2. Defining ‘drought’

Simple drought indices often rely solely on rainfall measures and are typically preferred by policymakers, including the Indian Meteorological Department (IMD), over more complex indices. Until 2016, the IMD recorded a ‘drought event’ when seasonal rainfall was below 75 per cent of its long-term average (between 1950 and 2000), and a ‘severe drought’ when rainfall was below 50 per cent (Indian Meteorological Department, Government of India, undated). Simple metrics of precipitation deficiency, which have the advantage of being easily interpretable, are also used to evaluate drought impacts on agricultural production (e.g., Pandey et al., Reference Pandey, Bhandari and Hardy2007; Auffhammer et al., Reference Auffhammer, Ramanathan and Vincent2012).

Simple definitions of drought based on rainfall are, however, problematic for our understanding of drought impact. Variables in addition to rainfall, in particular temperature, help determine the physical severity of a drought. A growing literature suggests critical turning points at which higher temperatures cease to have positive impacts on agricultural yield (e.g., Guiteras, Reference Guiteras2009; Schlenker and Roberts, Reference Schlenker and Roberts2009; Lobell et al., Reference Lobell, Sibley and Ortiz-Monasterio2012; Burgess et al., Reference Burgess, Deschênes, Donaldson and Greenstone2014). High temperatures have particularly acute effects on crop growth during periods of low precipitation since the rate of evapotranspiration increases as temperatures rise (Prasad et al., Reference Prasad, Staggenborg, Ristić, Ahuja and Saseendran2008; Lobell and Gourdji, Reference Lobell and Gourdji2012). In general, this increases a plant's demand for water at a time when water availability is already low due to deficient precipitation. Drought is documented to increase in severity as mean temperatures have risen. Higher temperatures, rather than the increased intensity of low rainfall events, have been held responsible for these drying trends (Vicente-Serrano et al., Reference Vicente-Serrano, Lopez-Moreno, Begueria, Lorenzo-Lacruz, Sanchez-Lorenzo, Garcia-Ruiz, Azorin-Molina, Moran-Tejeda, Revuelto, Trigo, Coelho and Espejo2014; Diffenbaugh et al., Reference Diffenbaugh, Swain and Touma2015). As such, neglecting the effect of temperature on the severity of a drought event could underestimate drought impact.

More complex indices tend to rely on data that are often not readily available in most economic datasets, e.g., for soil moisture levels. The lack of data needed to derive such measures, which can depend on factors such as wind, radiation and humidity, limits their applicability in empirical analysis of drought impacts. Bridging the gap between simple and complex indices, Yu and Babcock (Reference Yu and Babcock2010) propose a drought index that neatly captures the interaction between temperature and rainfall, thus giving it the potential to capture the combined effect of cumulative heat and water stress on yield. Applied to the study of drought tolerance of soybean and corn yields in the US, it takes a non-zero value for years of below-average rainfall and above-average values of CDD:

(1)\begin{equation} \textrm{DI}_{it} = [ { - \max ( {0,\textrm{CDD}_{it}^{\textrm{stand}}}) } ] \times [ {\min ( {0,\textrm{TR}_{it}^{\textrm{stand}}} ) } ],\end{equation}

where DI_it denotes the drought index for geographical unit i in year t; $\textrm{TR}_{it}^{\textrm{stand}}$ is standardized total monthly rainfall over the growing season; and $\textrm{CDD}_{it}^{\textrm{stand}}$ is standardized, cumulative CDD above 18°C.

The index described in (1) gives a value of zero whenever either $\textrm{CDD}_{it}^{\textrm{stand}}$ is below or $\textrm{TR}_{it}^{\textrm{stand}}$ is above their respective long-term averages. Thus, a drought ‘event’ or ‘year’ is defined when $\textrm{CDD}_{it}^{\textrm{stand}}$ is higher and $\textrm{TR}_{it}^{\textrm{stand}}$ is lower than their respective long-term averages. A strength of this index lies in its capacity to capture the potential of high temperatures to exacerbate the effects of low rainfall on crop production. Birthal et al. (Reference Birthal, Negi, Khan and Agarwal2015) adopt the index to study the tolerance of rice yields to drought in India.

