about 24 yearsand the oldest firm is 340 years of age (Table 1).10

3.2.3. Firm sizeThe extant literature discussed in the earlier section suggests that large firms are more likely to be ISO-

certified than small firms. Large firms are also likely to be less financially constrained than small firms(Beck et al., 2005, 2006; Schiffer &Weder, 2001). Beck et al. (2005) further show that when growth obstaclesare lowered, small firms benefit disproportionally more than large firms. Therefore, in our analysis of the ef-fect of ISO certification on firm financial constraints and performance, we control for firm size (Firm Size). Weuse the number of employees (WBES original data item: ‘l1’) as Firm Size variable. The WBES defines smallfirms as those having 5–50 employees; medium 51–500 employees; and large 500more or employees. As re-ported in Table 1, the mean and median numbers of employees in our sample are 123 and 25, respectively.This indicates that firm size distribution in our sample is right skewed with extreme large firms.11

3.2.4. ExporterAs discussed in the earlier section, exporters are more likely to adopt ISO certification. Beck et al. (2005)

find that exporters grow faster, perform better, and face less firm-level obstacles such as financial constraintsthan non-exporter. Thus, we control for exporting firms. We use a dummy variable, Exporter, to indicate if afirm exports (using WBES original data items: ‘d3a’, ‘d3b’ and ‘d3c’). In our sample, 27% of all firms exporttheir products abroad (Table 1).

10 For curiosity reason, we look it up and the oldest firm in our sample is a food-manufacturing firm in Jamaica.11 In fact, the largest firm in our sample has a total number of 21,955 full time employees, which is a food-manufacturing firm inMexico.

Table 2Univariate tests and correlation matrix.

Panel A: Univariate tests for ISO versus non-ISO firms

ISO Non-ISO Mean difference

Obs Mean Obs Mean

Financing 4590 1.461 17009 1.676 −0.216⁎⁎⁎

Productivity1 4632 13.817 17215 13.142 0.675⁎⁎⁎

Productivity2 2524 12.944 8652 11.863 1.081⁎⁎⁎

COGS 2643 0.606 9131 0.634 −0.028⁎⁎⁎

Firm Age 4530 29.702 16690 22.464 7.238⁎⁎⁎

Firm Size 4602 302.512 16718 73.113 229.399⁎⁎⁎

Exporter 4623 0.521 17206 0.208 0.313⁎⁎⁎

Ownership 4046 68.068 14160 70.434 −2.366⁎⁎⁎

Government 4455 0.009 16399 0.003 0.006⁎⁎⁎

Foreign 4457 0.276 16389 0.079 0.196⁎⁎⁎

Panel B: Correlation matrix of variables

ISO Financing Prod1 Prod2 COGS Firm Age Firm Size Exporter Ownership Govt. Foreign Priv GDP GDP/Cap Growth

Finance −0.0679⁎⁎⁎

Prod1 0.096⁎⁎⁎ −0.089⁎⁎⁎

Prod2 0.1551⁎⁎⁎ −0.1095⁎⁎⁎ 0.9735⁎⁎⁎

COGS −0.0405⁎⁎⁎ 0.0566⁎⁎⁎ −0.0633⁎⁎⁎ −0.2037⁎⁎⁎

Age 0.1524⁎⁎⁎ −0.0641⁎⁎⁎ 0.0654⁎⁎⁎ 0.1085⁎⁎⁎ −0.0064Size 0.1715⁎⁎⁎ −0.0398⁎⁎⁎ 0.0276⁎⁎⁎ 0.0251⁎⁎⁎ −0.0381⁎⁎⁎ 0.1572⁎⁎⁎

Export 0.2872⁎⁎⁎ −0.0018 0.0489⁎⁎⁎ 0.1062⁎⁎⁎ 0.0002 0.1463⁎⁎⁎ 0.1298⁎⁎⁎

Owner. −0.0361⁎⁎⁎ −0.0349⁎⁎⁎ −0.1456⁎⁎⁎ −0.1561⁎⁎⁎ −0.0283⁎⁎⁎ −0.0715⁎⁎⁎ −0.0126⁎ −0.0412⁎⁎⁎

Govt. 0.0383⁎⁎⁎ −0.0063 0.0048 −0.0008 −0.0063 0.0237⁎⁎⁎ 0.0357⁎⁎⁎ 0.0189⁎⁎⁎ −0.0304⁎⁎⁎

Foreign 0.2467⁎⁎⁎ −0.0775⁎⁎⁎ 0.0523⁎⁎⁎ 0.0763⁎⁎⁎ −0.0361⁎⁎⁎ 0.0404⁎⁎⁎ 0.1311⁎⁎⁎ 0.2159⁎⁎⁎ 0.0078 0.0565⁎⁎⁎

Priv 0.0111⁎ −0.0129⁎ 0.1381⁎⁎⁎ 0.2477⁎⁎⁎ −0.014 0.0244⁎⁎⁎ −0.0103 −0.0297⁎⁎⁎ −0.0222⁎⁎⁎ 0.0067 0.0131⁎

GDP 0.0824⁎⁎⁎ 0.0427⁎⁎⁎ 0.036⁎⁎⁎ 0.0086 −0.0462⁎⁎⁎ 0.0283⁎⁎⁎ 0.0759⁎⁎⁎ 0.0231⁎⁎⁎ −0.0221⁎⁎⁎ −0.0207⁎⁎⁎−0.0595⁎⁎⁎ −0.3156⁎⁎⁎

GDP/Cap 0.0757⁎⁎⁎ 0.04⁎⁎⁎ −0.0464⁎⁎⁎ −0.0103 0.0381⁎⁎⁎ 0.1058⁎⁎⁎ 0.0386⁎⁎⁎ 0.0703⁎⁎⁎ −0.0769⁎⁎⁎ −0.0069 0.0204⁎⁎⁎ 0.15⁎⁎⁎ 0.28⁎⁎⁎

Growth 0.0102 −0.0008 −0.0289⁎⁎⁎ −0.0863⁎⁎⁎ 0.0119 0.0264⁎⁎⁎ 0.0151⁎⁎ 0.0365⁎⁎⁎ 0.0671⁎⁎⁎ −0.015⁎⁎ −0.0081 −0.2253⁎⁎⁎ 0.2457⁎⁎⁎−0.14⁎⁎⁎

Inflation −0.0212⁎⁎⁎ −0.0053 0.2315⁎⁎⁎ 0.0893⁎⁎⁎ 0.071⁎⁎⁎ 0.0285⁎⁎⁎ −0.0184⁎⁎⁎ −0.0462⁎⁎⁎ −0.0414⁎⁎⁎ −0.003 0.0213⁎⁎⁎ −0.2594⁎⁎⁎ −0.0308⁎⁎⁎ 0.052⁎⁎⁎ 0.143⁎⁎⁎

Note: Panel A presents univariate tests for the differences of relevant variables between ISO and non-ISO firms and Panel B presents the correlationmatrix of the key variables. Refer to Appendix Table A.2for detailed variable descriptions.⁎ Indicate significance level of 10%.⁎⁎ Indicate significance level of 5%.⁎⁎⁎ Indicate significance level 1%.

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3.2.5. Ownership variablesLafuente et al. (2009) show that firm ownership structure has a significant influence on its ISO adoption

policy. Highly concentrated ownership allows the owner to make important business decisions quickly anddecisively. However, it also reduces the power and benefits of monitoring byminority owners. Extant empir-ical evidence on the relation between ownership concentration and firm performance has been mixed.Demsetz and Lehn (1985) and McConnell and Servaes (1990) find a nonlinear, U-shaped relation betweenownership concentration and firm performance. On the contrary, Morck, Shleifer, and Vishny (1988) andWruck (1989)findpositive a relation between ownership concentration and firmperformance. Thus, we con-trol for ownership concentration in our analysis.We use the fraction of the shares owned by the largest share-holder as ownership concentration (Ownership) (WBES original data item: ‘b3’). As reported in Table 1, for theaverage firm in our sample, around 70% of the firm is owned by the largest owner(s).

