ORIGINAL PAPER

Quantifying the costs of drought: new evidence from lifesatisfaction data

Nick Carroll & Paul Frijters & Michael A. Shields

Received: 18 May 2006 /Accepted: 10 August 2007 /Published online: 2 October 2007# Springer-Verlag 2007

Abstract We estimate the cost of droughts bymatching rainfall data with individual lifesatisfaction. Our context is Australia over the period 2001 to 2004, which included aparticularly severe drought. Using fixed-effect models, we find that a drought in springhas a detrimental effect on life satisfaction equivalent to an annual reduction in incomeof A$18,000. This effect, however, is only found for individuals living in rural areas.Using our estimates, we calculate that the predicted doubling of the frequency of springdroughts will lead to the equivalent loss in life satisfaction of just over 1% of GDPannually.

Keywords Drought . Life Satisfaction . Income-equivalence

JEL Classification I31 . Q10 . R10

1 Introduction

Recent years have seen an increased interest in the economic consequences of extremeclimate conditions such as hurricanes, floods and drought (see, for example, Bin and

J Popul Econ (2009) 22:445–461DOI 10.1007/s00148-007-0174-3

Responsible editor: Junsen Zhang

N. CarrollResearch School of Social Sciences, Australian National University, Canberra, Australia

P. FrijtersSchool of Economics and Finance, Queensland University of Technology, Brisbane, Australiae-mail: p.frijters@qut.edu.au

M. A. ShieldsDepartment of Economics, University of Melbourne, Melbourne, Australiae-mail: Michael.shields@unimelb.edu.au

N. Carroll (*)Economics Research Unit (12M62), Department of Employment and Workplace Relations,GPO Box 9879, Canberra, ACT 2601, Australiae-mail: nicholas.carroll@dewr.gov.au

Polasky 2004; Horridge et al. 2005; Troy and Romm 2004). This increased interest ispartly due to the fact that such extreme conditions are projected to become morefrequent in the future with the advent of man-induced climate change (IPCC 2001;CSIRO 2001a). Yet, it remains a difficult task to obtain an estimate of the total cost ofextreme conditions because many of the costs are unobserved, psychological, orindirect. The one-off nature of extreme conditions also limits the use of the traditionaltool of economic analysis, namely revealed preference methods such as whenresearchers attempt to use the difference in housing prices between regions to ascertainthe compensating differential for risks of climate events.1

In this paper, we use a hedonic technique to value a specific form of climate event,namely periods of drought in Australia.2 Following the small literature that haspreviously used various self-reported measures of well-being to ascertain the value ofclimate (Frijters and Van Praag 1998; Rehdanz and Maddison 2005), in this paper, weuse geographical and temporal spacing of responses to life satisfaction surveys inAustralia to compute the costs of drought conditions.

The period that our data covers, 2001–2004, happens to include a widespread anddeep drought that occurred in 2002 which has been described by Australian PrimeMinister John Howard as ‘one of the most severe in a hundred years’.3 It was beenestimated by Horridge et al. (2005) that this particular drought directly resulted in afall in the Australian gross domestic product of 1.0% below what it would haveotherwise been.4 This estimate primarily contains the direct reduction in agriculturalyields in the year of the drought. We believe, however, that this measure is limited andseverely understates the true total cost of a drought.

Costs of drought beyond the fall in agricultural yields will include the depreciationof capital stock in a year when capital is idle. It will also include the psychologicalcosts of the uncertainty associated with a drought year, the flow-on effect of the pre-dicament of farmers on the rest of the rural economy, the effects of the drought on thefuture carrying capacity of the soil (for instance, when rains do come after a drought, partof the top soil will be washed away because there is no vegetation to hold it) and the lossof psychological well-being from being underemployed, which is known to have a large

1 Blomquist et al. (1988) used variation in wages and housing across regions in the USA to ascertain thecompensating wage differential for many socioeconomic and climate conditions, which, in principle,means one gets an estimate of the long-run willingness to pay or accept conditions. In that type ofanalysis, however, one needs to fully account for differences in economic opportunities across regions, toseparate the compensating variation in wages from unobserved individual heterogeneity.2 The Australian Bureau of Meteorology (http://www.bom.gov.au/climate/glossary/climate.shtml) statesthat: “the weather of any place refers to the atmospheric variables for a brief period of time. Climate,however, represents the atmospheric conditions for a long period of time and generally refers to the normalor mean course of the weather.” We refer to drought as a climate event, rather than a weather event,because we are using measured rainfall over a period of time (most commonly seasonal rainfall) in ourregressions below.3 Australian Prime Minister John Howard, media release, 9th December 2002 (http://www.pm.gov.au/news/ media_releases/2002/media_release2025.htm). The Australian Bureau of Meteorology described the2002-03 drought as “one of the worst on record” (see: http://www.bom.gov.au/climate/drought/).4 Horridge et al. (2005) also estimated that multiplier effects, which could have occurred in urban or ruralareas from the drop in agricultural production, would have resulted in GDP being 0.6% further below whatit would have otherwise been. However, as our primary interest is in the direct effects on ruralcommunities, the comparison figure we use is the direct effect of drought on GDP.

446 N. Carroll et al.

negative effect on well-being. It is also the case that a drought period may revealsomething to individuals about likely future climatic change. Droughts may lower theexpected returns in the region and increase the expected variation in income from oneyear to the next. These lower future returns and increased insecurity also have a cost towell-being. For all these reasons, it is important to be able to calculate a morecomprehensive measure of well-being to ascertain the full costs of droughts.

