Ole Kristian Hope

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Financial Reporting Quality and Investment Efficiency of

Private Firms in Emerging Markets

Feng ChenRotman School of Management

University of [email protected]

Ole-Kristian Hope

Rotman School of ManagementUniversity of Toronto

[email protected]

Qingyuan LiSchool of Economics and Management

Wuhan [email protected]

Xin Wang

School of AccountancyChinese University of Hong Kong

[email protected]

March 16, 2010

.


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Financial Reporting Quality and Investment Efficiency of

Private Firms in Emerging Markets

Abstract

Prior research shows that financial reporting quality is positively related to investment efficiency

for large, U.S. publicly traded companies. However, it is not obvious whether this finding

extends to smaller, privately-held companies in emerging markets (i.e., a very different

environment characterized by low investor protection and more concentrated ownership

structures). Using firm-level survey data from the World Banks Enterprise Survey, this paper

studies the relation between accounting quality and investment efficiency for a sample of

relatively small private firms in emerging markets. We find strong evidence that accounting

quality positively affects investment efficiency (i.e., is negatively related to both

underinvestment and overinvestment) for our sample of firms. Second, we show that the relation

between financial reporting quality and investment efficiency is stronger if a firms investment is

funded relatively more through bank financing than other sources of financing. Third, we

document that, for firms with the strongest incentives to manage earnings for tax purposes, the

positive association between accounting quality and investment efficiency is reduced. Such a

connection between tax-minimization incentives and the informational role of earnings has often

been asserted in the literature, but to date there is limited empirical evidence on this issue.

Keywords: Investment Efficiency, Over- and Under-Investment, Accounting Quality, Private

Firms, Emerging Markets, Financing Sources, Tax Incentives


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1. Introduction

One objective of financial reporting information is to facilitate the efficient allocation of

capital in the economy. An important aspect of this role is to improve firms investment

decisions. Specifically, theory suggests that improved financial transparency has the potential to

alleviate both over- and underinvestment problems and recent studies support this prediction

(e.g., Biddle and Hilary 2006; Hope and Thomas 2008; McNichols and Stubben 2008; Biddle,

Hilary, and Verdi 2009). This evidence, however, has been mostly limited to large, publicly

traded companies in the United States. It is not obvious that the findings documented in prior

studies extend to different settings. In this study, we extend the literature by examining the

relation between financial reporting quality and investment efficiency for a sample ofprivate

firms in emerging markets.

It is important to study private firms from emerging markets for several reasons. First,

private firms (i.e., firms that are not traded on public stock exchanges) are the predominant

organization in most countries. More than 99% of limited liability companies are not listed on a

stock exchange for most countries (e.g., Nagar, Petroni, and Wolfenzon 2005; Berzins, Bhren,

and Rydland 2008). In the aggregate, non-listed firms have about four times more employees,

three times higher revenues, and twice the amount of assets than listed firms (Berzins et al. 2008).

In spite of their economic importance and likely differences from public companies,

comparatively little is known about financial reporting by private firms. In particular, based on

prior literature discussed further in Section 2, it is not clear whether accounting information

plays the same role in mitigating information asymmetries for private firms as it does for public

firms.

Second, compared with a large literature on developed countries accounting systems and


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managers reporting incentives, much less is known about the role of accounting in developing

countries. The extent to which findings for public firms from developed countries apply to our

setting is unclear. Some authors (e.g., Ball, Kothari, and Robin 2000) argue that the role of

accounting information is more limited in environments that are characterized by low investor

protection and more concentrated ownership structures. However, due to the lack of alternative

sources of information available, accounting information may be especially useful for our sample

of relatively smaller, privately-held firms.1 Ultimately it is an empirical question whether

accounting information and especially the quality of reported earnings matters for investment

decisions in private firms from emerging markets. Our research also addresses an area of direct

interest for the World Bank. Guidance as to investment efficiency in emerging markets, and what

factors affect this efficiency, is directly relevant to the World Banks mission of offering

assistance to developing countries around the world.2

Furthermore, financing and tax factors influence the financial reporting incentives of our

sample firms. For example, high quality accounting information is likely more desirable to

mitigate information asymmetry for private firms when they are in need of external financing.

Thus, we examine whether private firms investment efficiency is more sensitive to the quality of

accounting numbers when these firms seek external bank financing. In addition, some authors

argue that, compared with public firms, private firms financial reporting is more influenced by

taxation (e.g., Ball and Shivakumar 2005). It is also often asserted that greater emphasis on taxes

(and tax minimization in particular) would reduce the informational role of earnings. However,

there is little empirical evidence on this important issue to date. We have data that allow us to

explicitly test the role of tax incentives.

1 Section 2 provides a fuller discussion of these issues.2 See http://www.enterprisesurveys.org.


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We obtain our data from the World Banks Enterprise Survey (WBES), a major cross-

sectional firm survey conducted around the world by the World Bank. This database has been

used in a number of prior studies (e.g., Beck and Demirguc-Kunt 2006; Beck, Demirguc-Kunt,

and Maksimovic 2005, 2006, 2008; Carlin, Shaffer, and Seabright 2007; Brown, Chavis, and

Klapper 2008; Hope, Thomas, and Vyas 2010).3 In contrast to studies on the role of accounting

information on investment decisions that use data on public firms only, our data source allows us

to examine small and medium-size private enterprises. Further, the WBES database includes

information on both the sources of financing (e.g., bank financing, internal financing, informal

financing, etc.) used in making new investments and the degree to which the firm faces income

tax pressures (higher tax rates and stronger tax enforcement), without having to rely on proxies

based on accounting data. This unique information allows us to investigate whether the

usefulness of accounting information varies both with the source of financing and with the firms

tax incentives.

We use WBES data from 2002 to 2005 and proxy for financial reporting quality using

both earnings- and revenue-based accounting quality measures. Our main findings are as follows.

First, consistent with economic theory, we show that our proxies for financial reporting quality

are statistically and economically significantly related to investment efficiency. Specifically, we

find that accounting quality is significantly negatively associated with both over- and

underinvestment. Second, we show that the importance of accounting information is increasing

in the degree of bank financing. This likely reflects the use of financial statements by banks in

granting credit. Third, we document that for firms facing greater income tax pressures, the

relation between accounting information and investment efficiency is reduced. It is often argued

3 For a more extensive list, please see http://www.enterprisesurveys.org/ResearchPapers/.


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in the literature that a focus on minimizing taxes can negatively affect the usefulness of financial

statement information; however to date the evidence on this issue has been limited. Our results

are robust to the inclusion of firm-level control variables, industry and country fixed effects, and

to the use of alternative measures of both investment efficiency and earnings quality.

Our study advances the existing literature by providing empirical evidence that

accounting quality relates to the economic fundamentals of firm-level investment efficiency in a

sample of small, private firms across 21 emerging markets. We also examine how the importance

of accounting varies with financing sources and tax incentives. These findings complement and

extend current research on the relation between accounting quality and investment efficiency

(e.g., Biddle and Hilary 2006; McNichols and Stubben 2008; Biddle et al. 2009). Existing

empirical studies rely on databases of listed firms so that even the smallest firms in their samples

are relatively large. In contrast, we focus on a sample of private firms, for which there is limited

extant research but which are important drivers of economic growth.4

In addition, research shows that small- and medium-sized enterprises (SMEs) not only

incur higher financing obstacles than large firms, but that the effect of these financing constraints

is stronger for SMEs than for large firms (e.g., Beck and Demirguc-Kunt 2006). Policymakers in

governmental and international aid organizations believe that in developing countries, small

firms have inadequate access to external financing due to market imperfections (World Bank

2007). In other words, compared with public firms in developed countries, our sample firms are

more likely to be underinvestment firms, and our empirical evidence supports this idea. Given

the importance of financing for these firms, financial reporting quality could play a major role in

their investment decisions, and our results are consistent with this notion. Further, compared with

4 Our evidence may thus also be of importance to accounting standard setters, for example to the IASB and its SMEworking group.


