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How does skills mismatch affect remittances? a study of Filipino migrant workers.


The economic literature is rich with evidence showing high prevalence of skills mismatch among migrants. A skills mismatch occurs when a job holder has a higher level of skill or qualification than what the job requires. Mismatch among immigrants has been shown to lower wages, result in significant labor market scarring, and affect one's economic integration in the host country overall (see, e.g., Borjas 2003: Chiswick and Miller 2010: Mattoo, Neagu, and O/den 2008). As well, it can bring increased risk of personal frustration, lower job satisfaction, and a diminished sense of self-worth compared to better-matched migrant peers (Cabral-Vieira 2005: Chiswick, Lee, and Miller 2008). The welfare losses associated with skills mismatch are deemed large for individuals who have invested significant amounts of resources in acquiring particular skills and/or educational qualifications in their home economies and who then migrate to a different country expecting to be able to reap the benefits of that investment postmigration. Prevalent skill mismatch can also mean significant losses in terms of the underutilized skills that could have been used for more productive activities in the country of origin. Recent studies on skills mismatch include Green. Kler, and Leeves (2007) for Australia. Miller (2008) for the United Kingdom, Yuen (2010) for Canada, and Chiswick and Miller (2009a, 2009b) for the United States.

Given the increased incidence of skills mismatch among international migrants in recent times, as well as the growing dependence of many economies on international remittances, it is useful to examine the impact that skills mismatch has on remittance behavior. If a significant number of skilled migrants end up in overseas jobs considered to be below their level of skills or qualifications. they as a group would tend to earn less and are therefore able to remit less than they otherwise could. From the point of view of the national economy, this could indicate significant waste of resources in terms of the underutilized skills that could have been used for more productive activities in the country of origin. For the individual migrant, this can also represent lower returns to investments in education in addition to the social and psychological losses (loss of self-worth/self-confidence. lower job satisfaction, etc.) that they may also experience as a result. And as skills mismatch invariably implies an income loss for a migrant compared to a matched situation, our results can provide potentially useful insights into how migrants and transnational families cope in the face of such adversity. Reduced remittances can imply that the burden of a mismatch is shared between all members of such transnational households; if however, the level of remittances is found to be unaffected by skills mismatch. this can indicate greater personal sacrifices on the part of the migrant so as to mitigate the negative impact of the mismatch situation on family members back home.

The specific research questions we address in the article are therefore as follows: How does a skills mismatch outcome affect a migrant's remittance behavior? Does it cause migrants to send proportionally less remittances than they would have if they were in better-matched jobs? Or, does it not affect remittance behavior at all? Other factors affecting this relationship will also be investigated. In particular, we are interested in the extent to which the relationship varies by the gender of the migrant worker as well as other personal characteristics such as age, education, and marital status.

As far as we are aware, these interactions have not been systematically investigated before, mainly due to the absence of suitable data that can support the required analysis. Recent developments on the accessibility and form of Philippine data 011 migrants now provide a good opportunity to address these particular questions. The database we use was formed by linking previously unrelated household surveys using unique identifiers and allows us to investigate the skills mismatch-remittance relationship using a single-country source migrant group with observed outcomes in multiple-country destinations--an analytical perspective that is still quite uncommon in the migration literature. This expanded database is ideal for our study as it enables us to better standardize initial endowments in skills and educational attainments, and obtain more accurate measures of differences in behavioral responses in labor, remittances, and other postmigration outcomes.

The outline for the article is as follows. Section II provides an overview of the relevant literature and sets out the conceptual framework of the study. Section III describes the datasets, the variables, and the statistical model used. This section also discusses the estimation issues we faced and the empirical approach we adopted to address them. Section IV presents the empirical findings and the main analytical results of the study. Section V concludes.


In this article, the job situation of a migrant worker is considered a skills mismatch if his or her educational background indicates possession of work capabilities that are over and above the requirements of his or her current job. Such individuals are seen to be capable of handling more complex tasks than what is required to be performed and their educational qualifications are underused in their current employment. (1)

There are various explanations that have been put forth as to why migrants might have a higher propensity to end up in a skills mismatch situation than nonmigrants. The most promoted explanation in the migration literature is based on human capital theory and suggests that those apparently overqualificd are, in fact, not. Accordingly, it is misleading to assume that all individuals with the same qualifications are homogenous: instead, it is more appropriate to acknowledge that there will be a range of skills and abilities embodied in an individual beyond formal qualification, such as skills acquired 011 the job. (2) language skills. (3) local experience, and job tenure. (4) that may or may not be observed by the researcher. It is therefore implied that those classed as overqualified may simply be those workers from a particular qualification category w ho have low values of these other unobserved aspects of human capital. In the context of immigrants, skills mismatch occurs because the local labor market provides little or 110 recognition of both qualification and work experience acquired overseas. As Borjas (2003) put it, it is highly unlikely that similarly educated workers in the labor market with different levels of work experience are perfect substitutes. For an overview of migrants' labor market mismatch, see Piracha and Vadean (2013).

An alternative explanation for skills mismatch emanates from the job-competition model of Thurow (1979) and the assignment theory of job allocation due to Sattinger (1993). The idea is that there is a skill level attached to a job regardless of the attributes of the person who fills it and that individuals are then assigned to these jobs according to their characteristics. High-skilled individuals are thus likely to be matched with job vacancies requiring a higher level of skills. Skills mismatches can however occur because the matching process is not perfect. Such may be the case if a migrant fails to find the most appropriate job for his or her skills/education because of systematic barriers he/she faces in the labor market. For a migrant, these can be such things as limited information (e.g., no/small/limited network), regulatory limits on credential recognition or personal financial constraints faced when maximizing joint utility functions. Such constraints for example can result in the secondary income earner taking on a mismatched job to be able to attend common family chores such as after-school child care, or supporting the primary earner in his/her job search or participation in further education. (5)

Another explanation for an imperfect skill-to-job matching process is the limited supply of skilled jobs in the labor market. In this case, some skilled individuals including migrants who lose out in the race for appropriate jobs will end up taking on occupations for which they are overqualified. However, strong evidence against this "too many skilled workers" theory is provided by the wage inequality literature. If wages respond to market forces, then an oversupply of skilled workers would be expected to lower the wage they receive, relative to those of low-skilled workers. The economic literature however does not bear this out. Psacharopoulos and Patrinos (2004), for instance, show sustained increasing returns to private education in a vast range of countries; this point is also reinforced by studies showing persistent skill shortages in popular immigrant destination countries like Australia (Green. Kler, and Leeves 2007), Canada (Yuen 2010), the United Kingdom (Green and Macintosh 2007), and the United States (Borjas 2003: Chiswick and Miller 2009a, 2009b. 2010).

The decision to remit is the key variable that we wish to understand in this study, in particular, how this is affected by skill mismatch. On remittances, the literature provides a wealth of easily identifiable motives for migrants to remit, each of which classifies broadly under either the altruism model or the exchange and insurance model. (6) Houle and Schellenberg (2008) identify some practical drivers and find that a migrant's capacity to remit is a function of the personal characteristics of the remitter him/herself as well as of particular demographics of the origin household. For the remitter migrant, gender, age, and marital status have been found important, while for the family remaining in the home country, age, gender, and marital status of the household head, as well as the presence of children in the household and the position of the remitter in the family can also be a significant influences. More recently, Dustmann and Mestres (2010) show that the likelihood of remitting and the amounts remitted are both significantly associated with intentions to return to the home country. This complements well the earlier finding of Lucas and Stark (1985) which showed that the flow of remittances tends to decline with the length of the migrants' stay.

