The influence of climate and the role of water security on economic growth are topics of growing interest. Few studies have investigated the potential role that climate hazards, which water security addresses, and their cumulative effects have on the growth prospects for a country. Owing to the relatively stationary spatial patterns of global climate, certain regions and countries are more prone to climate hazards and climate variability than others. For example, El Nino/Southern Oscillation patterns result in greater hydroclimatic variability in much of the tropics than that experienced at higher latitudes. In this study, we use a precipitation index that preserves the spatial and temporal variability of precipitation and differentiates between precipitation maxima (e.g. floods) and minima (e.g. droughts). The index is a more precise instrument for hydroclimate hazards than that used in any previous studies. A fixed effects, for year and country, regression model was developed to test the influence of climate variables on measures of economic growth and activity. The results indicate that precipitation extremes (i.e. floods and droughts) are the dominant climate influences on economic growth and that the effects are significant and negative. The drought index was found to be associated with a highly significant negative influence on gross domestic product (GDP) growth, while the flood index was associated with a negative influence on GDP growth and lagged effects on growth. The flood index was also found to have a negative effect on industrial value added in contemporary and lagged regressions. Temperature was found to have little significant effect. These results have important implications for economic projections of climate change impacts. Perhaps more important, the results make clear that hydroclimatic hazards have measurable negative impacts, and thus lack of water security is an impediment to growth. In addition, adaptation strategies should recognize the importance of managing hazards given the identification of precipitation extremes as the key climate influence on historical GDP growth.
Growing awareness of the possible harmful effects of climate change has focused the attention of policy makers on the potential economic impacts of climate. Economists and climate scientists have made use of the latest model-based projections of changes in temperature and precipitation produced by a suite of general circulation models (GCMs) for this purpose. The results of these studies often range from large negative impacts to relatively modest impacts when aggregated at a global scale [1–3].
Interestingly, economists and others have been concerned with the link between climate and economic wellbeing since well before the recent attention spawned by climate change. In the late nineteenth century, the British government charged successive scientists in India with predicting the quality of monsoon rains in an attempt to mitigate the devastating effects of drought on the subcontinent. One of these scientists, Sir Gilbert Walker, pioneered the use of correlation coefficients and used them to establish the Southern Oscillation index, a pressure differential between Darwin, Australia, and Tahiti that still serves as an indicator of the El Nino/Southern Oscillation (ENSO).
More fundamentally, societies have seen investments in infrastructure as economically efficient responses for addressing hydroclimate risk. Primarily through infrastructure, many countries have achieved protection from most but not all hydroclimate hazards. This attribute of maintaining hydroclimatic risk at tolerable levels has been recently termed ‘water security’ . With some measure of water security achieved, the return on investments beyond water security becomes more attractive and attention shifts elsewhere. Perhaps consequently, there is less conviction especially in developed countries that infrastructure investments for water security are worth the economic, ecological and socio-cultural costs.
However, there is reason to believe that water security is not achieved in many nations, and the consequences are greater poverty in the poorest nations. Current understanding of the Earth's climate makes clear that different regions of the world differ not only in terms of their mean climate conditions, for example the average temperature and precipitation, but also in terms of the variability of the climate. Parts of the world are more prone to large swings in the year-to-year amount of rainfall they receive, or the average temperature that occurs. As a result, there are places that have higher, and in some cases much higher, exogenous exposure to droughts and floods. To achieve water security under these conditions is likely to require greater investments in infrastructure .
Sub-Saharan Africa (SSA) provides an example where climate conditions are particularly challenging owing to the frequency and severity of hydroclimatic extremes. In an earlier study, the effects of climate extremes on economic growth in SSA were found to be negative and substantial . The analysis used a fixed effects approach that accounted for factors that show little year-to-year variation, like institutions and infrastructure, allowing the effects of climate extremes, which show strong year-to-year differences, to be isolated. Otherwise, the effects of water security in countries that have achieved it would mitigate the climate signal and would also potentially conflate climate effects on growth with other non-climate-related factors, for example poor institutions. The results showed that the climate effects were significant, particularly in the case of drought, leading to reductions of per capita economic growth of the order to 2–4% per year for each percentage point increase in country area affected by drought. Investments to improve water security are likely to be necessary and rewarding.