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publicly traded companies in the U.S. and other developed countries, our sample firms face

fewer mandatory accounting recognition and measurement (as well as disclosure) requirements.

Therefore, our sample firms have greater flexibility to choose financial reporting quality and

their choices have the potential to be especially important for improving investment efficiency.

The next section reviews related research and develops our hypotheses. Section 3

describes our data and our measures of accounting quality and investment efficiency. Section 4

presents our empirical tests and results. Section 5 contains several robustness tests and Section 6

concludes.

2. Background and Hypotheses Development

Our study relates to the growing literature on the role that accounting information plays

in corporate investment decisions. While in theory, all positive NPV projects would be funded

and implemented, in practice, firms often face financing constraints that limit their ability to fund

potential projects, suggesting that underinvestment may take place.5 Even under the assumption

that financing is available, managers allocation of resources may not be efficient, resulting in a

reduction in investor value. In other words, there is prior research on managers overinvestment

or empire building (e.g., Jensen 1986; Stein 2003; Hope and Thomas 2008) as well as

underinvestment (e.g., Bertrand and Mullainathan 2003).

The literature suggests that financial reporting and disclosure has the potential to mitigate

both under- and overinvestment problems, and hence to increase overall investment efficiency.

There are several mechanisms through which financial reporting can play such a role. First,

accounting information can aid investment efficiency by reducing adverse selection, liquidity

5 Hubbard (1998) provides an overview of the extensive literature in this area.


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risk, and information risk (Diamond and Verrecchia 1991; Leuz and Verrecchia 2000; Easley

and OHara 2004; Lambert, Leuz, and Verrecchia 2007).6 Second, disclosed financial

information aids corporate control mechanisms in preventing managers from expropriating

wealth from investors or creditors (e.g., Fama and Jensen 1983), hence providing external

suppliers of capital with greater assurance about managers activities as well as aiding in internal

stewardship functions (e.g., board supervision of management). Third, improved accounting

information can enhance investment decision-making by managers. As McNichols and Stubben

(2008, 1571) point out, investment decisions depend on expectations of investment benefits.

These benefits in turn depend on expectations of future growth and product demand. In other

words, high quality information can help managers form more accurate expectations and identify

better investment opportunities, thereby improving investment efficiency even in a world without

adverse selection and/or moral hazard (Bushman and Smith 2001; McNichols and Stubben 2008).

Several recent studies provide empirical evidence supporting the role of financial

disclosure for investment decisions. Using a sample of U.S. publicly traded companies, Biddle,

Hilary, and Verdi (2009) predict and find that higher quality financial reporting increases

investment efficiency. Two related studies by McNichols and Stubben (2008) and Kedia and

Philippon (2009) document that U.S. firms that manage their earnings upward tend to over-invest

(see also Bens and Monahan 2004; Beatty, Liao, and Yu 2009; and Kang, Liu, and Qi 2009). 7

6 As a derivative to the adverse selection problem, misleading accounting information, produced either within or

beyond the confines of GAAP, may allow outside capital suppliers to form unrealistic expectations, which in turn

pressures managers to deviate from optimal operation decisions (e.g., Lai, Debo, and Nan 2008).7 Similar to McNichols and Stubben (2008), Kedia and Philippon (2009) predict and find that firms overstating their

financial results pool with better performing firms to avoid detection. These firms over-invest to mimic the better

performing firms.


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To our knowledge, few studies examine international settings. Biddle and Hilary (2006)

use country-level measures of accounting quality. In their full sample of firms from 34 countries,

they find that higher quality of financial reporting lowers investment-cash flow sensitivity (a

proxy for investment inefficiency).8,9 Biddle et al. (2009) note that investment-cash flow

sensitivities can reflect either financing constraints or an excess of cash (see also Kaplan and

Zingales 2000; Fazzari, Hubbard, and Peterson 2000). Similar to Biddle et al. (2009), we directly

test whether higher financial reporting quality enhances investment efficiency by reducing over-

and/or underinvestment.

In this study, we examine the role that financial reporting quality plays in private firms

from emerging markets. Although private firms make up the vast majority of economic activity

around the world (e.g., Pacter 2004; Chaney, Jeter, and Shivakumar 2004), they have received

limited attention in academic research. We are not aware of prior research on the relation

between accounting information and investment efficiency in a private firm setting.

Private firms are different from publicly traded firms in several respects. Almost by

definition, they tend to have more concentrated ownership, although some large private firms can

have relatively more dispersed ownership. With greater ownership concentration, the large

shareholder can take advantage of her controlling position and direct private benefits for personal

consumption, which is the typical problem of expropriation of minority shareholders and

8 Biddle and Hilary (2006) hypothesize that the effect should be stronger when capital is provided through arms-

length transactions. Although they observe a more negative coefficient on accounting quality in the subsample of

countries with above-median stock market value to credit market value, their tests do not allow them to test whether

this difference is statistically significant.9 Focusing on conditional conservatism as their proxy for accounting quality, Bushman, Piotroski, and Smith (2007)

find that countries with more timely loss recognitions exhibit higher propensities to abandon negative NPV projects.

In another country-level study, Francis, Huang, Khurana, and Pereira (2009) find that corporate transparency

facilitates the allocation of resources across industry sectors.


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creditors (e.g., Morck, Shleifer, and Vishny 1988). Prior studies using data from developed

countries find evidence suggesting that private firms have lower earnings quality on average than

public firms (e.g., Ball and Shivakumar 2005; Burgstahler et al. 2006). It is important to note that

it does not follow from this observation that accounting is less important for private firms than

for public firms. Private firms typically have a weaker information environment compared with

the relatively stronger disclosure environment of public firms (e.g., Burgstahler et al. 2006),

which suggests that accounting information could play an important role since there are fewer

competing sources of information. For example, Indjejikian and Matejka (2009) highlight the

importance of accounting information for private firms in compensation contracts. As noted

above, McNichols and Stubben (2008) emphasize the role that accounting information plays in

internal decision making. Small firms are unlikely to have management accounting systems that

are separate from financial accounting (e.g., Drury and Tayles 1995), potentially enhancing the

role of accounting in internal decision making. McNichols and Stubben (2008) emphasize how

low-quality financial information implies distorted information to decision makers that in turn

leads to inefficient investment decisions.10

We are also interested in the role of financial information in emerging markets. There is

far less research on the usefulness of accounting in such environments than in developed

countries. Although prior studies do not include our exact sample of countries (which are mostly

low-income countries as described in Section 3), there is some evidence in the literature

suggesting that the value relevance of accounting information (for publicly traded companies)

is lower in less developed economies. Again, this does not necessarily mean that financial

10 For example, investment decision makers within the firm could believe the misstated growth trend from

overstated revenues, either because they are over-optimistic or because they are unaware of the misstatement, and

invest accordingly (McNichols and Stubben 2008, 1572).