While Yu and Babcock's (Reference Yu and Babcock2010) approach has the advantage of being a relatively simple way to account for both temperature and precipitation, the index restricts the definition of drought to events characterized by below-average $\textrm{TR}_{it}^{\textrm{stand}}$ accompanied by above-average values of $\textrm{CDD}_{it}^{\textrm{stand}}$, that is, our Type 1 drought. It does not consider events characterized by below-average values of $\textrm{CDD}_{it}^{\textrm{stand}}$ as well as below-average $\textrm{TR}_{it}^{\textrm{stand}}$, that is, our Type 2 drought. Despite being common in many settings, the impacts of such events on agricultural production remain unknown, due to either being omitted altogether (as in Birthal et al., Reference Birthal, Negi, Khan and Agarwal2015) or joined with Type 1 droughts in arbitrarily-defined rainfall indices.

Type 2 droughts should not be omitted a priori because, as explained in the introduction, focusing only on years with an above-average value of $\textrm{CDD}_{it}^{\textrm{stand}}$ (Type 1 drought) ignores the possibility that, in years with a below-average value of $\textrm{CDD}_{it}^{\textrm{stand}}$, there may still be a number of very hot days sufficient to negatively impact agricultural productivity.Footnote ³ Thus, a class of potentially destructive dry events would not be defined as ‘drought’ per equation (1), which may underestimate the aggregate impact of all dry events. The classification of these events as non-droughts could lead to biased estimates of drought impact. Thus, if Type 2 droughts do have a significant negative impact on productivity, then the application of Yu and Babcock's index potentially underestimates drought impacts due to the inclusion of Type 2 drought events in the ‘no drought’ control group. Finally, since we expect crops to respond differently to increasing deviations from mean rainfall, depending on whether the value of $\textrm{CDD}_{it}^{\textrm{stand}}$ is below- or above-average, Type 2 droughts ought not only to be included but also to be modelled separately from Type 1 droughts.

Our definitions of drought – Type 1 and Type 2 – are important for understanding and predicting crop yields. We argue that they are an improvement on measures and indices used in earlier research. Table 1 lists previous studies that have adopted both simple and more complex nonlinear functions of temperature and rainfall as predictors for crop yields. In general, these measures focus on either heat or rainfall, thus neglecting the combined impact on yield from cumulative heat and water stress. One possible reason is that the temperature bins approach is often used and requires a very large number of coefficients to estimate the combined effect of heat and rainfall. Previous work on drought impacts also typically adopts a single binary definition to estimate the impact of drought on yields. However, the use of a binary definition makes it difficult to assess the relationship between drought and yield.

Table 1.

List of previous studies

3. A new index to define drought in India

Weather data on daily rainfall and daily average temperature at the district level are sourced from the IMD for figures 1 and 2.Footnote ⁴ We define our growing season as June-SeptemberFootnote ⁵ and both long-term average rainfall and CDD are defined vis-à-vis their 1956–2009 averages.Footnote ⁶

Figure 1.

Proportion of affected districts, by drought type. (a) Proportion affected (Type 1); (b) Proportion affected (Type 2).

Notes: The numbers above the bars represent the proportion of districts (rounded to two decimal places) that were affected by a given drought type in a given year.Source: Authors' own calculations.

Figure 2.

Type 1 droughts in excess of Type 2 droughts (June-September only).

Notes: Bar graphs show the number of districts affected by Type 1 droughts in excess of the number affected by Type 2 droughts. As a result, a value of 50 would mean that there were 50 more districts affected by a Type 1 drought than affected by a Type 2 drought in a given year. The converse applies to a negative number, which highlights a higher number of districts affected by type 2 droughts in a given year. The solid vertical lines represent the years considered by the Indian Government as All-India drought years.Source: Authors' own calculations.

Panel (a) of figure 1 shows the proportion of districts, by year, limited to events characterized by both below-average rainfall and above-average values of CDD (Type 1). The vertical lines indicate All-India Drought Years.Footnote ⁷ Panel (b) of figure 1 shows the proportion of districts in years characterized by below-average rainfall and below-average values of CDD (Type 2).

Figure 2 shows why the omission of Type 2 droughts is likely to be problematic. For each year, we estimate the number of districts affected by Type 1 droughts net of the number of those affected by Type 2 droughts, with a positive number (in darker grey) denoting a year in which the former exceeds the latter. A negative number (in lighter grey) indicates a year in which the latter exceeds the former. Overall, Type 1 droughts are slightly more prevalent (55 per cent) than Type 2 droughts (45 per cent). In the 1990s, most of the drought-affected districts were affected by Type 1 droughts. Since 1999, Type 2 droughts have increased, with the number of districts affected by Type 2 droughts outnumbering the number of districts affected by Type 1 droughts in seven out of 11 years.

