Temporary international migration, shocks and informal finance: Analysis using panel data

Chakraborty, Tanika; Pandey, Manish Article Temporary international migration, shocks and informal finance: Analysis using panel data IZA Journal of Development and Migration Provided in Cooperation with: IZA – Institute of Labor Economics Suggested Citation: Chakraborty, Tanika; Pandey, Manish (2022) : Temporary international migration, shocks and informal finance: Analysis using panel data, IZA Journal of Development and Migration, ISSN 2520-1786, Sciendo, Warsaw, Vol. 13, Iss. 1, pp. 1-19, https://doi.org/10.2478/izajodm-2022-0008 This Version is available at: https://hdl.handle.net/10419/298717 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Tanika Chakraborty and Manish Pandey* Temporary international migration, shocksand informal finance: analysis usingpanel data Abstract We examine households’ temporary international migration response when faced with shocks in rural Kyrgyzstan. Using a household fixed effects model, we find that while a drought shock increases migration, a winter shock reduces migration. We argue that this difference is because of the trade-off between two effects of a shock for a household: loss of income and increase in the need for labor services. Migration increases when the former effect of a shock dominates and it reduces when the latter effect dominates. We explore these mechanisms further, and find that when households have easier access to informal finance the migration response is muted only for shocks for which the adverse income effect dominates. These findings provide evidence in favor of our proposed mechanisms through which shocks affect migration. Current version: June 11, 2022 Keywords: temporary migration, shocks, insurance, informal finance, Asia,Kyrgyzstan JEL codes: J61, O15, O16 Corresponding author: Manish Pandey [email protected] Chakraborty and Pandey. IZA Journal of Development and Migration (2022) 13:08 © The Author(s). 2022. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Cite as: Chakraborty and Pandey. IZA Journal of Development and Migration (2022) 13:08 https://doi.org/10.2478/izajodm-2022-0008 Economics Group, Indian Institute of Management Calcutta, Diamond Harbour Road, Joka, Kolkata (Calcutta) 700104, West Bengal, India Page 2 of 19 Chakraborty and Pandey. IZA Journal of Development and Migration (2022) 13:08 1 Introduction There is a large literature that examines the causes and consequences of international migration.1 However, one aspect with regards to temporary international migration has received less attention.2 Except Halliday (2006), no other study distinguishes between different types of shocks faced by households that affect their temporary international migration decision. While past studies have examined the effects of agricultural shocks (Kubik and Maurel, 2016; Dillon et al., 2011; Giannelli and Canessa, 2022), direct income shocks (Angelucci, 2015), and weather shocks (Gröger and Zylberberg, 2016) in isolation, they do not distinguish between the effects of various types of shocks on the migration decision. This distinction is important as the decision to migrate in response to a shock may depend on the nature of the shock. We fill this gap in the literature. We examine the migration response of households to different types of natural shocks and provide evidence on potential mechanisms driving the effects. We use a unique household panel data from the Life in Kyrgyzstan (LiK) surveys, which allows us to observe the dynamics of temporary migration (LIK, 2010/2013) at the household level. The LiK surveys provide a nationally representative panel data comprising about 3,000 households in Kyrgyzstan. We use data from four waves of the survey (2010–2013). To our knowledge, the LiK survey data is the only panel data available for a low income source country that allows for tracking and analyzing temporary international migration decisions of households.3 The longitudinal nature of the LiK data allows us to use a household-specific fixed effects (FE) model to address the issue of unobserved heterogeneity in migration decisions (McKenzie et al., 2010). Further, to our knowledge, the LiK data has only been utilized by two other studies to study international migration – Chakraborty et al. (2015) and Zhunusova and Herrmann (2018). The former uses the 2010 and 2011 waves of the data to examine the consequences of migration on private transfers between households in Kyrgyzstan. The latter uses the panel between 2010 and 2013 to study the impact of migration on income in the sending community. We use the information on whether a household was affected by one of five natural shocks – drought, rain and landslide, winter and frost, earthquake, and pest. We restrict our analysis to natural shocks as they are most likely to be exogenous to a household’s migration decision. Further, given that weather shocks are more likely to affect the livelihoods of rural households, we restrict our analysis to