Impact of Natural Disasters on the Economy of Pakistan
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Natural disasters are an increasingly phenomena that we all evidently observe and identify that may have a direct bang on the interests of an area where it hits and also on explicit domestic meters in such areas. Depending of where we live, hurricanes, earthquakes, floods, droughts, etc, are intimidation to living, belongings, industrious assets, and also can have an impact on societal pointers.
The increasing occurrence of natural disasters is extremely interrelated to the increasing susceptibility of homes and communities in emergent nations, as earlier socioeconomic vulnerabilities may aggravate the shock of a natural disaster, making harder the course of revitalization (Vatsa and Krimgold, 2000). Therefore, the impact of such events could consequence in an instant raise in poverty and deficiency (Carter et al, 2007). The literature has been still conflicting to a few amounts. For example Benson and Clay (2003) have discussed that the long-standing shock on development of natural disasters is depressing, at the same time as Skidmore and Toya (2002) explain that such tragedy may upbeat impact development in the long run as there is a decrease to returns on physical assets but a boost in human capital, leading to advanced development. Strobl (2008) for the US coastal areas discover that tornados reduce county's development originally by 0.8 per cent, whereas getting your strength back after in 0.2 per cent. This writer also figures out for Central America and the Caribbean that the impact from a critical cyclone is a diminution of 0.8 percent of development (Strobl, 2008a).
The impact of a natural disaster may also origin discriminations. The poor, who undergo from profits rise and fall, and also have imperfect access to monetary services, in the consequences of a disaster may be extra flat to lessen use and have a declining upset in other domestic indicators as a result. Additionally, there are a many non poor, or close to be, who are not insured in opposition to such threats, and then may plunge into scarcity as result of recapitalizing when dealing with with the upset, depending the shock and probability of diminishing into scarcity of the original stock assets and coping means.
Furthermore, susceptibility to natural disasters is a multifaceted issue, as it is strong-minded by the financial structure, the phase of growth, prevailing of communal and fiscal conditions, coping means, risk evaluation, rate of recurrence and concentration of catastrophes, etc. The impact on deprived ones could be losing contact with a few vital services, reversals in accretion of corporeal and human funds, and possibly an augment in child employment and unlawful behavior.
Lindell and Prater (2003) summarize the significance of shaping the impact and the pretentious agents in natural disasters. First, that information is helpful for policy makers, as they can be acquainted with the need for peripheral support and which may be more efficient; second, definite sections of affected can be acknowledged, e.g. how low income families are affected; and third, it may be also practical for setting up assistance for natural disasters and the latent results.
Overall, growing literature has emerged over the last few years on the macroeconomic and development impacts of natural disasters. Amusingly, there is as up till now no harmony on whether disasters are significant from a macroeconomic point of view, and two situations can be identified. The first believes natural disasters a hinder for economic development and is well symbolized by the following reference:
It has been argued that although individuals are risk-averse, governments should take a risk-neutral stance. The reality of developing countries suggests otherwise. Government decisions should be based on the opportunity costs to society of the resources invested in the project and on the loss of economic assets, functions and products. In view of the responsibility vested in the public sector for the administration of scarce resources, and considering issues such as fiscal debt, trade balances, income distribution, and a wide range of other economic and social, and political concerns, governments should not act risk neutral (OAS, 1991).
The other position sees disasters as entailing little growth implications and consider disasters and their reduction a problem of, but not for development (e.g. Albala-Bertrand, 1993, 2006; Caselli and Malhotra, 2004). These authors find natural disasters do not negatively affect GDP and "if anything, GDP growth is improved" (Albala-Bertrand, 1993: 207). This paper can be understood as an attempt at reconciling this body of literature. There are two entry points for the analysis. The first is to look at counterfactual vs. observed GDP, the second entry point is to assess disaster impacts as a function of hazard, exposure of assets (human, produced, intangible), and, importantly vulnerability.
Overall, the evidence reveals adverse macroeconomic consequences of disasters on GDP. In a medium-term analysis, natural disasters on average seem to lead to negative effects on GDP. The negative effects may be small, yet they can become more pronounced depending on the size of the shock. We tested a large number of vulnerability predictors and found that higher aid rates as well as higher remittances lessen the adverse macroeconomic consequences, while capital stock loss is the most important predictor for the negative consequences.