There is already a small literature that has investigated the well-being consequencesof climate. Rehdanz and Maddison (2005) found that after controlling for a widevariety of factors, climate variables, particularly those relating to temperature andrainfall, have a highly significant effect on well-being using cross-country data. Frijtersand Van Praag (1998) found that people require large amounts of income to com-pensate for uncertain climates in Russia. For example, individuals living in Gurjewrequired only half the income of Moscovites to have the same welfare levels. Van Pragg(1988) used a cross-sectional survey of European countries, namely the LuxemburgIncome Study, to show the large effect that climate conditions have on financial well-being where the main finding was that very cold climate conditions were financiallycostly.

Van Pragg and Baarsma (2005), Welsch (2002, 2006) specifically use a lifesatisfaction approach to value environmental conditions. All use the relative variationsin life satisfaction associated with income and environmental conditions to find theamount of income required to compensate for variations in environmental conditions.Welsch (2006) finds that the environmental improvements achieved in Western Europein the 1990s are valued at about $750 per capita per year in the case of nitrogendioxide and about $1,400 per capita per year in the case of lead. Welsch (2002) findsthat a representative German citizen would need to be given more than $1,900 per yearto accept the typical urban air pollution level prevailing in Japan. Van Pragg andBaarsma (2005) find that people are not fully compensated in life satisfaction terms byprices for noise variations associated with Amsterdam airport.

In addition to the above, three recent papers have measured the amenity value ofclimate using an hedonic pricing methodology. This approach examines the relationshipbetween explanatory variables and variations in wages and prices. Using Italian data,Maddison and Bigano (2003) examine the effect of variations in temperature andrainfall on housing costs and wages. Furthermore, Bin and Polasky (2004) and Troyand Romm (2004) use US data to calculate the value of flood risk and hurricanes onproperty values. All three papers find evidence of a large willingness to pay by indi-viduals for mild weather conditions.

In this paper, we contribute to this important line of research by combining data fromthe Australian Centre on Quality of Life’s ‘Australian Unity Wellbeing Index’ withrainfall data from the Australian Bureau of Meteorology. Our dataset is constructed forthe period 2001 to 2004, which included a major nationwide variation in rainfall con-ditions stemming from a deep drought experienced in 2002. In each year, however,variations in climate occurred with a small number of regions experiencing drought.

To gain a reliable estimate of the cost of drought, we use fixed-effects panel datatechniques to analyse the data. Our main result is that droughts that occur in thespringtime are associated with a large decline in life satisfaction for rural communities.This makes sense, as spring is the key period in agricultural production during whichtime crops are sown, and sufficient rain is needed for crops to grow. Our results show

Quantifying the costs of drought: new evidence from life satisfaction data 447

that rain in other periods is not very relevant for measured life satisfaction. Nor does itmatter how much rainfall is below the level needed for crops to grow; once rainfall islower than about 60 mm a quarter, the crops fail. Moreover, this result is not sensitive toour measure of drought.We have conducted a variety of robustness checks using severaldifferent measures of drought and found that our results remain strong.

We estimate, at the mean level of income observed in the data, that having very lowrainfall in regional Australia has an equivalent effect on well-being of a loss in income ofaround A$18,000, which is about US$14,500. Importantly, however, no such effect isfound for individuals living in urban areas of Australia. This estimate of the cost ofdrought holds current income constant and therefore reflects the effects of drought viafuture incomes, increased uncertainty and psychological costs. Finally, the total effect ofthe 2002 drought on well-being is estimated to be equivalent to a drop in nationalincome of A$5.4 billion.5

This paper is structured as follows. In Section 2, we provide a detailed description ofthe data used in this study and define our key variables. This is followed in Section 3by a presentation of the empirical methods adopted for the analysis. Section 4 presentsthe results and also quantifies the costs of drought using various measures of droughtshocks. We conclude the paper in Section 5.

2 Data and definitions

The quality of the data used is central to the reliability of the results that we can obtain. Inthis respect, our data are drawn from two separate sources, namely, the AustralianBureau of Meteorology (BOM) and the Australian Centre on Quality of Life based atDeakin University. We merge together information from these two data sources. All ofthe data that we use in this study are publicly available.

Turning first to the meteorological data that we use to define drought conditions, weobtain rainfall and temperature measures that are collected by the BOM from over 900weather stations, in the case of temperature readings, and over 7,000 weather stations forrainfall data. The primary weather measure that we have chosen as our main measure ofdrought is based on quarterly rainfall by season, and we focus on the period betweenMay 2001 and December 2004. As previously noted, this period included a severedrought that affected many areas of Australia. We do, however, observe a large amountof variation throughout the entire 4 years.

The well-being and demographic variables that we use are drawn from individuals’survey responses collected as part of the Australian Centre on Quality of Life’s‘Australian Unity Wellbeing Index’. The stated aim of the ‘Index’ is to provide relevantand accurate data to promote greater public and political awareness of the social factorsunderpinning well-being, as well as enhance scientific understanding of subjective well-

5 An important point to note here is that although droughts are costly to rural communities, this does notdirectly imply that individuals should be compensated for this cost. Climate phenomena are natural eventsthat, to some extent, are part and parcel of agricultural production and part of the normal ‘business-risk’ ofan agricultural enterprise for which the usual equilibrium argument of Blomquist et al. (1988) applies.Whilst one can make the case that capital market imperfections should mean a government takes over aninsurance role, the case for compensating farmers for droughts seems weak from an economic point ofview.

