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The Impact of Natural Disasters on Human Development and Poverty at the
Municipal Level in Mexico
Article · January 2010
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The Impact of Natural Disasters on Human Development and Poverty at the
Municipal Level in Mexico
Eduardo Rodriguez-Oreggia
EGAP, ITESM, Campus State of Mexico
[email protected]
Alejandro de la Fuente
World Bank
[email protected]
Rodolfo de la Torre
UNDP Mexico
[email protected]
Hector Moreno
UNDP Mexico
[email protected]
The authors acknowledge comments from Luis Felipe Lopez Calva, Javier Baez, Indira
Santos and assistants to the seminars for the UNDP disasters report.
Corresponding author: Eduardo Rodríguez-Oreggia. Email:
[email protected] EGAP ITESM CEM, Carretera Lago Guadalupe Km 3.5,
Atizapan, Estado de Mexico, CP 52926, Tel + (52 55) 5864 5643 Fax: +(52 55) 5864
5651
The Impact of Natural Disasters on Human Development and Poverty at the
Municipal Level in Mexico
ABSTRACT
This paper seeks to analyze the impact of natural disasters on human development and
poverty at the municipal level in Mexico. We control for a set of geographical and
natural location characteristics which make municipalities more prone to the occurrence
of these events. We also control for a set of institutional, economic and demographic
pre-shock characteristics. Using a difference-in-difference approach with data for 2000
and 2005, results show a significant decrease of social indicators for general events, and
especially from floods and droughts.
Key words: natural disasters, impact, poverty, human development, geography
JEL classification: C52, I31, O10, O54, Q54
1
1. Introduction
The social and economic consequences of recent natural disasters across the world have
reiterated the need to place more attention to natural disaster as part of the global
poverty agenda. In parallel, there is mounting evidence that global climate change is
increasing the recurrence and virulence of climatic hazards in vast parts of the world,
such as hurricanes and floods (IPCC, 2007).
Mexico cannot be indifferent to any of these trends. The country lies within one of the
world’s most active seismic regions; prone to constant droughts in its northern cone and
in the path of hurricanes and tropical storms originating in the Atlantic and Pacific
Oceans. This wide geographic exposure renders that a high share of the country’s
population and GDP may be at hazard risk.
And yet, with a few exceptions (UNISDR, 2009), the foreseeable effect of geological
and climatic hazards on poverty has not translated into a systematic research agenda that
illustrates their connection. Major reviews on poverty dynamics have noted, for
instance, that only a few studies account for this type of risk impacts (Baulch and
Hoddinott, 2000; Dercon and Shapiro, 2007). Perhaps, the single most important
explanation for this shortcoming is data availability. The standard tools for measuring
poverty (household surveys) lack risk modules upon which one could create
counterfactuals to explore actual impacts; an alternative is to import disaster data into
them.
Disentangling the causal impact of natural disasters on social welfare indicators in a
credible way is also a complex task. While the occurrence of a natural hazard could be
considered exogenous, its transformation into a disaster may not. It is the number of
people located in certain areas combined with the human, material and environmental
circumstances of households and the localities where they live that shapes their chances
of weathering a natural hazard or not, and certainly less resourceful households located
in hazard-prone areas are more vulnerable. In addition, institutional factors affect the
magnitude of the impact. So estimating the impact of disasters on poverty requires data
2
spanning many object categories and techniques to address the existence of a double
causality.
In this context, at least three streams of literature can be identified. The first focus on
natural hazard and what are the determinants of vulnerability to disasters (e.g.: McGuire
et al, 2002; Pelling, 2003; Wisner et al, 2004; among others); a second one focuses on
how when a disaster occurs there is an impact the macro level of the economy (e.g.:
Auffret, 2003; Benson and Clay, 2003; Skidmore and Toya, 2002; Strobl 2008 and
2008a) and the third one studies the impact that disasters have at the micro level or
households within localities localities (e.g.: Carter et al, 2007; De Janvry et al, 2006;
Dercon 2004; Dercon et al, 2005; Guarcello et al, 2007; Kahn, 2005). A less developed
area is how disasters affect local activities (Yamano et al, 2002; Burrus et al, 2002,
among others). We will insert this paper in this last stream of the literature.
The purpose of this paper is to contribute to the scant literature on the impact of natural
disasters on poverty in Mexico. More concretely, this paper aims to answer if natural
disasters occurring between 2000 and 2005, time for which data is available, affected
poverty and long-term indicators such as human development at the municipal level.
In order to analyze such issue, the paper draws on a unique poverty panel dataset of
municipalities across Mexico and merges it with a database of natural disasters
(DesInventar) at municipal level too. We then bring baseline data from other public
sources to account for the natural, geographic and socio-demographic characteristics of
municipalities, as well as for their institutional capacities to cope with disasters. Our
main results show that natural disasters reduce human development and increase
poverty, and this effect can be sizeable: The average impact on human development in
the affected areas is similar to going back about 2 years in terms of their human
development gains over the 5-year period reviewed.
The paper is structured as follows. Section 2 presents the literature related to this topic.
Section 3 presents the data and an overview of the various social indicators considered
in the present analysis, as well as the methodology used. Section 4 introduces the
different results. Section 5 outlines the conclusions.
3
2. The literature on natural disasters
It is first important to differentiate between a natural hazard and a natural disaster.
According to Hyndman and Hyndman (2006), a natural hazard occurs whenever there is
a natural process threatening human life or property; but if this threat becomes real and
affects significantly damages life and property, then is called a natural disaster. We are
focusing on the impact from natural disasters.
The literature on natural disasters and its social and economic consequences is still
scarce and can be divided mainly in three strands. One strand of the literature has
focused on how some factors exacerbate vulnerability to natural events. They have
developed a framework considering changing climate, deforestation and geophysical
factors (McGuire, Mason and Kilburn, 2002), in addition to increasing urbanization
which brings environmental hazards and exposure to risk from lack of adequate urban
planning and dual political discourse (Pelling, 2003 and 2003a), or even geographical
proximity to exposure, access to assets and public facilities as well as political and
social networks (Bosher, 2007).
All these factors become a thread to population, their belongings and possessions, and
their productive capacity, becoming then a natural hazard. And when such hazard is
realized, then it becomes a natural disaster (see McGuire, Mason and Kilburn, 2002).
Even though this strand of the literature recognizes that such hazard factors affect the
impact of the disasters, they only briefly mention basically the number of fatalities, or
some rough costs.
