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Numerous studies have concluded that immigration has very small effects on wages or unemployment, even when the immigration flow is very large. Three reasons suggested for this are that immigration: (1) is not supply-push, but may instead be driven by demand-pull factors; (2) is likely to cause some out-migration; and (3) may induce flows of other factors across the economy. Surprisingly, few studies consider another obvious explanation: immigrant workers also consume locally, which means immigration stimulates the local demand for labor. Previous researchers have generally ignored the measurement of immigration's effects on labor demand, perhaps because when immigration, out-migration, and immigrant consumption occur simultaneously in the same labor market, it is very difficult to isolate immigration's effect on labor demand. This paper measures the labor demand-augmenting effects of immigration using a two-sector model of a very special case in which the receiving economy consists of: (a) an export industry employing both immigrants and natives; and (b) a retail industry employing native labor that is driven by local demand. The model can incorporate both supply-push and demand-pull immigration as well as out-migration. The model's important implication is that since immigration is exogenous to the retail sector, an unbiased estimate of the demand effect of immigration can be obtained without having to use instrumental variables estimation or other statistical procedures that may introduce new sources of bias. Fortunately, the economy in our model matches a very convenient test case: Dawson County, Nebraska. Dawson County recently experienced a surge in demand-pull immigration due to the location of a large export-driven meatpacking plant. This exogenous capital shock pulled in many Hispanic immigrant workers, who did not immediately seek work in the retail sector because of social and language barriers. This immigration led to higher retail wages and housing prices, confirming that immigration is capable of exerting significant effects on local labor demand.

Student mobility from the south to the north/centre of Italy is an increasingly crucial topic because the most important consequence is the continual depletion of universities situated in the southern regions. Using micro-data from the Italian Ministry of Education, University and Research (MIUR), the purpose of this paper is to investigate how contextual factors affect this mobility.

Empirical evidence is provided by developing a multilevel logit model of student decisions to move at university enrolment that allows us to identify the unique effects that student and province characteristics have on out-migration. Multilevel analysis is appropriate because the research questions focus on the role of province characteristics, variables that are measured at the macro level, on student-level outcome (out-migration) while controlling for student-level characteristics, variables that are measured at the micro level.

The present paper intends to contribute to the literature by quantifying the way in which contextual factors affect student mobility from the south to the north/centre of Italy. Findings show that province differences remain significant even after controlling for individual characteristics stressing the importance of the geographical dimension for student mobility. These findings have important policy implications for the future of university system in Italy.

This paper is relevant to the literature concerning this issue because most of the empirical applications are based on spatial models that do not take into account individual characteristics of the students. In addition, the multilevel methodology proposed can be easily generalise to other countries.

The Sundarbans is a unique ecosystem, the most expansive mangrove system in the world covering the Ganga–Brahmaputra–Meghna delta system and spread over 10,000 sq.km across India and Bangladesh. The Indian Sundarbans have been witnessed to large-scale conversion of mangroves to settled agriculture and steady growth of population over the decades. With time, while population growth has taken place, there has been no significant change in the development scenario, as the agriculture and fishing dependent communities have remained trapped in the vicious cycle of poverty. Its deltaic location and its position in the path of cyclones, tidal surges and seasonal floods have made the people and landscape of Sundarbans more vulnerable than ever before. Cyclones affecting this region have grown both in frequency and intensity over the years and have caused devastation to the land, homes, lives and livelihoods. The problem of salinity has also affected the region relentlessly. Given this scenario, scenario of progressing vulnerabilities, livelihood losses have led to a gradual increase in out-migration of the adult male population. This disaster-led outmigration from Sundarban region as a whole has affected the social fabric of displaced communities to a great extent. This chapter makes an attempt to discuss results of sample surveys across six villages in three CD Blocks of Sagar, Gosaba and Kultali to trace the nature and dimension of the migration patterns of the region.

Increasing trends of climatic risk pose challenges to the food security and livelihoods of smallholders in vulnerable regions, where farmers often face loss of the entire crop, pushing farmers (mostly men) out of agriculture in destitution, creating a situation of agricultural making agriculture highly feminization and compelling male farmers to out-migrate. Climate-smart agricultural practices (CSAPs) are promoted to cope with climatic risks. This study aims to assess how knowledge related to CSAPs, male out-migration, education and income contribute to the determinants of male out-migration and CSAPs adoption and how they respond to household food security.

Sex-disaggregated primary data were collected from adopter and non-adopter farm families. STATA 13.1 was used to perform principle component analysis to construct knowledge, yield and income indices.

