Democracy and famine
prevention
A cross sectional time-series analysis of the
relationship between democracy and famine
STV4020 - Forskningsmetode og statistikk
Antall ord: 4990
Semesteroppgave i statistisk analyse
Kandidatnr: 1314
Høst 2011
Table of contents
1.0 Introduction ................................................................................................................................ 3
2.0 Sen’s theory of democracy and famine ............................................................................ 4
2.1 Sen’s understanding of democracy ............................................................................... 4
2.2 Famine...................................................................................................................................... 5
3.0 Hypotheses .................................................................................................................................. 5
4.0 Dataset .......................................................................................................................................... 6
5.0 Famine reports (Y) ................................................................................................................... 6
5.1 The Independent Variables .............................................................................................. 7
5.1.1 Freedom House Index (FHI) .................................................................................... 7
5.1.2 Political Rights and Civil Liberties ........................................................................ 8
5.2 Control Variables ................................................................................................................. 8
5.2.1 GDP per capita .............................................................................................................. 8
5.2.2 State Fragility ................................................................................................................ 9
5.2.3 African dummy ............................................................................................................. 9
5.2.4 War intensity ................................................................................................................. 9
5.2.5 Population density and land area ......................................................................... 9
5.3 Missing values .................................................................................................................... 10
5.4 Measurement validity ..................................................................................................... 10
6.0 Method ....................................................................................................................................... 10
6.1 OLS-regression in panel data ....................................................................................... 11
7.0 Results........................................................................................................................................ 12
7.1 H1: Democracy reduces the risk of famine ............................................................. 13
7.2 H2: Political Rights and Civil Liberties ..................................................................... 14
7.3 Fixed effects ........................................................................................................................ 16
8.0 Validity and reliability ......................................................................................................... 17
8.1 Validity .................................................................................................................................. 17
8.2 Reliability ............................................................................................................................. 19
9.0 Conclusion ................................................................................................................................ 19
2
1.0 Introduction
In one of his most famous essays, Amartya Sen (1999: 178) states that famines do not
occur in democracies.
Sen has made two influential contributions to modern famine research. The
first, ‘The Entitlement Approach’, rejected earlier causal famine theories by focusing
on the lack of access to food rather than a lack of availability as the key determinant
of famine (Sen 1981). His second major contribution was to claim that democratic
institutions in combination with a free press provide effective protection against
famine (Sen 1999:180-181).
Sen’s theory has received great attention, especially among famine theorists.
Yet having been exposed to criticism by several academics (Banik 2007; Currie 2000
De Waal 1997, 2000; Myhrvold Hansen 2003; Neumayer and Plümper 2009), Sen’s
causal theory of how democracies effectively reduce the risk of famines, still stands
largely uncontested (Rubin 2009:700).
There is extremely limited systemized cross-country empirical evidence of the
relationship between the political system and famine, and this paper seeks to elaborate
this relationship. The quantitative analysis is based on an interpretation of Sen’s
definition of democracy, combined with a discursive famine index proposed by
Olivier Rubin (2011).
The research question is: Do democratic institutions decrease the chance for famine
occurrence?
Before the analysis, Sen’s theory of democracy and famine is discussed,
followed by an interpretation of how Sen understands democracy. Thereafter the term
‘famine’ is discussed and how it is operationalized in this paper. Next, the hypotheses
are presented, followed by the dataset and an explanation of the variables included in
the analysis. The data is investigated with linear regression on a cross-sectional timeseries dataset. Finally, results in relation to the hypotheses are commented, before
validity and reliability is discussed. Concluding remarks then follow.
3
2.0 Sen’s theory of democracy and famine
Many scholars have disputed several aspects of Sen’s writings, and especially how his
‘entitlement failure’ comes short of explaining conflict-related famines in subSaharan Africa (Neumeyer and Plümper 2009: 5). Having achieved much attention
for his entitlement theory, the estimated death of 30 million people in the great
Chinese famine in the late 50’s appears to have inspired Sen to complement his
entitlement approach with dynamics at the political level (Rubin 2009:700).
Sen argued in his Coromandel Lecture (1982) that although China had reduced
chronic undernourishment and child mortality, the Chinese famine was allowed to
happen due to the absence of democracy. Democracies are more likely to prevent
famines because of the inherent political system and the importance of a free press.
With a free press in combination with periodic elections and active opposition parties,
“no government can escape severe penalty if it delays preventive measures and allows
a real famine to occur” (Sen 1990:50).
2.1 Sen’s understanding of democracy
There is a vast literature on ‘democracy’ but no consensus on a definition of the
concept. Sen has devoted a lot of time writing on democracy throughout his
authorship, but when discussing democracy and famine he is less distinct about a
proper definition.
In order to grasp what constitutes a democracy to Sen, this paper seeks to
provide a fair and nuanced interpretation of his understanding. When discussing
famine in ‘Democracy as a Universal Value’ (1999: 5), Sen argues that “[t]he positive
role of political and civil rights applies to the prevention of economic and social
disasters in general”.
Having studied quotes by Sen from 1982-2009 on the subject, Rubin
(2011:40) argues that according to Sen “[t]he virtues of democracy regarding famine
protection rely on the existence of political rights, while civil rights do not receive the
same attention”. Rubin’s argument is noteworthy, all the time Sen stresses the
importance of a free press (1999: 152), which falls into the category of civil liberties
in our operational definition.
Rubin (2011:10) argues that Sen’s democracy theory builds on a
‘responsiveness mechanism’: because of political competition in democracies (i.e.
fight for re-election), the electorate and the free press will hold the government
4
accountable in times of crisis. The elected government will be more responsive to
popular demand, because the voters have the power to replace the government after
the next election. The press is important because it serves as a mediator of
information and ensures that a famine cannot be hidden from the public.
This distinction is appealing as it can help the further understanding of the
particular elements within a democracy that constitutes more effective famine
prevention. Is it the dynamics at the political level with opposition parties and
elections free from corruption, or is it the virtues of a society with civil rights and
liberties?
2.2 Famine
Despite the huge amount of literature on famines, no watertight or good operational
definition exists (Banik 2003:61; Banik 2007:27; Deveroux 1993; Howe 2002; Howe
and Deveroux 2004).