A second strand of the literature focuses on the impact of natural disasters on
macroeconomic indicators. Auffret (2003) analyzed the impact of natural disaster on
Latin America and the Caribbean, and found the impact very significant, especially for
the Caribbean, where the volatility of consumption is higher than in other regions of the
world, where inadequate risk-management mechanisms have been available in the
region.
4
This strand of the literature has been even contradictory to some extent. For example
Benson and Clay (2003) have argued that the long term impact of natural disasters on
national economic growth is negative, while Skidmore and Toya (2002) argue that such
disaster may have a positive impact in the long run growth, derived from a reduction to
returns to physical capital but an increase in human capital, leading to higher growth.
Strobl (2008) finds for the US coastal regions that hurricanes decrease county’s growth
initially by 0.8 per cent, while recovering after in 0.2 per cent. This author also finds for
Central America and the Caribbean that the impact from a destructive hurricane is a
reduction of 0.8 percent of economic growth (Strobl, 2008a).
When analyzing what additional factors reduce or increase the impact of the disasters on
macro indicators, Noy (2009), Kahn (2005) and Toya and Skidmore (2007) find that
institutions, higher education and trade openness, as well as strong financial sector and
smaller governments are important factors in determining the impact that natural
disasters have on development at the international level. However, the impact may
differ according to levels of aggregation of economic sectors and for different disasters.
For example, Loayza et al (2009) separated the effects from different disasters on
economic sectors, finding that they use to affect economic growth but not always in a
negative way, and while droughts affects growth negatively, other disasters may have a
positive effect on some economic sectors, to he extent that they are moderate.
The third stream of the literature measures the impact and coping mechanism for natural
disasters mostly at the household and village levels. Here, natural disasters are shocks
that households have to face as they are adverse events leading to a decrease in income
or consumption, and also a loss in productive assets.
Alderman et al (2006) using data for households in Zimbabwe focused on height
development of children as consequence of a drought and civil war in Zimbabwe,
finding that children affected by such shocks have achieved lower education levels and
could have been taller otherwise. Dercon (2004) used growth in consumption among
household in selected villages in Ethiopia, and did not find that shocks have an effect in
the reduction of assets due to the 1980s famine, but some covariates for the famine are
related to subsequent low growth. Dercon et al (2005) also find for Ethiopia that
5
droughts and illness shocks are associated to low levels of per capita consumption in
household for shocks between 1999 and 2004.
Carter et al (2007) analyzed the impact of droughts in Ethiopia and of hurricane Mitch
in Honduras on growth of assets at the household level. For Ethiopia, they find a pattern
of assets smoothing among low wealthy families, i.e. such household hold on their
assets even if they are few in periods when income and consumption decreases, such as
after the big drought occurred. They find for Honduran households that relatively
wealthy families recovered faster from the shock than low income families, and that a
poverty trap is set below a given level of income. Baez and Santos (2007) also analyzed
the effects of Mitch on households indicators, finding no effect on school enrollment of
children, but a significant increase in their labor participation.
Others have analyzed how some coping mechanisms within households affect recovery
from a shock derived from a natural disaster. De Janvry et al (2006) shows that
conditional cash transfers availability previous to a disaster serve as a safety net for
those exposed to the disaster, while those uninsured and vulnerable non extremely poor
use as coping mechanism an increase in child labor, and savings in nutrition and school
costs. Alpizar (2007) also finds that access to formal financial services mitigates the
negatives effects from natural disaster shocks for farmers in El Salvador, as it leads to
more efficient production.
However, a less developed area in the analysis is the impact at the regional level.
Yamano et al (2007) focus on manufacturing and business centers. These authors use
district level data for employment and output, estimating that economic loses are not in
proportion to the distribution of industrial activities and population concentration,
suggesting that policies to alleviate loses should be considered from a higher order.
Burrus et al (2002) also analyzed how low intensity hurricanes can impact local
economies through interruption of activity. They use data from the local Chambers of
Commerce surveys in North Carolina and because of their frequency the impact could
be a reduction between 0.8 and 1.23 per cent of annual output and up to 1.6 per cent of
regional employment. In addition, Ewing et al (2009) found for Oklahoma that after the
big tornado that affected the area, the labor market improved in the aggregate, being
positive for most of the economic sectors.
6
The impact of hurricane Katrina, a well diffused disaster, also sparked some analysis on
the effects on the local economy. Thompson (2009) for example found that the impact
of the hurricane impacts the local economy in about 5.2 per cent decline, representing
about eight years of development. The BLS (2006) also found a reduction in
employment for about a year since the hurricane impact, increasing therefore the
unemployment rate to about 12 per cent. Groen and Polivka (2008) also find for Katrina
evacuees that those who did not return to the area of the impact were performing worse
than those who did return. A similar pattern is found by Belasen and Polachek (2009)
for hurricanes hits in Florida, finding an increase of about 4.5 per cent on wages on
those areas directly hit. Whether due to Katrina, or the hurricanes in Florida, wages tend
to decrease in areas where evacuees migrate (McIntosh, 2008; Belasen and Polachek,
2009). In related issues, for Mexico, Saldaña and Sandberg (2009) correlated natural
disasters and migration at the local level, finding a positive correlation at the municipal
level.
However, there is still a gap in the analysis of how local social indicators are affected by
natural disasters, since the evidence seems to point to a local and sectorial impact from
natural disasters. It is important to bring this to the fore since policies to address those
shocks can be better planned. It is in this context that this paper tries to put together
different databases at the municipal level in order make a contribution to the literature
of local impact on social indicators derived from shocks of natural disasters related to
poverty and human development, especially for a region constantly affected for such
events and where this kind of studies are still necessary.
3. Methodology, Data and Variables
Methodology
Previous studies have tried to determine the impact of natural disasters as a national
shock affecting macroeconomic variables (see for example: Auffret, 2003; Heger et al,
2008, Crowards, 2000; Toya and Skidmore, 2005; Jaramillo, 2007; Smith et al, 2005;
among others), but only a few have delved into the regional-local effects within a
country on specific social variables, taking geographic units at different levels of
7
aggregation, for example Dercon (2004), Dercon et al (2005), Belasen and Polachek
(2009), or focusing specifically in a hit area, like in Ewing et al (2009).