The connection between skills mismatch among migrants and their remittance behavior has not been explored previously but some research on remittances and skilled migration can provide some starting points. The main question here is, "Do skilled migrants remit more?" Lucas and Stark (1985), Osili (2007) and Bollard et al. (201 I) provide empirical evidence of a strong positive relationship between the amount remitted and the educational attainment and the skill level of the emigrant. On the other hand, Faini (2007) used national level figures to show that skilled migrants are no more likely to remit more to their home economies than their low-skilled counterparts and concludes that skilled migration is unlikely to boost the flow of remittances to the source country. This work is also consistent with earlier works of Funkhouser (1995) and Rodriguez and Horton (2000) which show that the education level of migrants has no impact on the amount of remittances for both Mexico and the Philippines. Funkhouser (1995) in particular finds that higher levels of educational attainment among immigrants from Central America are negatively correlated with the incidence of remitting, but among migrants who do remit, those with higher levels of education send more.

Several other variables have been explored for their role in remittances, and the significance of this role under conditions of a mismatch. These include one's legal status in the host country. (7) as well as strict immigration policies of countries involved, (8) where these variables represent increased risks due to uncertainty. In the economic literature though, what appears most relevant appears to be the remitter's income. Lucas and Stark (1985) and Amuedo-Dorantes and Pozo (2006) find a positive relationship between income and amounts remitted, while Knowles and Anker (1981) find a negative effect of migrant's income on the probability of remitting. Aisa, Andaluz, and Larramona (2011) on the other hand, find no obvious relationship between the migrant's income and the annual remittances sent bank home, but suggests (hat the bargaining power of the migrant within the (sending) family can be used to explain variations in remittances sent.

We acknowledge well-established differences in labor preferences between men and women, and consider how the impact of skills mismatch on remittance behavior may differ along gender lines. In their review paper, Azmat and Petrongolo (2014) point out that gender inequality continue to exist in the labor market because of differences in preferences for risk and competition between men and women, as well as different attitudes toward negotiation. (9) There is also overwhelming evidence in the economic literature showing that females are more generous and more altruistic compared to males (10) and that female migrants tend to remit more compared to male migrants." Given all that, the approach we take in this study is to investigate this issue allowing for the possibility that the impact of skills mismatch on remittances can differ by the gender of the remitter.


A. Household Datasets

Data used in this study come from two linked household surveys--the Survey of Overseas Filipinos (SOF) and the Family Income and Expenditure Survey (FIES)--obtained from the National Statistics Office of the Philippines. The SOF is a nationwide survey that seeks to gather information on Filipino citizens who left for another country during the last 5 years. The survey collects data on a range of migrant characteristics, including demographic characteristics, work experience, visa category, family relationships, and the amount and mode of remittances repatriated in the last 6 months. The FIES, on the other hand, is a nationwide survey of households conducted every 3 years that collects a comprehensive range of information on the demographic structure, income, and expenditures of households in the Philippines. An attractive feature of this linked FIES-SOF surveys is that it allows us to simultaneously study the economic characteristics of the migrant and that of the corresponding origin household in the Philippines. In this study, we use three waves of linked cross-sectional data: 1997, 2000, and 2003 FIES-SOF dataset,

where these are pooled to generate the sample used for analysis.

II. Identifying Skills Mismatch Cases

A skills mismatch in this article is identified using a simplified version of what is called the job analysis method due to Hartog (2000). We compare the educational requirements of the migrant's job to their educational qualifications, and identify a mismatch if the former is less than the latter. In determining the educational requirements of particular occupations, guidance is taken from the International Labour Organization (ILO) occupational classification system called ISCO-OS which, at its broadest level, identifies four broad occupation skill levels and the educational qualifications required for each.

Accordingly, we have the following classifications ranked from highest (1) to lowest (4):
Occupation Class (A)                   Qualification Required (B)

1. Professional managerial/        1. Require at least a lull degree
   administrative occupations         completed
2. Technical occupations/trades    2. Require some form of
   and plant/machine operator         postsecondary education
3. Clerks and other moderately     3. Require a high school degree or
   skilled occupations                3-4 years of high school plus
                                      work experience
4. Retail, laborer and other       4. Require less than the threshold
   low-skilled occupations            for moderately skilled
                                      occupations. (a)

Source: International Labour Organization (2010). (a) In the SOF
data, there is also a residual category for current occupation that
includes such activities as unpaid homemakers, unemployed, those in
military, and so on. However, this is a very small proportion of the
total, and for convenience they are grouped in with the lowest
occupation skill category. Omitting (hem from the sample makes no
difference to the results.

In the empirical analysis, the skills mismatch variable is the difference in the ranks: that is. occupation class rank in column (A) minus the educational qualification rank in column (B). with higher values indicating higher degrees of skills mismatch. For example, the migrant in a low-skilled occupation who has a full degree will have a skills mismatch value of 3 = (4)-(1). Similarly. a migrant in a moderately skilled occupation who has had some years of college studies will have a skills mismatch value 1 = (3)-(2). In those relatively few cases where a person is undereducated and is working in an occupation normally associated with a higher level of qualification. the skills mismatch value is set equal to zero.

As observed, we define the education levels for only four broad occupational groups, something which is substantially less detailed than what is typically carried out in the literature. However, unlike most previous work where all respondents are located within the same host country, our data involve the work experiences of migrants working in a large number of countries around the world. Given that countries differ in the educational requirements for more narrowly defined occupations, it is not practical to gather job evaluation data for each of the 86 countries in which our sample of individuals are working in. Therefore, our approach is to identify clearly unambiguous instances of a skills mismatch by, for example, focusing on degree holders working in unskilled occupations. We investigate the robustness of our results to alternative specifications of the mismatch variable.

C. Econometric Specification

Our empirical strategy involves the estimation of the following basic equation form:

(1) [] = [alpha] + [beta][] + [gamma][] + [theta]'[] + [delta][] + [eta][] + [lambda]'[T.sub.t] + [[epsilon]]

where [] is a measure of the receipt of remittance by the household from overseas (which can be zero). [] is a measure of a skills mismatch. [] is a vector of migrant characteristics. [] is a vector of household characteristics. [] is a vector of destination country variables, [] is a vector of home region dummy variables [T.sub.t] is a vector of indicator variables for survey year. [[epsilon]] is the error term. i = 1, ..., n and pertains to the ith individual working abroad, and t = 1997.2000, or 2003 representing each survey year.

The vector of household characteristics ([]) includes the number and age of children in the household, the area or region of residence in the Philippines, whether the household received dividend income during the year, whether they received pension income during the year, gender, marital status, highest education level achieved, and other personal characteristics of the household head. The vector of migrant characteristics ([]) meanwhile includes personal characteristics as age, gender, marital status, and educational attainment of the migrant, the migrant's relationship in the household, and his or her type of visa or legal status in the host country. We also include a vector of destination country and home province dummy variables.

D. Estimation Issues

An important consideration for estimation is the potential endogeneity of the skills mismatch variable arising from unobserved factors also affecting the remittance decision. A migrant's decision to accept or not accept a mismatch job overseas would depend on a wide range of personal characteristics such as ability and education quality, as well as on destination country immigration and labor market policies, existing family networks in both home and destination countries, and community/cultural ties--at least some of which could impact on remittance decisions directly. For example, relatively close family networks could provide entry into a lower-skilled family business but could also increase the remittance motive. Similarly, lower unobserved ability would be more likely to lead to a mismatched job but also convey lower family resources and a greater remittance motive. More generally, there are reasons to expect that individuals considering working abroad may not have a lull range of country-employment options available to them because of incomplete information or lack of community connections so that location (and hence employment) choice and remittance behavior may be jointly determined.

