metadata: authors: 0: author: Narloch,Ulf Gerrit
count: Vietnam
docna: 0: docna: The varying income effects of weather
            variation : initial insights from rural Vietnam
docty: Policy Research Working Paper
subtopic: Climate Change and Agriculture,Science of Climate Change,Climate Change and Health,Forests and Forestry,Climate Change and Environment,Crops and Crop Management Systems,Livestock and Animal Husbandry,Forestry,Coastal and Marine Resources,Energy and Natural Resources,Inequality
teratopic: Poverty Reduction,Science and Technology Development,Rural Development,Environment,Health, Nutrition and Population,Macroeconomics and Economic Growth
abstracts: cdata!: To estimate the impact of weather on
            rural income changes over time, this study combines data
            from the panel subsample of the latest Vietnam Household
            Living Standard Surveys 2010, 2012, and 2014 and gridded
            weather data from the Climate Research Unit. The analyses
            show: (i) crop cultivation, livestock management, forestry
            and fishing activities, and agricultural wages remain
            important income sources in rural Vietnam—especially for
            poorer households; (ii) rural communes are exposed to
            substantial inter- and intra-annual weather variation, as
            measured by annual, seasonal, abnormal, and extreme weather
            conditions and weather events; and (iii) these types of
            weather variation are indeed related to income variation. In
            particular, warmer temperatures and heat extremes can have
            negative income effects in all climate contexts and for all
            socioeconomic groups and most income activities. Only staple
            crops, forestry, and fishing seem to be less sensitive to
            hotter conditions. The effects of rainfall are more
            difficult to generalize. Some findings indicate that more
            rainfall is beneficial in drier places but harmful in wetter
            places. Interestingly, the incomes of poorer households seem
            to be negatively affected by wetter conditions, while those
            of wealthier households are more impacted by drier
            conditions. An increase in rainfall levels and flood
            conditions between 2012 and 2014, which were relatively wet
            years, is related to reduced income growth between these two
            years. Altogether these findings suggests that greater
            attention has to be paid to making rural livelihoods more
            resilient to weather variation which, is very likely to
            increase because of climate change.
geo_reg_key: 517198,517191
repnme: repnme: The varying income effects of weather
            variation : initial insights from rural Vietnam
display_title: The varying income effects of
            weather variation : initial insights from rural Vietnam
listing_relative_url: /research/2016/07/26597193/varying-income-effects-weather-variation-initial-insights-rural-vietnam
topicv3: Crop Production m1326627 174,Forestry m1326646 436,Poverty m1326687 695,Climate Change m1326829 567,Livestock m1504490 4024
publishtoextweb_dt: 2016-07-25T00:00:00Z
historic_topic: Poverty Reduction,Science and Technology Development,Rural Development,Environment,Health, Nutrition and Population,Macroeconomics and Economic Growth
no_of_pages: 61
author_keys: 0: author_key: 1253358
model_extractions: WPS7764 Policy Research Working Paper 7764 Climate Change and Poverty in Vietnam _Background Paper_ The Varying Income Effects of Weather Variation Initial Insights from Rural Vietnam _Ulf Narloch_ Development Economics Environment and Natural Resources Global Practice Group & Climate Change Cross-Cutting Solutions Area July 2016
Policy Research Working Paper 7764

# **Abstract**

To estimate the impact of weather on rural income changes over time, this study combines data from the panel subsample of the latest Vietnam Household Living Standard Surveys 2010, 2012, and 2014 and gridded weather data from the Climate Research Unit. The analyses show: (i) crop cultivation, livestock management, forestry and fishing activities, and agricultural wages remain important income sources in rural Vietnam—especially for poorer households; (ii) rural communes are exposed to substantial inter- and intra-annual weather variation, as measured by annual, seasonal, abnormal, and extreme weather conditions and weather events; and (iii) these types of weather variation are indeed related to income variation. In particular, warmer temperatures and heat extremes can have negative income effects in all climate contexts and for all socioeconomic groups and most income activities. Only staple crops, forestry, and fishing seem to be less sensitive to hotter conditions. The effects of rainfall are more difficult to generalize. Some findings indicate that more rainfall is beneficial in drier places but harmful in wetter places. Interestingly, the incomes of poorer households seem to be negatively affected by wetter conditions, while those of wealthier households are more impacted by drier conditions. An increase in rainfall levels and flood conditions between 2012 and 2014, which were relatively wet years, is related to reduced income growth between these two years. Altogether these findings suggests that greater attention has to be paid to making rural livelihoods more resilient to weather variation which, is very likely to increase because of climate change.

This paper is a product of the World Bank Environment and Natural Resources Global Practice Group and the Climate Change Cross-Cutting Solutions Area and is a background paper for the World Bank work on “Climate Change and Poverty in Vietnam.” It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at unarloch@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **The Varying Income Effects of Weather Variation: Initial Insights from Rural Vietnam**<sup>*****</sup>

## Ulf Narloch

Sustainable Development Practice Group, World Bank, Washington, DC, USA

**Keywords:** Climate Change, Consumption, Households, Incomes, Livelihoods, Poverty, Shocks, Vulnerability

**JEL:** I30, I32, O10, Q10, Q54, R20

* **Acknowledgements:** This work is part of the programmatic AAA on Vietnam Climate Resilience and Green Growth (P148188) and was developed under the oversight of Christophe Crepin. It contributed to the global program on Climate Change and Poverty (P149919) under the oversight of Stephane Hallegatte. I am very thankful to the World Bank Vietnam team for providing the Vietnam Household Living Standards Survey (VHLSS) data and to Linh Hoang Vu and Ha Thi Ngoc Tran for helping with data questions. Mook Bangalore prepared the weather and geo‐spatial data. Maros Ivanic and Anne Zimmer contributed to earlier data preparations. Very helpful suggestions and comments on earlier versions of this work were received from Tuan Anh Le, Diji Chandrasekharan Behr, Gabriel Demombynes, Linh Hoang Vu, Christopher Jackson, Frederik Noack, Pamela McElwee, and Maurice Rawlins.
# **1. Introduction**