In this paper, we expand the analysis to assess on a global basis whether climate variability is an impediment to economic growth and a contributor to poverty levels in countries where exposure to variability is high. From an episodic standpoint, the direct effects of a drought on a region or of a flood on a river basin are fairly obvious. These direct effects can be, and often are, quantified in terms of the damage they cause. However, do these effects accumulate and contribute to a general reduction of welfare or a drag on economic growth? Perhaps surprisingly, previous studies do not provide a convincing answer to this question.
The analysis makes use of a new rainfall statistic that captures climate extremes in rainfall and their spatial extent that was previously applied in the SSA study . The econometric model is applied to national-level growth and poverty statistics representing most countries of the world. The results of this study will have significant implications for evaluating the costs of climate change and the continuing dialogue on the best means of adapting to climate change. Typically, climate change impact analyses have focused on changes in mean conditions, effectively ignoring the damages or benefits that might be associated with changes in variability. Furthermore, adaptation planning often proceeds without an understanding of the climate effects that have the strongest impact on society. As a result, temperature is usually used as the lone predictive variable. Through this analysis, we provide some evidence that climate variability, and particularly extremes of precipitation, are the factors that should be the foremost in adaptation planning.
2. Review of the influence of water security on economic development
The hypothesis that lack of water security has had negative effects on economic growth derives from the ample evidence of direct negative impacts of hydroclimate extremes on society locally. While there are other aspects of water security, including water quality and access to clean water and sanitation, the present analysis focuses on the effects of droughts and floods. In the USA, drought has been estimated to be the most costly form of natural disaster, with an annual average cost of $6–8 billion . Drought is also the leading single cause of deaths due to natural disasters, representing 50% of the global total . The devastating impacts of drought have caused massive famines, such as those in 1980s Ethiopia and nineteenth-century India, and have led to mass migration, as in the Dust Bowl of the 1930s USA and repeatedly in Northeast Brazil, and possibly contributed to the conflict in Sudan recently. Floods affect societies in different ways and at different scales of time and space, but with similar devastation. Floods destroy homes and infrastructure, the ‘physical and social capital of society’ , and are frequently followed by disease. In China, floods on the Yangtze River in the early twentieth century repeatedly killed hundreds of thousands, and flood damages in 1998 were estimated at $30 billion. Damages due to flooding in 1998 on the Ganges and Brahmaputra in India, Bangladesh and Nepal were estimated at $5 billion, and the 2010 floods on the Indus caused $10 billion in damage .
Country-level studies provide evidence that the direct damages due to hydroclimate extremes can have a significant effect on national economies. An investigation of drought and flood impacts on Ethiopia using an economy-wide model found that their effects reduced economic growth by greater than a third . A study of Kenya found that the variability effects due to ENSO between 1997 and 2000 resulted in losses that ranged from 10 to 16% of gross domestic product (GDP) as a result of the associated floods and droughts .
The direct effects of these extreme events on a society or a region are straightforward to quantify. There is also evidence of indirect effects on societies due to repeated exposure. The most relevant to this analysis is the evidence that repeated exposure to climate extremes leads to risk aversion and a counterproductive reduction in investment, leading to a reinforcing negative wealth cycle or ‘poverty trap’ [13,14]. This may be described as a hidden cost of a lack of water security. This evidence is largely derived from information gathered at the household and village level. For example, Rosenzweig & Binswanger  studied household wealth, investment choices and rainfall for six villages in India and found that risk aversion due to rainfall variability negatively influenced choices, leading to less profitable investments. Farmers are less likely to make investments in fertilizers or high-value crops if there is a significant probability that the investment will be lost owing to insufficient rainfall or a flood. As a result, there are major opportunity costs imposed by climate variability that impedes their ability to accumulate savings that might see them through the next shock. They remain trapped in a low-level subsistence equilibrium. Dercon  identified rainfall shocks (droughts) as the primary reason that households fell into poverty in a study of six Ethiopian villages. Income growth was reduced by up to 20% relative to growth without the rainfall variability. The level of wealth was found to play an important role in the climate effect. In a study of six villages in Burkina Faso, the average farmer experienced food shortfalls in 1 out of 5 years owing to income variability associated with rainfall, whereas the poorest farmers (the bottom quartile in land holdings) experienced shortfalls in 4 out of 5 years, and the top quartile had shortfalls in only 1 out of 10 years . It was estimated that poor farmers forego about 18% of their income to buffer against climate risks, whereas the better off farmers forego only 0.4% of income to buffer risk .