households residing in villages. We begin our analysis by estimating the effect of each shock on a household’s decision to have a migrant. We find that while receiving a drought shock in the last period increases the likelihood of migration, winter shocks reduce the likelihood of migration.4 However, we do not find evidence that the other three shocks we consider affect the decision to migrate. We then analyze the decision to migrate and control for various household characteristics in our baseline specification. Consistent with previous studies, we find that the likelihood of migration is 1 See Kerr and Kerr (2011) and Gaston and Nelson (2013) for reviews of the literature. A large number of studies examining the determinants of migrants have used data for migrants from Mexico to the U.S. These studies include Chiquiar and Hanson (2005), Ibarraran and Lubotsky (2007), Mishra (2007), Mckenzie and Rapoport (2010), Vincenzo (2011), and Kaestner and Malamud (2014) among others. 2 See Christian and Görlach (2016) for a review of the literature on temporary international migration. 3 For details on the LiK survey data see Brück and Esenaliev (2014). 4 While most previous studies in the literature find an increase in migration in response to shocks, Halliday (2006) finds that adverse agricultural conditions increase net migration while earthquakes reduce net migration from El Salvador to the United States. Our findings of differences in effects of shocks for Kyrgyzstan are similar to these results. Page 3 of 19 Chakraborty and Pandey. IZA Journal of Development and Migration (2022) 13:08 positively related to household size, fraction of adults, and wealth of the household [see, e.g., Kaestner and Malamud (2014)]. Importantly, we find that the estimated coefficients for the shocks remain unaffected by the inclusion of these covariates. When examining temporary migration, an important distinction is between the decision to send a migrant and to recall a migrant. The longitudinal nature of the LiK data allows for examining these dynamics of temporary migration and evaluate whether the effect of shocks on the two decisions depends on the nature of the shock. For analyzing the effect of shocks on recalling a migrant, we compare two households that both have a migrant in the current period and study the change in their migrant status in the next period. Analogously, for analyzing the effect of shocks on a household’s decision to send a migrant, we compare two households that do not have a migrant in the current period and one of them switches to be a migrant in the next period. We find that while a drought shock affects the decision to send a migrant, a winter shock does not affect this decision. Even though the effect of winter shocks on the recall decision is large and positive, it is imprecisely estimated. To understand why the migration response to these shocks differ, we explore the underlying mechanisms. We argue that depending on the nature of income generating activities that households are engaged in, local labor markets, and liquidity constraints faced by a household, a natural shock might reduce or increase migration. For instance, a drought shock is likely to reduce income for rural households that are largely engaged in agricultural activities. Migration, in such a situation, helps affected households to mitigate the adverse income effect of the shock (Morten, 2016). On the other hand, a potential explanation for the migration-lowering effects of winter shocks could be that colder months lead to an adverse labor situation by increasing the need for household labor in rural Kyrgyzstan. For instance, we find that time spent on various labor-intensive household activities is much higher in colder months than in milder months. We examine the mechanisms driving different responses to different shocks by studying a household’s access to informal finance. It is well established that in the absence of financial markets, rural households use informal networks to insure against income shocks (Townsend, 1994).5 Hence, the decision to migrate in response to a shock is likely to depend on the extent to which households can mitigate the adverse income effects using their access to alternate sources of finance. For example, Morten (2019) examines the link between informal networks and domestic migration within India and finds that availability of informal finance reduces migration. We hypothesize that the availability of informal finance, controlling for household wealth, is likely to affect the migration response of shocks that adversely affect household income but not the migration response of shocks that affect the household’s labor requirement. We use information on informal borrowing opportunities of households in the LiK data and find that access to finance reduces the likelihood of migration only for shocks that reduce incomes of households (drought shock) but not for shocks that increase the demand for labor service (winter shock). These findings provide support for