what was the primary motivation behind rural-to-urban migration

INTRODUCTION

Population movement has a significant impact on the development process. Even though migration provides benefits for migrants, information technology is not necessarily beneficial for social club. With respect to developing countries, migration may lead to high population density in an urban surface area, unemployment, and a gap betwixt rural and urban areas (United nations, 2016). With a population of 265 one thousand thousand people in 2018 (BPS-Statistics Indonesia, 2018), Republic of indonesia has the fastest rate of urbanization in Asia, with an annual urban population growth of 4.4% on average over the last four decades. It is predicted that 68% of the population will alive in an urban area in the next 10 years. Notwithstanding, unlike other countries, urbanization in the country does not automatically improve the economic welfare of society (both in urban and rural areas).

One of the of import factors that drives urbanization in Indonesia is migration activities from rural to urban areas (Figure 1(a)). Another factor is that Indonesia is characterized by uneven population distribution amongst its islands. Java is the most populous island with 57% of Indonesia's population living on the island, fifty-fifty though Java accounts for only 7% of the full area of Indonesia. This has resulted in high population density on the island, while accounting for 1.055 people/kmⁱⁱ (Figure 1(b)). These factors undoubtedly contribute to the success and failure of development both at the national and regional levels.

Understanding migration motives and its touch on on household welfare: evidence from rural–urban migration in Indonesia

Published online:

04 May 2020

Figure i. (a) Projected distribution of the urban and rural population, 2010–2035; and (b) percentage distribution of the population by region and population density (people/km²), 2015. Sources: Statistics Indonesia (2015).

Figure 1. (a) Projected distribution of the urban and rural population, 2010–2035; and (b) percentage distribution of the population by region and population density (people/kmⁱⁱ), 2015. Sources: Statistics Indonesia (2015).

Theoretically, debates about the strong relationship between migration and development remain. The optimist assumes that migration is a form of optimal allocation of production factors to favour the sending and receiving areas. In the perspective of 'counterbalanced growth', the reallocation of labour from an agronomical sector-based rural area to an industrial sector-based urban area is considered a precondition for economic growth and a component of all development processes (Harris & Todaro, 1970; Penninx, 1982). In dissimilarity, pessimists believe that migration is the reason for increasing spatial inequality (both interregional and international) in development processes. Migration is considered a problem that burdens sending areas: causing an uncontrolled decrease in skilled labour availability and a decrease in the number of the near good for you, dynamic and productive members of the population ('brain drain'; Baldwin, 1970; Myrdal, 1957; Papademetriou, 1985). Autonomously from the ambivalence between migration and evolution and the uncertainty of the benefit gained from migration, the number of people who take migrated has increased over the years. Specially, internal migration has reached 768 million people worldwide (United Nations, 2016).

Although at that place are many reasons why people migrate, 2 major motives are commonly underlined in the context of the developing state. Starting time, migration can solve negative income shock. If households are experiencing negative stupor (for instance, agricultural shock because of drought or another natural disaster) and toll fluctuation, they might send a household member to a different location in order to gain extra income. This migration strategy is seen equally an alternative to other hazard-coping strategies, such as reducing savings, selling avails, increasing one's labour supply locally and reducing consumption. Second, migration can exist applied as an investment strategy with the goals of increasing and diversifying expected income in the future and obtaining benefits from a higher wage in another place (e.g., an urban area). However, similar to investment, this strategy often requires a loftier upfront cost. Hence, it is non possible for a household with limited liquidity to make such an investment even though it is profitable.

These 2 migration strategies are observed and documented empirically, even so they are described as separate phenomena. Migration motive as a risk-coping strategy was reported past Kleemans and Magruder (2018) and Morten (2016), who mentioned that rainfall daze has motivated people to participate in internal migration. Evidence from investment strategy was documented past Bryan et al. (2014), who plant that profitable migration is prevented by liquidity constraint. Thus, solving this problem by subsidizing migration or through positive income shock volition increase migration. However, there is still limited study (specially in Indonesia) that performs the assay of patterns and the impacts of migration based on migration reason/motive either for risk-coping or investment. Information on migration pattern, behaviour and impacts is necessary. In fact, obtaining this information enables the policy of migration management to be implemented in order to maximize the benefit of migration for the household and the society and minimize the negative externalities of migration.

