Article Review- Corporate Finance
This week deals with international business in emerging economies and risks associated with international corporate finance. To understand international business and risks of emerging economies, you will review these two articles and respond to the questions:
You are required to combine the two articles in the article review.
Ferguson, M. (2011). Lessons on managing risk in emerging markets. Journal of Accountancy
McGowan, C. (2008). Evaluating the impact of foreign exchange rate risk on the capital budgeting for multinational firms. International Business & Economic Research Journal, 7(8), 47 – 58
Address the following questions as you read the article:
What corporate finance problems are the articles addressing?
What method of study (qualitative, quantitative, or mixed study) do the authors use to address the problem?
What are the significant findings or ideas of the study?
Summarize the main ideas of these articles.
What are the strengths and limitations of the study?
Make a proposal for future research on the topic that needs to be investigated.
International Business & Economics Research Journal August 2008 Volume 7, Number 8
47
Evaluating the Impact Of Foreign Exchange
Rate Risk On The Capital Budgeting
For Multinational Firms
Carl B. McGowan, Jr., Norfolk State University, USA
ABSTRACT
Capital budgeting analysis has evolved to the point where large firms universally use
sophisticated capital budgeting techniques.
1
However, small firms are less likely to use
sophisticated capital budgeting techniques.
2
Even large firms do not generally use simulation for
risk analysis in multinational project capital budgeting analysis.
3
This paper provides a
discussion and example of the use of simulation in evaluating the impact of foreign exchange rate
volatility on multinational project capital budgeting analysis.
Keywords: capital budgeting, foreign exchange risk, and simulation
INTRODUCTION
arragher, Kleiman, and Sahu (2001) discuss eight stages in the capital budgeting process. The first
three stages encompass finding appropriate projects for consideration: strategic analysis,
establishment of corporate goals, and searching for investment opportunities. The next three stages
involve the analysis of the project under consideration: forecasting cash flows, evaluating the projected cash flows,
and making the decisions to accept or to reject the project. The final two steps are implementing the decision and
post-auditing operating performance. In this paper, we deal primarily with the middle three stages of the mechanics
of project evaluation and selection.
FKS report that 55% of respondents perform quantitative risk analysis. Of this number, 95% use sensitivity
analysis and 79% use scenario analysis. However, only ten percent use simulation analysis. Graham and Harvey
(2001) report that 14% of respondents use simulation analysis. The use of simulation for risk analysis has not
increased significantly over the past 30 years. Klammer (1972) reports that 13% of respondents use simulation,
Klammer, Boch, and Wilner (1991) report that 12% of respondent use simulation, and Ho and Pike (1991) report
that 11% of respondents use simulation. Thus, the proportion of firms using simulation as a part of the capital
budgeting process has stayed level at just over ten percent while the use of sophisticated capital budgeting
evaluation techniques has increased substantially.
1
Bierman, Harold, Jr. Capital Budgeting in 1991: A Survey, Financial Management, Autumn 1993, pp. 21-29.
2
See, for example, Block, Stanley. Integrating Traditional Capital Budgeting Concepts into an International Decision-Making
Environment, The Engineering Economist, 45(4), 2000, pp. 309-325 or Graham, John R. and Campbell R. Harvey. The
Theory and Practice of Corporate Finance: Evidence from the Field, Journal of Financial Economics, 60, 2001, pp. 187-243.
3
See, for example, Farragher, Edward, Robert Kleiman, and Anandi, Sahu. The Association Between the Use of Sophisticated
Capital Budgeting Practices and Corporate Performance, The Engineering Economist, 46(4), 2001, pp. 300-31, Ho, Simon S. M.
and Richard H. Pike. Risk Analysis in Capital Budgeting Contexts: Simple or Sophisticated?, Accounting and Business
Research, 21(83), 1991, pp. 227-238, Klammer, T. Empirical Evidence of the Adoption of Sophisticated Capital Budgeting
Techniques, The Journal of Business, July 1972, pp. 387-397, and Klammer, T., B. Koch, and N. Wilner. Post-auditing Capital
Assets and Firm Performance: An Empirical Investigation, Managerial and Decisions Economics, (12), 1991, pp. 317-327.
F
International Business & Economics Research Journal August 2008 Volume 7, Number 8
48
The first step in making a capital budgeting decision is to forecast future cash flows. The second step is to
evaluate the projected cash flows. The third step is to make the decision to accept or to reject the project. Projects
with positive net present value (NPV) are accepted and projects with negative NPV are rejected. Alternatively,
projects with an internal rate of return (IRR) that is greater than the cost of capital are accepted and projects with an
IRR less that the cost of capital are rejected.