our proposed mechanisms through which shocks affect the decision to migrate. We contribute to the existing literature in two ways. First, with the exception of Halliday (2006), previous studies have focused on only one shock as a determinant of migration 5 A large literature examines the importance of informal networks in providing insurance in rural areas, see Morten (2019) for a brief review of the literature. Page 4 of 19 Chakraborty and Pandey. IZA Journal of Development and Migration (2022) 13:08 [see,e.g.,Angelucci (2015), Dillon et al. (2011), and Gröger and Zylberberg (2016)]. Second, we discuss the underlying mechanisms that explain differences in the migration response of households to different shocks. We then empirically investigate these mechanisms and find evidence in support of the proposed mechanisms. The rest of the paper is organized as follows. Section 2 discusses the data, and provides an overview of migration and shock experiences of households. Section 3 outlines the empirical specification that we use for our analysis. Section 4 reports our findings for the effects of shocks on the decision to migrate. Section 5 provides a discussion of the mechanisms driving the findings and empirically investigates the mechanisms. Finally, Section 6 provides a brief conclusion. 2 Data We use four waves of the LiK survey, a panel data collected annually between 2010 and 2013. The survey was conducted in roughly 120 communities across the country and covers all provinces of Kyrgyzstan. We study the effects of weather and other natural shocks on a household’s decision to have a migrant member. These shocks are more likely to affect the livelihoods of rural households predominantly employed in agriculture and related activities. Hence, we restrict our analysis to households that reside in villages. To do so, we follow Chakraborty et al. (2015) and use a variable in the data that provides information on whether a household resides in a city or village. The data consists of about 3,000 households, of which about 59% reside in villages. We consider the decision to send a migrant to another country as a joint household decision. Accordingly, our outcome variable is the international migrant status of a household in a specific year. We construct this variable from the survey question that asks each household whether any of the household members lived in another country for more than 1month (excluding business trips, vacations, and visits) during the last 12months.6 The migrant status is an indicator variable that takes on a value of 1 for households that have at least one member who lived in another country for more than 1month during the last 12months, and 0 otherwise. Table 1 provides the number of migrant and non-migrant households that reside in villages in each of the 4years of the survey. The last column of the Table indicates that the percentage of households with migrants has gradually increased from 15% to 21% between 2010 and 2013. 6 Based on the information available in the LiK data, more than 90% of the migrants from Kyrgyzstan go to Russia. While there were some requirements for workers to register in Russia, there was free mobility of workers between Central Asian countries and Russia over the period of our analysis. Table 1 Migrant and non-migrant households Year Non-migrant Migrant Fraction migrant 2010 1,513 271 0.152 2011 1,446 320 0.181 2012 1,438 341 0.192 2013 1,329 357 0.212 Notes: Households residing in villages were used in the analysis. Non-migrant refers to households who do not have any migrants in the reference period. Migrant refers to households with at least one migrant in the reference period. Fraction migrant is the fraction of migrant households in the reference period. Page 5 of 19 Chakraborty and Pandey. IZA Journal of Development and Migration (2022) 13:08 Table 2 Patterns of migration (1) (2) (3) (4) Year 0 to 0 0 to 1 1 to 0 1 to 1 2011 0.761 0.088 0.058 0.093 2012 0.736 0.083 0.071 0.110 2013 0.699 0.107 0.090 0.105 Notes: 1 indicates migrant household; 0 indicates non-migrant household. The columns provide fraction of households that are in the four types of switches. (0 to 1) indicates households that switch from being a non-migrant in the previous year to being migrant in the current year; other switches indicated accordingly. Figure 1 Migration by month: year 2010. The data also provide information on the month in which a migrant leaves home to go to another country. In Figure 1, we use this information to plot the percentage of migration in each month in 2010. The figure indicates that while migrants leave all year round, the highest percentage of migration is around the month of March (Spring) and September (Fall). In comparison, migration is very low in winter between November and February. This pattern of migration is similar for other years in the data. While the information on migration status, in any year, is the primary outcome of interest, we also examine the dynamic nature of temporary migration decisions. In