Neumeyer and Plümper (2009:4) argues that famine theorizing can be
distinguished between “before and after Sen”. ‘Old definitions’ often treated famine
as a sudden decline in food supply (Brown and Eckholm 1974:25) or as a result of an
exogenous shock (Ackroyd 1974:1). The ‘old definitions’ of famine differ from the
‘new definitions’ where the new famines are inherently political as they are
preventable and often a result of human action or inaction (Deveroux 2007:11).
According to Banik (2007: 27) Sen seems to operate with a dualistic
distinction between famine and chronic hunger, when there in real life are many
phases between these two extremes that need to be recognized. Therefore, without
trying to determine what famine definition is the best, the following analysis rather
tries to make a contribution by using a different measurement than usual, being
explained in section 5.0.
3.0 Hypotheses
This paper investigates two hierarchically ordered hypotheses, whereas the first is
based on a probabilistic interpretation of Sen’s theory on democracy and famines. The
second hypothesis differentiates the first, and is based on Rubin’s study and
discussion of Sen’s understanding of democracy, as explained in section 2.1.
5
Hypothesis I: Democracy decreases the chance for famine occurrence in general.
Hypothesis II: Political rights decrease the chance for famine occurrence relatively
more than civil liberties.
4.0 Dataset
This paper makes use of a dataset constructed by my co-student Tarald Laudal Berge
and myself. The dataset involves 74 countries, restricted to those classified as low or
low-income by the World Bank and those with a population above one million.1
Small states with population below one million are excluded due to potential biases
and to make the analysis less complex. The dependent variable is country specific
reports voicing the word ‘famine’, extracted from the OCHA Reliefweb database. The
independent variables utilized are the Freedom House democracy index (FHI)
extracted from the Freedom House webpage, which is analysed both as an aggregate
index and separated in political rights (PR) and civil liberties (CL). The control
variables are GDP per capita, a dummy variable determining whether the country is
African or not, a proxy for state fragility, a civil war intensity index, population
density and land area in square kilometres. Total number of observations is 888.
5.0 Famine reports (Y)
The dependent variable is a discursive famine index, first introduced by Olivier Rubin
(2011), and will serve as a proxy for famine occurrence in the analysis. The index is
based on counting reports mentioning the word ‘famine’, extracted from the Office
for the Coordination of Humanitarian Affairs (OCHA) Reliefweb database2. From a
methodological standpoint counting key words is a simple, but yet, valuable form of
content analysis, especially when there are huge amounts of text to be analysed. The
standardized usage of words will most likely outdo potential errors due to changing or
unclear meanings of the words (Klandermans and Staggenborg 2002).
Knowledge on the origin of the data is also important in content analysis
(Krippendorff 1980:27). In 2006, the OCHA conducted a comprehensive evaluation
of the success, value and usage of ReliefWeb, concluding that the project so far had
1
It is well documented that famines almost solely occur in development countries (Neumayer and
Plümper 2009: 56).
2
Fore more information, visit http://reliefweb.int/.
6
been a success (ReliefWeb 2011a). Banik (2003:81) argues that ‘famine’ can be used
both as an early warning system by the international media when a crisis unfolds, and
as a describing term when mass deaths already are taking place. Our famine proxy
attempts to encapsulate these two arguments, in measuring both vulnerability and
intensity. The figures below illustrate this. Howe & Deveorux (2004: 353) identified
two famines in this millennium: Ethiopia in 1999-2000 and Malawi in 2002. The two
figures effectively show monthly famine reports in respectively Ethiopia and Malawi
from 2000 – 2011.
Figure 1: Reliefweb reports on Ethiopia and Malawi, 2000 – 2011
As observed, two pinnacles are identified in both Ethiopia in 2000 and in Malawi in
2002. The two famines are thus both measured by the count, suggesting that it
captures disasters of famine proportions.
Counting the occurrence of the word famine in country specific reports suggests
that the word ‘famine’ includes a certain range of famine-related situations,
investigating the continuum in Sen’s definition.
5.1 The Independent Variables
In order to capture Sen’s definition of democracy, this paper makes use of The
Freedom House Index.
5.1.1 Freedom House Index (FHI)
The FHI seeks to measure the rights and freedoms enjoyed by individuals, and are
therefore “suitable for operationalizing a substantive democracy concept” (Knutsen
7
2011: 86-87). The FHI is used as a weighted average of the political rights and the
civil liberties-index, and is utilized for testing our first hypothesis in the first analysis.
The original FHI scales have been transposed to make the interpretation easier: higher
values indicate more democracy and vice versa.
5.1.2 Political Rights and Civil Liberties
According to Oliver Rubin (2011: 40), Sen’s definition of democracy lies closer to the
electoral definition of democracy, when arguing what constitutes the most effective
famine prevention. The political rights index is based on questions about the electoral
process, political pluralism and participation, and the functioning of the government
(FHI 2011b). More importantly and relating to the Sen’s ‘responsiveness mechanism’
mentioned in section 2.1; the PR checklist also include whether a country experiences
political opposition or not.
The civil liberties index seeks to measure degrees of freedom of expression
and belief, organization rights, rule of law and personal autonomy and individual
rights (FHI 2011b). In relation to the ‘responsiveness mechanism’, the CL checklist
also includes whether there is a free media in a country, one of the cornerstones in
Sen’s theory of democracy and famine. The distinction between PR and CL is rather
similar to the distinction made by Sen himself, thus being of vast interest to our
second hypothesis.
5.2 Control Variables
In an attempt to avoid the problem of omitted variable bias, a certain number of
control variables are included in the analysis, theoretically or/and empirically linked
to famine. Omitted variable bias occurs when you exclude important variables that
“might influence a seeming causal connection between our explanatory variables and
that which we want to explain” (King et al. 1994: 28), leading to an under- or
overestimation of the effects in the regression analysis.
5.2.1 GDP per capita
GDP per capita has turned to have a positive effect on reducing famine in earlier
analyses (Rubin 2011; Neumeyer and Plümper 2009; Besley and Burgess 2002) and is
therefore included in the analysis, assuming that the more developed the country, the
less famine prone. To reduce the effects of extreme values, the variable is log
transformed. The interpretation is then the relative effects of GDP per capita rather
8
than absolute (Skog 2009: 309).
5.2.2 State Fragility
The State Fragility Index extracted from Center for Systemic Peace (CSP) is also
included. The index ranges from 0 = ‘no fragility’ to 25 = ‘extreme fragility’.