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reporting information has less value in such countries, and in fact it is possible that the lack of

analyst coverage, lower media coverage, and overall lower-quality institutions makes accounting

information a relatively greater component of the overall information set used for decision

making by insiders or outsiders.11,12

Based on the above discussion, theory supports the idea that high-quality information

leads to higher investment efficiency in private firms. But as discussed, based on prior research

this relation is not a foregone conclusion and it is ultimately an empirical question what the

outcome will be. For our first hypothesis, we predict that higher quality accounting information

will mitigate both over- and underinvestment.

H1: Accounting quality is negatively associated with both underinvestment and

overinvestment.

In addition to examining the overall effect of earnings quality on investment efficiency,

we explore two conditional effects: financing sources and tax incentives. We first investigate the

effect of financing sources on the importance of accounting earnings. Using the WBES data, we

have access to a detailed breakdown of financing sources as a percentage of new investment.

Private firms in emerging markets fund investment from external sources such as bank lending,

issuance of private equity, leasing, trade credit, financing from special development agencies or

11 Abu-Nassar and Rutherford (1996), Mirshekary and Saudagaran (2005), and Al-Razeen and Karbhari (2007)

conduct surveys and conclude that accounting information is the most important information source for users in

emerging markets.12 Accounting information may be useful for bank financing, in attracting new equity capital (for the private firms

with more dispersed ownership and/or firms that are selling shares to new investors), to suppliers in their decisions

to grant trade credit, and to other providers of finance such as leasing companies.


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governments, and informal financing (i.e., financing from moneylenders, family, and friends), or

from internal sources (i.e., retained earnings and additional contributions by owners) (Beck et al.

2008). A large body of literature has documented how the availability of external or internal

funds affects investment decisions (Myers and Majluf 1984; Fazzari, Hubbard, and Peterson

1988; Blanchard, Lopez-de-Silanes, and Shleifer 1994). Prior studies have used samples of

public firms that rely mainly on equity and debt financing. In contrast, for private firms, the

external financing sources are usually limited and mainly consist of bank loans and trade credit.

We argue that, given differential demand for accounting information, the financing

source will affect the relation between accounting quality and investment efficiency. Specifically,

we are interested in whether firms that rely more on bank financing have a relatively stronger

relation between accounting and investment efficiency than do other private firms in emerging

economies.

Bank lending is the most common source of external capital for private firms in

developing countries (Brown et al. 2008; Beck et al. 2008).13 Banks may well have access to

additional information beyond the financial statements, potentially reducing the importance of

accounting information. However, besides the large body of research documenting the role of

accounting information for lending decisions in the U.S. and other developed countries, there is

also extensive evidence that banks rely on borrowers financial reports in credit decisions in

emerging markets and for small firms (e.g., Danos, Holt, and Imhoff 1989; Berry, Faulkner,

Hughes, and Jarvis 1993; Berry, Grant, and Jarvis 2004; Kitindi, Magembe, and Sethibe 2007).

Compared with other external sources of informal financing that rely more on mutual trust and

private communication, banks are likely to more carefully screen the financial statements of

13 In our sample firms, on average bank financing accounts for 42% of all external financing of new investments.


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corporate clients. Most importantly, banks not only lend larger amounts compared with other

sources of informal financing, but also extend loans with longer maturities, which make them

more vulnerable to information and incentive problems. Failure rates are also higher among

smaller firms, and this further encourages banks to carefully examine financial information in

their lending decisions. Examining a clients financial statements helps banks to determine the

firms assets that can serve as collateral, to evaluate its future cash-flow generating capability, to

gauge the firms debt capacity, and to analyze the riskiness of the firm in determining a lending

rate. Based on this discussion, our second hypothesis is:14

H2: The relation between financial reporting quality and investment efficiency is

stronger if a firms investment is mainly funded through bank financing.

Finally, we explore the conditional effect of tax incentives. The alignment between

financial and tax accounting is likely higher for our sample than for publicly traded firms in

developed countries. High alignment means that financial statements serve as the basis for

taxation or that tax laws explicitly require an equal treatment for certain items in both sets of

accounts (e.g., Burgstahler et al. 2006). In such an environment, a focus on minimizing taxes can

directly impact financial reporting quality. That is, for firms that face especially high income tax

rates and strong enforcement by tax authorities (i.e., high tax pressure), the primary objective

of financial reporting may be to minimize income taxes rather than to provide information to

14 Note that our hypothesis differs from Biddle and Hilary (2006) who compare bank financing with public equity

financing across countries. In contrast, as private firms do not rely on public equity financing, we compare the role

of bank financing primarily with other non-public debt, private equity, and informal financing.


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suppliers of capital or to management, which may reduce the role of reported accounting

information in informing users. Our final hypothesis is thus:

H3: For firms with higher tax pressure, accounting quality is less negatively associated

with both underinvestment and overinvestment.

3. Sample and Measurement of Main Variables

3.1 Data Source

We obtain our data from World Banks Enterprise Survey (WBES) conducted during

2002-2005 by the World Bank in 79countries, including many low-income countries. Prior

studies that have used this database include Beck and Demirguc-Kunt (2006), Beck et al. (2005,

2006, 2008), Carlin et al. (2007), Brown et al. (2008), and Hope et al. (2010).

The primary goal of WBES is to provide quantitative data that allows an assessment of a

countrys investment climate in an internationally comparable manner.15 The surveys are

administered in face-to-face interviews with managing directors, accountants, human resource

managers, and other relevant company staff.16 Samples are stratified by size, industry, and

location. Although the surveys are conducted with the knowledge and support of relevant

government authorities, governments are not provided with the raw data or other information that

15 This discussion draws on Productivity and Investment Climate Survey: Implementation Manual (2003).16 WBES begins from a minimum core set of questions that are common across all countries. However, survey

managers at the country or region level are allowed to extend the survey. To maintain cross-country comparability,

core questions cannot be reworded except in translating to well-understood phrases with the same meaning. In

addition, all core questions are asked using standardized instructions provided by the surveys designers.


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allows them to identify the responses of individual firms, and businesses are informed of this

confidentiality prior to interviews to encourage truthful responses.17

The dataset includes a large sample of firms across multiple sectors (manufacturing,

services, agriculture, and construction). Usable data include both quantitative and qualitative

information on firm characteristics, including sources of finance, barriers to growth, access to

infrastructure services, legal difficulties, and corruption. The dataset also includes measures of

firm performance, such as multiple years of historical data on capital investment and operating

performance.

A limitation of using the survey data is that financial statement information is necessarily

restricted. Thus we do not have access to some potential control variables used in prior studies.

However, the survey does contain data on a number of important firm characteristics, and

consistent with prior research that employs the WBES data, we include these firm characteristics

as well as industry and country fixed effects in our empirical tests.

We describe our sample selection procedure in Table 1. The main constraints on our

sample size are the availability of data to compute the investment efficiency measure and the two

accounting quality measures. Each sample firm appears in only one survey year and hence the

number of observations equals the number of unique firms. However, conditional on data

availability, the dataset provides three consecutive years of accounting information from a given

survey year. We end up with 6,862 unique firms for the test of H1 involving discretionary

revenues (described below) as a proxy for accounting quality, and our sample size is reduced to

6,697 firms when we use discretionary accruals as another proxy for accounting quality. The

17 Carlin et al. (2007), Beck et al. (2008), and Hope et al. (2010) contain several validity tests of the survey data.


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sample size is reduced further for the tests of H2 and H3 for which we require additional data for

the firms financing sources and tax burden.

Table 2 provides a distribution of the sample by country. We have a total of 21 countries

represented, with the greatest number of observations coming from Thailand, Brazil, Pakistan,

Vietnam, and India. These five countries make up 65% of the total sample. Although all of our

sample countries are considered developing or emerging markets as per United Nations

classifications, there is considerable variation in income per capita across our sample countries.