Table 2 we summarize the four types of year-to-year migration status changes for households. First, if a household was a non-migrant (migrant status = 0) in the previous year and continues to be a non-migrant in the current year, it is indicated as (0 to 0) in Column 1. Thus in 2011, 2012, and 2013, roughly 76%, 74%, and 70%, respectively, of the households did not have a migrant and also did not have one in 2010, 2011, and 2012, respectively. Second, if a household was a non-migrant in the previous year but decides to send a migrant (migrant status = 1) member in current year it is indicated by (0 to 1) in Column 2. In 2011, roughly 9% of the households had a migrant member in 2011 but did not have one in 2010. The third change is (1 to 0), that is, the household recalls a migrant. In 2011, roughly 6% of the households recalled the migrant. In other words, there were 15% of households that had a migrant in 2010, of which 6% recalled Page 6 of 19 Chakraborty and Pandey. IZA Journal of Development and Migration (2022) 13:08 a migrant in 2011; or, conditional of having a migrant, about 40% of households recalled the migrant between 2010 and 2011. Finally, if a household had a migrant in the previous year and continues to have a migrant in the current year it is indicated as (1 to 1) in Column 4. Between 2010 and 2011, roughly 9% of the households continued to have a migrant member. The pattern of migration indicates that there are households of all types in the data and there are substantial changes in the migration status of households over the period of our analysis. We also find that almost all of the international migration from Kyrgyzstan is temporary in nature: there are only 3% of households that have a migrant status of 1 in all 4years of the data. Following the literature on the socio-economic determinants of migration, in our analysis, we control for household demographics, education, and wealth. The demographic control variables we include are: total number of members in a household (Household size); gender composition of the household using the ratio of the number of male members in the household and the household size (Male fraction); age composition of the household using two variables: the fraction of members in the household older than 18years of age (Adult fraction) and the fraction of household members older than 65years of age (Elderly fraction). To account for education as a determinant of migration, we construct a measure of education at the household level. We use the highest years of schooling achieved by any member within the household (Education years). For wealth of a household, we follow Chakraborty et al. (2015) and combine various asset indicators to create a wealth index using a principal component method that serves as a proxy for household income.7 We then use the wealth index to construct and classify each household into wealth quintiles (Wealth1–Wealth5). Table 3 provides a summary of the data for migrant and non-migrant households for the year 2010.8 Relative to non-migrant households, migrant households are larger in size, have more male and adult members but few elderly members, and are more educated. In addition, migrant households are relatively poor – they are more likely to belong to the lower wealth quintiles (Wealth1–Wealth3) than higher ones. 2.1 Shocks Our main explanatory variable of interest is the household exposure to shocks in a specific year. The survey asks each member of the household whether he/she experienced any of the listed shocks during the year preceding the day of the survey.9 We create a binary variable for each shock that takes a value of 1 if any member of the household indicates that they received the shock, and 0 otherwise. While the survey lists a number of shocks, for our analysis we use all the shocks in the list that are caused by nature – drought, excessive rain or flood and landslide, severe winter and frost, earthquake, and pests.10 The other questions on shocks that are included in the survey are specific to a region or a household, such as riots, deaths, and illnesses, 7 Specifically, we use a weighted average of whether the household owns a house, car, refrigerator, gas-stove, microwave, washing machine, vacuum cleaner, television, computer, mobile-phone, and livestock. 8 We only present summary statistics for 2010 as household migration status changes across years. Summary statistics for migrant and non-migrant households for other years are similar to that for 2010. 9 While it would be better to use objective measures for the shocks, the LiK data do not provide enough information for us to be able to construct such measures. Specifically, the community links in LiK data are anonymized and hence we cannot connect them to the community geocodes. 