Do weather extremes induce people to move? Evidence from Vietnam

Economic Analysis and Policy 69 (2021) 118–141

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Economic Analysis and Policy journal homepage: www.elsevier.com/locate/eap

Modeling economic policy issues

Do weather extremes induce people to move? Evidence from Vietnam Cuong Viet Nguyen

∗

Institute of Theoretical and Applied Research (ITAR), Duy Tan University, Hanoi 100000, Viet Nam National Economics University, Hanoi, Viet Nam

article

info

Article history: Received 21 April 2020 Received in revised form 11 November 2020 Accepted 13 November 2020 Available online 23 November 2020 JEL classification: Q54 O15 R23 Keywords: Weather extremes Climate change Migration Households Population census Vietnam

a b s t r a c t This study examines the effect of extreme weather events on inter-province migration in Vietnam using a migration gravity model. It finds that high rainfall extremes encourage the out-migration of highly educated people but attracts the in-migration of the poorly educated. A possible reason is that high rainfall increases household income from agricultural production but reduces wage income. Thus, high rainfall extremes increase the migration of the highly educated, who mainly have salaried jobs, while attracting the poorly educated, who are mainly self-employed in agricultural work. Low temperature extremes in source provinces reduce out-migration, while low temperature extremes in destination provinces increase in-migration, possibly because very hot weather reduces wages and affect people’s health, and as a result influences migration. © 2020 Economic Society of Australia, Queensland. Published by Elsevier B.V. All rights reserved.

1. Introduction In recent decades, the world has experienced natural hazards with increasing frequency and severity (e.g., EM-DAT, 2015). According to the estimation of Guha-Sapir et al. (2013), natural hazards and extreme weather events caused approximately 107,000 deaths each year during the 2002–2012 period. Numerous studies show the adverse effect of natural hazards on household welfare and poverty (e.g., Dercon, 2004; Masozera et al., 2007; Arouri et al., 2015). Migration has been an important livelihood strategy for coping with income variability, especially for those in low- and middle-income countries (Stark and Bloom, 1985; Molloy et al., 2011). There is growing concern that extreme weather events may cause migration (McLeman and Smit, 2006; Black et al., 2011a, 2013; Belasen and Polachek, 2013). In this study, we assess the impact of extreme weather events on migration across provinces in Vietnam using a gravity model. We measure weather events by extremes in temperature and precipitation, which reflect the hazard shocks. The main advantage of using these hazard shocks is that these variables are exogenous, and we can estimate the effect on migration. We explore whether income and health are channels through which weather extremes affect migration. Our hypothesis is that temperature and precipitation extremes can reduce household income and deteriorate health, and as a result causing people to migrate. This study expects to contribute to the literature of development economics, as follows. First, it provides empirical findings about the effect of extreme weather events on internal migration in Vietnam — an important case study. Vietnam ∗ Correspondence to: Institute of Theoretical and Applied Research (ITAR), Duy Tan University, Hanoi 100000, Viet Nam. E-mail address: [email protected]. https://doi.org/10.1016/j.eap.2020.11.009 0313-5926/© 2020 Economic Society of Australia, Queensland. Published by Elsevier B.V. All rights reserved.

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Economic Analysis and Policy 69 (2021) 118–141

is a country with relatively high migration. According to Coxhead et al. (2015), the percentage of individuals aged 15–59 who have migrated during the past 12 years is 7.6%. In Vietnam, younger people and those with high education are more likely to migrate (e.g., Coxhead et al., 2015; Nguyen and Minh, 2016), and the main motive for migration is to find better economic and employment opportunities (e.g., De Brauw and Harigaya, 2007; Phan and Coxhead, 2010; Coxhead et al., 2015). Using a gravity model, Phan and Coxhead (2010) find that people move from low-income to high-income provinces. Vietnam has been exposed to a wide range of extreme weather events, such as floods and droughts (UNISDR, 2009), and several studies look at the effect of climate change on migration. Migration is mentioned as a scoping strategy to climate change in Vietnam (Garschagen, 2013). Gröger and Zylberberg (2016) find that people in areas affected by typhoons are more likely to migrate to other areas for nonfarm employment opportunities in Vietnam. Compared with previous studies, our study uses more updated and nationally representative data to measure the effect of weather extremes and explore potential mechanisms through which the weather extremes affect migration. Secondly, this study compares the effect of two main types of extreme weather events, namely high/low rainfall and temperatures, on migration. Most studies focus on a single type of hazards, such as hurricanes, droughts or floods. For example, Findley (1994) and Erza (2001) find that droughts increase migration out of rural areas in Mali and Ethiopia, respectively. Gröger and Zylberberg (2016) find that people in areas affected by typhoons are more likely to migrate to other areas for nonfarm employment opportunities in Vietnam. Black et al. (2011b) report that in Bangladesh, migration from rural to urban areas has become a common strategy to cope with flooding. On the other hand, Gray and Mueller (2012) find that droughts tend to increase people move out of rural areas in Ethiopia. Different weather events can have different effects on people and migration strategies. Thus, it is important to compare the effect of rainfall and temperature extremes using a comparable framework, data and estimation methods. Thirdly, using a gravity model, this study examines not only the push effect but also the pull effect of weather events on inter-province migration. Previous studies often focus on the effect of hazards on rural–urban migration and international out-migration (e.g., see the review by Black et al., 2013; Belasen and Polachek, 2013). There is less evidence of the pull effect of weather on in-migration. Fourthly, using different outcome variables (including income sources and health) and different types of migration (migration by age and education), this study can be able to examine two potential channels through which extreme weather events can induce migration in Vietnam. The first channel is through the economic effect (income and employment), while the second channel is through health problems. Climate and weather can have both direct and indirect effects on migration through various channels (e.g., McLeman and Smit, 2006; Black et al., 2011a). While there are a large number of empirical studies on the effect of climate change and wealth events on migration, a key challenge is to test the mechanisms of this effect (Black et al., 2013; Belasen and Polachek, 2013). The paper is organized into seven sections. The second section discusses a theoretical framework that this study relies on and several hypotheses that are tested in this study. The third section describes the datasets used in this study. The fourth section presents the descriptive analysis of migration and weather in Vietnam. The fifth and sixth sections, respectively, present the estimation method and the empirical results. Finally, the seventh section concludes the paper. 2. Conceptual framework and hypotheses To illustrate the mechanism of the effect of weather events on migration, we follow the conceptual framework of Black et al. (2011a). According to this framework (summarized in Fig. 1), the difference in climate and weather between source and destination areas can affect the migration through five main groups of drivers including environmental, political, demographic, social, and economic factors. Migration is affected by climate and weather of not only source but also destination areas. Climate change and weather events have a direct effect on migration through the environmental drivers and indirect effects on migration through the other four groups of drivers. In this study, depending on the availability of the data set, we estimate the effect of the temperature and precipitation extremes on migration and examine whether the effect happens through demographic and economic drivers. According to Black et al. (2011a), the demographic drivers include age, fertility, mortality, and ill-health within a community. The environment can have harmful effects on health of people, and people can mitigate these effects by migration. Economic shocks caused by climate change and weather events can push or pull migration. Since migration can be induced by weather of both source and destination areas, we use the gravity model to estimate the effect of weather extremes of source and destination provinces on inter-province migration. Then, using householdlevel and individual-level data from Vietnam Household Living Standard Surveys, we explore the effect of weather shocks on household income and individual health. The economic drivers are the main motives for migration. It is well documented that people move from areas with lower wages or income to those with higher wages and income. Weather extremes can affect agricultural production and labor demand, thus promoting or attracting migration. According to Cattaneo and Peri (2016), temperature affects the outmigration through the agricultural production. The sign of the effect depends on the income level and liquidity constraints of the source countries. High temperature extremes decrease agricultural productivity, therefore reducing household income. In middle-income countries, where the liquidity constraint does not bind, households can cover migration cost and they are more likely to migrate to cope with income loss caused by high temperature extremes. On the other hand, 119

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Economic Analysis and Policy 69 (2021) 118–141

Fig. 1. Conceptual framework of migration. Source: Preparation from Black et al. (2011a).

households in low-income countries face the liquidity constraints and cannot cover migration cost. The out-migration decreases as a result of high temperature extremes. Several empirical studies find the effect of precipitation and temperature extremes on agricultural production and economic growth but find that the evidence is mixed (e.g., Deschênes and Greenstone, 2007; Dell et al., 2012; Cattaneo and Peri, 2016). The effect of environmental changes on household income is examined in many studies such as Masozera et al. (2007), Mendelsohn et al. (2007), and Arouri et al. (2015). Feng et al. (2010) find a significant effect of climate-driven changes in crop yields in Mexico on emigration to the United States. In the conceptual framework of Black et al. (2011a), climate and weather can affect migration through demographic drivers. An important demographic driver is disease and ill-health. High temperatures result in discomfort and tiredness, reducing productivity and the labor supply, especially of outdoor workers (e.g., Zivin and Neidell, 2010; Tian et al., 2013). Exposure to cold weather can also cause health problems for children, even infant mortality (e.g., Deschenes and Moretti, 2009). Sharon and Yang (2009) find that in Indonesia, higher rainfall during early life has substantial positive effects on outcomes for women, but not for men. Temperature and precipitation are found to strongly affect the spread of dengue, cholera, malaria, diarrhea and several infectious diseases (e.g., Kuhn et al., 2005; Levy et al., 2016). Unhealthy people may be less likely to migrate (e.g., Finney et al., 2011). People can migrate from an area with more extreme weather to another with less extreme weather for health reasons. Fig. 1 shows that there are heterogeneous effects depending on characteristics of individuals and households, and intervening factors, either obstacles or facilitators. Age and education are important intervening factors. People with different age and education levels can respond to weather extremes in different ways. In this study, given the data availability, we examine whether the effect of weather extremes on migration of different people groups: children, adult, older people, and people with different education levels. 3. Data sources To examine the effect of weather events on inter-province migration, we combine migration data and weather data. To measure inter-province migration, we need to use large samples, which not only contain data on migration but are also representative at the provincial level. We use the 2009 Population and Housing Census and the 2014 Vietnam Intercensal Population and Housing Survey. The data were collected by the General Statistics Office of Vietnam with technical support from the United Nations Population Fund (UNFPA). The 2009 Population and Housing Census (henceforth referred to as the 2009 VPHC) was conducted in April 2009 nationwide, collecting data on basic demographic and housing characteristics. The Vietnam Intercensal Population and Housing Survey (the 2014 IPS) was conducted in April 2014. This is the first Population and Housing Census carried out at the midpoint between two national censuses (the 2009 and the 2019 censuses). The 2014 IPS consists of 5% of the total number of households in the whole country selected from 20% of the national survey sites. Like the 2009 VPHC, the 2014 IPS contains basic information concerning basic demographic and housing characteristics. 120

