THEORETICAL FRAMEWORK
Literature of migration and remittances
In recent decades, remittances have emerged as a significant form of international capital flow, serving as a vital channel for economic growth in developing countries and supporting development in labor-exporting nations This concept is closely tied to migration theory, as highlighted by Yoko Niimi et al (2006), who discuss the intricate relationships between migration and remittances, indicating that migration often directly correlates with remittance flows, as noted by the IMF.
Standard economic theory posits that remittances are closely linked to international labor migration, primarily driven by income disparities between the migrant's home and destination countries (Holst et al., 2010) Hein de Haas (2007) emphasizes that remittances and migration are often interconnected, leading to the development of various theoretical models to explain this relationship.
Neoclassical theory: Macro and Micro Economics
The oldest theory of remittance and migration is Neo-classical theory This theory looked remittance and migration in two aspects including macro and micro aspects
Migration occurs in response to labor market imbalances and wage disparities, as workers from regions with surplus labor and low wages relocate to areas with labor shortages and higher wages, ultimately achieving labor market equilibrium This migration is often accompanied by capital flows, including remittances, which move from capital-rich to capital-poor countries The mechanisms driving this equilibrium are illustrated in Oberg's 1995 framework.
In the realm of microeconomics, Neo-classical theory, as articulated by Todaro (1969), explains migration through the lens of individual choice, particularly among high-skilled laborers who anticipate higher real wages in countries with greater marginal productivity These individuals weigh the benefits against the costs of migration, which include expenses related to language acquisition, relocation, job searching, and adapting to new work environments Additionally, Massey et al (1993) highlight Borjas' (1990) perspective on migration behavior, emphasizing that migrants seek to maximize their net returns by moving to alternative countries.
Figure 2 1 Neo-classical mechanism leading to equilibrium
High-wage region Low-wage region
Keynesian theory, as outlined by Roel Jennissen in 2007, presents an alternative perspective on migration and remittances compared to Neo-classical theory According to Keynes, labor migration is driven by disparities in unemployment rates across regions; workers from areas with high unemployment are motivated to relocate to regions with lower unemployment in search of job opportunities.
Behind the moving of labors is capital flows to pure their home
The theory posits that large-scale capital transfer and industrialization can drive rapid economic development and modernization in poorer countries Proponents of this developmentalist perspective believe that migration facilitates a North-South transfer of investment capital, exposing traditional communities to liberal, rational, and democratic ideas, as well as modern knowledge and education Consequently, developing countries have begun to promote emigration, anticipating that return migrants will bring back remittances, skills, knowledge, and experience gained abroad, thereby contributing to the economic development of their home countries (Hein de Haas, 2007).
Dual labor market theory, as proposed by Piore (1979) and Massey et al (1993), divides the labor market into two distinct sectors: the primary sector and the secondary sector The primary sector is characterized by capital-intensive methods, offering stable, skilled jobs with access to advanced equipment and tools In contrast, the secondary sector relies on labor-intensive methods, resulting in unstable, unskilled jobs that often place workers at the bottom of the labor market hierarchy, where they face the risk of being laid off without employer support.
Migration, whether international or domestic, serves as a vital strategy for individuals and their families to address unemployment and poverty Alongside the movement of labor across borders, migrants often send a portion of their earnings back home, known as remittances This creates a significant link between migration and remittance, highlighting their interconnected roles in economic stability for families and communities.
Dual labor market theory highlights the connection between migration and remittances within the global economy A shortage of labor in the secondary sector of developed countries drives the movement of workers from developing nations This migration facilitates the flow of capital in the form of remittances from developed to developing countries, further reinforcing economic ties.
The New Economics of Labor Migration (NELM)
The new economics of labor migration (NELM) offers a contrasting perspective to Neo-classical theory by emphasizing the family or household as the primary decision-making unit for migrants This approach highlights how households engage in risk-sharing behaviors, diversifying income sources to mitigate income risks and maximize earnings (Stark and Levhari, 1982) According to Lucas and Stark (1985) and Hein de Haas (2007), households are motivated to migrate as a strategy to address income risks, with migrant remittances acting as a form of income insurance for those left behind This theoretical framework helps explain migration even in the absence of significant income disparities Additionally, migration serves as a crucial source of investment capital, particularly in developing countries where credit and insurance markets are often inadequate.
The theory highlights the social role of remittances in the lives of migrants and their households, emphasizing their function as resources exchanged within social networks A social network consists of recurring associations among individuals connected through occupational, familial, cultural, or emotional ties When migrants send remittances, these resources are integrated into their social networks, fostering connections and support among members.
(i) Based on traditional or sentimental value, the migrant thought that they accumulate social obligation from the people to whom they remit for example childcare, sending goods
(ii) The migrant remitting, maybe, conforms to moral values learn as being a member of the group
(iii) Remittances increase their social visibility in the sending and receiving countries, in addition to avoiding the sanction by the social group if they do not remit.
Theory of Remittances
Family affection creates intangible ties among members, influencing migrants' remittance behavior Lucas and Stark (1985, 1988) identified altruism as a key motive for remittances, as migrants feel obligated to support their families due to their care for their well-being and the families' support during their education Rapoport and Docquier (2005) further emphasized that family sentiment drives migrants to send money home, with remittances increasing in tandem with migrants' income Glytsos (2001) noted that the purpose of remittances varies based on the duration of a migrant's stay abroad; permanent residents tend to remit for altruistic reasons, while temporary migrants often do so for investment and future consumption Rapoport and Docquier (2005) reinforced the connection between altruism and remittances, building on Funkhouser's (1995) model of remittance behavior.
“(i) Emigrants with higher earnings potential remit more;
(ii) Low-income household receiving more;
Remittances are likely to rise when there is a closer proximity between the migrant and their household members, as well as when the migrant has intentions to return Conversely, the amount of remittances sent by an individual migrant tends to decrease as the number of other emigrants from the same household increases.
(v) The time profile of remittances should depend on the comparison between the migrants’ time-discount factor and their earnings profile abroad”
Impure altruism, or pure self-interest, contrasts with pure altruism Lucas and Stark (1985) identified three key reasons that explain the motivations behind migrants sending remittances to their families without altruistic intent.
Firstly, a migrant would send money home to increase their visibility hence eligible for inheritance, esteem or other resources in the community of origin
Secondly, migrants send remittances in order to reimburse the household for past expenditures such as schooling or the cost directly related to migration
Migrants often act out of self-interest by planning to return to their home countries, which encourages them to send remittances These funds are typically used to purchase durable goods and invest in housing, land, livestock, or businesses back home.
Tempered Altruism or Enlightened Self-Interest
The theory of "tempered altruism" or "enlightened self-interest," proposed by Lucas and Stark (1985) and further developed by El Mouhoub Mouhoud et al (2008) and Jessica Hagen-Zanker et al (2007), identifies three key motivations for remittances within family arrangements: exchange, insurance, and investment.
