Labor supply responses to rainfall shocks

Key Question Being Explored in This Insight:

How do households in developing countries cope with unpredictable rainfall affecting agricultural productivity?

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How do households in developing countries cope with unpredictable rainfall affecting agricultural productivity?

Image courtesy of interviewee. December 9, 2024

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This video discusses the reliance of households in developing countries on agriculture as their primary source of income, particularly noting how agricultural production is largely dependent on rainfall due to inadequate irrigation facilities. It highlights the impact of rainfall variability—both insufficient and excessive—on agricultural output and reflects on the decline of research on this critical issue despite its continued significance.

Further, the text emphasizes consumption smoothing strategies employed by households to cope with income fluctuations caused by rainfall shocks. It examines gender differences in responses to these shocks through empirical data analysis. The study investigates how men and women allocate their time to different activities during periods of rainfall shocks, discovering that women tend to bear the brunt of adjustments, often reducing their educational engagement, while men may shift toward more regular wage work.

The research uses historical rainfall data and a national sample survey dataset from India to analyze the effects of rainfall variability on labor allocation. It determines that women often reduce time spent in education in response to economic pressures, revealing a negative long-term effect on women's human capital.

Additionally, the text critiques the effectiveness of India's National Rural Employment Guarantee Scheme (NREGS) as a form of social insurance against such shocks, suggesting the program's role may not be as robust as previously thought. It calls for policy changes to address the increased volatility associated with climate change that complicates traditional coping strategies used by households. The recommendation includes government interventions, like conditional cash transfers, to prevent the withdrawal of girls from educational institutions and to better support affected households during challenging times.

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Here are the five key points from the video:

1. **Dependence on Agriculture**: A large majority of households in developing countries rely on agriculture as their main source of livelihood. This reliance makes them highly susceptible to rainfall variability due to a lack of irrigation facilities, leading to fluctuations in agricultural output.

2. **Consumption Smoothing Strategies**: To mitigate income fluctuations caused by rainfall shocks, households employ various strategies such as increasing labor supply, reducing investments in education for children, and leveraging microfinance for public works programs, indicating complex behavior in response to shocks.

3. **Gender Differences in Response**: The research indicates that men and women respond differently to rainfall shocks. While men tend to shift towards regular wage work during adverse conditions, women often reduce their educational engagement, suggesting that women bear a disproportionate burden during these shocks.

4. **Limitations of Employment Programs**: The effectiveness of employment programs like the National Rural Employment Guarantee Scheme (NREGS) in providing insurance against shocks is called into question. Many women may not seek jobs in these programs due to perceived limitations and competition for available positions during times of need.

5. **Implications for Policy and Climate Change**: The findings highlight the need for policymakers to consider the timing of job availability in rural employment schemes to align with agricultural cycles, as well as the emerging challenges posed by climate change, which may disrupt traditional coping mechanisms that families have relied on. Furthermore, there is an urgent need for programs aimed at enhancing women’s education and human capital, as their education impacts the future human capital of the next generation.

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Here are some key quotes extracted from the video you :

1. **Dependence on Agriculture**: "Large majority of households in developing countries are dependent on agriculture as the main source of livelihood."

2. **Impact of Rainfall on Agriculture**: "Their production is heavily rainfall-dependent... bad rainfall year [is] shock to the system, agricultural output is low."

3. **Consumption Smoothing**: "You want to smooth consumption... this is this whole [concept of] consumption smoothing that is called."

4. **Women's Educational Impact**: "Women... seem to be bearing the brunt of adjustment... they spend less time attending educational institutions."

5. **Negative Effects of Rainfall Shocks**: "The fact that women's educational attainment goes down... is not a due result, right? Because there are other cases from other countries which we see where we've seen this happen."

6. **NREGA's Limited Role**: "We don’t seem to find... the insurance role of NREGA... while you can get a job, there is rationing of jobs... women kind of anticipate that and don’t seem to be trying to get NREGA jobs."

7. **Need for Government Planning**: "Governments need to plan for [the timing of demand for NRGS jobs... when there might be excess demand]."

8. **Impact of Climate Change**: "Climate change means... weather is going to become more volatile... the role of policymakers becomes very important."

9. **Long-term Effects on Human Capital**: "Women are bearing the burden... and that has long-term effects... women's human capital today has big effects on the next generation's human capital."