To mitigate the effects of endogeneity issues, we employ instrumental variable (IV) estimation techniques. We include a number of time varying measures of the education profiles of the host countries: the proportion of the population that did not complete primary school, the proportion that did not complete secondary school, and the average number of years of total education. The logic is that these measures reflect the relative scarcity of domestically available skilled workers in the destination countries; they also could reflect the pressure on overseas employers to recognize Philippine education credentials because of skilled labor shortages. We think it is a reasonable assumption that these variables would not be expected to exert an independent effect on an individual's decision to remit. To further enhance our identification strategy, we also include in the remittance equation a series of time- and country-specific aggregate measures of population, economic activity, investment, economic structure, per capita gross domestic product (GDP), and exchange rates. These are in addition to our rich set of personal, household variables, and host country and home region fixed effects.

Another issue in modeling the amount remitted is the treatment of those who do not remit. In the absence of a suitable instrument that we can use to address this selection bias, we estimate effects using a number of empirical specifications that treat zero remittances in different ways and evaluate the robustness of our results across those methods. These include estimating by IV the amounts remitted based on the full sample including zeros, an IV-Tobit. and IV estimation conditional on positive amounts remitted.


Table 1 presents summary information from our unified databases for years 1997, 2000. and 2003. In the upper panel, it is seen that a significantly greater proportion of male migrants are married and older than the average woman migrant. Amongst women, about half have found employment in East Asia (Hong Kong, Korea, Japan, and Taiwan), and between 55% and 68% work in low-skilled occupations. In contrast, about 40% of men were employed in the Middle East, and less than a quarter end up in East Asia. Also among men. over 70% work in skilled to highly skilled occupations and less than 10% work in low-skilled jobs. In contrast, women appear concentrated in low-skilled jobs.

The international migration process in the Philippines requires significant financial resources to initiate, and so will tend to attract those with greater financial capacities. This is somewhat is reflected in the lower panel of Table I where over 55% of male migrants come from households in the top quintile of the income distribution, while 60% of women migrants are drawn from the middle 60th percentile. The poorest quintile accounts for just 2% of sender households, overall. The educational attainment of the household head does not appear to be a correlate of whether a household has a migrant sender or not, although we suspect that it may be so once we control for other factors in the regression analysis.

Table 2 presents the occupational distribution of Filipino workers abroad, grouped into the four broad occupational groups identified earlier. (12) It shows that the largest degree of skills mismatch occurs in the English-speaking background (ESB) countries of the United States. United Kingdom. Canada, and Australia, with 35% of male degree holders and 48% of female degree holders employed in low-skilled occupations. Twenty-three percent of male degree holders in the Middle East and Southeast Asia work in professional/managerial occupations, but the numbers for other regions are much smaller. For each region, the majority of male degree holders are employed as technicians, in the trades or as machine operators. For women, about 40% of degree holders in the ESB countries and the Middle East find employment in highly skilled occupations, but at the same time, very high percentages of them in Western Europe, East Asia, and Southeast Asia are employed in low-skilled jobs, at 85%. 73%. and 66%. respectively. For high school graduates, the differences in employment outcomes between men and women are very pronounced. A large majority of men appear to find work in occupations requiring technical skills, particularly those in East Asia, the Middle East, and Southeast Asia. Meanwhile, prospects for employment at this level are virtually nonexistent for female certificate holders or less, because as many as 94% of them end up working in low-skilled occupations, in particular in ESB and Western European countries.

Given the popular conjecture that Filipinos are relatively generous. (13) the proportions of remitters to total migrant workers and their level of remittances are also worth noting. In Table 3. we see that around 70% of individuals send remittances to their origin household, and the figures are marginally higher for men than for women. Among men. the highest proportions of remitters are found in the Middle East (about three in every four male migrants remit), while for women, the migrant population in East Asia has this distinction. For both groups, those working in Southeast Asia are found to have comparatively smaller proportions of migrants sending monies back home, but this still averages 54% across the education groups. In terms of conditional amounts remitted, we see that those remitted by men are markedly higher than those remitted by women, and are positively correlated with education level. Because remittance amounts are a direct function of the migrants' financial capacity, and that wages in ESB countries are generally higher than elsewhere, we also find that for both men and women alike, the largest median amounts remitted arc from degree holders in the ESB countries. (14) The smallest amounts remitted are from women working in the Middle East and in Southeast Asia. In order to provide a general context for the amounts remitted, median total family expenditure in 2003 among households in the FIES data was 79.000 pesos. Among households with a family member working abroad (that is, present in the SOF data), the median total expenditure was 169,000 pesos.

As a prelude to the econometric analysis, we present Table 4 which shows selected results of remittance choices by education level and current occupation in the host country. Remittance incidence and median amount are shown for four groups of workers abroad, disaggregated by gender. These are: people with a degree who are working in a highly skilled occupation (professionals and managers/administrators), people with a degree who arc working in a moderately skilled occupation (technical and clerks/bookkeepers), people with a degree who are working in a low-skilled occupation (laborer, service worker, and agricultural worker), and for comparison, people with less than a high school degree who are working in a low-skilled occupation. For men. the incidence of remittance and the amount of remittance are lower for degree holders employed in lower-skilled occupations, but degree-holding men working in the lowest skilled occupations still remit substantially more than less-educated men in the same low-skilled occupations. For women, the patterns are similar for the amounts remitted but the incidence of remittance is almost the same across education/occupation groups.

Table 5 presents our first-stage ordered probit regression results for determinants of skill mismatch for migrants aged 25-69years old. (15) Our results show that the educational attainment of the household head is an important predictor of a skill mismatch for both men and women. If the head of the sending household has had no high school education, this increases the migrant's chances of a skill mismatch by 5% in men and by 6% in women. In contrast, if the household head back home has completed a lull degree, the chance of a skill mismatch for the migrant decreases by 7% for men and by 13% for women. Pension income also appears to he significant--household receipt of a pension decreases the chances of a skill mismatch outcome for men but not for women.

With regards to the migrants themselves, we find that one's educational attainment is a strong determinant of a skill mismatch outcome, particularly for men. Our results show that having a full undergraduate degree or higher increases the chances of a male migrant to be skill mismatched by at least 45%. whereas the same did not make it better or worse for the case of female migrants. We also find that having a postgraduate degree improves a female migrant's chances of a being in a matched outcome by 5%. In contrast, being widowed or separated increases male migrants' chances of a mismatch outcome by 14% compared to that of married men; at the same time, the older men get. the less chances they will be in a mismatch situation. For women, marital status and age are not significant predictors, but moving overseas to immigrate (as opposed to fulfill a work contract) increases the chances of a skill mismatch outcome by 10%. Last, Table 5 shows that the host country exerts a significant influence on whether a migrant's skills or qualifications will be well matched to the overseas job or not. For men, the chances of a mismatch are reduced by 22% if they migrated to Southeast Asia instead of the United States/United Kingdom/Canada. For women, those who find themselves in East Asia and Western Europe have a lower chance of a being in a mismatch situation compared to those in the United States/United Kingdom/Canada. Finally, a joint test of the instruments strongly confirms that education composition in the host country is a highly significant determinant of skill mismatch outcomes for its Filipino migrant workers.