<mark>Especially for poor people with climate‐sensitive livelihoods, weather variation can be linked to living standards. Despite significant progress in poverty reduction, around 20 percent of Vietnam’s population still lived in extreme poverty in 2010 – about 27 percent in rural areas (World Bank, 2012). At the same time, Vietnam is also particularly sensitive to increasing climate hazards, including short‐lived natural disasters and inter‐ and intra‐annual variation in weather conditions, both of which are likely to be exacerbated by climate change.</mark><sup>1</sup> <mark>Such climate hazards can affect poor and other vulnerable people through various channels, such as agriculture and ecosystems, natural disasters, and health (Hallegatte et al., 2016).</mark> Many of the poverty impacts will unfold through changes in household incomes, which are hardly quantified. Existing studies for Vietnam investigate the effects of global warming on agricultural production using crop and hydrological models (Gebretsadik et al., 2012; Van Hoang et al., 2014; Yu et al., 2010). Other work estimates the macroeconomic cost of climate change in Vietnam through sectoral impacts (Arndt et al., 2012; World Bank, 2010). Global‐level work, including Vietnam‐specific results, estimates poverty impacts of generalized climate shocks using micro‐simulation techniques and Computable General Equilibrium (CGE) modeling (Ahmed et al., 2009; Hertel et al., 2010; Rozenberg and Hallegatte, 2015). Recent studies have shown the current income and welfare impacts of short‐lived natural disasters or extreme events on Vietnamese households (Arouri et al., 2015; Bui et al., 2014; Thomas et al., 2010). More work, however, is needed to understand household‐level income effects of more subtle variation in rainfall and temperature conditions and gradual changes to this variation.

A growing number of studies in a variety of contexts investigates how observed weather conditions affect economic outcomes, mainly in an attempt to quantify the potential economic impacts of future climate change (Auffhammer et al., 2013; Dell et al., 2014).<sup>2</sup> Yet a quantification of climate change impacts remains challenging even when the current income effects of weather variation are understood. Firstly, how weather conditions will alter due to climate change is highly uncertain and even existing Global Circulation Models (GCMs) bring diverging forecasts for some regions and countries. Moreover, income 1 In line with existing literature, this paper defines weather variation to describe shorter‐run temporal variation (variation between and within years), while climate is used for longer‐term variability of conditions and changes decades (Dell et al., 2014).

2 Many of these studies are based on cross‐country analyses comparing country income and production and weather conditions at various points in time (e.g. Burke et al., 2015, 2011; Dell et al., 2012; Hsiang, 2010; Schlenker and Lobell, 2010). Another strand of the literature focuses on the impacts on agricultural profits mostly using US data at the county level (Deschênes and Greenstone, 2012, 2007; Fisher et al., 2012; Mendelsohn et al., 1994; Schlenker et al., 2006; Schlenker and Roberts, 2009). Other studies use weather variables to explain household income in order to test income effects on other variables (Feng et al., 2010; Hidalgo et al., 2010; Yang and Choi, 2007). Interesting new studies estimate current weather impacts on incomes and living conditions (Baez et al., 2015; Noack et al., 2015; Park et al., 2015). Yet results from these micro‐level studies have not yet been used to simulate income effects under future climate change.
changes in the future are subject to adaptive responses and socioeconomic changes, such as the shift to less climate‐sensitive activities.

While it is hard to predict what happens in the future, it is even difficult to establish the links between weather and incomes in the present. While some activities are negatively affected by wetter or warmer rainfall conditions, others may benefits from it. And Income changes do not only depend on direct weather impacts on output, but also on indirect impacts such as price or production adjustments. Decreases in crop income through yield declines could be reduced or even offset through resulting increases in prices and wages (Hertel et al., 2010; Jacoby et al., 2014). Different income activities also play various functions. Some may actually serve as a coping strategy to compensate income shortfalls from other activities so that incomes from these activities increase in times of adverse weather conditions (Noack et al., 2015). In addition, weather effects depend on socioeconomic factors that determine the extent to which households choose risker (but more profitable) activities and can manage negative impacts through production adjustments (e.g. use more irrigation).

Another difficulty results from identifying the types of weather variation that are relevant for income fluctuations. Many studies use temperature and rainfall levels defined as annual means (Burke et al., 2011; Dell et al., 2009; Feng et al., 2010; Schlenker and Lobell, 2010). Yet weather impacts depend on their timing and the seasonality of income activities, so that some studies measure seasonal weather conditions (Hsiang, 2010; Mendelsohn et al., 1994; Welch et al., 2010; Yang and Choi, 2007). Moreover, excess rain and heat waves can be as harmful as rainfall scarcity and cold spells. To capture this non‐ linearity, some studies break rainfall and temperature levels into different intervals or only measure them above or below a certain threshold (Deryugina and Hsiang, 2014; Deschênes and Greenstone, 2011; Schlenker and Lobell, 2010).<sup>3</sup> To control for abnormal values, some studies measure the deviation from the long‐term mean normalized for location‐specific variability (Baez et al., 2015; Hidalgo et al., 2010; Noack et al., 2015). Weather extremes measured by minimum and maximum temperature and rainfall levels matter too (Welch et al., 2010). Very importantly, all of these forms of weather variation can have different impacts in different places depending on location‐specific climate conditions (Park et al., 2015).

In order to define recommendations for poverty eradication in face of increasing climate risks in Vietnam, this paper attempts to disentangle the current impacts of weather variation on income changes. To better understand the income effects of different types of weather variation, the analyses differentiate between annual, seasonal, abnormal and extreme weather conditions and weather events related to rainfall and temperature. They also investigate how weather impacts vary by socioeconomic group, climate zones and income activities. By covering this breadth of impacts, this study adds to the existing literature showing that income effects depend on the type of weather variation and different contexts.

These analyses can build on two novel methodological aspects. First, this study combines data from the latest Vietnam Household Living Standard Surveys (VHLSS) 2010, 2012 and 2014 and gridded weather data

> 3 Related to this approach is the concept of degree days, which assumes a piecewise‐linear function in temperatures defines as the sum of degrees above a lower threshold and below an upper threshold intervals (Schlenker and Lobell, 2010; Schlenker and Roberts, 2009) .

> .

3
from the Climate Research Unit (CRU). Second, this work takes advantage of the panel structure of this data set, which includes about half of the households in at least two of the three survey rounds. Regression techniques based on the panel data set can estimate the weather impact on income changes over time while reducing omitted variable bias. Despite the strength of these data and methods, a quantification of future impacts subject to uncertain climatic, environmental and socioeconomic changes is beyond reach.