We examine the effects of financial reporting quality on investment efficiency across different

income groups in Section 5.

3.2 Proxy for Investment Efficiency

The two key constructs in the analysis are investment efficiency and financial reporting

quality, and we investigate how accounting quality in the current year affects nextyears

investment efficiency. Conceptually, investment efficiency refers to firms undertaking all and

only projects with positive net present value. Consistent with prior research (e.g., Biddle et al.

2009), we measure investment efficiency as deviations from expected investment using a model

that predicts investment as a function of growth opportunities. Thus, both underinvestment

(negative deviations from expected investment) and overinvestment (positive deviations from

expected investment) are considered inefficient investments. Specifically, we estimate a

parsimonious model for expected investment as a function of revenue growth (see, e.g.,

Modigliani and Miller 1958; Hubbard 1998). As the relation between investment and revenue

growth could differ between revenue decreases and revenue increases (e.g., Eberly 1997;

McNichols and Stubben 2008), we allow for differential predictability for revenue increases and


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revenue decreases by employing a piecewise linear regression model:18

, 0 1 , 1 2 , 1 3 , 1 ,*i t i t i t i t i t Invest NEG RevGrowth NEG RevGrowth = + + + + (1)

Following Biddle et al. (2009), we define,i tInvest as the sum of new investment in machinery,

equipment, vehicles, land, buildings, and research and development expenditures, less the sale of

fixed assets, and scaled by lagged total assets for firm i in year t., 1i t

RevGrowth

is the annual

revenue growth rate for firm i in year t-1. The indicator variableNEGi,t-1 takes the value of one

for negative revenue growth, and zero otherwise.

We estimate the investment model cross-sectionally with at least ten observations in each

WBES industry by country. The sample consists of 9,992 firm-year observations with available

data to estimate Equation (1). To mitigate the influence of outliers, we winsorize all variables at

the 1% and 99% levels. We then classify firms into two groups based on the residuals of

Equation (1) (i.e., the deviations from the predicted investment levels). To ease exposition, we

multiply the underinvestment variable by -1 so that a higher value suggests a more severe

underinvestment.

3.3 Proxies for Financial Reporting Quality

There is no one universally agreed upon measure of accounting quality (e.g., Dechow, Ge,

and Schrand 2009). To ensure the robustness of our conclusions, we apply two measures that

have been widely used in prior research. The first measure is performance-adjusted discretionary

18 Note that we describe results of using two alternative investment models in Section 5.2 (including a model that

contains seven additional explanatory variables beyond revenue growth). Furthermore, adding lagged investment to

Equation (1) does not change our inferences but significantly reduces our sample size.


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accruals as developed by Kothari, Leone, and Wasley (2005).19 Specifically, we estimate the

following model by country and for each industry that has at least 16 observations:

, 0 1 , 1 2 , 3 , 4 , ,(1 / )i t i t i t i t i t i t Accr Assets Sales PPE ROA = + + + + + (2)

where,i t

Accr is measured as the change in non-cash current assets minus the change in current

liabilities, minus depreciation and amortization expense for firm i at year t, scaled by lagged total

assets (, 1i tAssets ); ,i tSales is the annual change in sales revenues scaled by lagged total assets;

,i tPPE is property, plant, and equipment for firm i at year t, scaled by lagged total assets; ,i tROA

is return on assets for firm i at year t. The residuals from the regression model are discretionary

accruals (DisAccr). In our main tests, we use the absolute values of discretionary accruals as a

proxy for accounting quality.20 We multiply the absolute values of discretionary accruals by -1.

Thus, higher values ofDisAccrrepresent higher earnings quality.

To calculate the second proxy, we follow McNichols and Stubben (2008) and Stubben

(2010) and estimate discretionary revenues. Specifically, we use the following regression:

it it it AR Sales = + + (3)

where AR represents the annual change in accounts receivable and Sales is the annual change

in revenues, each scaled by lagged total assets. Discretionary revenues (DisRev) are the residuals

from Equation (3) which is estimated separately for each industry-country group that has at least

eight observations. We multiply the absolute values of discretionary revenues by -1. Thus, higher

values ofDisRev represent higher accounting quality.

19 Data availability precludes the use of the Dechow and Dichev (2002) measure of accrual quality.20 We redo all tests using signed values of discretionary accruals. Results are presented in Section 5.1.


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3.4 Bank Financing and Tax Burden Variables

For the purpose of examining H2, we use an indicator variable (Bank) to capture bank

financing as a major external financing source for firms. Specifically, this indicator takes a value

of one if bank financing is the dominant source of external financing and constitutes more than

5% of all sources that fund new investments.21 Bank financingincludes financing from both

local and foreign banks.

Our measure of income tax pressure refers to managers perceptions of whether tax rates

and tax administration impose a major or severe obstacle in the operation and growth of their

business. In the WBES database, higher ratings of tax rates and/or tax administration suggest a

heavier tax burden borne by the firm, implying that firms have greater incentives to manage

earnings for tax purposes. We use an indicator variable, Tax, to test H3. The indicator takes on

the value of one when the average rating of managers responses is 3 or greater.22

3.5 Descriptive Statistics

Table 3 provides descriptive statistics for our measures of investment efficiency, earnings

quality, bank financing, tax burden, as well as for our main control variables. Panel A shows that

only about a quarter of firms belong to the overinvestment group, while an overriding majority of

sample firms belong to the underinvestment group. This intuitive result confirms that private

firms in emerging markets, due to their difficulty in securing external financing, are more likely

subject to the problem of underinvestment rather than overinvestment.

21 The 5% criterion ensures that bank financing is substantial compared with internal financing. As a sensitivity test

we change the threshold to 10% and no inferences are affected.22 Specifically, in the WBES database, a rating of zero denotes no obstacle; one denotes a minor obstacle; two, a

moderate obstacle; three, a major obstacle; and four, a very severe obstacle.


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For the 4,657 firms that provide information about their financing sources, about 38% of

these firms have bank financing as their primary source of external financing (and that

constitutes more than 5% of all sources that fund new investments). For the 6,830 firms that

provide information about the firm-level tax burden, about 17% of these firms report facing

severe tax obstacles. The average firm has been in existence for 18 years. As expected, there is a

wide variation in firm size measured by total assets. But most firms are quite small, with half the

firms having total assets of $912,110 or less. In all tests we use log transformations of both firm

age and firm size.

Panel B reports Pearson correlations. As predicted, the two proxies of accounting quality

are significantly negatively correlated with the proxy for investment inefficiency. In addition, the

two accounting quality proxies are positively and significantly correlated. As correlation results

do not control for differences in firm, industry, or country characteristics, we now turn to

multivariate tests.

4. Research Design and Results

4.1 Basic Empirical Model

Since we are interested in how accounting quality affects investment efficiency, we test

Hypothesis 1 by regressing the measure of investment efficiency in year t+1 on the measures of

accounting quality in year t. As well, similar to Biddle et al. (2009), we estimate equation (4)

separately for overinvestment and underinvestment. This research design allows us to test

whether higher financial reporting quality mitigates over- as well as underinvestment, which is

important given that our sample firms presumably are more likely to experience underinvestment

problems. The basic model is:


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, 1 0 1 , , ,i t i t n i t i t InvEff EQ Control Variables Industries Countries

+= + + + + + (4)

Where:

InvEff = Excess investment (overinvestmentor underinvestment) is the residual of the

investment model as described in the section 3.2.EQ = Accounting quality measured as either the absolute residuals of the Kothari

et al. (2005) performance-adjusted discretionary accruals model, multipliedby -1; or the absolute residuals of the McNichols and Stubben (2008)discretionary revenue model, multiplied by -1.