As noted by Anderson et al. (1999), a significant number of government agencies began adopting ISO cer-tification following the European Commission acceptance of ISO 9000 certification. Beck et al. (2005) showthat government-owned firms have lower growth rates and are subject to higher financial obstacles. In ouranalysis, we control for government ownership using a dummy variable, Government, that takes on thevalue 1 if firm is owned by government/state (WBES data item ‘b2c’), 0 otherwise. As presented in Table 1,in our sample, a small fraction of firms (less than 1% of all firms) have government ownership stakes.

Firms with foreign ownership tend to face greater internal pressure and hence, are more likely to obtainISO certification (Hudson & Orviska, 2013; Pekovic, 2010). Fisman and Svensson (2007) suggest that firmswith foreign ownership possess better access to markets and technical expertise, resulting in better financialperformance than pure domestic firms. Beck et al. (2005, 2006) find that foreign ownership has largely pos-itive effect on firmperformance and foreignfirms face fewerfinancing obstacles. Therefore, we control for for-eign ownership in our regression analysis. We use a dummy variable, Foreign, to indicate if any foreigncompany or individual has a financial stake in the ownership of the firm (WBES original data item: ‘b2b’).As presented in Table 1, in our sample, about 12% of all firms have foreign ownership stakes.

3.2.6. Macroeconomic factorsMacroeconomic factors also influence firm level performance (Beck et al., 2005). In our analysis, we con-

trol for country level GDP, GDP growth rate, GDP per capita, and inflation. In our sample, as shown in Table 1,the mean log of GDP is 24.74, the mean GDP per Capita is US$ 4507, and the mean GDP growth is 3.8%. Table 1also shows that these macro-variables vary widely across LAC countries. The mean Inflation is 6.3% and alsovaries widely across countries, from the low of 1.3% to the high of 27.1%. Because sales values are reportedin local currencies, inflation must be controlled for. As a control for country-level financial development, fol-lowing Beck et al. (2005), we use Priv, which is given by the ratio of domestic banking credit to the privatesector divided by GDP.

4. Methodology and results

This section presents methodologies and empirical results of our study. We first conduct univariate testsfor the differences of key variables between ISO-certified and non-ISO-certified firms. We then employ alogit model to examine the determinants of the adoption of ISO certification separately for the full sample,manufacturing firms, and service firms. Thirdly, we employ OLS, country fixed effects, and ordered probitmodel to investigate the effect of ISO certification on firm's financial constraints. Finally, we employ OLSand country fixed effects model to investigate the effect of ISO certification on firm performance. Like allcross-section and cross-country studies, we control for industry effects, year effects, and country effects.

4.1. Univariate tests and correlation matrix

We present our univariate test results for the differences of key variables between ISO-certified and non-certified firms in Panel A of Table 2. T-test is used to test the mean differences. The results show that, mea-sured in mean differences, ISO certified firms exhibit significantly lower level of financial constraints, higherlevel of productivity, and lower level of cost of sales than non-ISO firms. The results also provide preliminaryevidence that ISO firms have been in business longer, are larger in size and more likely to export, and haveforeign and government ownership stakes and lower ownership concentration than non-ISO firms.

Table 3Adoption of ISO certification.

Dependent variable: ISO Full sample Manufacturing firms Service firms

(1) (2) (3)

Firm Age 0.1068⁎⁎⁎ 0.1322⁎⁎⁎ 0.0271⁎

(0.030) (0.037) (0.056)Firm Size 0.5159⁎⁎⁎ 0.6410⁎⁎⁎ 0.3387⁎⁎⁎

(0.018) (0.024) (0.028)Exporter 0.9485⁎⁎⁎ 0.9506⁎⁎⁎ 0.6922⁎⁎⁎

(0.047) (0.058) (0.096)Ownership 0.0000 −0.0001⁎⁎ −0.0004

(0.001) (0.001) (0.001)Government 0.2358 0.5250 0.1559

(0.286) (0.367) (0.551)Foreign 0.7963⁎⁎⁎ 0.7369⁎⁎⁎ 0.8353⁎⁎⁎

(0.059) (0.076) (0.101)Intercept −60.5068 −99.5170 −10.4507

(51.557) (69.194) (84.604)Macro-controls Yes Yes YesIndustry dummies Yes Yes YesYear dummies Yes Yes YesCountry dummies Yes Yes YesLog pseudo-likelihood −7226.9074 −4670.1896 −2179.8526Pseudo-R2 0.2208 0.2763 0.1177Obs. 17352 11698 5023

Note: The dependent variable is ISO which is a dummy variable equal to 1 if a firm is ISO certified, and 0 otherwise. Refer toAppendix Table A.2 for the descriptions of independent variables. Macro-controls (Priv, GDP, GDP per capita, GDP growth, andinflation) and dummies for industry, year, and country are also included. For brevity, the coefficients are not presented but areavailable upon request. The regressions are run with logit with heteroskedasticity-robust standard errors.⁎ Indicates significance level of 10%.⁎⁎ Indicates significance level of 5%.⁎⁎⁎ Indicates significance level of 1%.


Panel B of Table 2 reports the correlation matrix for the relevant variables. Our primary interests are thecorrelations of the indicator variable for ISO certifiedfirms, ISO. As expected and consistentwith theunivariatetest results, we find that ISO is significantly negatively correlated with financial constraints (Financing) andcost of sales (COGS) and positively correlatedwith both productivitymeasures (Productivity1 and Productivity2).The correlation between ISO and the other variables is also consistent with our univariate test results.

4.2. Adoption of ISO certification: logit analysis

In this section, we investigate the determinants of a firm's decision to adopt ISO certification. Because thedependent variable, ISO, is a binary indicator variable, we employ a logit regressionmodel estimated bymax-imum likelihood. The model is specified as follows:

Adoption of ISO ¼ β0 þ β1Firm Ageþ β2 Firm Size β3 Exporterþ β4 Ownershipþβ5 Government þ β6Foreign þ β7Privþ β8GDP þ β9GDP per Capitaþβ10GDP Growthþ β11Inflationþ β12Industryþ β13Yearþ β14Countryþ ε:

ð4Þ

Table 3 presents the results. Our results for the full sample (Column 1) show that the coefficients for bothFirm Age and Firm Size are positive and significant at the 1% level, indicating that older and larger firms aremore likely to seek ISO certifications than younger and smaller firms. However, ownership concentrationand government ownership are not as important. The coefficients of both Exporter and Foreign are positiveand significant at the 1% level, indicating that exporters and firms with foreign ownership are more likelyto adopt ISO certification than their respective counterparts. These results suggest that for LAC firms, cross-border business activities, such as exporting and foreign investment in the firm (foreign ownership stake),are important determinants for the adoption of ISO certification.

Table 4The effect of ISO certification on firm financial constraints.

Dependent variable Financing Dummy Financing

(1) OLS (2) Country FE (3) Ordered probit

ISO −0.0300⁎⁎⁎ −0.0332⁎⁎⁎ −0.0552⁎⁎⁎

(0.010) (0.008) (0.022)Firm Age −0.0340⁎⁎⁎ −0.0330⁎⁎⁎ −0.0814⁎⁎⁎

(0.005) (0.005) (0.011)Firm Size −0.0217⁎⁎⁎ −0.0214⁎⁎⁎ −0.0568⁎⁎⁎

(0.003) (0.005) (0.007)Exporter 0.0084 0.0116 0.0434⁎⁎

(0.009) (0.012) (0.020)Ownership −0.0003⁎ −0.0003⁎⁎ −0.0008⁎⁎⁎

(0.000) (0.000) (0.000)Government 0.0515 0.0482 0.1010

(0.056) (0.059) (0.131)Foreign −0.0710⁎⁎⁎ −0.0700⁎⁎⁎ −0.1836⁎⁎⁎

(0.012) (0.014) (0.026)Intercept −5.0117 0.7693⁎⁎⁎

(8.680) (0.031)Macro-controls Yes No YesIndustry dummies Yes Yes YesYear dummies Yes Yes YesCountry dummies/FE Yes Yes YesR2 0.0692 0.0227 0.0273Obs. 17153 17153 17153

Note: FinancingDummy, the dependent variable of specifications (1) and (2), is a dummy variable equal to 1 iffinancing obstacles equal to2 (Moderate), 3 (MAJOR), or 4 (Very Severe), and 0 otherwise. Financing, the dependent variable of specification (3), takes values between0 and 4, where 0 indicates no financing obstacle and 4 indicates a very severe financing obstacle. Refer to Appendix Table A.2 for thedescriptions of independent variables. Macro-controls (Priv, GDP, GDP per capita, GDP growth, and inflation) and dummies forindustry, year, and country are also included. For brevity, the coefficients are not presented but are available upon request. Theregressions are run with (1) OLS, (2) country fixed effects, and (3) ordered probit with heteroskedasticity-robust standard errors.⁎ Indicates significance level of 10%.⁎⁎ Indicates significance level of 5%.⁎⁎⁎ Indicates significance level of 1%.