In July-August 2010, Pakistan experienced "the worst floods in its history... The floods have affected 84 districts out of a total 121 districts in Pakistan, and more than 20 million people - one-tenth of Pakistan's population... More than 1,700 men, women and children have lost their lives, and at least 1.8 million homes have been damaged or destroyed" (UN 2010, p.1). In attacking poverty in developing countries, due considerations need to be paid to the vulnerability of households against natural disasters. Poor households are likely to suffer not only from low income and consumption on average but also from fluctuations of their welfare once such disasters occur. These households are vulnerable to a decline in their welfare level because they have limited ability to cope with shocks and also they are subject to substantial shocks, such as weather variability (Dercon, 2005; Fafchamps, 2003).
This concern has led to an emerging literature on vulnerability measures in development economics (Ligon and Schechter, 2003; 2004; Kamanou and Morduch, 2005; Calvo and Dercon, 2005; Kurosaki 2006a). We broadly think people as vulnerable when (i) they cannot mitigate income volatility and (ii) their consumption expenditure is volatile over time (they lack reliable coping mechanisms). Vulnerability is thus a forward-looking concept.
As an example of low-income countries subject to substantial vulnerability, this paper examines the case of Pakistan. Pakistan is located in South Asia, where more than 500million people or about 40% were estimated to live below the poverty line at the turn of the century (World Bank, 2001). Economic development in South Asia has been characterized by a moderate success in economic growth with a substantial failure in human development such as basic health, education and gender equality (Dr`eze and Sen, 1995). This characteristic is most apparent in Pakistan (World Bank, 2002). Although the overall economic growth rates were improved during the 2000s, poverty reduction was slower than expected. Using a two period panel dataset spanning three years from the North-West Frontier Province (NWFP), one of the four provinces comprising Pakistan, Kurosaki (2006a) and Kurosaki (2006b) show that rural households were indeed vulnerable to substantial welfare fluctuations. Using a three-year panel dataset from Pakistan's Punjab, Kurosaki (1998) shows that farmers' consumption was excessively sensitive to idiosyncratic shocks to their non-farm income. Similar findings have been accumulated for rural India as well (Townsend, 1994; Kurosaki 2001).
The paper is organized as follows. Section 2 reviews the literature on the macroeconomic impacts of disasters and locates the proposed analysis within the disaster risk management paradigm. In section 3, we present the data and methodology used for projecting the economic impacts for a medium term horizon (up to 5 years after an event), as well as the regression analysis used for identifying predictor variables explaining potential impacts. Section 4 ends with a discussion of possible implications of our analysis.
The literature on impacts of natural disasters and economic effects is still inadequate and can be separated generally into three different categories. One part of the literature has focused on how several factors intensify susceptibility to natural events. They have maintained a natural vulnerability framework in view of climate change, deforestation and geophysical factors (McGuire, Mason and Kilburn, 2002), other than rising urbanization which brings ecological risks and exposure to threats from deficiency of sufficient urban development and dual political discourse (Pelling, 2003 and 2003a), or even environmental immediacy to exposure, access to property and public conveniences as well as political and social networks (Bosher, 2007).
All these parts become a thread to population, their assets and possessions and their dynamic competence, becoming then an expected risk. And when such danger is realized, then it turns out to be a natural adversity (see McGuire, Mason and Kilburn, 2002). Although this thread of the literature distinguishes that such risk factors influence the impact of the natural tragedy, they just briefly point out essentially the number of losses, or some irregular overheads.
A second thread of the literature spotlights on the impact of natural disasters on macroeconomic pointers. Auffret (2003) examined the impact of natural catastrophe on Latin America and the Caribbean, and figured out the impact very considerable, particularly for the Caribbean, where the explosive nature of expenditure is higher than in other parts of the world, where insufficient risk-management instruments have been available in the region.