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The 2009 VPHC and the 2014 IPS collected data on inter-province migration by asking which province individuals lived in during the 5 years prior to the census.1 This migration is defined as the mobility of individuals across provinces over the past 5 years. More specifically, there is a question on which province respondents were living 5 year ago. In this study, an individual is defined as a migrant if she or he lived in another province 5 years ago rather than the current province. A limitation of the definition is that it cannot capture the mobility within the five-year period. If a migrant moved several times during the past five years, the data would not capture this intra-period mobility. In the 2009 VPHC, there are no data on whether the previous areas of migrants are rural or urban. Thus, we cannot model the rural–urban migration in this study. It should be noted that we used provinces instead of districts as the geographic unit for migration analysis. There are 63 provinces and around 700 districts in Vietnam. Districts are quite small, and people can easily travel across districts daily. A potential problem with using the definition of inter-province migration is overestimation of distance effects. There can be a large flow of migration between adjacent provinces. However, in our data sets, there are no information on migration between areas within a province. Thus, we cannot measure the inter-district or inter-commune migration. Moreover, provinces are the main unit for administrative management. For example, the registration book (ho khau), health insurance, and education are mainly managed at the provincial level. To our knowledge, all studies using the gravity model of migration in Vietnam use the same definition (e.g., Phan and Coxhead, 2010; Giang et al., 2020). Weather extremes in this study are measured by the extremes in temperature and precipitation of the provinces. The weather data were obtained from Willmott and Matsuura (2015). This dataset provides worldwide (terrestrial) monthly mean temperature and precipitation data at high resolution. These are gridded data which are interpolated into a 0.5 degree by 0.5 degree of latitude/longitude grid with the grid nodes centered at 0.25 degrees (Willmott and Matsuura, 1995). We used geospatial software to compute the province-level variation data in monthly temperature and precipitation over time. For a province which lies within different grids, the average temperature and precipitation are the weighted average of the grid temperature and precipitation, with the weight of a grid equal to the share of that grid’s area in the overall area of the province. The main advantage of this weather data set is the coverage of a long-time period so that we can construct the distribution of weather variable over time. The weather shocks are then measured by very low or very high values in the historic distribution. We used the province-level shape file in 2009 and 2014 (which are the same), and overlaid this shape file with the grid data of weather variables over time. We then obtained estimates of weather data at the provincial level over the period 1900–2014. There are 63 provinces in Vietnam. A province is paired with 62 other provinces. We have two points of data. Thus, the total number of observations (province pairs) used in this study is 63 ∗ 62 ∗ 2, which is equal to 7812. 4. Migration and weather in Vietnam 4.1. Migration trend Vietnam is a low middle-income country in Southeast Asia with an area of 331,000 km2 . Its population increased from 86 to 90.7 million during the 2009–2014 period. In this study, migrants are defined as those who lived in a province other than their current one during the previous 5 years. The percentage of inter-province migration in 2009 and 2014 was 4.3% and 3.2%, respectively. Fig. 2 shows the percentage of inter-province out-migration and in-migration during the 5 years previous to 2014. The migration rate decreased over this period. Possibly, the reduction in the migration rate is partially explained by a decrease in the economic growth of Vietnam in the recent years. The annual growth rate of GDP decreased from 7.5% during the 2004–2009 to 6.6% during the 2009–2014 period.2 Further studies need to be conducted to understand reasons for the decrease in internal migration in Vietnam. By region, people in the Mekong River Delta are most likely to migrate, followed by those in the Southeast and Central Coast. The Southeast – the richest region – has the highest rate of in-migration. The Southeast region includes Ho Chi Minh City, the largest city in Vietnam, and also other provinces with many industrial zones, such as Dong Nai and Binh Duong provinces. The Northern Mountains, the poorest region, has the lowest rate of in-migration as well as of out-migration. Figs. 3 and 4 present the inter-province out-migration and in-migration rates (as a percent) in 2009 and 2014. For comparison, we use the same scale of legend for both types of migration in those two years. It shows that within a region, both the out-migration and the in-migration rate vary across provinces. The maps indicate that the variation in the migration rate across provinces was lower in 2014 than in 2009. This is because the migration rate for most provinces in 2014 was lower than that in 2009. 1 The dataset contains no information on migration over varying time periods, such as 1 year or 10 years, so we can only describe migration over the past 5 years in this study. 2 These data are from World Development Indicators of World Bank. Available at: https://data.worldbank.org/indicator/NY.GDP.MKTP.KD?locations= VN. 121

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Fig. 2. The percentage of inter-province in-migrants and out-migrants by regions in 2014. Note: The figure presents the number of Out-migrants and in-migrants in a province as a percentage of the total population of the province. Out-migrants or immigrants are defined as those who lived in a different province (rather the current one) during the previous 5 years. Source: Estimation from the 2009 VHPC.

Fig. 3. Percentage of out-migrants from Vietnamese provinces in 2009 and 2014. Note: This figure presents inter-province out-migration (as a percent) in 2009 and 2014. The out-migration rate of a province is the number of people out-migrating from the province over the past 5 years as a proportion of the province’s total population. Source: Estimation from the 2009 VHPC and the 2014 IPS.

4.2. Weather in Vietnam Vietnam has a tropical country with two regions of different climates. North Vietnam has four seasons with different temperature and precipitation. The winter has significantly lower temperature and precipitation than the summer. South Vietnam has two seasons with the dry season from November to April and the rainy season from May to October. The UNISDR (2009) ranks Vietnam fourth in the world in terms of the absolute number of people exposed to floods, tenth to high winds from tropical cyclones, and sixteenth to drought. In recent years, typhoons with higher intensity tend to occur more frequently (FAO, 2011). According to our estimates using data from Willmott and Matsuura (2015), the average surface temperature of Vietnam has increased by approximately 0.5 degree Celsius over the past 50 years. This is 122

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Fig. 4. Percentage of in-migrants from Vietnamese provinces in 2009 and 2014. Note: This figure presents inter-province in-migration (as a percent) in 2009 and 2014. The in-migration rate of a province is the number of people in-migrating to the province over the past 5 years as a proportion of the province’s total population. Source: Estimation from the 2009 VHPC and the 2014 IPS.

consistent with the estimated increase in global average temperature. The average temperature is predicted to increase by the range of 1.1–3.6 ◦ C in this century (MONRE, 2009). With a long coastal line Vietnam will be serious affected by sea level rise. Although rainfall has not been increased over time, the annual precipitation is generally projected by MONRE (2009) to increase in the range of 1–10 percent until 2100. Using climate data from Willmott and Matsuura (2015), we can compute the monthly precipitation and temperature at the provincial level over time. In 2014, the average temperature was 24.6 ◦ C, and the average monthly precipitation was 133.7 mm (millimeters per square meter). There is a large variation in climate across provinces within each region (Fig. 5). In northern Vietnam, a large proportion of the terrain consists of mountainous areas, and the temperature in the mountains is lower than other areas. North Vietnam has four seasons, with temperatures and precipitation much lower in the winter than in summer. South Vietnam has two seasons, dry and rainy. On average, temperatures are lower in the north than in the south of Vietnam. The average temperature is highest in the southeast and in the Mekong River Delta. In precipitation, the Central Coast and Central Highlands have the highest level of rainfall. The link between migration and weather is explored in Figs. 5 and 6. The percentage of in-migration and out-migration is estimated for each province using both the 2009 VHPC and the 2014 IPS. Thus, for each province, there are two observations on migration: one from the 2009 VHPC and the other from the 2014 IPS. Fig. 6 shows a positive correlation between migration and temperature. Provinces with high temperatures experience a higher migration rate than those with low temperatures. The correlation between in-migration and temperature also is positive, but the magnitude of this correlation is smaller than that between out-migration and temperature. Regarding rainfall, there is a small, negative correlation between out-migration and the level of rainfall in the provinces, while there is a positive correlation between in-migration and rainfall (Fig. 7). This means that people tend to move from areas with lower rainfall to areas with high rainfall. 5. Econometric model According to the conceptual framework of Black et al. (2011a), the difference in climate and weather between source and destination areas can affect the migration through five main groups of drivers of migration including environmental, political, demographic, social, and economic factors. In order to model the effect of weather extremes of both source and destination areas, we use a gravity model. Gravity models are widely used to estimate the push–pull effects of factors on migration (e.g., Volger and Rotte, 2000; Phan and Coxhead, 2010; Kim and Cohen, 2010; Ortega and Peri, 2013). The gravity model follows the idea behind Newton’s law of universal gravitation. According to this law, two objects attract each other with a force proportional to their masses and inversely proportional to the squared distance between them. In the case of migration between two geographic areas, the gravity model states that the migration flow between the two areas is positively associated with their population size, and negatively associated with the distance between them, as 123

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Fig. 5. Average precipitation and temperature over 1900–2014 in Vietnam. Note: This figure presents the average monthly temperatures and precipitation in the provinces from 1900–2014. Source: Authors’ computation using data from Willmott and Matsuura (2015).

Fig. 6. The percentage of migrants and average temperatures. Note: In this figure, the vertical axis presents the percentage of provincial out-migration and in-migration during the past 5 years previous to the year of the censuses (2009 & 2014). The horizontal axis presents the provincial average monthly temperature from 1900–2014. Source: Author’s preparation using the 2009 VPHC & the 2014 IPS, & climate data from Willmott and Matsuura (2015).

follows: β

Mijt = g

Popαit Popjt γ

Distanceij

,

(1)

where Mijt is the migration flow from province i to province j in year t during the past 5 years; Popit and Popjt are the sizes of the population of provinces i and j in year t, respectively; Distance ij is the distance between the two provinces. For estimation, we take the log of both sides of the above equation: log(Mijt ) = log (g ) + α log (Popit ) + β log Popjt − γ log Distanceij .

(

)

(

)

(2)

In empirical studies, the basic gravity model is extended to include additional explanatory variables, such as GDP, of the original and destination areas. In our case, we are interested in the effect of weather extremes on migration flows between provinces. Thus, in Eq. (2) we include variables measuring weather extremes in the originating and destination provinces. 124

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Fig. 7. The percentage of migrants and average precipitation. Note: In this figure, the vertical axis indicates the percentage of out-migration and in-migration from/to provinces during the past 5 years previous to the year of the censuses (2009 & 2014). The horizontal axis gives the average monthly precipitation in the provinces from 1900–2014. Source: Author’s computation using the 2009 VPHC & the 2014 IPS, and climate data from Willmott and Matsuura (2015).