Pantuliano (2007) argues that fragility of states make people more vulnerable in
emergency situations, such as humanitarian crises and famine. An intuitive
interpretation of the index, leaves us with the possibility that the more fragile the
state, the more famine prone.
5.2.3 African dummy
To investigate whether famine is an African idiosyncrasy of societal collapse, an
African dummy is included in the analysis. Countries from Africa are given the value
1, and all other countries included in the analysis are coded as 0. The dummy thus
measures the effect of being African, and captures elements that may lead to famine
that are specific to the continent.
5.2.4 War intensity
Damaged infrastructure due to civil conflict can make famine mortality prevention
more difficult (Neumeyer and Plümper 2009:24). D’Souza (1994: 370) argues that
famines and food shortage often is related to civil unrest and war, and thus famines
almost always occur in the context of war. To account for war intensity, the
UCDP/PRIO Armed Conflict Dataset is utilized. The intensity variable is originally
an ordinal variable (0, 1, 2) and is therefore recoded into two dummy variables, where
dummy1 = Minor war, and dummy2 = War3.
5.2.5 Population density and land area
Population density is midyear population divided by land area in square kilometers.
The variable is included, assuming that famine can be a result of densely populated
areas, implying ‘more mouths to feed’. Also, presupposing that bigger countries have
more NGO’s in place, representing more famine reports, the land area variable is
included to control for potential bias. The variables are extracted from the World
Development Indicators dataset and both are log transformed, on the same basis as
argued in section 5.3.1.
3
‘Minor war’ = 25 and 999 battle-related deaths and ‘war’ is at least 1,000 battle-related deaths in a
given year (Themnér & Wallensteen 2011).
9
5.3 Missing values
Fortunately, the dataset used in the paper is almost free from missing data. Kosovo
has missing data on almost all of the variables, and is therefore excluded from the
analysis. GDP per capita had some missing values, and they have been substituted
with values from the UN dataset (appendix). State fragility only has 3 missing values,
and is therefore considered as a non-problem.
5.4 Measurement validity
In order to maximize the validity of our measures, it is important that our
operationalization reflects the concepts we seek to measure (Adcock and Collier
2001: 539). High validity is achieved when we are “measuring what we think we are
measuring” (King et al. 1994: 25). As illustrated in Figure 1, our proxy for famine
indicates famine intensity and vulnerability. The proxy is not relying on universal
threshold values but rather on relative changes and comparisons. The measurement
validity is therefor considered as good, but in any practical application of the index, it
should be used as an additional source of information, and not as a stand-alone tool.
Regarding democracy, there runs a possibility that the operationalized definitions
applied in this paper does not capture Sen’s definition in a satisfactory manner. The
ideal analysis would solely focus on the ‘responsiveness mechanism’ portrayed by
Sen, but proxying these mechanisms have proven difficult in previous efforts (Rubin
2011: 17; 160). Additionally, case studies could probably constitute a more intimate
analysis of Sen’s theory, in studying the actual mechanisms rather than the mere
effects (Gerring 2007: 44-45). Nevertheless, as argued under section 5.2.3, the
advantage with the FHI as opposed to i.e. the polityIV-index is that it enables us to
analyze the separate effects of political rights and civil liberties, thereby hopefully
measuring Sen’s definition in an adequate manner.
6.0 Method
The data is cross-sectional time-series data, often referred to as panel data, containing
observations for n different entities observed at t different time periods (Stock and
Watson 2007: 350). The analysis includes observations from 74 different countries
from 2000 – 2010. 85 % of the famine reports are produced after 2000, which is the
reason for the relatively short time frame.
10
6.1 OLS-regression in panel data
This paper makes use of a continuous dependent variable, and ordinary least squares
(OLS) is therefore utilized in the statistical analysis. Regression diagnostics shows
that our dependent variable fulfils the normality assumption after being log
transformed, with a skewness on .39. The same applies for our residuals.
Figure 2: Residuals and log transformed dependent variable
When studying observations in a cross sectional time series-design, some
thoughtfulness is compulsory. To ensure OLS to be optimal, we must assume that the
error terms are homoscedastic and that errors for a specific unit at one time are
unrelated to errors for that unit at all other times (Beck and Katz 1995: 636). The
absence of the latter is often referred to as serial correlation. Serial correlation can be
problematic, because it violates the OLS assumption, thus leading to incorrect
estimates of the standard errors (Keele and Kelly 2006: 187). In turn, results can turn
out significant when they actually are not. To avoid the problem of serial correlation
the analysis is conducted with panel-corrected standard errors, as proposed by Beck
and Katz (1995: 634).
Another common way to reduce potential serial correlation is to include a
lagged dependent variable in the model specification (Kristensen and Wavro 2003:4;
Keele and Kelly 2006: 187). Since the analysis already includes panel corrected
standard errors, the lagged dependent variable is only included in the analysis as a
further robustness check of the results.
In regression analyses “[the] regressors (…) can be conceptualized as
alternative indicators of the same underlying construct” (Fox 1991: 14). They were
therefore checked for multicollinearity. If two or more of the independent variables
are highly correlated, the standard error of their coefficients are affected and proving
11
significant results can thus be complicated (Christophersen 2009: 161). As seen in the
correlation matrix (appendix) none of the variables included in the analysis represents
problems of multicollinearity, except a small deviance observed in state fragility.
State fragility and GDP per capita correlate on point 0.57. This is probably because
economic effectiveness, being constructed on GDP per capita, partly constitutes the
State Fragility Index (SFI 2010). This is not treated as a problem, and the variable is
included in the analysis. There was also run a VIF-test (appendix), just confirming the
same as the matrix, as each variable’s tolerance value exceeded the critical value .20.
7.0 Results
Model 1-4 in Table 1 reports the results from an OLS-regression with FHI as the
independent variable. The analyses are performed using Stata version 10.0. Model 1 is
a baseline model, consisting only of the independent variable FHI and the dependent
variable famine reports. Model 2 includes the control variables GDP per capita,
Africa, land area, population density and the two dummy variables for war intensity.