This part of the literature has been still conflicting to some extent. For example Benson and Clay (2003) have also explained that the lasting impact of natural events on economic development of any country is negative, while Skidmore and Toya (2002) reveal that such tragedies may also have a constructive impact in the future growth, resulting from a decrease to returns to physical assets but an enlargement in human capital.
Strobl (2008) discovers for the US coastal counties that cyclones cut district's intensification at first by 0.8 per cent, at the same time as recuperating after in 0.2 per cent. This writer also figures out for Central America and the Caribbean that the impact from a unhelpful storm is a decline of 0.8 percent of fiscal increase (Strobl, 2008a).
When investigating what extra features cut or amplify the impact of such natural tragedies on macro pointers, Kahn (2005) and Toya and Skidmore (2007) explain that organizations, top education and trade openness, in addition to well-built economic segment and smaller governments are significant aspects in shaping the impact that natural events have on growth at global level.
The third tributary of the literature takes care of the impact and coping means for such tragic events generally at the domestic and township levels. At this point, natural adversities are upsets that family units have to face as they are unpleasant proceedings leading to a decline in earnings or utilization, and in addition a loss in industrious property.
Alderman et al (2006) by means of data for family units in Zimbabwe spotlighted on height growth of kids as result of a deficiency and civil war in Zimbabwe, result that kids influenced by such upsets have less schooling and could have been tall; if not. Dercon (2004) focused on development in utilization amongst family units in chosen villages in Ethiopia, and did not discover that upsets have an effect in the diminution of assets.
Carter et al (2007) examined the impact of droughts in Ethiopia and of cyclone Mitch in Honduras on development of belongings at the village level. For Ethiopia they uncover a model of assets leveling between low income family units, i.e. such families keep hold of their assets even they are little in phases where profits and usage drops off, for instance the big deficiency aroused. They discover for Honduran families that comparatively well-off families recovered earlier from the upset than short income households, and that a poverty corner is put below a specified point of income. Baez and Santos (2007) also examined the sound effects of Mitch on households' pointers, discovering no outcome on school admissions of kids, but a noteworthy add to their labor contribution.
Others have investigated how some coping methods inside families have an effect on revival from a shock resulting from such an adversity. De Janvry et al (2006) explains that uncertain cash transfer accessibility before a disaster provide as a shelter for those who are affected, while those dependent and helpless people utilize as coping method an add to child labor, and savings in food and school expenses. Alpizar (2007) also discovers that access to proper economic services takes the edge off pessimistic outcomes from natural disaster upsets for farmers in El Salvador, as it leads to further proficient production.
On the other hand, a less urbanized region is the impact at local level. Yamano et al (2007) explain about industries and production. These writers makes use of region-wise data for employment and production, guessing that financial fatalities are not in proportion to the sharing of manufacturing activities and people attention, signifying that strategies to improve losses should be measured from a top order. Burrus et al (2002) also examined how low intensity typhoons can shock local financial systems from side to side interruption of actions. They exercise statistics from the local Chambers of Commerce surveys and as a result of their regularity the bang could be a decrease between 0.8 and 1.23 per cent of yearly production and up to 1.6 per cent of local employment.
Though, there is a slit in the study of how local communal indicators are exaggerated by natural events. This is significant to bring to the front as the effects give the impression of being stretch around all unlike points, macro, micro and local, and how strategies to deal with those upsets can be premeditated in a good way.
Whereas families emerge as the natural component of investigation for researching the consequences of natural disasters, it can also seem right to balance the study up as families react to risks are frequently influenced by the broader strategy framework. Certainly, households have substantial and insubstantial assets at their clearance, and their capability to preserve or gather together such assets in such situations will be produced by the arrangements and procedures for instance - governance and institutional planning, broader strategies and open circumstances at metropolitan and district level.
Additionally, the experience of family units to danger loss can and has been conventionally balanced up to top levels of aggregation (UNDP, 2008). It is the number of citizens situated in definite parts joint with the individual, material and ecological conditions of families and the regions where they live that forms their communal potential to deal with a natural disaster. For that reason, we refer to the community level of study while thinking of the inferences that dangers can have.