- Exchange motive derived from migrants’ expectation for members in their family to receive the better quality of welfare services (health, education, and so on) from their remittances
Insurance motives serve as intra-family strategies among migrants and their families to mitigate income volatility in rural areas Rapoport and Docquier (2005) suggest that families often send members abroad or to urban centers to secure financial stability in the event of poor harvests, with all costs associated with migration covered by the family This dynamic creates a strong incentive for migrants to remit funds back to their families.
Migrants often transfer their savings to invest in their home countries when they perceive potential returns to be greater than those available in their host countries Consequently, their decision to remit funds is influenced by the disparity in interest rates between their home and host nations.
The relationship between migration and remittances is influenced by various factors, including pure altruism, pure self-interest, and tempered altruism Migrants often aim to send money back home to enhance the living conditions of their family members Catalina Amuedo-Dorantes and Susan Pozo summarize three key motives behind remittances, highlighting the complexity of these financial transfers.
Source: Catalina Amuedo-Dorantes and Susan Pozo (2006)
2 2 Literature review: Remittances, Income, Savings, Asset, Insurance and
Remittances sent by migrants to their families in their home countries are influenced by various theories that explain their purpose At the microeconomic level, the impact and determinants of remittances on recipient countries depend on how households utilize these funds, whether for consumption, health, or education This raises important discussions about the effects of remittances on the receiving households and how these funds are effectively managed.
According to Richard H Adams et al (2008), Adriana Castaldo and Barry Reilly
In 2007, remittances were analyzed through three key perspectives Firstly, they are recognized as an essential source of household income, contributing to increased earnings and reduced poverty levels Conversely, a more pessimistic view suggests that remittances may alter the behavior of recipient households, potentially diminishing the positive impact of income from other sources This perspective is supported by the findings of Chami et al.
Family-provided insurance (response of risks in host country)
Altruism (nope response of risks in host country)
Self-insurance (response of risks in host country)
(2003), Yéro Baldé (2010) gives judgments including (i) remittance is major spent for
Remittances play a crucial role in household financial management, with a significant portion allocated to status-oriented consumption, loan repayments, and investments in land or housing Research by Khawaja Mamun and Hiranya K Nath (2010) indicates that savings constitute 3-7% of remittances, loan repayments account for 10-19%, while healthcare and food & clothing make up 0-4% and 20-36%, respectively Furthermore, the relationship between remittances and insurance highlights the need for financial security against income volatility and wage risks, suggesting that migrants and their families often invest in insurance (Catia Batista & Janis Umblijs, 2014; Dean Yang & HwaJung Choi, 2005).
Beside observations above, empirical studies as follows show the impacts of remittances on income, assets, borrowing, insurance, and savings in household receiving remittances
Relationship between Remittance and Income
Research has explored the relationship between remittances and household income in developing countries, including Egypt, Small Island Developing States (SIDS), Vietnam, and various Asian and Pacific nations.
Richard H Adams Jr and John Page (2005) conducted a study examining the impact of remittances on poverty alleviation in 71 developing countries categorized as low-income and middle-income Their research highlights the significant relationship between remittances and improved economic conditions in these nations.
The study analyzes data from 18 Sub-Saharan African countries and 53 other developing nations, comprising 184 observations related to income, poverty, and inequality over time Utilizing instrumented and non-instrumented OLS estimates alongside the growth-poverty model by Ravallion, the findings indicate that an increase in per capita official remittances significantly reduces the number of individuals living on less than $1.00 per day Specifically, the instrumented OLS estimates reveal that a 10% rise in per capita remittances correlates with a 3.5% decrease in extreme poverty, while non-instrumented OLS estimates show a 1.8% reduction for the same increase in remittances.
Juthathip Jongwanich's 2007 working paper provides empirical evidence on the influence of workers' remittances on economic growth and poverty reduction in 17 Asian and Pacific countries Utilizing the Generalized Method of Moments (GMM) for panel data analysis from 1993 to 2003, sourced from the World Bank and IMF's Balance of Payments Yearbook, the study reveals that remittances significantly boost the income of households that receive them.
Papers of Cuong Nguyen Viet (2008) and Wade Donald Pfau & Long Thanh Giang
RESEARCH METHODOLOGY
Assumptions of PSM method
According to the foundational work of Rosebaum and Rubin (1983) and further research by Marco Caliendo and Sabine Kopeinig (2005), Carolyn Heinrich et al (2010), and Shahidur R Khandker et al (2010), key assumptions are necessary for accurately identifying the intervention effects between treated and control groups.
Assumption 1 – Conditional Independence Assumption (CIA)
There is a set of observed covariates X not affected by treatment, the potential outcomes Y are independent of treatment status T:
The assumption of unconfoundedness, also referred to as selection on observable characteristics, is crucial for accurately identifying the impact of interventions The Conditional Independence Assumption (CIA) helps minimize selection bias by ensuring that differences between the treated and control groups are reduced This is why a control group is essential for constructing a counterfactual, which represents the unobserved outcomes of the treated subjects.
Assumption 2 – Common Support or Overlap Condition [Figure 3.1]
The expression indicates that the ratio of treated to untreated groups must exceed zero for the respective values of X The overlap condition, or common support, is essential to ensure sufficient similarity in characteristics between the treated and control groups, allowing for effective matching The Average Treatment Effect on the Treated (ATT) or Average Treatment Effect (ATE) is defined within the common support region, and treatment assignment is considered strongly ignorable when the aforementioned assumptions are met.
Figure 3 1 Example of Common Support
Source: Shahidur R Khandker et al (2010)
Model Specification of Propensity Score Matching
Propensity score matching involves several steps to assess the effects of remittances on household income, savings, insurance, borrowing, and assets for both remittance-receiving and non-receiving households in the years 2006, 2008, and 2010 This process includes determining the region of common support to ensure accurate evaluation.
Step 1: Choosing Model and Variables for estimation of Propensity Score
Marco Caliendo and Sabine Kopeinig (2005) suggest that logit and probit models are preferred over linear probability models in PSM regression due to their ability to maintain predicted probabilities within the [0,1] range Among these, the logit model is often favored because it is based on stronger assumptions compared to the probit model, which can be more complex and cumbersome to compute.
The next step in the process is variable selection, which is crucial because omitting key variables can lead to increased bias in the resulting estimates To ensure the best outcomes, the selected variables must meet the Conditional Independence Assumption, meaning they should be independent of the treatment condition based on the propensity score, and these variables should remain consistent over time For effective matching, the chosen variables must also be independent of remittances and originate from the same source.
Improving the precision of the propensity score involves incorporating fixed characteristics over time into a regression model Key variables include household composition, gender and age of the household head, living area, ethnicity, proficiency in Vietnamese, marital status, and internet access.
Household’s total area land for cultivated, breed or for rent; Toilet type of household, Energy for cooking; Type of house; and 11 dummy variables of 12 provinces - [Table 3.1]
Command of PSM determining the propensity score and satisfy the balancing property in Stata platform is “pscore” command
The weighting for propensity scores in the VARHS dataset is derived from population data across twelve provinces, as reported in the Statistical Yearbook of Vietnam for the years 2006, 2008, and 2010 This weighting is compared to the households surveyed in these provinces after integrating data from VARHS 2006, VARHS 2008, and VARHS 2010, as illustrated in Tables 3.2 and 3.3.