For this paper, the main idea is to use a framework for geographic units, which are the
municipal level. We can treat the natural disaster occurrence as a natural experiment, or
just as an exogenous shock that allows for getting an exogenous variation in the
explanatory variables. With at least 2 periods of data to make comparison, we calculate
the effect of such disaster with a difference in difference specification and random
effects model as in the following specification:
(1) Yjt=α0+α1D1+α2D2+α3D1D2+α4Xjt+εj+ujt
Where Y denotes the indicator for a social variable in geographic unit i at time t. D1
denotes a dummy for areas considered under treatment, D2 is a dummy variable taking
the value of 1 for year 2005, X is a set of characteristics of the area. The term α3
measures the impact of a natural disaster on the outcome variable Y. Treatment (D1) can
be defined as those areas that are subject of a natural disaster at any time in the period
covered by the data.
We will include in X different sets of variables pre-shock for which municipalities may
be more heterogeneous in their vulnerability and response to shocks from a natural
disaster, or making the municipality more prone to a natural disaster. We also interact
the treatment dummy with these pre-shock covariates in order to control for existent
observed variations between treatment and control groups, that may be determinant of
the impact and reduces as well selection bias in the sample.
In addition, it may be argued that despite controlling for pre-shock variables there
would be some unobservable that may affect the magnitude of the impact, therefore we
will include a fixed effect model at the municipal level in the following form:
(2) Yit= dj + αDj+α4Xjt+ujt
8
Where Y is the level of social indicator chosen (poverty incidence, poverty depth,
poverty severity, child labor, school attendance) in municipality j at time t, dj is a
geographical unit fixed effect (state, municipality, province), Dj is a dummy variable
taking the value of 1 if the geographical unit suffered a shock from a natural disaster
during the period, and X is a set of time-varying characteristics. The coefficient α
measures the effect of the shock derived from a natural disaster for that period.
In the next subsection we present the sets of variables, their sources, and basic statistics.
Data and Variables
We are using data from different sources. We are interested in dependent variables such
as the Human Development Index (HDI), as published by the UNDP at the municipal
level for years 2000 and 2005, and also the poverty levels in three definitions (food,
capacities, assets) as published by CONEVAL (2008) also for 2000 and 2005 at the
municipal level. We separate different events as: floods, frost, droughts, rains, and other
events.1
Data for natural disasters was extracted from the DESINVENTAR database, covering
events at municipal level (see Annex 1 for a description of this database). Comments on
benefits and drawbacks using this kind of databases can be found in Wisner et al (2004),
although the Desinventar database does not focus only on casualties but in a more broad
definition, including economic losses, for collecting data. Its must be noted that there
are no other sources for natural disasters at the local level, at least in Mexico.
The geographical distribution of natural disasters is shown in the Maps in the Annex 2,
where we can see that some patterns can be seen but they do not seem to be too strong
in some cases. For example, floods events seem to be distributed all around the country,
droughts seem to be more concentrated to some extent in the north areas of the country,
while frosts also seem to be concentrated in the north, finally rains seem to be
concentrated in northern and southern areas mostly in coastal zones.
1
Other events comprises a set of events with lower frequency including: landslide, avalanche, eruption,
hailstorm, surge, snowstorm, earthquake, electric storm, tornado, strong winds.
9
At the municipal level, there are numerous characteristics that might be associated with
poverty, including the geographical isolation, and other climatic conditions which
would ideally need to be captured. We therefore control for relevant natural and
geographical characteristics of municipalities (Geography and Nature), including
measures of latitude, altitude, surface length, percentage of arid and semiarid areas
within the municipality, deforestation rates, and maximum and minimum average
temperatures and rainfall. Data under this category is from years previous to 2000, and
were collected from several public sources including the National Agency for Water
(CONAGUA in Spanish), and the National Institute for Statistics and Geography
(INEGI in Spanish).
We also account for hazard mitigation practices, and emergency preparedness at the
municipality (Institutional /Local Capacity) in year 2000. This includes a set of
variables that may affect the response capacity of local governments to natural events,
such as the existence of hazard maps, civil defense units and plans against
contingencies, as well as the share of local financial resources (tax base). This set of
data was collected from the National Survey of Municipalities 2000 applied by the
National Institute for Social Development (INDESOL). We control for the availability
of coping funds with a dummy if the municipality received federal resources after the
disaster as reported by the agency in charge of allocating such funds (CENAPRED in
Spanish).
We also constructed a number of pre-shock (2000) municipal-level variables that may
affect the vulnerability of the municipal population to natural disasters, or the capacity
to recover from such shocks. This include the share of individuals or households with
the following characteristic within the municipality: proportion of rural population;
migration intensity; shares of population working in different economic sectors; share of
population with social security; share of indigenous population; demographic
composition of population; and degree of inequality within the municipality measured
with the Gini index. This dataset was collected from the National Population Census
2000 by INEGI and the UNDP. In addition we will consider in the model state level
effects.
All basic statistics are presented in the next Table 1 with their corresponding source.
10
INSERT TABLE 1 ABOUT HERE
4. Results
We estimated equations (1) and (2) to assess the impact of natural disasters on human
development and poverty. We present results using, first, a sample comprising all
municipalities, thus the control group here is with those municipalities that have no
disasters in the period; then we also want to test the robustness of the estimations
comparing the results using different sample groups, which restrict the controls to group
to different sets. In order to do so, we first restrict the sample to those municipalities
without reported disaster in the previous decade, where the control group are those
without disasters previously reported; and then after we restrict the sample to those
municipalities that only suffered a disaster in the period under analysis, with the control
group being those municipalities with disaster in the period but experienced disasters
grouped in the “other” category, this model being applied only when we disaggregate
the disasters by type.
Estimations are presented with the panel structure using random and fixed effects at the
municipal level, and standard errors are clustered at the municipal level. All regressions
also include interactions between the treatment dummy and all sets of pre-shock
characteristics, and also include state level fixed effects.
Table 2 introduces the results from a general natural disaster, using all municipalities,
then restricting to those municipalities without reported previous disaster in following
tables.
INSERT TABLE 2 (EFFECT FROM GENERAL NATURAL DISASTERS)
From Table 2 we can note that the effect of a general disaster on the Human
Development Index (HDI) is about -0.00688 or -0.00684, using random or fixed effects
and using all sample. This represents about 0.98 or 0.97 per cent decrease on such index
11
from the impact of a natural disaster. Using in the sample only municipalities without
previous disasters to the period, we find that there is a decrease of 0.58 or 0.55 per cent.