The first stage in the capital budgeting project risk analysis process is to estimate the future cash flows of
the project. Each variable that affects the future cash flows is estimated with a probability distribution. Probability
distributions can range from a simple high, low, best guess estimate to complex distributions of various natures.
4
Each probability distribution is chosen to best reflect the decision makers prediction of the nature of the underlying
variable process.
Once all of the probability distributions are estimated for the input variables, the simulation is run. A
simulation is implemented by selecting a value for each variable and combining all of the values to compute an
NPV/IRR for the project. Two options are available for the random selection process, Monte Carlo selection and
Latin hypercube selection. Monte Carlo selection selects each value from the full probability distribution. Latin
hypercube uses stratified sampling, which restricts the number of observations from each part of the probability
distribution. This process is repeated as many times as practicable given the speed of the computer and the time
available. In fact, current technology allows for simulation of 100,000 simulations easily. The result is probability
distribution of outcomes NPV/IRR.
This probability distribution of possible outcomes allows the decision maker to get a broad view of what
might happen to the capital budgeting project under consideration. The decision maker has the option to do
sensitivity analysis to determine which variables affect the outcome the most. That is, which variables affect the
decision to accept or to reject the project the most.
DOODAD COMPANY: A CAPITAL BUDGETING EXAMPLE
Doodad Company currently exports doodads to a low income country. To take advantage of incentives
provided by the host country government, and to avoid future political risk, Doodad has decided to begin
manufacturing in the host country (LIC). This project will be treated as a stand alone, new venture analysis.
The cost of building and equipping the manufacturing plant in LIC is $1,000,000 and will be depreciated
over the five year life of the project. Doodad uses straight line depreciation. Doodad believes that the risk level of
this project requires a 12.5% required rate of return. Sales volume in the first year (2000) will be 100,000 units and
demand will rise by 10% each year. The initial price of a unit will be 12 FC and will rise by 15% each year.
Variable cost per unit will begin at 6 FC and rise by 7.5% per year.
Doodad will repatriate all earnings after taxes as dividends which are subject to a 10% withholding tax. In
addition, Doodad will repatriate the depreciation. To simplify the exposition, US taxes are assumed to be the same
as the tax credit for taxes paid in LIC, so no US tax is due.
We construct a table of cash flows for the project and compute the net present value and internal rate of
return for Doodad. Table 1 provides the solution to the capital budgeting example for Scenario One. For scenario
one, all of the input variables are assumed to be deterministic, that is, know with certainty. The first three rows
show the value of the three input variables: sales volume, sales price, variable cost per unit and the expected future
spot rate. The level of unit sales volume begins at 100,000 units in year 2000 and grows by ten percent each year to
end at 146410 units. The beginning selling price is $12 and grows by 15% each year to end at $20.99 per unit.
Variable cost per unit begins at $6 and grows at 7.5 percent each year to end at $8.00 per unit. The fourth variable is
the expected future spot rate which begins at 2.00 foreign currency per dollar, is 2.15 foreign currency per dollar, at
4
In this paper, we use the simulation analysis package @RISK published by Palisades Corporation which includes over thirty
different probability distributions.
International Business & Economics Research Journal August 2008 Volume 7, Number 8
49
the end of 2000, and grows by 7.5 percent each year to end at 2.87 foreign currency per dollar. The IRR for this
scenario is 18.9 percent and the NPV for this scenario is $182,704.
Table 2 provides a solution to the same capital budgeting example but with Sales volume starting at
120,000 units.
5
With increased sales volume, the IRR increases to 25.14% and the NPV increases to $375,881. The
financial decision maker can change variable inputs to determine the sensitivity of IRR to changes in each input
variable. Scenario analysis allows the decision maker to determine which input variable has the most significant
impact of IRR. The capital budgeting project can be restructured to mitigate the effect of those input variables
where only a small adverse change in the input variable changes the IRR decision.
A significant scenario level for each input variable is the level at which the IRR is equal to the required rate
of return, 12.5%. The NPV is zero at this point. For sales volume, the zero NPV level is 81,804 units. For sales
price, this level is $10.72 per unit. For unit variable cost, this level is $7.47 pre unit. For the foreign exchange rate
variable, the break-even, starting level is $2.54. The break-even level for the cost of the project is $1,182,704.