The results of Table 3 (Columns 2 and 3) suggest that the determinants of ISO certification are similar forboth manufacturing and service firms except Firm Age; age of the firm is not a major determinant for servicefirms. A plausible explanation for this could be that it requires substantial amount of time for manufacturingfirms to reach certain standard to achieve ISO certification whereas for service firms, achieving such standardis less time consuming.

4.3. ISO certification and firm financial constraints: multivariate analysis

The univariate test results in Table 2 show that ISO-certified firms exhibit significantly lower level of finan-cial constraints than non-certified firms. However, other factors may also affect firm financial constraints. Toinvestigate the impact of ISO certification on firms' financial constraints, we employ the following multivari-ate regression to control for relevant firm characteristics, country-level macro-variables (Priv, GDP, GDP percapita, GDP growth, and Inflation), industry effects, year effects, and country effects:

Financial Constraints ¼ β0 þ β1ISOþ β2Firm Ageþ β3FirmSizeþ β4Exporterþ β5Ownershipþβ6 Government þ β7Foreign þ β8Privþ β9GDP þ β10GDP per Capitaþβ11GDP Growthþ β12Inflationþ β13Industryþ β14Yearþ β15Countryþ ε:

ð5Þ

The dependent variable, Financial Constraints, ismeasured by FinancingDummy and Financing.Wehypoth-esize that ISO-certified firms experience lower financial constraints than non-certified firms. Therefore, weexpect the coefficient of ISO, β1, to be negative and significant.


Columns (1) and (2) of Table 4 present the regression results where Financing Dummy is the dependentvariable. Column (1) shows that the coefficient of ISO, β1, is negative and significant at the 1% level,consistent with our hypothesis that ISO certification is related to lower financial constraints at the firmlevel. This finding holds after controlling for relevant firm characteristics, country-level macro-variables, in-dustry effects, year effects, and country effects. The results further show that Firm Age and Firm Size have neg-ative and significant coefficients, indicating that larger andmore established firms face lower level of financialconstraints. Firmswith high ownership concentration and foreign ownership are also related to lower level offinancial constraints.

Since OLS model does not account for unobservable country specific factors that may also contribute tofirm financial constraints, we mitigate this shortcoming by re-estimating Eq. (5) with country fixed effectsmodel. The fixed effects model is a flexible generalization of OLS which allows for response variables tohave non-normal distribution. The country FE model is more appropriate to control for unobservablecountry-specific factors that also affect firm-level financial constraints. The results based on country FEmodel presented in Column (2) of Table 4 show that the coefficient of ISO, β1, remains negative and significantat the 1% level, after controlling for relevant firm characteristics, industry effects, year effects, and countryfixed effects.

The observed firm-level financial obstacles are polychotomous variables with a natural order, i.e. firmsrank the financial obstacles by five levels, from 0 (no obstacle) to 4 (severe obstacle). We follow Beck et al.(2006) and Barth, Lin, Lin, and Song (2009) and re-estimate Eq. (5) with the ordered probit model, estimatedby the standardmaximum likelihood estimationwith heteroskedasticity robust standard errors.12 The resultsbased on ordered probit model presented in Column (3) of Table 4 further establish our finding that LAC ISO-certified firms exhibit significantly lower level of financial constraints comparing to similar firmswithout anyISO certification.

We further examine whether the ISO certification effect on firm financial constraints varies between ISO-rich and ISO-poor countries, between ISO-rich and ISO-poor industries, and between publicly listed and pri-vate firms.13 The main results are presented in Table 5 (Panel A–Panel F). The results provide evidence thatas expected, the beneficial effect of ISO certification in reducing firm financial constraints is more pronouncedin ISO-rich countries/industries than ISO-poor countries/industries (Panel A to Panel D). The impact of ISO cer-tification in reducing firm financial constraints is significant in both publicly listed and private firms (Panel Eand F).

4.4. Robustness check: additional evidence on ISO certification and financial constraints

As a robustness check, we examine if ISO certification is positively related to other measures of firm finan-cial strength and/or liquidity position.We consider firm's collateral requirement (Collateral), overdraft facility(Overdraft), and capital expenditure (Capex) since all these are related to a firm's financial strength and/or li-quidity position. Collateral is a dummy variable equal to 1 if creditors require collateral for themost recent lineof credit or loan (WBES original data item: ‘k13’), and 0 otherwise. Overdraft is a dummy variable equal to 1 iffirmhas an overdraft facility (WBES original data item: ‘k7’), and 0 otherwise. Capex is a dummy variable equalto 1 if a firm purchased fixed assets, such as machinery, vehicles, equipment, land, or buildings during themost recent year (WBES original data item: ‘k4’), and 0 otherwise.

Existing evidence suggests that for firmswith substantial financial strength, the collateral requirement forcredit or loans should be lower (Ayyagari, Demirgüç-Kunt, & Maksimovic, 2008) and these firms are morelikely to invest in fixed assets (Shin & Park, 1999). Overdraft protection is directly related to a firm's liquidityposition. Overdraft protection has been found to be a substantially large source of liquidity for US firms (Sufi,

12 Angrist (2001) argues that linear probabilitymodel (such asOLS) is just as efficient as the ordered probitmodelwhen dependent var-iable is binary.13 We take the upper quartile of the sample countries and industrieswithmore number of ISO certifiedfirms for defining ISO-rich coun-tries and industries, respectively and lower quartile of the sample countries and industries with less number of ISO certified firms for de-fining ISO-poor countries and industries, respectively.

Table 5The effect of ISO certification on firm financial constraints by country, industry, and ownership.

Dependent variable Financing Dummy Financing

(1) OLS (2) Country FE (3) Ordered Probit

Panel A: Firms in ISO-rich countriesISO −0.0224⁎ −0.0252⁎⁎ −0.0597⁎⁎

(0.012) (0.008) (0.028)R2 0.0729 0.0254 0.0281Obs. 10883 10883 10883

Panel B: Firms in ISO-poor countriesISO −0.0749 −0.0749 −0.2747⁎⁎

(0.065) (0.048) (0.131)R2 0.089 0.0269 0.0227Obs. 577 577 577

Panel C: Firms in ISO-rich industriesISO −0.0459⁎⁎⁎ −0.0495⁎⁎⁎ −0.0894⁎⁎⁎

(0.014) (0.011) (0.030)R2 0.0698 0.0194 0.0269Obs. 9333 9333 9333

Panel D: Firms in ISO-poor industriesISO 0.0401 0.0456 0.1686

(0.057) (0.042) (0.127)R2 0.1796 0.0315 0.0714Obs. 527 527 527

Panel E: Publicly listed firmsISO −0.0644⁎ −0.0577⁎ −0.1522⁎

(0.036) (0.034) (0.087)R2 0.1349 0.0456 0.0545Obs. 1005 1005 1005

Panel F: Private firmsISO −0.0254⁎⁎ −0.0286⁎⁎⁎ −0.0464⁎

(0.011) (0.008) (0.025)R2 0.0729 0.0234 0.0298Obs. 12760 12760 12760

Note: Refer to Table 4 for the description of dependent variables. Refer to Appendix Table A.2 for the descriptions of independentvariables. Firm-controls (Firm Age, Firm Size, Exporter, Ownership, Government, and Foreign), macro-controls (Priv, GDP, GDP percapita, GDP growth, and inflation) and dummies for industry, year, and country are also included. For brevity, the coefficients are notpresented but are available upon request. The regressions are run with (1) OLS, (2) country fixed effects, and (3) ordered probit withheteroskedasticity-robust standard errors.⁎ Indicates significance level of 10%.⁎⁎ Indicates significance level of 5%.⁎⁎⁎ Indicates significance level of 1%.


2009; and Yun, 2009), as well as firms around the world (Lins, Servaes, & Tufano, 2010). Based on the abovediscussion, we expect ISO to be negatively related to Collateral and positively related to both Overdraft andCapex.

