· WEEK 1 Databases and SecurityLesson· Databases and Security.docx

· WEEK 1: Databases and SecurityLesson
· Databases and Security
Databases are in just about everything we use today. When you
are performing any task, think to yourself, Does this involve a
database in some way?
As a daily process, communication occurs between people by
many mediums, but there is no other medium more utilized than
the large internetwork of computer systems we know as the
Internet. When we look at some of the transactions that are
performed on a daily basis, it is highly likely that there is a
database involved. For example, if you open a web page to
www.google.com and type a keyword in the textbox to search
for, this process starts a series of searches through multiple
databases. Another example is when searching for a book in the
APUS library, this search is conducted using a database of
books known as a catalog. so databases play an integral part in
our daily lives; they store millions of pieces of data and more is
collected each day (Basta, 2012).
In recent years, we find that technology has expanded to the
reaches of utilities and production environments. Many of the
utilities we come to rely on so heavily, such as gas, oil and
electric, have been tied into the networks we use today. This
interconnection allows for many new innovations in keeping
everything in working order, but at the same time it also
presents some very real threats to security. In reality, an
intruder could take down an entire electrical grid which would
remove power to millions of customers. An article in CIO
Insight gives a great perspective on this and other issues in
security where databases play such an important role
(CIOInsight, 2011).
With the importance of securing the database infrastructure, we
need to look at a multilayered approach to security. As can be
seen in many security programs, multiple layers allow for
strong security because it adds another roadblock that an

intruder has to bypass to get to these systems. This same
approach leads us to begin with the foundation of security; the
CIA Triad. It all begins with the most basic approach, computer
security and moves forward from that point on. Below is a
detailed description of the components of the CIA Triad from
(Basta, 2012):
· Confidentiality: For a system to provide confidentiality, it
needs to do two things: ensure that information maintains its
privacy by limiting authorized access to resources; block
unauthorized access to resources.
· Integrity: This refers to the efforts taken through policy,
procedure, and design in order to create and maintain reliable,
consistent, and complete information and systems.
· Availability: This refers to the efforts taken through policy,
procedures, and design to maintain the accessibility of resources
on a network or within a database. These resources include, but
are not limited to, data, applications, other databases,
computers, servers, applications, files, drives, shares, and
network access.
Database Structure, Models and Management
A database alone is just a single collection of data that has some
organization based on the various groupings necessary for that
data. In most cases, the database follows some model of
organization, but by itself it is merely just a file filled with
information. So what is needed is a way to bring all of these
together, which allows us to introduce the database management
system (DBMS). Quite simply, a DBMS is an application that
is used to combine databases and allow the addition and
modification of data held within a database. A DBMS also
allows for added functionality to manipulate data in many ways.
For example, going back to the Google search example, we can
search for specific criteria, which then creates a search of
databases within that criteria; this is also known as a query.
A database is made up of several components that aid in the
organization of data. The highest level is the table then moves
on to records, columns, rows, fields, etc. You can see an

example in the text by reviewing figure 2.1.
Database records usually need a starting point, such as an
identifier to make the record unique from others. This can be
done in many ways, but what this is known as in a database is a
key. There are two major categories of a database key; the
primary key and the foreign key but there are other keys that
could be used. Below is the definition given by (Basta, 2012):
· Primary Key: It is a best practice, but not necessary, to use
keys that are meaningful to the data being stored. Examples of
primary keys are employee ID numbers, student IDs, ISBNs,
and Social Security numbers.
· Foreign Key: A foreign key is a field within a table that
contains a label that is used to build a relationship between two
tables. Use Figure 2-2 to aid the discussion.
· Other Keys: The use of the following keys depends on the
administrator, the DBMS, and the database model within an
environment: secondary or alternative key, candidate key, sort
or control key, and alternate key.
Keys help to map relationships in databases and make it easier
to create queries that group data that is being searched.
There are many database models to consider. A database model
is a representation of the way data is stored. Note that the model
for which a database is constructed also determines the way the
data can be retrieved and manipulated. Below are examples of
database models that are used.
· Hierarchical Model
· Network Model
· Relational Database
· Object-Oriented Databases
References/Works Sited:
Basta, A. and Zgola, M. (2012). Database Security, 1st Edition.
Florence, KY. Delmar Cengage Learning. ISBN-10/13:
1435453905/9781435453906
CIOInsight (2011). Stronger Database Security Needed, Cyber
Attacks Show. CIO Insight.