The regression model is written as follows: log(Mijt ) = log (g ) + α log (Popit ) + β log Popjt − γ log Distanceij

(

)

(

)

+ β1 Weatherit + β2 Weatherjt + Xit′ θ1 + Xjt′ θ2 + Tt δ + uij + vijt

(3)

where Weatherit and Weatherjt are the variables of weather extremes of provinces i and j in year t, respectively. In addition, we also control for a vector of variables X and time dummies T. uij and vijt are time-invariant and time-variant unobserved variables, respectively. The push effect of weather extremes is measured by the coefficients β1 , while the pull effect of weather extremes is measure by β2 . If people tend to move from provinces with more weather extremes to those with fewer, we will have β1 > 0 and β2 < 0. In this study, climate extremes are measured by the number of months with very high or very low levels of temperature and precipitation over the past 5 years in comparison with the average trend over time. This definition has been used to capture weather extremes in various studies (e.g., Deschênes et al., 2009; Deschenes and Greenstone, 2011). We measure weather extremes during the past 5 years, since our migration variable is defined for that period of time. The variables ‘‘low temperature’’ and ‘‘high temperature’’ in a province are measured by the number of months (over the previous 5 years), which had temperatures below the 5th percentile and above the 95th percentile, respectively, of the monthly temperature distribution in the same province from 1900–2014. Similarly, the variables ‘‘low precipitation’’ and ‘‘high precipitation’’ are measured by the number of months (over the previous 5 years), which had precipitation below the 5th percentile and above the 95th percentile, respectively, of the monthly precipitation distribution. For robustness analysis, we also use the cut-off at the 10th and 90th percentile to define the low and high weather extremes, respectively. A limitation with this data set is that there are no daily data on weather. An idiosyncratic event like high rainfall or temperature can happen on a few days in a single month. This event can have different impacts than one in which weather is extreme throughout a month. We expect that months with more day with rainfall or temperature extremes tend to have a higher average rainfall or temperature. If the weather extremes occur persistently, it indicates a climate change trend. Because of this trend, households can change their income expectation and their behaviors. To separate the effect of climate trends, we control for the mean of the precipitation and temperature across the past 5 years in provinces. Another popular approach to estimate the climate effect is to define the distribution of weather as well as precipitation by different bins, e.g., the number of days with certain levels of temperature and precipitation (e.g., Deschenes and Greenstone, 2011; Dell et al., 2012; Deryugina and Hsiang, 2017).3 This allows for estimating the effect of the distribution of weather variables over a period of time. This way can also take into account adaption and scoping strategy of households to the climate. In this study, we also use this approach to examine the sensitivity of the estimates to the definition of the weather variables. A problem with this definition is that these weather variables can be correlated with other omitted variables which affect migration, and as a result the estimates can be biased. Thus, we use the measurement of the weather extremes of the main interpretation. 3 For other methods and definitions, see review from Hsiang (2016) and Kolstad and Moore (2019). 125

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A limitation of defining the temperature and precipitation extremes in this study is that it cannot fully capture hazard events typhoons, tornadoes, flooding, or drought. However, the main advantage of using weather extremes is its exogeneity, which allows for the unbiased estimate of the wealth effect (Dell et al., 2012; Kolstad and Moore, 2019). We control for the average temperature and precipitation of the provinces of origin and destination, and add dummies of pairwise origin–destination provinces. In other words, we estimate the over-time change in the weather extremes on migration. The province fixed-effects regression can address any endogeneity bias caused by time-invariant unobserved variables uij . An empirical problem with gravity models is the zero value of the dependent variable. For some pairs of provinces, where there is no migration between them, the dependent variable will be zero. As a result, the logarithm of migration cannot be taken. In this study, we use a two-part model, which is widely used to model a variable with a large number of zero values (Duan et al., 1983; Manning et al., 1987).4 In the first part, we model whether there is migration from province i to province j: I{Mijt > 0} = log (g ) + α log (Popit ) + β log Popjt − γ log Distanceij

(

)

(

)

+ β1 Weatherit + β2 Weatherjt + Xit′ θ1 + Xjt′ θ2 + Tt δ + uij + vijt ,

(4)

where I Mijt > 0 is a dummy variable denoting the occurrence of migration between provinces. In the second part, we model the effect of weather extremes on the log of migration for pairwise provinces with inter-province migration.

{

}

Log(Mijt |Mijt > 0) = log(g)∗ + α ∗ log (Popit ) + β ∗ log Popjt − γ ∗ log Distanceij

(

)

(

)

∗ + β1∗ Weatherit + β2∗ Weatherjt + Xit′ θ1∗ + Xjt′ θ2∗ + Tt δ ∗ + u∗ij + vijt ,

(5)

where subscript * in Eq. (5) is used to distinguish coefficients between this equation and Eq. (4). The effect of weather extremes on the probability of migrating from province i to province j is measured β1 and β2 . For pairwise provinces with migration, the effect of weather extremes on the migration flow is measured by β1∗ and β2∗ . In this study, we are interested in the effect of weather events on the unconditional dependent variable Log(Mijt ). We first write the unconditional migration flow: Mijt = I{Mijt > 0}.(Mijt |Mijt > 0),

(6)

and take the log of both sides of (6) to get: Log(Mijt ) = Log I{Mijt > 0} + Log Mijt ⏐Mijt > 0

[

]

(

⏐

)

(7)

We can get the marginal effect of Log(Mijt ) by first taking the partial derivative of (7) with respect to the weather variables, ∗ are uncorrelated with the weather variables. For example, noting that the error terms uij and u∗ij are fixed, and vijt and vijt the marginal effect of the migration flow with respect to Weatherit is computed as follows:

[ ] ∂ Log(Mijt )/∂ Weatherit = β1 . 1/I{Mijt > 0} + β1∗ .

(8)

The marginal ⏐ effect varies across the value of migration. We can estimate the average marginal effect by using the mean of Log(Mijt ⏐Mijt > 0) and I{Mijt > 0}: E ∂ Log(Mijt )/∂ Weatherit = β1 .E 1/I{Mijt > 0} + β1∗ .

[

]

[

]

(9)

Similarly, the full effect of weather extremes of destination provinces on the log of unconditional migration flow is expressed as follows: E ∂ Log(Mijt )/∂ Weatherjt = β2 .E 1/I{Mijt > 0} + β2∗ .

[

]

[

]

(10)

It should be noted that binary outcomes (Eq. (4)) are often estimated using a logit or probit model. However, there are no available fixed-effects probit estimators (Greene, 2004). Although a fixed-effects logit estimator is available, it is not efficient since it drops observations with time-invariant values of the dependent variable. Thus, we estimate Eq. (4) using a linear probability regression. Linear probability models are widely used when no non-linear probability estimators are available (e.g., Angrist, 2001; Angrist and Krueger, 2001). Linear probability models are also more robust to the assumption of error term distribution (Nichols, 2011). The standard errors can be correlated in panel data models (Bertrand et al., 2004). Thus, we follow Cameron et al. (2011) and Egger and Tarlea (2015) to estimate robust standard errors multi-way clustered at region pairs and country pairs.5 4 To address zero or missing values for the dependent variables, a Tobit model can be used. However, the Tobit model requires a strong assumption of error term normality for consistency, and the fixed-effects Tobit estimators are not available due to the incidental parameters problem (Greene, 2004). Heckman correct model can be used to model the sample selection (Heckman, 1979). However, this method requires an instrument that predict the probability of migration but not the flow of migration, and we cannot find such an instrument. The current model controls for province pairs fixed effects. Estimators for Heckman models with fixed effects are not available due to the incidental parameters problem (Greene, 2004). An alternative way to estimate a fixed-effect model is to control dummies of province pairs. However, the are 3906 province pairs, and the software cannot estimate a Heckman model with such as a large number of control variables. Another method to address zero-value observations is to use Poisson pseudo-maximum likelihood estimators to estimate an exponential function for Eq. (3) (Gourieroux et al., 1984; Santos and Tenreyro, 2006). However, this would not estimate the same function as model (3), and would change the interpretation of the original gravity model. 5 We use command ‘ivreg2’ in Stata 14. 126

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We calculate the standard errors of the average marginal effect on unconditional log of migration using a non-parametric bootstrap with 500 replications. In addition to the gravity model at the provincial level, for robustness analysis we also run regression of the decision to migrate of individuals using the individual-level data. Specifically, we estimate the following regression to model to the push effect of weather extremes on migration: ′ γs + Tt δs + Pp θs + ϵkpt , Out_migrantkpt = αs + βs Origin_Weatherpt + Xkpt

(11)

where Out_migrantkpt is a dummy variable indicating where individual k migrated out of province p in the period t. Origin_Weatherpt denotes variables of weather extremes of the source or original provinces. The weather extremes are defined as above. Xkpt are variables of characteristics of individuals. Pp and Tt are the dummy variables indicating provinces and the time period, respectively. ϵkpt denotes unobserved variables in the model. Similarly, we model the pull effect of the weather extremes on in-migration using the following regression: ′ In_migrantkpt = αd + βd Destination_Weatherpt + Xkpt γd + Tt δd + Pp θd + ϵkpt ,

(12)

where In_migrantkpt is a dummy variable indicate where individual k is an in-migrant in province p in the period t. This variable is equal to 1 for individuals who migrated into province p from another province. Destination_Weatherpt denotes variables of weather extremes of the destination provinces. We estimate models (11) and (12) using individual-level data. 6. Empirical results 6.1. Impact of weather extremes on migration using the gravity model This section discusses the effect of weather extremes on inter-province migration. As mentioned in the previous section, we measure weather extremes by the number of months with unusually low or high temperatures/precipitation during the past 5 years. A temperature or precipitation value is defined as low if it falls below the 5th percentile of the distribution over the 1900–2014 period (for the same provinces). A temperature or precipitation value is defined as high if it is above the 95th percentile of the distribution over the 1900–2014 period (for the same provinces). This definition has been used to capture weather extremes in various studies (e.g., Deschênes et al., 2009; Deschenes and Greenstone, 2011). For the period covered by our study (2004–2014), the average number of months with low precipitation extremes across provinces is 2.96 months, and the average number of months with high precipitation extremes is 2.61. Regarding temperatures, the average number of months with low temperature extremes is 2.56 months, while the average number with high temperature extremes is 4.35. This indicates that temperatures during this period were higher than the average during the 1900–2014 period. Figs. A.1 and A.2 in the Appendix present the distribution of provinces by the number of months during the past 5 years with unusually high or low temperatures/precipitation. The figures show that some provinces experienced unexpectedly high or low temperatures/precipitation during the past 5 years. We use a small set of exogenous variables as control variables. Variables which are caused by weather extremes should not be used as controls (e.g., Angrist and Pischke, 2009). Variables such as GDP, the share of urban or ethnic minority population of provinces are not controlled since these variables can be affected by weather extremes as well as the migration trend. This also means that we aim to estimate the total effect of weather extremes on migration rather than the partial effect of weather extremes with other variables held constant (Duflo et al., 2008). The control variables include population before migration, and the average temperature and precipitation of provinces during the past 5 years. It should be noted that in the fixed-effects model, the distance between pairwise provinces is time-invariant and is dropped from the model. The summary statistics of variables are presented in Table A.1 in Appendix. Table 1 presents an estimation of the effect of weather extremes on migration. Only coefficients of the weather extremes are reported in this table.6 The second and third columns present the coefficients from the two-part model. In the two-part model, the first dependent variable is a dummy variable indicating whether there is a flow of migration from a source province to a destination province. In our sample, around 27% of the paired provinces experienced no inter-province migration. The second dependent variable in the two-part model is the log of migrants from origin to destination provinces. The average marginal effects on log of unconditional migration flow are computed using Eqs. (9) and (10), and they are reported in the fourth column of Table 1. In the following, we use the average marginal effects of weather extremes on the log of unconditional migration for interpretation. Migration is not affected by low levels of precipitation in source provinces. However, high rainfall extremes have a positive effect on out-migration. An increase of one month with precipitation above the 95th percentile of the precipitation distribution increases the number of out-migrants by 7.1%. Since the average number of months with high precipitation is 2.61, one month is equal to 38.3% of the average. Using this information, we can compute the elasticity at the mean, which is approximately 0.19. We can infer that a 1% increase in the number of months with high precipitation extreme increases the number of out-migrants by 0.19%. As we will show in Table 2, the push effect of high precipitation is mainly 6 The full regressions are not reported in this paper, but they can be provided for readers on request. 127