10 Given the high correlation between excessive rain and landslide and severe winter and frost, we combine these natural shocks into one shock. Of the households that reported landslide, 64% reported excessive rain; and of the households that reported frost, 75% reported severe winter. Page 7 of 19 Chakraborty and Pandey. IZA Journal of Development and Migration (2022) 13:08 among others. Unlike weather shocks that are more likely to be exogeneous, the non-weather shocks are likely to be endogenous to a household’s characteristics and decisions. Hence, we restrict our analysis to the effect of natural shocks on a household’s decision to migrate. Table 4 provides a summary of the shocks. Given the geography of Kyrgyzstan, all the five shocks are reported with high frequency.11 Severe winter and frost is the most commonly reported shock while earthquake is the least reported. Table 5 provides the correlation between pairs of shocks for the year 2010. While the correlation coefficient between some pair of shocks 11 Kyrgyzstan is a landlocked Central Asian country with the Tien Shan mountain range and its valleys and basins comprising most of the country (https://www.cia.gov/the-world-factbook/countries/kyrgyzstan/). Table 3 Summary statistics (year 2010) Variable Non-migrant Migrant Household size 5.07 6.45 Male fraction 0.50 0.53 Adult fraction 0.65 0.72 Elderly fraction 0.07 0.03 Education years 13.35 14.50 Wealth 1 0.29 0.26 Wealth 2 0.24 0.31 Wealth 3 0.20 0.25 Wealth 4 0.16 0.12 Wealth 5 0.11 0.06 Notes: Adult fraction is the fraction of household members in the 18–65years age group; elderly fraction is the fraction of household members older than 65years. In all regressions, fraction of children, those below 17years of age, is the excluded category. Wealth1–Wealth5 refers to quintiles of a wealth index created using principle component analysis from a range of asset indicators for a household. Table 4 Summary of shocks (2010–2013) Variable Mean SD Min Max Drought 0.232 0.422 0 1 Rain and landslide 0.211 0.408 0 1 Winter and frost 0.379 0.485 0 1 Earthquake 0.162 0.368 0 1 Pest 0.193 0.394 0 1 Table 5 Correlation between shocks (year 2010) Drought Rain and landslide Winter and frost Earthquake Pest Drought 1.00 Rain and landslide 0.00 1.00 Winter and frost 0.14 0.23 1.00 Earthquake 0.02 0.26 0.17 1.00 Pest 0.11 0.24 0.25 0.24 1.00 Page 8 of 19 Chakraborty and Pandey. IZA Journal of Development and Migration (2022) 13:08 is as high as 0.26 (rain and landslide, and earthquake), as we discuss below, we do not find this to be an issue with the identification of the effects of shocks on the migration decision.12 3 Empirical Specification We examine the relation between shocks and the decision to migrate by estimating variations of the following baseline empirical model: ff - = + + + ++ ∑ 5 , 0 ,, 1 , , 1 *, it j i jt it t t i it j hhmig shock X region βγ β η= (1) where the dependent variable hhmig represents migration status for household i in year t. It takes a value 1 for a migrant household; and a value 0 for a non-migrant household. We include each shock j separately as well as all five shocks together in the regression equations that we estimate. The control variables, X, included in the regression are household size, fraction of males, fraction of adults, fraction of elderly, years of education, and wealth quintile of households. As illustrated in Figure 1, while migrants leave all year round, the highest percentage of migration is around the month of March (Spring) and September (Fall), and the survey rounds have typically been fielded between October and December. Thus, when a household is surveyed in 2013, specifically between October 2013 and January 2014, the migrant would have most likely left the household during March–June 2013. This suggests that within a year shocks and migration could occur at the same time. To ensure that for a household the shock precedes the migration decision, we use one-period lagged values (t - 1) of shocks as explanatory variables. Even though households might want to migrate in the months immediately after the shock, it is likely that there will be some lag in international migration as it takes time to make arrangements to find a job and travel. Another significant identification challenge arises from the possibility of unobserved heterogeneity. We cannot be sure that the observed correlates of the migration decision are not picking up the effects of other unobserved household characteristics. The longitudinal nature of our data enables us to address this issue by introducing household FE (ηi), which allows us to control for any time-invariant differences between households. In addition, we include time FE (φt) and the interaction of time and region FE (φt*region) to control for time-varying unobserved heterogeneity at a regional level. We use the oblast of residence for the household as the region. There are nine oblasts in our data.13 Including household FE in non-linear models, like probit or logit, can lead to severely biased estimates because of an incidental parameter problem, particularly given that we have limited time variation for each household (Greene et al., 2002; Greene, 2004). Hence, the regressions are estimated using a linear probability model. In addition, to address spatial correlation, we cluster the standard errors (SE) for all regressions at the community-year level since migration outcomes of households are likely to be correlated across households from the same geographic region in a specific year.14 12 We find similar correlations between pairs of shocks for other years in the data. 13 While we also have community identification in the data, weather shocks are unlikely to vary across households within these small geographic areas with very few households. 