URL: /access/content/group/science-and-technology-
common/ISSC/ISSC431/Reading-Materials/Stronger-Database-
Security-Needed-Cyber-Attacks-Show.pdf
Full Terms & Conditions of access and use can be found at
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Feminist Economics
ISSN: 1354-5701 (Print) 1466-4372 (Online) Journal homepage:
https://www.tandfonline.com/loi/rfec20
Gender Disparity in Education and the
International Competition for Foreign Direct
Investment
Matthias Busse & Peter Nunnenkamp
To cite this article: Matthias Busse & Peter Nunnenkamp (2009)
Gender Disparity in Education
and the International Competition for Foreign Direct
Investment, Feminist Economics, 15:3, 61-90,
DOI: 10.1080/13545700802528315
To link to this article:
https://doi.org/10.1080/13545700802528315
Published online: 23 Jul 2009.
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G E N D E R D I S P A R I T Y I N E D U C A T I O N A N D
T H E
I N T E R N A T I O N A L C O M P E T I T I O N F O R
F O R E I G N D I R E C T I N V E S T M E N T
Matthias Busse and Peter Nunnenkamp
A B S T R A C T
With few exceptions, the empirical literature on foreign direct

investment (FDI)
continues to be gender blind. This paper contributes to filling
this gap by
assessing the importance of gender inequality in education as a
determinant of
FDI. The authors estimate a standard gravity model on bilateral
FDI flows that is
augmented by educational variables, including different
measures of gender
inequality in education. The analysis covers an unprecedented
number of both
host and source countries of FDI, thereby reducing the risk of
distorted results
because of a sample selection bias. The results support the view
that foreign
investors are more likely to favor locations where education-
related gender
disparities are small. However, the discouraging effects of
gender disparity on
FDI are restricted to middle-income (rather than low-income)
developing host
countries and to investors from developed (rather than
developing) countries.
K E Y W O R D S
Foreign direct investment, gender inequality, education
JEL Codes: F23, I21, J16
I N T R O D U C T I O N
The question of whether gender inequality hinders or helps the
integration
of countries into the international division of labor has received
only scant
attention in the empirical literature. Some evidence exists on

the links
between gender inequality and trade. Matthias Busse and
Christian
Spielmann (2006) find that wage inequality is positively
associated with
comparative advantage in labor-intensive exports, whereas
inequality in
terms of labor-market participation and education is negatively
related with
such exports. According to Stephanie Seguino (1997), wage
inequality may
have contributed to the export success of countries such as
South Korea.1
However, the role of gender inequality has been largely ignored
in studies
about the countries’ attractiveness for foreign direct investment
(FDI).
Feminist Economics 15(3), July 2009, 61–90
Feminist Economics ISSN 1354-5701 print/ISSN 1466-4372
online � 2009 IAFFE
http://www.tandf.co.uk/journals
DOI: 10.1080/13545700802528315
This is fairly surprising in light of the fierce international
competition for
FDI. Policy-makers are falling over themselves to entice foreign
investors,
for example, by offering tax breaks and outright subsidies, in
the hope that
FDI inflows would induce higher growth and employment. Yet,