Yield and income index of adopters was higher for men than women. The probability of out-migration reduced by 21% with adoption of CSAPs. An increase in female literacy by 1 unit reduces log of odds to migrate by 0.37. With every unit increase in knowledge index, increase in log-odds of CSAPs adoption was 1.57. Male:female knowledge gap was less among adopters. Non-adopters tended to reduce food consumption when faced with climatic risks significantly, and the probability of migration increased by 50% with a one-unit fall in the nutrition level, thus compelling women to work more in agriculture. Gender-equitable enhancement of CSAP knowledge is, therefore, key to safeguarding sustainable farming systems and improving livelihoods.

The enhancement of gender equitable knowledge on CSAPs is key to safeguard sustainable farming systems and improved livelihoods.

This study is based on the robust data sets of 100 each of male and female from 100 households (n = 200) using well-designed and validated survey instrument. From 10 randomly selected climate-smart villages in Samastipur and Vaishali districts of Bihar, India, together with focus group discussions, the primary data were collected by interviewing both men and women from the same household.

Reviews a number of contributions to migration analysis. Discusses some of the rudiments of migration modelling before turning attention to specific applications. Pays particular attention to articles which have modelled the interaction between labour and housing markets.

The purpose of this paper is to understand the factors determining smallholder farmers’ adaptation strategies to climate change, in the semi‐arid region of Nguru, Nigeria and toward sustainable management of their agricultural production and livelihood.

Both primary and secondary data were used. Primary data were collected by use of structured questionnaires, key informant interviews and focus group discussions. Based on the research plan, a total of 250 individual households were randomly selected and interviewed.

The stepwise multiple linear regression model ran using SPSS revealed nine variables significantly determining the adaptation of climate change strategies. The variables found significant were: agricultural labor force, level of education of the household head, land tenure arrangements, gender of the household head, extension service availability, out‐migration of labor, years of farming experience, household size and availability of farmer to farmer extension. The predicted R value of 0.87, R² of 0.63, and adjusted R² of 0.60 indicate high explanatory power of the model as a whole.

The acceptance of the variables included in the model helps very useful policy conclusions for climate change adaptations to be drawn. All these variables, except gender and out‐migration, have a positive influence on climate change adaptation strategies. The influence of agricultural labor force appeared to be strongest, indicating the very important role of this factor in adaptation and the need for promotion of less labour‐intensive, but more remunerative adaptation strategies that would enable smallholder farmers to manage all of their farm plots in an effective way.

Agricultural production in Honduras was devastated by the impact of hurricanes Eta and Iota in November 2020, placing people depending on agriculture in a vulnerable and food insecure situation. In addition to the COVID-19 pandemic and the economic downturn in the country, these extreme events are forcing more people to leave and head to North America in migrant caravans. Over the last decades, Honduras has been impacted by severe climate change events, including droughts and extreme tropical storms. According to the Global Climate Risk Index (CRI), developed by Germanwatch (Eckstein et al., 2019), Honduras was the second most affected country by climate change over a period of 20 years, from 1998 to 2017. Extreme rainfall and tropical storms, droughts, variation in rainfall patterns, and soil loss make agriculture more difficult, thus placing low-income rural families at the edge of hunger and food insecurity. In terms of migration policy, much focus has been given to economic instability, weak governance, violence and crime as push factors for migration, but the effect of food insecurity and climate change impact is often overlooked in this narrative. Agricultural areas in Honduras, traditionally the backbone of food production, have been identified as climate out-migration hotspots. The purpose of this chapter is to examine the most relevant data to understand the interactions between climate change, food insecurity and the current migration crisis in Honduras.

In this study, the authors examine the push and pull effects of extreme weather events on migration among governorates in Egypt.

To estimate the effect of extreme weather events on internal migration, the authors use migration gravity models and data from the 1996 and 2006 Population and Housing Censuses. The authors measure weather extremes by the number of months in the past 36 months with temperatures or precipitation of a governorate below the 5th percentile and above the 95th percentile of the distribution of monthly temperatures or precipitation of the corresponding governorate during the period 1900–2006.

This study’s results suggest that high temperatures in the origin area act as a push factor. High-temperature extremes have a positive effect on out-migration. A 1% increase in the number of months with high-temperature extremes in the original governorate results in a 0.1% increase in the number of out-migrants.

The study suggests that people may respond to weather extremes through migration. However, climate migrants in Egypt may encounter several significant risks that authorities must address.

This study is one of the first attempts to measure the push and pull effect of weather extremes on migration in Egypt.