Step 2: Determining Region of Common Support and Balancing Tests
In accordance with Shahidur R Khandker et al (2010), the common support region has to be specified in the propensity score distribution for treatment and control group overlap
The balancing test is utilized to determine whether the average propensity score and the mean of X are equivalent between treatment and control groups Following the matching process, the differences in covariate means between the two groups significantly decreased.
Step 3: Matching Treated Group and Control Group
Research by Marco Caliendo et al (2005), Shahidur R.K et al (2010), and Carolyn H et al (2010) highlights various techniques for matching treated individuals with control individuals This study implements several of these established methods.
Nearest-neighbor matching (NNM) is a statistical technique that pairs each treatment unit with the most similar comparison unit based on propensity scores or observed characteristics There are two variants of NNM: "with replacement" and "without replacement." In the "with replacement" approach, control group individuals can be reused as matches, enhancing the average quality of matching and reducing bias Conversely, "without replacement" limits each control unit to a single match, making the matching process sensitive to the order of observations, which must be randomized to ensure validity.
Table 3.1 Variables of Propensity score Matching
Variable Description Coding dmremit It is indication of household with remittance or without remittance
1= receiving-remittance household 0= receiving-no remittance household
Hhmem Total of members living in household shares lodging, income and expenditure for at least 6 in last 12 months Hhmem=1, 2, 3, …, 13
HeadGen Gender of Household Head 0 = Female, 1 = Male
Age of household Head In VARHS, age of Household head recoreded by year of birth, so we have to convert into age of year olds
Age in years Headstatus Marital status of household head 0 = Other, 1 = Married Livarea
Total area of a house in which total members of household lived including bedrooms, dining rooms, living rooms, study rooms
The article analyzes various household characteristics across twelve provinces, focusing on ethnic composition, language, and access to modern amenities It categorizes ethnicity into two groups: Kinh and other, while also noting whether all household members speak Vietnamese The article highlights internet access, indicating whether any member of the household can connect to online services Additionally, it examines the primary construction material of outside walls, distinguishing between brick and other materials, and identifies the main energy source for cooking, comparing electricity to other options.
Toilet With or without toilet in household 0 = No, 1 = Yes
Watersource The main source of cooking/drinking water of household 0 = Other, 1 = Tap water Weight
Density of observations represented in comparison with population in each province The observations kept after merging VARHS 2006, VARHS 2008, and VARHS 2010
Tinh_1 Represent of Ha Tay (Ha Noi) province, dummy variable 0 = Other
Tinh_2 Represent of Lao Cai province, dummy variable 0 = Other
Tinh_3 Represent of Phu Tho province, dummy variable 0 = Other
Tinh_4 Represent of Lai Chau province, dummy variable 0 = Other
Tinh_5 Represent of Dien Bien province, dummy variable 0 = Other
Tinh_6 Represent of Nghe An province, dummy variable 0 = Other
Tinh_7 Represent of Quang Nam province, dummy variable 0 = Other
Tinh_8 Represent of Khanh Hoa province, dummy variable 0 = Other
Tinh_9 Represent of Dak Lak province, dummy variable 0 = Other
Tinh_10 Represent of Dak Nong province, dummy variable 0 = Other
1 = Dak Nong province Tinh_11 Represent of Lam Dong province, dummy variable 0 = Other
Table 3.2 Population in each province
No Province Households in each year
Table 3.3 Weight of Population in VARHS 2006, VARHS 2008 and VARHS 2010
Caliper or radius matching (RM) addresses the limitations of nearest neighbor (NN) matching by imposing a tolerance on the maximum propensity score distance, reducing the risk of poor matches between treated and control individuals This method focuses on matching within a defined caliper or propensity range, ensuring closer alignment in propensity scores However, while this technique enhances match quality, it may lead to fewer available matches, resulting in increased variance of the estimates.
Stratification and Interval Matching (SM) is a technique that divides the common support of the propensity score into distinct intervals or strata, allowing for the assessment of program effects within each interval The program effect is determined by calculating the mean difference in outcomes between treated and control observations for each stratum To obtain the overall program impact, a weighted average of these interval impact estimates is computed, using the proportion of treated individuals in each stratum as weights.
Kernel and Local Linear Matching (KM):
Kernel matching (KM) and Local linear matching (LLM) are non-parametric matching techniques that utilize weighted averages from the entire control group to create counterfactual outcomes for treated individuals, leading to lower variance due to the use of more data However, a significant drawback is the potential inclusion of poor matches, making the proper application of the common support condition crucial for the effectiveness of these methods.
To ensure the consistency of findings, various matching methods can be employed Additionally, Shahidur R Khandker et al (2010) recommend utilizing direct nearest-neighbor matching, which can be executed in Stata using the command ‘nnmatch’.
Software
To implement matching and estimate treatment affects, we use “pscore” program of Becker and Ichino (2002) for PSM estimators with Stata platform
3 2 Difference in Difference (DD) Method
The Difference-in-Differences (DD) method was utilized to assess the impact of remittances on households receiving remittances (treated group) compared to those not receiving them (control group) during the periods of 2006-2008 and 2008-2010 This approach enables a clearer understanding of the effects of remittances over time by analyzing the differences between the two groups.
Theory
The Difference-in-Differences (DD) method, as outlined by Shahidur R Khandker et al (2010), is utilized when the parallel-trend assumption holds true This method involves comparing a treatment group, which receives the intervention, with a control group that does not, analyzing both pre- and post-intervention outcomes The DD estimate reveals the changes in outcomes for the treatment group relative to the control group over time, starting from a pre-intervention baseline Ultimately, the DD approach provides an estimate of the average impact of the intervention.
DD = E(Yt T – Yt-k T | T1 = 1) - E(Yt C – Yt-k C | T1 = 0) (1) Where:
+ t and (t-k) denote the time receiving remittance at t and (t-k) times;
+ Yt T and Yt C will be the outcomes of treatment and control groups at time t;
+ (Y1 C - Y0 C) will be the outcome changes of control group;
+ (Y1 T - Y0 T) will be the outcome changes of treatment group;
+ T1 = 1 denotes remittance-receiving households, and
+ T1 = 0 means no remittance-receiving households
In particular, estimative equation of DD presented by the following regression framework:
Yit= + βTi1 t + ρ Ti1 + γt + εit (2) Where:
+ Coefficient β: affect mutually between treatment variable Ti1 and time t with t 1…T, it is the average DD effect of remittance, and β = DD in (1)
+ Variables Ti1 and time t for any effect of time selected as well as the effect of remittance at (t-k) time in comparison with t time
Equation (1) is equivalent with equation (2) as follows:
E(Y1 C – Y0 C | T1 = 0) = ( + γ) – (4) Subtraction of equation (3) and equation (4) equals DD.
Implementing DID
The Difference-in-Differences (DID) approach utilizes panel data to analyze changes over time This method involves comparing participant and nonparticipant groups at two distinct points in time According to the parallel-trend assumption, the difference between the two trends remains consistent throughout the period under study.