it is still
open to debate what actually drives FDI inflows.2 In particular,
the sizeable
literature on FDI determinants has generally been gender blind
(Elissa
Braunstein 2006).
This paper attempts to fill this gap by assessing the role of
gender
disparity with respect to host countries’ attractiveness for FDI.
The focus
will be on education-related gender disparity and its effects on
FDI flows to
developing countries, for which the linkage is of particular
concern.3
Opposing hypotheses in this regard call for empirical analyses.
On the one
hand, gender disparity in education may stimulate FDI by
offering cost
advantages if it leads to lower average wages at a given level of
labor
productivity. On the other hand, FDI may be discouraged if
foreign
investors increasingly rely on the local availability of skilled
labor, which
gender disparity in education is likely to constrain.
We estimate a gravity model on bilateral FDI flows, covering as
many
(developing) host countries of FDI as possible to avoid a sample
selection
bias. The standard gravity model is augmented by educational
variables,
including different measures of gender inequality in education.
We chose

this rather indirect approach of assessing the FDI effects of
gender disparity
as disparity measures directly capturing wage costs, labor
productivity, and
the qualification of the workforce by gender are unavailable or
subject to
serious data constraints.
Our results clearly reject the view that foreign investors favor
locations
where education-related gender disparities may offer cost
advantages.
Rather, we find that gender disparity discourages FDI inflows.
However, the
strength of this relation depends on the level of education,
being most
pronounced with respect to secondary and tertiary education.
Additional
robustness tests reveal that the discouraging effect of gender
disparity
becomes statistically insignificant when considering only low-
income host
countries and developing source countries.
P R E V I O U S S T U D I E S A N D G E N D E R D I S P A R
I T Y M E A S U R E S
Even though the literature on FDI determinants does not address
gender
issues, a strand of this literature on social factors and FDI
relates to the
analysis in this paper. Several studies raise the question of
whether FDI
tends to go where social standards are low and worker rights are
repressed
to save costs, or rather where social and political conditions are

similar to
those prevailing in the home country.4 Howard J. Shatz (2003)
focuses on
education as a determinant of FDI but does not consider gender
gaps in
education. Shatz finds that better educated workers attract more
FDI. The
A R T I C L E S
62
counter-hypothesis is rejected, according to which FDI is
undertaken ‘‘in
countries with low levels of education to escape the high
compensation
costs with which higher levels of education and skill are
associated’’ (Shatz
2003: 188). The question addressed in the following analysis –
that is,
whether gender inequality attracts or rather discourages FDI
inflows –
resembles this strand of the literature on FDI determinants in
that there are
two opposing hypotheses.
On the one hand, gender disparity in education could be
associated with
higher FDI inflows. In the process of economic globalization,
multinational
companies appear to face mounting cost pressure. They
increasingly refer to
vertical types of FDI (also labeled efficiency-seeking FDI),
which provides a

means to allocate specific steps of the production process to
where the
relevant comparative advantages can be utilized. Consequently,
this type of
FDI tends to be sensitive to international cost differentials. In
particular,
vertical FDI is often associated with the relocation of labor-
intensive parts of
the value chain to lower-wage locations. This may strengthen
the incentives
of multinational companies to exploit less-skilled, low-wage
female labor. The
movement of FDI in so-called footloose industries, such as
textiles and cloth-
ing, to countries with segmented labor markets may provide
cases in point.5
On the other hand, multinational companies may be more
interested in
drawing on sufficiently qualified labor rather than just cheap
labor. David
Kucera (2002) refers to survey results in which the managers of
multinational companies rated the quality of labor in the host
country to
be more important than the cost of labor. Indeed, empirical
evidence
suggests that the labor demand of multinational companies is
biased toward
relatively skilled workers in developing host countries
(Overseas Develop-
ment Institute 2002). Furthermore, multinational companies are
increas-
ingly under pressure, notably from nongovernmental
organizations
(NGOs), to show good corporate behavior (Matthias Busse
2004). As a