14 We did not cluster at the oblast level since there are very few oblasts and the estimated standard errors are known to be biased with few clusters (Colin and Miller, 2015). Page 15 of 19 Chakraborty and Pandey. IZA Journal of Development and Migration (2022) 13:08 widely documented to be a common coping strategy in rural regions where formal financial markets are weak, or even absent (Fafchamps and Gubert, 2007). Hence, the increase in migration in response to income shocks is likely to be muted when the household has other options to mitigate the negative income effects. Therefore, we would expect the migration response to adverse income shocks to be less pronounced in the presence of informal transfers or informal borrowing arrangements. In addition, since monetary transfers are unlikely to compensate for the greater need for labor at home, their availability is unlikely to matter for shocks that change labor requirements. We empirically examine whether the migration decision of households in response to shocks depends on access to informal finance by estimating the following regression equation: () a ba a a ff h - - -- =+ + + + + + ++ ,, 0 , 1 ,1 2 ,1 3 ,1 ,1 , * * ict it it ct ct it t t i it hhmig X shock easyfin easyfin shock region u (2) where X is a vector of the control variables that we include for our analysis, shock is a one of the natural shocks, easyfin is a measure of the ease of access to informal finance for all households residing in community c, and easyfin*shock is the interaction variable between the shock and access to informal finance. In addition, we include controls for household and interaction of time and region FE. We define the availability of access to borrowing within a community as the fraction of households in a community that report that it is easy to borrow 2000 Soms, which approximately equals US$ 30 in 2017 (easyfin).18 The range of the variable is between 0, no households in a community report having access to informal finance, to as high as 87.5% of households in a community reporting having access to informal finance. Table 10 reports the findings from estimating Eq. (2) for each shock separately. In line with our findings in Table 7, the drought shock increases the likelihood of migration. However, the negative and significant estimates for the interaction term (easyfin*shock) in Column 1 suggest that a household’s access to informal finance lowers the likelihood of having a member migrate when the household is faced with a drought shock. In other words, migration response to income shocks, like drought, is muted when the household is more likely to find help within the community. These findings suggest that for income shocks such as drought, having access to informal finance reduces the need for a household to have a migrant to cope with the adverse effects of the shock. We find a similar effect for the earthquake shock. The findings in Table 7 suggested that earthquake reduces migration, though the estimate was not statistically significant. However, here we find that earthquake itself increases migration but access to informal finance largely offsets the effect of the shock and reduces migration. This results in the overall effect of the earthquake shock on migration to be zero or even negative. For winter shocks, the findings indicate that the migration response is not affected by the availability of informal finance. In Table 7 we found that migration is likely to go down when a household faces a winter shock. As discussed in Section 5, one possible explanation for this result is that a severe winter increases adult-labor requirement at home, which leads the household to not have a migrant member or to recall an existing migrant member. Unlike shocks that reduce income of households, access to finance is unlikely to play any mitigating role in the face of shocks that increase domestic labor requirement. Overall, these findings provide 18 The LiK survey was undertaken for 120 geographical clusters, which we define as communities. Page 16 of 19 Chakraborty and Pandey. IZA Journal of Development and Migration (2022) 13:08 evidence in favor of our proposed mechanisms through which shocks affect temporary international migration. 