The remainder of this paper is structured as follows: Section 2 explains the data and methods applied for the analyses. Section 3 shows that rural households remain highly reliant on agriculture and other ecosystem‐based activities. Section 4 demonstrates the extent of weather inter‐ and intra‐annual variation communes are exposed to. Section 5 presents the estimated income effects of weather variation from the various regression analyses. Section 6 concludes that in the face of climate change greater attention needs to be paid to make rural livelihoods more resilient to weather variation.

# **2. Data and methods**

Based on data from the Vietnam Household Living Standard Survey (VHLSS) collected in 2010, 2012, and 2014 with gridded weather data from the Climate Research Unit (CRU), this study fits a number of regression models to explain income differences.

## **2.1 Household and commune data**

Information on incomes and socioeconomic conditions is derived from the household and commune data from the VHLSS 2010, 2012, and 2014. These surveys are conducted by the General Statistics Office (GSO) with technical support from the World Bank in Vietnam. They are nationally representative and contain detailed information on individuals, households and communes. In total ca 9,400 households nationwide are included in each round with about half of the households in each round also being surveyed in the previous round so that the data set includes a short‐term panel.

These analyses focus on rural households and communes leaving a data set of about 20,000 household observations from ca. 2,250 communes. Each survey round covers about 6,600 and 6,700 rural households. About 1,400 households were interviewed in all three rounds, 1,600 in 2010 and 2012, and 1,400 in 2012 and 2014.

The household surveys include a wide array of socioeconomic data. At the individual level these data cover demographics, education, employment, health, and migration. At the household level the data comprise information on income and expenditures, employment and self‐production, durables, assets, and participation in government programs. Consumption estimates are based on per‐capita expenditure as calculated by the World Bank and GOS to determine the national poverty line. Incomes are calculated based on the raw data in line with classifications from the GSO (Section 3). All consumption and income values are expressed in 2010 prices using data on the Consumer Price Index from the World Development 4
Indicators. Other variables controlling for household demographics and assets are constructed from the data sets.

The commune surveys collect information about the occurrence of emergencies in the last 3 years listed by type and month in addition to information on population, economic conditions, agriculture and land. These data allow identifying communes exposed to weather events, such as storms, floods and droughts.

Although the data allow estimating nationally‐representative consumption and income estimates and their changes over time, several shortcomings for the purpose of this study are to be noted. First, the study relies on observations from only three years within a five year time period and only a subsample of the included households is observed in several years. This short‐term panel does not allow to understand time‐variant household factors or structural changes over longer time horizons. Second, little information is available about labor allocation, production inputs and returns, which are important to explain income differences over time. The lack of such information can limit the explanatory power of the regression models. Yet many of these variables could be determined by weather conditions and thus create potential endogeneity biases.

# **2.2 Weather data**

In addition to these self‐reported weather events in the commune surveys, this study uses weather data from the CRU of the University of East Anglia to control for rainfall and temperature conditions. From the global CRU TS3.21 data set, monthly time series of rainfall, minimum, mean and maximum temperature from 1961 to 2014 is available at 0.5 x 0.5‐degree grid.<sup>4</sup> This balanced panel of weather data was produced using statistical interpolation based data from 4,000 individual weather stations (Harris et al. 2014).

These data are merged at the commune level to construct current and long‐term weather variables for each month. For each commune, the household interview dates are specified and the current weather variables are defined for the 12 months before that interview date (e.g. if the interview date was in May 2012, the current weather variables cover June 2011‐ May 2012). The long‐term means and standard deviation of monthly rainfall, minimum, mean and maximum temperature are calculated for 30 years before these 12 months (e.g. in the above example June 1981– May 2011). Based on these data several variables measuring current weather conditions are constructed as explained in section 4.

Although gridded data set, such as CRU are commonly used in economic studies as they provide a balanced panel that adjusts for missing data and spatial factors (e.g. elevation), they suffer from some limitations for assessing weather variation at subnational level. Optimally such data would be measured based on ground station data, which, however, is not readily available for all of Vietnam. The precision of the CRU data at the subnational level depends on the interpolation method and the availability of station data for the areas of interest (Dell et al., 2014). Although this is an important concern that deserves further investigation, this is beyond the scope of this paper. Moreover, only monthly average rainfall and temperature data are available from CRU. Given high intra‐annual variations, the distribution of

> 4 http://www.cru.uea.ac.uk/cru/data/hrg/

5
temperature and precipitation within each day could include important information to identify extreme or unusual weather conditions (Deschênes and Greenstone, 2007; Fisher et al., 2012; Schlenker et al., 2006; Schlenker and Lobell, 2010; Schlenker and Roberts, 2009).

# **2.3 Regression analyses**

In the literature there are two econometric approaches used to estimate the income effects of weather variation (Dell et al., 2012; Deschênes and Greenstone, 2012, 2007; Fisher et al., 2012; Mendelsohn et al., 1994): Differences between households and locations are estimated from a cross‐section of data observed at one point in time following a Ricardian approach. Alternatively, changes over time are estimated from panel data observing the same households or locations at various points in time.

The data set used for this study allows estimating the following regression model: where Y denotes per‐capita income observed for individual _i_ in commune _j_ in year _t (i.e._ 2010, 2012, 2014 _)_ . W measures weather conditions n commune _j_ in year _t_ using five sets of weather variables as described in section 4. _β_ is the parameter of interest that indicates the income effects of weather variation. _X_ is a set of household‐specific controls that vary over time, such as education, labor and land endowments.<sup>5</sup> _T_ measures time‐fixed effects to neutralize common trends over time. _Z_ includes commune‐specific effects that do not change over time. _U_ is a random, idiosyncratic error term.

A particularity of this data set is that for some households information is available for two or three survey years. These households form a _Panel_ data set. Other households were only observed in one of the three years. Using this cross‐section data as well and treating all observation as independent observations provides a _Pooled_ data set.

A main concern when fitting models to estimate weather impacts on economic outcomes is endogeneity bias (dell et al., 2014). Reverse causation is unlikely to be a problem as weather conditions are exogenously determined. Yet the model is likely to suffer from omitted variable bias caused by the potential correlation of weather variables with other commune characteristics (e.g. long‐term climate, geo‐graphical, agro‐ecological conditions) that determine living standards. To the extent that such variables cannot be measured, the estimates of β will be biased.