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Table 1 Fixed-effects regressions of inter-province migration on weather shocks during the past 5 years. Source: Estimation using the 2009 VPHC and the 2014 IPS, and climate data from Willmott and Matsuura (2015). Explanatory variables

With inter-province migration

Log of migration flow

Average marginal effect

(1)

(2)

(3)

Number of months with low precipitation in provinces of origin

−0.0021 (0.0049)

−0.0086

−0.011

(0.0158)

(0.019)

Number of months with high precipitation in provinces of origin

−0.0037

0.0757** (0.0217)

0.071* (0.030)

Number of months with low temperatures in provinces of origin

0.0213** (0.0081)

−0.1228**

−0.093**

(0.0246)

(0.030)

Number of months with high temperatures in provinces of origin

−0.0019 (0.0061)

0.0254 (0.0197)

0.023 (0.026)

Number of months with low precipitation in destination provinces

0.0021 (0.0049)

0.0268 (0.0152)

0.030 (0.019)

Number of months with high precipitation in destination provinces

−0.0224** (0.0061)

0.0530* (0.0208)

0.022 (0.025)

Number of months with low temperatures in destination provinces

0.0237** (0.0084)

0.1155** (0.0265)

0.148** (0.031)

Number of months with high temperatures in destination provinces

−0.0117

−0.0485*

−0.065*

(0.0063)

(0.0218)

(0.029)

Control variables Pairwise province fixed-effects Observations R-squared

Yes Yes 7812 0.716

Yes Yes 5665 0.952

Yes Yes 7812

(0.0062)

Note: This table reports the effect on migration flow of weather extremes in provinces of origin and destination. In the two-part model, the first dependent variable is a dummy indicating if there is an inflow of migration from a province of origin to one of destination. The second dependent variable is the log of the number of migrants between provinces with migration. The average marginal effects on the log of unconditional migration flow are computed using Eqs. (9) and (10). Weather extremes are measured by the number of months with low or high temperatures/precipitation during the past 5 years. A temperature (and precipitation) value is defined as low if it is below the 5th percentile of the distribution over the 1900–2014 period. A temperature (and precipitation) value is defined as high if it is above the 95th percentile of the distribution over the 1900–2014 period. This table reports only the coefficient of weather extremes. The full regression with control variables are not presented, but they can be provided on request. Robust standard errors in parentheses. *p < 0.05. **p < 0.01.

on the highly-educated people. A sudden increase in rainfall is associated with floods and typhoons. Thus, our finding is consistent with findings from Gröger and Zylberberg (2016) that people in areas which are affected by typhoons are more likely to migrate to other areas for nonfarm employment opportunities. Cooler weather in the provinces of origin reduces out-migration. An increase of one month of temperatures below the 5th percentile of the temperature distribution reduces the number of out-migrants by 9.3%. Using the method outlined above for estimating elasticity, the elasticity of the migration flow in relation to the number of cold months is estimated at −0.24%. There are no significant effects of hot weather in source provinces on migration. In the destination provinces, the effects of precipitation extremes on the log of the flow of in-migration are positive and statistically significant (column 2 of Table 1). However, the effect of precipitation extremes on the unconditional migration flow is not statistically significant (column 3 of Table 1). Compared with the precipitation extremes, temperature extremes have a stronger effect on migration in Vietnam. This finding is consistent with several empirical studies such as BohraMishra et al. (2014), Mueller et al. (2014) and Gray and Wise (2016) which also find that temperature tends to have larger effects on migration relative to precipitation. Table 1 shows that low temperature extremes have a strong attraction effect on in-migration. An increase of one month with temperatures below the 5th percentile of the distribution increases the flow of in-migration by 14.8% (column 6 of Table 1). Elasticity is estimated at 0.38%. It should be noted that Vietnam is a tropical country with the lowest monthly temperature during 1900–2014 equal to only 7.3 ◦ C. Low temperatures mean coolness rather than extreme cold. As we will show in the later table, low temperatures have a stronger pull effect on migration of highly-educated people (column 6 in Table 2) than lowly-educated people (columns 4 and 5 in Table 2). Low temperatures also have a positive effect on wages (column 2 in Table 5). It means that people who are looking for a wage job prefer to move to an area with low rather than high temperatures, since high temperatures result in discomfort and tiredness, which lower productivity (Zivin and Neidell, 2010; Tian et al., 2013). On the other hand, high temperatures reduce the number of in-migrants. An additional month of high temperatures decreases the number of in-migrants by 6.5% (column 3 of Table 1). The elasticity of the in-migration flow with respect to the number of months with high temperatures in destination provinces is estimated at −0.28%. 128

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Table 2 Fixed-effects regressions of inter-province migration by age and education. Source: Estimation using the 2009 VPHC and the 2014 IPS, and climate data from Willmott and Matsuura (2015). Explanatory variables

Dependent variables Log of child migration flow

Log of adult migration flow

Log of elderly migration flow

Log of migration of people without high school education

Log of migration of people with high school education

Log of migration of people with tertiary education

(1)

(2)

(3)

(4)

(5)

(6)

Number of months with low precipitation in provinces of origin

0.016 (0.035)

−0.025

−0.006

−0.094

−0.019

−0.005

(0.019)

(0.060)

(0.072)

(0.026)

(0.032)

Number of months with high precipitation in provinces of origin

0.111* (0.049)

0.066* (0.026)

0.103 (0.084)

−0.172 (0.124)

0.028 (0.040)

0.107** (0.041)

Number of months with low temperatures in provinces of origin

−0.139*

−0.093**

−0.101

−0.235*

−0.081*

−0.083

(0.066)

(0.033)

(0.111)

(0.102)

(0.039)

(0.050)

Number of months with high temperatures in provinces of origin

0.048 (0.056)

0.027 (0.026)

0.015 (0.099)

0.060 (0.071)

0.065 (0.040)

0.036 (0.050)

Number of months with low precipitation in destination provinces

0.027 (0.040)

0.031 (0.021)

−0.023 (0.068)

0.030 (0.078)

0.082** (0.031)

0.018 (0.033)

Number of months with high precipitation in destination provinces

0.121* (0.049)

0.010 (0.027)

0.067 (0.073)

0.232* (0.110)

0.070 (0.038)

−0.004

Number of months with low temperatures in destination provinces

0.152* (0.064)

0.142** (0.035)

0.157 (0.115)

0.140 (0.107)

0.079 (0.046)

0.133* (0.059)

Number of months with high temperatures in destination provinces

−0.060 (0.055)

−0.061*

0.096 (0.096)

−0.024

−0.086*

−0.025

(0.030)

(0.133)

(0.039)

(0.040)

Control variables Pairwise province fixed-effects Observations

Yes Yes 7812

Yes Yes 7812

Yes Yes 7812

Yes Yes 7812

Yes Yes 7812

Yes Yes 7812

(0.045)

Note: The table reports the average marginal effects of weather extremes on the log of unconditional migration flow between provinces, computed using Eqs. (9) and (10). The full results of two-part regressions are not presented, but they can be provided on request. Weather extremes are measured by the number of months with low or high temperatures/precipitation during the past 5 years. A temperature (and precipitation) value is defined as low if it is below the 5th percentile of the distribution over the 1900–2014 period. A temperature (and precipitation) value is defined as high if it is above the 95th percentile of the distribution over the 1900–2014 period. Robust standard errors in parentheses. *p < 0.05. **p < 0.01.

Table 2 presents the effects of weather on different groups of migrants. The table reports the average marginal effects of weather extremes on the log of unconditional migration flow between provinces, computed using Eqs. (9) and (10).7 Overall, the coefficients of weather extremes on the migration of sub-groups have signs similar to the coefficients of weather extremes on total migration. However, there are some differences among groups in the effect of weather extremes. The weather effect on children are very similar to those on adults. In this study, children are defined as those below 16 years old, while older people are those from 60 years old.8 Children are not allowed for working in Vietnam. Thus, children migrate mainly because they follow their parents. This also explains why the effect of weather extremes is similar for children and adults. There are no significant effects of weather extremes on the migration of older people. It is well documented that migration is more common among younger than older people, especially in low- and middle-income countries where people migrate for better employment opportunities (Borjas, 2012). Migration is a type of a human capital investment, and older workers have a shorter period to collect migration investment returns (Borjas, 2012). Thus, older people are less likely to migrate, and the effect of weather extremes on their migration may be small. We also measure the effect of weather extremes on the migration of people with differing education levels. We do not investigate the effects on migrants with differing types of employment, since there are no data on employment before migration. Column 6 of Table 2 shows that in source provinces, high precipitation extremes have a positive (push) effect on the migration of highly educated people (i.e., those with a college degree or tertiary education) but not people with

7 The full results of two-part regressions are not reported in this paper, but they can be provided for readers on request. 8 This definition follows the definition of children in Vietnam’s Law on Children and the definition of older people in Vietnam’s Law on Older People. 129