In Model 3 there is included another control variable, state fragility. Finally, Model 4
includes a lagged dependent variable, serving as a robustness check on the results. All
the variables, except Africa and the dummy variables for war intensity, are included
with a one year lag-structure, to reduce the effect of potential reverse causation which
can be a problem in cross section designs (Bryman 2004: 76), and to facilitate a
proper cause-effect scenario.
12
Table 1: Democracy and Famine
(2)
Famine
-0.097**
(0.030)
(3)
Famine
-0.054
(0.034)
(4)
Famine
-0.015
(0.024)
ln(GDP per capita)
-0.473***
(0.057)
-0.343***
(0.063)
-0.131**
(0.048)
Africa
1.175***
(0.095)
1.019***
(0.107)
0.355***
(0.082)
ln(Population Density)
0.097**
(0.037)
0.117**
(0.037)
0.030
(0.033)
ln(Land Area)
0.144***
(0.036)
0.130***
(0.036)
0.033
(0.023)
War Intensity D1
0.621***
(0.111)
0.459***
(0.114)
0.249**
(0.094)
War Intensity D2
1.132***
(0.176)
0.840***
(0.177)
0.285*
(0.144)
0.0620***
(0.016)
0.015
(0.012)
Freedom House Index
(1)
Famine
-0.214***
(0.036)
State Fragility
0.654***
(0.030)
Famine t-1
Constant
Observations
r2
2.705***
(0.146)
801
0.0445
2.311***
(0.637)
799
0.414
0.632
(0.770)
797
0.428
0.508
(0.599)
726
0.684
*
p < 0.05, ** p < 0.01, *** p < 0.001
SE in parentheses, OLS with PCSE, dependent variable ln(Reliefweb reports).
Sources: Reliefweb, Freedom House, World Bank, Center for Systemic Peace, UCDP/PRIO Armed
Conflict Database
7.1 H1: Democracy reduces the risk of famine
In model 1 the coefficient for FHI is -0.214 and highly significant at the 0.1%-level,
suggesting that an increase in democracy reduces the risk of famine occurrence. As
expected and observed by the low r2, the one independent variable alone is
insufficient in explaining the variation on the dependent variable. In model 2 the
coefficient for FHI is -0.097 and now significant at the 1%-level, but the effect on
famine reporting is now halved due to the inclusion of almost all of the control
13
variables. All of the included control variables are significant at least at the 1%-level.
So far the analysis supports the first hypothesis: increased political rights and civil
liberties reduce the risk of famine.
In Model 3 state fragility is included, and democracy is no longer significant.
All of the other variables remain significant, with only trivial changes in effect on our
dependent variable. It seems like State Fragility accounts for all the aggregate effect
of democracy on famine. An explanation can be that the State Fragility Index scores
each country on effectiveness and legitimacy on four dimensions, one being a
political dimension measuring political effectiveness and political legitimacy (SFI
2010).
In Model 4 the lagged dependent variable is included, confirming that
democracy now is highly insignificant (p = .53) and almost without any effect. The
variables that remain significant are GDP per capita, Africa and war intensity. A
strong significant effect of GDP per capita seems intuitive – the more the developed
country, the more resources and capacity in preventing famine. The African dummy
also turned out significant, suggesting that the continent by itself triggers more famine
reporting. The two war dummies also remain significant, supposing that countries in
war are more famine prone than countries with no war.
The results of the analysis are in line with previous studies by Rubin (2011:
113-120), except his war dummy turned out insignificant and there was applied
another proxy for famine. But likewise Rubin’s analysis, the political system turned
out insignificant, leading us to reject our first hypothesis that democracy has a
positive effect on preventing famine occurrence.
7.2 H2: Political Rights and Civil Liberties
Although our democracy variable turned out insignificant, analysing the separate
effects of political rights and civil liberties can still shed light on what elements of a
democracy constituting the more effective famine prevention. A baseline model is not
included in Table 2, due to a positive significant effect (p = 0.00) on famine from both
of our independent variables (PR -.16 and CL -.26). The control variables are not
commented further, as they behave in the exactly same manner as in Table 1.
14
Table 2: Democracy and Famine
Political Rights
(1)
Famine
-0.033
(0.028)
(2)
Famine
-0.006
(0.021)
Civil Liberties
(3)
Famine
(4)
Famine
(5)
Famine
0.049
(0.038)
-0.079*
(0.039)
-0.029
(0.027)
-0.086
(0.050)
ln(GDP per
capita)
-0.343***
(0.063)
-0.130**
(0.048)
-0.342***
(0.062)
-0.132**
(0.048)
-0.129**
(0.048)
Africa
1.003***
(0.106)
0.348***
(0.081)
1.043***
(0.108)
0.367***
(0.083)
0.385***
(0.084)
ln(Population
Density)
0.117**
(0.037)
0.030
(0.033)
0.116**
(0.037)
0.030
(0.033)
0.029
(0.033)
ln(Land Area)
0.130***
(0.036)
0.033
(0.023)
0.130***
(0.036)
0.033
(0.030)
0.032
(0.030)
War Intensity D1
0.457***
(0.114)
0.247**
(0.094)
0.458***
(0.114)
0.250**
(0.094)
0.244**
(0.094)
War Intensity D2
0.841***
(0.178)
0.284*
(0.144)
0.840***
(0.176)
0.287*
(0.144)
0.286*
(0.143)
State Fragility
0.066***
(0.015)
0.016
(0.012)
0.058***
(0.016)
0.012
(0.012)
0.011
(0.012)
0.653***
(0.030)
0.652***
(0.030)
0.590
(0.603)
726
0.684
0.645
(0.601)
726
0.685
0.655***
(0.030)
Famine t-1
Constant
Observations
r2
0.513
(0.761)
797
0.427
0.450
(0.591)
726
0.684
0.770
(0.775)
797
0.430
*
p < 0.05, ** p < 0.01, *** p < 0.001
SE in parentheses, OLS with PCSE, dependent variable ln(Reliefweb reports).
Sources: Reliefweb, Freedom House, World Bank, Center for Systemic Peace, UCDP/PRIO Armed
Conflict Database
Political rights is not significant in model 1, and almost without any effect and highly
insignificant in model 2 (p = .78) when introducing the lagged dependent variable. Of
more interest, and in contrast to our second hypothesis, is that civil liberties is
significant at the 5%-level in model 3, even when controlled for state fragility. Recall
the results from Table 1, where state fragility seemed to be the decisive variable
15
making democracy insignificant. It seems like the functional aspects of a democracy
including freedom of speech and freedom of the press is relatively more effective on
famine prevention, rather than the instrumental manners of political rights as
proposed by Rubin (2011), though the effect is considerably weak. In general,
analyzing separate effects must be done with some caution, especially when both
effects are as weak as in this case. Therefore, in any practical purpose, our results
should not be interpreted substantially.