To minimize this omitted variable bias, a fixed‐effects ( _FE_ ) linear model is fitted by using a within‐ regression estimator based on the _Panel_ data set ( _Panel FE_ ).<sup>6</sup> These analyses allow investigating the determinants of income changes between 2010 and 2012 and 2014 due to differences in weather

> 5 The data sets allow to control for land and labor inputs of some income activities. These are, however, not included in the regression analyses due to potential endogeneity biases.

> 6 A within‐regression estimator is always consistent, but a General Least Squares estimator can be more efficient. The GLS –estimator based on a random‐effects model was also tested, but a Hausman test rejects that the GLS estimator is efficient.

6
conditions over time or other time‐variant household factors. The model regresses the household specific difference of the income in one specific year from the mean in years with observations using the difference of weather conditions in one year from their means in all observed years.<sup>7</sup> By taking differences, all time‐invariant commune and household‐fixed effects are taken out eliminating any omitted variable bias caused by time‐invariant factors.

To take advantage of the full data set and test the robustness of the results, additional models are fitted based on the _Pooled_ data set, which estimate the determinants of income differences between households. First an Ordinary Least Squares model (OLS) is fitted that controls for observable commune characteristics in _Z_ ( _Pooled OLS_ ). These commune controls include long‐term rain and temperature levels and variability calculated from the CRU data and other location‐specific factors (e.g. altitude, slope, soil conditions, tree cover, distance from city and roads) calculated from other geo‐spatial data sets.<sup>8</sup> Whereas the set of commune controls reduces omitted variable bias caused by time‐invariant commune characteristics, it is unlikely to eliminate it. To further reduce this bias, a fixed‐effects linear model is fitted by including a dummy for each commune in _Z_ ( _Pooled FE_ ). This specification can control for commune‐ fixed effects, but not for unobservable household‐fixed effects.<sup>9</sup> Where unobserved household‐factors are correlated with weather deviation (e.g. cultural preferences or risk aversion), they can also lead to omitted variable bias, which can only be reduced through the _Panel FE_ model.

These regression models are estimated for various sub‐samples to disentangle differences between socioeconomic groups and climate zones. To identify weather impacts on different income activities, the regression models are also applied to the household sub‐sample participating in the respective income activity.<sup>10</sup> Each model is calculated separately for each of the five sets of weather variables specified in section 4. Robust standard errors are estimated by clustering at the commune‐level in order to account for spatial correlation. The natural logarithm of the outcome variables is used in the regression to

> 7 For households with observations for only two years, this approach corresponds to regressing the difference in incomes between the two years on the difference in weather conditions.

> 8 The advantage of using observed time‐invariant commune factors is that they allow estimating their impact on income differences, which is not possible in a fixed effects model using the Pooled Cross‐section or Panel data set. 9 The effects of other observable, time‐invariant commune factors cannot be estimated.

> 10 In the _Panel FE_ model, only those households are included that have reported the respective income activity in both years. Although it would also be interesting to analyze how weather variability affects the choice to take‐up a new activity or to drop an existing activity, the limited number of households with such changes does not allow for a meaningful analysis. For the _Pooled_ data set selection models were tested, as the factors that drive participation in an activity can be very different from those that determine income levels. Such selection biases can be corrected by a Heckman selection model using limited information maximum likelihood (LIML) or full information maximum likelihood (FIML) estimators (Heckman, 1979). Yet such estimators are not robust when the model is not correctly specified or subject to collinearity (Puhani, 2000).  The levels of collinearity found in this data when estimating selection models for the different incomes exceed critical levels as defined in other work (Leung and Yu, 1996). This collinearity is possibly caused be the lack of sufficient exclusion restrictions, i.e. variables that determine income levels but not participation in the income activity. In the presence of such collinearity problems OLS for the sub‐ samples without correction of election biases provide more robust estimates (Puhani, 2000).

7
normalize the skewed distribution of outcomes (i.e. many observations of low income levels and a few observations of very high income levels).<sup>11</sup>

# **3. Rural incomes and changing living conditions**

This section shows the composition of rural income portfolios across different socioeconomic groups and regions, as well as changes in living conditions and the impediments to increasing prosperity – as perceived by households.

## **3.1 Income portfolios**

As calculated based on the VHLSS data, the following income categories are of importance in rural Vietnam:

- 1) _crop_ income: the output value minus production costs including rice, other staple crops, industrial crops<sup>12</sup> , fruit trees and crop by‐products;<sup>13</sup>

- 2) _livestock_ income: the value of animals and animal products produced (for selling and self‐ consumption) minus production costs;

- 3) _forestry_ income: the value of harvested trees and other forest products (such as firewood), hunted animals, as well as incomes from , forest protection and management minus the production costs;

- 4) _fishing_ income: the value of production and catch of fish and shrimp minus production costs;

- 5) _wage_ income: any cash and in‐kind wages and salaries including from agricultural wage employment and non‐agricultural wage‐employment in unskilled and skilled occupations;

- 6) _business_ income: revenues from businesses outside agriculture, forestry, aquaculture minus cost;

- 7) _transfer_ income include remittances, emergency assistance, insurance, donations, social benefits, support in health care and education;

- 8) _other_ income: including returns from investments, and earnings from weddings and funerals.

The data demonstrates that agriculture and other ecosystem‐based incomes remain an important income source for rural households. Crop, livestock, forestry and fishing incomes and agricultural wages make ca 45 percent of incomes of all rural households (Figure 1). Whereas a larger share of rural households is engaged in income activities related to crop cultivation, livestock, forestry or fishing, the incomes earned from wage‐employment in skilled occupations and from businesses are much higher (Table A.1). Interestingly, more than 90 percent of households across all groups receive some transfer income. Overall, there is little difference in the income composition for different socioeconomic groups between 2010, 2012 and 2014.

> 11 Taking the natural logarithm also improves the explanatory power of the various models and brings ease in interpreting the coefficients as percentage change of the outcome variable.

> 12 In order of importance, these include peanut, coconut, coffee, soya beans, tea, sugarcane, cashew, pepper.

> 13 While revenues are indicated for each crop, costs are only given for all crop production. To calculate incomes for different crops, it is assumed that each crop’s cost share is equivalent to it share in total crop revenues.