At the national scale, it has been hypothesized that poor countries lack the minimum level of infrastructure needed to engender economic growth . By any measure, the inventory of water infrastructure in poor countries is far below the level in most developed nations and probably far below what is needed to achieve water security when taking into account the distinct climate variability that each nation faces . The poverty trap that affects individuals through underinvestment may likewise apply to nations, where the resources needed to invest in infrastructure are instead spent on recovering from the last hydrologic extreme. The problem could be exacerbated if rich nation donors avoid the topic of infrastructure owing to concerns related to risk.
These studies provide anecdotal evidence that lack of water security impedes economic advancement and raise the question of whether water security is necessary for economic growth. However, do the direct and indirect effects of hydroclimate hazards aggregate to a detectable signal that influences economic growth at the scale of national economies? Few studies address this question directly. Studies that have attempted to quantify the effect of climate on economic growth have almost entirely focused on changes in mean climate conditions [1–3]. As Nordhaus describes ‘… current theories and empirical studies of economic growth give short shrift to climate as the basis for differences in the wealth of nations’ , pp. 3511–12. Other studies, while acknowledging the potential impact of climate and variability, have classified it as a subcomponent of ‘geography’ . Typically approached using a cross-country analysis method, the results were generally unable to specify significant geographical effects that could be distinguished from other institutional factors [20,21].
A small number of studies have begun to highlight the importance of year-to-year variability or changes in climate on economic growth. Brown & Lall  used statistics of rainfall and temperature variability in a cross-country analysis of economic level, finding that poor countries tended to have higher levels of precipitation variability. Dell et al.  evaluated the effects of annual variations in precipitation and rainfall over the previous 50 years as a way to estimate potential economic impacts of climate change. Using national-level economic and climate data in a global assessment, their results indicated that higher temperatures had negative consequences on poor countries, whereas there were no climate effects on wealthy countries. The effects on poor countries were not limited to agricultural production, as they found industrial output, investment growth and political stability to all be impacted. Several recent studies have investigated the effects of climate variability on economic growth in SSA, where the largely agrarian-based economies may be particularly vulnerable owing to low levels of infrastructure and lack of large-scale insurance mechanisms. In a review of a wide range of economic data, Christiaensen et al.  found that rainfall variations and ill health have profound effects on poverty, resulting in a need for social provision of protections against such shocks. Barrios et al.  provide a broad overview of the ways in which rainfall affects economic activities in Africa. Their empirical investigations of agricultural production and GDP growth found that the decline in rainfall that occurred between the 1960s and 1990s in much of SSA was a major contributor to the reduced agricultural production rates and growth rates during that period . According to their estimates, the rainfall decline accounted for a 9–23% drop in per capita GDP in SSA relative to levels without such decline in rainfall.
A common drawback in previous studies that attempt to assess climate impacts empirically is the form of the precipitation data that are used. As socioeconomic data are most widely available at the national level, climate data must also be aggregated to national spatial scales for use in analysis. Typically, a spatial average of rainfall and temperature over a given country's national borders is used. This averaging introduces a systematic bias in the resulting rainfall and temperature data owing to the smoothing effect that averaging causes. The calculation of a spatial average reduces the variability that is present across a country. As a result, it underestimates the climate challenges that countries face, especially for larger countries. The bias is worse for precipitation than for temperature, as precipitation tends to be more variable in space.