Formally shown in online appendix A, the first step of our index involves the calculation of the deviation of CDD over the growing season (June–September) from average long-run (1956–2009) CDD over the growing season, a variable we define as DCDD. Positive values of DCDD indicate above-average CDD while negative values indicate below-average CDD. A similar procedure is followed for rainfall in that we create a variable, DTR, which is defined as the deviation of district-specific, cumulative rainfall from long-term, mean cumulative rainfall between 1956 and 2009. Negative values of DTR represent below-average cumulative rainfall while positive values indicate above-average rainfall.

Next, we normalize DCDD and the negative of DTR, which we define as $\textrm{NCD}{\textrm{D}_{it}}$ and $\textrm{NT}{\textrm{R}_{it}}$, respectively. Normalizing the negative of rainfall, rather than rainfall directly, allows us to generate a variable bounded between 0 and 1, with higher values signaling more severe rainfall deficiency. Thus, $\textrm{NCD}{\textrm{D}_{it}}$ is increasing in temperature and $\textrm{NT}{\textrm{R}_{it}}$ is increasing in rainfall deficiency. Using the normalized negative of rainfall enables us to construct an index without running into the problem of negative values that emerges from the interaction of the standardized variables. Finally, a multiplicative relationship is generated between the two normalized variables, resulting in two drought indices. Type 1 droughts are denoted $\textrm{DI}{1_{it}}$ and Type 2 are denoted $\textrm{DI}{2_{it}}$:

(2)\begin{equation} \textrm{Drought} = \begin{cases} {\textrm{DI}{1_{it}} = \textrm{NT}{\textrm{R}_{it}} \times \textrm{NCD}{\textrm{D}_{it}}}& \textrm{if}\ \textrm{DT}{\textrm{R}_{it}} \lt 0\ \textrm{and}\ \textrm{DCD}{\textrm{D}_{it}} \gt 0;\enspace 0\ \textrm{otherwise}\\ {\textrm{DI}{2_{it}} = \textrm{NT}{\textrm{R}_{it}} \times \textrm{NCD}{\textrm{D}_{it}}}& \textrm{if}\ \textrm{DT}{\textrm{R}_{it}} \lt 0\ \textrm{and}\ \textrm{DCD}{\textrm{D}_{it}} \lt 0;\enspace 0\ \textrm{otherwise} \end{cases}.\end{equation}

As such, $\textrm{DI}{1_{it}}$ can be interpreted as a normalized version of Yu and Babcock's (Reference Yu and Babcock2010) index. It takes a strictly positive value for all events characterized by below-average rainfall and above-average $\textrm{CDD}_{it}^{\textrm{stand}}$. The second index, $\textrm{DI}{2_{it}}$, only takes non-zero values for events with below-average rainfall and below-average $\textrm{CDD}_{it}^{\textrm{stand}}$, the category that Yu and Babcock omit. Constructing these two indices separately allows us to test their respective statistical significance in the yield regressions.

Our indices are increasing in temperature but decreasing in rainfall and reflect that both higher temperatures and lower rainfall are expected to contribute to drought severity. A maximum value of one is obtained for the most severe droughts, and is only possible for the restricted set of drought events considered by Yu and Babcock. The similarity of their index to our own is illustrated in online appendix table A1, which shows the correlation coefficients and the Spearman correlation coefficient. As expected, our index DI1 is highly correlated with Yu-Babcock, displaying a correlation coefficient of 0.787 and a Spearman correlation coefficient in excess of 0.99. Our second index DI2, on the other hand, has a negative correlation coefficient with a correlation coefficient of −0.189 and a Spearman coefficient of −0.363. Since Yu-Babcock is invariant with a value of zero for these events, this result is also as anticipated. In addition, these two indices differ in terms of their maximum values. While the maximum value for Type 1 droughts is one (which occurs when the hottest year is also the year with the lowest rainfall), the maximum value of events in which both rainfall and CDD are below-average is 0.54 (see table 2). These two maximum values capture the fact that a combination of above-average CDD with below-average rainfall is likely to lead to a more severe drought than below-average CDD combined with below-average rainfall.

Table 2.

Summary statistics of observations in the sample

Notes: Rural population density is calculated by dividing total rural population by gross cropped area. Our cooling degree-days measure is calculated based on average daily district temperature in the months of June-September for the period 1956–2009.