We next present in Table 6 our second-stage results for determinants of remittances using IV methods to allow the endogeneity of the skill mismatch variable. Four models are presented: probit regression on the incidence of remittances, unconditional linear regression on amounts remitted including all zeroes. Tobit regression on amounts remitted including all zeroes, and linear regression on amounts remitted conditional on positive remittances.

Our probit regression shows that the incidence of remitting is determined by a combination of factors that pertain to both the sending household and the migrant. Significant household factors include education attainment, gender, and marital status of the household head as well as the number of children present in the origin household. Our results show that having a household head with a university degree causes migrants to remit less compared to the case of a high school graduate--the probability of remitting decreases by 6% for men and by 10% for women. Further, having a household head who is male, is widowed or separated, and who receives pension income arc all found to reduce the chances of a male migrant remitting by between 5% and 12%. For women, these characteristics do not influence their probability to remit. The receipt of dividend income also appears significant for women remitters but not for men.

Interestingly, we further find that the presence of young children in households increases the probability of remitting by men but not by women; and that having a second family member working overseas reduces women migrants' probability of remitting but it does not affect men.

On the matter of the migrant's personal characteristics. our results show that the educational attainment of the migrant affects the incidence of remitting, but the direction of the effect is differentiated along gender lines. For men, having a higher degree increases the incidence of remitting by at least 6% compared to that of a high school graduate. In contrast for women, having a higher degree reduces the incidence of remitting by about 10% compared to women with only a high school education. Single or never married men have a higher incidence of remittances compared to those of married men. Such men may be more focused on supporting the larger family back in the Philippines than one's own immediate family. For women, those who report as being widowed or separated have a 16% higher chance of remitting compared to a married woman. This in turn may indicate the absence of a primary earning partner and so greater family reliance on remittances. Relationship to household head in the Philippines is also important, with the default category of spouse of household head associated with significantly higher incidence of remittances than other categories; in particular, where the household head him or herself is identified as being the individual working overseas. We also find that age has a positive influence on the incidence of remitting for women but not for men.

In the foregoing, it will not be unreasonable to take educational attainment of the household head as a proxy for household wealth; and when this is combined with the other significant variables of pension and dividend receipts in the household, it can all be jointly taken to represent the total amount of financial resources that the household may have access to. Given that, our results imply that a higher level of resources of sender households causes a lower probability of remitting for migrants. This makes economic sense: overseas migrant from relatively well-off families may feel less obligated to send money back for purely altruistic reasons. Our results further show that the level of need of the household increases as the number of children in the family increases and this compels male migrants to send remittances, although we do not find this effect for female migrants.

On the migrant's side, we see that after controlling for household resources, having a high educational attainment causes opposite responses between men and women remitters. For men. having a higher degree increases the incidence of remitting by at least 6% compared to that of a high school graduate. In contrast for women, having a higher degree reduces the incidence of remitting by about 10%. Controlling for family resources and potentially the ability to fund higher education, a more-educated human may have a greater need to pay back resources or debt incurred to educate him to obtain that education. A higher degree can indicate relatively larger amounts of family resources have been invested in the migrant, and this result for men falls in line with the exchange motive for remittances where migrants remit to repay the family back home for this investment. Additionally, if a migrant's own higher education is taken to indicate his personal wealth in the Philippines, then remittances might be seen as repayments for services back home to maintain or manage these assets--whether they arc financial or physical assets. Repayments to spouse or parents for taking care of the house or land and children left back home can be pail of this.

In case of women, we find that controlling for household wealth, those with higher education are less likely to remit compared to their less-educated counterparts. One possible explanation can be that women with higher education are less altruistic than their less-educated counterparts and so less oriented to supporting family at home. Alternatively, higher education can be thought of as making women less responsive to short-term expenditure needs of their families back home; and perhaps induce them to save or invest their earnings in places where they can maximize returns in the longer run. We can however only speculate on this for now but this issue could be the subject of future research. Other factors have also been found as significant predictors of remittances, and generally, they are different for men and women migrants. More notably, the migrant's location is found not relevant for men; but for women being in East Asia, the Middle East, and Southeast Asia reduces the incidence of remitting by as much as 19% compared to those who are working in the United States/United Kingdom/Canada where earnings are likely to be higher, other things equal. On skills mismatch and remittances, our results show that the occurrence of a skill mismatch has no effect on the incidence of remittances for men but it has a positive effect for women. The skill mismatch effect on women effectively offsets the effect found earlier that highly educated women results in lower incidence of remitting. Taken together, this suggests that it is only highly educated women with a successful job match who are significantly less likely to remit than other women. These results also point to the importance of the need to consider separate analysis outcomes for men and for women.

Results of the IV instrumented ordinary least squares (OLS) regressions on the amounts remitted inclusive of zero remittances are thus found in the last four columns of Table 6. while the result for the Tobit regression and the unconditional OLS are tabulated in Table 7. We found a substantial overlap in the set of control variables that were significant with the signs of the estimated coefficients appearing consistent across the models. Rather than repeating the discussion for each model, we will focus on discussion here on the results from the conditional OLS model (Table 7. last four columns) that relate specifically to amount remitted conditional on positive remittances

Compared to results of the probit model discussed above, the variables found to be significant for explaining the amounts remitted in the conditional OLS model are broadly similar, but the conditional model provides sharper differences of effects between men's and women's remittances behavior. First, it is clear that having a second person in the family who is also a migrant results in lower remittances, both for men and women. Second, educational attainment of the household head appears to increase remittances of men but not of women. This affirms the exchange motivation to remit for men and also confirms the patriarchal orientation of family expectations of remittances from its migrant members. Men's remittances are also found here to be affected by the household head's marital status, gender, and age: these variables have no effect on women's remittances. What we see for women, however, is that remittance amounts respond negatively if the family back home has dividend income (amounts remitted are reduced by 55%), but this responds positively if the number of children present in the household back home increases (amounts remitted increase by 8%): there is no such change in men's remittances from these variables.

As for migrant characteristics, results from this conditional IV model show that variations in women's levels of remittances are not driven by any of their own personal characteristics, but the opposite is true for men's level of remittances. For example, men's educational attainment is positive and significantly associated with amounts remitted, while being a son or another nonspouse relative of the household head reduces remittance amounts. And as previously found, being a migrant who moved overseas for reasons other than to work or to immigrate (such as a diplomat or military personnel) is found to lower the amounts remitted by men, by as much as 33%. In contrast, none of these personal characteristics cause women's remittances to change.

We further find that the impact of the same macrovariables on amounts remitted also varies by gender. A currency appreciation of the peso causes men's remittances to decrease by 0.5%, but it has no effect on women's remittances. Location of overseas work also appears significant. For men, being in the Middle East or Southeast Asia reduces their remittances by 47% and 71%. respectively, compared to those who end up working in the United States/United Kingdom/Canada. This location effect is seen to affect women only in Southeast Asia, where the reduction in the amounts remitted are upwards of 95%.

Last, our results also show that the impact of skills mismatch on remittance amounts is well determined along gender lines. We find that the skills mismatch variable is strongly significant for men, with remittance amounts reduced by 32% if their education-to-job match worsens. For women, we find that the amount of remittances sent home is neither increased nor decreased as a result of skills mismatch. This suggests that where income is lower than would have been the case without a mismatch, men reduce the amount remitted but women appear to bear the effects by adjusting their own living expenses and/or work hours, thereby preserving the amount sent back to the Philippines.