As reported in Panel A of Table 6, among our sample firms, 64% were required collateral for their most re-cent line of credit or loan, 64% had overdraft facility, and 58% made investment in fixed assets. Panel B ofTable 6 presents univariate test results for the differences of the above alternative financial constraint mea-sures between ISO-certified and non-certified firms. The results show that ISO certified firms exhibit signifi-cantly lower Collateral and significantly higher Overdraft and Capex than non-certified firms, consistent withour conjectures. The correlationmatrix for these variables reported in Panel C of Table 6 also provides similarresults.

Table 6Alternative measures of firm financial constraints: summary statistics and univariate tests.

Panel A: Summary statistics

Variable Obs Mean Median Std Min Max

ISO 21852 0.212 0 0.409 0 1Collateral 11872 0.636 1 0.481 0 1Overdraft 21148 0.643 1 0.479 0 1Capex 21788 0.576 1 0.494 0 1

Panel B: Univariate tests: ISO versus non-ISO firms

ISO Non-ISO Mean difference

Obs Mean Obs Mean

Collateral 2886 0.614 8986 0.643 −0.029⁎⁎⁎

Overdraft 4500 0.757 16,648 0.612 0.145⁎⁎⁎

Capex 4618 0.706 17,170 0.542 0.164⁎⁎⁎

Panel C: Correlation matrix of variables

ISO Collateral Overdraft

Collateral −0.0259⁎⁎⁎

Overdraft 0.124⁎⁎⁎ −0.0626⁎⁎⁎

Capex 0.1359⁎⁎⁎ −0.0225 0.2103⁎⁎⁎

Note: Panel A presents summary statistics of the relevant variables; Panel B presents univariate tests for the differences of the variablesbetween ISO and non-ISO firms; and Panel C presents the correlation matrix of the variables. ISO is a dummy variable equal to 1 if afirm is ISO certified, and 0 otherwise. Collateral Requirement is a dummy variable equal to 1 if firm required collateral for the mostrecent line of credit or loan, and 0 otherwise. Overdraft is a dummy variable equal to 1 if firm has an overdraft facility at year t, where tis the year the survey was conducted and 0 otherwise. Capex is a dummy variable equal to 1 if firm purchased fixed assets, such asmachinery, vehicles, equipment, land or buildings at year (t − 1), and 0 otherwise. Refer to Appendix Table A.2 for detailed variabledescriptions.⁎ Indicates significance level of 10%.⁎⁎ Indicates significance level of 5%.⁎⁎⁎ Indicates significance level of 1%.


We present themultivariate regression results in Table 7. We employ logit model to examine the ISO cer-tification effect on Collateral in Column (1), Overdraft in Column (2) and Capex in Column (3). As we expect,ISO certified firms are significantly negatively related to Collateral and significantly positively related to bothOverdraft and Capex. The results are robust after controlling for relevant firm characteristics, country-levelmacro-variables, industry effects, year effects, and country effects. These additional results provide concreteevidence that ISO certified firms tend to have significantly higher financial strength and better liquidity posi-tion than non-certified firms.

4.5. ISO certification and firm performance: multivariate analysis

Weemploy bothOLS and country FEmethods to examine if ISO certification affects firmperformance aftercontrolling for relevant firm characteristics, country-level macro-variables, industry effects, year effects, andcountry fixed effects. The regression equations take the forms:

Firm Performance ¼ β0 þ β1ISOþ β2Firm Ageþ β3FirmSizeþ β4Exporterþ β5Ownershipþβ6 Government þ β7Foreign þ β8Privþ β9GDP þ β10GDP per Capitaþβ11GDP Growthþ β12Inflationþ β13Industryþ β14Yearþ β15Countryþ ε:

ð6Þ

Thedependent variables are twoalternativemeasures for labor productivity (Productivity1 and Productivity2)and COGS scaled by sales (COGS). These performancemeasures are defined in Eqs. (1), (2), and (3), respectively.

Table 7ISO certification and alternative measures of firm financial constraints.

Dependent variable Collateral Overdraft Capex

(1) (2) (3)

ISO −0.0960⁎ 0.2722⁎⁎⁎ 0.1684⁎⁎⁎

(0.059) (0.054) (0.047)Firm Age −0.1049⁎⁎⁎ 0.0943⁎⁎⁎ −0.1731⁎⁎⁎

(0.033) (0.027) (0.024)Firm Size 0.0677⁎⁎⁎ 0.4223⁎⁎⁎ 0.4851⁎⁎⁎

(0.020) (0.019) (0.017)Exporter 0.0813 0.2079⁎⁎⁎ 0.3150⁎⁎⁎

(0.055) (0.047) (0.043)Ownership −0.0011 −0.0026⁎⁎⁎ 0.0018⁎⁎⁎

(0.001) (0.001) (0.001)Government −0.2399 −0.8737⁎⁎⁎ 0.2212

(0.407) (0.264) (0.315)Foreign −0.2688⁎⁎⁎ −0.0253 −0.0083

(0.075) (0.063) (0.058)Intercept −139.4213⁎⁎ −51.2672 115.8470⁎⁎⁎

(57.156) (41.492) (40.283)Macro-controls Yes Yes YesIndustry dummies Yes Yes YesYear dummies Yes Yes YesCountry dummies Yes Yes YesLog pseudo-likelihood 5764.6211 −8638.8889 −10,326.982Pseudo-R2 0.1108 0.2095 0.1121Obs. 9959 17148 17310

Note: Refer to Table 6 for the descriptions of dependent variables. Refer to Appendix Table A.2 for the descriptions of independentvariables. Macro-controls (Priv, GDP, GDP per capita, GDP growth, and inflation) and dummies for industry, year, and country are alsoincluded. For brevity, the coefficients are not presented but are available upon request. The regressions are run with logit withheteroskedasticity-robust standard errors.⁎ Indicates significance level of 10%.⁎⁎ Indicates significance level of 5%.⁎⁎⁎ Indicates significance level of 1%.


Themain explanatory variable of our interest is ISO. If ISO certification has a positive effect on firm performance,we expect ISO to be positively related to Productivity1 and Productivity2, i.e. β1 N 0 in Eq. (6), and negativelyrelated to COGS, i.e. β1 b 0 in Eq. (6).

The OLS and country FE results are presented in Table 8. Columns (1) and (2) use Productivity1 as depen-dent variable. The coefficient of ISO, β1 is significantly positive at the 1% level in both columns, providingstrong evidence that LAC firms benefit from ISO certifications in terms of higher labor productivity. This find-ing holds after controlling for firm age, firm size, whether or not a firm exports, ownership concentration, andwhether or not a firm has government and foreign ownership. Country-level macro-variables, industry, time,and country effects are also controlled for. The result also holds after controlling for unobservable country-specific factors, as shown in Column (2). Columns (3) and (4) use Productivity2 as dependent variable andthe results provide further evidence to our finding of the beneficial impact of ISO certification on firm perfor-mance in terms of its labor productivity. Columns (5) and (6) use COGS as the performance measure. The co-efficient of ISO, β1 is significantly negative indicating that LAC firms benefit from ISO certifications in terms oflower cost of sales. Our overall results on the relation between ISO and firm performance show that LAC firmsbenefit from the adoption of ISO certifications.

We further explore whether the effect of ISO certification on firm performance varies betweenmanufacturing and service firms, between ISO-rich and ISO-poor countries, between ISO-rich and ISO-poorindustries, and between publicly listed and private firms. The main results are presented in Table 9 (PanelA – Panel H). The results provide evidence that the beneficial effect of ISO certification in enhancing firm per-formance is prevalent in bothmanufacturing and service firms (Panel A and B) and also in both publicly listed

Table 8The effect of certification on firm performance by sector.