This study is aimed at mapping rural–urban migration pattern and behaviour in Indonesia and analysing migration touch on migrant household welfare in an urban area based on two migration motives: (1) migration every bit a risk-coping strategy and (2) migration equally an investment strategy.

LITERATURE REVIEW

The economic literature on migration has causeless that an individual or household considering migration rationally considers many locations and selects i that maximizes the profit expected from migration. This expected do good depends on various factors, such as personal characteristics and experience, social networks, wealth or the reduction of vulnerability to poverty. Dissimilar theories and models take been developed to detail the importance of migration activity.

In his 'Law of Migration', Ravenstein (1885) connected migration patterns and the condition of labour surplus and shortage nether which people move from an area with a labour surplus to an area with a labour shortage to improve their living atmospheric condition. He besides developed the concept of 'pull' and 'push' factors to explain the force promoting migration. The pull cistron includes social incentives, economic system, politics or the surroundings of the destination such as employment opportunities, better education and living conditions. Moreover, the push button factor is the incentive in the place of origin that motivates people to migrate. Specific factors include a lack of employment opportunities, insecurity related to political, social or economical conditions, or a loss of wealth (Lee, 1966). Another classic migration model was introduced by Sjaastad (1962) and further adult by Harris and Todaro (1970) and Mincer (1978).

On the other hand, Sandell (1977) and Mincer (1978) considered migration equally a family determination. A whole family will migrate if the internet profit is positive. If simply one household member lands a meliorate task in the destination area, the family unit will migrate only if the profit/income gained from the one family member tin can internalize the income loss from another family unit fellow member. Therefore, the decision of family migration is basically an assemblage of individual migration utilities. Bigsten (1988) also causeless migration equally a family decision in which a family allocates laborers/workers to an urban or rural sector depending on the marginal product of combined wages.

As a modification of neoclassical approach, Stark and assembly adult a new economical theory of migration called The New Economic science of Labor Migration (NELM) (Katz & Stark, 1986; Lauby & Stark, 1988; Stark, 1985; Stark & Bloom, 1985; Stark & Levhari, 1982; Taylor, 1999). The key insight of this new approach is that the migration conclusion is not made by the isolated individual histrion, simply collectively past a family unit or household to maximize the expected profit and minimize the risk related to market failures. Massey et al. (1993) noted that the take chances on household income in adult countries is unremarkably minimized by the individual insurance market or government programmes. However, in developing countries, the mechanisms of such institutions are imperfect, do not exist or cannot be accessed by a low-income family unit. This status provides them with an incentive to perform run a risk diversification through migration.

According to the NELM, the economic variables that are relevant to explain migration are not the wage, but the take chances, necessity and admission to upper-case letter. Using the information collected from 25 Mexican communities, Massey and Espinosa (1997) empirically investigated the push factor in United mexican states–United states of america migration. They establish that, for the concluding 25 years, the probability of migration is more closely related to the forcefulness identified past the new migration economic theory than the individual cost–benefit calculation assumed by the neoclassical model. The NELM approach tin can be linked to the broader literature on risk and poverty (Dercon, 2004), where migration is i of the strategies applied by a poor household in a risky environment. To reduce the risk related to this new engineering science, a household tries to divert risk by performing diversification of its income portfolio.

Other studies on migration adopted the NELM approach to investigate factors backside migration migrants' welfare. For example, Agesa and Kim (2001) used data from Kenya to identify the determinants of the migration determination. They found that migration relatively occurs in workers experiencing a positive income difference between urban and rural areas, showing that skilled workers make their own decisions to migrate to an urban area. Giesbert (2007) showed evidence from West Kenya that indicated that the tendency to migrate depends on teaching and the migrant network, only it is not related to household wealth. Ezra and Kiros (2001) institute that individual members of a poor household, in terms of economic condition and in a community that is ecologically vulnerable in Ethiopia, tend to migrate more often than those from a less vulnerable area. Zhao (1999), Nguyen et al. (2015) and Rangkuti (2012) analysed factors related to the migration decision afflicted by household characteristics and the impact of migration activeness, such the effects of remittances on the welfare of the migrant household in the surface area of origin. However, to date, only a few studies conducted, particularly in Indonesia, performed a migration conclusion assay that differentiated the migration decision based on the migration reason/motive equally a strategy of risk-coping or investment.

