How climate reallocates labour and capital within countries: new evidence from Brazil

The economic and social impact of climate change is one of the major challenges of our time. According to the Intergovernmental Panel on Climate Change, human-induced climate change is already affecting many weather and climate extremes in every region across the globe, including heatwaves, heavy precipitation, and droughts.

Several studies have shown that increases in temperature and extreme weather events have negative effects on local economic activity and can generate migration away from affected areas (Dell et al. 2012). However, we still lack a clear understanding of how climate change affects the reallocation of workers and capital, including its impact on the regions they are displaced to.

In our recent paper, we use new data on extreme weather events in Brazil to study the impact of excessive dryness on the local economies of affected areas, on the magnitude and direction of labour and capital movements they generate, and on their allocation across sectors and firms in destination regions. We study the impact of excessive dryness on the local economies of affected areas, on the magnitude and direction of labour and capital movements they generate, and on their allocation across sectors and firms in destination regions.

We find that:

• long periods of excess dryness relative to historical averages see capital and labour move away from affected regions, consistent with an expected permanent reduction in agricultural productivity and limited capability of local adaptation responses;
• higher dryness can change the structure of economies in directly affected areas and of those integrated via labour and capital markets.

Dryness in Brazil over the past decades

We employed two measures of excessive dryness in Brazil:

• Data from the National System of Civil Protection, which contains every natural disaster reported by municipal administrations since 2000;
• The Standardised Precipitation Evapotranspiration Index (SPEI), which captures the moisture deficit in each location relative to its long-run average based on precipitation and temperature.

As shown in Figure 1, droughts are the most frequent natural disaster recorded, with 1,000 to 1,500 reports per year during the 2000s and a sharp increase to more than 2,000 yearly reports during the next decade. In our main analysis, we rely on the SPEI, which also indicates a strong increase in dryness during the 2010s across major parts of the country (Figure 2).

Agricultural production losses, short-run insurance and long-run capital flight

In the first part of our analysis, we document that excessive dryness leads to a sharp reduction in agricultural production. We then investigate how financial capital reacts to these production losses. For this, we use balance sheet data from all bank branches in Brazil to obtain overall local deposits and loans. Importantly, we not only estimate the direct effects of local dryness shocks but also the indirect effects arising from being connected via bank branch networks to regions suffering from droughts.

We find that regions directly affected by excessive dryness in a given year receive capital inflows, driven by an expansion of loans (Figure 3a). These funds are partly drawn from municipalities connected to dry ones through bank branches, as these locations experience capital outflows (Figure 3b).

This indicates that local economies are partially insured against dryness shocks in the short run through financial integration with other areas. However, when we study the impact on capital of excessive dryness over a full decade, we find that this leads to capital outflows, as lending is strongly reduced (Figure 4). This is consistent with the idea that a full decade of unusually dry meteorological conditions has (or is perceived to have) permanent negative effects on local productivity.

 

Movement of workers across sectors and regions due to dryness shocks

We investigate how dryness affects local labour markets and movements across regions, using the fact that migrants are more likely to relocate towards regions where they have social networks, measured by historical migration links. Based on this, we construct a measure of indirect dryness exposure for each destination.

We find a strong migration response to higher average dryness over ten years. Municipalities suffering from excessive dryness lose a significant portion of their population through higher outward and lower inward migration. Conversely, regions that are linked through historical migration patterns to those affected by dryness expand their population through higher inward migration.

How do dryness shocks and the population movements they trigger affect local employment?

Due to productivity losses and outward migration, employment falls in municipalities with excessive dryness, while employment expands in the regions connected through migration links.

We find that areas with a higher incidence of droughts experience a sharp decline in agriculture employment, a moderate decline in service sector employment and a sharp increase in manufacturing employment (Figure 5a).

This suggests that the fall in agricultural productivity reduces the demand for local non-traded goods, such as services, while expanding local traded goods, such as manufacturing, by reducing the price of labour.

In contrast, those regions that receive climate migrants see their employment expand in agriculture and services, but not in manufacturing (Figure 5b).

This might be driven by climate migrants lacking the skills required for manufacturing jobs in their destination. In this case, the absence of migrant reallocation into manufacturing would reflect an optimal labour allocation at destination, as they instead find jobs in other sectors that are better suitable for their skills.

Alternatively, this finding could be driven by migrants’ social networks being disconnected from manufacturing firms at destination. This asymmetry in labour market frictions across sectors would result in labour misallocation, as workers do not find their way into the manufacturing sector despite potentially having suitable skills. We explore these explanations in the final section by using micro-level data.

Why are workers displaced by climate shocks less likely to end up in manufacturing?

We study the employment history of workers leaving regions affected by climate change using a matched employer-employee data covering all formal workers in Brazil. Tracing their movements allows us to construct a firm-level measure of exposure to migrants from each region by using information on the origin region of currently employed workers.

We do this as firms that traditionally hire workers from certain regions are more likely to attract future migrants from those regions, as they might rely on referrals from their social networks to find employment.

We find that workers from dry areas tend to relocate towards firms with an already larger share of migrants from those origins. This implies that climate migrants do not amount to a symmetric increase in labour supply for all firms. Instead, labour market frictions direct them to connected firms.

We also find important differences across sectors. First, manufacturing is the least connected sector to areas exposed to excess dryness through migrant networks: only 2% of its workers come from those areas compared to 4% in services and 6% in agriculture. Second, even when manufacturing firms are connected to dry areas via referral networks, they are less prone to employ workers coming from these areas, potentially because such workers do not possess the required skills to be employed in manufacturing.

Overall, our paper attempts to contribute to our understanding of the potential effects of climate change on economic outcomes.

We expect that our estimates can be informative for recent quantitative models studying the effects of climate change on productivity and the spatial allocation of population and economic activity (Desmet and Rossi-Hansberg, 2015; Costinot et al., 2016; Balboni, 2019; Conte et al., 2020).

 

 

 

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