As expected, civil liberties become insignificant in model 4 when introducing
the lagged dependent variable. Although political rights and civil liberties are
extremely correlated (0.87), they are both included in the last model, confirming the
results from the former models. Civil liberties is technically not statistically but it
would have been at the 10%-level (p = .083). The results drawn from Table 2
therefore lead us to also reject our second hypothesis that political rights are more
effective than civil liberties in reducing the risk of famine occurrence.
As observed by the increase in the r2, both in Table 1 and in Table 2, the
inclusion of the lagged dependent variable increase the models explained variation on
the dependent variable. Also, the lagged dependent variable seems to steal much
effect from the variables in general. When lagged dependent variables are introduced,
there is a possibility that they will dominate the regression and “destroy the effect of
other variables whether they have any true causal power or not” (Achen 2001: 14).
This can be the case in our analysis, since we already have panel corrected standard
errors. Since democracy is relative constant over time with minor variation, the
lagged dependent variable must be interpreted as a strong test on our results.
7.3 Fixed effects
A typical concern in panel data analysis is that of omitted variable bias (Stock and
Watson 2007: 353). The results can be biased due to unobserved country-specific
effects that are constant over time, which correlates with both the dependent and the
independent variables. Green et.al (2001: 457) argues that the quantitative analysis of
panel data will be more robust and informative when testing for so called ‘fixed
effects’.
There is reason to believe that our analysis suffers from omitting important
variables that could affect famine vulnerability, i.e. quality of soil, cultural traits, and
16
historical dependency. Even though both of our hypothesis was rejected, there was
estimated a fixed effects model (appendix) with civil liberties as the only independent
variable, but without state fragility and the lagged dependent variable. The coefficient
turned out insignificant. Since democracy is the variable of interest and the time series
only stretches ten years, fixed effects is not necessarily sufficient. As democracy is
almost constant and vary little from year to year, the use of fixed effects could
produce strange estimates on our democracy variable, since the effect of democracy
then is ‘controlled’ for the fixed effects (Beck and Katz 2001: 491).
8.0 Validity and reliability
8.1 Validity
The results are now discussed in relation to Cook and Campbell’s (1979) four types of
validity: Statistical conclusion validity, internal validity, construct validity and
external validity (Lund 2002: 105).
Statistical conclusion validity refers to whether there is a strong significant
connection between the independent and the dependent variables (Lund 2002: 107).
As both of our hypotheses are rejected, there seems to be no significant relationship
between the political system and famine occurrence. Rather, GDP per capita, being an
African country and war intensity seems to have an effect on famine occurrence. The
inclusion of a lagged dependent variable in combination with panel corrected standard
errors proved to be a quite strong test on our results, removing a lot of effect.
Internal validity is achieved when there is a causal connection between the
independent and the dependent variables (Lund 2002: 107). Internal validity is not
regarded as one of the strengths in statistical analyses, and as argued in section 5.5,
case studies could have an advantage in being able to trace the mechanisms that
constitute Sen’s theory, and thereby securing a higher internal validity.
Construct validity, or measurement validity as referred to in this paper, have
been briefly discussed in section 5.5, but some additional information is necessary. A
first concern could be that the documents containing the word ‘famine’ extracted from
Reliefweb have different meanings. Some reports could use the word ‘famine’ when
perhaps praising countries for avoiding famine. Nevertheless, the enormous number
of reports extracted from the ReliefWeb will most likely outnumber potential biases
relating to what just described.
17
A second concern, could be that the organizations reporting to ReliefWeb, are
becoming more professional and numerous in their reporting, thereby causing more
documents of famine irrespective of famine vulnerability. The figure below shows the
total number of reports from 2000 - 2010. The increasing number of reports is
noticeable, but more important; the amount of reports referring to famine is frequently
decreasing. This declining share of famine is also in accordance with studies
documenting that the famine threat in terms of mortality is declining (Ó Gráda
2009:2).
60000
50000
40000
30000
20000
10000
0
Total
Famine
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Reports
Relationship between total reports and
famine reports
Figure 2: Total reports and famine reports
External validity refers to the degree to which the results of the study may be
generalized over time, settings or persons to other situations (Lund 2002: 107). The
analyses proved no significant relationship between democracy and famine
occurrence, indicating that the complexities of famine can relate to more than just the
political system. The analysis includes several weak and ‘new democracies’,
indicating that some democracies probably need time to establish strong institutions
and a democratic culture, before being able to generate the mechanisms proposed by
Sen and thus constitute effective famine prevention. Nevertheless, it is important to
address the uncertainty with our own research (King et al. 1994: 32), and this paper is
no exception. The external validity can always be improved in further analyses, in
extending the time series and including more variables.
18
8.2 Reliability
Reliability relates to the consistency of our measures (Bryman 2004: 70) and is a
necessary condition in order to consider our data to be valid (Hellevik 2006: 53). One
of the advantages with the discursive famine index is that it is instantly available. The
ReliefWeb database is open for everyone, and different scholars will get the exact
same results in their respective studies. This increases the external reliability of the
study. In addition, the analyses have been presented stepwise, thoroughly explaining
the variables and their origin, while also referring to their independent sources. In
total, the reliability is therefore considered as good.
9.0 Conclusion
The purpose of this paper has been to analyse the effect of democracy in preventing
famine occurrence, as theorized by Amartya Sen. The results from the first analysis in
Table 1, found no significant evidence between democracy and famine, leading to a
rejection of our first hypothesis. In Table 2, it turned out that civil liberties rather than
political rights had an effect on reducing famine occurrence, although the effect was
extremely weak and insignificant in the final models. Our second hypothesis was
therefore also rejected. The results indicates that famines are complex and solely
focusing on the political system is not necessary sufficient, as democracies are
different. In order to say something about the relationship between democracies and
famine in the future, in a further attempt to investigate Sen’s theory, the focus should
probably not be on democracy per se, but rather on the mechanisms clearly defined by
Sen. In a triangulation with other studies, and applying other proxies, future results
can hopefully identify other factors that seems to have an impact on famine
prevention.