Our main control variables are:

Size = Natural log of total assets.Age = Natural log of firm age.

Tang = Asset tangibility, measured as property, plant and equipment scaled bylagged total assets.

Slack = Financial slack, measured as cash divided by lagged total assets.

Motivated by prior research (e.g., Biddle et al. 2009), we include firm size, firm age,

asset tangibility, and financial slack as control variables, as well as industry fixed effects. 23 We

also include country fixed effects in all models, which is a common approach for controlling

country-specific effects and addressing correlated omitted country-level variable problems

(Doidge, Karolyi, and Stulz 2007).

To the extent that lower earnings quality (EQ) (i.e., larger absolute residuals from the

discretionary revenues or accruals models) leads to less efficient investment (H1) (i.e., larger

magnitude of over- and underinvestment), 1 is expected to be negative.

23 Untabulated tests show that our results are almost identical using number of employees or revenues as alternative

size proxies. Note further that we in Section 5.3 report results of tests that include 11 additional control variables

(which reduces the sample size by 54%).


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4.2 Empirical Results

Table 4 reports the regression results for the test of H1. The models have adjusted R2s of

between 14% and 46%, with a higher explanatory power for underinvestment (the most prevalent

scenario in our sample). Columns 1 and 3 (2 and 4) report the results using the DisAccr(DisRev)

measure for overinvestment and underinvestment, respectively.24 Across all four specifications,

the conclusion is the same: accounting quality is negatively related to inefficient investments.

Specifically, the estimated coefficients onDisAccrandDisRev are all negative and significant at

the 1% level. These results are also economically significant. For example, in the overinvestment

scenario, a one standard deviation decrease in the DisAccrmeasure implies a decrease of

overinvestment by 1.07% of total assets.

Firm size is negatively associated with both over- and underinvestment, consistent with

expectations and prior research. Asset tangibility is positively correlated with deviations from

expected investment, but is only significant for overinvestment cases. Financial slack is

positively but insignificantly related to investment efficiency.

Taken together, the results in Table 4 suggest that, although private firms in emerging

markets could have different reporting incentives than the companies previously examined in the

literature, we observe strong evidence that the quality of accounting numbers affects subsequent

capital investment efficiency in accordance with theory. Specifically, our results support H1 and

suggest that lower quality of financial reporting results in less efficient investment.

In addition to complementing and extending prior academic research, our findings should

be relevant to the World Bank, whose mission is to aid in improving living conditions in

developing countries, as it is likely that more efficient investments should lead to higher social

24 Note that we report results of analyzing positive and negative residuals from the two accounting quality proxies

separately in Section 5.1


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21

welfare. In fact, it is a reasonable conjecture that, compared with publicly traded companies in

the U.S. and similar countries, our sample firms face fewer mandatory accounting recognition

and measurement (as well as disclosure) requirements. Therefore, our sample firms choices

regarding accounting quality have the potential to be especially important for improving

investment efficiency.

For the tests of the relative role of bank financing (H2) and the effect of tax incentives

(H3), we add the main effects of these variables as well as interaction effects with the earnings

quality measures. We predict a negative coefficient onBankEQ and a positive coefficient on

TaxEQ.

Results for the conditioning effect of bank financing are presented in Table 5. The

additional data requirement for financing sources results in a reduced sample size, with 4,657

firms for the test employing discretionary revenues and 4,457 firms for the test using

discretionary accruals. We first note that bothEQ measures continue to load negatively and

significantly after controlling for the effect of bank financing. The main effect ofBankis not

significant. Our focus, however, is onBankEQ, and we observe negative estimated coefficients

using bothDisAccrandDisRev and for both overinvestment and underinvestment. Greater

significance levels are achieved for the underinvestment case, possibly due to the larger sample

size available for that test. The results suggest that the importance of accounting quality is

increasing in the presence of bank financing, which is consistent with banks making use of

financial statements in their lending decisions.

In Table 6, we test H3, the effect of tax incentives. We have 6,830 firms for the

discretionary revenues and 6,663 firms for the discretionary accruals tests. Recall that we argue

that the informational role of financial information will be reduced when the overriding objective


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22

is to minimize taxes. The main effect ofEQ continues to be negative and significant in this

specification. For the overinvestment sample, the coefficients on the interaction ofEQ and Tax

are positive and significant when using either theDisAccror theDisRev accounting quality

measures. In other words, consistent with H3, the effect of earnings quality in mitigating the

overinvestment problem is reduced in the extent to which firms incentives are primarily tax

oriented.25 We also observe positive but statistically weaker coefficients onEQTax in the

underinvestment scenario. Thus, there is at least some support for the often-argued (but seldom

tested) notion in the literature that tax motivations may distract from the usefulness of accounting

information.

Finally, to test whether bank financing and tax incentives have incremental effects in the

presence of the other, in Table 7 we report tests in which we include both Bankand Tax jointly

(and thus have a smaller sample). As before, the estimated coefficient onEQBankcontinues to

be negative in all specifications and statistically significant in three out of four regressions.

Moreover, the estimated coefficient onEQTax is significantly positive for the overinvestment

case but not significant for the underinvestment case. Equally importantly, the main effect ofEQ

is yet again significantly negative across test specifications. In addition to being interesting

results by themselves, our interaction tests also serve the function of providing additional

credence to H1 (e.g., related to correlated omitted variables or causality). We observe that the

estimated coefficients on the two interaction terms have the predicted signs and are statistically

significant in several cases. In other words, the effect is more (less) pronounced in subsamples in

which we predict the effect to be stronger (weaker). As Rajan and Zingales (1998) and Lang,

25 In fact, for both theDisAccrandDisRev measures of accounting quality, the sum of the coefficients onEQ and

EQTax (1+3) is not statistically different from zero in the overinvestment tests (the joint test results in F-

statistic=0.44 withp-value=0.51 forDisAccr; F-statistic=0.96 withp-value=0.33 forDisRev, respectively).


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23

Lins, and Maffet (2009) point out, it would be difficult to envision a consistent theory in which

causality is reversed yet the subsample results hold.

5. Additional Tests

In this section, we report results of additional tests that both lend robustness and extend

the reported results. For brevity, we only tabulate the results of the first sensitivity test.

5.1 Separate Regressions for Positive and Negative Values ofDisAccr andDisRev

To supplement our analyses based on the absolute value of the earnings quality measures,

we use a signedearnings quality measure (e.g., Hribar and Nichols 2007). Again, we estimate the

model for both over- and underinvestment. Unlike theEQ measures in the prior sections, we do

not multiply the proxies ofEQ by -1. Thus, for the main effect, we expect positive (negative) 1

for the sample with positive (negative) discretionary revenues or accruals. The results, which are

reported in Table 8, are consistent with our expectations. Specifically, as reported in panel A, the

positive discretionary accruals (DisAccr) are positively associated with both overinvestment and

underinvestment (t=2.42 and 1.63, respectively) while negative discretionary accruals are

negatively related to both overinvestment and underinvestment (t=-4.76 and -1.60, respectively).

The results are similar for signed discretionary revenues (DisRev) as reported in panel B.