Governments have a tendency to go on board in various approaches to deal with natural happenings. In the past, they have usually reacted through disaster relief, but more lately there has been a propensity to highlight cash transfers as well. Even if both methods are adopted extra efficiency could be consummate by adopting danger diminution and improvement means that deal with the structural aspects which make families more uncovered to natural risks. Having system in position previous to the awareness of dangers is primary.
At the macro level, premature warning systems and the public disaster-preparedness agenda look as if mostly significant, so as sufficient economic assets to promote revival, over and above tax inducements for households or public to take on mitigation procedures.
Another type of protecting the value of material goods at the macro level could be through financial diversification. Increasing primary, secondary and tertiary sector activities along with spatial activities in the economy, can offer an open pool to multiply the risk of anguish danger losses, and extra prospects to amplify and steady profits. Equally, the concentration of financial and sector-wise activities would be reliable with condensed capability of families to administer and react to natural disasters.
Still, there is a set of insubstantial facts which might improve the family hard work to get through the outcome of natural vulnerability on them, just as adverse socio-economic opportunities. The political economy and organizational aspects of the situation where assets are positioned together with the system of belief, norm and ideas set in the activities of communities' members might bear out elementary while utilizing and mobilizing assets for confronting disasters. If possible, one should be capable to clarify how civilization and supremacy provision come into play when they act together with the broader surroundings of risks, assets and wellbeing results. However, most of these features will be tough to get into work empirically for the period of our technical study.
Flourishing coping against natural disasters is difficult to achieve in a situation of small efficiency, staled financial development, not having access to industrious possessions, deficiency of economic reserves and safety nets in place, and broad difference crossways geographic, financial, or tribal lines. Lack of health conveniences, remoteness and low rate of education may also complex these susceptibility. Consequently, the covariate life of various natural hazards and the policy-tempted macro circumstances upsetting the rate and likelihood of effectively coping with them might reflect unreliable welfare shocks across region and sub-region levels.
At last, societies can make worse these natural, site and practice-specific aspects through not making any investment in substantial and communal infrastructure at the household and district level (roads and bridges). In case of rural areas, these deficiencies can be multifaceted by a high frequency of hazards because of being covered hazard-prone areas, extending the vulnerability of families to experience any losses.
Although the impact a natural disaster is an outside factor, susceptibility of causes, making the shock of the event high or low, is not. Susceptibility to natural hazards is a composite subject, as it is determined by the monetary model, the phase of growth, current social and fiscal situation, coping means, risk evaluation, rate of recurrence and greatness of hazards, etc.
Lindell and Prater (2003) summarize the significance of shaping the impact and the influenced agents in natural hazards. First, that information is helpful for policy makers, as they can recognize the need for outside support and which may be extra effectual. Second, exact sections of affected can be recognized, e.g. how short income families are influenced, uniqueness of regions etc; and third, it may be also helpful for setting up backing for natural hazards and the possible penalty. They also summarize how the impact of natural hazards should consider other means.
One of the main questions concerning the impact of natural hazards on families or towns is how accidental they may be. Donner (2007) examined the effects of hurricanes in the US and figured out that the effects are not accidental, because some aspects such as ecological, society, demographic, and scientific, have an occurrence on the impact of such events. On the whole the flow of impact of natural hazards can be sketched as in Figure 1.
Figure 1. Model of Disaster Impact
Other aspect is how establishment have defined practices concerning natural events and how they systematize help in the outcome can also be determinant of the crash. Such as, Peacok and Girard (1997) explain how the revitalization process after tornado in Florida was determined more by governmental obstructions rather than lack of resources.
Limited Literature is available which studies the quantitative relationship between the economy and the natural disasters. Zarrar et al (2009) studied the impact of natural disasters on the Iran's Gross domestic product. They adopted a auto regressive distributed Lag model in order to study the impact. The findings showed that natural disasters have negative impact on the GDP per capita and on Per captia investment. The result of the model test was that investment had a positive impact on the economy while negative impact on GDP from the damages from the loss of Physical capital.