Figures 1-4 show the probability distributions assumed for each of the input variables.
6
Sale volume is
assumed to be a triangular distribution with a minimum value of 95,000 units and a maximum value of 105,000
units. Sales price is assumed to be a histogram distribution with values between $11 and $13. The bottom and top
one-third each have a probability of 20% and the middle one-third has a probability of 60%. The growth rate of the
expected future foreign exchange rate is assumed to normally be distributed with a growth rate of 7.5% per with a
standard deviation of 1%. The exchange rate at time zero is assumed to be 2.00 foreign currency per dollar.
Figures 5-8 show the actual probability distributions for the input variables used in the simulation. Figures
9-10 show the actual probability distributions for the IRR and the NPV used in the simulation. Table 3 shows the
statistics generated by the simulation.
Unit volume has a mean of 100,000 units with a maximum of 104,991 units and a minimum of 95,018
units. Unit selling price has a mean of 12, a minimum of 11 and a maximum of 13. Unit cost has a mean value of 7
with a minimum of 5 and a maximum of 7. The foreign exchange rate has a mean of 2.15 foreign currency per
dollar with a minimum of 2.06 foreign currency per dollar and a maximum of 2.24 foreign currency per dollar.
The NPV for the project is $182,811 with a maximum of $514,545 and a minimum of -$122,330. The IRR
has a mean of 18.84% with a minimum of 7.93 percent and a maximum of 29.57. The probability of a positive NPV
is greater than 95%.
At this point, the decision maker can determine the critical variables which have the greatest impact of the
decision to accept or to reject the project. Managerial time, which is a limited resource, can be used where the time
will have the most impact, those variables whose volatilities have the most influence on the outcome. It is an easy
matter for the decision maker to develop various scenarios for the input variables or the probability distributions for
the input variables. This type of sensitivity analysis allows the decision maker to evaluate the impact of each input
variable on the possible outcome.
AUTHOR INFORMATION
Carl B. McGowan, Jr., PhD, CFA is a Professor of Finance at the School of Business at Norfolk State University.
Dr. McGowan received a BA in International Relations and an ROTC commission from Syracuse University, an
MBA (Finance) from Eastern Michigan University, and a PhD in Business Administration (Finance) from Michigan
State University. From 2003 to 2004, he was the RHB Bank Distinguished Chair in Finance at the Universiti
5
Scenario analysis is single iteration simulation. One variable is changed and the outcome is recomputed. The impact of each
variable can be determined for significant points such as the zero NPV point.
6
The probability distributions are chosen to show the variety of distributions available. @RISK provides thirty different
probability distributions.
International Business & Economics Research Journal August 2008 Volume 7, Number 8
50
Kebangsaan Malaysia. He has taught in Cost Rica, Malaysia, Moscow, Saudi Arabia, and The UAE. His special
area of interest is international risk analysis and foreign direct investment analysis analyzing the interaction between
political and economic risk and FDI. Professor McGowan has published over one hundred papers and presented
over one hundred and forty papers at conferences. Professor McGowan published in numerous journals including
Applied Financial Economics, Decision Science, Financial Practice and Education, The Financial Review, The
Journal of Applied Business Research, The Journal of Diversity Management, The Journal of Global Business, The
Journal of Real Estate Research, Managerial Finance, The Southwestern Economic Review, and Urban Studies.
BIBLIOGRAPHY
1. Bierman, Harold, Jr. Capital Budgeting in 1991: A Survey, Financial Management, Autumn 1993, pp.
21-29.
2. Block, Stanley. Integrating Traditional Capital Budgeting Concepts into an International Decision-Making
Environment, The Engineering Economist, 45(4), 2000, pp. 309-325.
3. Farragher, Edward, Robert Kleiman, and Anandi, Sahu. The Association Between the Use of
Sophisticated Capital Budgeting Practices and Corporate Performance, The Engineering Economist, 46(4),
2001, pp. 300-311.
4. Graham, John R. and Campbell R. Harvey. The Theory and Practice of Corporate Finance: Evidence
from the Field, Journal of Financial Economics, 60, 2001, pp. 187-243.
5. Ho, Simon S. M. and Richard H. Pike. Risk Analysis in Capital Budgeting Contexts: Simple or
Sophisticated?, Accounting and Business Research, 21(83), 1991, pp. 227-238.
6. Klammer, T. Empirical Evidence of the Adoption of Sophisticated Capital Budgeting Techniques, The
Journal of Business, July 1972, pp. 387-397.