Figure 3 shows how our index values change over time for all districts. There are clear spikes in the values of the index for a number of All-India Drought Years. The years 2002 and 2009 are associated with the largest deviations in rainfall. Similarly, 1972, 1979 and 1987 are also considered years with particularly high deviations and our index rises in these years. Throughout the 1990s, however, it is striking that, despite relatively modest deviations of rainfall from trend, our index still records high values. One possible explanation for this could be rising land-surface air temperatures over time (Pai et al., Reference Pai, Nair and Ramanathan2013).

Figure 3.

Average drought index value.

4. Impact of drought on rice productivity

To investigate drought impacts on aggregate rice productivity at the district level, we obtain agricultural data from the ICRISAT Meso-level Database.Footnote ⁸ For the period 1966–2009, this dataset contains detailed agricultural and socioeconomic information (ICRISAT, 2012). Data are available for annual crop production and area under crop production for a range of crops, for most districts. Focusing on rice, we create a balanced panel, which implies that, of the 311 districts available in the dataset only 159 are used in our empirical analysis due to missing data for irrigated rice area (see map in online appendix figure A1). Rice yield is estimated by dividing total rice production by total rice area. Table 2 summarizes the variables used in our analysis.

To model the relationship between rice yield and our drought index, we estimate the following fixed-effects modelFootnote ⁹:

(3)\begin{align} \textrm{ln}({y_{it}}) & = {\alpha _i} + {\gamma _t} + {\delta _{i1}} \times t + {\delta _{i2}} \times {t^2} + {\beta _{1q}} \textrm{D}{\textrm{I}_{itq}} + {\beta _{2q}} \textrm{DI}_{itq}^2 + {\beta _{3q}} \textrm{D}{\textrm{I}_{itq}} \times t\notag\\ & \quad + {\beta _{4q}} \textrm{DI}_{itq}^2 \times t + {\beta _{5q}} \textrm{D}{\textrm{I}_{itq}} \times \textrm{propirr}{\textrm{i}_{it}} + {\beta _{6q}} \textrm{DI}_{itq}^2 \times \textrm{propirr}{\textrm{i}_{it}} + {\epsilon _{it}}, \end{align}

where for district i in year t: In(y _it) denotes the natural logarithm of rice yield; ${\alpha _i}$ and ${\gamma _t}$ represent the district and year fixed effects, respectively; and ${\delta _{i1}}$ and ${\delta _{i2}}$ are the coefficients on the district-specific linear and quadratic trends, respectively. Quadratic terms are also included for the following variables, to account for potential nonlinearities in the relationship between drought type and yield. First, the coefficients associated with a type q (i.e., Type 1 – above-average CDD, or Type 2 – below-average CDD) drought index, which captures the marginal impact of a type q drought, are denoted ${\beta_{1q}}$ and ${\beta _{2q}}$. Coefficients ${\beta _{3q}}$ and ${\beta _{4q}}$ capture the interaction between drought type and time, t, while the coefficients ${\beta _{5q}}$ and ${\beta _{6q}}$ capture the interaction between drought type and the proportion of rice area under irrigation. Finally, ${\epsilon _{it}}$ represents the error term. Consistent with Yu and Babcock (Reference Yu and Babcock2010) and Birthal et al. (Reference Birthal, Negi, Khan and Agarwal2015), we do not include additional controls in our main specifications. This is also the norm in the climate impacts literature.

4.1 Regression results

We run a regression of the natural logarithm of yield on a set of district-specific quadratic trends and the drought indices. Specifically, we estimate the model in (3), in both log-levels and levels. First, we include only Type 1 drought events (columns 1 and 3 in table 3). Second, we estimate separate coefficients for Type 1 and Type 2 drought events (columns 2 and 4 in table 3).

Table 3.

Full sample results

Notes: Values in parentheses denote clustered standard errors at the district level. *, ** and *** denote statistical significance at the 10%, 5% and 1% level, respectively. Time trends denote quadratic district-specific trends.

Table 3 highlights two results.Footnote ¹⁰ First, at mean drought intensity (the average for all events with non-zero index values), both drought types have significant and negative effects when considered separately. Thus, Type 2 events have large and statistically significant, negative impacts on rice yield. Second, we find that at means of all variables, Type 2 droughts have a higher marginal effect on yield. However, as will be shown in the next section, the overall effects on yield and associated economic costs are higher for Type 1 droughts. This is because the index value of Type 2 droughts is typically around half of the index value of Type 1 droughts.Footnote ¹¹ Although both excess heat and reduced moisture have negative impacts on production, reduced rainfall carries greater weight in the Type 2 index than in the Type 1 index, explaining the greater marginal effect. Values of CDD are, by definition, higher in the latter than in the former. As a result, yields are likely to respond (more) negatively to changes in the Type 2 index than in the Type 1 index.