Macro economic variables determine the impact of these natural disasters on the long run economic growth. Aaron (2007) found that financial crises caused by these disasters hurt the long run growth through inflation. This inflation is the result of increased debt burden. Other reason for this inflation could be that central bank print excess notes to pay the external and internal debt. Also the tax collection is also affected which hurdles the government efforts in compensating the losses. However the loss in revenue is compensated by the help of the Loans and aid given by the international institutions. They include the World Bank, International Monetary Fund and the European Union. These loans and aid influence the economic growth in the short as well as the in the long run.
Pelling (2002) in his work identified that the most important macroeconomic impact of natural disaster can be studied by examining the inflation trends in the economy. More over the public expenditures by the government and the aid flowing as foreign direct investment influences the GDP growth rate. The used a comparative analysis technique of comparing different case studies to determine the macro-economic effects. These effects are measured by plotting the trends in GDP against macro economic factors i.e Inflation ,FDI and Loans.
The literature review discusses the direct and indirect impact of economic variables on the economy. However in this research work only the impact of macro economic variables is studied.
From the support of Literature review the macro economic variables which can be used to measure the quantitative impact of natural disasters on the GDP growth of Pakistan are Inflation, Internal and external debts, Foreign Aid and foreign direct investment flowing in the country.
In next section of research we will take into account the above macroeconomic variables with the purpose of concluding the impact of natural disasters on the economy of Pakistan.
In order to identify the macroeconomic effects of disasters, we suggest comparing a counterfactual situation ex-post to the observed state of the system ex-post. This involves assessing the potential trajectory (projected unaffected economy without disaster) versus the observed state of the economy. This contrasts with observing economic performance post-event and actual performance pre-event, as usually done in similar analysis. Our analysis requires projecting economic development into a future without an event. In short, the type of research would be purely Quantitative.
Sources of Data
Our two main sources of Data are:
The open-source EMDAT disaster database (CRED, 2008) maintained by the Centre for Research on the Epidemiology of Disasters at the Université Catholique de Louvain.
The proprietary Munich Re NatCat Service database.
Data type and Research Periods
Our sample consists of all major natural disaster events during 1950-2010. The sample is based on information from two databases and was compiled by Okuyama (2009) with the threshold for a large event defined arbitrarily to a loss exceeding 1 percent of GDP.One database is the open-source EMDAT disaster database (CRED, 2008) maintained by the Centre for Research on the Epidemiology of Disasters at the Université Catholique de Louvain. Primary data are compiled for various purposes, such as informing relief and reconstruction requirements internationally or nationally, and data are generally collected from various sources and, including UN agencies, non-governmental organizations, insurance companies, research institutes and press agencies. The other database is the proprietary Munich Re NatCat Service database, which mainly serves to inform insurance and reinsurance pricing. We focus on the monetary losses.
In both datasets, loss data follow no uniform definition and are collected for different purposes such as assessing donor needs for relief and reconstruction, assessing potential impacts on economic aggregates and defining insurance losses. We distinguish between sudden and slow onset events. Key sudden-onset events are extreme geotectonic events (earthquakes, volcanic eruptions, slow mass movements) and extreme weather events such as tropical cyclones, floods and winter storms. Slow-onset natural disasters are either of a periodically recurrent or permanent nature; these are droughts and desertification.
We broadly associate the loss data with asset losses, i.e. damages to produced capital. This is a simplification, as indirect impacts, such as business interruption, may also be factored into the data. Yet, generally, at least for the sudden onset events, analysts generally equate the data with asset losses, and an indication that this assumption can be maintained is the fact that loss data are usually relatively quickly available after a catastrophe, which indicates that flow impacts emanating over months to years are usually not considered. Losses are compared to estimates of capital stock from Sanderson and Striessnig (2009), which estimated stocks using the perpetual inventory method based on Penn World table information on investments starting in 1900 and assuming annual growth and depreciation of 4 percent.
Theoretical Framework and variables under consideration
Theoretical Framework to be used in this essay to explain Economical Impacts on Pakistan due to Natural Disasters.
Type of Hazard
The literature on the monetary impacts explained can be associated with framework above.
Independent Determinants of such impacts and dangers can be renowned as:
This variable is related to the type of Natural disaster/Hazard that jolts any part of Pakistan.