Dependent variable Productivity1 Productivity2 COGS

(1) OLS (2) Country FE (3) OLS (4) Country FE (5) OLS (6) Country FE

ISO 0.3080⁎⁎⁎ 0.2985⁎⁎⁎ 0.3636⁎⁎⁎ 0.3675⁎⁎⁎ −0.0141⁎ −0.0181⁎

(0.024) (0.023) (0.037) (0.043) (0.008) (0.011)Firm Age 0.0671⁎⁎⁎ 0.0713⁎⁎⁎ 0.1045⁎⁎⁎ 0.1043⁎⁎⁎ −0.0081⁎⁎ −0.0075

(0.012) (0.019) (0.019) (0.033) (0.004) (0.005)Firm Size 0.0631⁎⁎⁎ 0.0637⁎⁎⁎ 0.0912⁎⁎⁎ 0.0904⁎⁎⁎ −0.0065⁎⁎ −0.0061⁎⁎

(0.008) (0.016) (0.013) (0.021) (0.003) (0.002)Exporter 0.2303⁎⁎⁎ 0.2413⁎⁎⁎ 0.3056⁎⁎⁎ 0.3034⁎⁎⁎ −0.0010 0.0032

(0.021) (0.031) (0.032) (0.049) (0.007) (0.010)Ownership −0.0005 −0.0006 −0.0007 −0.0006 −0.0002⁎ −0.0002⁎⁎

(0.000) (0.001) (0.001) (0.001) (0.000) (0.000)Government 0.1820 0.1706 0.4806⁎⁎ 0.4793⁎⁎ −0.0337 −0.0339

(0.158) (0.128) (0.221) (0.234) (0.039) (0.051)Foreign 0.4598⁎⁎⁎ 0.4605⁎⁎⁎ 0.4412⁎⁎⁎ 0.4419⁎⁎⁎ −0.0224⁎⁎ −0.0226⁎⁎⁎

(0.030) (0.045) (0.047) (0.050) (0.009) (0.008)Intercept 0.9537 13.1666⁎⁎⁎ −40.7318 11.2788⁎⁎⁎ 16.9395⁎⁎ 0.8772⁎⁎⁎

(19.776) (0.095) (32.824) (1.114) (7.549) (0.082)Macro-controls Yes No Yes No Yes NoIndustry dummies Yes Yes Yes Yes Yes YesYear dummies Yes Yes Yes Yes Yes YesCountry dummies/FE Yes Yes Yes Yes Yes YesR2 0.8544 0.0267 0.8187 0.0443 0.0455 0.0096Obs. 17352 17352 9320 9320 9803 9803

Note: Productivity1, the dependent variable of specification (1) and (2), is log [sales (t − 1)/employees (t − 1)], where t is the year thesurvey was conducted. Productivity2, the dependent variable of specification (3) and (4), is log [{sales (t − 1) − COGS (t − 1)}/employees (t − 1)]. COGS, the dependent variable of specification (5) and (6), is a firm's annual cost of goods sold scaled by sales atyear (t − 1). Refer to Appendix Table A.2 for the descriptions of independent variables. Macro-controls (Priv, GDP, GDP per capita,GDP growth, and inflation) and dummies for industry, year, and country are also included. For brevity, the coefficients are notpresented but are available upon request. The regressions are run with OLS and country fixed effects with heteroskedasticity-robuststandard errors.⁎ Indicates significance level of 10%.⁎⁎ Indicates significance level of 5%.⁎⁎⁎ Indicates significance level of 1%.


and private firms (Panel G and H).14 However, such impact is less pronounced in ISO-poor countries (PanelD) and ISO-poor industries (Panel F) comparing to ISO-rich countries (Panel C) and ISO-rich industries(Panel E).

We next examine the effect of ISO certification on performance for different types of firms. We add threeinteraction terms to our baseline model: ISO × Firm Age, ISO × Firm Size, and ISO × Ownership. The results arepresented in Table 10. The coefficients of the three interaction terms in columns (1)–(4) suggest that olderand smaller firms and firms with more ownership concentration are more likely to benefit (in terms of pro-ductivity) from ISO certification.

5. Robustness tests

5.1. Unbalanced responses: a probit analysis

The main dependent variable in our study is firms' response to questionnaire concerning their financialconstraints. The response is unbalanced across the five levels of answers (0–4) and the unbalanced nature

14 Wedonot use Productivity2 and COGS as dependent variable for servicefirms because of the insufficientWBES data tomeasure cost ofservices and therefore, Productivity2 and COGS.

Table 9The effect of ISO certification on firm performance by sector, country, industry, and ownership.



Panel A: Manufacturing firmsISO 0.3138⁎⁎⁎ 0.3067⁎⁎⁎ 0.3649⁎⁎⁎ 0.3687⁎⁎⁎ −0.0132⁎ −0.0171⁎⁎

(0.028) (0.035) (0.037) (0.042) (0.008) (0.008)R2 0.8715 0.0351 0.8191 0.0408 0.0458 0.0093Obs. 11698 11698 9236 9236 9713 9713

Panel B: Service firmsISO 0.2254⁎⁎⁎ 0.2178⁎⁎⁎

(0.047) (0.046)R2 0.8332 0.0077Obs. 5023 5023

Panel C: Firms in ISO-rich countriesISO 0.3247⁎⁎⁎ 0.3153⁎⁎⁎ 0.4168⁎⁎⁎ 0.4199⁎⁎⁎ −0.0164⁎ −0.0207

(0.027) (0.027) (0.042) (0.042) (0.009) (0.013)R2 0.868 0.0162 0.8226 0.0292 0.0523 0.012Obs. 11003 11003 6630 6630 6922 6922

Panel D: Firms in ISO-poor countriesISO 0.1904⁎ 0.1904⁎ −0.0023 −0.0023 0.0206 0.0206

(0.112) (0.084) (0.196) (0.286) (0.036) (0.046)Obs. 580 580 220 220 225 225R2 0.0939 0.0692 0.1104 0.073 0.1914 0.0391

Panel E: Firms in ISO-rich industriesISO 0.3096⁎⁎⁎ 0.2979⁎⁎⁎ 0.3824⁎⁎⁎ 0.3867⁎⁎⁎ −0.0238⁎⁎ −0.0266⁎⁎⁎

(0.032) (0.034) (0.050) (0.047) (0.010) (0.008)R2 0.8618 0.0281 0.8269 0.0418 0.0418 0.0117Obs. 9447 9447 4773 4773 5018 5018

Panel F: Firms in ISO-poor industriesISO 0.2388⁎ 0.2352⁎⁎ 0.1302 0.1302 −0.0188 −0.0188

(0.134) (0.103) (0.324) (0.189) (0.046) (0.080)R2 0.7575 0.0167 0.5108 0.2287 0.1621 0.0574Obs. 532 532 265 265 277 277

Panel G: Publicly listed firmsISO 0.4173⁎⁎⁎ 0.3785⁎⁎⁎ 0.6485⁎⁎⁎ 0.6819⁎⁎⁎ −0.0139 −0.0233

(0.109) (0.111) (0.183) (0.142) (0.039) (0.040)R2 0.7783 0.0363 0.7567 0.1107 0.1075 0.0232Obs. 1022 1022 503 503 530 530

Panel H: Private firmsISO 0.3164⁎⁎⁎ 0.3123⁎⁎⁎ 0.3844⁎⁎⁎ 0.3883⁎⁎⁎ −0.0192⁎⁎ −0.0230⁎

(0.027) (0.030) (0.041) (0.046) (0.008) (0.013)R2 0.8666 0.0139 0.8329 0.0293 0.0518 0.0118Obs. 12888 12888 7007 7007 7333 7333

Note: Refer to Table 8 for the descriptions of dependent variables. Refer to Appendix Table A.2 for the descriptions of independentvariables. Firm-controls (Firm Age, Firm Size, Exporter, Ownership, Government, and Foreign), macro-controls (Priv, GDP, GDP percapita, GDP growth, and inflation) and dummies for industry, year, and country are also included. For brevity, the coefficients are notpresented but are available upon request. The regressions are run with OLS and country fixed effects with heteroskedasticity-robuststandard errors.⁎ Indicates significance level of 10%.⁎⁎ Indicates significance level of 5%.⁎⁎⁎ Indicates significance level of 1%.


of the responses might invalidate the estimates obtained from the ordered probit and other used regressions.A few outliers in one of the levels with a small number of responses could bias the overall results (see Barthet al., 2009 and Beck et al., 2006). To address these potential issues and obtain a somewhat balanced

Table 10The effect of ISO certification on performance for different types of firms.