The differences in impacts between Type 1 and Type 2 are tested into two ways. First, the confidence intervals of the marginal effects at means for the two drought types are shown in table 3 (see rows ‘95 per cent CI’). The DI2 marginal effect (evaluated at means of all variables) is outside the 95 per cent CI of the DI1 marginal effect (again at means of all variables) for the levels specification and it is just marginally inside the 90 per cent CI for the log-levels specification. Second, we tested whether all the DI1 coefficients (and interactions) are jointly different from all the DI2 coefficients (and their interactions) (see online appendix table A3). For the log-levels specification, the F-test was rejected at the 10 per cent level. For the levels specification, the hypothesis that the coefficients are equal could not be rejected. This may be due to the large number of interactions included in the model.

The estimated change in marginal effects by irrigation and over time are presented, respectively, in tables 4 and 5 for the levels specifications. The marginal effects for the log-levels specifications are shown in online appendix tables A4 and A5.

Table 4.

Levels specification: marginal elasticities (irrigation) proportion of area irrigated

Notes: *** denotes statistical significance at the 1% level. For both types of events, marginal effects are computed at the mean value when affected (DI1 = 0.493 and DI2 = 0.207).

Table 5.

Levels specification: marginal elasticities (time)

Notes: *, ** and *** denote statistical significance at the 10%, 5% and 1% level, respectively. For both types of events, marginal effects are computed at the mean value when affected (DI1 = 0.493 and DI2 = 0.207).

From table 4, the levels specification results suggest that absolute drought impacts either remain fairly constant (Type 1) or increase (Type 2) as the proportion of rice area under irrigation rises. Yet, as a proportion of total yield, the log-levels specification suggests that marginal impacts decrease substantially as the proportion of rice area under irrigation increases (see online appendix table A4). This can be explained by the fact that yields in irrigated areas tend to be higher and, even if losses remain constant or increase moderately in absolute terms, yield increases from improved irrigation imply a fall in losses as a proportion of the total. Our results also suggest that, as a proportion of the total, increases in the proportion of rice area under irrigation reduce the marginal impact more when considering Type 2 droughts compared with Type 1 droughts. With increasing proportion of rice area under irrigation (above 95 per cent irrigated), impacts of Type 2 droughts (at mean intensity) are not significantly different from zero (at the 5 per cent level). The same does not apply for Type 1 droughts: even when the proportion of rice area under irrigation is very high, we still find statistically significant effects on yields. This suggests that irrigation seems to be an effective strategy at substituting for water deficiency, but less effective at mitigating the combined effects of heat and water deficiency.

From table 5, the results over time suggest that absolute yield losses attributed to drought have increased. As a proportion of total production, the log-levels specification in online appendix table A5 shows that losses follow a different pattern depending on the type of drought.Footnote ¹² Type 1 drought impacts as a proportion of the total have fallen over time whereas Type 2 impacts have increased. Two potential explanations for this result can be derived from our data and are summarized in figure 4.

Figure 4.

Ratio of temperature to rainfall and drought intensity following dry years. (a) Ratio of temperature to rainfall over time; (b) Drought intensity over time (affected only); (c) Average proportion of dry events following a dry year, by type; (d) Average index value following a dry year, by type (affected only).

Notes: All figure panels use a bandwidth of 2 for the local polynomial. For panels (a), (b) and (d), the left y-axis refers to values for type 1 events whereas the y-axis on the right-hand side shows values for type 2 events. Panel (a) plots the ratio of the normalized cooling-degree days over the normalized negative cumulative rainfall for all events where the index is positive. A lower value of this ratio implies that the contribution of temperature to the drought index is lower. Panel (b) plots the changes in the index values over time for affected districts. Panel (c) plots the proportion of drought-affected districts if either index was positive the preceding year. Finally, panel (d) plots the index value if the preceding year was characterized by a positive index value.

First, our results could be driven by trends in the composition of Type 1 and Type 2 events, with the former increasingly driven by cumulative heat over the growing season and the latter by rainfall deficiency. Figure 4a shows the ratio of normalized CDD to normalized rainfall deficiency. A higher value indicates a higher contribution of CDD relative to rainfall in our index. The plotted linear trend in figure 4a suggests that the composition of the two types of drought has followed different patterns over time, with Type 1 droughts increasingly driven by CDD and Type 2 droughts increasingly driven by rainfall deficiency. As shown in figure 4b, this change in composition is not captured by the index value, which has followed very similar trends. However, should rainfall deficiency and CDD increases be associated with different impacts, the change in composition could partially explain the increase in impacts over time for Type 2 droughts.