Similarly, we obtain results consistent with H2 (the conditional effect of bank financing) and H3

(the conditional effect of tax burdens), although the coefficient estimates for some cases are not


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24

as statistically significant as the results reported in Table 7. In general, the use of the signed

earnings quality measure corroborates the conclusions above.26

5.2 Alternative (and Refined) Measures of Investment Efficiency

We repeat all tests employing two alternative measures of investment efficiency. Our first

alternative measure follows Biddle et al. (2009). Specifically, we sort firms, based on the

positive and negative residuals, respectively, into deciles. Within the positive-residual group, we

remove the bottom decile because these firms, whose unexpected investments are closest to zero

among all overinvesting firms, are more likely to be affected by measurement error in the

investment model (i.e., misclassified as overinvesting firms). Similarly, we remove the top decile

from the negative-residual group. We repeat all the tests using the remaining observations.

Results are similar to those tabulated in the paper, and no inferences are affected.

The second alternative measure of investment efficiency comes from Richardson (2006).

We first estimate optimal investment according to the following regression specification:

, 0 1 , 1 2 , 1 3 , 1 4 , 1 5 , 1

6 , 1 7 , 1 8 , 9 , 10 , ,

*i t i t i t i t i t i t

i t i t i t i t i t i t

Invest NEG RevGrowth NEG RevGrowth Size Age

Slack Lev BConIndex LConIndex PEConIndex

= + + + + +

+ + + + + +

In addition to the proxy for growth opportunity, we also include firm size, firm age,

financial slack, and financial leverage as controls for financing constraints. Moreover, based on

Brown et al. (2008), we augment the model with three additional firm-level variables related to

constraints in the operating environment: business constraints, legal constraints, and

26 As an additional sensitivity test, we have used factor analysis to compute an aggregate measure of accounting

quality. All inferences are unchanged.


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25

political/economic constraints.27 The unexplained portion (or residuals) is the estimate of

inefficient investment. Our conclusions are unchanged when we employ this alternative measure

of investment efficiency.

5.3 Additional Control Variables

Although we include several firm-level control variables as well as industry and country

fixed effects in our main tests, it is always possible that there are some omitted (and correlated)

variables. However, adding more control variables comes at the cost of reducing the sample size,

and thus a trade-off exists between sample size (and hence generalizability) and model

completeness. In untabulated tests, we add several additional controls to all regressions.

Following Biddle et al (2009), we include (1) long-term liabilities divided by total assets

(Lev); (2) the standard deviation of sales in the past three years (StdRev); (3) the standard

deviation of investments in the past three years (StdInvest); (4) the operating cycle calculated by

using both accounts receivables and sales (OperCycle); and (5) an indicator variable referring to

whether a firm reports a loss (Loss). We also (6) control for profitability (ROA). Furthermore, we

take advantage of the availability of certain unique data items from WBES and include: (7) an

indicator variable referring to whether the firm exports (Export); (8) an indicator variable

referring to whether any foreign company or individual has ownership interests in the firm

(Foreign); (9) an indicator variable that takes on the value of one if financing obstacles ( FinCon)

are severe; (10) an indicator variable that takes on the value of one if the largest shareholder

27 The Appendix contains detailed definitions of these additional variables. Note that with the expanded set of

control variables, we require more observations to estimate the model than for the estimation of Equation (1).

Therefore, we estimate this model by country and adjust both dependent and independent variables by the country-

specific industry median. The adjustment for industry median accounts for industry heterogeneity.


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26

owns more than 50% of the firm (Control); and (11) an indicator variable referring to whether a

firms financial statements are reviewed by independent auditors or not (Audit).

Requiring data on these additional controls reduces the sample size by approximately

54%, with only 2,987 firm-years for the sample using theDisAccrmeasure and only 3,166 firm-

years for the sample using theDisRev measure. Nevertheless, even with the reduced sample size

our conclusions for H1 to H3 remain unaltered.

5.4 Possible Variations across Sample Countries

Although all our sample countries are emerging economies, they clearly vary in terms of

income per capita and other factors, such as legal protection of investors. Recall that we control

for such effects using country fixed effects in all reported tests (e.g., Doidge et al. 2007). In this

section, we perform two additional tests. First, we replace country fixed effects with gross

national income per capita (GNI), a country-level variable that is available for all our sample

countries. No conclusions are altered with this specification (and in fact significance levels

increase somewhat). Second, we separate the sample based on median GNIand estimate the

regressions separately for the two groups. We find that our results hold in both sets of countries.

5.5 Possible Endogeneity Related to Bank Financing

Unlike our proxy for tax incentives, which we consider mostly outside of managers

control, the importance of bank financing could be driven by other firm characteristics. For this

reason we estimate a two-stage Heckman model in which the first stage (which is based on Beck

et al. 2008) predictsBankand the second stage controls for the Inverse Mills Ratio estimated

from the first stage. As an instrument we include the approximate value of collateral as a


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27

percentage of the loan value (Collateral). Banks may be less willing to lend to firms when the

amount of assets that can be pledged as collateral is limited. In untabulated tests we find that

Collateral is significantly correlated with the choice of bank financing but is not significantly

associated with over- or underinvestment. After controlling for potential self-selection, our

conclusions regarding H2 remain unchanged: the importance of earnings quality for investment

efficiency is increasing in bank financing.

6. Conclusions

We study the role of accounting information for a set of firms for which there is very

limited prior research evidence: private firms from emerging markets. On the one hand, prior

research sometimes argues that the role of accounting is more limited for both private firms and

for firms from emerging markets compared with publicly traded companies in developed

countries. The argument put forth is usually that these firms do not face the same market demand

for high-quality financial information and that tax incentives could be more important. On the

other hand, it is also true that there are more limited alternative information sources other than

accounting for private firms (and in countries with on-average lower disclosure levels). Also, the

link between financial accounting and management accounting is likely to be strong in these

settings, which suggests that the financial accounting information is largely the same set of

information used in managers decisions. Thus, it is an empirical question whether financial

statement quality is important for firms investment decisions for private firms in emerging

markets.

We find strong evidence that accounting quality is indeed positively associated with

investment efficiency for our sample firms. This result complements and extends extant research.


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In addition, we hypothesize that the source of financing as well as firms tax incentives will

affect the role of accounting information. We find evidence supporting these hypotheses.

Specifically, we find that greater use of bank financing increases the role that accounting

information plays. In addition, for firms in which tax incentives are likely to dominate incentives

to provide useful information for internal decision making as well as a source of information for

outside providers of capital, the informational role of accounting is significantly diminished.

Our results are robust to including a number of firm-level control variables as well as

industry and country fixed effects, and to a variety of sensitivity analyses. We hope that our

empirical findings will be of interest to researchers, standard setters and other regulators,

government officials in emerging markets, managers, and importantly to the World Bank and

others involved in improving economic conditions in developing countries.


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Appendix: Definitions of Variables

Variables in the investment modelInvest = The sum of new investment in machinery, equipment, vehicles, buildings,

land, and R&D activities, less the sale of fixed assets in the current year,

scaled by the lagged total assets.RevGrowth = The yearly growth rate of sales from year t-2 to year t-1.

Major variables in the investment efficiency testInvEff = A measure of investment efficiency, which is measured as the absolute

values of the residuals from the investment model:

, 0 1 , 1 2 , 1 3 , 1 ,*i t i t i t i t i t Invest NEG RevGrow th NEG RevGrowth = + + + + .

The indicator variableNEGi,t-1 takes the value of one for negative revenuegrowth, and zero otherwise. The investment model is estimated cross-sectionally with at least eight observations in each industry by country. Tomitigate the influence of outliers, we winsorize all variables at the 1% and

99% levels.Overinvestment = Positive residuals from the investment model.Underinvestment = Absolute value of the negative residuals from the investment model.EQ (measured

by DisAccr)

= The absolute residual of the Kothari et al. (2005) discretionary accrualmodel, multiplied by -1.