The deviation in the reasoning or the motive for migration past households based on household, region and customs characteristics in the expanse of origin would influence the behaviour toward migration, the option of region of destination, likewise equally its bear on on migrant households and conditions in the area of origin.

METHODS

This study used the longitudinal information sets of the Indonesia Family Life Survey (IFLS) of 2007 (Phase iv) and 2014 (Stage five). The unit of measurement of assay in this study was the household at the level of village/subdistrict, with a classification that the urban and rural areas selected were referred to equally the indicator applied by the BPS (Statistics Indonesia). To analyse migration, a migrant and migrant household should be divers. In this study, a migrant is a household fellow member who has lived outside the hamlet for not fewer than vi months during the period 2007–fourteen. A migrant household is a household that had at least one migrant during that period. Since there is the possibility that a household member migrated earlier 2007, the household has obtained a do good from the remittance that affects the variable of 'per capita income'. To avoid such an endogeneity problem, households with a migrant before 2007 were excluded from the sample. The number of respondents in this study was 2581 households, with 407 rural–urban migrant households.

The categories for the motive of migration were obtained through items from the IFLS questionnaire, which specifically asked: What was the main purpose for your movement to (DESTINATION)? The categories were dry out season/drought, natural and other disasters, political disturbance, new job opportunities because of job market limitations at the previous place (the gamble-coping motive or chance-coping strategy), and education/grooming-related to obtain work at the destination (the investment motive).

To investigate the pattern and behaviour of a migrated household member, the analysis of descriptive statistics, spatial analysis and mapping were performed to obtain the pattern and behaviour of migrants based on each migration motive. Furthermore, to analyse the consequence of migration for migrant household welfare in a rural area, the average treatment effect on the treated group was identified (Heckman & Navarro-Lozano, 2004), in which treatment is referred to equally the household migration status. The treatment effect exists by comparison the outcome betwixt a household with a migrant and a household without a migrant. It is expressed equally shown in the model below.

Difference-in-differences (DID) is a statistical technique applied in econometrics and quantitative inquiry in social sciences to imitate an experimental enquiry pattern using the information of the observational written report by studying the differential effect of treatment on a treatment group versus a control group in a natural experiment. It calculates the effect of treatment (namely, the explanatory variable or contained variable) on an outcome (i.e., the response variable or dependent variable) past comparing the average difference over fourth dimension in the outcome variable for the treatment group compared with the average difference over time for the control grouping. The objective is to reduce inapplicable factors and choice bias (Abadie, 2005; Angrist & Pischke, 2008). This approach also helps solve the endogeneity problem that usually hinders the identification of migration impact.