These key quotes reflect the video's discussion on agricultural dependence, the impact of rainfall variability on household income and education—particularly for women—and the necessity for effective policymaking in the convideo of climate change and rural employment programs.

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Large majority of households in developing countries,

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you know, are depend on agriculture,

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as the main source of livelihood.

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Uh, which also means, you know,

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in most developing countries,

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because there is lack

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of irrigation facilities and and so on.

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There, they're, there, there production is heavily,

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rainfall dependent.

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And that's true even now, which means, you know,

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bad rainfall, year shock, shock to the system,

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agricultural output is low.

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Um, the other cases also happened very good rainfall year.

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The excessive rainfall year can also have similar

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effects, right?

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Uh, so this, there's a large literature on this, you know,

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through the eighties, nineties, and two thousands and so on.

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Somehow it's kind of gone down, it become,

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even though the issue is not any less important now,

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the research has, has become rare in some sense,

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but it's kind of our way of going back.

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You know, some of my early work was on this issue.

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So the households, you know, income is fluctuating.

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You want to smooth consumption.

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So households use a number of different strategies to,

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you know, smooth consumption.

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This is this whole electrician consumption smoothing

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that is called, right?

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So that includes like things like supplying additional units

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of labor, additional labor, you know,

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reducing human capital investment in children

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or, you know, marrying daughters to distant households so

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that the, whether shocks are not correlated, you know,

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often marriage marriages outside the same district,

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you know, it could be selling productive assets,

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borrow from microfinance institutions

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or, you know, might be working more

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for public works programs and so on.

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So, there's a large literature and,

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and we kind of try to contribute to this.

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And looking at it, looking at it from a within household

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perspective, are there, you know, do men

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and women react differently in terms of

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how they allocate their time to different activities

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when they face a rainfall shock?

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Purely empirical, a large number of data sets.

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So obviously there are things you need.

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First of all, you need actual data on, you know,

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labor supply allocation, you know, different activities,

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how much time you spend on that,

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different activities, right?

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Then you need data on rainfall.

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Then you need data on actual production,

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even though it's just not central to the analysis.

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You can use rainfall shock.

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You assume make the assumption that rain bad,

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rainfall shock means, low out.

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But, but you have to also show this.

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But the main two data sets were,

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historical rainfall data.

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So there is this data available from the University

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of Delaware, I think, where they have rainfall data at,

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weather station level, gridded,

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weather station level from 1900 all the way to currently.

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So, and that's, I think, across all parts of the world,

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at least where there's weather stations were available.

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So what we did was we basically looked, used the geo codes

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of the weather stations, matched them to the

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Districts in which they were,

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those weather stations were based, between, you know,

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and, and we defined rainfall shocks as the rainfall.

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So we, you know, excess over the mean

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of the last 30 years in that district, right?

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And, we, we, we show it as a, you know, proportion

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of the standard deviation of rainfall.

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So that kind of gives you a, a measure of how,

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how far the rainfall in a particular year in a particular

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district is, from the

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long-term mean, so to say.

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Uh, and, and this is what is, we, we call this a Z score,

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and that's the most common way of calculating shock.

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So we define a negative rainfall shock or a dry shock

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or a rainfall shortage when this z is less than minus one,

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a positive rainfall shock,

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a wet shock when the Z is greater than one.

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Uh, that's the first dataset.

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Uh, the second dataset we use is this NSS data set,

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the national sample survey data set from India.

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You know, we used multiple rounds of this data set.

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So, one of the things that these data, at least from,

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a few rounds of that data they did was they asked

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households, you know, how much time they allocated

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to different activities in a day,

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in a particular reference week.

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Now, this MSS data has been going on for many years,

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though there's not been one after 2011,

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but this data on reference week is only available

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for a few rounds of the data, few rounds of the surveys.

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So we used those four rounds, 60 seconds, 64, 66,

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and 68 rounds of the surveys.

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And we looked at, you know, that the, we,

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we looked at how much did the,

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the survey asked the question in a particular way.

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They asked whether, you know, you, you, you,

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you'd work full day or half day.

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So it's not very continuous, very, very, you know,

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fine measure, but some kind of a, a course measure

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of time spent in different activities.