This variable deals with the geographical area and spatial scale of impact from the particular disaster.
This variable deals with the overall structure of the economy in the country and in particular region affected by the disaster (if needed).
This determinant deals with risks that might directly or indirectly affect the stage of the development of the country.
It is related to the current socioeconomic conditions in the country.
This takes care of the availability of formal and informal mechanisms to share risks in a particular part of the country.
The last four variables are related to economic susceptibility.
H0: Natural Disasters do not have any significant negative follow-on effects on the economy of Pakistan.
H1: Natural Disasters do have significant negative follow-on effects on the economy of Pakistan.
We use autoregressive integrated moving average models, also called ARIMA (p,d,q) (Box and Jenkins, 1976) for forecasting GDP into the future after the disaster event. ARIMA modeling approaches are chosen because they are sufficiently general to handle virtually all empirically observed patterns and often used for GDP forecasting (see for example Abeysinghe and Rajaguru, 2004). While such a type of modeling may be criticized for its black box approach (Makridakis and Wheelwright, 1989), it here serves well due to the large number of projections to be made and the difficulty identifying suitable economic model approaches.
The ARIMA process
Recall, an autoregressive process of order AR (p) can be defined as
x t = Ï†1x tâˆ’1 + Ï†2x tâˆ’2 +...+ Ï†px tâˆ’p + Îµt
A moving-average process of order MA (q) may be written as
xt =Îµ t +Î¸1Îµ tâˆ’1 +Î¸ 2Îµ tâˆ’2 +â€¦+Î¸ qÎµ tâˆ’q
and an ARMA(p,q) process, with p autoregressive and q moving average terms can be defined to be
xt =Ï†1xtâˆ’1 +...+Ï† p xtâˆ’ p +Îµ t +Î¸1Îµ tâˆ’1 +...+Î¸ qÎµ tâˆ’q
Where Ï† and Î¸ are parameters to be estimated and Îµ are white noise stochastic error terms. Now, let yt be a non-stationary series and define the first order regular difference of yt as
Î”yt = yt âˆ’ ytâˆ’1
or more generally using a back-shift operator denoted as Bk zt = ztâˆ’k
yt B d yt Î”d = (1âˆ’ )
An ARIMA (p,d,q) model can then be expressed as
yt q B t B B d Ï† p ( )(1âˆ’ ) =Î¸ ( )Îµ
B p Ï† p (B) = 1âˆ’Ï†1B âˆ’...âˆ’Ï† p
Bq Î¸ q (B) = 1âˆ’Î¸1B âˆ’â€¦âˆ’Î¸ q
The Box-Jenkins methodology (Box and Jenkins, 1976) is applied for determining the components of the ARIMA process; i.e. we test different ARIMA(p,d,q) models with p and q to be smaller or equal 4 (due to the limited amount of data) and estimate Ï† and Î¸ using Maximum likelihood techniques and the Akaike Information Criterion (AIC) as well as diagnostic checks to detect a suitable model. The data requirements were set thus that at least 5 observed data points are needed for projections into the future. This is the smallest number of observations which are needed to estimate ARIMA (4,1,4) models (however, the majority of the sample (greater 90 percent) has at least 10 data points).
Furthermore, all models are tested to be stationary (usually d=1 suffices to assure a stationary process) and all series are demeaned. To include uncertainty in the projections, also 95 percent confidence forecasts were calculated and analyzed. Forecasts into the future are performed with the selected models and then compared to the observed variables. Increases or decreases of GDP in future years are measured as a percentage increase or decrease to baseline GDP (i.e., baseline =100) which is defined to be GDP a year before the disaster event.
Furthermore, the differences between observed values and projected ones are calculated and called Diff(t), which indicates the percentage difference between the observed and projected value of GDP in year t. We focus on projections with a medium term perspective (up to 5 years into the future). This limitation is due to important data constraints for the ARIMA models within the sample and increasingly large uncertainties beyond the medium-term time horizon.
Disaster Impacts Projection on GDP
We project differences (in percent) between observed and projected GDP up to five years after a disaster event. A negative value indicates a situation where the projection surpasses the observation leading to a negative effect. The figure below charts out these differences for the years 1 to 5. Due to the heterogeneity of the data, it is not very surprising that the results are heavily skewed and as an average value the median should be looked at.