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lower education levels. Farmers are more ‘tied’ to the land and less likely to migrate to other areas.9 It is argued that more highly educated individuals have better information and employment opportunities, and are therefore more likely to migrate compared to people with less education (e.g., Levy and Wadycki, 1974; Faggian et al., 2007). Highly-educated people are more likely to have non-farm employment, whereas the poorly-educated tend to have farm employment. High precipitation extremes might cause difficulties for non-farm workers, especially for those who work outside and have to travel a long distance from their home to working places. In destination provinces, high precipitation has a positive (pull) effect on the migration of poorly educated people (columns 4 and 5 of Table 2). Possibly, high rainfall is better for crop production, and people with lower education tend to move to high-rainfall provinces to find farm employment, either employed or self-employed employment. According to the 2014 Vietnam Household Living Standard Survey, the percentage of people with tertiary education having a wagepaying job and a self-employment job in the agricultural sector was 0.2% and 4.1%, respectively. These corresponding figures for individuals without tertiary education was 4.2% and 38%, respectively. 6.2. Robustness analysis We conduct several robustness analyses. Firstly, in addition to the definition of weather extremes using the 5th and 95th percentiles, temperature (and precipitation) extremes are also measured by the number of months in which monthly temperature (and precipitation) are below the 10th percentile or above the 90th percentile of the distribution of monthly temperature (and precipitation) over the period 1900–2014. The results are very similar to those in Tables 1 and 2.10 Secondly, we use the absolute values of rainfall and temperature and compute the number of months within different bins. More specifically, we compute the number of months over the past 5 years that the precipitation level falls in different bins: 0 mm, 0–50; 50–100; 100–250; 250–400; 400–550; and above 550 mm. Similarly, temperatures are classified in different bins: 0–15 degree Celsius; 15–18; 18–21; 21–24; 24–27; 27–30; and above 30. We use the same model specification as those in Tables 1 and 2, and estimate the effect of the weather variables on the migration flows. The results are reported in Table A.3 in Appendix. We find that the results are very similar to those based on the number of months in which monthly temperature and precipitation are below the 5th percentile or above the 95th percentile of the distribution. Low precipitation and temperature tend to reduce the out-migration, while high precipitation and temperature tend to push people to migrate. Thirdly, there is a problem of the sample selection in showing income and health as channels through which weather extremes affect migration. A number of people might be already affected by weathers and migrated to their current provinces. To examine this issue, we limit the sample to people who have the registration book in their current provinces (ho khau). To apply for a registration book in a province, people are required to stay in the province at least for one year (according to the Vietnam’s Law on Residence). By focusing on people with registration books, we can exclude short-term migrants and some medium-term ones in the analysis. In other words, our sample includes only people who did not migrated at least in the short term. The results are put in Tables A.4 and A.5 in Appendix. It shows very similar results to those in Tables 4 and 5. This indicates that the selection problem is not serious. Fourthly, we estimate the effect of weather extremes on individuals’ decision to migrate. Table 3 reports regressions of migration of individuals on weather extremes. Column 1 present the push effect of weather extremes on out-migration (estimating Eq. (11)), while column 2 reports the pull effect of weather extremes on in-migration (estimating Eq. (12)). Compared with the gravity models, estimates from models using individual-level data are more significant. Overall, findings from the individual-level models are similar to those from the gravity models. The effect of low rainfall extremes on migration is not statistically significant. However, high rainfall extremes tend to push migration. An increase of one month with precipitation above the 95th percentile of the precipitation distribution increases the probability of outmigration by 0.0115 (column 1 in Table 3). Possibly, a sudden increase in rainfall is associated with floods and storms. At the same time, high rainfall extremes reduce the probability of in-migration. Consistent with the gravity model, the regression model using individual-level data shows that people tend to move from high to low temperature extremes. Sudden decreases in temperature in the source provinces reduce the probability of out-migration, while sudden increases in temperature increase the probability of out-migration. On the other hand, provinces with sudden decreases in temperatures can attract more in-migrants. 6.3. Mechanisms To understand the mechanism of the effect of weather extremes on migration, we estimate the effect of weather extremes on household incomes and individual healthcare. We use the Vietnam Household Living Standard Survey (VHLSS) conducted in 2010, 2012 and 2014. VHLSSs are conducted in Vietnam by the GSO with technical support from the World Bank every 2 years. Each VHLSS collects data on living standards from 9400 households nationwide. Individual data include demographics, education, employment, health, and migration. Household data cover durables, assets, production, income 9 It is important to examine whether the effect of weather extremes on rural–urban migration differs for farmers with different land sizes. There are no data on land areas from the 2009 VPHC and the 2014 IPS. Thus, in this study we cannot analyze this issue. However, this is an important issue which should be studied in the future research. 10 We do not report these results in this paper, but they can be provided on request. 130

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Table 3 Regressions of inter-province migration using individual sample. Source: Estimation using the 2009 VPHC and the 2014 IPS, and climate data from Willmott and Matsuura (2015). Dependent variable is the dummy of in-migration (equals 1 for in-migrants and 0 otherwise): the urban sample

Explanatory variables

Dependent variable is the dummy of out-migration (equals 1 for out-migrants and 0 otherwise)

Dependent variable is the dummy of in-migration (equals 1 for in-migrants and 0 otherwise)

Dependent variable is the dummy of in-migration (equals 1 for in-migrants and 0 otherwise): the rural sample

(1)

(2)

(3)

(4)

Number of months with low precipitation in provinces

0.0003 (0.0003)

0.0001 (0.0002)

−0.0063**

0.0032** (0.0002)

Number of months with high precipitation in provinces

0.0115** (0.0004)

−0.0021**

−0.0093** (0.0007)

−0.0000

(0.0003)

Number of months with low temperatures in provinces

−0.0129**

0.0169** (0.0005)

0.0392** (0.0013)

0.0098** (0.0004)

Number of months with high temperatures in provinces

0.0082** (0.0002)

−0.0010**

−0.0048** (0.0004)

−0.0001

−0.0002**

0.0001** (0.0000)

−0.0000 (0.0000)

0.0002** (0.0000)

0.0105** (0.0018)

0.0095* (0.0044)

0.0224** (0.0017)

−0.0059** (0.0003)

−0.0053** (0.0003)

0.0009** (0.0001)

0.0007** (0.0000)

Age

0.0010** (0.0000)

0.0007** (0.0000)

−0.0000** (0.0000)

−0.0000**

Age squared

−0.0000**

−0.0000**

−0.0048**

−0.0051**

(0.0000)

(0.0000)

(0.0008)

(0.0003)

−0.0049**

−0.0283**

−0.0680**

−0.0148**

Average precipitation of provinces Average temperature of provinces Male

Dummy year 2014

(0.0005)

(0.0000)

−0.0327** (0.0022)

(0.0002)

(0.0005)

(0.0003)

(0.0001)

(0.0000)

(0.0008)

(0.0007)

(0.0021)

(0.0007)

Province fixed-effects

Yes

Yes

Yes

Yes

Constant

0.8487** (0.0585)

−0.2581**

−0.1537 (0.1174)

−0.5876**

(0.0463)

2,521,233 0.016

2,521,233 0.070

1,825,878 0.044

695,355 0.084

Observations R-squared

(0.0436)

Note: This table reports the effect weather extremes in provinces of origin and destination on the decision to migrate using individual data. Weather extremes are measured by the number of months with low or high temperatures/precipitation during the past 5 years. A temperature (and precipitation) value is defined as low if it is below the 5th percentile of the distribution over the 1900–2014 period. A temperature (and precipitation) value is defined as high if it is above the 95th percentile of the distribution over the 1900–2014 period. Robust standard errors in parentheses. *p < 0.05. **p < 0.01.

and expenditure, and participation in government programs. Using these datasets, we run a regression of household per capita income from different sources on the weather extremes. Since the VHLSSs are conducted every 2 years, we measure the weather extremes during the past 2 years (instead of during the past 5 years, as in the case of migration). The summary statistics of variables used in this analysis are presented in Table A.2 in Appendix. Table 4 shows that weather extremes can affect household income. Low precipitation increases wage income. An additional month with low precipitation extreme increases wage income per capita by 7.9%. The coefficients of low precipitation on agricultural income are negative but not statistically significant at the conventional level. The increase in wages cannot offset the decrease in income from other sources and as a result, household per capita income is reduced by 2.2% if there is an additional month with low precipitation. On the other hand, high precipitation increases households’ per capita income. An additional month with high precipitation increases per capita income by 1.4%. The effects on income from different sources are not significant. However, the signs of both low precipitation and high precipitation indicate that high precipitation tends to reduce wage income but increase agricultural income. Regarding temperature, we find evidence that low as well as high temperature hamper household economic status. A one-month increase in the number of months with low and high temperatures reduces per capita income by 1.7 percent and 2.2 percent, respectively. This evidence is consistent with a well-known hypothesis that hot areas have lower economic growth (e.g., Nordhaus, 2006; Dell et al., 2012). Importantly, we find that low temperature extremes increase wages. As mentioned above, Vietnam is a tropical country, and low temperatures mean coolness rather than extreme cold. Cool weather, with the temperature around 20 degree Celsius, has been found to be associated with higher productivity (e.g., Lan et al., 2010; Tian et al., 2013). 131

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Economic Analysis and Policy 69 (2021) 118–141

Table 4 Regression of household income on weather extremes during the past 5 years. Source: Estimation using the VHLSSs in 2010, 2012, and 2014, and climate data from Willmott and Matsuura (2015). Explanatory variables

Dependent variables Log of per capita income

Log of per capita wages

Log of per Log of per capita crop capita income livestock income

Log of per capita income from forestry and aquaculture

Log of per capita nonfarm income

Log of per capita remittance income

Log of per capita income from other sources

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Number of months with low precipitation in provinces

−0.0224**

0.0787 (0.0411)

−0.0360

−0.0038

−0.0192

−0.0673

−0.0458

−0.0178

(0.0348)

(0.0302)

(0.0271)

(0.0416)

(0.0288)

(0.0380)

Number of months with high precipitation in provinces

0.0137* (0.0061)

0.0517 (0.0330)

0.0029 (0.0349)

0.0128 (0.0313)

0.0562 (0.0477)

0.0010 (0.0305)

−0.0516

Number of months with low temperatures in provinces

−0.0172*

−0.1066**

−0.0339

−0.0214

(0.0353)

(0.0646)

0.0385 (0.0497)

0.0406 (0.0623)

Number of months with high temperatures in provinces

−0.0222**

Average precipitation of provinces

−0.0009**

(0.0057)

(0.0079) (0.0064) (0.0002)

Average temperature of provinces

−0.0087 (0.0365)

Household size

−0.0527** (0.0036)

Proportion of children below 15 in households

−0.6743**

Proportion of people above 50 in households

−0.4477**

(0.0261) (0.0227)

−0.0576 (0.0456)

(0.0404)

0.1245* (0.0609)

(0.0399)

0.0170 (0.0419)

−0.1487** (0.0445)

0.0574 (0.0336)

0.0439 (0.0315)

0.0005 (0.0297)

0.0295 (0.0470)

0.0286 (0.0354)

0.0834* (0.0415)

−0.0021 (0.0018)

−0.0016 (0.0014)

0.0026 (0.0014)

0.0006 (0.0012)

−0.0010

−0.0036**

(0.0018)

(0.0013)

0.0046** (0.0016)

−0.2041 (0.2865)

0.1072 (0.1897)

0.0566 (0.1946)

−0.1284 (0.1733)

−0.8241**

−0.1160

(0.2872)

(0.2164)

0.2952** (0.0264)

0.2142** (0.0228)

0.0891** (0.0191)

0.0009 (0.0176)

0.1091** (0.0280)

−0.3349** (0.0159)

0.0288 (0.0202)

−2.1114** (0.1914)

−1.8380** (0.1646)

−1.8132** (0.1429)

−0.1867 (0.1264)

−0.6340**

0.5533** (0.1192)

0.0821 (0.1487)

−2.7865** (0.1526)

−0.9406** (0.1272)

−0.6214* (0.1013)

−0.4025** (0.0827)

−1.9240**

2.0031** (0.0771)

3.4974** (0.1066)

0.4934** (0.0985)

0.4096** (0.1192)

0.4776** (0.0915)

0.4091** (0.1128)

(0.2099) (0.1283)

0.3608 (0.2732)

Dummy year 2010

Reference

Dummy year 2012

0.3792** (0.0172)

0.7354** (0.1257)

−0.0453 (0.0900)

−0.1115 (0.0866)

−0.1022 (0.0803)

−0.2717*

0.5551** (0.0157)

0.9040** (0.1204)

−0.0997 (0.0886)

0.1350 (0.0822)

−0.0894 (0.0759)

−0.1788

Province fixed-effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Constant

10.3327** (0.9322)

10.5723 (7.3276)

1.3072 (4.8901)

0.2770 (5.0155)

4.3607 (4.4762)

23.1276** (7.3353)

9.0770 (5.4827)

−7.2493

Observations R-squared

28,197 0.317

28,197 0.088

28,197 0.242

28,197 0.231

28,197 0.235

28,197 0.041

28,197 0.189

28,197 0.096

Dummy year 2014

(0.1347) (0.1251)

(6.9328)

Note: This table reports province fixed-effects regressions of log of household income from different sources on weather extremes. The observations used in this regression relate to households. Weather extremes are measured by the number of months with low or high temperatures/precipitation during the past 5 years. A temperature (and precipitation) value is defined as low if it is below the 5th percentile of the distribution over the 1900– 2014 period. A temperature (and precipitation) value is defined as high if it is above the 95th percentile of the distribution over the 1900–2014 period. Weather extremes are measured at the provincial level, and these weather data are merged with household-level data on income. Robust standard errors in parentheses. Standard errors are corrected for sampling weights and cluster correlation. *p < 0.05. **p < 0.01.