The DID estimator tin can be identified using longitudinal data sets, where the aforementioned unit i is observed before and afterwards treatment. Assume we have data for 2 points in fourth dimension t = {0, ane}, as in the cross-section case. In a panel data set-up, DID is defined every bit the ordinary least squares (OLS) estimator of α in the following regression (Cerulli, 2015): $\left\{\begin{array}{c}t=1:Y_{i1}={\mu }_1+\alpha D_{i1}+{\mu }_{i\mathrm{one}}\\ t=0:Y_{i0}={\mu }_0+\alpha D_{i0}+{\mu }_{i0}\\ D_{i0}=0\end{array}\right.$ where interpretation is just carried out for those units that are untreated in t = 0. By subtracting, we then obtain: $\left\{\begin{array}{c}\mathrm{\Delta }{Y}_{i1}=\mu +\alpha \mathrm{\Delta }{D}_{i1}+\mathrm{\Delta }{\mu }_{i1}\\ D_{i0}=0\end{array}\right.$ with $\mu ={\mu }_{\mathrm{ane}}-{\mu }_0$ , which is equivalent to: $\left\{\begin{array}{c}\mathrm{\Delta }{Y}_{i1}=\mu +\alpha D_{i1}+\mathrm{\Delta }{\mu }_{i\mathrm{one}}\\ D_{i0}=0\end{array}\right.$ The previous relationship can exist written in matrix form as follows: $\mathrm{\Delta }{y}_1=\left[1;D\right]\left(\begin{array}{c}\mu \\ \alpha \end{array}\right)+\mathrm{\Delta }{u}_1$ Based on this, the consequences of migration to households estimated by OLS can be written as follows: $\mathbf{D}\mathbf{I}\mathbf{D}=\mathbf{East}[{\mathbf{Y}}_{2014}^{\mathbf{T}}-{\mathbf{Y}}_{2007}^{\mathbf{T}}|{\mathbf{X}}_{2007},{\mathit{T}}_{2014}=1\left]-\mathbf{E}[{\mathbf{Y}}_{2014}^{\mathbf{C}}-{\mathbf{Y}}_{2007}^{\mathbf{C}}|{\mathbf{X}}_{2007},{\mathbf{T}}_{2014}=0\right]$ where DID is difference-in-differences; ${\mathrm{Y}}_{2007}^{\mathrm{C}}$ is the outcome for households without migrant (command) in 2007; ${\mathrm{Y}}_{2007}^{\mathrm{T}}$ is the outcome for households with migrant (treatment) in 2007; ${\mathrm{Y}}_{2014}^{\mathrm{C}}$ is the outcome for households without migrant (command) in 2014; ${\mathrm{Y}}_{2014}^{\mathrm{T}}$ is the result for households with migrant (treatment) in 2014; and T is a dummy of determination for migration, where T = 1 if migrating (treatment) and T = 0 if not migrating (command).

RESULTS AND Word

Tabular array 1 shows the characteristics of Indonesian migrants in households. Well-nigh 407 households/migrants migrated during the period 2007–14. In full general, the migrants tended to take the feature of being unmarried, having a child condition in the family and existence in the productive historic period range of 15–30 years old. The strategy of adventure-coping and investment every bit a motive to migrate causes productive labour in a rural area to have a higher opportunity to migrate and may potentially affect economic activity in the rural/local surface area. Moreover, male migrants are more probable to migrate with the motive of hazard-coping compared with the motive of investment. This condition indicates that a vulnerable household has a higher trend to choose to send a male person member of household to another location in order to obtain extra income. Afterward, in terms of the destination area for migration, no significant difference was institute betwixt those who migrated based on the hazard-coping motive and those who migrated based on the investment motive.

Understanding migration motives and its impact on household welfare: evidence from rural–urban migration in Indonesia

Published online:

04 May 2020

Table one. Characteristics of Indonesian migrants.

Migration pattern and behaviour

To analyse the patterns and behaviours of migration, grouping based on three categories of region (area) was performed. The isle of Sumatra was categorized as region 1, the islands of Java and Bali were grouped into region ii, and the province of Kalimantan, the isle of Sulawesi and the province of Nusa Tenggara were included in region 3. This procedure was used since the structure of the sampling frame of the IFLS merely allowed aggregation at the level of island as the categorization practical.

The migrant households from regions 2 (Java and Bali) and three (Kalimantan, Sulawesi, and Nusa Tenggara) were constitute to have like patterns regarding the destination surface area for rural–urban migration between migration motives. In other words, there was no deviation concerning the destination expanse for migration/migration menses between motives (Figure 2(a)). In migrant households from region two, the destination area for migration based on the adventure-coping motive and investment motive was dominated by migration from a rural area to an urban area in other provinces (migration based on risk: 31.82%; migration based on investment: 30.93%) and to an urban expanse in the province where the migrant household lived (migration based on risk: 34.09%; migration based on investment: 38.14%). A blueprint similarity of the destination area for migration between motives was likewise plant in migrant households from region 3, in which the destination surface area for migration based on the two migration motives was dominated past migration from a rural surface area to an urban surface area inside the province (migration based on gamble: 63.64%; migration based on investment: 52.94%). Moreover, a deviation existed for migrant households from region one. For migration based on the chance motive, migrant households from region ane preferred to drift to other provinces (41.eighteen%) and islands (35.29%), while for migration based on investment motive, migrant households in region ane migrated to other areas within the province (39.58%) and to other islands (41.67).