While the bias may seem trivial, the way in which precipitation anomalies are expected to affect economic indicators amplified its significance. In typical economic systems that might be affected by rainfall, for instance agricultural production, small deviations from normal amounts are likely to have minor or negligible impacts, whereas large deviations may have very large impacts. This nonlinear response makes the use of spatially averaged rainfall problematic. The calculation of a spatial average over a country means that a small deviation below the normal rainfall can have the same value as results from a rainfall pattern of large deviation over a small part of the country and normal rainfall over other parts. An extreme example is the case where part of a country experiences drought while other parts receive normal or above-normal rainfall. The resulting spatial average may show normal rainfall. In addition, when used in regression analyses, there is an implicit assumption of symmetry in the effects of above-normal and below-normal rainfall. This assumption is not supported by the evidence of how such anomalies impact society. In addition, previous analyses use calendar year precipitation, which is not appropriate in tropical climates where the rainy season occurs over the end of the year, splitting a single season between 2 years. It is perhaps not surprising that rainfall rarely shows up as a significant explanatory variable when spatial averaging is used.
This analysis is distinct from previous work in two important ways. First, we explore the effects of hydroclimate hazards instead of the mean conditions that were the subject of previous studies. Second, the index used more effectively instruments precipitation variability than the country mean or population weighted mean used in previous studies. In this analysis, we use a precipitation statistic that preserves the spatial signal in rainfall by calculating the percentage of a country that falls below or above thresholds based on deviations from the long-term average. In doing so, we also separate and treat independently the effects of positive and negative precipitation anomalies. This allows the nonlinear effects of precipitation variability to be effectively investigated. The statistic, weighted anomaly standardized precipitation (WASP) , is discussed below.
3. Empirical methods
In this analysis, we use fixed effects regressions with economic indicators as dependent variables and climate data as independent variables to attempt to diagnose the economic effects of climate as manifested at the national level. In doing so, we use a precise measure of precipitation variability that has qualities that make it superior for identifying associated impacts than other methods typically used, such as spatially averaged or population weighted precipitation. Precipitation and temperature data were extracted from the New et al.  gridded 0.5° dataset. Calculation of national temperature and precipitation follows the usual methods of spatially averaging the annual average over the domain of each country. The data are available for 1901–2003. The calculation of the WASP indices, which are used to preserve the spatial and temporal variability of precipitation, begins with the calculation of the WASP time series for each 0.5° grid cell. The WASP index is based on deviations in monthly precipitation from their long-term mean, and summed and weighted by the average contribution of each month to the annual total, according to the following formula: 3.1In (3.1), Pi and are the observed precipitation in the ith month and the long-term average precipitation for the ith month, σi is the standard deviation of monthly precipitation for the ith month and is the mean annual precipitation. The index is calculated over N months; we use N=12 to capture annual precipitation anomalies. The index indicates rainfall anomalies as measured relative to the typical rainfall for a given month. Next, in order to produce a national-level value from the gridded values, a threshold level is designated (for example, 1 s.d.) and the total number of cells above and below those thresholds is counted.
This produces a measure of the portion of a country that is experiencing anomalously dry (WASP(−1)) or wet (WASP(+1)) over the time period measured. As Lyon & Barnston  show, the result for WASP(−1) is well correlated with drought indices, such as the commonly used Palmer drought severity index . The WASP(+1) index indicates a period of anomalously wet conditions but may not indicate flood events. Floods can occur on short time spans that are not captured by this index, which is calculated with monthly data. The WASP(+1) may capture flooding events caused by longer periods of rainy conditions that saturate soils and lead to intense flooding events over shorter time periods. Further details of the creation of the WASP index and its use in climate analyses can be found in Lyon & Barnston .
(a) Fixed effects regressions
To conduct an assessment of countries' historical sensitivities to climate variation, we use the data described above in regressions within several different specifications, including regressions with country fixed effects, regressions with year and country fixed effects together, regressions with a 1-year time lag and country fixed effects, and regressions with a 1-year time lag and country and year fixed effects.
For the purposes of this paper, we focus on four primary livelihood indicators as our outcome variables: (i) GDP growth, (ii) agricultural GDP value added, (iii) industrial GDP value added and (iv) poverty headcount ratio at national poverty line (percentage of population). The data used for this study cover the period 1961–2003.