EQ (measured

by DisRev)= The absolute value of the measure of discretionary revenues presented in

McNichols and Stubben (2008), multiplied by -1.Bank = An indicator variable that takes on the value of one if bank financing

accounts for the highest percentage of total financing among all externalsources of financing, and its percentage is larger than 5% among allfinancing sources; and zero otherwise.

Tax = An indicator variable that takes on the value of one if the average degree ofobstacle is 3 or greater for both tax rates and tax administration; and zerootherwise.

Control variables in the investment efficiency testSize = The log of lagged total assets.Age = The log of the age of the firm in years.Tang = The ratio of PPE to total assets in year t-1.Slack = The ratio of cash to total assets in year t-1.

Additional variables used in the additional tests

BConIndex = The business constraint index, which is the average of indicator variablesdefined as equal to 1 if the firm perceives major or very severe obstacles intelecommunications, electricity, transportation, access to land, and skillsand education of available workers, and zero otherwise.

LConIndex = The legal constraint index, which is the average of indicator variablesdefined as equal to 1 if the firm perceives major or very severe obstacles incorruption, crime, and anticompetitive practices, and zero otherwise.

PEConIndex = The political and economic index, which is the average of indicator


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variables defined as equal to 1 if the firm perceives major or very severeobstacles in customs regulations, labor regulations, business licensing,regulatory policy uncertainty, and the macroeconomic situation, and zerootherwise.

Lev = Financial leverage, measured as long-term liabilities divided by total

assets.StdRev = The standard deviation of sales in the past three years.StdInvest = The standard deviation of investments in the past three years.OperCycle = Operating cycle calculated by the following formula:

(average accounts receivables/sales)365 + (average inventory/cost ofgoods sold)365. We use the log of operating cycle for our tests.

Loss = An indicator variable referring to whether a firm reports a loss.Export = An indicator variable referring to whether the firm exports.Foreign = An indicator variable referring to whether any foreign company or

individual has a financial stake in the ownership of the firm.FinCon = An indicator variable that takes on the value of one if the degree of

obstacle for both access to financing and cost of financing is larger than 3;and zero otherwise.Control = An indicator variable that takes on the value of one if the largest

shareholder owns more than 50% of the firm.Audit = An indicator variable that takes on the value of one if annual financial

statement is reviewed by external auditor; and zero otherwise.Collateral = The approximate value of collateral required as a percentage of the loan

value.GNI = Gross national income per capita at a country level, which is provided by

the World Bank using the Atlas method. The Atlas method smoothesexchange rate fluctuations by using a three-year moving average, price-adjusted conversion factor.

All data are from the World Banks Enterprise Survey.


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Table 1: Sample Selection

All firm-years in the WBES database with listing status information 47,712

Less firms residing in Other sectors 740

Less publicly traded firms 3,200

Private firms with all necessary industry information 43,742

Less firms with missing data on revenue growth 27,804

Less firms with missing data on investment 5,869

Additional reduction due to the data requirement of the investment model (i.e., at leastten firms in each industry by country for Equation (1))

77

Firms with the investment efficiency variable (InvEff) 9,992

(1) Missing data on discretionary revenue (DisRev) and control variables (Table 4) 3,130Sample size for the main tests withDisRev as a proxy for accounting quality 6,862

(2) Missing data on discretionary accruals (DisAccr) and control variables (Table 4) 3,295Sample size for the main tests withDisAccras a proxy for accounting quality 6,697


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Table 2: Sample Distribution by Country

Country N Percentage

Bangladesh 489 7.13%Brazil

1,035

15.08%

Ecuador 56 0.82%El Salvador 88 1.28%Eritrea 24 0.35%Ethiopia 39 0.57%Guatemala 67 0.98%Honduras 43 0.63%India 528 7.69%Indonesia 337 4.91%Nicaragua 39 0.57%Oman 37 0.54%Pakistan 855 12.46%Philippines 373 5.44%South Africa 391 5.70%Sri Lanka 297 4.33%Syria 44 0.64%Tanzania 77 1.12%Thailand 1,342 19.56%Vietnam 591 8.61%Zambia 110 1.60%

Total 6,862 100%

This table shows the sample distribution by country. There are a total number of 6,862 observations from21 countries.


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Table 3: Descriptive Statistics of the Main Sample

Panel A: Descriptive Statistics

Variable N Mean 25% median 75% STD

InvEff 6,862 0.001 -0.05 -0.023 0.004 0.096Overinvestment 1,843 0.12 0.024 0.079 0.215 0.112

Underinvestment 5,019 0.043 0.019 0.037 0.06 0.03

DisRev 6,862 0 -0.029 -0.005 0.018 0.074

DisAccr 6,697 -0.001 -0.063 -0.004 0.048 0.151

Bank 4,657 0.384 0 0 1 0.486

Tax 6,830 0.171 0 0 0 0.377

Age 6,862 17.734 9 13 22 14.537

Size (in thousands) 6,862 60,900 170 912 6,073 201,000

Tang 6,862 0.364 0.124 0.338 0.563 0.266

Slack 6,862 0.069 0.007 0.028 0.09 0.103

Panel B: Correlation Matrix

InvEff DisRev DisAccr Bank Tax Age Size Tang

DisRev -0.161***DisAccr -0.049*** 0.426***Bank -0.033** -0.038** -0.054***Tax 0.014 0.019** 0.014* -0.085***Age -0.097*** 0.073*** 0.068*** -0.042*** 0.017Size -0.085** 0.076*** 0.137*** 0.085*** 0.077*** 0.150***Tang 0.038*** 0.181*** 0.201*** 0.095*** -0.008 -0.132*** 0.082***Slack 0.040*** -0.018 -0.039*** -0.102*** -0.029*** -0.042*** -0.180*** -0.136***Panel A presents descriptive statistics for the variables used in the analyses. Panel B presents Pearsoncorrelations for these variables. Variable computations are described in the Appendix. The exceptions arein Panel A, whereInvEffis the signed residual from the investment model, andDisRev andDisAccrrefer

to the signed residuals from the discretionary revenue model and the discretionary accruals models,respectively. *, **, and *** represent significance at the 10%, 5%, and 1% level (two-tailed test).


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Table 4: The Relation between Accounting Quality and Investment Efficiency

, 1 0 1 , , ,i t i t n i t i t InvEff EQ Control Variables Industries Countries + = + + + + + (4)

Predictions

Dependent Variable =

OverinvestmentDependent Variable =

Underinvestment

Variables

EQ measuredbyDisAccr

(1)

EQ measuredbyDisRev

(2)


(3)

EQ measured byDisRev

(4)

EQ - (H1)-0.094***(-4.04)

-0.091***(-2.49)

-0.020***(-6.09)

-0.060***(-7.47)

Size-0.023***(-7.01)

-0.024***(-7.19)

-0.001**(-2.37)

-0.001***(-2.46)

Age-0.010(-1.16)

-0.007(-0.85)

-0.001(-0.37)

0.001(0.36)

Tang0.0227*(1.73)

0.023*(1.78)

0.001(0.60)

0.001(0.51)

Slack0.018(0.70)

0.024(0.93)

0.003(1.05)

0.001(0.06)

Intercept0.259***(11.07)

0.265***(11.08)

0.047***(16.75)

0.047***(16.12)

Country fixed

effects

Yes Yes Yes Yes

Industry fixedeffects

Yes Yes Yes Yes

Adjusted R2

0.141 0.136 0.428 0.458

N 1,878 1,843 4,819 5,019

The table reports OLS regression results. Variables are defined in the Appendix.*, **, and *** representsignificance at the 10%, 5%, and 1% level, respectively, using one-tailed (two-tailed) tests for the testvariables (control variables). Huber-White robust standard errors are used to control for heteroskedasticity.