448 N. Carroll et al.

being.6 The data are collected initially on a twice yearly basis, then on a quarterlybasis, with the first survey undertaken in May 2001 (the survey months are shown inTable 2). Each survey contains around 2,000 randomly selected respondents drawnfrom across Australia, and in this study, we used data from the first 12 surveys.Importantly, as income information was not collected from survey respondents in May2001, we drop this wave of data from our empirical models, but retain it for thedescriptive statistics. After dropping respondents for which key information wasmissing, we are left with a working sample of 15,561 adults. The average age of thesample is 49 years, 53% are female and 78% reside in an urban location.

Some 20 questions are asked of survey respondents by telephone interview. Thesurvey also contains information regarding each respondent’s geographical location plusa wide range of demographic characteristics including age, gender, marital status,household income and employment status.7 Importantly, we are able to identify thepostcode in which each respondent resides, and it is at this level of geographicdisaggregation that we have merged the climate data.8 To do this, we assigned for eachpostcode a rainfall collection station by choosing the station that is closest to thecentroid of that postcode.9 Moreover, an important distinction we make is betweenrespondents living in an urban or rural location. We have defined these locations usingthe Accessibility/Remoteness Index of Australia (ARIA). If the postcode was coded inthe ARIA as in the vicinity of major cities, then we have classified the postcode as‘urban’, otherwise the postcode has been classified as ‘rural’.

The life satisfaction measure used in this paper is based on the question: ‘Thinkingabout your life and personal circumstances, how satisfied are you with your life as awhole?’ The responses range from 0 (very dissatisfied) to 10 (very satisfied). This is awidely used measure of well-being and has been shown to be closely related to a widerange of other potentially more objective measures of happiness (see Frey and Stutzer2002). Table 1 shows the distribution of responses to the life satisfaction question,with two thirds of the pooled sample reporting a life satisfaction score of 8 or above.

There are many alternative definitions of drought that include the level of rainfall in aregion (meteorological drought), the level of water supplies (hydrological drought), theeffect of the meteorological and hydrological drought on agricultural production

6 More details about the data can be found at http://acqol.deakin.edu.au/index_wellbeing/index.htm.7 The income measure used in this paper is derived from a question where individuals were required togive their total household income gross of tax in the previous 12 months. The income measure wasdeflated back to the May 2001 level using the Australian Bureau of Statistic’s CPI. One limitation is thatin our survey, like in many such surveys, income levels were given in bands. Subsequently, we simplytook the midpoint of each band to calculate our log household income measures. The original incomebands were: <$15,000, $15,000–<$30,000, $30,000–<$60,000, $60,000–<$90,000 and $90,000+. Wehave also examined the robustness of our main result by using a dummy variable for each income band tothe find the effect of drought on life satisfaction (holding income constant) and found the resultunchanged. The results are also robust to the amounts used to define the lowest and highest bands.8 A postcode is a four-digit number used by Australia Post to assist with mail delivery. There are 2,435postcodes with valid street delivery area (giving a mean number of people per postcode of approximately8,000). The size of the sample in each postcode in each sample is usually quite small. The range of thesample varies from 0 for some postcodes in some quarters to 21 with a median of two people (where thepostcode has people in the sample).9 Where there was missing data for the rainfall collection station, the next nearest rainfall collection stationto the centroid of the postcode was used.

Quantifying the costs of drought: new evidence from life satisfaction data 449

(pastoral drought) and various administrative definitions of drought.10 Due to dataavailability and measurement, the focus of this paper is on meteorological drought.11

In this paper, we focus on establishing the effect of low spring rainfall as our definitionof drought conditions as it is widely recognised that spring rainfall is the most crucialfor agricultural production and future crop yields. It is also important for pasturegrowth, winter crops and in wool growing amongst many others (see CSIRO 2001a;MCV 2004).12 The measure of drought we use throughout this paper is when thepostcode area receives less than 60 mm of rain in the quarter in the 3 months beforeinterview. We do, however, also examine the robustness of our results to a number ofother definitions of drought. Table 2 highlights the proportion of respondents living indrought conditions by month of survey and urban/rural area. It is clear that there is agreat deal of variation in the data, both overtime and across the urban/rural divide. Themost severe drought times were in August and November 2002 and in August 2004.These affected a significantly higher percentage of rural than urban residents.

Table 2 also shows the average life satisfaction by survey month for all respondentsand then separately for urban and rural respondents. The raw data clearly suggest thatthere is little difference in the life satisfaction reported by urban and rural communities.We now examine whether this is also the case when we use an empirical model thatcontrols for both observable and unobservable individual and postcode characteristics.

10 The Australian Bureau of Meteorology recognises that it is the State Government of Australia’sresponsibility to declare drought. Therefore, in Australia, the official definition of drought is anadministrative measure that considers the meteorological, hydrological and pastoral drought concepts.11 The key issue here is that very detailed geographical information is required to link the drought data tothe life satisfaction data. As the level of detail that rainfall is collected in Australia, it is possible to maprainfall data to the postcode level. To the authors’ knowledge, however, administrative, agricultural andhydrological drought measures cannot be matched to the same level of geographical detail.12 It should be noted that northern Australia has quite different rainfall patterns to the rest of Australia, andtherefore, we would expect quite different seasonal results there than from the rest of Australia. As most ofthe Australian population is located in southern Australia, the results presented in this paper will beprimarily influenced by the rainfall patterns there.