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And they ask, as with the national sample survey,

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they have data on a range of, activities,

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but we kind of, aggregate to,

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to get a few activities which we felt were crucial

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for our analysis.

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So those two were the main data sets that we used.

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Essentially, it's simple.

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The, the, what we did was left hand side variable

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is the amount of time, so aggregated over the week,

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over this reference week.

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Uh, so over the reference week amount of time

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spent in a particular activity is the left hand side

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variable by an individual, a male

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or a female in household h

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in district D in month M in year Y.

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And we regressed it on a set of variables.

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First two, the most important two were

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whether you faced a positive rainfall shock.

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I remember z less than minus one,

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less than plus one greater than plus one,

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or a negative rainfall shock Z less than minus one.

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Then we included some other household characteristics

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that could have an effect on, you know,

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how people allocate their resources,

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their time into different activities.

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And we also control for a set of district bond

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and year fixed effects.

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And the most important one is the district fixed effects,

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because we don't really want to compare

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across districts.

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Why? Because some districts have a lot

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of rainfall tradition, some districts have less rainfall.

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You don't really want to compare across districts

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that are very different.

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So you, we use this district fixed effects.

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So all comparisons are within

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a particular district across years in a particular district.

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So, district fixed effects allows us to make that, that,

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that, interpret our estimates

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of these zeds.

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Uh, in that particular way, what we wanted to look at was,

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whether men

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and be women kind of, react differently

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to this kind of, this rainfall shocks,

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whether it be positive or negative.

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And, we do find that,

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and sort of results are depressing

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because it is the women who seem to be bearing the brunt

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of, you know, adjustment.

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Um, men seem to be moving to regular

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when they're faced with rainfall shortage.

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They seem to be moving towards regular wage work.

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Now, the, puzzle

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to us was why were they not working more

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in regular wage work?

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Possibly regular wage work is,

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while low risk is also a low return output.

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So maybe only when there is, rain, you know,

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there's rainfall shortage

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and the the outside option,

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the other options are not very good.

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So they move to, working on re regular wage work.

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On the other hand, women, what we see is that they are,

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they, they spend less time

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attending educational institutions.

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So essentially when there's a rainfall shock, there is,

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women seem to be withdrawn from school or, or college

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or whatever it is, maybe to do other things in

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around the house, which men maybe have been doing before or,

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or, and now that they've, moved to other,

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other occupations to kind of, ensure against the shock

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that it's lack of income coming from, agriculture.

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So that's the first thing.

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The, so essentially women's education is

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Impacted. That's

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the negative result.

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The other result that we find,

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and which also surprised us a lot, look, the fact that

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women's educational attainment goes down, you know,

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they spend less time going to educ school

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education institutions.

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It is however negative that might be,

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is not a due result, right?

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Because there are other cases from other countries,

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which we see, where we've seen this happen.

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The other results, what we find was quite

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surprising was this, you know, the, the, the, the

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National Rural Employment Guarantee Scheme in India,

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this big work fair program, which has been you,

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which has been termed as a, you know, last resort

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or employer of the last, last resort, and so on so forth,

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and how the insurance big in insurance aspect.

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We don't seem to find, the N-R-E-G-S

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helping with insurance.

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Um, it, it's not

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that NN RRG GS is not doing things N rrg GS is helping

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thing, but there is this insurance role of N rrg GS is not

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as strong as, which as what we had thought it would be.

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At least that's what we seem to find from our data.

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It's possibly that lot of people

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think that when there is a shock,

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a negative shock, they won't get job in the N-R-E-G-S

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because there, there are, you know, while you can get a job,

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there are, there is rationing of jobs, there's more demand

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for jobs in certain cases, certain time periods,

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and there's not enough jobs going

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around in certain times of the year.

216
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So individuals and household members and men

217
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and women kind of anticipate that

218
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and don't seem to be, even trying to get NRGS jobs.

219
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Uh, there's nothing, no way we can kind of pin that down,

220
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but that seems to be what is happening and, and,

221
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and, maybe the way to go and,

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and I'm sure we'll talk a little bit about policy.

223
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Maybe the way to go is kind of, there are certain times

224
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of the year when this seems to be happening right there,

225
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rainfall shock in, in May, June

226
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for the given India's agricultural situation.