Understanding migration motives and its affect on household welfare: evidence from rural–urban migration in Indonesia

Published online:

04 May 2020

Effigy ii. Destination area for migration. Source: Information candy from the Indonesian Family Life Survey (IFLS) (2016).

Figure 2. Destination area for migration. Source: Data processed from the Indonesian Family Life Survey (IFLS) (2016).

Furthermore, Figure 2(b) shows the migration behaviour in each region. For the participation of household members in migration based on hazard motive, it was found that regions 1 and 2 had a similar design: about eighty% of the migrants migrated alone and did not take other household members with them. In contrast, in region 3, the proportion of migration in company with other household members was higher (60%). For migration based on investment motive, the majority of migrant households in region 1 migrated lone without taking other household members with them. Sandell (1977) and Mincer (1978) considered migration as a family decision that is basically an assemblage of individual migration utilities. A whole family will migrate if the net turn a profit is positive. If in that location is merely ane household member who will obtain a better job in the destination area, the family will but migrate if the turn a profit/income gained from the one family member can internalize the income loss from other family members.

The side by side stage was performing the analysis of the rural–urban migration pattern between regions/areas. For migration based on risk motive (Effigy iii), region 2 (Java and Bali) was establish to be the top destination for migration between regions. This condition reflected the beingness of the polarization of migration between regions with Java and Bali. In Figure iii, there is a migration flow from region one (Sumatra) and region iii (Kalimantan, Sulawesi, and Nusa Tenggara) to region 2 (Java and Bali). In region i, the number of households that migrated to region 2 reached 35.29%. The rest (64.71%) migrated from a rural area to an urban area within the region. This value is higher than the number of migrant households in region 3 (9.09%) who migrated to an urban area in region two. Moreover, in that location was no migration catamenia between regions 1 and 3 concerning migration based on chance motive. Later, the pattern of migration period in region 2 was dominated by migration to an urban area in the same region at the proportion of 77.27%, while the rest of the migrants migrated to region one (6.82%) or region iii (xv.91%).

Understanding migration motives and its touch on on household welfare: show from rural–urban migration in Republic of indonesia

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04 May 2020

Figure iii. Pattern of the destination areas for migrants between regions in Indonesia for migration based on risk. Source: Indonesian Family Life Survey (IFLS) (2014).

Figure iii. Pattern of the destination areas for migrants between regions in Republic of indonesia for migration based on risk. Source: Indonesian Family Life Survey (IFLS) (2014).

This status indicates that migration based on the risk motive for income diversification because of income shock in the origin area has motivated migrants to move to a region with the opportunity to get a meliorate income/job, such every bit Coffee Island compared with some other area. This is likewise supported by the access (transportation) between regions. The blueprint of bachelor transportation modes in Republic of indonesia is expected to affect the pattern and period of migration. Moreover, straight access from regions 1 and iii to region 2 is relatively easier and more than bachelor. Thus, the cost of migration from these regions to region 2 is lower than the toll of migration from region 1 to region 3.

The findings regarding this migration pattern confirm Newton's Constabulary of Gravity stating that two objects attract i another with a strength that is proportional to their size and inversely proportional to the altitude between them, which is further adopted for the analysis of migration design. This gravity model has the characteristic of the variables of population size and economic activity of an expanse. As well, altitude is a key factor. Thus, a region with larger economic activity will attract more people as the destination area for migration. Yet, it is as well influenced by the cost expended to reach the expanse. The variable of distance that is normally measured from travel time or transportation cost, in fact, can be used as an arroyo to calculate and capture physical costs that are actually immeasurable but affect migration flow (Etzo, 2008).