ISO × Firm Age 0.0994⁎⁎⁎ 0.0984⁎⁎⁎ 0.0949⁎⁎ 0.0969⁎ 0.0038 0.0032(0.029) (0.027) (0.045) (0.049) (0.009) (0.012)

ISO × Firm Size −0.0710⁎⁎⁎ −0.0714⁎⁎⁎ −0.0461⁎ −0.0457 −0.0012 −0.0015(0.017) (0.021) (0.026) (0.029) (0.005) (0.005)

ISO × Ownership 0.0028⁎⁎⁎ 0.0029⁎⁎⁎ 0.0027⁎⁎ 0.0026⁎⁎ −0.0002 −0.0001(0.001) (0.001) (0.001) (0.001) (0.000) (0.000)

ISO 0.1126 0.1026 0.0810 0.0792 −0.0093 −0.0146(0.111) (0.092) (0.184) (0.198) (0.038) (0.053)

Firm Age 0.0458⁎⁎⁎ 0.0503⁎⁎⁎ 0.0836⁎⁎⁎ 0.0829⁎⁎ −0.0090⁎⁎ −0.0083⁎

(0.013) (0.019) (0.022) (0.033) (0.005) (0.005)Firm Size 0.0816⁎⁎⁎ 0.0823⁎⁎⁎ 0.1037⁎⁎⁎ 0.1027⁎⁎⁎ −0.0062⁎⁎ −0.0057⁎

(0.009) (0.017) (0.015) (0.024) (0.003) (0.003)Exporter 0.2287⁎⁎⁎ 0.2396⁎⁎⁎ 0.3036⁎⁎⁎ 0.3012⁎⁎⁎ −0.0011 0.0031

(0.021) (0.031) (0.032) (0.049) (0.007) (0.010)Ownership −0.0011⁎⁎⁎ −0.0013⁎⁎ −0.0014⁎⁎ −0.0013 −0.0001 −0.0002⁎

(0.000) (0.001) (0.001) (0.001) (0.000) (0.000)Government 0.1939 0.1829 0.5116⁎⁎⁎ 0.5100⁎⁎ −0.0341 −0.0339

(0.160) (0.127) (0.221) (0.234) (0.039) (0.051)Foreign 0.4641⁎⁎⁎ 0.4646⁎⁎⁎ 0.4454⁎⁎⁎ 0.4461⁎⁎⁎ −0.0217⁎⁎ −0.0220⁎⁎⁎

(0.030) (0.045) (0.048) (0.047) (0.010) (0.008)Intercept 1.6699 13.2051⁎⁎⁎ −40.6309 11.3964⁎⁎⁎ 16.9686⁎⁎ 0.8758⁎⁎⁎

(19.762) (0.086) (32.794) (1.065) (7.553) (0.083)Macro-controls Yes No Yes No Yes NoIndustry dummies Yes Yes Yes Yes Yes YesYear dummies Yes Yes Yes Yes Yes YesCountry dummies/FE Yes Yes Yes Yes Yes YesR2 0.8547 0.0265 0.8189 0.0442 0.0456 0.0095Obs. 17352 17352 9320 9320 9803 9803

Note: Refer to Table 8 for the descriptions of dependent variables. Refer to Appendix Table A.2 for the descriptions of independentvariables. Macro-controls (Priv, GDP, GDP per capita, GDP growth, and inflation) and dummies for industry, year, and country are alsoincluded. For brevity, the coefficients are not presented but are available upon request. The regressions are run with OLS and countryfixed effects with heteroskedasticity-robust standard errors.⁎ Indicates significance level of 10%.⁎⁎ Indicates significance level of 5%.⁎⁎⁎ Indicates significance level of 1%.


responses, we follow Beck et al. (2006) and Barth et al. (2009) and construct a binary variable, the financialconstraints dummy (Fin_dum)which takes the value of zero if the response is “noobstacle” in assessingfinan-cial obstacles a firm faces, and one if the response is “minor”, “moderate”, “major”, or “very severe”. This tech-nique creates a binary variable for firms' responses, “no obstacle” or “obstacle”. We then use the Fin_dum asdependent variable and repeat the regressions in Table 4 with probit regression. The results presented inTable 11 are largely consistent with those obtained through using other models. The coefficient of ISO is neg-ative and significant at the 1% level, indicating that ISO certification is significantly related to lower financialconstraints.

5.2. Endogeneity tests

The regression models in Tables 4–10 assume a firm's decision to adopt ISO certification to be exogenousto firm-level financial obstacles. However, a firm's ISO adoption decision may also be endogenous, i.e. theremay be a reverse causality between the financial constraints and a firm's certification adoption decision.Since the decision is mostly voluntary (as opposed to regulatory requirement), a firm takes into accountmany factors when deciding whether to adopt quality certification such as ISO. For example, a firm is

Table 11Robustness check: a probit analysis.

Dependent variable: Fin_dum (1) Probit

ISO −0.0685⁎⁎⁎

(0.028)⁎

Firm Age −0.0998⁎⁎⁎

(0.015)⁎⁎

Firm Size −0.0293⁎⁎⁎

(0.009)Exporter 0.0650⁎⁎⁎

(0.026)Ownership −0.0017⁎⁎⁎

(0.000)Government −0.0631

(0.150)Foreign −0.2008⁎⁎⁎

(0.032)Macro-controls YesIndustry dummies YesYear dummies YesCountry dummies YesLog pseudo-likelihood −9649.1673Obs. 17153

Note: The dependent variable is Fin-Dum which takes the value of 0 if the response is “no obstacle” and 1 if the response is “Minor”,“Moderate”, “Major”, or “Very Severe” financial. Refer to Appendix Table A.2 for the descriptions of independent variables. Macro-controls (Priv, GDP, GDP per capita, GDP growth, and inflation) and dummies for industry, year, and country are also included. Forbrevity, the coefficients are not presented but are available upon request. The regressions are run with probit, which is based onstandard maximum likelihood estimation with heteroskedasticity-robust standard errors.⁎ Indicates significance level of 10%.⁎⁎ Indicates significance level of 5%.⁎⁎⁎ Indicates significance level of 1%.


financially constrained anddecides not to apply for ISO certification because it does not believe the applicationwould be approved due to its high level of existing debt or other observed factors. On the other hand, it couldbe that a firm will only adopt ISO certification if it is in relatively good financial health. The same rationale isalso applicable between firm performance and its ISO certification adoption decision. It could be possible thatonly relatively good performingfirms pursue ISO certifications. For already good performingfirms, an ISO cer-tification may be regarded as another “feather on the hat”. As a result, a situation may arise that poorperforming firms are clustered in the non-ISO group, while good performing firms are clustered in the ISO-certified group. Therefore, the potential self-selection bias needs to be accounted for.

We conduct endogeneity tests using the propensity score matching (PSM) technique. A firm's ISO adop-tion decision may be systematically related to certain firm-level characteristics. To control for the non-random nature of a firm's ISO adoption decision, we use the PSM technique to select matching firms foreach ISO certified firm. The PSM technique estimates the propensity scores (likelihood to participate, or re-ceive the treatment) of all observations andmatches each treated observationwith one ormore untreated ob-servations (the control) according to their propensity scores. As proposed by Heckman and Navarro-Lozano(2004) and Smith and Todd (2005), the logit model that estimates the propensity score should include onlythe variables that influence both the participation decision and the outcome variable. Based on finance theoryand existing empirical evidence as discussed in Section 3,we employ the following logitmodel to estimate thepropensity score for a firm to have ISO certification:

ISO ¼ β0 þ β1Firm Ageþ β2Firm Sizeþ β3Exporter þ β4Ownershipþβ5Government þ β6Foreignþ ε: ð7Þ

Table 12Propensity score matching.