7. Klammer, T., B. Koch, and N. Wilner. Post-auditing Capital Assets and Firm Performance: An Empirical
Investigation, Managerial and Decisions Economics, (12), 1991, pp. 317-327.
8. Pike, Richard H. An Empirical Study of the Adoption of Sophisticated Capital Budgeting Practices and
Decision-Making Effectiveness, Accounting and Business Research, 18(72), 1988, pp. 341-351.
9. Reichert, Alan K., James S. Moore, and Ezra Byler. Financial Analysis among Large US Corporations:
Recent Trends and the Impact of the Personal Computer, Journal of Business, Finance, and Accounting,
15(4), Winter 1988, pp. 469-485.
International Business & Economics Research Journal August 2008 Volume 7, Number 8
51
Figure 1
Probability Distribution Sales Volume
Figure 2
Probability Distribution Selling Price
Triang(95000, 100000, 105000)
X <= 103419
95.0%
X <= 96581
5.0%
0
0.5
1
1.5
2
2.5
94 96 98 100 102 104 106
Values in Thousands
V
a
lu
e
s
x
1
0
^
-4
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Histogrm(11, 13, {p})
X <= 11.1667
5.0%
X <= 12.8333
95.0%
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
10.5 11 11.5 12 12.5 13 13.5
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52
Figure 3
Probability Distribution Unit Cost
Figure 4
Probability Distribution Foreign Exchange Rate Change
Discrete({x}, {p})
X <= 5.0000
5.0%
X <= 7.0000
95.0%
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
4.5 5 5.5 6 6.5 7 7.5
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Normal(0.075, 0.01)
X <= 0.058552
5.0%
X <= 0.091448
95.0%
0
5
10
15
20
25
30
35
40
45
0.04 0.05 0.06 0.07 0.08 0.09 0.1 0.11
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Figure 5
Output Distribution Unit Volume
Figure 6
Output Distribution Selling Price
International Business & Economics Research Journal August 2008 Volume 7, Number 8
54
Figure 7
Output Distribution Unit Cost
Figure 8
Output Distribution Foreign Exchange Rate
International Business & Economics Research Journal August 2008 Volume 7, Number 8
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Figure 9
Output Distribution Net Present Value
Figure 10
Output Distribution Internal Rate of Return
International Business & Economics Research Journal August 2008 Volume 7, Number 8
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Table 1
NPV Computation
Scenario One
Year 2008 2009 2010 2011 2012
Volume 100000 110000 121000 133100 146410
Price 12.00 13.80 15.87 18.25 20.99
Variable cost per unit 6.00 6.45 6.93 7.45 8.01
Revenue 1200000 1518000 1920270 2429142 3072864
Variable costs 600000 709500 838984 992098 1173156
Depreciation 400000 400000 400000 400000 400000
EBT 200000 408500 681286 1037043 1499708
Taxes (30%) 60000 122550 204386 311113 449912
EAT 140000 285950 476900 725930 1049795
Dividend payment 140000 285950 476900 725930 1049795
Taxes 14000 28595 47690 72593 104980
Net 126000 257355 429210 653337 944816
FOREX rate 2.15 2.31 2.48 2.67 2.87
Depreciation ($) 186047 173067 160992 149760 139312
Dividend ($) 58605 111349 172749 244610 329060
Total ($) -1000000 244651 284415 333741 394370 468372
PV ($) 1182704
Cost ($) 1000000
NPV 182704
IRR 18.90%
International Business & Economics Research Journal August 2008 Volume 7, Number 8
57
Table 2
NPV Computation
Scenario One
Year 2008 2009 2010 2011 2012
Volume 120000 132000 145200 159720 175692
Price 12.00 13.80 15.87 18.25 20.99
Variable cost per unit 6.00 6.45 6.93 7.45 8.01
Revenue 1440000 1821600 2304324 2914970 3687437
Variable costs 720000 851400 1006781 1190518 1407787
Depreciation 400000 400000 400000 400000 400000
EBT 320000 570200 897544 1324452 1879649
Taxes (30%) 96000 171060 269263 397336 563895
EAT 224000 399140 628280 927116 1315755
Dividend payment 224000 399140 628280 927116 1315755
Taxes 22400 39914 62828 92712 131575
Net 201600 359226 565452 834405 1184179
FOREX rate 2.15 2.31 2.48 2.67 2.87
Depreciation ($) 186047 173067 160992 149760 139312
Dividend ($) 93767 155425 227583 312401 412425
Total ($) -1000000 279814 328492 388576 462161 551737
PV ($) 1375881
Cost ($) 1000000
NPV 375881
IRR 25.14%
International Business & Economics Research Journal August 2008 Volume 7, Number 8
58
Table 3
Output Statistics