consequence, they may shy away from host countries with
pervasive social
injustice in general and gender inequality in particular.6
It follows that the impact of gender inequality in education on
FDI is
theoretically ambiguous. Unit labor costs tend to decline to the
extent that
gender inequality in education involves lower average wages at
a given level
of labor productivity, with less educated women entering the
labor force.7
Locations where education-related gender inequality is more
pronounced
might then have a competitive edge in attracting cost-oriented
FDI of the
vertical type. However, gender inequality may also be
associated with higher
unit labor costs, and thus less vertical FDI, if it is mainly
associated with
lower average labor productivity. In other words, gender
inequality in
education has opposing effects on unit labor costs. Moreover,
the impact of
gender inequality in education on FDI inflows would still be
indeterminate
even if unit labor costs declined on balance. In contrast to
vertical FDI, the
horizontal type of FDI (also labeled market-seeking FDI) may
be unaffected
G E N D E R D I S P A R I T Y A N D F D I
63

by changes in unit labor costs. This type of FDI essentially
duplicates the
parent company’s production at home in the host countries.
Market access
motivations dominate over cost considerations, and factor
intensities of
production in the host countries largely resemble those at home.
Hence,
the importance of unit labor costs for overall FDI inflows is
likely to depend
on the composition of FDI, which – though difficult to measure
exactly –
tends to vary considerably across host countries (see also
Kucera [2002]).
Ideally, we would like to cover several aspects of gender
disparity and
their effects on FDI inflows, including gender wage gaps,
differences in
labor-force participation rates between males and females, and
education-
related differences. The focus on education-related disparity
measures
implies some limitations. Inferences about the FDI effects of
gender
disparity in general remain indirect and incomplete. First,
education-
related measures tend to capture the net effects of two
transmission
mechanisms running through wages and labor productivity,
without being
able to disentangle them. Second, any positive FDI effects of
less disparity in
education may be associated with gender wage disparity to the

extent that
an improved qualification of female workers does not lead to a
corresponding pay rise. Consequently, concerns about gender
equity and
fairness would not necessarily be overcome if only less gender
disparity in
education resulted in more FDI.
However, wage disparity measures and differences in labor-
force
participation rates are not particularly useful as possible
determinants of
FDI in the present context of a large panel of host countries and
a time
span of about twenty-five years because of the following
reasons:
. Data on wage differences are only available for selected years
and a
limited number of countries.8 The insufficient country coverage
especially may cause seriously biased results when analyzing
FDI
determinants (Shatz 2003; Matthias Busse, Jens Königer, and
Peter
Nunnenkamp 2008). Moreover, when available, wage data
typically
refer to the manufacturing sector only (Kucera 2002; Busse and
Spielmann 2006).9 This limitation is problematic, as FDI in
develop-
ing countries increasingly consists of FDI in the services sector
(United Nations Conference on Trade and Development
[UNCTAD]
2004; Braunstein 2006). And finally, the problem of reverse
causation
running from FDI to wages and wage disparity would be all but
impossible to resolve.

. Similar arguments apply to labor-force participation rates.
Again,
problems of reverse causality loom large (Braunstein 2006). The
statistically insignificant results Kucera (2002) achieves when
adding
the proportion of female workers in the industry to his list of
FDI
determinants may well reflect that causality between FDI and
female
employment shares goes both ways (see also Elissa Braunstein
A R T I C L E S
64
[2002]). Moreover, gender-specific labor-market participation
rates
do not necessarily reflect discrimination but rather may be
based on
voluntary decisions of female workers (Busse and Spielmann
2006).
Consequently, education-related gender disparity appears to be
the first
choice when analyzing FDI determinants. While theory
indicates that the
level of education in a host country should influence FDI
inflows (Shatz
2003), the possibility of reverse causation – meaning that higher
FDI results
in better education – seems to be rather remote in comparison
with wages
and employment. The empirical studies of Shatz (2003) as well