Using linked unit record datasets on household expenditure and overseas workers from the Philippines, this study provides strong evidence of skills mismatch among overseas Filipino workers. The empirical analysis reveals that significant proportions of highly educated individuals are working in low-skilled jobs, although there is substantial variation by gender and by country of work. The study provides evidence that the educational background of the sending family is a significant predictor of skill mismatch and remittance outcomes for the migrant worker. The higher the educational attainment for the sending family as indicated by the household head, the lesser is the chance of a skill mismatch for the migrant member. Similarly, a higher educational attainment of the family, the greater the likelihood that remittances are sent back, and the greater the amount if they are. We also find that different factors influence men's and women's decisions regarding their remittances. Male migrants appear to be more heavily influenced by factors pertaining to the sending households, such as marital status and age of household head, as well as the familial role that they have back home (such as being a son). These factors do not affect women and their remittances. Locational factors also tend to affect men more than women, except perhaps for women working in Southeast Asia who tend to have significantly reduced remittances overall.

A key finding of this study pertains to the impact of skills mismatch on the migrant's remittance behavior, with effects differentiated between the genders. Where there is a mismatch in the migrant's educational attainment and the overseas job he is in, we find significant reductions in remittances from mismatch for men but not for women. This result is certainly plausible. It is consistent with the empirical evidence that women are more altruistic than men, and that they are also more sensitive to the wider effects of their personal situations or decisions. In the face of a skills mismatch, women may be making adjustments in terms of decreasing personal consumption or increasing work hours so that they are able to contain the adverse welfare impact of the mismatch within themselves, thereby mitigating the effect of the adversity on their families back home. Men. on the other hand, appear to be more willing to share the burden of the skill mismatch by reducing the amounts they send home. These results align well with the idea that the gender role exercises different household bargaining power. We are not able to directly test this proposition in this article, but the findings obtained here are nonetheless significant forward steps in efforts to understand economic outcomes and family dynamics affecting transnational-type families, family types that have become increasingly common. Filipino overseas workers are at the forefront of this trend, and analyzing their experiences and behavioral responses to the misalignment of jobs vis-a-vis their qualifications and skills will assist in the general understanding of the various adjustments that such transnational families go through in their life cycles.

doi: 10.1111/coep. 12167


ESB: English-Speaking Background

HIES: Family Income and Expenditure Survey

GDP: Gross Domestic Product

ILO: International Labour Organization

IV: Instrumental Variable

OLS: Ordinary Least Squares

SOF: Survey of Overseas Filipinos


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McDonald: Professor. Department of Economics. University of New Brunswick, Fredericton. E3B6C4. Canada. Phone 1-506-447-3210. Fax 1-506-453-4514. E-mail

Valenzuela: Senior Lecturer. Department of Economics. Monash University. Caulfield East. 3145. Australia. Phone 61399032841, E-mail

(1.) This is sometimes called overselling or overqualification in the labor literature. We use the term skills mismatch to refer only to this type of labor mismatch, that is, we do not consider the case of underselling or underqualification in this article as it is deemed a positive outcome and therefore not within the boundaries of our research problem.

(2.) Chiswick and Miller (2009a, 2009b) provide evidence, among U.S. migrants, of less than perfect transferability of human capital across borders.

(3.) Green. Kler, and Leeves (2007) analyze the critical role of migrants' language skills in the Australian job market.

(4.) Piracha, Tani, and Vadean (2012) highlight the possibility that migrants may have been overeducated in the last job they held in their country of origin (called signalling in (he education literature).

(5.) Evidence of this effect may be found in Kalfa and Piracha (2013).

(6.) See Adams (2011) for a comprehensive overview. See also, Gibson and McKenzie (2012).

(7.) Amuedo-Dorantes and Pozo (2006) hypothesize that migrants with uncertain legal status arc prone to occupational mismatching, but may still remit more for insurance reasons.

(8.) See McDonald. Warman, and Worswick (2015) and Mahuteau, Piracha, and Tani (2010).

(9.) See Altonji and Blank (1999) for a summary of causes of gender inequalities and Croson and Gneezy (2009) for a detailed survey of experimental work on the issue.

(10.) Recent examples include DellaVigna et al. (2013). Dvorak and Toubman (2013), and McDonald, Warman, and Worswick (2015)

(11.) See Cooray (2014).

(12.) Given small sample sizes, statistics for workers with non-university postsecondary education are not reported in the tables. As well, the contents of some cells are suppressed because of small sample sizes.

(13.) See Alba and Sugui (2011). Fernan (2002). and Sundeen. Garcia, and Wang (2007).

(14.) At an exchange rate of 40 pesos per I USD. this is equivalent to 1315 and 903 USD.

(15.) Our baseline individual is the migrant worker who has completed high school, is married, is the spouse in the origin household, works in the United States/United Kingdom/Canada and comes from a household back home whose head had completed nothing more than high school certificate. For ease of exposition, we present marginal effects that give the change in the probability of experiencing some level of skills mismatch, evaluated at (he mean of the other explanatory variables.
Summary Statistics-Migrants and Households of Origin


Survey Year                                    1997     2000     2003

Sample size                                   1,412    1,584    1,193

Migrant information
  Age                                         37.0     38.4     37.8
  Marital status      Single                   0.255    0.235    0.218
                      Married                  0.731    0.747    0.769
                      Other                    0.014    0.018    0.013
  Location of         United States, United    0.096    0.133    0.169
    employment          Kingdom, Canada,
                        Australia, New
                      Western Europe           0.096    0.109    0.098
                      East Asia                0.259    0.235    0.227
                      Middle East              0.421    0.420    0.407
                      Southeast Asia           0.050    0.035    0.025
                      Others                   0.077    0.068    0.074
  Type of             Mgr/admin/prof           0.089    0.109    0.111
    employment        Tech/trades/plant        0.664    0.638    0.615
                      Clerk/service            0.129    0.122    0.117
                      Laborer                  0.096    0.077    0.093
                      Not reported             0.021    0.055    0.063
  Education           Higher degree            0.292    0.407    0.426
                      Bachelor degree          0.305    0.242    0.250
                      Postsecondary no         0.033    0.034    0.052
                      High school graduate     0.230    0.205    0.199
                      Less than high school    0.140    0.112    0.073
  Relationship to     Head (a)                 0.170    0.173    0.195
    household head    Spouse                   0.412    0.403    0.415
                      Child/child in law       0.365    0.378    0.331
                      Other                    0.052    0.047    0.059

Source household
  Per capita income   Bottom 20%               n.a.     0.015    0.013
    percentile        Middle 60%               n.a.     0.434    0.386
                      Top 20%                  n.a.     0.551    0.601
  Region              National Capital         0.174    0.165    0.190
                        Region (NCR)
                      Luzon (excluding NCR)    0.518    0.527    0.479
                      Visayas                  0.160    0.170    0.174
                      Mindanao                 0.149    0.138    0.158
  Urban/rural         Urban                    0.750    0.784    n.a.
                      Rural                    0.250    0.216    n.a.
  Household           Age of household head   47.6     49.2     46.9
    characteristics   Median household size    4        5        4
  Education of        Degree or more           0.161    0.247    0.286
    household head    Postsecondary no         0.188    0.190    0.220
                      High school graduate     0.242    0.246    0.230
                      Less than high school    0.268    0.220    0.171
                      Less than secondary      0.141    0.097    0.093