We also perform these regressions using both fixed effects and random effects. The specifications for these regressions are shown below.
(b) Country fixed effects
Using fixed effects with a linear model, we de-mean the variables to remove the time-invariant unobserved characteristics that are correlated with the other regressors in the equation. In these regressions, such time-invariant characteristics include, for example, the geographical characteristics of a country.
We begin with a basic fixed effects regression using the panel data for all 133 countries included in our sample: where Y it represents a livelihood measure of country i at time t, Xit represents climate measures for country i at time t, αi is the fixed effect, and therefore represents the sum of all time-invariant aspects of country i, and ϵit represents time-variant factors, which are typically not known by the countries before the time period occurs. We can also add controls for other variables, such as mean annual temperature and precipitation for country i. The unobserved country effects are coefficients on dummy variables for each country.
If we could observe all of the time-invariant country characteristics, then we could use a single cross-section regression of the livelihood indicators on the climate variables. But in such situations, we often cannot observe all of the relevant time-invariant country characteristics, and therefore cross-sectional estimates can be inconsistent. This is the benefit of using fixed effects instead of cross-country regressions; we control for the fact that the climate variability might depend (at least to some extent) on the time-invariant characteristics, which would therefore be correlated. For example, previous cross-sectional studies have encountered difficulty distinguishing between climate effects, local geographical characteristics and institutional effects [20,21].
Standard errors are clustered at the country level. Clustering the standard errors at the country level allows for potential correlation between observations for any given country at different times. Without clustering the standard errors, we may be overstating the relationship, and the significance of such a relationship, between variables included in the analysis.
These regressions are performed for each of the four above-mentioned outcome variables: (i) GDP growth, (ii) agricultural GDP value added (%), (iii) industrial GDP value added (%) and (iv) poverty headcount ratio at national poverty line (percentage of population).
In addition, these regressions are then repeated, using 1-year lagged values of independent variables on the right-hand side of the equation: For example, these regressions estimate the impact of last year's climate conditions on this year's GDP growth.
(c) Country and year fixed effects
In these regressions, we start with the same base regression as before, but we now add a year effect, Φt, to the fixed effects regression described above. Standard errors are clustered at the country level: The unobserved year effects are coefficients on dummy variables for each year included in the panel of data. As with the country fixed effects regressions, we repeat these regressions using 1-year lagged values of the independent variables on the right-hand side of the equation. The country and year fixed effects model is the most conservative model, as it can account for factors that affect multiple countries in the same year. This would eliminate the effect of a regional currency crisis, which should not be included, but also weaken the influence of a regional drought, which should be included.
4. Discussion of results
The regressions of economic statistics were conducted to attempt to identify whether there is evidence of a climate signal on the economic activity indicators of the nations of the world. The results are reported in tables 1–6 and are discussed below. The tables show the regression coefficient and the standard error in parentheses for each climate variable. Statistically significant regression coefficients are indicated with asterisks. Table 1 presents the summary results for each of the dependent variables for the most robust model specification, fixed country and year effects.
GDP per capita growth is the most widely used and available measure of economic growth at the national level. Here, the analyses were conducted with data from approximately 180 nations. The most striking result of the analysis of GDP growth is the negative influence of the WASP(−1) (moderate drought) statistic at the 99% level of statistical significance. This result was consistent in all regression specifications. Other results varied by specification. We note that the variance in the dependent variables explained is rather low in all models, implying that there is much that the independent variables do not capture. While not all details of the results are shown here, they are all available in an earlier report .
The results from the specification with fixed country and year effects are presented in table 2. This is the most robust model specification, and therefore the most meaningful results. The model with temperature and precipitation as the two independent variables showed temperature to be significant at the 95% level and precipitation at the 90% level. Interestingly, the signs of the regression coefficients were positive in both cases. The sign implies that a warmer year coincides with higher economic growth for the countries of the world, and similarly for (more) precipitation. However, when the WASP variables were included, temperature was no longer significant. Instead, the WASP(−1) variable was significant at the 99% confidence level and the WASP(+1) variable was significant at the 90% level. In both cases, the regression coefficients were negative, indicating that a greater area of a country in drought (WASP(−1)) coincided with less (or negative) economic growth. The coefficients indicate that a 1% increase in the fraction of a country's area undergoing drought causes a 2.7% reduction in GDP growth for a given year. More area with anomalously high rainfall or flooding (WASP(+1)) also coincided with reduced economic growth. In this case, a 1% increase in the area of a country experiencing high rainfall coincides with a 1.8% reduction in GDP growth. These results support the hypothesis of the economic importance of hydroclimate hazards, and consequently water security.