The effect on food poverty, or severe poverty, is to increase in about 3.7 per cent such
indicator when using all municipalities in the sample, and to increase in about 2.2 per
cent when using the restricted sample. There is also an increase in capacities poverty of
about 3 per cent for the whole sample, or 2 per cent for the restricted sample; and an
increase of about 1.5 per cent for assets poverty with all sample, or 1.4 with the
restricted sample. However, the impact from a specific type of disaster could be
different, and for that reason, we disaggregate the natural shocks into different natural
shocks as presented in Table 3.
In Annex 3 we also present the interactions of the treatment dummy with the control
variables. The coefficients present the marginal effect from such controls. Most of them
are not significant, although some are. For example the use of federal resources against
natural disasters effects (Cenapred) seems to have a positive effect but on poverty, even
though this variable may respond more to political factors for allocating such funds
rather than to palliate adverse effects. Attending seminars related to public
administration, as well as having a municipal development plan, have a negative effect
on poverty when using all sample. The political controls, same party than governor or
president, are mostly relevant in both samples. The share of population with social
security is highly relevant in reducing poverty with both samples for those localities
with a disaster. The share of indigenous population, as well as employment in the
tertiary sector and the gini coefficient are only relevant using all sample of
municipalities.2
INSERT TABLE 3 (DIFFERENT DISASTERS ALL SAMPLE)
Table 3 disaggregates the effects form different types of natural disasters on the social
indicators, using in the sample all municipalities. There is a significant decrease in the
HDI coming from floods (0.38 per cent), frost (0.78 per cent), droughts (1.34 per cent),
and other disasters (0.78 per cent). Even though there is an increase in such index from
2
We only present in the Annex the controls interacted for general disaster for matter of space.
12
rains (0.9 per cent), we will check in further runs in the next tables is that effect
sustains. These findings may be consistent with the international evidence from Loayza
et al (2009), where they found some disasters with a positive effect on growth, to the
extent, they suggest, that they are moderate disasters, and on different economic sectors.
In the case of poverty, there is an increase on all the measures, mainly from floods,
droughts and other disasters. In the case of floods the impact ranges from an increase in
1.9 per cent in assets poverty, 2.9 per cent in capacities poverty and 3.5 per cent in food
poverty. In the case of frost and rains, both are not significant. Droughts affect with a
higher increase in poverty levels, ranging from an increase of about 2.7 per cent in
assets poverty, 3.9 per cent in capacities poverty, and 4.3 per cent in food poverty. Other
disasters also increase capacities poverty levels in 2 per cent, and 2.3 per cent in food
poverty, but no significant regarding assets poverty.
INSERT TABLE 4 (DIFFERENT DISASTERS SAMPLE WITHOUT PREVIOUS)
In Table 4 we present results using as sample those municipalities that have not reported
disasters in the period previous to the one analyzed. Here we find a significant impact
on reducing the Human Development Index coming from droughts (2 per cent), or other
disasters (0.8 per cent), and also there is an increase in the index from rains (1 per cent).
Regarding poverty levels, there is an increase in all levels from floods and the other
category. Such effects are larger than hose found in Table 3, with sample of all
municipalities. However, here frost is significantly reducing food and capacities
poverty.
INSERT TABLE 5 (DIFFERENT DISASTERS SAMPLE ONLY DISASTERS)
Table 5 shows results using as sample only those municipalities that experienced a
natural disaster in the period. In this case, we are comparing different types of disasters
versus the group of localities that experienced that “other” category of disasters. Results
show that, compared to those with other disasters, frost and droughts reduce the Human
Development Index in 0.66 and 1.27 per cent, respectively. Rain has a positive effect
only compared to those with other disasters in about 1 per cent. For poverty levels, we
can note that the main effects are coming from floods and droughts, floods increasing
13
severe poverty in about 3.2 per cent, capacities poverty in 2.8 per cent, and assets
poverty in 1.9 percent, compared to those municipalities with other disasters. Droughts
increase food poverty in about 4.2 per cent, capacities poverty in 3.8 per cent, and assets
poverty in about 2.7 per cent, again compared to those localities with the aggregated
other types of disasters.
5. Conclusion
Natural disasters affecting different parts of Mexico have gained prominence, including
the 2005 hurricane season and the 2007 floods in the southeastern state of Tabasco (the
worst floods in the state’s recent history). The debate on whether such events affect the
development of affected areas is still under way. In this context, this paper analyzes the
effect of natural disasters on the Human Development Index and three different
measures of poverty (severe or food, capacities and assets).
We used a difference-in-difference strategy, where we control for different sets of preshock variables that may influence the magnitude of the impact of natural disasters. We
control for local variables such as geographical and natural characteristics, and preshock variables including socioeconomic factors, institutional and local administrative
capacity, as well as financial coping mechanisms and political covariates, and their
interactions with the treatment dummies.
Our results show a significant and adverse impact of natural disasters on both human
development and poverty. For affected municipalities, the impact on the Human
Development Index is similar to going back 2 years in human development over the
same period analyzed on average. Disaggregating by type of event we find that floods
and droughts have the more significant adverse effects. Political variables seem to be
relevant for explaining the magnitude of the impact of disasters, opening a room for
analysis of such issue.
This paper contributes to the debate on the impact of natural disasters in developing
countries with recurrent hazards. While such events reduce social welfare at the local
level, public policies for attenuating such impacts must be more focused on those under
the poverty lines and in implementing mechanisms for keeping elements considered in
14
the human development index that are affected due to these shocks. Additional research
could focus on how coping and preventive mechanisms at municipal level affect micro
effects on households, for which further data will be needed.
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18
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Annex 1
The DesInventar database
DesInventar is a conceptual and methodological tool for the construction of databases of
loss, damage, or effects caused by emergencies or disasters at the municipal level, in the
case of Mexico, as gathered by La Red. It defines a disaster as the combination of
effects produced by an event on human lives, infrastructure or economy in a
geographical unit, registering events as disasters only when there are deaths or missing
individuals, loss value, routes affected, affected agricultural and livestock, and effects
on housing, population, services, etc. This database differentiates from the EMDAT
database in that this last records disasters at the country level, while DesInventar records
them at the municipal level, reason for which it suits better for our purpose of analysis.
DesInventar is an initiative of the Social Studies Network for Disaster Prevention in
Latin America (LA RED). It contains information for a set of countries such as Mexico,
Guatemala, El Salvador, Costa Rica, Colombia, Ecuador, Peru and Argentina. This
system follows a methodology for recording information that includes characteristics
and effects of various types of disasters. This methodology was designed to capture the
effects of disasters on politico-administrative units.