In [Figure 3.2], the lowest line represents the true counterfactual outcomes, highlighting the distinction between the measured control outcomes and the actual counterfactual outcomes.
Figure 3 2 An example of Difference in Difference
Source: Shahidur R Khandker et al (2010)
Model of this study
This study is based on the parallel-trend assumption, which posits that the geographic and socio-economic conditions across provinces are uniform, leading to similar trends in household income changes over time It examines the impact of remittances by comparing households that receive remittances (the treated group) with those that do not (the control group), to determine whether remittances have affected the income levels of these households.
DD estimate of equation (1) regarding to impact of remittance on households receiving remittance and receiving no remittance is:
DD = E(Yt T – Yt-k T | T1 = 1) - E(Yt C – Yt-k C | T1 = 0) (5) Where:
+ T1 = 1 : Investigating Households in year t + T1 = 0: Investigating Households in year (t-k)
+ (Yt T – Yt-1 T | T = 1): outcome changes of receiving remittances households in year t
Receiving remittances households is called Treatment group
+ (Yt C – Yt-1 C | T = 0): outcome changes of receiving remittances households in year (t- k) Households receiving no remittance is called Control group
+ t is year considered, + (t-k) : the year before year t, and k is positive integer and (k < t)
Based on equation (2), regression model of this study as follows - [Table 3.4]
Yit = 0 + 1Year + 2Dmremiti+ 3 (Year*Dmremiti) + i X i + i (6) Where
+ Yit: outcomes changing between treatment group and control group in year t and (t-k), it will be logarit form
+ Xi : characteristic variables for extension of this module (age of head household, gender of head household, …)
A study by Khawaja A Mamun et al (2011) reveals a significant relationship between borrowing and remittances in Matlab, a rural area of Bangladesh The findings indicate that households receiving remittances experience a reduction in their borrowing levels, highlighting the positive impact of remittances on financial stability.
Research by Catalina A D and Susan Pozo (2002) highlights a significant relationship between remittances and insurance in Mexico Their study reveals that households receiving remittances exhibit a demand for insurance in two key forms: family-provided insurance, which ensures a secure place within the family, and self-insurance, achieved through the accumulation of precautionary savings.
Furthermore, Una Okonkwo Osili (2005) found out the connection of remittance and saving through remittance help reducing poverty and providing saving for receiving remittance household in country exporting labor
Based on relationship of remittance and saving, borrowing and insurance, the other outcomes extended in this study are saving, loan, insurance, the regression models were presented as follows
Income Ln(Income) = 0 + 1Year + 2Dmremiti+ 3 (Year*Dmremiti) + i Xi + i [Model 1]
Saving Ln(Saving) = 0 + 1Year + 2Dmremiti+ 3 (Year*Dmremiti) + i Xi + i [Model 2]
Assets Ln(Asset) = 0 + 1Year + 2 Dmremiti + 3 (Year* Dmremiti) + i X i + i [Model 3]
Insurance Ln(Insurance) = 0 + 1Year + 2 Dmremiti + 3 (Year* Dmremiti) + i Xi + i [Model 4]
Borrowing (Owe) Ln(Borrow) = 0 + 1Year + 2 Dmremiti + 3 (Year* Dmremiti) + i X i + i [Model 5]
+ The difference of outcome changes for control group in 2006(2008) and 2008(2010) is (1)
+ The difference of outcome changes for treatment group in 2006(2008) and 2008(2010) is (1 + 3)
+ The difference of outcome changes between control group and treatment group is (3)
We have summarized in comparison control group with treatment group in [Table 3.5]
Table 3.4 Variables of Difference in Difference Method
Variable Description Coding dmremit It is indication of household with remittance or without remittance
1= receiving-remittance household 0= receiving-no remittance household
Year Represent of dataset investigated in 2006, 2008 and 2010 0 = 2006 (2008)
1 = 2008 (2010) Remittance defined in VARHS is money or goods the households received from persons who are relatives, friends or neighbours [Annex 1]
VND lnIncome Total income of households calculated by VND % LnSav Total saving of households calculated by VND % LnAsset
Total value of asset of households converted to VND, based on instruction of investigator and estimation of households
LnInsurance Total value of insurance bought by households, calculated
LnBorrowing Total value of loans of households, calculated by VND %
Hhmem Total of members living in household shares lodging, income and expenditure for at least 6 in last 12 months Hhmem=1, 2, 3, …, 13
HeadGen Gender of Household Head 0 = Female, 1 = Male
Age of household Head In VARHS, age of Household head recoreded by year of birth, so we have to convert into age of year olds
Age in years Headstatus Marital status of household head 0 = Other, 1 = Married Livarea
Total area of a house in which total members of household lived including bedrooms, dining rooms, living rooms, study rooms
The article examines household characteristics across twelve provinces, focusing on the ethnicity of residents, specifically identifying Kinh as a significant group It highlights the prevalence of the Vietnamese language spoken by all household members and assesses internet accessibility, noting whether any member can access internet services Additionally, it discusses the primary construction material of external walls, emphasizing the use of brick, and evaluates the main cooking energy source, with a particular focus on electricity.
Toilet With or without toilet in household 0 = No, 1 = Yes
Watersource The main source of cooking/drinking water of household 0 = Other, 1 = Tap water Plotarea Total area of households’ possession Square meters
Table 3.5 DID estimation between treatment group and control group
C OMBINING PSM WITH DID METHODS
Shahidur R.K et al (2010) demonstrated the integration of the Difference-in-Differences (DID) method with propensity score matching (PSM) This process begins with PSM applied to baseline data to ensure the control group closely resembles the treatment group through two distinct steps The analysis utilizes data from the Vietnam Access to Resources Household Survey (VARHS) conducted in 2006 and 2008.
In 2010, variables were generated, including the year and dmremit08 variables The year variable was assigned a value of one for the year 2008 and zero for the year 2006 To establish a region of common support and conduct a balancing test using the 'pscore' command for both years, the dmremit08 variable was created to have identical values for 2006 and 2008, retaining the zero year for matching treated and control pairs When the balancing property of propensity score matching was achieved, it indicated that households with the same propensity score shared a consistent distribution of all covariates across all blocks.
Subsequent to, the matched households in year 2006 will be merged with data set of year 2006-2008 to keep only the matched households in sample data of 2006-2008
The application of the Difference-in-Differences (DID) method to a matched sample, along with sequential regression analysis, effectively addresses observable heterogeneity in panel data samples This combined approach yields superior results compared to using Propensity Score Matching (PSM) or DID methods independently A similar methodology is applied to the dataset from 2008 to 2010.
Using the Propensity Score Matching (PSM) method, we calculate the propensity score to ensure the balancing property between treatment and matched control groups with similar characteristics This matched sample enhances the results of the Difference-in-Differences (DD) method Additionally, we employed fixed-effects regression and ordinary least squares (OLS) to measure the double differences effectively.
D ATA
This study utilizes the Vietnam Access to Resources Household Survey (VARHS) data from 2006, 2008, and 2010 to analyze the effects of remittances on households receiving remittances compared to those that do not, across 12 rural provinces in Vietnam.