6 Conclusion Using panel data for households residing in rural Kyrgyzstan, we investigate the role of temporary international migration as a risk mitigation strategy. We ask whether the decision to migrate depends on the shock experiences of a household. We use a household FE model to Table 10 Migration, informal finance and shocks 1 2 3 4 5 b/se b/se b/se b/se b/se Easyfin 0.126 0.082 0.012 0.117 0.019 (0.121) (0.126) (0.144) (0.116) (0.134) Droughtt-10.183** (0.077) Easyfin × droughtt-1-0.346** (0.165) Rain and landslidet-10.091 (0.093) Easyfin × rain and landslidet-1-0.215 (0.199) Winter and frostt-1-0.051 (0.067) Easyfin × winter and frostt-10.028 (0.143) Earthquaket-10.176** (0.087) Easyfin × earthquaket-1-0.431** (0.192) Pestt-1-0.009 (0.057) Easyfin × pestt-10.060 (0.143) Observations 5,051 5,051 5,051 5,051 5,051 Households 1,781 1,781 1,781 1,781 1,781 Household FE Yes Yes Yes Yes Yes Year × oblast Yes Yes Yes Yes Yes Clustered SE Yes Yes Yes Yes Yes Notes: Migration decision is measured in period t and shocks are measured at time t - 1. All regressions include household, year, and year × oblast FE and all household control variables included in Table 7. SE, clustered by community-year, are reported in parenthesis below the estimated coefficients. ***, **, and * represent significance at the levels of 0.01, 0.05, and 0.10, respectively. FE, fixed effects; SE, standard errors. Page 17 of 19 Chakraborty and Pandey. IZA Journal of Development and Migration (2022) 13:08 account for unobserved correlates of a household’s shock experiences and its decision to have a member migrate. In line with the previous literature, we find that shocks with adverse income effects increase migration, indicating that households might use temporary migration as a coping strategy. However, in addition, we find that winter shocks reduce migration. We argue that the effect of a shock on the decision to migrate depends on the shock effect on households’ income and need for labor services. While a drought shock is likely to reduce agricultural production and income, a winter shock is less likely to affect agricultural income as agriculture is primarily done during the warmer months in Kyrgyzstan. Instead, a winter shock increases labor requirement in rural Kyrgyzstan. To substantiate our claim that migration is used as a mitigation strategy in the face of negative income shocks, we explore whether the decision to migrate responds to shocks differently in the presence of alternate coping mechanism. We find that a household’s migration response to negative income shocks decreases when access to informal finance is easier, that is, when households find it easier to borrow. However, access to informal finance has no impact on a household’s migration response to a winter shock. These findings provide evidence in favor of our proposed mechanisms through which shocks affect temporary migration. Acknowledgments This paper has benefited from comments received at various conferences. We are grateful to Damir Esenaliev for help with the LiK data and to Bakhrom Mirkasimov for useful discussions regarding the socio-economic context of Kyrgyzstan. We acknowledge funding support for this research from SSHRC, Canada. References Angelucci, Manuela (2015): Migration and Financial Constraints: Evidence from Mexico. Review of Economics and Statistics 97(1), 224-228. Atamanov, Aziz; Marrit Van den Berg (2012): Heterogeneous Effects of International Migration and Remittances on Crop Income: Evidence from the Kyrgyz Republic. World Development 40(3), 620-630. Brück, Tilman; Clotilde Mahé; Wim Naudé (2018): Return Migration and Self-Employment: Evidence from Kyrgyzstan. IZA Discussion Paper No. 11332. Brück, Tilman; Damir Esenaliev; Antje Kroeger; Alma Kudebayeva; Bakhrom Mirkasimov; Susan Steiner (2014): Household Survey Data for Research on Well-Being and Behavior in Central Asia. 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The European Journal of Development Research 30(5), 871-891. Page 19 of 19 Chakraborty and Pandey. IZA Journal of Development and Migration (2022) 13:08 Appendix Table A1 Migration and shocks: village versus city 1 2 b/se b/se b/se b/se b/se Droughtt-10.041* (0.022) City × droughtt-1-0.014 (0.035) Rain and landslide 0.001 (0.022) City × rain and landslidet-10.003 (0.045) Winter and frostt-1-0.026 (0.020) City × winter and frostt-10.028 (0.029) Earthquaket-1-0.018 (0.028) City × earthquaket-10.027 (0.041) Pestt-10.015 (0.018) City × pestt-1-0.022 (0.052) City significance test Ho: shock + city × shock=0 Accept Accept Accept Accept Accept Observations 8,347 8,347 8,347 8,347 8,347 Households 2,976 2,976 2,976 2,976 2,976 Household FE Yes Yes Yes Yes Yes Year × oblast Yes Yes Yes Yes Yes Clustered SE Yes Yes Yes Yes Yes Notes: Migration decision is measured in period t and shocks are measured at time t - 1. Information on households residing in villages and cities is provided in the data. All regressions include household, year, and year × oblast FE and all household control variables included in Table 7. SE, clustered by community-year, are reported in parenthesis below the estimated coefficients. ***, **, and * represent significance at the levels of 0.01, 0.05, and 0.10, respectively. FE, fixed effects; SE, standard errors.