8
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zones based on the long‐term (30 years) annual mean of monthly rainfall (157mm) and mean temperature (25.4°C) across the communes in the data set (Figure A2) in order to define _Dry‐cold_ , _Dry‐hot_ , _Wet‐cold_ and _Wet‐hot_ climate zones. Long‐term rainfall and temperature conditions as well as their inter‐annual variation greatly varies by zone (Figure A.3). Different climate zones mostly coincide with the different regions (Table A.3). In what follows current weather variation is described by five sets of variables measuring annual, seasonal, abnormal and extreme weather conditions and weather events.

# **4.1 Annual weather conditions**

Variables measuring annual rainfall and temperature levels control for the effect of average annual conditions and variation between years. The use of annual values is common in the literature exploring linkages between weather and economic outcomes (Burke et al., 2011; Dell et al., 2009; Feng et al., 2010; Schlenker and Lobell, 2010).

Based on the CRU data, annual values are calculated as the mean of the monthly rainfall and mean temperature values in the 12 months prior to the survey. Accordingly, 2010 values are not limited to the 2010 calendar year, but measure conditions in the 2009/10 season.

Figure 5 _Annual weather conditions in rural communes by climate zone in 2010, 2012 and 2014_

## _a. Annual rain_

## _b. Annual temperature_

<!-- Start of picture text --> dry_cold dry_hot wet_cold wet_hot dry_cold dry_hot wet_cold wet_hot<br>2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014<br>400 30<br>300<br>25<br>200<br>20<br>monthly rainfall in mm<br>100 monthly temperature in C<br>0 15<br><!-- End of picture text --> Notes: Box plot shows the distribution of commune observations for each climate zone. The boxes illustrate the 25 to 75 percentile with the median value represented by the line in the box. The whiskers indicate the lowest and highest adjacent value with the points outside below or above that identifying outlier observations. Values are measured as the mean of monthly rainfall levels and mean temperature in the last 12 months.

Source: Author’s calculation based on VHLSS 2010, 2012 & 2014 and CRU data.

Annual weather conditions vary between the zones and years. 2010 was the driest and warmest year with considerable variation between and within zones (Figure 5). Although on average 2012 is the wettest year in all zones, 2014 has a greater number of communes in the _Wet‐cold_ and _Wet‐hot_ zones which have rainfall levels far above the zone’s average. This skewed distribution reflects the heavy rainfalls that occurred in the Central Provinces at the end of 2013. These rainfalls resulted in severe flooding and livelihood damages (UN, 2013).

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# **4.2 Seasonal weather conditions**

Variables measuring seasonal rainfall and temperature levels can measure the impact of intra‐annual variation and their changes between years in order to test how income effects depend on the timing of weather shocks. Many studies measure temperature and rainfall levels at various points in the production year to reflect the seasonality of weather conditions and many income activities (Hsiang, 2010; Mendelsohn et al., 1994; Welch et al., 2010; Yang and Choi, 2007).

Based on the long‐term intra‐annual weather patterns (Figure A.3), this study differentiates between three seasons: January – April, which are drier and colder ( _S1_ ), May – August with wet months and the highest temperatures ( _S2_ ) and September – December with wet months and lower temperatures (S3). This division is also broadly in line with the growing cycle of some crops. For each of these periods in the last 12 months prior to the survey date, the mean of monthly rainfall and mean temperature level is calculated.

Seasonal conditions do not only vary between years but also zones. All survey years are affected by large intra‐annual variation in rainfall and temperature conditions (Figure A.4). In all three years the seasonal variation between zones and within zones is highest for temperature _S1_ and for rainfall in _S3_ (Figure 6). In 2010 dry season temperatures in _S1_ are higher than in the other years, while in 2012 and 2014 rainfall levels in _S3_ are larger than 2010. The rainfall outliers in _S3_ in the _Wet‐cold_ and _Wet‐hot_ zones capture the extensive rainfalls in November 2013 in the Central provinces.<sup>14</sup> Within 3 days some provinces experienced up to 400‐973 mm of rain (UN, 2013).

> 14 The weather variables are constructed so as to reflect the weather conditions in the 12 months prior to the survey. For example, for a household interviewed in March 2012, the first month indicates January 2012, while the 12<sup>th</sup> months indicate December 2011. Accordingly, the December values in the 2014 graph show the rainfall in December 2013.

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Figure 6 _Seasonal weather conditions in rural communes by climate zone in 2010, 2012 and 2014_

# _a. Seasonal rain S1 (Jan‐Apr)_

# _b. Seasonal temperature S1 (Jan‐Apr)_

<!-- Start of picture text --> dry_cold dry_hot wet_cold wet_hot dry_cold dry_hot wet_cold wet_hot<br>2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014<br>c. Seasonal rain S1 (May‐Aug) d. Seasonal temperature S2 (May‐Aug)<br>dry_cold dry_hot wet_cold wet_hot dry_cold dry_hot wet_cold wet_hot<br>2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014<br>e. Seasonal rain S3 (Sep‐Dec) f. Seasonal temperature S3 (Sep‐Dec)<br>dry_cold dry_hot wet_cold wet_hot dry_cold dry_hot wet_cold wet_hot<br>2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014<br>35<br>1,000<br>500 25<br>monthly rainfall in mm<br>monthly temperature in C<br>0 15<br>35<br>1,000<br>500 25<br>monthly rainfall in mm<br>monthly temperature in C<br>0 15<br>1,000 35<br>500 25<br>monthly rainfall in mm<br>monthly temperature in C<br>0 15<br><!-- End of picture text -->

# _c. Seasonal rain S1 (May‐Aug)_

# _e. Seasonal rain S3 (Sep‐Dec)_

Notes: Box plot shows the distribution of commune observations for each climate zone. The boxes illustrate the 25 to 75 percentile with the median value represented by the line in the box. The whiskers indicate the lowest and highest adjacent value with the points outside below or above that identifying outlier observations. Values are measured as the mean of rainfall levels and mean temperature in the respective months.

Source: Author’s calculation based on VHLSS 2010, 2012 & 2014 and CRU data.