The study utilizes the Propensity Score Matching (PSM) approach and the Difference in Difference (DD) method to analyze the characteristics of households By employing the PSM method, the research identifies matching characteristics between households that receive remittances and those that do not, specifically in the year 2006.
This study analyzes the impact of remittances on income, savings, borrowing, and assets from 2006 to 2010, utilizing a double differences method By comparing changes in outcomes between households receiving remittances and those not receiving them during the periods of 2006-2008 and 2008-2010, we employ a combination of Propensity Score Matching (PSM) and Difference-in-Differences (DID) estimations The results highlight the effects of remittances on financial variables across both periods, providing insights into the economic implications for receiving households.
General information of VARHS Data
The VARHS data sets, conducted by the Institute of Labor and Social Sciences under the Ministry of Labor, Invalids, and Social Affairs, utilize PSM and DD methods, ensuring that both participants and non-participants originate from the same source We selected VARHS data from the years 2006, 2008, and 2010, which were collected across 12 rural provinces in Vietnam, including Ha Tay (Ha Noi), Lao Cai, Phu Tho, Lai Chau, Dien Bien, Nghe An, Quang Nam, Khanh Hoa, Dak Lak, Dak Nong, Lam Dong, and Long An, with each province being coded individually.
The provinces of Ha Tay, Lao Cai, Phu Tho, Lai Chau, Dien Bien, Nghe An, Quang Nam, Khanh Hoa, Dak Lak, Dak Nong, Lam Dong, and Long An are represented by the codes 105, 205, 217, 301, 302, 403, 503, 511, 605, 606, 607, and 801, which are utilized for calculating weighting in Propensity Score Matching (PSM) and Difference-in-Differences (DID) analyses.
According to the Ministry of Labor, Invalids and Social Affairs, the average annual outflow of Vietnamese migrant workers from 2003 to 2008 was 77,500 In this study, the twelve provinces contributed 16,361 workers, while the remaining 51 provinces accounted for 61,139 workers This results in an average of 1,363 workers per province in the twelve provinces, compared to 1,199 workers per province in the other regions Thus, these twelve provinces serve as a representative sample for examining migrant labor trends across Vietnam.
The 2008 survey was conducted to build upon the findings from the 2006 household survey, expanding its reach to include additional households Investigators were tasked with updating information for households that had relocated since 2006, and this process was similarly applied in the 2010 survey As a result, the data collected from the VARHS surveys in 2006, 2008, and 2010 are largely consistent and compatible.
The investigation was implemented as follows
The interviewer will be given a list of households to be interviewed All the households have previously been interviewed by GSO and/or ILSSA in previous year
If a household has moved or split up, or the household head has died, the interviewer should follow these guidelines:
In cases where the household head and their spouse have divorced, it is essential to conduct the interview with the individuals currently residing at the household's previous location.
- If the household head has died, do the interview with those who live at the location where the household was living when they were previously interviewed
In cases where a household has separated, such as when a son marries and establishes a new household, interviews should be conducted with the individuals residing in the original family home.
- If the household has moved within the commune, they should be found and interviewed
If all members of a household have relocated outside the commune, it is necessary to conduct the "absent households" questionnaire This questionnaire should be administered to former neighbors or individuals familiar with the household It is permissible to gather information from multiple sources to accurately complete the absent household questionnaire.
The study analyzed data sets from the Vietnam Access to Resources Household Survey (VARHS) with observations from 2,324 households in 2006, 3,269 households in 2008, and 3,208 households in 2010 After merging the data from these years, a total of 1,059 households that were present in all three surveys were retained for analysis.
Information of VARHS data sets collected by questionnaire including ten sections as follows
Section 1 Cover page, Household roster, general characteristics and identification of household members
Section 2 Land use, General information about plots, Plots owned and operated, land rented in or borrowed, land law Section 3 Agricultural land and crop agriculture Section 4 Livestock, forestry, aquaculture, agricultural services and access to markets Section 5 Occupation, time use and other sources of income
Section 6 Training and supports in agricultural production Section 7 Food expenditures, other expenses, saving and household durable goods Section 8 Credit
Section 9 Shocks and risk coping Section 10 Social capital and networks
This study examines the effects of remittances on households receiving them, focusing on key variables such as remittances, assets, borrowing, savings, insurance, total income, household size, the age and gender of the household head, and total plot area Data for this research was gathered from various sections, specifically Sections 1, 2, 5, 7, 8, 9, and 10 in Appendix B.
EMPIRICAL ANALYSIS
4 1 Descriptive analysis of the sample
In the earlier mention, data sets of VARHS applied in this study will be VARHS
The VARHS studies conducted in 2006, 2008, and 2010 included 2,324, 3,269, and 3,208 households, respectively After merging and selecting variables for the Propensity Score Matching (PSM) and Difference-in-Differences (DID) models, a total of 3,177 households were retained, resulting in an average of 1,059 households for each year.
The VARHS data reveals significant changes in household remittance status over the years In 2006, there were 745 households receiving remittances and 314 not receiving them By 2008, the number of remittance-receiving households decreased to 351, while non-remittance households increased to 708, totaling 1,059 households In 2010, the trend shifted again, with 624 households receiving remittances and 435 without, maintaining the total of 1,059 households This information is summarized in Table 4.1, highlighting the dynamics of remittance distribution among households across the surveyed years.
Table 4 1 Number of HHs receiving remittance and receiving no remittance
No VARHS 2006 VARHS 2008 VARHS 2010 Total of HHs
(**) No: No remittances-receiving household
Number of households with remittance reduced from 745 in VARHS 2006 to 351 in VARHS 2008, and increased to 624 of households in VARHS 2010, it was accounted for 70.34% in 2006, 33.14% in 2008, and 58.9% in 2010 respectively
In 2006, average remittance received by each household is 3.019 million VND, and
In Vietnam, remittance patterns reveal significant trends over the years In 2008, the average remittance per household was 7.5 million VND, with half of the households receiving less than 1.5 million VND By 2010, the average increased to 7.9 million VND, and the threshold for the lower 50 percent rose to under 2 million VND Notably, 50 percent of households receiving remittances reported amounts below 500,000 VND, highlighting the varying levels of financial support among families.
Therefore, these shown that amount of remittance in 2008 increased in comparison with amount of remittance in 2006 and 2010
Besides average remittance received in each households in years of 2006, 2008 and
In 2010, data summarized in Table 4.2 revealed that households receiving remittances had higher income, savings, and assets compared to those not receiving remittances, with differences ranging from 24-25 percent However, in 2008, the financial advantages for remittance-receiving households were significantly lower, showing only a 4-11 percent increase over non-receiving households.
Table 4 2 Summary of Households with and without remittance
Year of 2006 Year of 2008 Year of 2010
Households without remittances have varying financial metrics, with total incomes ranging from 29,000,000 VND to 109,000,000 VND Savings also differ significantly, with amounts between 12,000,000 VND and 34,800,000 VND Insurance contributions are relatively low, with values from 305,000 VND to 2,400,000 VND Debt levels are notable, ranging from 8,050,000 VND to 19,500,000 VND Lastly, asset ownership varies, with figures between 15,100,000 VND and 37,800,000 VND, highlighting the financial diversity among these households.
