Thursday, August 10, 2017

Climate change, crop failure, and suicides in India (Guest post by Tamma Carleton)

[This is a guest post by Tamma Carleton, a Doctoral Fellow at the Global Policy Lab and PhD candidate in the Ag and Resource Econ department here at Berkeley]

Last week, I published a paper in PNAS addressing a topic that has captured the attention of media and policymakers around the world for many years – the rising suicide rate in India. As a dedicated student of G-FEED contributors, I focus on the role of climate in this tragic phenomenon. I find that temperatures during India’s main growing season cause substantial increases in the suicide rate, amounting to around 65 additional deaths if all of India gained a degree day. I show that over 59,000 suicides can be attributed to warming trends across the country since 1980. With a range of different approaches I’ll talk about here, I argue that this effect appears to materialize through an agricultural channel in which crops are damaged, households face economic distress, and some cope by taking their own lives. It’s been a pretty disheartening subject to study for the last couple years, and I’m glad to see the findings out in the world, and now here on G-FEED.

First, a little background on suicides in India. The national suicide rate has approximately doubled since 1980, from around 6 per 100,000 to over 11 per 100,000 (for reference, the rate in the U.S. is about 13 per 100,000). The size of India’s population means this number encompasses many lives – today, about 135,000 are lost to suicide annually. There have been a lot of claims about what contributes to the upward trend, although most focus on increasing risks in agriculture, such as output price volatility, costly hybrid seeds, and crop-damaging climate events like drought and heat (e.g. here, here, and here). While many academic and non-academic sources have discussed the role of the climate, there was no quantitative evidence of a causal effect. I wanted to see if this relationship was in the data, and I wanted to be able to speak to the ongoing public debate by looking at mechanisms, a notoriously thorny aspect of the climate impacts literature.

The first finding in my paper is that while growing season temperatures increase the annual suicide rate, also hurting crops (as the G-FEED authors have shown us many times over), these same temperatures have no effect on suicides outside the growing season. While the results are much less certain for rainfall (I’m stuck with state-by-year suicide data throughout the analysis), a similar pattern emerges there, with higher growing season rainfall appearing to cause reductions in suicide: 

These effects seem pretty large to me. As I said above, a degree day of warming throughout the country during the growing season causes about 65 suicides throughout the year, equivalent to a 3.5% increase in the suicide rate per standard deviation increase in growing season degree days. 

The fact that the crop response functions are mirrored by the suicide response functions is consistent with an agricultural mechanism. However, this isn’t really enough evidence. Like in other areas of climate impacts research, it’s difficult to find exogenous variation that turns on or shuts off the hypothesized mechanism here –  I don’t have an experiment where I randomly let some households’ farm income be unaffected by temperature, as others’ suffer. Therefore, aspects of life that are different in the growing and non-growing seasons could possibly be driving heterogeneous response functions between temperature and suicide.  Because this mechanism is so important to policy, I turn to a couple additional tests.

I first show that there are substantial lagged effects, which are unlikely to occur if non-economic, direct links between the climate and suicidal behavior were taking place (like the psychological channels linking temperature to violence discussed in Sol and Marshall’s work). I also estimate spatial heterogeneity in both the suicide response to temperature, as well as the yield response, and find that the locations where suicides are most sensitive to growing season degree days also tend to be the locations where yields are most sensitive: 

The fact that higher temperatures mean more suicides is troubling as we think about warming unfolding in the next few decades. However, I’m an economist, so I should expect populations to reallocate resources and re-optimize behaviors to adapt to a gradually warming climate, right? Sadly, after throwing at the data all of the main adaptation tests that I’m aware of from the literature, I find no evidence of adaptation, looking both across space (e.g. are places with hotter average climates less sensitive?) and across time (e.g. has the response function flattened over time? What about long differences?):

Keeping in mind that there is no evidence of adaptation, my last calculation is to estimate the total number of deaths that can be attributed to warming trends observed since 1980. Following the method in David, Wolfram and Justin Costa-Roberts’ 2011 article in Science, I find that by end of sample in 2013, over 4,000 suicides per year across the country can be attributed to warming. Integrating from 1980 to today and across all states in India, I estimate that over 59,000 deaths in total can be attributed to warming. With spatially heterogeneous warming trends and population density, these deaths are distributed very differently across space:

While the tools I use are not innovative by any means (thanks to the actual authors of this blog for developing most of them), I think this paper is valuable to our literature for a couple reasons. First, while we talk a lot about integrating our empirical estimates of the mortality effects of climate change into policy-relevant metrics like the SCC, this is a particular type of death I think we should be incredibly concerned about. Suicide indicates extreme distress and hardship, and since we care about welfare, these deaths mean something distinct from the majority of the deaths driving the mortality rate responses that we often study. 

Second, mechanisms really matter. The media response to my paper has been shockingly strong, and everyone wants to talk about the mechanism and what it means for preventative policy. While I have by no means nailed the channel down perfectly here, a focus on testing the agricultural mechanism has made my findings much more tangible for people battling the suicide epidemic on the ground in India. I look forward to trying to find ways to improve the tools at our disposal for identifying mechanisms in this context and in others.

Finally, as climate change progresses, I think we could learn a lot from applying David, Wolfram, and Justin’s method more broadly. While attribution exercises have their own issues (e.g. we can’t, of course, attribute with certainty the entire temperature trend in any location to anthropogenic warming), I think it’s much easier for many people to engage with damages being felt today, as opposed to those likely to play out in the future. 


  1. Sad but very interesting paper, congratulations! I got couple of questions on the econometric estimation you followed. I am also working on climate and econometrics and I'd be happy to read your insights:
    - have you tried normalizing temperature (deviation from mean / trend, etc.)? if so, what are the results?
    - same for precipitation, for example using SPI or SPEI - considering the range of precipitation in the different seasons - and particularly what a 100-mm increment means in the different regions, could this have implications on your results?
    - how does the linear trend interact with your results, where could we find the value of the linear trend coefficient in your paper, as well as the time fixed effects for the different model specifications?

  2. Hello Florent,

    Thanks for your comments. I hope these responses are helpful, happy to clarify if you still have questions.

    Normalizing temperature:
    • I haven’t experimented with regressing on normalized temperature. However, with state fixed effects, I’m already using within-state deviations of temperature exposure from the sample average to identify the causal effect on suicides. Despite that, you are absolutely right that it’s possible a given level of temperature increase has a differential effect on suicides in locations with heterogeneous average exposures, and/or heterogeneous within-location variances. While I haven’t tested this possibility directly, the results shown in Figure 3A in the main text show that locations with higher average temperatures do tend to have a distinct response function, although this result is noisy.

    Normalizing precipitation:
    • As you mentioned, it’s very possible that with different average levels of rainfall, the suicide rate in different regions will respond heterogeneously to the same treatment of rainfall. In my main model, I don’t estimate this heterogeneity. However, in the drought and surplus rainfall regressions shown in Figure 2B (main text) and S8 (SI), I do use a normalized measure, where the definition of “surplus” and “drought” is location-specific.

    Temporal effects:
    • This is a great question. As is pretty standard, I don’t report the coefficients on either year fixed effects or on state-specific linear trends. Of course, there are many non-climatic drivers of suicide that are changing over the years in my sample, and these trends and year effects are statistically significant in many specifications. However, take a look at Tables S3 and S8 (SI) to see that my main findings are not very sensitive to the types of temporal controls I use. I’m working on getting my final dataset and replication code up on my website, at which point you can take a look at the coefficients on these time controls directly.