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# **4.3 Abnormal weather conditions**

Not only absolute rainfall and temperature levels matter, but also the extent to which these levels differ from long‐term normal climate conditions. Unusually wet, dry, hot and cold conditions can all have detrimental impacts, which depend on their timing and location. For example, more rain may be beneficial in a dry month and dry locations, but harmful in wet months and wet locations. Existing studies define unusual weather conditions as the deviation from the long‐term mean (Hidalgo et al., 2010; Baez et al., 2015; Noack et al., 2015). These studies put the calculated deviation into the context of the location‐ specific variability by normalizing by the location’s long‐term standard deviation (Lobell et al., 2011).

Figure 7 _Abnormal weather conditions in rural communes by climate zone in 2010, 2012 and 2014_

## _a. Wet months_

## _b. Hot months_

<!-- Start of picture text --> 2010, dry_cold 2010, dry_hot 2010, wet_cold 2010, wet_hot 2010, dry_cold 2010, dry_hot 2010, wet_cold 2010, wet_hot<br>2012, dry_cold 2012, dry_hot 2012, wet_cold 2012, wet_hot 2012, dry_cold 2012, dry_hot 2012, wet_cold 2012, wet_hot<br>2014, dry_cold 2014, dry_hot 2014, wet_cold 2014, wet_hot 2014, dry_cold 2014, dry_hot 2014, wet_cold 2014, wet_hot<br>0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6<br>number of months number of months<br>c. Dry months d. Cold months<br>2010, dry_cold 2010, dry_hot 2010, wet_cold 2010, wet_hot 2010, dry_cold 2010, dry_hot 2010, wet_cold 2010, wet_hot<br>2012, dry_cold 2012, dry_hot 2012, wet_cold 2012, wet_hot 2012, dry_cold 2012, dry_hot 2012, wet_cold 2012, wet_hot<br>2014, dry_cold 2014, dry_hot 2014, wet_cold 2014, wet_hot 2014, dry_cold 2014, dry_hot 2014, wet_cold 2014, wet_hot<br>0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6 0 1 2 3 4 5 6<br>number of months number of months<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>Density .20 Density .20<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>Density 0 Density 0<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br><!-- End of picture text -->

## _c. Dry months_

Notes: Histograms show the distribution of commune observations for each climate zone. Abnormal months are measured by the deviation of the current month’s value from the 30 years mean being greater than 1.5 standard deviations. Source: Author’s calculation based on VHLSS 2010, 2012 & 2014 and CRU data.

To measure the number of wet, dry, hot and cold months, this study calculates the current deviation from the long‐term mean for each month (Figure A.5). Wet conditions are defined as positive deviation from 15
the long‐term rainfall level and dry conditions as negative deviation. Hot conditions are measured by the month’s positive deviation of current maximum temperature from the long‐term mean of maximum temperature and cold conditions by the month’s negative deviation of current minimum temperature from the long‐term mean of minimum temperature. For all measures each month’s deviation is then divided by the month’s long‐term standard deviation. Abnormal months are defined as months with a deviation exceeding 1.5 standard deviations.

Abnormal weather conditions vary mostly by year (Figure 7). The limited variation between zones results from accounting for some of the regional variation through the normalization by the commune’s standard deviation. In all zones number of wet months is highest in 2014 and the number of dry months in 2010. Hot months prevail in 2010, while the number of cold months is largest in 2014.

# **4.4 Extreme weather conditions**

To control for extreme weather conditions maximum and minimum values in rainfall and temperature levels can be measured. A limited period without any rain or with intensive rain can be as harmful to livelihoods as a limited number of days with heat extremes or with frost conditions. For example, a recent study for Vietnam defines excessive rainfall if it exceeds 300, 450 or 600mm within a 5 day period (Thomas et al., 2010). Another study from Asia differentiates between daily minimum and maximum temperatures to control for temperature variation (Welch et al., 2010).

Based on the monthly rainfall and temperature values (Figure A.4), extreme weather conditions are measured as follows: the maximum of rain as the precipitation level in the wettest month, the minimum of rain as measured by the precipitation level in the driest month, the maximum of temperature as the maximum temperature in the hottest month, and the minimum of temperature as the minimum temperature in the coldest month.

These extreme weather conditions broadly reflect earlier findings. Maximum rain is lowest in 2010 at around 400mm and highest in 2012 exceeding 500mm in _Dry‐hot_ and _Wet‐cold_ zones, but with some considerable variation within the _Wet‐cold_ zone in 2014 (Figure 8). Minimum rain is below 50mm and is close to zero in the _Dry‐cold_ zone. Whereas this zone had the lowest annual and seasonal temperature means (Figure 5 and 6), their maximum temperature is higher than in the Wet‐cold zone and their minimum temperature is at the level of the _Dry‐hot_ and _Wet‐hot_ zone.

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Figure 8 _Extreme weather conditions in rural communes by climate zone in 2010, 2012 and 2014_

## _b. Maximum temperature_

## _a. Maximum rain_

<!-- Start of picture text --> dry_cold dry_hot wet_cold wet_hot dry_cold dry_hot wet_cold wet_hot<br>2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014<br>c. Minimum rain d. Minimum temperature<br>dry_cold dry_hot wet_cold wet_hot dry_cold dry_hot wet_cold wet_hot<br>2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014<br>40<br>1,500<br>35<br>1,000<br>rainfall in mm<br>500 temperature in C 30<br>0 25<br>100 30<br>25<br>50<br>rainfall in mm<br>temperature in C 20<br>0 15<br><!-- End of picture text -->

## _c. Minimum rain_

Notes: Box plot shows the distribution of commune observations for each climate zone. The boxes illustrate the 25 to 75 percentile with the median value represented by the line in the box. The whiskers indicate the lowest and highest adjacent value. Maximum rainfall is measured by the rainfall level in the wettest month. Minimum rain is measured by the rain level in the driest month. Maximum temperature is measured by the mean of maximum temperature in the hottest month. Minimum temperature is measured by the minimum temperature in the coldest month. Source: Author’s calculation based on VHLSS 2010, 2012 & 2014 and CRU data.

# **4.5 Weather events**

Variables describing the occurrence of weather events, such droughts, floods and storms allow to measure the impact of weather shocks and natural disasters. A recent study for Vietnam uses monitored data on riverine floods and cyclones to identify natural disasters and assess their welfare impacts (Thomas et al., 2010). Notwithstanding the limitations of self‐reported data, which often suffers from subjective judgements and is highly correlated with welfare outcomes, other studies from Vietnam use self‐reported data to assess the welfare impacts (Arouri et al., 2015; Bui et al., 2014).