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Um, timing of agriculture, majority crop, A lot

228
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of the crops are grown during this, you know,

229
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the curry season and so on, that's planted in May, June,

230
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and harvested in, in,

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in the big rice crops are grown during that time.

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If there is a bad rainfall shock in may, June,

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that means you are likely to have an effect

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because this moisture content of, of the soil goes down.

235
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There is a possibility that outcomes, that,

236
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that the production is going to be less later in the,

237
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even though rainfall might be good over June and July.

238
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So households might be wanting to work more in NRGS

239
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during May, June,

240
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but that's when there seems to be a shortage of,

241
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jobs available because more people are all going into work.

242
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And therefore, how, you know, there's a lack of jobs,

243
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how government should be very careful,

244
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think carefully about this timing of the demand

245
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for NRGS jobs, right?

246
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Uh, it is not going to be, there is going to be a demand,

247
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excess demand in certain times of the year.

248
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So governments need to plan for it.

249
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Uh, the right to education act in India says

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that any rural household can get up to a hundred days

251
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of job, a hundred days of work as a part

252
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of this N-R-H-G-S through the year.

253
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Now that's great, but you know, for obvious reasons

254
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and from some other reasons that we,

255
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we talked about just now, there is going to be a kind

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of a time element to when the demand is going to be high,

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the government needs to take this account into account.

258
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And if knowing that there might be excess demand

259
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for jobs in certain times of the year, they kind of need

260
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to plan ahead.

261
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That, that's one of the things.

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Um, second thing is, you know,

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climate change, means that

264
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not just, it's not that it's going to become warmer,

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it's going to become, more volatile.

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The weather is going to be more, become more volatile.

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You're going to have more variations in rainfall.

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You're going to have more variations in temperature.

269
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Uh, so, the, you know, things that have worked

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for many years, like in the absence of government policies,

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you know, there have been,

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households have historically learned

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from rainfall patterns,

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and they have, you know, designed informal policies, right?

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This climate change means this, these, these things

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that have been, how should I, what, what's the word?

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I should, these, these things that have been in,

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have been used by households for years

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and years might no longer work

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because all of a sudden you're faced with an environment

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that is very different from

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what households have been, are used to.

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So the role of the policy makers

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becomes very important.

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And designing the right institutions to help would I,

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is, is, is very, very crucial.

287
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Um, and the third thing in my mind is that,

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at least from our data

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and from data from other countries, we, it seems

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as if in terms of rain, when, when faced

291
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with rainfall shocks, women are bearing the burden, right?

292
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They are being withdrawn from, education.

293
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So that has long-term effects, right?

294
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You're not going to school your human capitalism lower.

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And we know women's human capital today has big effects on

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the next generation's human capital, right?

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So, maybe it is the government now has

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to step in and, and

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Introduce programs that will encourage households not

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to withdraw their daughters, the,

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the women from educational institutions,

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maybe something like a conditional cash transfer

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program, right?

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Government needs to think about alternative,

305
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ways in which they can mitigate some of these effects

306
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of shocks and not let, let the, you know, it be kind

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of diffused and decentralized base and, and,

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and let households do what they have always done.

Impact of Rainfall Dependency on Agricultural Livelihoods

00:00:00 - Large majority of households in developing countries, you know, are depend on agriculture, as the main source of livelihood. which also means, you know, in most developing countries, because there is lack of irrigation facilities and and so on. There, they're, there, there production is heavily, rainfall dependent. And that's true even now, which means, you know, bad rainfall, year shock, shock to the system, agricultural output is low. the other cases also happened very good rainfall year.

00:00:33 - effects, right? so this, there's a large literature on this, you know, through the eighties, nineties, and two thousands and so on. Somehow it's kind of gone down, it become, even though the issue is not any less important now, the research has, has become rare in some sense, but it's kind of our way of going back. You know, some of my early work was on this issue. So the households, you know, income is fluctuating. You want to smooth consumption.