Figure: Differences between experiential and projected GDP
The mean, median, standard deviation as well as the skewness coefficients for the whole sample are shown as under.
summary results for difference.jpg
Table: Summary results for differences of experiential and predictable GDP levels
According to the skewness and standard deviation the results are asymmetric with a large spread. The results, however, clearly indicate a trend. All post-disaster years show negative values with an increasing "gap," indicating that "on average" one can expect negative economic follow-on consequences in the short-medium term, leading to a median reduction of GDP of about 4% points (of baseline GDP in to) in year 5 after the event.
We further test whether the differences are statistically different from zero and, due to non-normality of the data, used the non-parametric one-sample Wilcoxon test. The null hypothesis H0 is that the median is equal to zero, while the alternative hypothesis H1 is that the median is smaller than zero. Next Table shows the p-values for this test using the projections.
p-values for the test.jpg
Table: p-values of the Wilcoxon test
Clearly, the null hypothesis is rejected for all post-disaster years, and therefore one can conclude that there are significant negative follow-on effects. Furthermore, also 95 percent forecast confidence intervals to include uncertainty of the projections within the analysis are used. Additionally, also sub-sample analysis to include uncertainty regarding the influence of multiple occurrences of disasters is performed. The sub-sample is chosen so that only events are considered with no other event (with losses higher than 1 percent of GDP) occurring 5 years before and 5 years after the event considered in the sample. Results related to this sub-sample corroborate our findings on the negative economic Consequences.
As a next step, we test key variables, particularly those relating to economic vulnerability, as to their suitability as predictors for explaining the differences of projected and observed GDP in year 5 post event.
We first use multivariate models, then employ general linear regression modeling approaches (GLM) using fixed factors, covariates and mixed models as independent variables and Diff(5) as the dependent variable.
First, exploratory analyses are performed. Pearson correlation analysis between the continuous variables and Diff(5) leads to significant results with capital stock losses (correlation of - 0.317, p-value 0.000). Interestingly, such a correlation cannot be found for GDP losses, indicating that capital stock losses may serve as a better predictor. Furthermore, total population (correlation of 0.200, p-value 0.013) as well as aid is found to be significant (correlation of 0.187, p-value 0.032).
Descriptive statistics for Diff(5) within sub-groups according to the income, indebtedness, SIDS and hazard type indicators were considered next. Using the income indicator, the mean of Diff(5) for all sub-groups exhibits negative values. Also, with regards to the indebtedness indicator, there are negative mean values. As to the type of hazard, storms and earthquakes as well as droughts show negative values. In addition, additional "layers" are examined; however, the number of observations quickly becomes very small, and therefore average values should be treated with caution. Overall, however, a general interpretation of these results is difficult as no clear trend can be discerned. Therefore, we use regression models in the following.
Multivariate regression model
A forward stepwise regression procedure to detect the most important independent variables from table 5 for the dependent variable Diff(5) is employed. In the first round of the iteration, the independent variables are each added to the starting model, and the improvement in the residual sum of squares for each of these resulting models is calculated. Next, for each model the p-value for the change in the sum of squares is determined. The variable associated with the lowest p-value is the first model candidate. If the p-value is below 0.1 (significance at the 10% level), then this model is taken. In the next round, this model will be the starting model and the subsequent rounds follow the same Procedure as the first. The forward procedure stops if the lowest candidate p-value in subsequent rounds is not lower than 0.1. Next Table lists the initial model 1 and the final model 2.
Multivariate Regression results.jpg
Table: Multivariate Regression Results
The final regression model is already reached at step 2, which indicates that the selected variables already have good predictive power. Regarding the fit of the model, while not very satisfactory from a predictive point of view (R square is around 19 percent); two variables are significant at the 5 percent level: capital stock losses (p 0.007) and remittances in the disaster year (p 0.036). While the capital stock loss variable has a negative coefficient suggesting a larger direct shock will lead also to larger negative GDP effects, the remittances parameter has a positive value suggesting that stronger remittances inflow will decrease negative consequences. In line with the exploratory analysis, the direct impacts variable (capital stock losses) seems to be a strong predictor.