Life satisfaction Frequency Proportion

0 77 0.491 55 0.352 97 0.623 207 1.334 259 1.665 1,085 6.976 858 5.517 2,726 17.528 5,162 33.179 2,650 17.0310 2,385 15.33Total 15,561 100

Table 1 Distribution of lifesatisfaction in Australia

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3 Modelling approach

To establish the relationship between drought and economic insecurity on the one handand life satisfaction and well-being on the other, and to fully utilise the variations in ourdata, we need to adopt an empirical modelling approach that allows for individual, timeand postcode-level variations. Importantly, as we have a panel of postcode-level data,that is, we observe many individuals living in the same postcode area, we can control forunobserved regional variation. A number of papers have demonstrated that estimates ofthe determinants of life satisfaction are virtually unchanged whether one models theordinal nature of the variable (for example, using an ordered probit model) or treats theresponses as continuous (and thus cardinal). Therefore, for ease of estimation andcalculation of the costs of drought, we assume cardinality here. A detailed discussion ofthis issue can be found in Ferrer-i-Carbonell and Frijters (2004).13

Therefore, we adopt the following fixed-effects framework (see Greene 2003):

lsict ¼ αc þ β10xict þ β2

0zct þ "ict ð1Þ

where lsict denotes life satisfaction of person i in postcode c at time t, xict denotes arange of explanatory variables of person i in postcode c at time t, including loghousehold income log (y). The variable zct denotes a range of weather variables atpostcode c at time t. The postcode-specific unobserved effect is allowed for by theintercept term αc. The estimation is undertaken by differencing the variables from thepostcode level means at a base period. Importantly, this modelling approach controlsfor unobserved heterogeneity by allowing each postcode to have a different intercept.

Table 2 Proportion living in drought and average life satisfaction by month of survey

Month of survey % Living in drought Average life satisfaction

All Urban Rural All Urban Rural

May 2001 3.3 0.8 14.2 7.5 7.5 7.8September 2001 9.4 5.7 24.7 7.7 7.7 7.9March 2002 19.6 18.5 23.3 7.8 7.8 7.8August 2002 20.1 15.2 38.4 7.7 7.7 7.8November 2002 25.5 20.7 43.5 7.8 7.8 7.8March 2003 11.7 11.8 11.1 7.8 7.8 7.9June 2003 4.5 0.4 21.6 7.8 7.8 7.8August 2003 3.9 0.4 18.0 7.8 7.8 7.9December 2003 7.3 7.1 7.9 7.8 7.7 8.0February 2004 17.5 17.7 16.8 7.8 7.8 7.8May 2004 9.8 6.2 24.3 7.8 7.8 7.8August 2004 22.5 17.8 41.7 7.9 7.9 8.0

The drought measure used here is a measure of very low rainfall. The postcode is considered to be indrought if it receives less than 60 mm of rain in the quarter.

13 An anonymous referee pointed out that the marginal rate of substitution (MRS) of income for othervariables (such as climate) is an ordinal concept. That is, if true latent life satisfaction is a monotonicallyincreasing function of the discrete observed variables (as is assumed in ordered choice models), the MRSshould be unaffected by the details of the function.

Quantifying the costs of drought: new evidence from life satisfaction data 451

It also allows the unobserved time-invariant heterogeneity component to be correlatedwith the explanatory variables. This specification allows us to combine repeated cross-sections of different regions as an effective panel of the satisfaction levels of a region.

We then use the estimates from the fixed-effects model to quantify:

1. The effect of drought in terms of the change in life satisfaction, for an individualliving in a rural area

2. The effect of the 2002 drought on Australia-wide life satisfaction3. The effect that a doubling in drought springs would have on Australia-wide

life satisfaction.

To do this, we use the estimated coefficient on log household income together withthe coefficients on the event of interest (such as drought or marriage). That is, thequantifications are based on the amount of log income that a person would need to begiven to record the same level of life satisfaction if they were to suffer a shock, namely,experience drought conditions. If life satisfaction (ls) was dependent, holding othercharacteristics constant, on the shock (s) and log income (log (y)), then the amount oflog income required to compensate for a shock (Δlog (ycomp)) will be:

Δ log ycomp

� � ¼ @ ls=@sð Þ �Δs

@ ls=d log yð Þð Þ ð2Þ

What is kept constant here is everything else we observe in the year of the shock.Hence, this measure of the cost of a shock includes the effect of the shock on othervariables in future years, such as future incomes, as well as the psychological costs ofa shock in the year of the shock. Note that this formula holds for any size of the shockΔs, as both s and log income enter the (transformed) life satisfaction equation linearly.Therefore, this cost is a simple proportion of income.

The total log income-equivalent life satisfaction effect of a shock equals the logincome compensation needed for the effect of the shock plus the direct effect of theshock on log income:

Δ log ytotð Þ ¼ Δ log ycomp

� �þ @ log yð Þ=@sð Þ �Δs ð3Þwhere @ log yð Þ=@sð Þ � $s denotes the direct income effect of a shock in the year of theshock. In our case, this is the effect of the drought on log incomes. We use the term‘direct log income effect’ here in the sense of a change in income observed to occur witha change in climate. It includes the effect of government intervention in incomes. It alsoincludes the income effect that arises via the changed demand from other actors and thusincludes the effect of drought on the non-farm incomes of those in the rural sector.