Survey Year                                    1997     2000     2003

Sample size                                   1,007    1,299    1,263

Migrant information
  Age                                         33.2     34.1     33.9
  Marital status      Single                   0.498    0.489    0.432
                      Married                  0.430    0.428    0.481
                      Other                    0.072    0.083    0.087
  Location of         United States, United    0.084    0.107    0.109
    employment          Kingdom, Canada,
                        Australia, New
                      Western Europe           0.062    0.043    0.045
                      East Asia                0.457    0.541    0.525
                      Middle East              0.315    0.268    0.290
                      Southeast Asia           0.063    0.021    0.018
                      Others                   0.020    0.020    0.013
  Type of             Mgr/admin/prof           0.124    0.147    0.102
    employment        Tech/trades/plant        0.096    0.095    0.135
                      Clerk/service            0.080    0.092    0.142
                      Laborer                  0.688    0.607    0.550
                      Not reported             0.011    0.058    0.070
  Education           Higher degree            0.243    0.309    0.276
                      Bachelor degree          0.272    0.227    0.238
                      Postsecondary no         0.027    0.033    0.043
                      High school graduate     0.251    0.269    0.306
                      Less than high school    0.207    0.162    0.136
  Relationship to     Head (a)                 0.014    0.028    0.022
    household head    Spouse                   0.332    0.291    0.333
                      Child/child in law       0.576    0.595    0.550
                      Other                    0.078    0.085    0.094

Source household
  Per capita income   Bottom 20%               n.a.     0.032    0.043
    percentile        Middle 60%               n.a.     0.606    0.600
                      Top 20%                  n.a.     0.362    0.357
  Region              National Capital         0.100    0.133    0.113
                        Region (NCR)
                      Luzon (excluding NCR)    0.488    0.483    0.487
                      Visayas                  0.139    0.133    0.145
                      Mindanao                 0.273    0.250    0.255
  Urban/rural         Urban                    0.554    0.635    n.a.
                      Rural                    0.446    0.365    n.a.
  Household           Age of household head   51.4     53.1     51.6
    characteristics   Median household size    5        5        5
  Education of        Degree or more           0.107    0.125    0.124
    household head    Postsecondary no         0.118    0.139    0.135
                      High school graduate     0.193    0.220    0.228
                      Less than high school    0.360    0.326    0.337
                      Less than secondary      0.221    0.189    0.176

(a) About 2% of location and about 5% of job types are not reported,
n.a., not available.

Occupation in Host Country, by Education Level and Region of

                                     United States,
                                     United Kingdom,   Western   East
                                         Canada        Europe    Asia

Men's occupations
  Skill/Educational level: Degree
      or more
    Professional                          0.092                  0.093
    Technical                             0.512         0.805    0.712
    Moderately skilled                    0.050                  0.019
    Low skilled                           0.347         0.142    0.176
  Skill/Educational level: High
      school only
    Technical                             0.451         0.517    0.684
    Moderately skilled
    Low skilled                           0.479         0.431    0.271
  Skill/Educational level: Less
      than high school
    Technical                             0.361                  0.627
    Moderately skilled
    Low skilled                           0.611         0.688    0.328

Women's occupations
  Skill/Educational level: Degree
      or more
    Professional                          0.322                  0.101
    Technical                             0.095                  0.149
    Moderately skilled                    0.091                  0.023
    Low skilled                           0.492         0.851    0.727
  Skill/Educational level: High
      school only
    Technical                                                    0.086
    Moderately skilled                                           0.133
    Low skilled                           0.844         0.897    0.769
  Skill/Educational level: Less
      than high school
    Moderately skilled                                           0.101
    Low skilled                           0.913         0.917    0.823

                                     Middle   Southeast    Other
                                      East      Asia      Country

Men's occupations
  Skill/Educational level: Degree
      or more
    Professional                     0.230      0.231      0.156
    Technical                        0.526      0.585      0.706
    Moderately skilled               0.082      O.000
    Low skilled                      0.162      0.185
  Skill/Educational level: High
      school only
    Professional                     0.039
    Technical                        0.737      0.667      0.500
    Moderately skilled               0.024
    Low skilled                      0.200                 0.413
  Skill/Educational level: Less
      than high school
    Professional                     0.055
    Technical                        0.747      0.375      0.741
    Moderately skilled
    Low skilled                      0.186      0.609

Women's occupations
  Skill/Educational level: Degree
      or more
    Professional                     0.301      0.227
    Technical                        0.094
    Moderately skilled               0.074
    Low skilled                      0.530      0.659      0.550
  Skill/Educational level: High
      school only
    Moderately skilled               0.063
    Low skilled                      0.918      0.938      0.846
  Skill/Educational level: Less
      than high school
    Moderately skilled               0.085
    Low skilled                      0.911      0.884

Remittances by Region of Employment and Broad Education Level

Proportion                       United States,
Sending                          United Kingdom,   Western    Hast
Remittances                          Canada        Europe     Asia

  University degree                   0.724         0.757    0.778
  Postsecondary no degree                                    0.667
  High school graduate               0.7.12         0.776    0.763
  Less than high school               0.667         0.750    0.701
Median amount (conditional on
    remittances >0)
  University degree                  52.585        52.423    45.226
  Postsecondary no degree                                    27.169
  High school graduate               34.382        43.321    36.810
  Less than high school              37.770        32.604    36.101
  University degree                   0.617         0.729    0.719
  Postsecondary no degree                                    0.700
  High school graduate                0.667         0.846    0.773
  Less than high school               0.674         0.667    0.717
Median amount (conditional on
    remittances >0)
  University degree                  36.101        28.531    25.126
  Postsecondary no degree                                    18.090
  High school graduate               26.985        26.293    24.121
  Less than high school              15.644        20.477    21.911

Sending                          Middle   Southeast    Other
Remittances                       Fast      Asia      Country

  University degree              0.806      0.615      0.794
  Postsecondary no degree        0.766
  High school graduate           0.802      0.542      0.783
  Less than high school          0.767      0.391      0.852
Median amount (conditional on
    remittances >0)
  University degree              42.068    43.821     52.585
  Postsecondary no degree        36.101
  High school graduate           31.941    26.474     37.248
  Less than high school          30.151               30.069
  University degree              0.719      0.523      0.750
  Postsecondary no degree        0.633
  High school graduate           0.683                 0.615
  Less than high school          0.630      0.628
Median amount (conditional on
    remittances >0)
  University degree              25.126     9.627     30.151
  Postsecondary no degree        19.254
  High school graduate           17.528
  Less than high school          17.090    10.830

Notes: Further to the SOF. the sample of migrants identities
themselves as remitters remitting money back to the Philippines or
not. For those who do, the reported values are reported in terms of
Philippine pesos remitted during the 6 months prior to the survey
date and are adjusted for inflation.