The results for the country fixed effects are reported in table 3. They are similar to the results with country and year fixed effects. With country fixed effects only, events that affect many countries in a given year but are not related to the country in particular, say a currency crisis that affects a region, are not accounted for. However, drought can affect multiple countries in a given year owing to its often regional or larger span. For example, droughts caused by ENSO have a global pattern that affects different parts of most continents. Controlling for year fixed effects may cause an underestimation of the drought effect. Therefore, we generate these results with only country fixed effects.
Again, the WASP variables were significant, with signs that were consistent with expectations. The WASP(−1) variable was again significant at the 99% confidence level. Similarly, WASP(+1) was significant at the 90% level. Each had negative regression coefficients and the WASP(−1) coefficient is larger (−2.9) than when year fixed effects were controlled for. Temperature was not significant when the WASP variables were included. However, when temperature and precipitation were included as the only independent variables, precipitation was significant but temperature was not. The regression coefficient for precipitation was positive. This result implies that the significance of national average precipitation and temperature is sensitive to specification and does not provide much confidence in the meaning of the statistical significance.
The regressions on the agricultural value added for the nations of the world provided our most difficult interpretation of results. Overall, in our most robust model specification, none of the climate variables were statistically significant. In some specifications, temperature and the WASP(−1) variable were significant. This is probably due to the selection of agricultural value added as the dependent variable. As this is a percentage of a nation's GDP, its value can increase even when actual agricultural GDP decreases, if total GDP decreases at a greater rate. For these reasons, we recommend caution in the interpretation of these results.
With fixed effects for the country and the year (table 4), the results of regression on temperature and precipitation as the independent variables showed temperature to be significant at the 95% confidence level. Somewhat strangely, in this case, the regression coefficient was negative, which is opposite in direction to the effect on per capita GDP growth. When the WASP(−1) variable is included with temperature, the WASP variable is significant at the 95% level while temperature is no longer significant. The sign of the regression coefficient for the WASP(−1) variable is again negative, which is consistent through all model specifications. When the WASP(+1) is included with WASP(−1) and temperature, none of the variables retain their significance. This is the result reported in the summary of table 1. The case of the fixed effects for country only was similar (not shown).
The regression analysis using industrial value added and poverty headcount as the dependent variables did not yield any statistically significant results (not reported here). In the case of industrial value added, this represents a marked difference with the findings of the SSA-only analysis. In that case, industrial value added was sensitive to drought. The large percentage of electricity generated through hydroelectricity and the portion of industrial activity that is related to agriculture are the likely causative links. However, using the global dataset, the wide heterogeneity of industrial activity and energy sources reduces the effects of climate on industry in most developed countries. The analysis of poverty headcount was not informative owing to the small sample size (not reported here). For most countries, only a small number of observations were available.
(a) Lagged regression results
Lagged regression analyses were conducted using the data for dependent variables for the year that follows the values of the independent variables. These analyses are an attempt to identify impacts that might be delayed in their effect on economic activities. For example, when a drought hits, a household may be able to maintain a portion of their consumption through the year of the drought through accessing some form of savings. But in smoothing consumption in the current year, there is a negative impact in their productivity in the following year. It may be a reduction in their ability to invest in productive activities (e.g. fertilizer or other inputs) or a reduced appetite for investment after suffering a year of loss. Therefore, the impacts of a climate anomaly, for example drought, may be expected to affect the current year and the following year's economic activities negatively. This effect is investigated through the lagged regressions.