The proxy for famine utilized in this paper should therefore be perceived as an
augmentation – not a competing method – for famine diagnosing. If being used in
combination with other proxies for famine, the famine index could prove additional
information regarding the relationship between the political system and famine.
19
Appendixes:
1. References
2. Descriptive statistics
3. Regression diagnostics
4. Additional diagnostics
5. Syntax files (do-files)
Tables in the text:
Table 1: Results from the first analysis with FHI as independent variable, page 13
Table 2: Results from the second analysis with Political Rights and Civil Liberties as
independent variables, page 15
Figures in the text
Figure 1: Reliefweb reports on Ethiopia and Malawi, 2000 – 2011, page 7
Figure 2: Total reports and famine reports, page 18
1. References
Achen, Christopher (2001). “Why Lagged Dependent Variables Can Suppress The
Explanatory Power of The Other Independent Variables”. American Political Science
Association, July 20 – 22.
Banik, Dan (2003). Democracy, drought and starvation in India: Testing Sen in
theory and practice. PhD dissertation. Oslo: University of Oslo
Banik, Dan (2007). Starvation and India’s Democracy. London: Routledge.
Beck, Nathaniel and Jonathan N. Katz (1995). “What to do (and not to do) with TimeSeries Cross-Section Data” in American Political Science Review, 89(3): 634 – 647
Beck, Nathaniel and Jonathan N. Katz (2001). “Throwing Out the Baby With the Bath
Water: A Comment on Green, Kim, and Yoon” in International Organization 55(2):
487 – 495
Besley, Timothy and Robin Burgess (2002). “The political economy of government
responsiveness: theory and evidence from India” in The Quarterly Journal of
20
Economics 117(4): 1415 – 1451
Bryman, Alan (2004). Social Research Methods, Second Edition. New York: Oxford
University Press
Center for Systemic Peace (CSP) (2010). ‘State Fragility Index and Matrix 2010’.
[URL]: http://www.systemicpeace.org/SFImatrix2010c.pdf. Visited October 29th,
2011.
Christophersen, Knut Andreas (2009). Databehandling og statistisk analyse med
SPSS. Oslo: Unipub
Deveroux, Stephen (2007). The New Famines: Why famines persist in an era of
globalization. New York: Routledge
Dreze, Jean and Amartya Sen (1989). Hunger and Public action. Oxford: Clarendon
Press
D’Souza, Frances (1994). “Democracy as a Cure for Famine”. Journal of Peace
Research. 31(4): 369-373
Fox, John (1991). Regression diagnostics. Quantitative Applications in the Social
Sciences. Sage Publications
Freedom House Index (FHI) (2011a). ‘Freedom in the World’. [URL]:
http://www.freedomhouse.org/template.cfm?page=15&year=0, visited October 15th,
2011
Freedom House Index (FHI) (2011b). ‘Freedom in the World’, [URL]:
http://freedomhouse.org/template.cfm?page=351&ana_page=363&year=2010, visited
Desember 2nd, 2011
Gerring, John (2007). Case Study Research. Principles and Practices. New York:
Cambridge University Press
Howe, Paul and Stephen Deveroux (2004). “Famine Intensity and Magnitude Scales:
A Proposal for an Instrumental Definition of Famine”. Disasters 28(4): 352-372.
Keele, Luke and Nathan J. Kelly (2006). “Dynamic Models for Dynamic Theories:
The Ins and Outs of Lagged Dependent Variables” in Political Analysis 14: 186 – 205
King, Gary, Robert O. Keohane og Sidney Verba (1994). Designing Social Inquiry:
Scientific Inference in Qualitative Research. Princeton University Press.
Klandermans, Bert and Suzanne Staggenborg (2002). Methods of Social Movement
Research. University of Minnesota Press.
Knutsen, Carl Henrik (2011). The Economic Effects of Democracy and Dictatorship.
PhD thesis. University of Oslo.
21
Kristensen, Ida Pagter and Gregory Wawro (2003). “Lagging the Dog?: The
Robustness of Panel Corrected Standard Errors in the Presence of Serial Correlation
and Observation Specific Effects” Working Paper. Columbia University
Lund, Torleif (red.) (2002): Innføring i forskningsmetodologi. Oslo: Unipub
Ó Gráda, Cormac (2009). Famine: A Short History. New Jersey: Princeton University
Press.
Plümper, Thomas and Eric Neumayer (2009). ’Famine Mortality, Rational Political
Inactivity, and International Food Aid”. World Development 37(1): 50-61
ReliefWeb (2006). ‘Evaluation of ReliefWeb’. [URL]:
http://reliefweb.int/node/22934, visited Septempber 29th, 2011.
Rubin, Olivier (2009). “The Merits of Democracy in Famine Protection – Fact or
Fallacy?” in European Journal of Development Research, 21: 699-717.
Rubin, Olivier (2011). Democracy and Famine. London and New York: Routledge
Sen, Amartya (1981). Poverty and Famines. New York: Oxford University Press
Sen, Amartya (1990). ’Food Economics, and Entitlements’ in The Political Economy
of Hunger, 34-52. Oxford: Clarendon Press
Sen, Amartya (1999). ‘Democracy as a Universal Value” in Journal of Democracy
10.3: 3-17
Themnér, Lotta & Peter Wallensteen (2011). "Armed Conflict, 1946-2010." in
Journal of Peace Research 48(4).
22
2. Descriptive Statistics
Histogram famine reports
Histogram famine reports logged
Histogram GDP per capita
Histogram GDP per capita
logged
Histogram land area
Histogram land area logged
23
Histogram population density
Histogram population density
logged
Descriptive statistics independent variables
Variable |
N Min
Max Mean
Sd
Skewness
-------------+---------------------------------------------------------------------Famine
803 0
6.5
1.97 1.495
.390
Civil Liberties 801 1
6
3.51 1.336
-.229
Pol. Rights
801 1
7
3.34 1.712
.144
FHI
801 1
6.5
3.43
1.474
-.016
GDP per cap 880 4.4
8.4
6.37
.851
.145
SFI
875 5
25
14.8
4.434
.129
Arica
888 0
1
.52
.499
-.103
Pop dens
887 .43
7.04 4.0
1.201
-.359
Land area
888 9.2
14.9 12.3
1.410
-.445
dummy2
888 0
1
.167
.373
1.778
dummy3
888 0
1
.065
.247
3.518
------------------------------------------------------------------------------------
Kurtosis
2.357
2.240
1.770
1.870
2.416
2.388
1.010
3.242
2.347
4.161
13.38
NOTE: The missing values on the independent variables and the dependent, are due
to the one year lag structure, where the year 1999 is defined as missing.