Although the agricultural sector has been shrinking in Vietnam, it is still an important sector in Vietnam, accounting for 15% of GDP in 2018. In the 2014 VHLSS, 42% of working people remained in the agricultural sector. Several studies such as Ravallion and van de Walle (2008) and Nguyen and Ngoc Tran (2013) show a poverty-reducing effect of agricultural growth in Vietnam. Estimates from the 2014 VHLSS show that crops account for 63% of agricultural outputs of households. The remaining output, 27%, were from aquaculture, forestry and livestock. We show that weather extremes matter to agricultural income. Overall, higher temperatures and rainfall tend to benefit agricultural production. Although extreme weather can hurt farmers when they are working outside, temperature and rainfall are still important inputs for agricultural production. The effect of weather events on agricultural production is complex and inconclusive in empirical studies, since this effect depends not only on the range of weather but also the country context (e.g., see Schlenker et al., 2005; and Deschênes and Greenstone, 2007). For the case of Vietnam, high temperature and rainfall bring households higher income from agriculture, but lower income from wages. Low temperature extremes reduce crop income. The optimal temperature for crops is between 25 and 30 degree Celsius (e.g., Hatfield and Prueger, 2015). Column 3 of Table 4 shows that an increase of one month with precipitation below the 5th percentile of the temperature distribution decreases the per capita income from crop production by around 10.7%. The results from Table 4 help interpret the effect of weather extremes on migration. Highly educated people with tertiary education are more likely to have a salaried job. According to the 2014 VHLSS, 87% of working people with 132

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Economic Analysis and Policy 69 (2021) 118–141

Table 5 Regression of health of individuals. Source: Estimation using the VHLSSs in 2010, 2012, and 2014, and climate data from Willmott and Matsuura (2015). Explanatory variables

Number of health care contacts

Number of out-patient health care contacts

Number of in-patient health care contacts

Log of out-of-pocket spending on health care

Log of out-of-pocket spending on out-patient health care

Log of outof-pocket spending on in-patient health care

(1)

(2)

(3)

(4)

(5)

(6)

−0.0162

−0.0147

−0.0015

−0.0110

−0.0078

−0.0052

(0.0232)

(0.0231)

(0.0024)

(0.0179)

(0.0177)

(0.0095)

0.0515** (0.0168)

0.0487** (0.0165)

0.0028 (0.0028)

0.0479** (0.0182)

0.0448* (0.0174)

0.0115 (0.0113)

Number of months with low temperatures

0.0222 (0.0269)

0.0277 (0.0263)

−0.0054 (0.0035)

0.0084 (0.0267)

0.0124 (0.0254)

0.0031 (0.0147)

Number of months with high temperatures

0.0508** (0.0173)

0.0490** (0.0170)

0.0018 (0.0026)

0.0476** (0.0183)

0.0442* (0.0174)

0.0050 (0.0102)

Average precipitation of provinces

0.0012 (0.0008)

0.0009 (0.0008)

0.0003** (0.0001)

0.0025** (0.0007)

0.0019** (0.0007)

0.0010* (0.0004)

Average temperature of provinces

−0.3230*

−0.3245*

(0.1379)

(0.1359)

0.0014 (0.0167)

0.0336 (0.1226)

0.0445 (0.1186)

0.0195 (0.0633)

−0.3368** (0.0217)

−0.3173**

−0.0195**

−0.4699**

−0.4104**

−0.1377**

(0.0211)

(0.0032)

(0.0203)

(0.0183)

(0.0130)

−0.0498**

−0.0466**

−0.0032**

−0.0214**

−0.0173**

−0.0048**

Number of months with low precipitation Number of months with high precipitation

Male Age

(0.0025)

(0.0024)

(0.0003)

(0.0021)

(0.0019)

(0.0012)

Squared age

0.0010** (0.0000)

0.0009** (0.0000)

0.0001** (0.0000)

0.0007** (0.0000)

0.0005** (0.0000)

0.0002** (0.0000)

Dummy year 2010

Reference

Dummy year 2012

−0.3192** (0.0607)

−0.2915**

−0.0276**

−0.1304*

−0.0774

−0.0442

(0.0600)

(0.0077)

(0.0537)

(0.0524)

(0.0296)

−0.2783**

−0.2694**

−0.0088

−0.0610

−0.0776

Dummy year 2014

(0.0634)

(0.0627)

(0.0074)

(0.0515)

(0.0500)

0.0190 (0.0276)

Province fixed-effects

Yes

Yes

Yes

Yes

Yes

Yes

Constant

9.6469** (3.5166)

9.6012** (3.4680)

0.0457 (0.4284)

0.7696 (3.1165)

0.2816 (3.0154)

−0.1958

Observations R-squared

109,731 0.096

109,731 0.094

109,731 0.020

109,731 0.088

109,731 0.093

109,731 0.021

(1.6144)

Note: This table reports province fixed-effects regressions of the number of annual health care contacts and individual health spending on weather extremes. The observations used in this regression are for individuals. Annual health care contacts include visits to health care services and doctors, and invitations of doctors to individuals’ house for health treatment during the past 12 months. Weather extremes are measured by the number of months with low or high temperatures/precipitation during the past 5 years. A temperature (and precipitation) value is defined as low if it is below the 5th percentile of the distribution over the 1900–2014 period. A temperature (and precipitation) value is defined as high if it is above the 95th percentile of the distribution over the 1900–2014 period. Weather extremes are measured at the provincial level, and these weather data are merged with individual-level data on health care. Robust standard errors in parentheses. Standard errors are corrected for sampling weights and cluster correlation. *p < 0.05. **p < 0.01.

tertiary education had a salaried job, while the rate for those without tertiary education was just 36%. This implies that high precipitation in source provinces pushes highly educated people to migrate to find a better salaried job. They tend to move to a province with low temperatures, which are assumed to increase their productivity and work time. It has been well documented that high temperatures result in discomfort and tiredness, especially for people working outdoors (Zivin and Neidell, 2010; Tian et al., 2013). Poorly educated people, on the other hand, are more likely to work on farms and do not migrate because of high rainfall. According to the 2014 VHLSS, 45% of workers with less than high school education worked in the agricultural sector, whereas the rate for those with tertiary education was below 4%. If they migrate, the poorly educated tend to move to a province with high rainfall. This finding again indicates the important role of rainfall for agricultural production. Weather can affect the decision to migrate because of its effects on health. There are no data on the health status such as diseases or illness of individuals in the VHLSSs. We use the number of annual health care contacts and the log of annual spending on health care as indicators of health. Annual health care contacts include visits to health care services and doctors, and invitations of doctors to individuals’ house for health treatment. In Table 5, we regress health care contacts and health spending on the weather extremes. It shows clear evidence that high precipitation as well as high 133

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temperatures increase the number of individuals’ health contacts. The effect is mainly on out-patient health care since there are no significant effects on in-patient health care. Exposure to high precipitation and temperatures also increases individuals’ expenditure on out-patient health care. There are two problems with the health data in this study. Firstly, the number of health care contacts and spending might not reflect the poor health status. For example, people with higher income are more likely to use health care services. Secondly, the weather extremes can increase health care through the income effect. To explore this issue, we control for log of per capita in the regression of health care on weather extremes. The results are very similar to those without controlling for per capita income.11 It implies that the weather extremes affect health care not through the income effect. After the income level is controlled for health care contacts and spending are more correlated with the health status. This finding is consistent with the abundant evidence on the positive association between extreme weather and health (e.g., Huynen et al., 2001; Phalkey et al., 2015; Levy et al., 2016). The negative effect on health can partly explain the push-effect of high precipitation and pull-effect of low temperatures on migration. Among the reasons why people tend to move to cooler provinces rather than hot ones may be not only higher wages but also better health conditions. 7. Conclusions In this study, we examine the push and pull effects of weather extremes on migration in Vietnam using a gravity model. We find that high rainfall extremes in source provinces increase out-migration. The push effect of high rainfall extremes on migration differs for highly educated and poorly educated people. High rainfall extremes tend to push the highly educated but not the poorly educated to migrate. On the other hand, high rainfall extremes in the destination provinces attract poorly educated people. A possible explanation is that high rainfall can increase income from agricultural production but reduces wage income for households. Thus, the highly educated who mainly work in salaried jobs are more likely to move when exposed to high precipitation extremes, while the poorly educated, who are mainly self-employed in agricultural production, are less likely to migrate as a result of high rainfall extremes. Low temperature extremes reduce the flow of out-migration and increase the in-migration flow. At the same time, high temperature extremes tend to reduce in-migration. Using data from VHLSSs, we show that high temperature extremes reduce wages and are harmful to health. These are probable channels through which low temperature extremes can affect migration. A limitation with the data set used in this study is that there are no daily data on weather. As a result, this study cannot capture idiosyncratic events like high rainfall or temperature can happen on a few days. Another related issue is the disasters such as storms and floods that can have a strong effect on households in Vietnam. These issues are not addressed in this study, but certainly important for further studies. The findings in this study suggest several policy implications. First, Vietnam is a tropical country, where high temperature extremes can be harmful for health and labor productivity, especially of outdoor workers. Thus, the government should provide health care services and other supports for people who are affected by weather extremes. Health care services are now less developed in rural and mountainous areas, and they should be improved. Secondly, high precipitation helps households increase their agricultural production and income. This suggests the important role of irrigation, and the government can strengthen irrigation systems in rural areas. Thirdly, people can mitigate the economic and health shocks caused by weather extremes by migration. However, weather-motivated migrants may face several significant risks, such as high migration costs, and lack of social protection and social networks in destination areas. Recently, there are more concerns about the urbanization process which leads to problems such as environment pollution, a shortage of public services, and an increase in housing prices. These issues also require attention and support policies for migrants from the government. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments I would like to thank Prof. Clevo Wilson and three anonymous reviewers for their useful comment on the early version of this paper. This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under grant number 502.01-2016.08. 11 The regression results are not presented in this paper, but they can be provided for readers on request. 134

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Appendix

Fig. A.1. Distribution of provinces by the number of months with low and high precipitation during the past 5 years. Note: This figure reports the distribution of provinces by the number of months with low/high precipitation during the past 5 years. A temperature or precipitation value is defined as low if it is below the 5th percentile of the distribution over the 1900–2014 period. A temperature or precipitation value is defined as high if it is above the 95th percentile of the distribution over this period. Source: Estimation using the VHLSSs in 2010, 2012, and 2014, and climate data from Willmott and Matsuura (2015).

