1. Introduction

The increasing impact of climate change has become a global threat, accelerating noticeably over the last decade [ IPCC, 2014 ], including documented impacts on agriculture [ Mendelsohn, 2007; Deschenes & Greenstone, 2007 ], human health [ Markandya & Chiabai, 2009 ], and ecosystems [ Munang et al., 2013 ]. However, the costs and benefits of climate change are uncertain and unevenly distributed. For instance, the cost of dealing with its impact falls disproportionately on developing countries, whilst developed countries are trying to cut pollutant emissions to mitigate those impacts on the future economy. Climate change is also expected to increase the frequency and intensity of current extreme climatic events (e.g. heat waves, droughts, floods, and wildfires). These extreme events might have negative impacts not only on our life in general [ McMichael et al., 2012 ], but also in our communities [ Frame et al., 2020 ]. Both climate change and its consequences have always attracted the interest of policymakers and the public.

Researchers have focused on the potential for climate change to undermine progress towards economic development [ Hallegatte et al., 2016; Leichenko & Silva, 2014 ], while one small research stream focuses on the unequal distribution of the effects of climate change [ Islam & Winkel, 2017; Marchiori & Schumacher, 2011; Mendelsohn et al., 2006 ]. Its impacts are particularly disastrous for developing countries, and further degrade the resilience of the poor and vulnerable groups within them [ Mendelsohn et al., 2006; Mall et al., 2011 ]. In developing countries, many people depend heavily on agriculture for income, but they have fewer resources to fall back on, and lower adaptive capacity with regard to climate change [ Hallegatte et al., 2018 ]. For example, the poor tend to settle in risky areas where land is available and affordable, but where climate hazards are more likely to occur; therefore, their assets and livelihoods are more likely to be destroyed. Furthermore, they tend to work in ‘exposed conditions’ (i.e. they work outdoors, and directly exposed to the weather), so they are more vulnerable to environmental shocks and stressors. From a socio-economic perspective, the most negative effects of climate change are likely to occur in locations that are already economically marginal, and where livelihoods are already precarious [ Samson et al., 2011; Reyer et al., 2017; World Bank, 2013 ]. As a result, the impact of climate change accentuates existing location-based inequalities and gives further momentum to the dynamics and incentives that drive economic migration.

However, poor and vulnerable groups are not homogenous. Disproportionate household and familial burdens, together with a relative lack of control over productive assets due to climate change, can enhance female vulnerability beyond that of men [ Goh, 2012 ]. In particular, women are more likely to be impoverished than men, less capable of adapting to the impact of present and future climate change, and less likely to participate and contribute towards improving knowledge of the processes that facilitate gender-specific adaptation or mitigation efforts [ Van Aelst & Holvoet, 2016 ]. Eastin (2018) argued that gender disparities in climate change vulnerability not only reflect pre-existing gender inequalities, but also reinforce them. Due to the gendered divisions of household labour, women often face greater challenges adapting to changes in environmental conditions, thereby reducing their livelihood opportunities and heightening resource scarcities.

Because of environmental impact, such as climate change, through its negative impacts on agricultural productivity (see Mendelsohn, 2007 for overview, and Trinh, 2018 for Viet Nam), the availability of water resources and flood frequency will alter the utility that a location offers to its population. Indeed, migration acts as a coping mechanism to climate change [ McLeman & Smit, 2006; Perch-Nielsen et al., 2008 ]. However, migration outcomes rarely emerge in a simple stimulus-response way, but are instead modified and shaped by the interaction of environmental changes and human, social, economic, and cultural processes [ Hunter, 2005 ]. Migration is often undertaken to secure livelihoods in adverse environmental conditions [ Black et al., 2011 ].

There is a growing body of literature on the impact of climate on human migration. For example, Barrios et al. (2006) show the effect of rainfall on rural-to-urban migration in sub-Saharan Africa countries, whilst Mueller et al. (2014) emphasize the impact of heat stress on long-term migration in rural Pakistan. By following province-to-province movement of more than 7,000 households in Indonesia, Bohra-Mishra et al. (2014) also suggest that permanent migration is influenced by climatic variations (i.e. temperature, and rainfall changes), whereas episodic natural disasters seem have little impact on such migration. Climate change not only impacts domestic migration, but also international migration (see Cai et al., 2016). Cai et al. (2016) provide an in-depth study about the climate-migration link across countries, which suggests a positive relationship between temperature and international emigration in the most agriculture-dependent countries. In addition, they show that migration flows to current major destinations are temperature sensitive. Impacts caused by climate change directly affect not only individuals, but also firms’ operations and productivity. There is growing literature about the effects of climate change on labour productivity [ Heal and Park, 2016, Burke, Hsiang, and Miguel, 2015 ], and on aggregate macroeconomic productivity [ Nath, 2020; Burke, Hsiang, and Miguel, 2015; Hsiang, 2010 ]. For example, Nath (2020) first attempts to use firm-level data to estimate the effect of temperature on productivity in manufacturing and services. He found that extreme heat reduces non-agricultural productivity, but less so than in agriculture. These productivity effects of climate change reduce welfare by an average of 1.5–2.7% overall, and up to 6–10% for the poorest quartile.

This study tries to understand the multi-dimensional influences of climate in Viet Nam. Firstly, it examines the effects of weather shocks on household income, especially agricultural income. Using the VHLSS dataset, we find that climate change would harm household agricultural income (from fruit and non-crop components), especially when the temperature is above 33°C. We also find the negative effect of weather shocks household income inequality as weather shocks damage income of poor households more than other groups. Secondly, this study also investigates how labour supply changes with regard to climate change. By using the LFS dataset, we find a negative relationship between climate change and working hours/hourly wage. Thirdly, we identify how temperature affects firms’ productivity. By using the Viet Nam Enterprise Survey, we find that an increase in temperature and precipitation would reduce firms’ revenue, total factor productivity, output, and size.

Furthermore, we evaluate how climate variability affects individuals’ behaviours and percep-