Panel A: Propensity scores: logit estimation

Variable Coef. Std. Err. z

Firm Age 0.2342 0.039 6.03Firm Size 0.5883 0.024 24.54Exporter 0.8207 0.059 13.8Ownership −0.0008 0.001 −0.81Government 0.5536 0.389 1.42Foreign 0.8580 0.079 10.87Intercept −4.6375 0.159 −29.14Log pseudo-likelihood −4018.9881Pseudo-R2 0.2135Obs. 9229

Panel B: Average treatment effects on the treated (ATT)

Matching algorithm Outcome Sample Treated(N = 2236)

Controls(N = 6993)

Difference treated-controls

K-nearest neighbor matching (1) Financing Unmatched 1.5045 1.7203 −0.2158⁎⁎⁎

ATT 1.5050 1.6180 −0.1130⁎⁎⁎

(2) Productivity1 Unmatched 14.0648 13.1255 0.9393⁎⁎⁎

ATT 14.0693 13.2683 0.8010⁎⁎⁎

(3) Productivity2 Unmatched 13.0257 12.0210 1.0047⁎⁎⁎

ATT 13.0271 12.1650 0.8621⁎⁎⁎

(4) COGS Unmatched 0.5716 0.5956 −0.0240⁎⁎⁎

ATT 0.5733 0.5993 −0.0260⁎⁎⁎

Note: Panel A presents the logit regression estimates and Panel B presents the average treatment effects (ATT) for firm financialconstraints and performance variables. Refer to Appendix Table A.2 for the descriptions of variables. The regressions are run with logitwith heteroskedasticity-robust standard errors.⁎ Indicates significance level of 10%.⁎⁎ Indicates significance level of 5%.

⁎⁎⁎ Indicates significance level of 1%.


The variables are defined as before and in Appendix Table A.2. We drop observations that have missingvalues in any variables in Eq. (5) or in any of the outcome variables (financial constraints and performancemeasures). We then apply the “psmatch2” module in STATA with common support constraint that dropsany treated observation whose propensity score is higher than the maximum or lower than the minimumscore of the controls. We also trim at the bottom 1% those treated observations with the lowest propensityscore density. We then apply the k-nearest neighbors matching method to construct a matching sample ofthe controls (non-ISO) with the treated sample (ISO).

The PSM results are presented in Table 12. Panel A presents the logit regression results (Eq. (7)), whilePanel B presents the average treatment effect on the treated (ATT). As shown in Panel B, the final numberof treated observations is 2236 and the number of controls is 6993, indicating one treated observation hasone or more matching controls. Panel A shows that most of the right-hand side variables in Eq. (7) are signif-icantly related to the propensity score of a firm's decision to participate. Panel B further shows that the aver-age treatment effect on the treated (ATT) is significantly lower than the controls when the outcome isfinancial constraints (Financing). ATT for the treated is significantly higher than the controls when the out-come variables are Productivity1 and Productivity2 and significantly lower when the outcome is COGS.

We then re-estimate OLS, country FE, and ordered probit models using only the propensity score matchedsample for Financing Dummy (specifications (1) and (2)) and Financing (specification (3)). The results are

Table 13The effect of ISO certification on financial constraints with propensity score matching approach.

Dependent variable Financing dummy Financing

(1) OLS (2) Country FE (3) Ordered Probit

Treated (ISO) −0.0319⁎⁎ −0.0366⁎⁎⁎ −0.0740⁎⁎⁎

(0.014) (0.013) (0.031)Firm Age −0.0336⁎⁎⁎ −0.0328⁎⁎⁎ −0.0759⁎⁎⁎

(0.007) (0.007) (0.016)Firm Size −0.0223⁎⁎⁎ −0.0222⁎⁎⁎ −0.0545⁎⁎⁎

(0.005) (0.007) (0.010)Exporter 0.0163 0.0221 0.0542⁎⁎

(0.012) (0.018) (0.027)Ownership −0.0005⁎⁎ −0.0005⁎⁎⁎ −0.0012⁎⁎⁎

(0.000) (0.000) (0.000)Government 0.1285⁎ 0.1288 0.2178

(0.075) (0.084) (0.199)Foreign −0.0802⁎⁎⁎ −0.0796⁎⁎⁎ −0.1859⁎⁎⁎

(0.017) (0.018) (0.038)Intercept −5.6780 1.3113⁎⁎⁎

(13.043) (0.092)Macro-controls Yes Yes YesIndustry dummies Yes Yes YesYear dummies Yes Yes YesCountry dummies/FE Yes Yes YesR2 0.0724 0.0258 0.0266Obs. 9229 9229 9229

Note: Refer to Table 4 for the description of dependent variables. Refer to Appendix Table A.2 for the descriptions of independentvariables. Macro-controls (Priv, GDP, GDP per capita, GDP growth, and inflation) and dummies for industry, year, and country are alsoincluded. For brevity, the coefficients are not presented but are available upon request. The regressions are run with (1) OLS,(2) country fixed effects, and (3) ordered probit with heteroskedasticity-robust standard errors.⁎ Indicates significance level of 10%.⁎⁎ Indicates significance level of 5%.⁎⁎⁎ Indicates significance level of 1%.


reported in Table 13. As shown in Columns (1), (2), and (3), the coefficients of the treated dummy (Treated orISO) are negative and significant indicating that ISO certification is related to lower firm level financialconstraints.

We further re-examine the effects of ISO certification on firm performance using only the propensityscore matched sample of the treated and the controls. We re-estimate Eq. (6) using OLS and country fixedeffects. The results are reported in Table 14. The results are consistent with those in Table 8 and Table 9.ISO has a positive and significant effect on Productivity1 and Productivity2 and negative and significant ef-fect on COGS.

The above results suggest that after controlling for potential endogeneity caused by selection bias, ouroverall findings remain robust.

6. Conclusions

Using a unique survey dataset, we first investigate the major determinants of a firm's adoption of ISOcertification for 21,852 sample firms in 31 LAC countries over the period between 2006 and 2010. Sec-ondly, we examine whether ISO certification could signal a firm's financial constraints and strength. Fi-nally, we investigate the firm-level benefits of ISO certification in terms of labor productivity and costof sales.

Our empirical evidence suggests that larger and older firms are more likely to adopt ISO certification.While ownership concentration and government ownership are not important determinants of ISO

Table 14The effect of ISO certification on firm performance with propensity score matching approach.

DV Productivity1 Productivity2 COGS

(1) OLS (2) Country FE (3) OLS (4) Country FE (5) OLS FE (6) Country FE

Treated (ISO) 0.3055⁎⁎⁎ 0.2978⁎⁎⁎ 0.3611⁎⁎⁎ 0.3649⁎⁎⁎ −0.0233⁎⁎⁎ −0.0272⁎⁎⁎

(0.030) (0.029) (0.037) (0.044) (0.006) (0.008)Firm Age 0.0748⁎⁎⁎ 0.0766⁎⁎⁎ 0.1064⁎⁎⁎ 0.1064⁎⁎⁎ −0.0063⁎⁎ −0.0056

(0.015) (0.027) (0.020) (0.034) (0.003) (0.004)Firm Size 0.0895⁎⁎⁎ 0.0900⁎⁎⁎ 0.0928⁎⁎⁎ 0.0919⁎⁎⁎ −0.0017 −0.0012

(0.010) (0.018) (0.013) (0.021) (0.002) (0.002)Exporter 0.2945⁎⁎⁎ 0.3036⁎⁎⁎ 0.3065⁎⁎⁎ 0.3043⁎⁎⁎ −0.0003 0.0036

(0.025) (0.038) (0.032) (0.050) (0.005) (0.007)Ownership −0.0007⁎ −0.0008 −0.0006 −0.0005 −0.0002⁎⁎ −0.0002⁎⁎⁎

(0.000) (0.001) (0.001) (0.001) (0.000) (0.000)Government 0.3462⁎ 0.3445 0.4124⁎ 0.4103⁎ −0.0136 −0.0135

(0.201) (0.214) (0.216) (0.223) (0.033) (0.041)Foreign 0.3953⁎⁎⁎ 0.3959⁎⁎⁎ 0.4467⁎⁎⁎ 0.4476⁎⁎⁎ −0.0216⁎⁎⁎ −0.0217⁎⁎⁎

(0.040) (0.044) (0.047) (0.050) (0.008) (0.005)Intercept −8.0468 12.1264⁎⁎⁎ −39.1844 11.2693⁎⁎⁎ 10.3647⁎ 0.5044⁎⁎⁎

(25.323) (0.769) (33.031) (1.112) (5.862) (0.186)Macro-Controls Yes No Yes No Yes NoIndustry Dummies Yes Yes Yes Yes Yes YesYear Dummies Yes Yes Yes Yes Yes YesCountry Dummies/FE Yes Yes Yes Yes Yes YesR2 0.8833 0.0424 0.8196 0.0432 0.0626 0.0191Obs. 9229 9229 9229 9229 9229 9229

Note: Refer to Table 8 for the descriptions of dependent variables. Refer to Appendix Table A.2 for the descriptions of independentvariables. Macro-controls (Priv, GDP, GDP per capita, GDP growth, and inflation) and dummies for industry, year, and country are alsoincluded. For brevity, the coefficients are not presented but are available upon request. The regressions are run with OLS and countryfixed effects with heteroskedasticity-robust standard errors.⁎ Indicates significance level of 10%.⁎⁎ Indicates significance level of 5%.⁎⁎⁎ Indicates significance level of 1%.


adoption, we find that exporters and firms with foreign ownership are more likely to adopt ISO certifica-tion than their respective counterparts.