Outputs Volume Price Cost FX rate NPV / 2000 IRR / 2000
Minimum 95018 11.00 5.00 2.06 -122330 0.0793
Maximum 104991 13.00 7.00 2.24 514545 0.2957
Mean 100000 12.00 6.00 2.15 182811 0.1884
Standard Deviation 2041 0.46 0.63 0.02 105266 0.0358
Variance 4166707 0.21 0.40 0.00 1.11E+10 0.00
Skewness 0.00 0.00 0.00 0.00 0.02 -0.07
Kurtosis 2.40 2.62 2.50 3.00 2.79 2.81
Number of Errors 0 0 0 0 0 0
Mode 96030 11.29 6 2.10 -39537 0.1847
5% 96581 11.17 5 2.12 13327 0.1298
10% 97236 11.33 5 2.12 46295 0.1416
15% 97739 11.50 5 2.13 68073 0.1493
20% 98162 11.67 6 2.13 89273 0.1568
25% 98535 11.72 6 2.14 110876 0.1643
30% 98873 11.78 6 2.14 130148 0.1710
35% 99183 11.83 6 2.14 146014 0.1765
40% 99472 11.89 6 2.14 158994 0.1809
45% 99743 11.94 6 2.15 171012 0.1850
50% 100000 12.00 6 2.15 182432 0.1889
55% 100257 12.06 6 2.15 194259 0.1929
60% 100528 12.11 6 2.16 206160 0.1969
65% 100817 12.17 6 2.16 219424 0.2013
70% 101127 12.22 6 2.16 235059 0.2065
75% 101464 12.28 6 2.16 253990 0.2128
80% 101838 12.33 7 2.17 275030 0.2198
85% 102261 12.50 7 2.17 296645 0.2269
90% 102764 12.67 7 2.18 320516 0.2346
95% 103419 12.83 7 2.18 355048 0.2459 Retrieved from Journal of Accountancy
Lessons on Managing Risk in Emerging
Markets
BY MICHAEL FERGUSON, CPA
July 28, 2011
In recent years, as economies in developed countries have slipped and stagnated, a
number of U.S. and other companies have sought to fuel growth by investing in
emerging markets. There are many benefits to employing such a strategy: By and
large, developing countries promise access to new, untapped markets; rising levels
of consumption, driven by rapidly growing middle classes; and access to inexpensive
labor and materials.
Indeed, with each passing year, the barriers to international trade are being whittled
away. Common currencies, more-liberal trade agreements and enhanced
communication and cooperation between countries have eased the process of finding
lucrative new markets. The possibilities for expansion are immense.
However, emerging markets also pose significant perils, as I learned while on a
recent consulting engagement in India. These perilsin this case, bureaucratic
delays, unanticipated expenses and fluctuating currenciesare often invisible to a
company entering the market for the first time. But they can easily turn a promising
venture into a losing proposition if they arent dealt with quickly and effectively.
In India, my team was enlisted to help a large financial services firm build an
agricultural extension services practice in the state of Andhra Pradesh. Our team
came equipped with a diverse array of skills, and our expertise was sought in
developing a feasible operational model. In support of this, we also had to develop
financial models and accounting procedures. Both were measures whose importance
was underestimated by management.
From the beginning of the project, we experienced some big, unexpected hurdles.
For example, we determined at the front end of the project that marketing would be
a large component of the firms success and that the materials and labor needed for
this activity could be obtained for a low cost. However, we didnt realize some of
the hidden expenses that the company would face on the marketing side, such as an
expensive, informal registration fee required for participation in an important
government-sponsored forum. Despite the name, this fee was more of an off-the-
record transaction, arbitrarily determined by government officials, with the only
basis for the amount being the clients ability and willingness to pay. We refused to
pay this fee, since doing so could be a violation of the Foreign Corrupt Practices Act,
and instead worked to find other ways to spread awareness of our clients service.
In addition, to make its business model viable, this socially minded firm planned to
rely on government contracts to help it reach impoverished farmers. But because so
much of the governments funding ebbed and flowed from year to yeareither due
to economic or legislative issues, or bureaucratic corruptionthe company was
unable to determine whether national or local officials would ever be able to fund
the project, or whether other funding vehicles should be pursued.