Remittances by Education Level and Change in
Job Status (Selected Results)

                                             Incidence of    Amount
                                              Remittance    Remitted

Men's occupation
  Skill/Educational level: Degree or more
    Professional                                0.791        60.235
    Technical/Moderately skilled                0.770        43.821
    Low skilled                                 0.655        38.026
  Skill/Educational level: Less than high
    Low skilled                                 0.574        21.661
    Women's occupation
  Skill/Educational level: Degree or more
    Professional                                0.703        31.989
    Technical/Moderately skilled                0.701        25.125
    Low skilled                                 0.699        24.067
  Skill/Educational level: Less than high
    Labor/service/agriculture                   0.665        18.864

Determinants of Skills Mismatching


                                          Marginal Effects   p Values
                                                (1)            (2)

  No primary school                            0.047          0.185
  No secondary school                          0.050#         0.047#
  Some postsecondary                           0.022          0.395
  Degree                                      -0.073#         0.006#
  Second person                                0.043          0.086
  Single                                      -0.052          0.396
  W/S/D (a)                                    0.052          0.098
  Male                                         0.014          0.513
  Age                                          O.000          0.928
  Pension income                              -0.082#         0.013#
  Dividend income                              0.070          0.123
  No. Children <15 years                      -0.008          0.291
  Some primary school                         -0.050          0.251
  Undergraduate degree-                        0.450#         0.000#
  Higher degree                                0.466#         0.000#
  Single                                       0.016          0.556
  W/S/D (a)                                    0.141#         0.006#
  Age                                         -0.004#         0.004#
  Household head (b)                          -0.011          0.644
  Child of household head                     -0.084#         0.025#
  Other relationship to household head        -0.037          0.430
  Immigrant                                    0.010          0.902
  Oilier reason                                0.040          0.242
  East Asia                                   -0.064          0.061
  Middle East                                 -0.137          0.088
  Southeast Asia                              -0.215#         0.012#
  Western Europe                               0.049          0.195
  Other country                               -0.044          0.591
  Instruments for second stage                                0.035#


                                          Marginal Effects   p Values
                                                (3)            (4)

  No primary school                            0.088#         0.000#
  No secondary school                          0.062#         0.001#
  Some postsecondary                          -0.052          0.031
  Degree                                      -0.127#         0.000#
  Second person                                0.009          0.778
  Single                                      -0.041          0.148
  W/S/D (a)                                   -0.004          0.890
  Male                                         O.000          0.495
  Age                                          0.034          0.339
  Pension income                              -0.007          0.851
  Dividend income                             -0.009          0.682
  No. Children <15 years                       0.004          0.505
  Some primary school                          0.073#         0.000#
  Undergraduate degree-                        0.013          0.452
  Higher degree                               -0.050#         0.016#
  Single                                      -0.027          0.393
  W/S/D (a)                                    0.001          0.204
  Age                                          0.090          0.069
  Household head (b)                          -0.050          0.072
  Child of household head                     -0.061          0.070
  Other relationship to household head         0.028          0.478
  Immigrant                                    0.098#         0.000#
  Oilier reason                               -0.015          0.730
  East Asia                                   -0.359#         0.000#
  Middle East                                 -0.163          0.072
  Southeast Asia                               0.083          0.055
  Western Europe                              -0.616#         0.001#
  Other country                                0.082#         0.001#
  Instruments for second stage                                0.000#

Notes: These are the first-stage regression results. Baseline
individual is from a household with the head of household having
finished high school, has an undergraduate degree, is married, is the
spouse of the household head, is in the host country, went overseas
to work legally, and worked in the United States/United Kingdom/
Canada. Low-skilled jobs include laborer, service worker, and
agricultural worker. Medium-skilled jobs are clerks:
medium-high-skilled jobs are technician, plant operator/driver, and
jobs in trades. High-skilled jobs are manager and professional.
Regressions also include controls for survey year and region of
residence of the household in the Philippines. Variables used as
instruments in the second stage include the proportion of adults in
the host country with less than secondary school education, average
number of years of education, and the proportion of people aged 18-25
who are enrolled in tertiary education.

(a) Widowed/Separated/Divorced.

(b) Informal ion on the head of the household relates to the person
nominated as household head among those individuals resident in the
house at the time of the survey.

Bold indicates significant at the 5% level or better.

Note: Significant at the 5% level or better are indicated with #.

Determinants of Remittances: Probit and OLS. IV Estimation

                                    Probit on
                              Incidence of Remitting


                             Marginal   p > [absolute
                             Effects     value of z]

                                DepVar: Remittance

                               (1)           (2)

Household (a)
  Second person              -0.034        0.059
  No primary school          -0.016        0.525
  No secondary school         0.000        0.990
  Some postsecondary         -0.033        0.106
  Degree                     -0.055#       0.005#
  Single                     -0.036        0.366
  W/S/D (b)                  -0.080#       0.000#
  Male                       -0.116#       0.000#
  Age                         0.000        0.603
  Pension income             -0.056#       0.010#
  Dividend income            -0.036        0.319
  No. kids < 15 years         0.015#       0.010#
  Degree                      0.074#       0.033#
  Higher degree               0.063#       0.042#
  Single                     -0.058#       0.002#
  W/S/D (h)                   0.039        0.478
  Age                        -0.001        0.142
  Head of household          -0.193#       0.000#
  Child of household head    -0.068#       0.010#
  Other relationship to      -0.113#       0.000#
    household head
  Immigrant                   0.053        0.314
  Other reason               -0.104#       0.000#
  Exchange rate               0.000        0.377
  Log(population)             0.004        0.473
  Log(GDP per capita)        -0.011        0.286
  Growth rate GDP             0.000        0.880
  East Asia                   0.001        0.982
  Middle East                 0.020        0.487
  Southeast Asia             -0.040        0.409
  Western Europe             -0.003        0.926
  Other country              -0.028        0.501
Skills mismatch              -0.037        0.185

                                    Probit on
                              Incidence of Remitting


                                        p > [absolute
                               COEF      value of z]

                                DepVar: Remittance

                               (3)           (4)

Household (a)
  Second person              -0.067#       0.001#
  No primary school          -0.083        0.004
  No secondary school        -0.029        0.220
  Some postsecondary         -0.017        0.529
  Degree                     -0.099#       0.001#
  Single                      0.017        0.685
  W/S/D (b)                  -0.023        0.479
  Male                       -0.030        0.330
  Age                         O.000        0.658
  Pension income              0.025        0.348
  Dividend income            -0.091#       0.032#
  No. kids < 15 years         0.001        0.865
  Degree                     -0.056        0.226
  Higher degree              -0.096#       0.020#
  Single                      0.035        0.136
  W/S/D (h)                   0.157#       0.000#
  Age                         0.007#       0.000#
  Head of household          -0.323#       0.000#
  Child of household head     0.042        0.189
  Other relationship to      -0.077#       0.029#
    household head
  Immigrant                  -0.135#       0.009#
  Other reason               -0.308#       0.000#
  Exchange rate              -0.001        0.027
  Log(population)            -0.016        0.041
  Log(GDP per capita)        -0.020        0.247
  Growth rate GDP            -0.004        0.118
  East Asia                  -0.118#       0.002#
  Middle East                -0.144#       0.000#
  Southeast Asia             -0.189#       0.004#
  Western Europe             -0.021        0.659
  Other country              -0.127        0.123
Skills mismatch               0.065#       0.026#

                                  OLS on Amount
                              Remitted (Includes (O)


                                        p > [absolute
                               COEF      value of z]

                               DepVar: Logremitreal

                               (5)           (6)

Household (a)
  Second person              -0.379        0.066
  No primary school           0.069        0.809
  No secondary school         0.022        0.918
  Some postsecondary         -0.114        0.589
  Degree                     -0.324        0.121
  Single                     -0.741        0.112
  W/S/D (b)                  -1.351#       0.000#
  Male                       -1.585#       0.000#
  Age                         0.008        0.344
  Pension income             -0.592#       0.019#
  Dividend income            -0.400        0.305
  No. kids < 15 years         0.163#       0.007#
  Degree                      0.816#       0.023#
  Higher degree               0.746#       0.022#
  Single                     -0.768        0.000
  W/S/D (h)                   0.830        0.187
  Age                        -0.013        0.221
  Head of household          -2.074#       0.000#
  Child of household head    -0.475        0.101
  Other relationship to      -1.182#       0.001#
    household head
  Immigrant                   0.175        0.747
  Other reason               -1.881#       0.000#
  Exchange rate              -0.002        0.326
  Log(population)             0.005        0.930
  Log(GDP per capita)        -0.016        0.885
  Growth rate GDP            -0.015        0.542
  East Asia                   0.109        0.697
  Middle East                -0.026        0.929
  Southeast Asia             -0.679        0.199
  Western Europe              0.073        0.821
  Other country              -0.014        0.975
Skills mismatch              -0.418        0.146