In general, the results of the lagged regressions are consistent with the results of the simultaneous regressions. For per capita GDP growth (table 5), the WASP variables, both drought and flood, were the sole statistically significant factors. The signs of the regression coefficients were negative in all cases, another sign of consistency. The results for WASP(+1) were slightly stronger (99%) than those for WASP(−1) (90%) in the lagged regressions, a reversal in ordering from the concurrent regressions. This result may indicate that flood effects, which WASP(+1) may be indicative of, have greater longer-term effects owing to their destructive capacity. For example, while a drought may reduce savings and investment, a flood can destroy savings and capital, damage infrastructure, for example roadways, and contribute to disease, for example cholera. All told, these impacts may have longer-term effects on economic activities than a drought.
The lagged regression results for agricultural value added (not reported) were also generally consistent with the concurrent regressions except for the significance of the variables. Here, temperature was weakly significant (90% level) while no other variables were significant.
The lagged regressions with industrial value added provide further credence to the impacts of flood on economic activities (table 6). Here, the WASP(+1) variable stands out as the sole significant explanatory climate variable over all model specifications. It is significant at the 95% level and the regression coefficients are all negative. Damage to capital and infrastructure is probably the cause of the negative impact of excessive rainfall. This result is especially significant given the large body of anecdotal evidence of major flood damages and expected economic effects and yet the non-existent evidence of these effects in any multi-country econometric analyses. This is due to the lack of a consistent database of flood events and flood hazard exposure that is climate-based. Instead, existing flood databases are collected by impacts, and thus are biased in reporting flood significance, not by their hydrologic magnitude, which is largely exogenous, but rather by their economic or social impact, which is largely endogenous. As a result, the economic effects of flood risk and effects are likely to be underestimated in the literature. This result is the first that the authors are aware of that demonstrates a statistically significant flood effect on national-level economic variables in a multi-country analysis.
5. Policy implications
This analysis of hydroclimate effects on economic activity benefited from a more precise measure of precipitation variability than has been used in previous analyses. The current literature conflicts on the significance of climate variables that affect GDP growth. The results of this analysis strongly support the hypothesis that anomalously low rainfall (drought) and anomalously heavy rainfall (possibly associated with flooding) are the key climate effects on GDP growth, overwhelming any temperature effect. This implies the importance of water security, that is, protection from these negative effects, as a pathway for engendering economic growth.
Future climate change may cause water security to be a necessary condition for economic growth. Projections of climate change impacts on GDP growth often use projected temperature changes from GCMs applied to an estimated temperature effect. The results of this analysis imply that the greater economic impacts from climate change will be due to changes in precipitation variability. Most significantly, an increase in drought area in a country of 1% was found to cause a 2.8% reduction in annual GDP growth rate. Unfortunately, estimates of precipitation from GCM tend to be less skilful than projections of temperature. The projections of mean changes in precipitation are often characterized by wide ranges and, in some cases, disagreement on the direction of change. The ability to project variability of rainfall is even less skilful than mean projections of temperature or precipitation. Lacking improved ability to project future precipitation variability, investing in water security is likely a low regrets climate change adaptation given that these results imply that even current climate hazards are an impediment.
While estimating future exposure to drought and flood remains problematic, it is clear from these results that historical levels of precipitation variability already impede economic progress. Managing this hydroclimatic risk in the future is likely to be more difficult because of changing climate conditions that may alter the historical frequency of floods and drought. Nonetheless, these results imply that water security would aid economic growth under current climate conditions and can be expected to do so in the future as well.
In many developing nations, investment in water infrastructure is needed. The challenge, however, is determining how to invest to achieve water security in the present and future. In many developing nations, there is a significant gap with the levels of infrastructure that wealthier nations possess. The prospect of climate change raises concerns that infrastructure may be stranded by changing climate conditions. Richer nations may also feel that infrastructure investment is by nature too risky. The greater risk, however, probably lies in facing continued climate variability and change without investing. In addition, there are important components of a comprehensive strategy for water security that go beyond infrastructure. For example, many water-scarce basins successfully manage current droughts through strong institutions that facilitate the economic allocation of water. Hydrologic monitoring and forecasting provide the foresight to anticipate and mitigate climate extremes. The exact balance of these sorts of investments that best achieves water security is an open question.