00:01:06 - you know, smooth consumption. This is this whole electrician consumption smoothing that is called, right? So that includes like things like supplying additional units of labor, additional labor, you know, reducing human capital investment in children or, you know, marrying daughters to distant households so that the, whether shocks are not correlated, you know, often marriage marriages outside the same district, you know, it could be selling productive assets, borrow from microfinance institutions

Gendered Responses to Rainfall Shocks: An Empirical Analysis

00:01:36 - for public works programs and so on. So, there's a large literature and, and we kind of try to contribute to this. And looking at it, looking at it from a within household perspective, are there, you know, do men and women react differently in terms of how they allocate their time to different activities when they face a rainfall shock? Purely empirical, a large number of data sets. So obviously there are things you need. First of all, you need actual data on, you know,

00:02:08 - how much time you spend on that, different activities, right? Then you need data on rainfall. Then you need data on actual production, even though it's just not central to the analysis. You can use rainfall shock. You assume make the assumption that rain bad, rainfall shock means, low out. But, but you have to also show this. But the main two data sets were, historical rainfall data. So there is this data available from the University of Delaware, I think, where they have rainfall data at,

00:02:41 - weather station level from 1900 all the way to currently. So, and that's, I think, across all parts of the world, at least where there's weather stations were available. So what we did was we basically looked, used the geo codes of the weather stations, matched them to the Districts in which they were, those weather stations were based, between, you know, and, and we defined rainfall shocks as the rainfall. So we, you know, excess over the mean

Measuring Rainfall Shocks: Z-Scores and Data Analysis

00:03:17 - And, we, we, we show it as a, you know, proportion of the standard deviation of rainfall. So that kind of gives you a, a measure of how, how far the rainfall in a particular year in a particular district is, from the long-term mean, so to say. and, and this is what is, we, we call this a Z score, and that's the most common way of calculating shock.

00:03:48 - or a rainfall shortage when this z is less than minus one, a positive rainfall shock, a wet shock when the Z is greater than one. that's the first dataset. the second dataset we use is this NSS data set, the national sample survey data set from India. You know, we used multiple rounds of this data set. So, one of the things that these data, at least from,

00:04:19 - households, you know, how much time they allocated to different activities in a day, in a particular reference week. Now, this MSS data has been going on for many years, though there's not been one after 2011, but this data on reference week is only available for a few rounds of the data, few rounds of the surveys. So we used those four rounds, 60 seconds, 64, 66,

Analyzing Time Allocation in Rural Households

00:04:50 - And we looked at, you know, that the, we, we looked at how much did the, the survey asked the question in a particular way. They asked whether, you know, you, you, you, you'd work full day or half day. So it's not very continuous, very, very, you know, fine measure, but some kind of a, a course measure of time spent in different activities. And they ask, as with the national sample survey, they have data on a range of, activities,

00:05:22 - to get a few activities which we felt were crucial for our analysis. So those two were the main data sets that we used. Essentially, it's simple. The, the, what we did was left hand side variable is the amount of time, so aggregated over the week, over this reference week. so over the reference week amount of time spent in a particular activity is the left hand side variable by an individual, a male

00:05:54 - in district D in month M in year Y. And we regressed it on a set of variables. First two, the most important two were whether you faced a positive rainfall shock. I remember z less than minus one, less than plus one greater than plus one, or a negative rainfall shock Z less than minus one. Then we included some other household characteristics that could have an effect on, you know,

Gendered Responses to Rainfall Shocks in Agricultural Households

00:06:24 - their time into different activities. And we also control for a set of district bond and year fixed effects. And the most important one is the district fixed effects, because we don't really want to compare across districts. Why? Because some districts have a lot of rainfall tradition, some districts have less rainfall. You don't really want to compare across districts that are very different. So you, we use this district fixed effects.

00:06:55 - a particular district across years in a particular district. So, district fixed effects allows us to make that, that, that, interpret our estimates of these zeds. in that particular way, what we wanted to look at was, whether men and be women kind of, react differently to this kind of, this rainfall shocks,

00:07:25 - And, we do find that, and sort of results are depressing because it is the women who seem to be bearing the brunt of, you know, adjustment. men seem to be moving to regular when they're faced with rainfall shortage. They seem to be moving towards regular wage work. Now, the, puzzle to us was why were they not working more

Impact of Rainfall Shock on Women's Education and Labor Dynamics

00:07:55 - Possibly regular wage work is, while low risk is also a low return output. So maybe only when there is, rain, you know, there's rainfall shortage and the the outside option, the other options are not very good. So they move to, working on re regular wage work. On the other hand, women, what we see is that they are, they, they spend less time attending educational institutions. So essentially when there's a rainfall shock, there is,

00:08:31 - or whatever it is, maybe to do other things in around the house, which men maybe have been doing before or, or, and now that they've, moved to other, other occupations to kind of, ensure against the shock that it's lack of income coming from, agriculture. So that's the first thing. The, so essentially women's education is Impacted. That's the negative result.