Fig. A.2. Distribution of provinces by the number of months with low and high temperatures during the past 5 years. Note: This figure reports the distribution of provinces by the number of months with low/high temperatures during the past 5 years. A temperature or precipitation value is defined as low if it is below the 5th percentile of the distribution over the 1900–2014 period. A temperature or precipitation value is defined as high if it is above the 95th percentile of the distribution over this period. Source: Estimation using the VHLSSs in 2010, 2012, and 2014, and climate data from Willmott and Matsuura (2015). 135

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Table A.1 Summary statistics of variables used in the gravity models. Source: Estimation using the 2009 VPHC and the 2014 IPS. Variables

The 2014 IPS

With inter-province migration (yes = 1, no = 0) Number of migrants With child migration (yes = 1, no = 0) Number of migrating children With adult migration (yes = 1, no = 0) Number of migrating adults With elderly migration (yes = 1, no = 0) Number of migrating elderly people Migration of people with less than high school education (yes = 1, no = 0) Number of migrants with less than high school education Migration of people with high school education (yes = 1, no = 0) Number of migrants with high school education Migration of people with tertiary education (yes = 1, no = 0) Number of migrants with tertiary education Population of provinces Average precipitation in provinces Average temperature in provinces Number of months with low precipitation in provinces Number of months with high precipitation in provinces Number of months with low temperatures in provinces Number of months with high temperatures in provinces

The 2009 VPHC

Obs.

Mean

Std. Dev.

Obs.

Mean

Std. Dev.

3906 3477 3906 3477 3906 3477 3906 3477 3906 3477 3906 3906 3906 3906 3906 3906 3906 3906 3906 3906 3906

0.89 976.92 0.61 95.49 0.88 865.90 0.37 15.53 0.85 716.64 0.73 152.55 0.57 79.14 1 246 202 158.08 24.17 3.16 2.38 1.98 3.54

0.31 3904.68 0.49 362.59 0.32 3528.10 0.48 52.98 0.36 2919.28 0.44 677.46 0.50 386.97 1 045 350 28.92 2.40 1.95 1.44 1.37 1.23

3906 2188 3906 2188 3906 2188 3906 2188 3906 2188 3906 3906 3906 3906 3906 3906 3906 3906 3906 3906 3906

0.56 1202.24 0.22 111.06 0.55 1067.33 0.10 23.85 0.48 792.70 0.33 126.03 0.28 103.38 1 317 051 156.50 24.39 2.76 2.86 3.19 5.16

0.50 3367.13 0.42 342.97 0.50 3031.60 0.29 89.37 0.50 2411.73 0.47 528.62 0.45 486.86 1 151 468 30.49 2.35 1.54 2.01 1.99 2.41

Table A.2 Summary statistics of variables from VHLSSs. Source: Estimation using data from VHLSSs 2010, 2012 and 2014. Variables

VHLSS 2010

VHLSS 2012

VHLSS 2014

Mean

Std. Dev.

Mean

Std. Dev.

Mean

Std. Dev.

Household-level data Per capita income (thousand VND) Per capita wages (thousand VND) Per capita crop income (thousand VND) Per capita livestock income (thousand VND) Per capita income from forestry and aquaculture (thousand VND) Per capita nonfarm income (thousand VND) Per capita remittance income (thousand VND) Per capita income from other sources (thousand VND) Household size Proportion of children below 15 in households Proportion of people above 50 in households Number of sampled households

18 691 7820 2329 889 541 3936 1717 1460 3.871 0.205 0.125 9399

37 044 11 795 9726 22 366 2765 12 769 8088 7359 1.547 0.208 0.261

25 097 10 920 3399 929 778 4901 2134 2035 3.844 0.200 0.143 9399

28 267 15 337 8918 3899 5715 13 205 6897 14 725 1.556 0.206 0.280

32 193 15 303 3469 1148 873 6536 2552 2313 3.796 0.197 0.151 9399

30 039 20 767 8865 4798 5215 19 261 7978 9300 1.564 0.202 0.286

Individual-level data Number of health care contacts Number of out-patient health care contacts Number of in-patient health care contacts Out-of-pocket spending on health care (thousand VND) Out-of-pocket spending on out-patient health care (thousand VND) Out-of-pocket spending on in-patient health care (thousand VND) Male (male = 1, female = 0) Age Number of individuals

1.496 1.378 0.118 570.6 287.0 283.7 0.489 31.8 36,999

3.760 3.685 0.541 3257.9 1568.9 2715.2 0.500 20.6

1.352 1.249 0.103 705.3 338.5 366.8 0.489 32.7 36,655

3.241 3.168 0.466 4185.0 1866.7 3581.5 0.500 21.0

1.356 1.245 0.111 808.4 377.0 431.4 0.488 33.4 36,077

3.226 3.145 0.516 4477.9 1853.1 3893.5 0.500 21.4

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Table A.3 The effect of precipitation and temperature on migration flow. Explanatory variables

Dependent variables Log of migration flow

Log of child migration flow

Log of adult migration flow

Log of elderly migration flow

Log of migration of people without high school education

Log of migration of people with high school education

Log of migration of people with tertiary education

Precipitation in provinces of origin Number of months with −0.0798* precipitation 0 mm (0.0372)

0.0440 (0.0344)

−0.0908*

0.0211 (0.0269)

−0.1458**

(0.0366)

−0.0264 (0.0339)

0.0096 (0.0337)

Number of months with precipitation 0–50 mm

0.0166 (0.0271)

0.0614* (0.0243)

0.0058 (0.0266)

0.0086 (0.0193)

−0.0551

0.0445 (0.0250)

0.0317 (0.0243)

Number of months with precipitation 50–100 mm

−0.0125

0.0294 (0.0173)

−0.0174

0.0146 (0.0135)

−0.0547

(0.0186)

(0.0297)

0.0220 (0.0179)

0.0312 (0.0173)

Number of months with precipitation 100–250 mm

Reference

Number of months with precipitation 250–400 mm

0.0101 (0.0201)

0.0139 (0.0180)

0.0091 (0.0200)

0.0176 (0.0147)

0.0683* (0.0313)

−0.0151 (0.0187)

−0.0283

Number of months with precipitation 400–550 mm

0.0066 (0.0372)

−0.0225

0.0114 (0.0258)

0.0303 (0.0544)

−0.0242

−0.0040

(0.0326)

0.0070 (0.0369)

Number of months with precipitation above 550 mm

0.1248* (0.0598)

0.0919 (0.0513)

0.1311* (0.0590)

0.0346 (0.0430)

−0.0470

0.0213 (0.0524)

−0.0455*

−0.0366 (0.0413)

−0.0223

(0.0188)

Temperature in provinces of origin Number of months with degree −0.0483* 0–15 (0.0223)

(0.0210)

(0.0482) (0.0406)

(0.0180)

(0.0350)

(0.0336)

0.0856 (0.0558)

0.1230* (0.0544)

(0.0787)

−0.0298 (0.0535)

0.0330 (0.0496)

(0.0875)

Number of months with degree 15–18

−0.0150

−0.1402**

−0.0045

(0.0389)

(0.0405)

−0.0783* −0.0568 (0.0320) (0.0574)

0.0176 (0.0401)

−0.0618

(0.0408)

Number of months with degree 18–21

0.0116 (0.0334)

0.1319** (0.0314)

0.0167 (0.0332)

0.0503 (0.0259)

−0.0172

0.0197 (0.0322)

0.0394 (0.0319)

Number of months with degree 21–24

Reference

Number of months with degree 24–27

−0.0202

−0.0326

−0.0108 (0.0264)

(0.0331)

0.0379 (0.0257)

−0.0039

(0.0244)

0.0142 (0.0197)

−0.0410

(0.0270)

Number of months with degree 27–30

−0.0235

−0.0099

−0.0115

(0.0329)

(0.0357)

0.0522 (0.0531)

0.0262 (0.0342)

−0.0231

(0.0364)

0.0055 (0.0267)

Number of months with degree above 30

0.0950* (0.0466)

0.0529 (0.0746)

0.1293 (0.0746)

−0.0087

0.0902 (0.0734)

−0.0234

(0.0620)

0.1888* (0.0944)

Precipitation in destination provinces Number of months with 0.0065 precipitation 0 mm (0.0370)

0.0062 (0.0325)

0.0152 (0.0364)

−0.0026 (0.0254)

0.0444 (0.0549)

0.0140 (0.0335)

−0.0079

−0.0007

−0.0281

0.0172 (0.0181)

−0.0375

0.0377 (0.0244)

−0.0226

0.0358** (0.0124)

−0.0002

0.0384* (0.0171)

−0.0224

0.0165 (0.0151)

−0.0481 (0.0308)

−0.0241 (0.0194)

0.0422* (0.0178)

0.0162 (0.0259)

0.0484 (0.0493)

−0.0777* (0.0344)

−0.0029

0.0482 (0.0409)

−0.0317 (0.0798)

0.0127 (0.0561)

0.0801 (0.0512)

(0.0619)

(0.0389)

(0.0229) (0.0318) (0.0722)

(0.0323)

Number of months with precipitation 0–50 mm

−0.0292 (0.0267)

(0.0243)

(0.0264)

Number of months with precipitation 50–100 mm

−0.0180

0.0425* (0.0172)

−0.0224

Number of months with precipitation 100–250 mm

Reference

Number of months with precipitation 250–400 mm

−0.0285 (0.0203)

0.0288 (0.0190)

−0.0271

Number of months with precipitation 400–550 mm

−0.0044

0.0834* (0.0337)

−0.0085

Number of months with precipitation above 550 mm

−0.0497

−0.0673

(0.0614)

0.0812 (0.0534)

Temperature in destination provinces Number of months with degree −0.0481 0–15 (0.0550)

0.0096 (0.0513)

−0.0246 (0.0543)

−0.0288 (0.0379)

0.0245 (0.0827)

0.0203 (0.0518)

0.0089 (0.0477)

Number of months with degree 15–18

0.1270** (0.0415)

0.0026 (0.0385)

0.1079** (0.0411)

0.0026 (0.0291)

−0.0216

−0.0100 (0.0386)

−0.0393

(0.0661)

Number of months with degree 18–21

0.0222 (0.0331)

0.0451 (0.0313)

0.0282 (0.0326)

0.0671** (0.0236)

0.0055 (0.0554)

0.1076** (0.0303)

0.0401 (0.0295)

Number of months with degree 21–24

Reference

(0.0187)

(0.0374)

(0.0185)

(0.0202) (0.0371) (0.0615)

(0.0397) (0.0308)

(0.0228) (0.0155)

(0.0313)

(0.0357)

(continued on next page)

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Table A.3 (continued). Explanatory variables

Dependent variables Log of migration flow

Log of child migration flow

Log of adult migration flow

Log of elderly migration flow

Log of migration of people without high school education

Log of migration of people with high school education

Log of migration of people with tertiary education

Number of months with degree 24–27

0.1069** (0.0247)

0.0576* (0.0251)

0.0986** (0.0240)

0.0423* (0.0197)

0.0859 (0.0519)

−0.0055 (0.0243)

0.0046 (0.0236)

Number of months with degree 27–30

−0.1581**

−0.1018**

−0.1382**

(0.0343)

(0.0350)

−0.0400 (0.0263)

0.1530* (0.0685)

0.0102 (0.0337)

−0.0044

(0.0356)

Number of months with degree above 30

−0.1154*

−0.0205

−0.0991

−0.1574* −0.0268

−0.0813

−0.0146

(0.0556)

(0.0744)

(0.0777)

(0.0644)

(0.1314)

(0.0739)

(0.0723)

Control variables Pairwise province fixed-effects Observations

Yes Yes 7812

Yes Yes 7812

Yes Yes 7812

Yes Yes 7812

Yes Yes 7812

Yes Yes 7812

Yes Yes 7812

(0.0318)

Note: The table reports the average marginal effects of weather extremes on the log of unconditional migration flow between provinces, computed using Eqs. (9) and (10). The full results of two-part regressions are not presented, but they can be provided on request. Robust standard errors in parentheses. *p < 0.05. **p < 0.01.