Substitute values, GDP per capita, extracted from the UN database:
http://data.un.org/Data.aspx?d=SNAAMA&f=grID%3A101%3BcurrID%3AUSD%3
BpcFlag%3A1
Afghanistan 2000, 2009
Burma (Myanmar) 1999-2009
Iraq 2003
North Korea 1999-2009
Namibia 2002
Somalia 1999-2009
24
Correlation matrix
Famine
FHI
Civil
Political GDP
Liberties Rights
Africa
Land
Area
Pop.
Density
SFI
Famine
1.00
FHI
Civil
Liberties
-0.20
-0.22
1.00
0.95
Political
Rights
-0.18
0.975 0.87
1.00
GDP
-0.44
0.17
0.21
0.14
1.00
Africa
0.47
-0.05
-0.01
-0.08
-0.34
1.00
Land Area
0.17
-0.06
-0.08
-0.05
0.01
0.07
1.00
Pop.
Density
-0.07
0.04
0.03
0.05
0.01
-0.25
-0.50
1.00
State
Fragility
Dummy 2
Dummy 3
0.55
-0.39
-0.42
-0.34
-0.57
0.47
0.24
-0.18
1.00
0.23
0.19
-0.07
-0.16
-0.11
-0.18
-0.04
-0.14
-0.13
-0.07
0.04
-0.04
0.23
0.06
0.07
0.05
0.29
0.29
D2
D3
1.00
-0.11
1.00
1.00
VIF- test for multicollinearity
Variables
Famine
Civil Liberties
Political Rights
GDP
Africa
Land Area
Population Density
State Fragility
Dummy2
Dummy3
VIF
1.76
5.03
4.46
1.67
1.81
1.59
1.57
2.90
1.34
1.31
Tolerance
0.57
0.20
0.22
0.60
0.55
0.63
0.64
0.34
0.74
0.76
25
3. Regression Diagnostics
Residuals
4. Additional diagnostics
Fixed Effects with Civil Liberties as independent variable
Civil Liberties
(1)
Famine
-0.0647
(0.0746)
ln(GDP per capita)
-0.197
(0.108)
Africa
Dropped
ln(Population Density)
-2.749***
(0.582)
ln(Land Area)
Dropped
Constant
14.36***
(1.905)
799
0.0933
N
r2
Standard errors in parentheses
Fixed Effects, dependent variable Reliefweb famine report count.
*
p < 0.05, ** p < 0.01, *** p < 0.001
26
5. Do files
***Settings***
clear
set more off, perm
set memory 200m
**Dataset**
cd
"/Users/xxx/Desktop/xxx/UiO/STV402
0/Hjemmeoppgave - Statistikk"
insheet using
"Datasett_fullstendig_manglerkunWA
R.csv", delimiter(;)
**Drop variables**
drop if id == .
/*Merge
sort gwnoloc year
merge gwnoloc year using
"64464_UCDP_PRIO_ArmedConflict
Dataset_v42011-2.dta", keep(intensity
type)
save, replace
***Replace missing***
replace intensity = 0 if intensity ==.
replace type = 0 if type==.
drop if id==.
drop if var21==1
drop var21
***EXPORT TO WORD**
ssc install estout, replace
*****Variables*******
egen location_id = group(location)
xtset location_id year
***Log transform variables***
gen ln_relief_rep =
ln(1+reliefweb_rep)
gen ln_gdp_cap = ln(gdp_cap)
gen ln_land_area = ln(land_area)
gen ln_pop_dens = ln(pop_dens)
***Lag variables***
gen fhi_lag = fhi[_n-1]
gen fhipr_lag = fhipr[_n-1]
gen fhicl_lag = fhicl[_n-1]
gen fhfp_lag = fhfp[_n-1]
gen sfi_lag = sfi[_n-1]
gen pop_dens_lag = pop_dens[_n-1]
gen ln_relief_rep_lag =
ln_relief_rep[_n-1]
gen ln_gdp_cap_lag = ln_gdp_cap[_n1]
gen ln_pop_dens_lag =
ln_pop_dens[_n-1]
***Invers variables***
gen fhi_inv = 8-fhi_lag
gen fhipr_inv = 8-fhipr_lag
gen fhicl_inv = 8-fhicl_lag
**Creating dummy**
tab intensity, gen(dummy)
***Variable Names***
lab var ln_relief_rep Famine
lab var ln_gdp_cap
GDP_per_capita_logged
lab var fhi_lag FHI_lagged
lab var fhipr_lag
Politcal_Rights_lagged
lab var fhicl_lag
Civil_Liberties_lagged
lab var ln_relief_rep_lag
Famine_lagged
lab var sfi_lag State_Fragility_lagged
lab var fhipr_inv
Political_Rights_inverse
lab var fhicl_inv
Civil_Liberties_inverse
lab var fhi_inv FHI_inverse
lab var africa Africa
lab var pop_dens_lag
Population_Density_lagged
lab var ln_land_area
Land_Area_logged
lab var ln_gdp_cap_lag
GDP_per_capita_lagged
lab var ln_pop_dens_lag
Population_density_logged
lab var intensity War_intensity
lab var dummy3 War_intensity_2
27
lab var dummy2 War_intensity_1
lab var dummy1 War_intensity_0
tabulate ln_pop_dens, sort miss
tabulate intensity, sort miss
***Descriptive statistics***
*ORIGINAL VALUES*
tabstat reliefweb_rep fhicl fhipr
gdp_cap sfi africa land_area pop_dens
dummy2 dummy3, stats(n min max
mean sd skewness kurtosis) col(stat)
***REGRESSION
DIAGNOSTICS***
*AFTER LOG
TRANSFORMATION*
tabstat ln_relief_rep fhicl_inv
fhipr_inv fhi_inv ln_gdp_cap sfi_lag
africa ln_pop_dens_lag ln_land_area
dummy2 dummy3, stats(n min max
mean sd skewness kurtosis) col(stat)
**FAMINE REPORTS
sum ln_relief_rep, detail
hist reliefweb_rep, normal
hist ln_relief_rep, normal
**GDP PER CAP
sum gdp_cap, detail
sum ln_gdp_cap, detail
hist gdp_cap, normal
hist ln_gdp_cap, normal
**LAND AREA
sum land_area, detail
hist land_area, normal
sum ln_land_area, detail
hist ln_land_area, normal
**POP DENSITY
sum pop_dens, detail
hist pop_dens, normal
sum ln_pop_dens, detail
hist ln_pop_dens, normal
*MISSING*
tabulate reliefweb_rep, sort miss
tabulate ln_relief_rep, sort miss
tabulate fhi, sort miss
tabulate fhipr, sort miss
tabulate fhicl, sort miss
tabulate sfi, sort miss
tabulate land_area, sort miss
*RESIDUALS
predict residuals, xb
pnorm residuals
hist residuals, normal
scatter residuals location_id
twoway (scatter residuals location_id)
(lfit residuals location_id)
***Testing for collinearity***
correlate reliefweb_rep fhicl fhipr
gdp_cap sfi africa pop_dens land_area
intensity
correlate ln_relief_rep fhi_inv
fhicl_inv fhipr_inv ln_gdp_cap_lag
africa ln_land_area ln_pop_dens_lag
sfi_lag dummy2 dummy3
***VIF-TEST FOR
MULTICOLLINEARITY***
collin ln_relief_rep fhicl_inv fhipr_inv
ln_gdp_cap_lag africa ln_land_area
ln_pop_dens_lag sfi_lag dummy2
dummy3
*OLS WITH PCSE - 4 MODELS.