17
Unfortunately, the monthly weather data from CRU do not allow to identify weather events, such as floods, droughts, and storms, which often depend on extremes within a short time period. Instead self‐ reported data from the commune surveys are used, which include questions about the five main emergencies in the last three years.<sup>15</sup> Based on this data, those communes that have experienced floods, droughts and storms within the 12 months before the survey are identified. Although the data also include information on crop diseases, fires and epidemics, these events are not included as they are supposedly less directly related to weather conditions.

Overall, more than a fifth of all rural households reports some weather event. The variation between zones and years is lowest for the occurrence of storms (Figure 9). In line with earlier findings suggesting that 2010 is the hottest and driest year, the self‐reported data indicates a higher occurrence of droughts in 2010 – mostly in the _Wet‐cold_ zone. Surprisingly, the share of communes reporting floods is highest in 2010 in the _Dry‐hot_ and _Wet‐cold_ zone, which is in contrast to the rather dry conditions in that year and the severe flood events observed in the _Wet‐cold_ zone in 2014. The self‐reported data does not correspond to the flood events experienced by communes in. Auxiliary regressions, however, reveal that self‐reported floods indeed are related to higher annual and seasonal rainfall levels, number of wet months and maximum rainfall (Table A.4).

Figure 9 _Weather events in rural communes by climate zone in 2010, 2012 and 2014_ <!-- Start of picture text --> a. Floods b. Droughts c. Storms<br>dry_cold dry_hot wet_cold wet_hot dry_cold dry_hot wet_cold wet_hot dry_cold dry_hot wet_cold wet_hot<br>201020122014 201020122014 201020122014 201020122014 201020122014 201020122014 201020122014 201020122014 201020122014 201020122014 201020122014 201020122014<br>.25 .25 .25<br>.2 .2 .2<br>.15 .15 .15<br>.1 .1 .1<br>share of communes affected share of communes affected share of communes affected<br>.05 .05 .05<br>0 0 0<br><!-- End of picture text --> Notes: Bars show the average share of communes in each climate zone being affected by the event based on self‐reported data. Source: Author’s calculation based on VHLSS 2010, 2012 & 2014 and CRU data.

> 15 These emergencies are listed by year and month, including crop diseases, storms, floods, droughts, fires and epidemics.

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# **5. Weather sensitivity of incomes**

This section shows the impacts of the different sets of weather variables on incomes as estimated by the regression model described in section 2.3.<sup>17</sup> The emphasis is placed on the results from the _Panel FE_ model as this allows to estimate the effects on income changes over time eliminating any potential omitted variable bias from time‐invariant factors. All regression models also include a set of control variables which are not the focus of this discussion (Table A.5). The fitted models indicate the income effects of weather variation (5.1) and how these weather impacts vary by socioeconomic group (5.2), climate zone (5.3), income activity (5.4) and severity of conditions (5.5).

## **5.1 Weather variation relates to income changes**

The results indicate that many weather variables have a significant impact on consumption and income changes over time. The direction, order of magnitude and statistical significance of the results for consumption and total incomes are almost identical for the different weather variables (Figure 10 and Table A.6). This finding suggests that the impact of weather variation on incomes directly translates into consumption changes and that there is limited smoothing of the consumption effects. Hence, the calculated weather effects on incomes also provide an estimate of the overall welfare impacts.

The significant results for temperature variation indicate that hotter conditions relate to lower incomes. An average annual temperature that is 1 ° C warmer decreases total income by 20 percent – mostly driven by a 10% reduction in in season _S1_ and _S3_ (Figure 10). Similarly, an additional hot month lowers income by 6 percent, while an additional cold month increases income by 4 percent. A 1°C increase in temperature in the hottest month relates to 10 percent income reduction and a 1°C increase in the coldest month to a 5 percent reduction.

The impacts of rainfall variation suggest that drier conditions can have negative impacts. An additional 100m of rain in season _S3_ has an income‐increasing effect of 3 percent, while an additional dry month reduces income by 6 percent. All other variables are insignificant possibly because wetter conditions can also have negative impacts when rainfalls become too intensive. Accordingly, having experienced a drought event relates to a 9 percent and a flood event to a 5 percent income reduction. The income effects of rainfall variation are further disentangled in the next analyses.

As would be expected weather variation is related to some but not all income differences between years. As per the R2 statistics the models with the measured weather variables explain about 8 to 9 percent of the variation in total income, whereas the explanatory power of the models with the self‐reported weather events

> 17 Other specifications of weather conditions are also tested, which do not offer much additional insights. For example, including the quadratic form of annual or seasonal rainfall and temperature levels does yield similar results and makes the interpretation of the results from the _Panel_ regressions difficult.  Similarly, using deviations from the long‐term mean instead of absolute rainfall and temperature levels produces almost identical results in the _Panel_ regressions, because differences from the mean are already captured in the _Panel FE_ model.

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<!-- Start of picture text --> 4050 Rainfall. variationa<br>a 30 a<br>= 20 Al<br>ea€ 10 ss| 1 o = I<br>Ss o lrog i Ti #50 a<br>2 ° i. ei°8<br>#710 AD : ani an<br>7320 T Bt<br>| by<br>bis R258£ £ PeETTESEST£ = 7 £ IEE:= £ ZEPSRRRE£ = £<br>Begs Paes Peeers Seteraes<br>E532 eEs28F8ss rss FeeEE SE<br>$1 82 $3 Wet Ory<br>(1) Annual (2) Seasonal [3) Abnormal months<br>(100mm) (200mm) (number)<br><!-- End of picture text -->

<!-- Start of picture text --> 5040 Temperature variation.<br>: 30 |<br>5 20 oo<br>= 10 x - ;| | _ T I .<br>So a | 1 | 7a mote<br>® “Ty oq "sin<br>€-10 +; d b tt] Ol g _<br>-pont, te<br>30 4 " ”<br>as SYes= [x RtSeSesese= = =a = | SERRE= =EE?<br>B= ; es : : :<br>£522 g8ehe5eies22 FELT EERE<br>$1 s2 53 Het Cold<br>(1) Annual (2) Seasonal [3] Abnormal months<br>(asc) (12°C) (number)<br><!-- End of picture text -->
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Warmer weather conditions have a negative effect on most income activities. Three exceptions are to be noted. First, the weather impacts for staple crops are not significant or even positive for maximum temperatures. This finding indicates that these crops – mostly grown in the colder regions – are less sensitive to warmer temperatures or could even benefit. Second, hotter conditions and even drought events are related to higher forestry incomes. This finding may indicate that the extraction of forest resources is a copying mechanism applied to compensate shortfalls from other activities under heat stress. Similarly, fishing incomes go up under heat extremes expressed by the number of abnormally hot months and the maximum temperature in the hottest months. These results corresponds to other studies from rural Vietnam showing that incomes from the extraction of environmental resources tend to go up during weather shocks (Völker and Waibel, 2010).