In the case that drought subsidies and other income-compensation mechanisms wouldfully offset the production effects of a drought, we should find that @ log yð Þ=@sð Þ �Δsequals zero. In the case that subsidies and other offsets would completely compensatefor the effect of a drought in the year of the drought, then we should find that Δlog(ytot) equals zero, i.e. that the direct income effect, @ log yð Þ=@sð Þ �Δs, completelyoffsets the effect of drought on life satisfaction, Δlog(ycomp). We should note,however, that it is entirely possible, even when people are fully rational maximisersand when moving between regions is costless, that incomes in the year of the shock donot fully compensate. Other compensating factors can be long-term income, housing

452 N. Carroll et al.

prices and higher base levels of life satisfaction due to compensating amenities, i.e.nice weather or a pleasant community. Nevertheless, Δlog(ycomp) should then still beunderstood as the total log income-equivalent life satisfaction effect of a shock in theyear of the shock. It can be interpreted as the equivalent log income amount that is lostfrom a shock, and hence, can be interpreted as the amount that would be lost per futureoccurrence of such shocks. The fact that some outside agency, or indeed the portfoliochoices of the individual herself, compensates for the income effects of the shock is amatter of transfers and base levels of variables, not of the cost of the occurrence of theshock.

4 Results

We now turn to the estimates from fixed-effects models shown in Table 3. The modelenables us to determine whether any relationship between life satisfaction and droughtis likely to be causal or whether it could be due to postcode areas differing in ob-servable and/or unobservable ways, i.e. areas prone to drought might also have char-acteristics related to low life satisfaction. In all of the estimations, we control for thelog of household income so that the results should be interpreted as the effect ofdrought on well-being, holding current income constant.

To provide additional insights into the effect of droughts on life satisfaction, we beginby estimating the model using all of the samples. We then provide separate estimates forurban and rural areas with the expectation that any effect of droughts will beconsiderably greater in rural than urban areas. Finally, we undertake the same exercise,but focus only on respondents from New South Wales, which was the State mostaffected by severe drought conditions. Our expectation is that any drought effect will begreater for rural communities in New SouthWales than in other Australian rural areas. Inaddition to including our main measure of drought, namely very low rainfall in spring,we control for age (and its quadratic), gender, marital status, unemployment and loghousehold income in each of the models. Each of these individual characteristics hasbeenwidely shown to be key factors in explaining variations in life satisfaction (see Freyand Stutzer 2002).

4.1 Main results

Starting with the general determinants of life satisfaction, the validity of our data isconfirmed by the estimates for age, gender, marital status, unemployment and loghousehold income, which are all in line with the results from previous national andinternational studies (see Frey and Stutzer 2002; Kahneman et al. 1999). We see thatfemales report significantly higher life satisfaction than males (+0.223), age takes theusual ‘u-shaped’ profile, there is a marriage satisfaction premium (+0.493), un-employment is associated with significantly lower life satisfaction (−0.514), andincome is positively associated with life satisfaction (roughly a doubling of householdincome is associated with a 0.447 increase). Interestingly, both marriage and incomeare more important in urban than rural locations.

Turning to look at our measure of drought, we find that having very low rainfall (lessthan 60mmof rain in the quarter) in spring is negatively related to life satisfaction for the

Quantifying the costs of drought: new evidence from life satisfaction data 453

full sample. This result is statistically significant at the 1% level and is associated with a0.202 drop in life satisfaction. As expected, we find that drought conditions in summer,autumn and winter do not have a significant impact on life satisfaction. The results arealso sensible in that the effect of drought is far larger for rural than urban communities,with a spring drought reducing life satisfaction by 0.399 in rural areas (significant at the1% level) compared to 0.193 (not significant) in urban areas. It is clearly the case that thelow levels of rainfall will restrict output to a much larger extent in rural locations, asprimary production makes up a larger share of production in those areas. It is also thecase that the much larger effect in rural areas suggests that the adverse effect of droughton life satisfaction is primarily driven by economic effects (increased uncertainty/reduced financial security, given that we are controlling for income) rather than phys-iological drivers. Clearly, such increased uncertainty would need to be traded off bylarge changes in personal circumstances for an individual to remain at the same pre-drought level of life satisfaction.

Table 3 Fixed-effect estimates of life satisfaction

All Urban Rural NSW Urban NSW Rural

Female 0.223a 0.240a 0.197a 0.106 0.064[7.10] [6.77] [2.91] [1.74] [0.59]

Married 0.493a 0.538a 0.351a 0.624a 0.079[10.73] [10.30] [3.61] [6.79] [0.51]

Unemployment −0.514a −0.504a −0.629 −0.654b −0.834[3.55] [3.15] [1.83] [2.30] [1.82]

Log household income 0.447a 0.462a 0.351a 0.459a 0.372a

[19.01] [17.59] [6.61] [10.54] [4.47]Age −0.055a −0.057a −0.048a −0.054a −0.042b

[10.42] [9.61] [4.06] [5.14] [2.29]Age2/10 0.007a 0.008a 0.007a 0.008a 0.006a

[13.90] [12.74] [5.62] [7.44] [3.11]Spring × low rainfall −0.202a −0.193 −0.399a 0.314 −1.010a

[2.67] [1.83] [3.41] [0.71] [3.51]Summer × low rainfall 0.152 0.186 −0.087 – −0.701

[1.25] [1.38] [0.30] – [1.13]Autumn × low rainfall −0.074 −0.152 0.225 −0.672 −0.067

[0.97] [1.74] [1.33] [1.39] [0.19]Winter × low rainfall 0.025 0.l28 −0.056 0.129 −0.22