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Table 5: The Conditional Effect of Bank Financing on the Relation between Accounting

Quality and Investment Efficiency

, 1 0 1 , 2 , 3 , , ,

,

i t i t i t i t i t n i t

i t

InvEff EQ Bank Bank EQ Control Variables

Industries Countries

+= + + + +

+ + + (5)

Predictions



Underinvestment

Variables


(1)

EQ measuredbyDisRev

(2)


(3)


(4)

EQ - (H1)-0.112***(-3.63)

-0.139***(-2.74)

-0.009**(-2.04)

-0.034***(-2.76)

Bank0.004

(0.42)0.008

(0.95)0.001

(1.29)-0.002*

(-1.49)

EQBank - (H2)-0.053(-1.01)

-0.141*(-1.60)

-0.015**(-2.24)

-0.047***(-2.46)

Size-0.024***(-6.41)

-0.025***(-6.60)

-0.001(-0.93)

-0.001(-1.26)

Age-0.012(-1.21)

-0.009(-0.94)

-0.001(-0.51)

0.001(0.32)

Tang0.032**(2.30)

0.032**(2.21)

0.003(1.43)

0.002(0.98)

Slack 0.028(0.97)0.032(1.10)

0.010***

(2.51)

0.004(0.99)

Intercept0.261***(10.13)

0.268***(10.12)

0.043***(12.07)

0.045***(12.28)

Country fixedeffects

Yes Yes Yes Yes


Yes Yes Yes Yes

Adjusted R2

0.091 0.087 0.267 0.298

N 1,433 1,409 3,024 3,248

The table reports OLS regression results. Compared with Table 4, we further require availability of datafor a firms financing sources. Variables are defined in the Appendix.*, **, and *** representsignificance at the 10%, 5%, and 1% level, respectively, using one-tailed (two-tailed) tests for the testvariables (control variables). Huber-White robust standard errors are used to control for heteroskedasticity.


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Table 6: The Conditional Effect of Tax Burdens on the Relation between Accounting

Quality and Investment Efficiency

, 1 0 1 , 2 , 3 , , ,

,

i t i t i t i t i t n i t

i t

InvEff EQ Tax Tax EQ Control variables

Industries Countries

+= + + + +

+ + + (6)

Predictions



Underinvestment

Variables


(1)

EQ measuredbyDisRev

(2)


(3)


(4)

EQ - (H1)-0.121***(-4.71)

-0.122***(-3.07)

-0.021***(-6.13)

-0.067***(-7.85)

Tax0.018**

(2.13)0.012*

(1.41)-0.001

(-1.34)-0.001

(-0.28)

EQTax + (H3)0.150***(3.06)

0.194***(2.41)

0.004(0.43)

0.033*(1.53)

Size-0.023***(-6.93)

-0.024***(-7.20)

-0.001**(-2.20)

-0.001**(-2.32)

Age-0.010(-1.17)

-0.007(-0.85)

-0.001(-0.34)

0.001(0.37)

Tang0.020(1.56)

0.022*(1.69)

0.001(0.73)

0.001(0.56)

Slack 0.021(0.83)0.026(1.00)

0.003(1.09)

0.001(0.11)

Intercept0.255***(10.87)

0.263***(10.97)

0.047***(16.58)

0.046***(15.95)


Yes Yes Yes Yes


Yes Yes Yes Yes

Adjusted R2

0.142 0.137 0.428 0.459

N 1,870 1,836 4,793 4,994

The table reports OLS regression results. Compared with Table 4, we further require availability of datafor a firms tax burden. Variables are defined in the Appendix.*, **, and *** represent significance at the10%, 5%, and 1% level, respectively, using one-tailed (two-tailed) tests for the test variables (controlvariables). Huber-White robust standard errors are used to control for heteroskedasticity.


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Table 7: The Conditional Effect of Bank Financing and Tax Burdens on the Relation

between Accounting Quality and Investment Efficiency

, 1 0 1 , 2 , 3 , , 4 , 5 , ,

, ,

i t i t i t i t i t i t i t i t

n i t i t

InvEff EQ Bank Bank EQ Tax Tax EQ

Control variables Industries Countries

+= + + + + +

+ + + + (7)

Predictions



Underinvestment

Variables


(1)

EQ measuredbyDisRev

(2)


(3)


(4)

EQ - (H1)-0.153***(-4.48)

-0.187***(-3.38)

-0.010**(-1.96)

-0.039***(-3.02)

Bank0.005

(0.54)

0.010

(1.11)

0.002*

(1.44)

-0.002

(-1.30)EQBank - (H2)

-0.068(-1.33)

-0.167*(-1.63)

-0.014**(-2.13)

-0.045**(-2.31)

Tax0.017*(1.76)

0.009(0.99)

-0.002*(-1.40)

-0.001(-0.66)

EQTax + (H3)0.182***(3.44)

0.226***(2.53)

0.001(0.04)

0.015(0.54)

Size-0.023***(-6.28)

-0.025***(-6.57)

-0.001(-0.89)

-0.001(-1.26)

Age-0.012(-1.20)

-0.009(-0.94)

-0.001(-0.42)

0.001(0.39)

Tang0.030**

(2.17)

0.032**

(2.21)

0.003

(1.56)

0.002

(1.07)Slack

0.031(1.07)

0.035(1.17)

0.010***(2.58)

0.005(1.06)

Intercept0.255***(9.87)

0.265***(9.95)

0.042***(11.99)

0.045***(12.20)


Yes Yes Yes Yes


Yes Yes Yes Yes

Adjusted R2

0.094 0.088 0.266 0.298

N 1,426 1,403 3,002 3,226

The table reports OLS regression results. Compared with Table 4, we further require availability of datafor firms financing sources and tax burden. Variables are defined in the Appendix.*, **, and ***represent significance at the 10%, 5%, and 1% level, respectively, using one-tailed (two-tailed) tests forthe test variables (control variables). Huber-White robust standard errors are used to control forheteroskedasticity.


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Table 8: The Conditional Effect of Bank Financing and Tax Burdens on the Relation

between Accounting Quality and Investment Efficiency

, 1 0 1 , 2 , 3 , , 4 , 5 , ,

, ,

i t i t i t i t i t i t i t i t

n i t i t

InvEff EQ Bank Bank EQ Tax Tax EQ

Control variables Industries Countries

+= + + + + +

+ + + + (7)

Panel A: Discretionary Accruals (DisAccr) as a proxy forEQ

Predictions



Underinvestment

Variables

DisAccr> 0

(1)

DisAccr< 0

(2)

DisAccr> 0

(3)

DisAccr< 0

(4)

DisAccr + ifDisAccr>0;- ifDisAccr0;- ifDisAccr0;+ ifDisAccr


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Panel B: Discretionary Revenue (DisRev) as a proxy forEQ

Predictions



Underinvestment

VariablesDisRev > 0

(1)DisRev < 0

(2)DisRev > 0

(3)DisRev < 0

(4)

DisRev+ ifDisRev>0;- ifDisRev0;- ifDisRev0;+ ifDisRev

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Ole Kristian Hope