For migration based on investment motive, the pattern of the destination expanse for migration between regions is depicted in Figure 4. In migration based on investment motive, region ii was also constitute to exist the middle of migration menstruum: particularly the flow from region i. In addition, the migration flow in region 3 was concentrated within the region.

Understanding migration motives and its impact on household welfare: prove from rural–urban migration in Republic of indonesia

Published online:

04 May 2020

Figure 4. Pattern of the destination areas for migration between regions in Republic of indonesia for migration based on investment. Source: Indonesian Family Life Survey (IFLS) (2014).

Figure 4. Pattern of the destination areas for migration betwixt regions in Indonesia for migration based on investment. Source: Indonesian Family Life Survey (IFLS) (2014).

Unlike from migration based on risk, the migration flow between regions i and three was observed regarding migration based on investment motive. This status denotes that migration based on investment to improve migrant resources in the future is supported by the initial majuscule to face up difficulties and barriers concerning the access between these 2 regions. This condition is too supported past the office of networks in migration based on an investment method that is expected to suppress/reduce the cost of migration.

A person'due south social network is widely accepted to affect the migration decision. Many studies have found that a wider network of friends and family from former migrants promotes migration (Davis & Winters, 2001; Dolfin & Genicot, 2010; Massey & Espinosa, 1997; Grossman, 1989; Munshi, 2003; Palloni et al., 2001) and tends to attract family members to migrate to the same geographical area (Bartel, 1989). The community network also plays a part in reducing migration price, especially for those who are less educated (McKenzie & Rapoport, 2007; Morten, 2016). The migrant network may facilitate migration in three ways: (i) past providing information on the migration process; (2) by providing information on the destination area and employment and helping the migrant to integrate after arriving; and (3) by helping the migrant pay the migration cost.

Migration'south impact on the welfare of migrant households in rural areas

The remittances sent home by migrants represent a major factor contributing to an understanding of the effect of migration on the origin expanse. The office played past remittances at the household level and the touch information technology has on the local community can be significant, depending on how the remittances are received by the household.

To encompass the impact of rural–urban migration on migrant household welfare in the origin area of the migrant, the not-experimental method was applied. This method was used since the household group analysed was found to have migrated. Thus, the control grouping was estimated using the DID technique. This method applies the counterfactual approach: that is, a condition that may exist if a migrant household or a household that has migrated (the treatment group) actually does not migrate and thus results in a command group that minimizes bias (Ravallion & Chen, 2005). The DID approach observes the treatment and control group for two periods of time: before and after migration. The divergence between before and after migration in each grouping (treatment and control) will remove the time-invariant unobserved gene. Hence, no bias exists. The variable of household welfare in this report was analysed through the approach of difference in the amount of real consumption expenditure per capita in 2007 and 2014 with the base yr 2010.

Table 2 shows the results of estimation using the DID approach in four estimation models. In the starting time model, analysis was performed using all observations of 4014 households, while analysis for the other three estimation models was performed in each category of region practical in this written report. The impact of migration on difference in the level of real consumption expenditure per capita in the rural area that was the origin expanse of migration was seen from the variable of interaction betwixt the variable of migration status (migration based on risk and migration based on investment) and the variable of year (risk_mig#year and inv_mig#yr variable). To generate a model that is more than precise and unbiased, the authors include control variables that affect the per capita consumption expenditure (pce) such as worker characteristics, household characteristics, and community and region characteristics.

Understanding migration motives and its impact on household welfare: evidence from rural–urban migration in Indonesia

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04 May 2020

Tabular array 2. Result of regression with the divergence-in-differences (DID) arroyo.

Based on the upshot of estimation, it was found that migration based on risk motive (risk_mig#year) did not significantly affect the deviation in household consumption expenditure per capita in the rural area in all of the models applied. In contrast, migration based on investment motive (inv_mig#year) positively and significantly afflicted the difference in household consumption expenditure per capita in the rural area in all of the models used, except for model 4 (region three). This indicates that migration based on risk motive, which is i of the strategies practical by households in rural areas in Republic of indonesia to cope with risk because of marketplace failure in the rural areas, cannot provide a real bear on on the improvement of migrant household welfare.