as Jonathan
Eaton and Akiko Tamura (1996), considering education among
the
determinants of FDI, find that better educated workers in host
countries
attract higher FDI inflows. However, both studies cover only
selected FDI
source countries (United States FDI in the case of Shatz, US
and Japanese
FDI in the case of Eaton and Tamura). Furthermore,
Braunstein’s (2006)
verdict that most FDI studies are gender blind applies to both
Shatz (2003)
and Eaton and Tamura (1996).
To the best of our knowledge, Kucera (2002) is the only
exception in that
he considers gender-specific educational variables as
determinants of FDI.
He does not find evidence suggesting that education-related
gender
disparity resulted in higher FDI inflows. Yet, his results are far
from robust.
The positive effect of (relative) female educational attainment
on FDI is
statistically significant only when high-income host countries
are included
in the sample, and the coefficient of this variable even changes
its sign once
the regressions are run with regional dummies.
Moreover, Kucera’s study has some shortcomings that we
attempt to
overcome in the following analysis. First of all, it is purely
cross-sectional,
while we use a panel analysis to examine changes over time in

the relation
between gender gaps in education and FDI. Second, we employ
a gravity
model on bilateral FDI flows, and we explicitly account for the
fact that
various host countries have not attracted any FDI flows from
particular
source countries. Third, we draw on a large, new dataset to
cover essentially
all (developing) host countries as well as a large number of
source
countries and thereby avoid, or at least substantially reduce, a
sample
selection bias.
In the regressions reported below, we measure gender gaps in
education
by comparing females and males with respect to average years
of schooling.
While we also consider three different levels of education when
estimating
the Tobit model later in this paper, we confine the subsequent
presentation
of stylized facts to gender gaps in education at all levels of
schooling
combined, in order to save space. We compare the situation
prevailing in
1980 with that in the most recent years (average of 2000 and
2005). The
mean and the range of gender differences at specific levels of
schooling are
presented in the Appendix.
65

In Figure 1, ratios far below one reflect larger gender gaps in
education
working against women. On the other hand, women are
overrepresented in
some countries with ratios above one (notably in several Latin
American
countries). Not surprisingly, high-income countries, on average,
have a
relatively narrow gender gap in education, whereas the gap is
widest in low-
income countries. This applies to both 1980 and the most recent
years. In
contrast to what one might expect, however, there is also
considerable
variation over time.10 Middle-income countries, on average,
caught up with
high-income countries in terms of narrowing the gender gap; in
recent
years, middle-income countries resembled the high-income
group in that
the gender gap in education was less than 10 percent. At the
same time,
low-income countries, while still lagging behind, made
remarkable progress
in expanding the schooling of females relative to males.
Moreover, the group averages reported in Figure 1 conceal
considerably
different developments in particular countries. This may be
exemplified by
three middle-income countries in Latin America. Colombia and
Honduras
started with a ratio of close to one in 1980 but through

subsequent
developments diverged: females spent 24 percent more time in
education
than males in Colombia in recent years, whereas the ratio of
females to
males deteriorated to 0.67 in Honduras. Bolivia, starting with a
pronounced
gender gap (0.68), made substantial progress in closing this gap
(to 0.88 in
2000/2005). Similar discrepancies apply to low-income
countries in sub-
Saharan Africa. Mozambique reported a large gender gap (0.23)
at the
beginning of the period of observation but a relatively narrow
one recently
Figure 1 Gender disparity in schooling,a 1980 and 2000/2005
Note: aAverage years of schooling at all levels combined:
females divided by males;
2000/2005 represents the average for 2000 and 2005.
Sources: Robert J. Barro and Jong-Wha Lee (2001) and
UNESCO (2007).
A R T I C L E S
66
(0.69). Ghana and Sudan both started at a ratio of females to
males in
education of about 0.4. While this ratio increased to 0.62 in
Sudan, it
declined slightly in Ghana.
A P P R O A C H A N D D A T A