The analysis presented in this paper was conducted to identify the influences of hydroclimate hazards on economic activities in the countries of the world. Based on previous research and the published literature, precipitation, temperature and statistics of precipitation extremes (droughts and floods) were investigated as explanatory variables in fixed effects regressions of economic activity statistics over several decades. Several model specifications were used to increase the robustness of any findings derived from the regression results. The analysis resulted in several significant findings.
The most important result of this analysis is the identification of drought (WASP(−1)) and excess precipitation (WASP(+1)) variables as the most significant climate factors in the regressions on GDP growth. The results suggest that a 1% increase in drought area causes a 2.8% reduction in economic growth per year. A 1% increase in flood-impacted area causes a 1.8% reduction in economic growth in that year, with possible important lagged effects. The results are important, for several reasons. First, they contradict the findings of previous studies that identify temperature as the most influential climate variable on GDP growth. Previous studies have used a coarse measure for precipitation that may obscure the precipitation effect. The WASP variables preserve the spatial variability, and the nonlinearity and asymmetry of precipitation effects, and hence better capture their impacts on economic activity. The results are consistent with a previous study using these variables in SSA .
Second, the prevailing thinking on climate change presumes that temperature is the key climate impact on economic activities. Estimates of temperature effects on economic growth are typically used in static projections of the economic impacts of climate change. This analysis shows that those projections are likely to be too simplistic. Precipitation variability has a stronger influence on economic growth than temperature. This complicates climate change impact assessments, because much more uncertainty surrounds projections of precipitation than of temperature. Finally, these results suggest the importance of achieving water security for economic growth and should influence strategy for adaptation to climate change. The findings indicate that national economies are impeded by hydroclimate hazards, periods of too much or too little precipitation. To prevent increasing damage to economic progress as a result of a changing climate, the results imply that adaptation strategies should focus on reducing the negative consequences of precipitation extremes. Hence, water security should be a priority topic for adaptation planning.
Another interesting finding from this analysis was the significant effect of the WASP(+1) variable on GDP growth and on industrial value added. The WASP(+1) is indicative of excess rainfall that may be associated with flooding. As discussed earlier, flooding is difficult to specify using monthly precipitation data. Unfortunately, there is a lack of globally available daily precipitation or streamflow data that could be used to more precisely describe flood risk for a particular country. Nonetheless, the results of the regression analysis using WASP(+1) are consistent with expectations for a flood effect. The WASP(+1) variable was found to be a significant explanatory variable for GDP growth at a slightly lower significance than the WASP(−1) for the concurrent regressions. Interestingly, the effects were stronger for regressions on GDP and industrial value added at a 1-year lag. In both cases, the WASP(+1) was significant at a 95% confidence level. Regression coefficients were consistently negative, implying a negative impact of excess rainfall. The greater lagged effect may imply that the excess rainfall was associated with flooding that impacted infrastructure. The damage to infrastructure then resulted in reduced output in terms of GDP and industrial value added, both of which may be expected to be more dependent on infrastructure than is agriculture. This offers the tentative evidence of a flood effect on these variables in a multi-country analysis. Evidence from case studies of individual flood impacts and of single countries is consistent with this finding. However, a better flood index is needed to explore this effect with more confidence.
In summary, this study has found evidence that hydroclimate hazards have a statistically significant impact on economic growth of the countries of the world, and precipitation variability, as characterized by the WASP indices, is the most significant effect. These represent new findings with important implications for how we conceive of the relationship between water security and economic growth, and how we prioritize adaptation activities.
This research was supported by the Earth Institute of Columbia University and the Bank-Netherlands Water Partnership Program.
The authors acknowledge the encouragement, contributions and comments of Claudia Sadoff, David Grey, John Bricoe, Nagaraja Harshadeep, Marianne Fay and Alexander Lotsch. The views expressed in this paper are solely those of the authors, as are any errors.
One contribution of 16 to a Theme Issue ‘Water security, risk and society’.
- © 2013 The Author(s) Published by the Royal Society. All rights reserved.