To summarize, the size of the direct impact (losses) strongly predicts the magnitude of follow-on effects. The fact that it significantly explains the variation in Diff(5), which is based on the time series approach, seems to suggest some validity of the regression results so far. However, interdependencies between variables are not used in this model and are looked at next.
General linear regression model
A general linear regression modeling approach, which also allows for inclusion of interdependencies of several indicator variables, is used next. The model is restricted to selected key variables first identified in the literature review, the further limited by the exploratory analysis. The model has 4 fixed factors (indicators), including country income group, indebtedness, countries relating to SIDS and hazard type (shown as under).
Indicators used for GLM Regression.jpg
Table: Indicators used for GLM Regression
We thus define different sub-samples according to these indicator variables. For example, the whole sample can be split by the income group indicator into 3 sub-samples, the high income sub-sample, the middle and low income subsamples. As mentioned, the limitation of higher order effects is mainly due to the decreasing number of observations within sub-groups. Table below shows the tests for the different main factors as well as their interactions with the indicators. Whereas a least squares criterion is used to obtain estimates of the parameters models.
GLM Regression Findings.jpg
Table: GLM Findings: Test of Effects (Between Subjects)
As to the model specification, the model itself is significant (p-value 0.021) with about 83 percent of the variation explained (R-square 0.829), which is quite satisfactory. Significant variables (p-value smaller than 0.05) include aid (in percent of import and exports), capital stock loss , aid (in percent of GNI), remittances, and interactions of capital stock losses and remittances with some of the other indicators, such as indebtedness, income and hazard.
GLM models usually have systematic co-linearity between the dependent variables and therefore the impact of one single dependent variable is not captured within the parameter estimate. Hence, the variables found to be significant in above table are analyzed according to scatter-plots, profile plots as well as comparisons of averages. In line with the observations made above the results lead to the conclusion that especially the direct impact, measured in percent of capital stock loss leads to negative long-term consequences. Remittances as well as various forms of aid decrease the negative effects, however, not as strongly as direct losses. Overall, we also find that in general natural disasters can be expected to entail negative consequences in the medium term (five years after an event). As in the multivariate regression, adverse macroeconomic effects can be related to the direct impact in terms of asset losses. Higher aid rates as well as higher remittances seem important in lessening the adverse macroeconomic consequences.
There is an ongoing debate on whether disasters cause significant macroeconomic impacts and are truly a potential impediment to economic development. Given the divergent positions, this analysis aimed at better defining a sort of "middle ground" identifying circumstances under which disasters have the potential to cause significant medium-term economic impacts. In a medium-term analysis, comparing counterfactual GDP derived by time series analysis with observed GDP, natural disasters on average lead to significant negative effects on GDP. The negative effects may be small, yet can become more pronounced depending on the direct impact measured as a loss of capital stock. Using regression analysis, we further test a large number of predictors and find that higher aid rates as well as higher remittances importantly lessen the adverse negative macroeconomic consequences, while direct capital stock losses had the largest effects in causing adverse GDP effects. A number of other variables, such as country debt, seemed promising in terms of explaining the variability of GDP, yet it was not possible to further refine the analysis due to small number of observations. Beyond these findings, final conclusions are difficult to draw and the uncertainty in loss data and socioeconomic information has to be acknowledged. One reason is the challenge associated with determining the size and type of impacts as well as identifying additional key predictors.
For example, particularly for middle and high income countries, capital stock losses probably play a minor role and other variables such as human and natural capital increasingly become important. Obvious steps for improving the analysis should thus focus on increasing the sample size and quality of data generated, particularly as relates to those lower income and hazard-prone countries supposed to be most vulnerable and of highest interest for the analysis. Finally, another key extension of the analysis would be to also look at disaster impacts on human and environmental capital and its economic repercussions, in isolation as well as in conjunction with produced capital.
Impact of Natural Disasters on the Economy of Pakistan
Ms Zehra Raza
Dawood Ahmed Chaudhry
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