[0.30] [1.18] [0.43] [0.86] [1.10]Spring 0.187b 0.142 0.343b −0.059 0.377

[2.42] [1.62] [2.06] [0.39] [1.50]Winter −0.019 −0.023 −0.054 −0.096 −0.044

[0.29] [0.32] [0.37] [0.78] [0.20]Autumn −0.058 −0.05 −0.158 −0.023 0.088

[0.93] [0.70] [1.13] [0.19] [0.39]Observations 12311 9850 2461 3309 887Number of postcodes 1475 1069 406 322 130R2 0.07 0.07 0.06 0.09 0.07

t Statistics in parenthesis. Dummies for missing values on marriage and unemployment, and a dummy fornot in the labour force are included as controls. Summer is the omitted category for the seasonal dummiesand employment for the labour force dummies. There are an insufficient number of city observations tocalculate coefficient on summer × low rainfall in NSW.a Significance at the 1% levelb Significance at the 5% level

454 N. Carroll et al.

Overall, the periods of drought observed over our sample period affected the EastCoast more heavily than other parts of Australia.We now turn to a separate investigationof New South Wales to see if the estimated effect of spring drought conditions differedfrom other areas of Australia. Due to a much smaller sample size, we generally see lessstatistical significance than in the previous models, but it is clear that as expected, springdrought leads to a very large loss of life satisfaction (−1.010) in rural New South Walescompared with the estimate of −0.399 for all rural areas.

4.2 Some robustness checks

A number of supplementary fixed-effects estimates are provided in Table 4 to shed lighton the robustness of our results. Each of these additional models is estimated usingonly respondents residing in rural areas. For ease of visual comparison, we include inthe second column the results for the rural only model from Table 3.

Table 4 Robustness checks, fixed-effect estimates of life satisfaction

Rural No income Length ofdrought

Quarter-specific 10th Percentile

Marriage 0.351a 0.470a 0.350a 0.547a 0.347a

[3.61] [4.88] [3.60] [5.04] [3.58]Unemployment −0.629 −0.847b −0.631 −0.610 −0.574

[1.83] [2.46] [1.84] [1.78] [1.68]Log household income 0.351a – 0.350a 0.300a 0.359a

[6.61] [6.59] [5.55] [6.77]Spring × low rainfall −0.399a −0.406a −0.392a −0.380a –

[3.41] [3.44] [3.34] [3.25]Summer × low rainfall −0.087 −0.112 −0.077 −0.094 –

[0.30] [0.38] [0.26] [0.32]Autumn × low rainfall 0.225 0.244 0.210 0.248 –

[1.33] [1.43] [1.24] [1.47]Winter × low rainfall −0.056 −0.032 −0.059 −0.035 –

[0.43] [0.24] [0.45] [0.26]Long drought – – 0.118 –

[0.88]Spring × q10_rain – – – −0.995a

[3.96]Summer × q10_rain – – – −0.565

[0.46]Autumn × q10_rain – – – 1.447

[1.19]Winter × q10_rain – – – −0.147

[0.55]Quarter dummies No No No Yes NoObservations 2461 2461 2461 2461 2461Postcodes 406 406 406 406 406R2 0.06 0.04 0.06 0.07 0.06

As with the models in Table 3, female, season, age and age2 are included in models—means that thevariable is not included in the model. All specifications are based on individuals living outside of urbanpostcodes. For additional information, see Table 3 notes. Season dummies included in all specification,except quarter-specific specification where quarterly dummies are included instead.

Quantifying the costs of drought: new evidence from life satisfaction data 455

Our first robustness check is to exclude household income to see what happens to theother estimates. When we do this, the coefficients on marriage, unemployment andspring drought all increase. This is reassuring because it suggests that part of thetransmission mechanism of these variables occurs through a lowering (or increase in thecase of marriage) of current income. In other words, in the case of spring drought, wefind that such a drought is associated with a drop in household income and when we donot hold this constant, we get the total effect of drought on life satisfaction.

A second robustness check is when we include dummy variables for very longdrought (length of drought) and annual drought (annual rainfall).14 Both of these arefound to be small and statistically insignificant. This is somewhat surprising, giventhat we might expect that agricultural and other rural output would be more adverselyaffected by long rather than short droughts. However, a few of points should be noted.Firstly, the measure used for length of drought is equivalent to a rolling average ofdrought. Rolling average variables are often less relevant for the current periodbecause they often pick up information relevant for a prior period. Secondly, it ispossible, as is the case with disability that people may adapt to some extent to longperiods of drought and their changing circumstances. Thirdly, for very long periods ofdrought, there may be outward migration by the people most affected. This would leadto the estimated effect of long droughts being biased to more positive values.

In the next robustness check, we investigate whether the results are sensitive to theway that the seasonal and time dummies are specified. In particular, instead of includingseason-specific dummies, we include quarter-specific dummies. This controls forquarter specific macroeconomic shocks that, by chance, may be correlated with bothdrought and life satisfaction. With the more finely specified time dummies, our resultsare unchanged.

A fourth robustness check pertains to what should be the definition of drought, asthere is controversy about the definition of drought. Our results are not sensitive to thedrought definition. In the final model in Table 4, we use a relative measure of droughtrather than an absolute measure of low rainfall.15 Here, we include a dummy variablethat takes a value of 1 if rainfall is below the 10th percentile in the postcode and 0otherwise. We then interact this variable with season as in the previous models.Importantly, using this alternative measure of drought, we find very similar results. Inparticular, a spring drought has a large significant negative effect on life satisfaction,but once again, drought at other times of the year has no substantive effect. What isnotable, however, is that this alterative definition leads to a near tripling of the size ofthe effect, with a spring drought estimated to reduce life satisfaction by −0.995.