Remittances 3,019,000 VND 0 VND 7,500,000 VND 0 VND 7,900,000 VND 0 VND
As share of Total household income
The share of remittances in total household income varied between 8.38% and 13.63% during the years 2006, 2008, and 2010 This fluctuation is illustrated in [Table 4.3], which reflects the anticipated outcomes of the PSM method for these three years.
For DID method, the expected signs of variables are in [Table 4.4], when applying regression models (from [Model 1] to [Model 5]) for year pairs of 2006-2008 and 2008-2010
Table 4.3 Expected sign in PSM Model
Variables Description Unit Expected Sign
This is dummy variable, it indicates Households (HHs) receiving remittance or not receiving remittance dmremit = 1 or dmremit = 0 Lnincome
Amount of Income in each HH was investigated continuously in three years of 2006, 2008 and
Amount of Saving in each HH was investigated continuously in three years of 2006, 2008 and
Amount of asset in each HH was investigated continuously in three years of 2006, 2008 and
Amount of Insurance in each HH was investigated countinuously in three years of 2006,
Amount of Borrowing in each HH was investigated continuously in three years of 2006,
(*) Expected sign (+) for Lnborrowing variable explain that receiving-remittance households received the support loans of banking for expanding household business; or
Expected sign (-) for Lnborrowing variable explain that received remittance paid for owes of households
Table 4.4 Expected sign of variables in DD model
Variable Description Symbol Expected Signs
Year This is dummy variable, it indicates year of
Year =0 if nam 06 Year =1 if nam 08 (+) dmremit
This is dummy variable, it indicates HHs receiving remittances or receiving no remittances dmremit = 1 dmremit = 0 (+)
Year*dremit Difference of remittance was received between receiving-remittance households and receiving- no remittance households
Year This is dummy variable, it indicates year of
Year =0 if nam 08 Year =1 if nam 10 (+) dmremit
This is dummy variable, it indicates HHs receiving remittances or receiving no remittances dmremit=0 dmremit=1 (+)
Year*dmremit Difference of remittance was received between receiving-remittance households and receiving- no remittance households
4 2.1 Propensity score matching (PSM) model
The algorithm developed by Sascha O Becker and Andrea Ichino (2002) utilizes Propensity Score Matching (PSM) to adjust the estimation of treatment effects while controlling for confounding factors This approach reduces bias by comparing outcomes between treated and control subjects The output from the “pscore” command indicates that the average propensity scores of treated and control groups differ across all five blocks The algorithm continues to refine these blocks until the average propensity scores are equivalent In this study, the balancing property is tested for each covariate, revealing a common support region of [.066641, 99769314], ensuring that the mean propensity scores for treated and control groups are similar within each block The common support condition, enforced by the “comsup” option, results in the exclusion of five control observations outside this range, leading to a total of 1,054 observations instead of 1,059, as detailed in Appendix C.
This study analyzes the impact of remittances on wealth accumulation in households by comparing those receiving remittances with those that do not for the years 2006, 2008, and 2010 Utilizing various matching techniques outlined in Chapter III, we estimate the effects of remittances, with the findings summarized in Table 4.5 and detailed results of the Propensity Score Matching (PSM) method provided in Appendix B.
In the 2006 Income regression analysis, the null hypothesis was rejected, as evidenced by the t-statistic values from various matching techniques: Nearest-neighbor matching yielded a t-statistic of 2.005, Stratification matching produced 2.036, Radius matching showed 2.056, and Kernel matching reached 2.210 All these t-values exceed the critical value of 1.645, confirming their statistical significance This indicates that remittances positively influenced the total income of households receiving them, with an increase in total income ranging from 14% to 26%, and the impact is significant at the ten percent level.
The results were verified using direct Nearest-neighbor matching rather than estimating the propensity score equation, confirming consistency with four previous techniques The Average Treatment Effect on the Treated (ATT) was found to be 23.8%, with a significant p-value at the five percent level.
In the 2006 insurance model, the null hypothesis was rejected, indicating significant findings The matching techniques revealed t-statistic values for Average Treatment Effects on the Treated (ATT) that exceeded the critical t-value of 1.645, with values of 1.692, 2.505, 1.709, and 2.228 These results were robust at the 5% significance level, suggesting that remittances significantly influence the likelihood of households receiving remittances to purchase insurance.
In the 2006 Savings model, the null hypothesis is accepted, indicating no significant relationship between the dependent and independent variables This is evidenced by the t-statistic values obtained through various matching techniques: Nearest-neighbor matching shows a t-statistic of 0.634, Stratification matching yields 1.000, Radius matching presents 0.949, and Kernel matching results in 0.865 All these values are lower than the t-critical value of the student distribution, reinforcing the conclusion of no connection.
Consequently, there is not the impact of remittance on saving of receiving-remittance households These findings are unchanged when checking robustness
In the regression analysis conducted for the year 2006, which examined the variables of Asset and Borrowing, the null hypothesis was accepted This indicates that remittances do not have a significant impact on the assets, insurance, and borrowing behaviors of households receiving remittances.
The results indicate that the t-statistic values of the ATT do not meet the t-critical value of the Student's t-distribution at a ten percent significance level Furthermore, the robustness testing confirms these findings remain consistent.
The these findings were verified by omitting dummy variables such as Tinh_1, Tinh_2, Tinh_3, Tinh_4, Tinh_5, Tinh_6, Tinh_7, Tinh_8, Tinh_9, Tinh_10, Tinh_11
The identified region of common support is [.09676315, 90167309], with seven control observations lacking block identifiers outside this range, resulting in a total of 1,052 observations instead of 1,059 The final count of blocks is six T-statistics for Average Treatment Effect on the Treated (ATT) values, estimated through various matching methods including Nearest-neighbor, Stratification, Radius, and Kernel matching, indicate that remittances do not significantly affect the accumulated wealth of households receiving them.
CONCLUSIONS AND RECOMMENDATIONS
This thesis investigates the relationship between remittances and various financial factors at the household level, including income, saving, borrowing, insurance, and assets The study's findings indicate that remittances positively impacted the income of receiving households in 2006, but showed no effects in 2008 and 2010 Additionally, there was no significant relationship between remittances and savings, borrowing, insurance, or asset accumulation among remittance-receiving households in twelve rural provinces of Vietnam This raises questions about the consistency of these results with previous research, as the empirical literature presents mixed conclusions While some studies suggest that remittances enhance income, savings, and asset accumulation, others indicate a lack of connection to these financial aspects Furthermore, remittances may alter household behaviors, leading to decreased motivation for income improvement, as recipients may feel less inclined to seek employment due to the regular financial support they receive.
This study aligns with previous research in Vietnam, as it utilizes distinct data sets and is conducted by different organizations employing varied survey methods and timelines Additionally, the remittances received by migrant households are primarily used for consumption, reflecting a pessimistic viewpoint Consequently, remittances can influence the behaviors of recipient households, which may explain the lack of correlation between remittances and income, savings, borrowing, insurance, and assets.