The module for Mexico contains information from 1980 to 2006 with a total of 17
thousand 177 disasters. Over 60% of these disasters are due to flood (22.1%), drought
(7.0%), frost (6.6%), forest fire (5.6%), fire (5.6%) and rains (4.6%). For studies that
have also used this database check Saldaña and Sandberg (2009).
19
Source: La Red (2003). Guía Metodológica de DesInventar 2003. Available online
www.desinventar.org.
20
Annex 2.
Maps on the distribution of natural hazards for 2005 using DesInventar database
Floods distribution
Droughts distribution
21
Frost distribution
Rains distribution
22
Table 1. Descriptive statistics
Mean Std. Dev. Min
Dependent
HDI *
Food poverty incidence*
Capacities poverty incidence*
Assets poverty incidence*
Natural Disasters Occurrence
Any event /2
Flood /2
Frost /2
Drought /2
Rains /2
Landslide /2
Others /2
Geography and Nature
Altitude *
Latitude *
Length *
Arid surface *
Semiarid surface*
Deforestation rate *
Minimum temperature *
Maximum temperature *
Minimum rain*
Maximum rain *
Socioeconomic
Rural municipalities **
Max
Source
0.7079
0.4438
0.5141
0.6828
0.0758
0.2423
0.2427
0.2119
0.3915
0.0160
0.0280
0.0920
0.9165
0.9680
0.9810
0.9950
PNUD (2008)
CONEVAL (2008)
CONEVAL (2008)
CONEVAL (2008)
0.4234
0.2326
0.0835
0.0831
0.0811
0.0590
0.1716
0.4942
0.4226
0.2768
0.2761
0.2730
0.2356
0.3771
0
0
0
0
0
0
0
1
1
1
1
1
1
1
Desinventar (2008)
Desinventar (2008)
Desinventar (2008)
Desinventar (2008)
Desinventar (2008)
Desinventar (2008)
Desinventar (2008)
1304
198388
985658
6.49
16.38
-18.27
7.25
27.46
15.76
175.78
819
33461
43623
17.04
20.22
17.89
5.74
2.90
10.17
51.93
2
2924
143827 322993
865878 1166813
0.00
97.50
0.00
72.50
-62.57
-0.56
0.00
24.00
16.00
30.00
1.50
57.40
8.00
315.50
0.8350
0.3713
0.0000
1.0000
INEGI (2006)
INEGI (2006)
INEGI (2006)
CNA (2007)
CNA (2007)
Davis, R. (1997)
CNA (2007)
CNA (2007)
CNA (2007)
CNA (2007)
INEGI (2001)
Economic dependency rate *
Population with social security **
Population living in the same state 5 years
before **
Indigenous population **
Gini coefficient*
Employed at primary sector ** /1
Employed at secondary sector ** /1
Employed at tertiary sector ** /1
Coping Funds and Covariates
With CENAPRED resources 2000-2005 **
Same political party at municipal and state level
when hazard occur
Same political party at municipal and federal
level when hazard occur
Institutional/Local Capacity
NGO for consultation or courses
Seminar attendance
No NGO
Associated services
Municipal regulations
Municipal development plan
Civil defense unit
Civil defense program
Natural contingency in tne 1990s
Hazard map
Tax resources **
Federal resources **
0.8333
0.2148
0.1693
0.1824
0.3945
0.0000
2.3700
0.8055
INEGI (2001)
INEGI (2001)
0.9677
0.0379
0.4044
0.1284
0.0705
0.0968
0.0252
0.0993
0.0556
0.0781
0.0458
0.0618
0.6714
0.0000
0.1955
0.0005
0.0000
0.0023
1.0000
0.7682
0.5978
0.5533
0.3461
0.4098
INEGI (2001)
INEGI (2001)
PNUD (2008)
INEGI (2001)
INEGI (2001)
INEGI (2001)
0.8014
0.3990
0.0000
1.0000
CENAPRED (2008)
0.0348
0.1833
0
1
SNIM
0.0168
0.1285
0
1
SNIM
0.2645 0.4412
0.1716 0.3771
0.3014 0.4590
0.2158 0.4115
0.2121 0.4089
0.7211 0.4485
0.5733 0.4947
0.4607 0.4986
0.5909 0.4918
0.3055 0.4607
6.4960 8.1633
40.9682 19.8749
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
1
1
1
90
100
ENGM (2002)
ENGM (2002)
ENGM (2002)
ENGM (2002)
ENGM (2002)
ENGM (2002)
ENGM (2002)
ENGM (2002)
ENGM (2002)
ENGM (2002)
ENGM (2002)
ENGM (2002)
1
Notes: *average ** proportion. /1 Relative to total population. / 2 between 2000-2005. Data for year 2000
except coping
Statistical references
CNA. 2007. Estadisticas del Agua en México. Edición 2007. Comisión Nacional del
Agua. Secretaría de medio ambiente y recrusos naturales. 2a. Reimpresión. México
INEGI. 2001. “XII Censo General de Población y Vivienda 2000”. Consulta interactiva de
datos.
INEGI. 2006. “II Conteo de Población y ivienda 2005” Consulta interactiva de datos.
PNUD. 2008. Índice de Desarrollo Humano Municipal en México 2000-2005. Programa
de Naciones unidas para el Desarrollo. México
CONEVAL. 2008. Mapas de pobreza 2000-2005. Consulta en red
[www.coneval.gob.mx]
Desinventar. 2008. Consulta en red [www.desinventar.org/]
Davis, R. (1997). Mexico country brief: Interim forest cover assessment for SOFO. FAO.