Table 3 shows the magnitude of the touch of the rural–urban migration based on migration motive. According to the consequence of interpretation, at that place was a different migration touch on on household welfare for each migration motive. In general, migration based on investment motive statistically led to a greater and more pregnant bear on compared with migration based on adventure concerning the difference of household welfare in the rural area. This finding is supported past the issue of previous studies. Migration equally an investment made by households in a rural area has improved household welfare through remittances sent by the migrant from the migration destination expanse (Stark & Bloom, 1985).

Understanding migration motives and its touch on on household welfare: testify from rural–urban migration in Indonesia

Published online:

04 May 2020

Table 3. Impact of migration on real household consumption expenditure per capita based on migration motives using the departure-in-differences (DID) approach (United states$).

In migration based on investment motive, the migrant household in region 1 (Sumatra) obtained the greatest affect of migration compared with other migrant households in other regions. The migrant households in region 1 experienced an increase in consumption expenditure per capita of 280% on average from 2007 to 2014, which was college than that obtained by migrant households on Coffee and Bali that faced a difference in consumption expenditure per capita of 64.23% over the same menstruation. Moreover, the result obtained for households in region 3 showed that migration based on investment motive did not significantly improve household welfare.

CONCLUSIONS

There is a polarization of migration betwixt regions in Indonesia. The islands of Coffee and Bali are areas at the center of the urban–rural migration flow in the country both for migration based on risk and migration based on investment motives. This condition has caused cities on Coffee and Bali to feel higher pressure and negative externalities: particularly in times of high migration because of risk-coping performed by households in rural areas in Indonesia.

Migration based on adventure motive did not significantly affect the difference in the household consumption expenditure per capita in the rural area, while migration based on investment motive positively and significantly affected the difference in the household consumption expenditure per capita in the rural area. Region 1 (Sumatra) was plant to be the region that obtained the greatest impact of migration (specifically, migration based on investment motive) on the improvement of household welfare in the migrant origin region compared with the migrant household in other regions.

When analysing the migration by rural households to urban areas in Indonesia, it is important to pay attending to the motive that drives the migration. The difference in the motives of migration volition be reflected in the pattern and behaviour of migrant households and the socioeconomic impacts associated with migration. The study shows that the pattern, behaviour and impact of migration is influenced strongly past the areas of origin. Based on this observation, policies associated with direction of migration, particularly at the regional level, must consider the motivational factors and characteristics of the areas of origin.

Rural–urban migration has been identified as one of the strategies carried out by poor rural households facing risky environments. These risks are mostly associated with market uncertainties such as falling in agronomical commodities, harvest failures, natural disasters and lack of employment opportunities in rural areas. All the same, it is hardly proved that this gamble-motive-based migration could consistently better the economic and welfare of rural households. To overcome this situation, households in rural areas require other instruments to improve their welfare during poor seasons such every bit unconditional authorities help and improved infrastructure.

Regions (especially those with a high trend of rural–urban migration such as the Sumatra regions) could initiate policy interventions to adjourn migration through social security programmes, social insurance and depression interest-rate loans. Such programmes should be complemented by man resources development programmes and include training for entrepreneurs, capacity-edifice for communities and the development of non-agricultural economic activities for rural people. All these programmes are needed past rural residents in Indonesia to finance new ventures or to ready new businesses, likewise as to implement technological innovations to mitigate the pressure from market fluctuation and harvest failures.

Policies on migration likewise other regional development policies must be in line with other policy initiatives in the neighbourhood regions, especially those regions that accept not received the do good of migration activities. Policies that are designed to empower the household economics of migrants are urgently needed, such as raising household financial literacy, assisting migrants to send money to family members in their area of origin, profitable group of recipients to manage funding from remittances to be used for rural development programmes and encouraging productive investment activities. Other complementary programmes, such as policy intervention to encourage migrants to maintain their social connection to their area of origin through sharing cognition, ideas and other intangible assets, should be pursued. These programmes could be carried out in cooperation with migrant associations that have been established in the areas of both origins and destinations.

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Source: https://www.tandfonline.com/doi/full/10.1080/21681376.2020.1746194

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