Apart from the robustness analyses shown in the results tables, we also ran variousadditional analyses which we only mention here but are available from the authors onrequest. In particular, we analysed various alternative climate measures and theirinteractions, such as using the interaction between temperature and rainfall. We found

14 The annual rainfall specification is not shown, but is available from the authors upon request.15 There are several reasons why this should not be our preferred definition of drought. In particular, thelevel of the 10th percentile will vary widely between northern Australia and the rest of the country. Anabsolute measure of rainfall is more important because Australia’s primary production tends to be relianton a relatively narrow range of production (such as pastoral farming, and certain types of crops). Thismeans that a similar level of rainfall and water stocks will be required.

456 N. Carroll et al.

that only our favoured definitions of drought achieved statistical significance. Weanalysed whether lagged climate variables affected current incomes, but foundinsignificant additional explanatory power. We also found that allowing the error termto have a region-specific component (i.e. spatial clustering) reduced the significance ofour results somewhat (our main variable that of a drought in springtime attained a t valueof 1.8).16 However, this is exactly what you would expect, as adding a regional-specific error term reduces the amount of identifying information from observedregionally varying characteristics such as climate.

Other robustness tests we undertook showed that more extreme spring droughts, lessthan 30 mm in the season, had larger effects on life satisfaction than less extreme springrainfall deficiencies, i.e. between 30 and 60 mm in the season. We also found that whenwe dropped the socioeconomic status variables in the estimation, our results wereunchanged. We furthermore found that adding household size left the coefficients onincome and the other explanatory variables largely unchanged.17

A final robustness check is to see whether our results are robust to a treatment of theincome variable, which is reported in five categories. In all the analyses above, wereplaced the categorical income variable with the midpoint of the categories. This, ineffect, presumes that the distribution of income is uniform within its category. If the truedistribution is not uniform within categories, then the midpoint is going to be a biasedestimate of the true conditional mean within the category which will lead to attenuationbias, i.e. to a downward bias in the coefficient of income.18 When there are very manycategories, the assumption of a uniform distribution within each category is going toapproximate the true distribution well, but with only five categories one may wonder ifwe can do better.

We presumed that the true income distribution was log-normal and found that themaximum likelihood value in our sample of the log-mean and log-variance was ln(34841) and 0.681, respectively (whichmeans that the median income is A$34,841).Wethen calculated the conditional means within the income categories and used that toreplace the categorically measured income in our estimates. The results of this exerciselead to an income coefficient of 0.343 compared to 0.351 with the previous incomemeasure. This difference is both relatively minute as well as statistically insignificant,nor did any of the other variables change significantly. We may finally note that thevariance of the estimated log-income variable is 0.6172, which is about 91% of thevariance of the underlying distribution and implies a fairly low loss of signal variance.

16 We also investigated whether the influence of weather differed by region by using an interactionbetween the weather variables and whether the region concerned was in Queensland and the NorthernTerritories (which are in the north of Australia where a monsoon climate prevails). We found nosignificance of such interaction terms. The limited additional explanatory power of additional variablesprobably shows that our data can only pick up the large climate effects and are not suitable for a moredisaggregated analysis.

18 Its worth pointing out that in the case of a truly exogenous regressor, the mere act of averaging thatregressor within categories does not bias its coefficient in a linear regression as long as the meandifferences between those categories correspond to the true mean differences. Indeed, averaging wouldincrease the coefficient if there is measurement error in the original series, as averaging overcomesmeasurement error bias.

17 We are grateful to two anonymous referees for the suggestion to undertake these robustness checks.

Quantifying the costs of drought: new evidence from life satisfaction data 457

4.3 Quantifying the cost of drought

Here, we use the coefficients from the model estimated for the whole sample to calculatethe income-equivalence of changes in individual circumstances, which we term the‘cost’ of that individual circumstance. As the found effects are in terms of proportionaldifferences due to the logarithmic specification, the cost differs with the initial level ofhousehold income. We therefore show the costs in thousands of dollars for incomes inspecific parts of the income distribution in Table 5. We undertake the calculationsseparately at the mean of household income and at the 10th and 90th percentiles. Forpurposes of brevity, we will focus here on the results calculated at the mean of house-hold income, which coincide with the average cost of a change in an individualcircumstance for the sample population.

The first income-equivalence we provide is for being married, which has a positiveeffect on life satisfaction equivalent to an increase in income in real annual householdincome of A$67,000 (in May 2001 dollars). Similarly, the effect of moving fromunemployment to employment is equivalent to the gain of A$72,000. These figures areroughly comparable to the results found in the international literature about the relativesizes of the marriage, unemployment and income effects.Most importantly, we calculatethat in rural areas, a spring drought is equivalent to a drop in household income of$18,000 at the mean of log household income.19 This latter effect is very largeconsidering that the model includes people not working in farming and those peoplethat may work on farms where production is less reliant on rainfall.

We now turn to obtaining an estimate of the total cost of the 2002 drought. Overall,there was an increase in very dry areas in the spring of 2002 of 11% compared to thesame quarters in 2001, 2003 and 2004. We therefore assume that an additional 11% ofrural households is affected by drought (or 298,000 households). Each quarterly droughtepisode is equivalent to the loss of $18,000 in household income. By multiplying thesefigures together, we find that the 11% point rise in dry springs across Australia in 2002was equivalent to the lowering of national income byA$5.4 billion. This estimate showsthat the psychological costs of the drought (potentially through increased insecurity)were almost as large as the economic costs (which we estimate to have lowered GDP byA$6.6 billion).

Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO)estimate that very dry springs will double after 2020 (see CSIRO 2001b). Whilst thisnumber is subject to a lot of uncertainty, it is nevertheless useful as a point estimate ofthe future. Using the estimates of income and dry springs on life satisfaction fromTable 5, we can now calculate what the effect of a doubling of dry springs would haveon life satisfaction. It is estimated that the proportion of regions experiencing verydry springs will increase from 14.5 to 29.0%. Given that there are approximately2.7 million Australian households outside the main centres and that there is expectedto be a doubling in an event that results in a drop in life satisfaction equivalent to a fallin annual household income by $18,000, the doubling of dry spring is predicted to

19 A wide 95% confidence interval was calculated for this effect. It was calculated using the 95%confidence intervals on income and drought in spring separately. Using this method, the 95% confidenceinterval on the drought quantification ranges from $8,300 to $25,000.

458 N. Carroll et al.

have an equivalent affect on life satisfaction as the loss of over 1% of GDP annually(or A$7.3 billion per year).20

5 Conclusion

In this study we undertook an investigation into the effect of drought conditions on lifesatisfaction in Australia by matching meteorological data from the Australian Bureau ofMeteorology at the postcode level with individual-level life satisfaction anddemographic data collected as part of the ‘Australian Unity Wellbeing Index’. Weestimated a number of fixed-effects model that effectively control for unobservablepostcode area characteristics, which provides us with more confidence in our mainresults. We used the resulting estimates to calculate the cost of drought and also topredict the costs of an increased prevalence of droughts as predicted by the AustralianCommonwealth Scientific and Industrial Research Organisation (CSIRO).

We found that the experience of drought in springtime, defined as less than 60 mm ofrainfall in the quarter in our preferred specification (but also robust to alternativedefinitions), leads to a substantial loss in life satisfaction for residents of rural areasequivalent to a fall in annual household income of A$18,000. This loss, however, isfound to be far larger for those living in rural New South Wales where the most severeperiods of drought were experienced between 2001 and 2004. Importantly, droughtsexperienced in any other season had no effect on life satisfaction in either rural or urbanareas. This drop in life satisfaction due to spring droughts may be associated with a dropin expected future income, the psychological stress associated with a large drop in

Table 5 The costs of events quantified (in A$2001 thousands)

10th Percentile Mean 90th Percentile

Level of annual household income 9.5 33.5 74.1Marriage +19 +67 +149Unemployment −20 −72 −160Low rainfall in winter/spring (all) −1 −5 −11Low rainfall in winter/spring (non-city) −5 −18 −39Spring (non-city) +4 +14 +31

Calculations based on specification (1) in Table 3 (except the estimate for the rural cost of drought andspring). The “10th percentile” and “90th percentile” columns are calculated at the 10th and 90th percentileof log(household income), the “mean” column is calculated at the mean of log(household income). Theestimates of spring rainfall and spring are divided by 4 to take account of the fact that these effects onlyoccur one fourth of the time (i.e. in the other three season where there is no effect). While the mean of loghousehold income is 10.42 (giving an estimate of household income at this level of $33,500), the meanhousehold income is $47,400. The mean household income value in Unity Wellbeing Index is close to the$50,800 given by the ABS.

20 The expected well-being cost of climate change is an upper estimate. If there is indeed a doubling of drysprings, we would expect that that there would be out-migration from these regions, and, within theregions, that the industry composition would change and that alternative production methods would beemployed (for example, there may be a drop in the harvesting of water-intensive crops, such as rice andcotton). In addition, part of the large costs associated with dry springs is likely to be transitional aspeople’s expectations change about their futures.

Quantifying the costs of drought: new evidence from life satisfaction data 459

income or other factors related to very low rainfall. Moreover, our results were found tobe robust to a variety of model specifications and alternative definitions of drought. Asmight be expected, we found no evidence that urban communities in Australiaexperience a loss of life satisfaction from drought conditions.

We also used the model estimates to calculate (1) the total cost of the 2002 droughtand (2) the potential costs of a doubling in the frequency of spring droughts, as predictedto be the case by CSIRO. In relation to (1), we estimate that the 2002 drought wasequivalent to a lowering of national income by A$5.4 billion. In terms of the cost ofpredicted future climate change in Australia, we estimate that a doubling of episodes ofspring drought will lead to the equivalent loss in life satisfaction of just over 1% of GDPannually or A$7.4 billion per year.

Whilst we believe that this study is the first to calculate the costs of an exogenousnatural disaster by means of looking at the temporal variation in life satisfaction, weshould emphasise that one should view the results with some caution. In particular, thedata available to us lack the ability to distinguish between farmers and non-farmers, orothers directly connected to agricultural production. This means that we cannot discernthe precise transmission mechanisms of drought on life satisfaction. Such an attemptwould need more detailed data on the type of activities done to identify the separateeconomic channels that affect life satisfaction. However, the coefficient on drought inthis paper will provide the correct average effect as long as the unobserved variable isorthogonal to the drought shock, which is all that is required for a correct valuation of theaverage impact of climate shocks. Similarly, we would require more information on thevarious ways in which individuals could adapt to a greater drought frequency to be morecertain about the costs of changes in future climate risks. Despite these caveats, webelieve that the analysis provided in this paper provides a good building block for futurework, in particular investigating whether our main results are also found for othercountries.

Acknowledgements The authors would like to thank Alison Booth, John Freebairn, Andrew Oswaldand two anonymous referees for helpful comments.

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