We follow a standard approach in the large empirical FDI
literature11 and
estimate a gravity-type model on the determinants of FDI.
Gravity models
are widely used to analyze the movement of goods, services,
and factors of
production between different locations within or across
countries. The
common intuition is to portray spatial transactions analogous to
Newton’s
Law of Gravity. Consequently, mass and distance are core
elements of this
class of models. In cross-country contexts as the present one,
the focus is on
economic size (in terms of income and/or population) and
geographical
distance between each pair of countries. Economic interaction is
supposed
to be an increasing function of the economic size of partner
countries and
a decreasing function of the distance between them; larger
countries with
higher income are thus expected to be involved in more
transactions with
nearer-by and larger countries. Various extensions of this basic
model
structure have been suggested in the literature. The extended
version we
use is specified below.
As noted by Alan V. Deardorff (1998), this class of models first
appeared in
the empirical economics literature on bilateral trade flows.
Deardorff also
shows that simple gravity models can be derived from standard

trade theories.
More recently, gravity models have also been applied to analyze
financial flows.
The explanatory power of gravity models on financial flows is
comparable with
that of models on trade flows (Philippe Martin and Hélène Rey
2004).
According to Richard Portes and Hélène Rey, this is hardly
surprising as the
gravity approach ‘‘emerges naturally’’ from theories of asset
trade (2005: 275).
Recent examples employing gravity models to analyze bilateral
FDI include
Shatz (2003) and John H. Mutti and Harry Grubert (2004).
Hence, in contrast to Avik Chakrabarti’s earlier verdict of
‘‘measurement
without theory’’ (2001: 90), there appears to be widespread
agreement by
now on the appropriate analytical framework to guide empirical
work on the
determinants of FDI. Indeed, variables such as market size and
openness to
trade that the extreme bounds analysis of Chakrabarti (2001)
found to be
fairly robust determinants of FDI represent important
cornerstones of the
gravity model. The particular advantage of the extended gravity
model in
explaining the determinants of bilateral FDI flows is the fact
that differences
between source and host country characteristics can be used as
explanatory
variables. A standard FDI analysis using aggregated FDI flows
for each
country would not be suitable for that task.

While the core variable set of gravity models helps prevent
fragile results
due to ad-hoc choices on control variables, the estimation
results may still
67
be sensitive to sample selection. Shatz’s (2003) analysis of US
FDI clearly
reveals that sample selection matters for empirical results.12
Consequently,
we cover as many countries as possible in our baseline
regressions and, at
the same time, perform robustness tests for specific sub-
samples.13
Furthermore, when applying gravity models to FDI flows, one
must take
into account the concentration of FDI in a few host countries.
During the
period under consideration (1978–2004), about 80 percent of
FDI flows to
all (150) middle- and low-income countries were concentrated
in just
twenty countries (World Bank 2006). Bilateral FDI flows are
often equal to
zero; this applies to roughly three-quarters of all observations
in our
sample. The censored nature of this variable implies that the
results from
OLS estimations would very likely be biased. Therefore, we use

a non-linear
method of estimation such as Tobit.
The Tobit model represents our preferred option among
alternative
approaches suggested in the literature to avoid biased results
when the
dependent variable is censored.14 This model estimates FDI
flows between
a particular pair of countries in one step. The underlying
assumption is
that the explanatory variables have the same impact on (1) the
probability
of receiving any bilateral FDI at all (selection decision) and (2)
the amount
of FDI allocated thereafter (allocation decision). This
assumption
appears to be reasonable in the context of bilateral FDI flows; it
would
be difficult to find an exclusion variable that affects selection
but does not
affect allocation, as two-step models such as the Heckman
model would
require.
In our empirical approach, we principally follow David L. Carr,
James R.
Markusen, and Keith E. Maskus (2001), who estimate the so-
called
knowledge-capital model that integrates the previously separate
concepts
of horizontal (market-seeking) and vertical (efficiency-seeking)
FDI into a
single model by considering the determinants of both types of
FDI within a
single estimation equation.15