00:08:59 - and which also surprised us a lot, look, the fact that women's educational attainment goes down, you know, they spend less time going to educ school education institutions. It is however negative that might be, is not a due result, right? Because there are other cases from other countries, which we see, where we've seen this happen. The other results, what we find was quite surprising was this, you know, the, the, the, the

Challenges of NREGA: Employment and Insurance During Shocks

00:09:31 - this big work fair program, which has been you, which has been termed as a, you know, last resort or employer of the last, last resort, and so on so forth, and how the insurance big in insurance aspect. We don't seem to find, the N-R-E-G-S helping with insurance. it, it's not that NN RRG GS is not doing things N rrg GS is helping thing, but there is this insurance role of N rrg GS is not

00:10:06 - At least that's what we seem to find from our data. It's possibly that lot of people think that when there is a shock, a negative shock, they won't get job in the N-R-E-G-S because there, there are, you know, while you can get a job, there are, there is rationing of jobs, there's more demand for jobs in certain cases, certain time periods, and there's not enough jobs going around in certain times of the year.

00:10:37 - and women kind of anticipate that and don't seem to be, even trying to get NRGS jobs. there's nothing, no way we can kind of pin that down, but that seems to be what is happening and, and, and, maybe the way to go and, and I'm sure we'll talk a little bit about policy. Maybe the way to go is kind of, there are certain times of the year when this seems to be happening right there, rainfall shock in, in May, June for the given India's agricultural situation.

Impact of Rainfall on Agricultural Production and Employment

00:11:10 - of the crops are grown during this, you know, the curry season and so on, that's planted in May, June, and harvested in, in, in the big rice crops are grown during that time. If there is a bad rainfall shock in may, June, that means you are likely to have an effect because this moisture content of, of the soil goes down. There is a possibility that outcomes, that, that the production is going to be less later in the, even though rainfall might be good over June and July.

00:11:42 - during May, June, but that's when there seems to be a shortage of, jobs available because more people are all going into work. And therefore, how, you know, there's a lack of jobs, how government should be very careful, think carefully about this timing of the demand for NRGS jobs, right? it is not going to be, there is going to be a demand, excess demand in certain times of the year. So governments need to plan for it.

00:12:17 - that any rural household can get up to a hundred days of job, a hundred days of work as a part of this N-R-H-G-S through the year. Now that's great, but you know, for obvious reasons and from some other reasons that we, we talked about just now, there is going to be a kind of a time element to when the demand is going to be high, the government needs to take this account into account. And if knowing that there might be excess demand

Adapting to Climate Change: The Need for Policy and Institutional Support

00:12:45 - to plan ahead. That, that's one of the things. second thing is, you know, climate change, means that not just, it's not that it's going to become warmer, it's going to become, more volatile. The weather is going to be more, become more volatile. You're going to have more variations in rainfall. You're going to have more variations in temperature.

00:13:19 - for many years, like in the absence of government policies, you know, there have been, households have historically learned from rainfall patterns, and they have, you know, designed informal policies, right? This climate change means this, these, these things that have been, how should I, what, what's the word?

00:13:50 - have been used by households for years and years might no longer work because all of a sudden you're faced with an environment that is very different from what households have been, are used to. So the role of the policy makers becomes very important. And designing the right institutions to help would I, is, is, is very, very crucial. and the third thing in my mind is that,

Mitigating the Impact of Rainfall Shocks on Women's Education

00:14:22 - and from data from other countries, we, it seems as if in terms of rain, when, when faced with rainfall shocks, women are bearing the burden, right? They are being withdrawn from, education. So that has long-term effects, right? You're not going to school your human capitalism lower. And we know women's human capital today has big effects on the next generation's human capital, right? So, maybe it is the government now has to step in and, and Introduce programs that will encourage households not

00:15:15 - the women from educational institutions, maybe something like a conditional cash transfer program, right? Government needs to think about alternative, ways in which they can mitigate some of these effects of shocks and not let, let the, you know, it be kind of diffused and decentralized base and, and, and let households do what they have always done.

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