Further, by its very nature, agricultural extension work is a relatively low-margin
business. Thus, when the rupee appreciated or depreciated sharply against the U.S.
dollar, the effects on the companys balance sheet and income statements were
immediate and substantial. Depending on the magnitude of these swings, the
companys entire investment in the country could be called into question.
As is typical of many businesses pursuing international diversification, my client
overcame some of these issues, but on less favorable terms than it had anticipated.
Because of the potential corruption issues in dealing with government-sponsored
marketing forums, it had to market independently, in a manner that was more
expensive and had less reach. Since local government agencies and small businesses
in India were unreliable, it had to seek partnerships with larger, international
corporations, which had significantly more bargaining clout. And as Indias currency
fluctuated wildly, the company had to keep most of its profits in India through
reinvestment, even when there was a lack of attractive projects to justify this action.
FORMIDABLE RISKS
As countless other companies have learned the hard way in recent years, while
investing in the emerging world can be very rewarding, it carries formidable risks as
well. These risks, which often slide by without notice in the rush to seize an
opportunity, can undermine an otherwise sound operation.
The following are some of the biggest threats to anticipate when entering an
unfamiliar, developing market:
Corruption. Emerging economies, more so than those in the developed world, are
often saddled with corrupt politicians, bureaucrats and businesspeople. The E7
nationsChina, India, Brazil, Russia, Indonesia, Mexico and Turkey, considered
the primary sources of the worlds economic growth through 2050are among the
poorest performers on Transparency Internationals Corruption Perception Index,
which measures public sector corruption. With a score of 3.3 out of 10 (where a
score of 10 is highly clean and 0 is highly corrupt), India ranked 87th out of 178
countries in the 2010 index. Often, corruption is the legacy of previous governments
that, while no longer in power, continue to have an influence through people or
policies that remain in place. And even when a new government has cleaned house
and old institutions and bureaucrats are gone, social norms may continue to keep
corruption alive and slow the progress of economic liberalization. For instance,
Russia has long suffered from quasi-legal forms of bribery. This practice seems to
be a remnant of the countrys former communist rule.
Moreover, even if local governments are not corrupt in initial dealings, their
incentives may change after a foreign firm commits to making an investment. A
government that was initially cooperative and willing to provide assistance may
impose onerous taxes or restrictions once the host company is heavily invested and
doesnt have the option of a quick withdrawal.
Operating within this business climate, we even found that the farmers with whom
we were interacting were initially unwilling to trust us and were highly skeptical of
our motives. From their perspective, we were working with the government and
regarded simply as one more intermediary that would prevent funding from getting
to its intended targets.
Any U.S. company seeking to do business in a foreign country needs to have a clear
understanding of its responsibilities under the FCPA and other international laws,
such as the United Kingdoms Bribery Act 2010. The FCPAs anti-bribery
provisions make it illegal to offer or provide money or anything of value to officials
of foreign governments or foreign political parties with the intent to obtain or retain
business. The provisions apply to U.S. issuers, other domestic concerns (individuals
and businesses), U.S. parent companies of foreign subsidiaries, and foreign
companies and individuals, including agents.
To protect against charges of corruption, companies need to keep books, records and
accounts that accurately reflect their transactions and disposition of assets. In
addition, companies need to devise and maintain internal accounting controls aimed
at preventing and detecting FCPA violations. They also must have clear policies and
http://www.justice.gov/criminal/fraud/fcpa/
https://www.journalofaccountancy.com/news/20114098.html
procedures that explain how business is to be conducted as well as ongoing training
for employees and business partners.
Lack of transparency. Even when corruption per se is not present, there often is very
little transparency into the inner workings of government and business. Granted,
improving the visibility of financial reporting in the U.S. and other developed
nations is still a work in progress. But, in many emerging markets, the commitment
to improving transparency lags far behind the norm.
As a result, major Western corporations have been hesitant to invest substantially in
emerging economies, realizing that market opaqueness often can be a smokescreen
for shady behavior. In their home countries, large multinationals have very stringent
reporting guidelines, and they may be fearful of being unable to meet these standards
when working in new countries if transparency is minimal.
Lack of transparency proved to be a major obstacle during our engagement. Not only
did we have little insight into the decision-making process of the government
agencies that would be granting us contracts (making financial planning
extraordinarily difficult and imprecise), but it was even a struggle to get financial
and operational information from potential corporate partners who would have
benefitted from such communication. A cultur