Our basic specification reads as follows:
InðFDI ijtÞ¼ a0 þ a1lnðFDI ijt�1Þþ g
0X jt þ f0Y ijt þ a2 GenderInequalityjt
þ lt þ eijt ð1Þ
where FDIijt stands for foreign direct investment of country i in
country j at
period t, FDIijt-1 corresponds to FDI inflows in the previous
period t-1, Xjt
represents a set of host country control variables, Yijt denotes
the differ-
ence between source and host country characteristics, lt is a set
of year
dummies, and GenderInequalityjt corresponds to gender
inequality in
education between males and females in the host country. The
error term
of the random effects estimation can be written as:
eijt ¼ nijt þ uijt ð2Þ
A R T I C L E S
68
where uijt is the random unobserved bilateral effect and vijt
represents the
remaining error.16
For the dependent variable, we use two measures of FDI: first,
FDI flows

from the source to the (developing) host country in percent of
host country
GDP (the variable is labeled FDI1), and second, the share of
FDI attracted
by a specific (developing) host country in total FDI flows from
the source
country under consideration to all developing host countries
(FDI2)
included in our sample. The second measure captures the
attractiveness of
a particular country relative to other host countries. We
calculate three-year
averages to smooth the considerable fluctuation of annual
bilateral FDI
flows. At the same time, this approach ensures that we have
enough
variation in the data.17
The limited host country coverage of previous analyses of
bilateral FDI
flows is overcome by fully exploiting the (largely unpublished)
data
available upon request from UNCTAD’s Data Extract Service.
Yet, some
data limitations remain. Most importantly, it is not possible to
differentiate
between different types of bilateral FDI flows. For instance,
vertical FDI
should be affected more strongly by gender inequality in
education than
horizontal FDI; it is mainly the former type of FDI that is
supposed to
depend on international cost differences as well as the
availability of
sufficiently qualified labor in the relevant literature. Likewise,
the impact of

gender inequality in education may be stronger in the case of
greenfield
FDI, compared with mergers and acquisitions (M&As), which
amount to a
change in ownership of existing production facilities and may
be driven by
asset-seeking motives in the first place.18 Tax-induced
distortions in
international FDI patterns are minimized by excluding FDI
flows to
offshore financial centers (see also below); but the problem
remains that
FDI channeled through offshore centers to the ultimate host
country
cannot be accounted for appropriately.
We include the lagged dependent variable on the right-hand side
of the
regression equation for two reasons. First, this solves the
potential problem
of autocorrelation in the pooled time-series regressions.19
Second, this
procedure is theoretically plausible as foreign investment in the
previous
period is highly relevant for FDI in the current period. Above
all, countries
that already have considerable FDI inflows are much more
likely to attract
multinational corporations. This has been shown, for example,
by Victor M.
Gastanaga, Jeffrey B. Nugent, and Bistra Pashamova (1998); the
lagged FDI
variable is always highly statistically significant in their
regressions. By
including lagged FDI flows, the econometric specification
becomes a

dynamic panel.
We employ a fairly standard set of controls, including total host
country
population and real GDP growth for market-seeking FDI
(labeled
population and growth, respectively),20 host country inflation
(inflation),
host country openness to trade (openness), the difference in
GDP per capita
69
between the source and the host country for vertical FDI
(DiffGDPpc), and a
dummy for the existence of a bilateral or regional trading
agreement – that
is, a free trade agreement or customs union (RTA). We expect a
positive
association of population, growth, DiffGDPpc, and RTA with
FDI; the opposite
applies to inflation, as this variable can be interpreted as a
proxy for
macroeconomic distortions. Exact definitions and data sources
for all
variables as well as descriptive statistics can be found in the
Appendix.
As for time invariant variables, we also closely follow the
empirical
literature on gravity models and incorporate dummies for a
common border, a