Table A.4 Regression of household income on weather extremes using the sample of households with registration books in their current provinces. Source: Estimation using the VHLSSs in 2010, 2012, and 2014, and climate data from Willmott and Matsuura (2015). Explanatory variables

Dependent variables Log of per Log of per Log of per Log of per capita crop capita capita capita livestock income wages income income (1)

(2)

Log of per capita remittance income

Log of per capita income from other sources

(3)

(4)

(5)

(6)

(7)

(8)

−0.0078

−0.0209

−0.0759

−0.0470

−0.0316

(0.0414)

(0.0356)

(0.0308)

(0.0277)

(0.0421)

(0.0291)

(0.0380)

−0.0572

0.0511 (0.0332)

0.0027 (0.0351)

0.0117 (0.0315)

0.0566 (0.0480)

−0.0001

−0.0517

−0.1099**

0.0172 (0.0428)

−0.0332

−0.0113

(0.0361)

0.0423 (0.0318)

−0.0013

−0.0226** 0.0795 (0.0057)

Number of months with high precipitation in provinces

0.0139* (0.0061)

Number of months with low temperatures in provinces

−0.0149

Number of months with high temperatures in provinces

−0.0203** −0.1516** 0.0555 (0.0064)

Log of per capita nonfarm income

−0.0350

Number of months with low precipitation in provinces

(0.0079)

Log of per capita income from forestry and aquaculture

(0.0459) 0.1263* (0.0616)

(0.0407)

(0.0404)

(0.0647)

0.0162 (0.0618)

0.0362 (0.0477)

0.0251 (0.0356)

0.0770 (0.0418)

−0.0035** (0.0013)

0.0047** (0.0016)

(0.0451)

(0.0339)

Average precipitation of provinces −0.0008** −0.0021 (0.0002) (0.0018)

−0.0016

0.0027* (0.0014)

0.0005 (0.0012)

−0.0008

(0.0014)

Average temperature of provinces

−0.1576 (0.2911)

0.1442 (0.1944)

0.0902 (0.1982)

−0.1137

−0.8988**

(0.1762)

(0.2941)

−0.1214 (0.2174)

0.4470 (0.2767)

−0.0508** 0.3271**

0.2044** (0.0230)

0.0859** (0.0194)

0.0078 (0.0180)

0.0845** (0.0278)

−0.3414** (0.0163)

0.0142 (0.0204)

0.5252** (0.1189)

0.0273 (0.1483)

1.9901** (0.0775)

3.4415** (0.1062)

0.4902** (0.1000)

0.4020** (0.1200) 0.4053** (0.1145)

−0.0140 (0.0372)

Household size

(0.0037)

(0.0263)

(0.0301)

(0.0306) 0.0347 (0.0496)

(0.0019)

Proportion of children below 15 in households

−0.6723** −2.0332** −1.8643** (0.0264) (0.1919) (0.1664)

−1.8414** (0.1447)

−0.1754

−0.7217**

(0.1281)

(0.2116)

Proportion of people above 50 in households

−0.4396** −2.6629** −1.0136**

−0.6610** (0.1027)

−0.3866**

−2.0326**

(0.0843)

(0.1286)

Dummy year 2010

Reference

Dummy year 2012

−0.0966 (0.0881)

−0.0910

−0.2824*

(0.0819)

(0.1359)

0.1428 (0.0838)

−0.0823

−0.2112

(0.0774)

(0.1275)

0.4801** (0.0931)

(0.1514)

(0.1284)

0.3818** (0.0174)

0.7429** (0.1267)

−0.0319

0.5547** (0.0159)

0.9243** (0.1221)

−0.0864

Province fixed-effects

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Constant

10.4329** (0.9473)

9.1890 (7.4260)

0.5382 (4.9999)

−0.4856

3.9964 (4.5379)

25.0866** (7.4914)

9.2461 (5.4999)

−9.2302

Dummy year 2014

(0.0228)

(0.0922) (0.0909)

(5.0971)

(7.0087)

(continued on next page)

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Economic Analysis and Policy 69 (2021) 118–141

Table A.4 (continued). Explanatory variables

Observations R-squared

Dependent variables Log of per Log of per Log of per Log of per capita capita capita crop capita income wages income livestock income

Log of per capita income from forestry and aquaculture

Log of per capita nonfarm income

Log of per capita remittance income

Log of per capita income from other sources

(1)

(2)

(3)

(4)

27,734 0.304

27,734 0.084

27,734 0.230

27,734 0.227

(5)

(6)

(7)

(8)

27,734 0.233

27,734 0.043

27,734 0.192

27,734 0.093

Note: Weather extremes are measured by the number of months with low or high temperatures/precipitation during the past 5 years. A temperature (and precipitation) value is defined as low if it is below the 5th percentile of the distribution over the 1900–2014 period. A temperature (and precipitation) value is defined as high if it is above the 95th percentile of the distribution over the 1900–2014 period. Weather extremes are measured at the provincial level, and these weather data are merged with household-level data on income. Robust standard errors in parentheses. Standard errors are corrected for sampling weights and cluster correlation. *p < 0.05. **p < 0.01.

Table A.5 Regression of health of individuals on weather extremes using the sample of individuals with registration books in their current provinces. Source: Estimation using the VHLSSs in 2010, 2012, and 2014, and climate data from Willmott and Matsuura (2015). Explanatory variables

Number of health care contacts

Number of out-patient health care contacts

Number of in-patient health care contacts

Log of outof-pocket spending on health care

Log of out-of-pocket spending on out-patient health care

(1)

(2)

Number of months with low precipitation

−0.0140

−0.0124

(0.0237)

Number of months with high precipitation

0.0531** (0.0168)

Number of months with low temperatures

(3)

(4)

(5)

(6)

−0.0016

−0.0101

−0.0050

−0.0077

(0.0235)

(0.0024)

(0.0179)

(0.0178)

(0.0095)

0.0502** (0.0166)

0.0029 (0.0028)

0.0477** (0.0181)

0.0442* (0.0174)

0.0119 (0.0113)

0.0244 (0.0273)

0.0299 (0.0268)

−0.0055 (0.0035)

0.0058 (0.0267)

0.0115 (0.0254)

0.0000 (0.0146)

Number of months with high temperatures

0.0499** (0.0177)

0.0478** (0.0173)

0.0022 (0.0026)

0.0449* (0.0185)

0.0389* (0.0176)

0.0066 (0.0103)

Average precipitation of provinces

0.0011 (0.0008)

0.0008 (0.0008)

0.0003** (0.0001)

0.0024** (0.0007)

0.0017* (0.0007)

0.0011* (0.0004)

Average temperature of provinces

−0.3126*

−0.3113* (0.1399)

−0.0012 (0.0165)

0.0570 (0.1247)

0.0651 (0.1209)

0.0253 (0.0640)

−0.3150** (0.0213)

−0.0194** (0.0032)

−0.4662** (0.0201)

−0.4061** (0.0182)

−0.1382**

(0.0218)

−0.0492**

−0.0460**

−0.0032**

−0.0208**

−0.0165**

−0.0049**

(0.1420) Male Age

−0.3344**

Log of out-of-pocket spending on in-patient health care

(0.0131)

(0.0024)

(0.0024)

(0.0003)

(0.0021)

(0.0019)

(0.0012)

Squared age

0.0010** (0.0000)

0.0009** (0.0000)

0.0001** (0.0000)

0.0007** (0.0000)

0.0005** (0.0000)

0.0002** (0.0000)

Dummy year 2010

Reference

Dummy year 2012

−0.3123**

−0.2840** (0.0618)

−0.0284** (0.0078)

−0.1287* (0.0544)

−0.0701 (0.0532)

−0.0484

(0.0624)

−0.2768**

−0.2658**

−0.0110

−0.0591

−0.0724

0.0154 (0.0279)

Dummy year 2014

(0.0297)

(0.0654)

(0.0647)

(0.0074)

(0.0525)

(0.0510)

Province fixed-effects

Yes

Yes

Yes

Yes

Yes

Yes

Constant

9.3660** (3.6125)

9.2547** (3.5609)

0.1113 (0.4224)

0.1915 (3.1618)

−0.2238

−0.3424

(3.0664)

(1.6321) (continued on next page)

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Table A.5 (continued). Explanatory variables

Number of health care contacts

Number of out-patient health care contacts

(1) Observations R-squared

108,141 0.097

Log of out-of-pocket spending on out-patient health care

Log of out-of-pocket spending on in-patient health care

(4)

(5)

(6)

108,141 0.088

108,141 0.093

108,141 0.022

Number of in-patient health care contacts

Log of outof-pocket spending on health care

(2)

(3)

108,141 0.094

108,141 0.021

Note: This table reports province fixed-effects regressions of the number of annual health care contacts and individual health spending on weather extremes. The observations used in this regression are for individuals. Annual health care contacts include visits to health care services and doctors, and invitations of doctors to individuals’ house for health treatment during the past 12 months. Weather extremes are measured by the number of months with low or high temperatures/precipitation during the past 5 years. A temperature (and precipitation) value is defined as low if it is below the 5th percentile of the distribution over the 1900–2014 period. A temperature (and precipitation) value is defined as high if it is above the 95th percentile of the distribution over the 1900–2014 period. Weather extremes are measured at the provincial level, and these weather data are merged with individual-level data on health care. Robust standard errors in parentheses. Standard errors are corrected for sampling weights and cluster correlation. *p < 0.05. **p < 0.01.

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