Our cross-section, multi-country study shows that ISO-certified firms exhibit significantly lower level offinancial constraints than non-ISO firms. This result is robust after controlling for several relevant firm andcountry-level characteristics and country fixed effects. The finding is also robust to several alternative mea-sures of financial strength and/or liquidity. These results generally support the conjecture that the adoptionof an ISO certification plays the role of “guarantee” and signals a firm's financial strength. Accredited by athird-party, ISO certification reduces information asymmetry between firms and creditors/investors. Ourstudy provides the first empirical evidence in the ISO and financial constraint literature that ISO certificationis linked to lower financial constraints.

In addition, our study provides concrete evidence that ISO-certified firms exhibit significantlyhigher labor productivity and lower cost of sales than non-certified firms, consistent with the findingsin Starke et al. (2012), Elmuti and Kathawala (1997), Sharma (2005), Lafuente et al. (2009), and Terlaakand King (2005a). The adoption, implementation, and maintenance of ISO certification help firms cre-ate continuous quality monitoring and measurement, follow sufficiently well-defined and documentedprocedures to ensure that appropriate corrective action is taken whenever defects occur, and producemore effective worker training programs. These mechanisms inherent in the ISO certification help im-prove labor productivity and costs effectiveness in certified firms. These findings are consistent withthe predictions of the established theories in the academic literature in explaining the possible sourcesof gains following ISO certification, i.e. the internal improvement theory and the external improvementtheory.

ISO and non-ISO firms by country and industry.Sources of data: WBES = World Bank Enterprise Survey (WBES).

Panel A: ISO and non-ISO firms by country

Country Survey Year(s) Total ISO Non-ISO

N % N %

Antigua and Barbuda 2010 133 5 4 128 96Argentina 2006, 2010 1947 590 30 1357 70Bahamas 2010 115 41 36 74 64Barbados 2010 128 31 24 97 76Belize 2010 148 4 3 144 97Bolivia 2006, 2010 722 147 20 575 80Brazil 2009 1622 314 19 1308 81Chile 2006, 2010 1742 528 30 1214 70Colombia 2006, 2010 1796 382 21 1414 79Costa Rica 2010 407 68 17 339 83Dominica 2010 141 2 1 139 99Dominican Republic 2010 322 63 20 259 80Ecuador 2006, 2010 882 191 22 691 78El Salvador 2006, 2010 908 154 17 754 83Grenada 2010 129 46 36 83 64Guatemala 2006, 2010 896 137 15 759 85Guyana 2010 135 36 27 99 73Honduras 2006, 2010 639 119 19 520 81Jamaica 2010 314 70 22 244 78Mexico 2006, 2010 2645 671 25 1974 75Nicaragua 2006, 2010 720 127 18 593 82Panama 2006, 2010 629 110 17 519 83Paraguay 2006, 2010 787 75 10 712 90Peru 2006, 2010 1475 321 22 1154 78St. Kitts and Nevis 2010 125 27 22 98 78St. Lucia 2010 139 2 1 137 99St. Vincent and Grenadines 2010 142 32 23 110 77Suriname 2010 152 28 18 124 82Trinidad and Tobago 2010 323 57 18 266 82Uruguay 2006, 2010 971 156 16 815 84Venezuela 2006, 2010 618 98 16 520 84Total 21852 4632 21 17220 79

Panel B: ISO and non-ISO firms by industry

Industry ISO Non-ISO

N % N %

Auto and auto components 69 1.49 60 0.35Chemicals and pharmaceuticals 603 13.02 1112 6.46Construction and transportation 160 3.45 597 3.47Electronics 70 1.51 73 0.42Food 694 14.98 2178 12.65Garments 177 3.82 1666 9.68Hotels and restaurants 74 1.6 261 1.52Metals and machinery 519 11.2 1161 6.74Non-metallic and plastic materials 252 5.44 665 3.86Other manufacturing 694 14.98 2870 16.67Other services 506 10.92 1804 10.48Retail and wholesale trade 589 12.72 3184 18.49Textiles 217 4.68 1436 8.34Wood and furniture 8 0.17 152 0.88Total 4632 100 17219 100

Appendix A


Variable definitions and data sources.Sources of data: WDI = World Development Indicators; WBES = World Bank Enterprise Survey (WBES), IFS = International FinancialStatistics.

Variable Definition — t is the survey year Original source

ISO Dummy variable equal 1 if a firm is ISO certified, and 0 otherwise(WBES data item ‘b8’)

WBES

Financing “How problematic is access to finance for the currentoperations of a business?” No Obstacle = 0, Minor Obstacle = 1,Moderate Obstacle = 2, Major Obstacle = 3, and Very Severe Obstacle = 4.

WBES

Financing Dummy Dummy variable equal to 1 if financial obstacles equal to 2 (moderate),3 (major), or 4 (very severe), and 0 otherwise.

WBES

Productivity1 Log of the ratio of a firm's sales in year (t − 1) (WBES data item ‘d2’)over its number of permanent, full-time employees at (t − 1) (WBES data item ‘l1’).

WBES

Productivity2 Log of the ratio of the difference between a firm's sales in year (t − 1)(WBES data item ‘d2’) and its total annual cost of labor(including wages, salaries, bonuses, social payments), raw materials andintermediate goods used in production (WBES data item ‘n2a’ and ‘n2e’)in year (t − 1) over its number of permanent, full-timeemployees at (t − 1) (WBES data item ‘l1’).

WBESWBES

COGS Ratio of firm's total annual cost of labor (including wages, salaries,bonuses, social payments), raw materials and intermediate goods used inproduction (WBES data item ‘n2a’ and ‘n2e’) to sales at the end ofyear (t − 1) (WBES data item ‘d2’).

WBES

Firm Age Logarithm of a firm's actual age, age = survey year — firm foundingyear (WBES data item ‘b5’).

WBES

Firm Size Logarithm of number of permanent, full-time employees at the endof year (t − 1) (WBES data item ‘l1’).

WBES

Exporter Dummy variable equal to 1 if firm exports (using WBES dataitems ‘d3a’, ‘d3b’ and ‘d3c’), 0 otherwise.

WBES

Ownership Percentage of firm owned by the largest owner(s) (WBES data item ‘b3’). WBESGovernment Dummy variable that takes on the value 1 if firm is owned by government/state

(WBES data item ‘b2c’), 0 otherwise.WBES

Foreign Dummy variable equal to 1 if any foreign company or individual has afinancial stake in the ownership of the firm (WBES data item ‘b2b’), 0 otherwise.

WBES

Priv Private credit by deposit money banks to GDP, calculatedusing the following deflation method: {(0.5) ∗ [Ft/P_et + Ft − 1/P_et − 1]}/[GDPt/P_at]where F is credit to the private sector, P_e is end-of period CPI, andP_a is average annual CPI.

IFS

GDP GDP in current US$, the average over year (t − 3), (t − 2) and (t − 1). WDIGDP per Capita Real per capita in US$, the average real GDP per capita over year

(t − 3), (t − 2) and (t − 1).WDI

GDP growth GDP growth rate, the average over year (t − 3), (t − 2) and (t − 1). WDIInflation Log difference of consumer prices, the average over year (t − 3), (t − 2) and (t − 1). WDICollateral Dummy variable equal to 1 if firm required collateral for the most recent

line of credit or loan (WBES data item ‘k13’), and 0 otherwise.WBES

Overdraft Dummy variable equal to 1 if firm has an overdraft facility at yeart (WBES data item ‘k7’), and 0 otherwise.

WBES

Capex Dummy variable equal to 1 if firm purchased fixed assets, such as machinery,vehicles, equipment, land or buildings at year (t − 1)(WBES data item ‘k4’), and 0 otherwise.

WBES


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