common language, and colonial ties as well as the distance
between the source
and the host country (distance). The first three control variables
are
expected to be positively associated with FDI flows, whereas
the sign of
distance is unclear. On the one hand, management and transport
costs are
likely to increase if two countries are located far away from
each other; on
the other hand, remote markets might be better served through
local
production, that is, FDI in the host country. Hence, the net
impact on FDI
is uncertain.
To reduce the skewness in the data, we take the natural
logarithm of
population, FDI1, FDI2, DiffGDPpc, distance, and inflation.
But this would
mean that we would lose observations with negative values or
zeros. To
overcome this problem, we use the following logarithmic
transformation
that reduces the skewness in the data and, at the same time,
retains negative
and zero observations:
y ¼ ln x þ
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
x2 þ 1ð Þ
p� �
ð3Þ
Using this transformation leaves the sign of x unchanged, while

the values
of x pass from a linear scale at small absolute values to a
logarithmic scale at
large values.
In addition to these standard control variables, we include the
institutional development of host countries, proxied by political
constraints
on the executive branch (political constraints). Poor institutions
may
discourage FDI by giving rise to uncertainty (for example, with
respect to
the protection of property rights [Jeong-Yeon Lee and Edwin
Mansfield
1996; Witold J. Henisz 2000]) and additional costs (for
example, in the case
of corruption [Shang-Jin Wei 2000]). We use the index for
political
constraints that was developed by Henisz (2000). In contrast to
alternative
institutional indicators, this variable is available for a large
number of
countries and years. Political constraints focuses on the
political discretion of
the executive branch. Less discretion is supposed to render
credible
commitments to (foreign) investors more likely. The indicator
…
Stronger Database Security Needed, Cyber-Attacks Show
http://www.cioinsight.com/print/c/a/Latest-News/CyberAttacks-
Highlight-Need-to-Focus-on-Stronger-Database-Security-
342260[2/2/2017 9:31:02 AM]

Stronger Database Security Needed, Cyber-Attacks Show
By CIOinsight | Posted 06-03-2011
When cyber-attackers breach an organization's network, the
database is usually their target. However, many organizations
are so focused on protecting the perimeter that they don't think
abo
protecting the database itself, according to several security
experts.
Many organizations still think that protecting the perimeter is
sufficient to protect the data, but as recent data breaches at
Epsilon and Sony have shown, traditional perimeter security
can't
relied on to protect the data, Josh Shaul, CTO of Application
Security, told eWEEK. It's a "losing battle" to try to protect
every single endpoint within the organization, Shaul said.
That's not to suggest that organizations shouldn't be investing in
firewalls and other security products. Shaul recommended the
layered model, where attackers have to get past multip
gatekeepers before they even get to the database. Organizations
should be thinking, "When the perimeter fails, what's next?" and
combining all the layers to pinpoint when something is wron
according to Shaul.
It's ironic that "the closer we get to the data, we see fewer
preventive controls and more detection measures," Shaul said.
IT departments are more likely to have deployed products that
send o
alerts that a breach has occurred, than ones that actively block
the threat from getting in to the database. Most blocking
technologies are still deployed on the perimeter, according
Shaul.

Organizations still assume that all activity hitting the database
is "untrusted," Shaul said. Instead, they should monitor all
requests to figure out whether the activity is normal or
malicious.
Continuous, real-time monitoring is crucial to detect suspicious
or unauthorized activity within the database, Phil Neray, vice
president of data security strategy and information management
IBM, told eWEEK. Database activity monitoring allows security
managers to catch anyone who is trying to get access to
information they shouldn't be able to obtain.
To read the original eWeek article, click here: Cyber-Attacks
Highlight Need to Focus on Stronger Database Security.
http://www.cioinsight.com/c/a/Security/Epsilon-Data-Breach-
Hits-Banks-Retail-Giants-154971/
http://www.cioinsight.com/c/a/Security/Sony-Networks-Lacked-
Firewall-Ran-Obsolete-Software-Testimony-103450/
http://www.eweek.com/c/a/Security/CyberAttacks-Highlight-
Need-to-Focus-on-Stronger-Database-Security-
342260/cioinsight.comStronger Database Security Needed,
Cyber-Attacks Show

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