CENAPRED. 2008. base de datos proporcionada por CENAPRED a la Oficina de
Investigación en Desarrollo Humano. PNUD. México
INEGI. 2003. Encuesta Nacional a Gobiernos Municipales 2002. ]Consulta
interactiva de la base de datos
INAFED. 2008. Sistema Nacional de Información Municipal. Secretaría de
Gobernación. http://www.inafed.gob.mx/wb/inafed09/descargas
2
Table 2. Effect of a natural disaster on social municipal indicators
All municipalities in
Municipalities without
sample
previous disaster
Social indicator
(1)
(2)
(3)
(4)
Random
Fixed
Random
Fixed
Effects
Effects
Effects
Effects
-0.00688*** -0.00684*** -0.00392** -0.00371**
Human Development Index
(0.00108)
(0.00108)
(0.00181)
(0.00179)
R-squared
0.8447
0.756
0.8082
0.786
Observations
4836
4836
2370
2370
0.0367***
0.0371*** 0.0225*** 0.0222***
Food Poverty (severe)
(0.00495)
(0.00493)
(0.00873)
(0.00860)
R-squared
0.8116
0.472
0.0181
0.549
Observations
4884
4884
2404
2404
0.0300***
0.0305***
0.0206**
0.0199**
Capacities Poverty
(0.00485)
(0.00483)
(0.00842)
(0.00828)
R-squared
0.8209
0.427
0.7881
0.508
Observations
4884
4884
2404
2404
0.0154***
0.0160***
0.0147**
0.0135**
Assets Poverty
(0.00431)
(0.00427)
(0.00693)
(0.00679)
R-squared
0.8150
0.210
0.7753
0.318
Observations
4884
4884
2404
2404
yes
yes
yes
yes
Natural Disasters Occurrence
yes
yes
yes
yes
Geography and Nature
yes
yes
yes
yes
Socioeconomic
yes
yes
yes
yes
Coping Funds
yes
yes
yes
yes
Institutional/Local Capacity
3
yes
yes
yes
yes
Inequality
*** p<0.01, ** p<0.05, * p<0.1. Robust standard errors clustered at the municipal level in
parentheses.
Note: All regressions are also controlled for state fixed effects and interaction of the
treatment dummy with local declaratories.
Table 3. Effects of different natural disasters on social municipal indicators. All municipalities in sample
Human Development Index
Food Poverty (severe)
Capacities Poverty
Assets Poverty
(1)
(2)
(1)
(2)
(1)
(2)
(1)
(2)
Random
Fixed
Random
Fixed
Random
Fixed
Random
Fixed
Effects
Effects
Effects
Effects
Effects
Effects
Effects
Effects
0.0354***
0.0358***
0.0295***
0.0299***
0.0188***
0.0193***
-0.00271**
-0.00269**
Flood
(0.00544)
(0.00538)
(0.00545)
(0.00539)
(0.00520)
(0.00512)
(0.00124)
(0.00123)
-0.00814
-0.00815
-0.00555***
-0.00556***
-0.00605
-0.00608
0.00108
0.00100
Frost
(0.00847)
(0.00839)
(0.00204)
(0.00202)
(0.00833)
(0.00824)
(0.00763)
(0.00753)
0.0434***
0.0440***
0.0390*** 0.0397*** 0.0266*** 0.0274***
-0.0101***
-0.0100***
Drought
(0.00784)
(0.00774)
(0.00810)
(0.00798)
(0.00807)
(0.00790)
(0.00195)
(0.00193)
-0.000950
-0.00116
-0.00599
-0.00625
-0.0133*
-0.0137*
0.00641***
0.00640***
Rains
(0.00785)
(0.00777)
(0.00189)
(0.00187)
(0.00793)
(0.00785)
(0.00760)
(0.00749)
-0.00558*** -0.00557*** 0.0232*** 0.0234*** 0.0200*** 0.0202***
0.00705
0.00726
Others
(0.00132)
(0.00130)
(0.00576)
(0.00570)
(0.00572)
(0.00567)
(0.00528)
(0.00522)
0.8457
0.7604
0.8151
0.4814
0.8233
0.4357
0.8166
0.2173
Adjusted R-squared
4,836
4,836
4,884
4,884
4,884
4,884
4,884
4,884
Observations
yes
yes
yes
yes
yes
yes
yes
yes
Natural Disasters Occurrence
yes
yes
yes
yes
yes
yes
yes
yes
Geography and Nature
yes
yes
yes
yes
yes
yes
yes
yes
Socioeconomic
yes
yes
yes
yes
yes
yes
yes
yes
Coping Funds
4
yes
yes
yes
yes
yes
yes
yes
Institutional/Local Capacity
yes
yes
yes
yes
yes
yes
yes
Inequality
*** p<0.01, ** p<0.05, * p<0.1. Robust standard errors clustered at the municipal level in parentheses
Note: All regressions are also controlled for state fixed effects and interaction of the treatment dummy with local declaratories
Note: Overall R-squared is reported when using random effects, meanwhile within R-squared is reported when using fixed effects.
yes
yes
Table 4. Effects of different natural disasters on social municipal indicators restricted to municipalities without natural disaster in
previous period
Human Development
Food Poverty (severe)
Capacities Poverty
Assets Poverty
Index
(1)
(2)
(1)
(2)
(1)
(2)
(1)
(2)
Random
Fixed
Random
Fixed
Random
Fixed
Random
Fixed
Effects
Effects
Effects
Effects
Effects
Effects
Effects
Effects
0.0459***
0.0462***
0.0402*** 0.0399*** 0.0274*** 0.0260***
-0.00395
-0.00350
Flood
(0.0112)
(0.0111)
(0.0106)
(0.0104)
(0.00864) (0.00838)
(0.00250)
(0.00247)
-0.0747***
-0.0749***
-0.0615*** -0.0617***
-0.0233
-0.0236
0.00528
0.00526
Frost
(0.0181)
(0.0178)
(0.00374)
(0.00367)
(0.0183)
(0.0180)
(0.0156)
(0.0153)
0.0306
0.0304
-0.0142*** -0.0143***
0.0223
0.0222
-0.000326 -0.000243
Drought
(0.0205)
(0.0201)
(0.0209)
(0.0205)
(0.0185)
(0.0182)
(0.00492)
(0.00483)
-0.0262
-0.0265
-0.0283
-0.0283
-0.0271** -0.0266**
0.00772*
0.00744*
Rains
(0.0225)
(0.0221)
(0.0203)
(0.0199)
(0.0127)
(0.0125)
(0.00412)
(0.00403)
-0.00589** -0.00546** 0.0342*** 0.0345*** 0.0339*** 0.0337*** 0.0241**
0.0227**
Others
(0.00250)
(0.00246)
(0.0113)
(0.0111)
(0.0113)
(0.0110)
(0.01000) (0.00970)
0.8080
0.7879
0.7889
0.5607
07915
0.5183
0.7771
0.3238
Adjusted R-squared
2,370
2,370
2,404
2,404
2,404
2,404
2,404
2,404
Observations
Natural Disasters
yes
yes
yes
yes
yes
yes
yes
yes
Occurrence
yes
yes
yes
yes
yes
yes
yes
yes
Geography and Nature
5
yes
yes
yes
yes
yes
yes
yes
Socioeconomic
yes
yes
yes
yes
yes
yes
yes
Coping Funds
Institutional/Local
yes
yes
yes
yes
yes
yes
yes
Capacity
yes
yes
yes
yes
yes
yes
yes
Inequality
*** p<0.01, ** p<0.05, * p<0.1. Robust standard errors clustered at the municipal level in parentheses
Note: All regressions are also controlled for state fixed effects and interaction of the treatment dummy with local declaratories
Note: Overall R-squared is reported when using random effects, meanwhile within R-squared is reported when using fixed effects.