5 2 Limitations and suggestions for further researches
This study relies on secondary data from the Institute of Labor and Social Sciences, collected in 2006, 2008, and 2010, which limits the analysis of remittance impacts on wealth accumulation among receiving households The surveys lacked expenditure data for rural households across twelve provinces, highlighting the need for a more comprehensive survey that includes a broader range of elements to gain a clearer understanding of remittance-related issues.
Adriana C., Barry R (2007) Do Migrant Remittances Affects the Consumption Patterns of
Albanian Households? University of Sussex South-Eastern Europe Journal of Economics 1 (2007), p.25-54
Amuedo-Dorantes et al (2009) New Evidence on the Role of Remittances on Health Care
Expenditures by Mexican Households IZA DP No 4617 (617543798.pdf)
Aree J and Aphichat C (2009) The Impact of Circular Migration and Remittance on Relative
Household Wealth in Kanchanaburi province, ThaiLan Asia and Pacific Migration Journal, Vol.18, No.2, 2009, p.283-301
Becker and Ichino (2002) Estimation of Average Treatment Effects Based on Propensity
Score The Stata Journal 0026 (2002) Number 4, pp.358-377
Bichaka Fayissa et al (2008) The Impact of Remittances on Economic Growth and
Development in Africa Department of Economics and Finance Working Paper Series • February 2008
Bichaka Fayissa et al (2010) Can Remittances Spur Economic Growth and Development?
Evidence from Latin American Countries (LACs) Department of Economics and Finance Working Paper Series • March 2010
Carolyn Heinrich et al (2010) A Primer for Applying Propensity-Score Matching Inter-
American Development Bank Impact-Evaluation Guidelines Technical Notes No.IDB-TN-161
Catalina Amuedo-Dorantes and Susan Pozo (2006) Remittances as Insurance: Evidence from
Mexican Immigrants Journal of Population Economics, Vol.19, No.2 (Jun., 2006), pp.227-254 Published by Springer
Catalina Amuedo-Dorantes and Susan Pozo (2011) Remittances and Income Smoothing
Center for Research and Analysis of Migration (CReAM) Discussion Paper Series, CDP No.07/11
Catia B., janis U (2014) Do Migrant Send remittances as a Way of Self-Insurance? Evidence from a Representative Immigrant Survey NORFACE MIGRATION Discussion Paper No.2014-05
Chandar Henry et al (2009) Motives for Sending Remittances to Jamaica: An Application of the BPM6 definition of Remittances Bank of Jamaica
Cuong Nguyen Viet (2008) Impacts of International and Internal Remittances on Household
Welfare: Evidence from Viet Nam Munich Personal RePEc Archive MPRA Paper
Douglas S Massey et al (1993) Theories of International Migration: A View and Appraisal
Population and Development Review, Vol 19, No.3 (Sep., 1993), pp 431-466
Edward Funkhouser (1995) Remittances from International Migration: A Comparison of El
Salvador and Nicaragua The review of Economics and Statistics, Vol.77, No.1, pp.137-146
Elke Holst et al (2010) Gender, Transnational Networks and Remittances: Evidence from
Germany Discussion Paper 1005 DIW Berlin German Institue for Economic Research
George J Borjas (1990) Economic Theory and International Migration The center for
Migration Studies of New York, Inc International migration review, Vol 23, No.3, Special Silver Anniversary Issue: International Migration an Assessment for the 90’s, pp.457-485
Hein de Haas (2007) Remittances, Migration and Social Development: A Conceptual Review of the Literature Social Policy and Development Programme Paper Number 34 October 2007
Hein de Haas (2008) Migration and Development: A Theoretical Perspective International
Migration Institute, University of Oxford Working Paper 9
Hillel Rapoport and Frédéric Docquier (2005) The Economics of Migrants’ Remittances The
Institute for the Study of Labor (IZA) in Bonn IZA Dicussion Paper No.1531
International Monetary Fund (2009) International Transactions in Remittances – Guide for
Irene Brambilla et al (2011) Adjusting to trade policy: Evidence from U.S Antidumping
Duties on Vietnamese Catfish The review of Economics and Statistics 94(1): 304-
During the global financial crisis, migration and remittances played a crucial role in economic stability, as highlighted by Cohen et al (2012) in their World Bank study This research emphasizes the resilience of remittance flows amidst financial turmoil Additionally, Reinke (2007) discusses the challenges and potential enhancements in the balance of payments framework regarding remittances, as outlined in the International Monetary Fund's statistics department report Together, these studies underscore the significance of remittances in supporting economies during crises and the need for improved frameworks to better capture their impact.
Jessica Hagen-Zanker and Melissa Siegel (2007) The Determinants of Remittances: A
Review of The Literature Maastricht University Working Paper MGSoG/2007/WP003
John Foster (1995) The Relationship between Remittances and Saving in Small Pacific Island
States: Some Econometric Evidence Asian and Pacific Migration Journal ,Vol 4, No
Juthathip Jongwanich (2007) Workers’ Remittances, Economic Growth and Poverty in
Developing Asia and the Pacific Countries UNESCAP Working Paper WP/07/01
Khawaja A.Mamun and Hiranya K Nath (2010) Workers’ Migration and Remittances in
Bangladesh Journal of Business Strategies, Vol 27, No.1, pp.29-52
Khawaja A Mamun et al (2011) Borrowing, Migration, and Remittances in Bangladesh:
Lisa Andersson (2014) Migration, Remittances and Household Welfare in Ethiopia UNU-
MERIT Working Papers ISSN 1871-9872, Maastricht Economic and Social Research Institute on Innovation and Technology, UNU-MERIT
Marco Caliendo and Sabine Kopeinig (2005) Some Practical Guidance For The
Implementation of Propensity Score Matching Institute for the Study of Labor IZA Discussion Paper No.1588
Michael J Piore (1979) Birds of Passge: Migrant labor and Industrial Societies Journal of
Social History, Vol.14, No.4, pp 775-778
Michael P Todaro (1969) A Model of Labor Migration and Urban Unemployment in Less
Developed Countries The American Economic Review, Vol.59, No.1 (1969), 138-
Mmaduabuchukwu Mkpado (2012) Global Financial Crisis and Agrarian Households’
Income, Remittance and prices in Rural Nigeria Amid policy Responses Federal University Oye-Ekiti, Ekiti State, Nigeria
Nguyen Minh Thao (2008) Migration, Remittances, and Economic Development: Case of
Viet Nam Central Institute for Economic Management and Report No 16731190 Oded Stark and David E Bloom (1985) The new economics of labour migration American
Oded Stark and Robert E.B Lucas (1988) Migration, Remittances and the Family Economic
Development and Culture Change, Vol.36, No.3, pp.465-481
Paul R Rosenbaum and Donal B Rubin (1983) The Central Role of the Propensity Score in
Observational Studies for causal Effects Biometrika, Vol.70, No.1, pp.41-55
Rafael A P.C (2004) Remittances as a Strategy to Cope With Systemic Risk: Panel Results from Rural Households in El Salvador The Degree Doctor of Philosophy of The Ohio State University
Rahila Munir et al (2011) Effect of Workers’ Remittances on Private Saving Behavior in
Pakistan Asian Economic and Financial Review, 1(3), pp.95-103
Ralph Chami et al (2008) Macroeconomic Consequences of Remittances International
Richard H Adams, Jr (1991) The Effect of International Remittances on Poverty, Inequality, and Development in Rural Egypt International Food Policy Research Institute
Richard H Adams Jr and John Page (2005) Do International and Remittances Reduce
Poverty in Developing Countries? World Development Vol.33, No.10, pp.1645-1669
Richard H.Adams, Js et al (2008) Remittances, Consumption and Investment in Ghana The