Interestingly, weather variables do not only relate to agricultural and other ecosystem‐production related incomes, but also to supposedly less sensitive activities, such as non‐agricultural wages and business activities. This result may imply that in rural areas there are strong inter‐sectoral (demand and supply) linkages through which all income activities are negatively affected when weather shocks hit the agricultural sector. Moreover, some weather extremes may also affect non‐agricultural activities through their impacts on labor availability and productivity. Interestingly, all wage‐related activities are negatively affected by higher temperature, but are less sensitive to rainfall variation. This finding is in line with Park et al. (2015) who find that heat extremes can have a negative impact on labor productivity.

Some note of caution is needed. The predictive power of the models –especially those with the self‐reported weather events – become weak for some income activities. This finding mainly applies for income activities undertaken by a small sub‐sample of households and with limited income variation over time, such as forestry and fishing. Possibly these activities are less weather sensitive, so that the weather variables combined with the set of households controls cannot sufficiently explain income variation across time.

This finding may also be due to the dependency of effects on the specific locations within climate zonesand on specific activities within each income category. To further test this dependency, additional regressions were run for each income activity at the regional level. The results for the regional models with the highest predictive power are summarized in Tables A.18‐A.23. For example, in the Mekong River Delta less rain in the dry season _S1_ reduces income from summer‐autumn rice, while less rain during season _S3_ increases income from winter‐ spring rice. Unusually dry months are harmful for incomes from both winter‐spring and summer‐autumn rice (Tables A.18‐A.19). Coffee incomes in the Central Highlands suffer from high temperatures in _S1_ and temperature and rainfall extremes, as well as flood and drought events (Table A.20). In the North West region forestry incomes decrease in drier years with more cold months, while fishing incomes increase in warmer years with more hot months (Table A.21). Moreover, weather variation can explain a larger extent of the variation in wage and business‐related incomes for some regions (Tables A.22‐A.23). These findings suggest that a further disaggregation by income activities and regions would allow to refine the results.

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<!-- Start of picture text --> 30<br>Pr i Rainfall variation<br>I |<br>»5 10 fie[ #47 i<br>2 0 LT T a a i 3] I<br>8 -10 ] ? J Th<br>a<br># -20 / .<br>-40 r<br>“30<br>4s a<br>a9 Ns as\98 S298 Sas as<br>RR gssagissRRRARAR gagesRRARR gaaRRRAR aRa4<br>$1 $2 $3 Wet Dry Max Min Flood<br>(1) Annual (2) Seasonal (3) Abnormal months —‘(4) Extreme (5) Event<br>(100mm) (100mm) (number) (100mm) (occurrence)<br><!-- End of picture text -->

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variation between years can hide some of the actual weather impacts, extreme conditions in one of the few survey years can bias some of the results. To mediate some of these shortcomings, panel data sets that cover larger time horizons are needed.

# **5. Conclusions**

The results in this paper show that weather variation and income changes over time in rural Vietnam are related. They also warn against an oversimplification of this relationship as there is a breadth of weather impacts, which are highly dependent on socioeconomic groups, climate zones, and income activities, and even the severity of weather conditions. In addition, income effects do not only depend on the intensity of the rainfall or temperature shock, but also on the timing of the shock and the location‐specific optimal rainfall and temperature levels.

Despite this complexity the data allow identifying some general patterns. Warmer temperatures and heat extremes have income‐reducing effects in all climate contexts and for all socioeconomic groups, including poorer households and ethnic minorities. While most income activities are negatively affected by hotter conditions, staple crops, forestry and fishing seem to be less sensitive to temperatures. The income effects of rainfall are more complex. Some findings indicate that more rainfall is beneficial in drier places but harmful in wetter places. Interestingly, the incomes of poorer households seem to be negatively affected by wetter conditions, while those of wealthier households are more impacted by drier conditions. This finding implies that the livelihoods of poor rural people are more vulnerable to severe rainfalls and flooding, which for example occurred at the end of 2013. The data indeed confirm that an increase in rainfall and wet months compared to 2012, which was also a wet year, had a negative impact on income growth between 2012 and 2014.

Bringing the variety of income effects from different types of weather variation together with the uncertainty about how weather conditions will be altered by climate change makes any effort to quantify the income and poverty impacts of climate change extremely challenging – not even considering that the weather sensitivity of income activities may change due to structural changes or adaptive responses. While future temperature increases due to climate change can be predicted with some level of confidence, there is less agreement on future changes in precipitation patterns. Yet variation between locations and between seasons is likely to increase (IMHEN and UNDP, 2015; ISPONRE, 2009; MONRE, 2009). Overall, however, it remains difficult to translate the global climate change scenario from the IPCC (2014) into localized impacts. While some locations could benefit from more favorable conditions, the overall variability of weather conditions is expected to increase with abnormal or extreme conditions likely to become more frequent and intense.

Notwithstanding the difficulties to quantify any future climate change impacts on rural incomes, the initial insights provided by this paper bring important implications for rural development in times of climate change. The findings demonstrate that high weather variation between years, seasons and locations is already the norm in rural Vietnam and that the incomes of most people including poor households are currently very sensitive to this variation. Consequently, rural households are already vulnerable to weather variation. Climate change could increase these vulnerabilities in many unpredictable ways. Hence it is important to make rural 26
livelihoods more resilient to weather variation by promoting income strategies that are more robust to uncertain weather conditions (e.g. weather‐resistant crops and production practices) and by enabling households to be better prepared for weather shocks (e.g. weather‐focused information systems) or to cope with them ex‐post (e.g. weather‐proofed safety nets). All in all, policies and investments should pay greater attention not only to short‐lived disasters, but also to more subtle weather variation and gradual changes in weather conditions.

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