yes
yes
yes
yes
Table 5. Effects of different natural disasters on social municipal indicators restricted to municipalities with natural disaster
only
Human Development
Food Poverty (severe)
Capacities Poverty
Assets Poverty
Index
(1)
(2)
(1)
(2)
(1)
(2)
(1)
(2)
Random
Fixed
Random
Fixed
Random
Fixed
Random
Fixed
Effects
Effects
Effects
Effects
Effects
Effects
Effects
Effects
0.0326***
0.0327***
-0.000621
-0.000614
0.0278*** 0.0280*** 0.0193*** 0.0195***
Flood
(0.00675)
(0.00664)
(0.00677) (0.00665) (0.00636) (0.00624)
(0.00153)
(0.00151)
-0.00777
-0.00791
-0.00529
-0.00546
0.00202
0.00180
-0.00478** -0.00479**
Frost
(0.00857)
(0.00844)
(0.00844) (0.00831) (0.00787) (0.00772)
(0.00204)
(0.00200)
0.0425***
0.0430***
0.0386*** 0.0392*** 0.0271*** 0.0277***
0.00908*** 0.00905***
Drought
(0.00816)
(0.00802)
(0.00840) (0.00824) (0.00834) (0.00814)
(0.00200)
(0.00197)
-0.00176
0.00721*** 0.00719*** -0.00145
-0.00606
-0.00643
-0.0128*
-0.0133*
Rains
(0.00799)
(0.00787)
(0.00193)
(0.00190)
(0.00805) (0.00792) (0.00773) (0.00757)
0.8717
0.7606
0.8299
0.4612
0.8359
0.4134
0.8154
0.1866
Adjusted R-squared
6
2,062
2,062
2,068
2,068
2,068
2,068
2,068
2,068
Observations
Natural Disasters
yes
yes
yes
yes
yes
yes
yes
yes
Occurrence
yes
yes
yes
yes
yes
yes
yes
yes
Geography and Nature
yes
yes
yes
yes
yes
yes
yes
yes
Socioeconomic
yes
yes
yes
yes
yes
yes
yes
yes
Coping Funds
Institutional/Local
yes
yes
yes
yes
yes
yes
yes
yes
Capacity
yes
yes
yes
yes
yes
yes
yes
yes
Inequality
*** p<0.01, ** p<0.05, * p<0.1. Robust standard errors clustered at the municipal level in parentheses
Note: All regressions are also controlled for state fixed effects and interaction of the treatment dummy with local declaratories
Note: Overall R-squared is reported when using random effects, meanwhile within R-squared is reported when using fixed effects.
7
Annex 3. Controls with treatment interaction for table 2 (random effects)
All municipalities in sample
Municipalities without previous disaster
Interacted controls
Annual deforestation rate
Altitude *
Latitude *
Length *
Coast
Arid surface
Semiarid surface
Minimum temperature
Maximum temperature
Minimum rain
Maximum rain
Human
Development
Index
Food
Poverty
(severe)
Capacities
Poverty
Assets
Poverty
-0.000127
(0.000108)
-1.11e-06
(2.04e-06)
-7.53e-08
(9.63e-08)
0.000658**
(0.000322)
1.11e-05*
(5.98e-06)
4.32e-07
(2.80e-07)
0.000560*
(0.000316)
9.32e-06
(5.91e-06)
3.70e-07
(2.86e-07)
0.000177
(0.000283)
2.09e-06
(5.28e-06)
1.00e-07
(2.75e-07)
-5.76e-08
Human
Development
Index
Food
Poverty
(severe)
Capacities
Poverty
Assets
Poverty
0.000462* -0.000112 -0.000214 -0.000457
(0.000259) (0.000682) (0.000650) (0.000542)
-5.88e-06 1.85e-05* 1.70e-05* 1.00e-05
(3.89e-06) (1.04e-05) (1.01e-05) (8.20e-06)
-1.99e-07 1.14e-06** 1.11e-06** 6.90e-07
(2.13e-07) (5.75e-07) (5.42e-07) (4.34e-07)
6.05e-1.36e-1.33e-9.80e1.21e-07
1.21e-07 1.73e-07
07***
06***
06***
07**
(1.93e-07) (1.98e-07) (1.91e-07) (2.06e-07) (5.04e-07) (5.03e-07) (4.51e-07)
-0.0210*
-0.0201*
-0.0143
0.0138
-0.0350
-0.0344
-0.0302
(0.0109)
(0.0110)
(0.0106)
(0.00863)
(0.0229)
(0.0221)
(0.0193)
(6.52e-08)
0.00916**
(0.00360)
0.000351 0.000484 0.000503 -0.00206** 0.00627***
0.000482***
(0.000170) (0.000487) (0.000501) (0.000483) (0.000953) (0.00235)
-6.55e-05
9.81e-06
2.69e-05 6.57e-05 -4.66e-05
0.000736
(0.000111) (0.000343) (0.000348) (0.000325) (0.000270) (0.000794)
0.000234
-0.00108
-0.00102 -0.000746 0.00186***
0.00481***
(0.000296) (0.000846) (0.000846) (0.000774) (0.000666) (0.00170)
0.00250*** -0.00363* -0.00379* -0.00342* 0.00601*** -0.00747*
(0.000595) (0.00198) (0.00200) (0.00182) (0.00134) (0.00386)
-0.000343
0.000883 0.000703 0.000178 0.00134** -0.00120
(0.000209) (0.000614) (0.000619) (0.000585) (0.000618) (0.00155)
-0.000118* 0.000185 0.000154 2.39e-05 -0.000118 0.000410
8
0.00599*** 0.00423**
(0.00230)
0.000671
(0.000766)
0.00468***
(0.00165)
-0.00688*
(0.00354)
-0.00132
(0.00154)
0.000337
(0.00201)
0.000467
(0.000650)
0.00366**
(0.00145)
-0.00504
(0.00312)
-0.00156
(0.00140)
0.000126
(6.23e-05)
With CENAPRED resources
2000-2005