Second, Type 2 droughts seem to be increasingly preceded by dry years, which could increase the impact of this type of drought, turning what we have defined in this study as a meteorological drought into a potential hydrological drought. Figure 4c plots the proportion of affected districts (by drought type) in a given year, conditional on the previous year being drier than average (i.e., either a Type 1 or Type 2 drought). This figure shows contrasting trends for Type 1 and Type 2 events, with the proportion of the former declining slightly while the proportion of the latter follows an increasing trend. Figure 4d, which plots the average intensity of droughts (by type) if preceded by a drier-than-average year, also suggests that the intensity of Type 2 droughts increased at a faster rate than that of Type 1 droughts.Footnote ¹³

Supporting evidence that lagged dry events might be associated with larger drought impacts is given by Shah and Kishore (Reference Shah and Kishore2009), who argue that in years of below-average precipitation, more groundwater tends to be extracted to compensate for rainfall deficiency and minimize production losses. However, the extent to which losses can be minimized depends on the availability of groundwater. For example, 2002–2003 was an exceptionally dry period preceded by two moderately dry years, which put additional pressure on groundwater resources. These resources had not sufficiently recovered by 2002–2003, thus limiting their capacity to minimize production losses.

5. Estimating yield and economic losses

We estimate yield impacts and economic costs by running simple simulations using our estimated regressions in table 3 (see appendix B). Column 1 shows the predicted impacts of Type 1 droughts when Type 2 droughts are excluded (i.e., using results from columns 1 and 3 in table 3) and columns 2–4 show the impacts of both types of drought (i.e., using results from columns 2 and 4 in table 3). Specifically, we estimate the: (i) average yield loss for an affected district over the sample period; (ii) average total production loss for an affected district over the sample period; (iii) average value of production loss for an affected district; (iv) average yearly production loss across all the Indian districts in our sample; and (v) the average yearly cost of predicted production losses across sampled districts. A summary of estimates is presented in table 6.

Table 6.

Cost estimates

Notes: Rice prices use the 2008 prices converted into US$ using the average monthly exchange rate for 2008. All numbers were rounded to two decimal places. The results presented in column 4 are simply the aggregation of the results presented in columns 2 and 3.

From table 6, we note that, despite a higher estimated coefficient, total yield and economic losses from Type 2 droughts are smaller than those from Type 1 droughts. This is due to the index values for Type 2 droughts being substantially lower (approximately half) in affected districts. Depending on the specification used, we estimate the range of average yield loss per district at 130–155 kg/ha (table 6, column 2) and 84–121 kg/ha (table 6, column 3) for Type 1 and Type 2 droughts, respectively. These smaller impacts on yield translate into lower total economic costs. We estimate that, in a given year, the total economic cost of a Type 1 drought ranges, on average, between US$224–265 million (table 6, column 2),Footnote ¹⁴ whereas this falls to US$121–185 million for a Type 2 drought (table 6, column 3). We note that the estimated impact of Type 1 droughts increases when Type 2 droughts are included. This is due to the fact that, when Type 2 droughts are excluded, they are part of the ‘no drought’ counterfactual, which is likely to bias the Type 1 drought impacts downwards.

Omitting Type 2 droughts can lead to a lower estimate of Type 1 drought impacts. These effects are quantifiably large as we illustrate by comparing the first two columns of table 6 for the full sample. Average yield losses are estimated to be approximately 25–27 per cent higher (from 104–122 kg/ha to 130–155 kg/ha) when Type 2 droughts are included. These estimates have a substantial effect on the estimated average annual cost. This ranges from US$179–204 million (table 6, column 1) when Type 2 droughts are omitted compared to US$224–265 million (table 6, column 2) when they are included, which represents a 25–30 per cent increase. Thus, if estimating the economic cost of Type 1 droughts without accounting for Type 2 droughts, the average yearly total costs of drought would approximate US$179–204 million. Including Type 2 droughts raises this total cost by 121–124 per cent to US$402–450 million (table 6, column 4). Overall, both specifications suggest that Type 2 droughts are responsible for about 35–40 per cent of the total ex-post economic value of yield losses.