COMPRESSEd*
***FHI
xtpcse ln_relief_rep fhi_inv, ///
hetonly pairwise
estimates store mod_10
***FHI & GDP_CAP & AFRICA &
POP_DENS & LAND_AREA &
INTENSITY & WAR_INTENSITY_1
& WAR_INTENSITY_2
xtpcse ln_relief_rep fhi_inv
ln_gdp_cap_lag africa
ln_pop_dens_lag ln_land_area
dummy2 dummy3, ///
hetonly pairwise
estimates store mod_11
28
***FHI & GDP_CAP & AFRICA &
POP_DENS & LAND_AREA &
WAR_INTENSITY_1 &
WAR_INTENSITY_2 & SFI
xtpcse ln_relief_rep fhi_inv
ln_gdp_cap_lag africa
ln_pop_dens_lag ln_land_area
dummy2 dummy3 sfi_lag, ///
hetonly pairwise
estimates store mod_12
***FHI & GDP_CAP & AFRICA &
POP_DENS & LAND_AREA &
WAR_INTENSITY_1 &
WAR_INTENSITY_2 & SFI & LAG
xtpcse ln_relief_rep fhi_inv
ln_gdp_cap_lag africa
ln_pop_dens_lag ln_land_area
dummy2 dummy3 sfi_lag
ln_relief_rep_lag, ///
hetonly pairwise
estimates store mod_13
esttab mod_10 mod_11 mod_12
mod_13 using
"OLS_PCSE_FHI_4MODELS_WITH
_N.rtf", ///
se label title(Democracy and famine)
replace stat(N r2) ///
addnotes(OLS with PCSE, dependent
variable Reliefweb famine report
count.)
**OLS FHIPR AND FHICL - 5
MODELS**
***FHIPR & GDP_CAP & AFRICA
& POP_DENS & LAND_AREA &
DUMMY1 & DUMMY2 & SFI
xtpcse ln_relief_rep fhipr_inv
ln_gdp_cap_lag africa
ln_pop_dens_lag ln_land_area
dummy2 dummy3 sfi_lag, ///
hetonly pairwise
estimates store mod_100
DUMMY1 & DUMMY2 & SFI &
LAG
xtpcse ln_relief_rep fhipr_inv
ln_gdp_cap_lag africa
ln_pop_dens_lag ln_land_area
dummy2 dummy3 sfi_lag
ln_relief_rep_lag, ///
hetonly pairwise
estimates store mod_101
***FHICL & GDP_CAP & AFRICA
& POP_DENS & LAND_AREA &
DUMMY1 & DUMMY2 & SFI
xtpcse ln_relief_rep fhicl_inv
ln_gdp_cap_lag africa
ln_pop_dens_lag ln_land_area
dummy2 dummy3 sfi_lag, ///
hetonly pairwise
estimates store mod_102
***FHI & GDP_CAP & AFRICA &
POP_DENS & LAND_AREA &
DUMMY1 & DUMMY2 & SFI &
LAG
xtpcse ln_relief_rep fhicl_inv
ln_gdp_cap_lag africa
ln_pop_dens_lag ln_land_area
dummy2 dummy3 sfi_lag
ln_relief_rep_lag, ///
hetonly pairwise
estimates store mod_103
***ALL VARIABLES
xtpcse ln_relief_rep fhipr_inv fhicl_inv
ln_gdp_cap_lag africa
ln_pop_dens_lag ln_land_area
dummy2 dummy3 sfi_lag
ln_relief_rep_lag, ///
hetonly pairwise
estimates store mod_104
esttab mod_100 mod_101 mod_102
mod_103 mod_104 using
"OLS_PCSE_FHIPR_FHICL_5MOD
ELS_WITHN.rtf", ///
se label title(Democracy and famine)
replace stat (N r2) ///
***FHIPR & GDP_CAP & AFRICA
& POP_DENS & LAND_AREA &
29
addnotes(OLS with PCSE, dependent
variable Reliefweb famine report
count.)
*** FIXED EFFECTS WITH FHICL
WITHOUT SFI AND WITHOUT
LAGGED FAMINE****
xtreg ln_relief_rep fhicl_inv
ln_gdp_cap_lag africa
ln_pop_dens_lag ln_land_area, fe
estimates store model007
esttab model007 using
"FHICL_FIXED.rtf", ///
se label title(Fixed Effects) replace
stat(N r2) ///
addnotes(Fixed Effects, dependent
variable Reliefweb famine report
count.)
30