NGO for consultation or courses
Seminar attendance
No NGO
Associated services
Municipal regulations
Municipal development plan
Civil defense unit
Civil defense program
Natural contingency in 10 years
Hazard map
Tax resources
Federal resources
(0.000180) (0.000181) (0.000164) (0.000142) (0.000404) (0.000394) (0.000329)
-0.00294
0.0226*** 0.0242*** 0.0212***
-0.00274
0.0217*
0.0239**
0.0223**
(0.00228)
-0.00783*
(0.00441)
0.0116**
(0.00468)
0.00376
(0.00278)
-0.00303
(0.00265)
-7.67e-05
(0.00245)
(0.00726)
0.0112
(0.0111)
-0.0255**
(0.0125)
-0.0144*
(0.00786)
0.0110
(0.00736)
-0.00216
(0.00743)
(0.00403)
0.00127
(0.00638)
0.00116
(0.00775)
0.00391
(0.00495)
-0.00115
(0.00478)
0.00292
(0.00609)
(0.0119)
0.00653
(0.0177)
-0.0210
(0.0203)
-0.0207
(0.0137)
0.0109
(0.0126)
-0.0103
(0.0163)
(0.0118)
0.00869
(0.0174)
-0.0179
(0.0202)
-0.0155
(0.0133)
0.00626
(0.0122)
-0.00997
(0.0160)
(0.0102)
0.00471
(0.0150)
-0.000615
(0.0183)
-0.00184
(0.0108)
-0.00599
(0.0103)
-0.00594
(0.0136)
0.000420
-0.000672
0.000667
0.00344
(0.00426)
0.00137
(0.00522)
-0.00706
(0.00548)
0.00862*
(0.00496)
0.00987*
(0.00515)
0.0225
(0.0256)
0.0187*
(0.0123)
-0.00265
(0.0142)
0.0144
(0.0149)
-0.0135
(0.0134)
-0.00976
(0.0140)
0.0137
(0.0750)
-0.0285
(0.0120)
-0.00392
(0.0138)
0.0110
(0.0143)
-0.0132
(0.0127)
-0.00597
(0.0134)
-0.00543
(0.0739)
-0.0257
(0.0101)
-0.00612
(0.0121)
0.00628
(0.0120)
-0.0120
(0.0102)
-0.00313
(0.0115)
-0.0468
(0.0660)
-0.0146
(0.00728)
0.0104
(0.0113)
-0.0231*
(0.0128)
-0.0123
(0.00795)
0.00816
(0.00738)
-0.00461
(0.00760)
0.00305
-0.0238***
0.0235***
(0.00243)
(0.00724) (0.00719)
0.00266
-0.00361
-0.00434
(0.00321)
(0.00947) (0.00955)
3.76e-05
-2.93e-05
0.00190
(0.00289)
(0.00874) (0.00874)
0.00853*** -0.00656
-0.00398
(0.00244)
(0.00718) (0.00723)
-0.000784
0.00259
0.00147
(0.00255)
(0.00800) (0.00800)
0.0132
0.0356
0.0234
(0.0146)
(0.0414)
(0.0423)
0.00319
-0.00588
-0.00682
9
(0.00669)
0.00428
(0.0111)
-0.00978
(0.0126)
-0.00493
(0.00748)
0.000504
(0.00682)
-0.00849
(0.00730)
0.0178***
(0.00640)
-0.00771
(0.00900)
0.00727
(0.00816)
0.00168
(0.00674)
-0.00277
(0.00741)
-0.00884
(0.0403)
-0.00583
(0.00641)
Same political party at municipal
and state level when hazard occur
Same political party at municipal
and federal level when hazard
occur
Population with social security
Population living in the same state
5 years before
Indigenous population
Employed at primary sector
Employed at secondary sector
Employed at tertiary sector
Rural municipalities
Gini coefficient 2000
Constant
(0.0189)
(0.0191)
(0.0182)
(0.00972)
(0.0289)
(0.0279)
(0.0231)
-0.00557
0.0256*** 0.0256*** 0.0225**
0.0266***
-0.0486**
-0.0491**
-0.0431**
(0.00346)
(0.00918)
(0.00990)
(0.0112)
(0.00817)
(0.0202)
(0.0198)
(0.0179)
0.00626**
0.0167
0.00789
-0.0122
-0.0298**
0.0692*
0.0684*
0.0567*
(0.00281)
(0.0105)
(0.0105)
(0.0118)
(0.0354)
(0.0352)
(0.0310)
0.0548***
-0.0323
-0.0603*
0.0386**
-0.139***
-0.153***
-0.125**
(0.0115)
(0.0309)
(0.0326)
(0.0105)
0.0955***
(0.0332)
(0.0178)
(0.0530)
(0.0551)
(0.0514)
0.0540
-0.334*
-0.308*
-0.195
0.0795
-0.385
-0.263
0.137
(0.0613)
-0.0298*
(0.0170)
0.00914
(0.0310)
0.0468
(0.0399)
-0.115***
(0.0367)
0.000415
(0.00307)
0.0469*
(0.0240)
0.979***
(0.0963)
(0.180)
0.174***
(0.0471)
-0.0301
(0.0792)
-0.178
(0.114)
0.285***
(0.107)
0.000104
(0.0101)
-0.163**
(0.0655)
-0.176
(0.306)
(0.371)
0.0491
(0.0543)
-0.0665
(0.0938)
-0.157
(0.182)
0.0286
(0.208)
-0.0290
(0.0219)
-0.0832
(0.0851)
0
(0)
(0.369)
0.0356
(0.0506)
-0.0907
(0.0924)
-0.191
(0.181)
0.121
(0.211)
-0.0202
(0.0228)
-0.0910
(0.0853)
0
(0)
(0.343)
0.0228
(0.0394)
-0.109
(0.0787)
-0.218
(0.162)
0.307
(0.194)
0.00491
(0.0232)
-0.115
(0.0760)
0
(0)
(0.182)
(0.163)
(0.126)
0.150*** 0.0962***
0.0207
(0.0439)
(0.0328)
(0.0233)
-0.0492
-0.0980
0.0305
(0.0778)
(0.0658)
(0.0383)
-0.178
-0.141
0.0279
(0.115)
(0.109)
(0.0703)
0.314*** 0.317*** -0.000401
(0.108)
(0.102)
(0.0786)
0.00196
0.00533
9.35e-05
(0.0104)
(0.0107)
(0.00780)
-0.150** -0.122**
0.0648**
(0.0665)
(0.0611)
(0.0301)
-0.245
-0.175
0
(0.308)
(0.272)
(0)
10
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