World Bank Development Economics Department & Africa Region Policy Research Working Paper 4515
Robert E.B Lucas and Oded Stark (1985) Motivations to Remit: Evidence from Botswana
Journal of Political Economy, Vol.93, No.5, pp.901-918
Roel Peter Wilhelmina Jennissen (2004) Macro-economic Determinats of International
Migration in Europe Dutch University Press, 2004 - Business & Economics
Roel Jennissen (2007) Causality Chains in the International Migration Systems Approach
Popul Res Policy Rev (2007) 26:411-436 Springer Science + Business Media B.V.2007
Samual Munzele maimbo and Dilip Ratha (2005) Remittances Development Impact and
Future Prospects The International Bank for Reconstruction and development/ The World Bank
Shahidur R Khandker et al (2010) Handbook on Evaluation – Quantitative Methods and
Simon Feeny et al (2012) Remittances and Economic Growth: Larger Impacts in Smaller
Countries? Alfred Deakin Research Institute, Deakin University, Geelong, Australia
Sture Oberg (1995) Theory on International Migration: An Over View International Institue for Applied Systems Analysis WP-95-47
The World Bank (2011) Migration and Remittances Factbook 2011 – Second Edition 2011
The International Bank for Reconstruction and Development / The World Bank
Una Okonkwo Osili (2006) Remittances and saving from international migration: Theory and evidence using a matched sample Department of Economics, Indiana University–
Purdue University at IN, United States Received 25 June 2003; accepted 10 June 2006
Wade Donald Pfau & Long Thanh Giang (2010) The Growing Role of International
Remittances in the Vietnamese Economy: Evidence from the Vietnam (Household) Living Standard Surveys Munich Personal RePEc Archive MPRA Paper No 24945, posted 13
Wade Donald Pfau (2008) Detreminants and Impacts of International Remittances on
Household Welfare in Viet Nam MPRA paper No 19038, posted 8
Yéro Baldé (2010) The Impact of Remittances and Foreign Aid on Savings/Investment in
Sub-Saharan Africa (SSA) Laboratoire d’Analyse et de Prospective Economique (LAPE), Université de Limoges, France
Yoko Niimi and Caglar Ozden (2006) Migration and Remittances: Causes and Linkages
Yu Zhu et al (2009) Do Migrants Really Save More? Understanding The Impact of
Remittances on Saving in Rural China University of Kent, Department of Economics Discussion Papers KDPE 0923
Yu Zhu et al (2009) Where Did All Remittances Go? Understanding The Impact of
Remittances on Consumtion Patterns in Rural China Department of Economics Discussion Paper, University of Kent, KDPE0907
GSO: http://www.gso.gov.vn/default_en.aspx?tabidG1&idmid=3&ItemID368 Viet Nam report: http://www.vietnam-report.com/vietnam-fdi/
World Bank: http://data.worldbank.org/indicator
APPENIX A REMITTANCES DEFINED IN BALANCE OF PAYMENT OF INTERNATIONAL MONETARY FUND
In IMF Balance of Payments Manual, the Fifth Edition (BPM5) in 1993, the concept of remittance is defined as the sum of three items in Chapter 15 Current Transfers – Item Classification
Workers' remittances refer to the financial transfers made by migrants who are employed in new economies and regarded as residents there, typically staying for a year or more These remittances often involve transactions between related individuals Conversely, those who work and reside in new economies for less than a year are classified as non-residents, and their transactions primarily fall under the category of compensation for employees.
Relating workers’ remittance, there are two supplementary items in BPM5 including
“compensation of employee” and “migrants’ transfers”
“Compensation of employees” defined in Chapter 14 Income in BPM5 from paragraph 269 to paragraph 272 as follows:
Compensation of employees encompasses wages, salaries, and various benefits earned by individuals working in foreign economies, paid by residents of those economies This includes employer contributions to social security, private insurance, or pension funds aimed at securing employee benefits The term "employees" also covers seasonal and short-term workers, as well as border workers who maintain economic ties to their home economies.
Embassies and consulates are regarded as extraterritorial entities, meaning that the compensation for local staff is categorized as payments made by nonresident entities to resident entities within the host country.
Compensation paid to employees by international organizations is considered as payments to residents from nonresident entities if the employees reside in the host economy Employees from other economies who work for one year or more are classified as residents of the host economy, and their compensation is treated similarly However, if these employees are employed for less than one year, their payments do not count as payments to residents For details on the treatment of technical assistance personnel on assignments of one year or more, refer to paragraph 69.
Personal expenditures by nonresident seasonal and border workers, as well as those involved in installation projects, are categorized under travel in the economies where they work Additionally, tax payments and pension fund contributions in these economies are classified as current transfer payments The Manual advises for gross recording of compensation and expenditures, although practical considerations may lead to net income estimates in certain cases.
Differentiating between individuals whose earnings are categorized as employee compensation, despite not residing in the economies where they work, and migrants who have established residency due to an expected stay of a year or longer, can be challenging in practice.
The actions of both residents and migrants do not influence the overall economic transactions of a country with the global market.
To ensure accuracy in balance of payments statements, it is essential to differentiate between nonresident workers and migrants Failing to make this distinction may lead to inconsistencies in the statistical treatment of individuals across different economies, ultimately affecting the comparability of the data.
“Migrants’ transfers” was defined in Chapter 17 - Capital Transfers and Acquisition or Disposal of Nonproduced, Nonfinancial assets - in BPM5 from paragraph 352 to paragraph
352 In the strictest sense, these transfers are not transactions between two parties but contra-entries to flows of goods and changes in financial items that arise from the migration
(change of residence for at least a year) of individuals from one economy to another The transfers to be recorded are thus equal to the net worth of the migrants
All household and personal belongings of migrants, along with any movable capital goods transferred from the old economy to the new, are categorized as general merchandise These goods flows and their corresponding offsets should be accounted for accordingly.
During migration, it is essential to document the flows accurately If these flows are not based on trade returns, no timing adjustments to the figures are recommended Instead, offsets should be recorded in the same period as the corresponding exports and imports.
When migrants retain ownership of enterprises, including land, structures, and movable capital goods, after their departure, these assets become foreign claims linked to the migrants and the economies they have moved to Additionally, any claims or liabilities that migrants have with residents of their former economies or those in a third economy are also classified as foreign claims or liabilities in their new economies These financial relationships and offsets are documented at the time of migration.
355 In practice, it is recognized that few countries are in a position to record all assets