                                  OLS on Amount
                              Remitted (Includes (O)


                                        p > [absolute
                               COEF      value of z]

                               DepVar: Logremitreal

                               (7)           (8)

Household (a)
  Second person              -0.632#       0.003#
  No primary school          -0.780#       0.012#
  No secondary school        -0.554#       0.026#
  Some postsecondary         -0.305        0.282
  Degree                     -1.207#       0.000#
  Single                      0.126        0.774
  W/S/D (b)                  -0.107        0.755
  Male                       -0.538        0.100
  Age                        -0.002        0.806
  Pension income              0.255        0.357
  Dividend income            -0.991#       0.030#
  No. kids < 15 years         0.075        0.306
  Degree                     -0.027        0.958
  Higher degree              -0.416        0.366
  Single                      0.038        0.878
  W/S/D (h)                   1.183#       0.001#
  Age                         0.059#       0.000#
  Head of household          -3.078#       0.000#
  Child of household head     0.394        0.245
  Other relationship to      -0.246        0.512
    household head
  Immigrant                  -0.829        0.140
  Other reason               -2.973#       0.000#
  Exchange rate              -0.017#       0.008#
  Log(population)            -0.199#       0.016#
  Log(GDP per capita)        -0.132        0.477
  Growth rate GDP            -0.043        0.147
  East Asia                  -0.836        0.040
  Middle East                -1.115#       0.009#
  Southeast Asia             -2.211#       0.002#
  Western Europe              0.200        0.682
  Other country              -0.336        0.693
Skills mismatch               0.304        0.340

(a) Age of men and women restricted to 25-69 bracket to allow
university to be completed: personal characteristics pertain to
those of household head.

(b) Widowed/Separated/Divorced.

Bold indicates significant at the 5% level or better.

Note: Significant at the 5% level or better are indicated with #.

Determinants of Remittances; Tobit and Conditional OLS. IV

                                   Tobit on Amount Remitted

                                 Men                    Women

                                 p > [absolute           p > [absolute
                         COEF     value of z]    COEF     value of z]

                                     DepVar: Logremitreal

                          (1)         (2)         (3)         (4)
Household (a)
  Second person         -0.763#     0.004#      -1.381#     0.000#
  No primary school      0.016      0.965       -1.255#     0.004#
  No secondary school    0.040      0.882       -0.708#     0.038#
  Some postsecondary    -0.238      0.374       -0.316      0.417
  Degree                -0.533#     0.045#      -1.717#     0.000#
  Single                -0.909      0.136        0.299      0.626
  W/S/D (b)             -1.618#     0.000#      -0.114      0.809
  Male                  -2.017#     0.000#      -0.539      0.234
  Age                    0.011      0.290       -0.005      0.684
  Pension income        -0.875#     0.007#       0.354      0.354
  Dividend income       -0.529      0.285       -1.479#     0.019#
  No. kids <15 years     0.199#     0.009#       0.081      0.416
  Degree                 1.218#     0.009#      -0.496      0.5
  Higher degree          1.076#     0.011#      -1.005      0.133
  Single                -1.055#     0.000#       0.269      0.441
  W/S/D (b)              0.991      0.224        1.981#     0.000#
  Age                   -0.020      0.148        0.090#     0.000#
  Head of household     -2.734#     0.000#      -5.310#     0.000#
  Child of household    -0.577      0.118        0.632      0.180
  Other relationship    -1.461#     0.002#      -0.837      0.114
    to household head
  Immigrant              0.416      0.545       -1.612#     0.041#
  Other reason          -2.427#     0.000#      -4.919#     0.000#
  Exchange rate         -0.007      0.250       -0.022      0.012
  Log(population)        0.014      0.846       -0.265      0.021
  Log(GDP per capita)   -0.070      0.616       -0.226      0.371
  Growth rate GDP       -0.016      0.602       -0.057      0.159
  East Asia             -0.033      0.932       -1.481#     0.009#
  Middle East           -0.106      0.785       -1.813#     0.002#
  Southeast Asia        -1.112      0.107       -3.402#     0.001#
  Western Europe        -0.081      0.853       -0.023      0.973
  Other country         -0.184      0.746       -1.009      0.388
Skills mismatch         -0.711      0.060        0.728      0.118

                                    OLS on Amount Remitted
                                   (Conditional on Positive
                                       Amount Remitted)

                                 Men                    Women

                                 p > [absolute           p > [absolute
                         COEF     value of z]    COEF     value of z]

                                     DeVVar: Logremitreal

                          (5)         (6)         (7)         (8)
Household (a)
  Second person         -0.949#     0.000#      -1.225#     0.000#
  No primary school      0.136      0.259       -0.115      0.44
  No secondary school    0.029      0.743       -0.248#     0.035#
  Some postsecondary     0.167#     0.053#      -0.016      0.907
  Degree                 0.261#     0.003#      -0.048      0.758
  Single                -0.275      0.199        0.016      0.943
  W/S/D (b)             -0.231#     0.035#       0.198      0.242
  Male                  -0.149#     0.054#      -0.135      0.398
  Age                    0.007#     0.030#      -0.002      0.610
  Pension income         0.001      0.949       -0.011      0.936
  Dividend income       -0.093      0.564       -0.548#     0.014#
  No. kids <15 years     0.008      0.747        0.079#     0.024#
  Degree                 0.259#     0.043#       0.251      0.261
  Higher degree          0.320#     0.006#       0.314      0.126
  Single                -0.154      0.092       -0.014      0.911
  W/S/D (b)              0.497      0.076        0.193      0.265
  Age                    0.006      0.236        0.002      0.798
  Head of household      0.021      0.816       -0.278      0.472
  Child of household    -0.266#     0.029#      -0.090      0.597
  Other relationship    -0.394#     0.016#      -0.079      0.684
    to household head
  Immigrant             -0.058      0.794        0.310      0.284
  Other reason          -0.333#     0.010#       0.143      0.477
  Exchange rate         -0.005#     0.023#      -0.004      0.273
  Log(population)       -0.024      0.309       -0.042      0.320
  Log(GDP per capita)    0.019      0.673        0.081      0.377
  Growth rate GDP       -0.008      0.435        0.004      0.786
  East Asia             -0.130      0.309        0.017      0.931
  Middle East           -0.476#     0.000#      -0.021      0.920
  Southeast Asia        -0.706#     0.002#      -0.949#     0.010#
  Western Europe        -0.248      0.089        0.047      0.841
  Other country         -0.042      0.818        0.457      0.274
Skills mismatch         -0.317#     0.001#      -0.040      0.771

(a) Ago of men and women restricted to 25-69 bracket to allow
university to be completed: personal characteristics pertain to
those of household head.

(b) Widowed/Separated/Divorced.

Bold indicates significant at the 5ch level or better.

Note: Significant at the 5ch level or better are indicated with #.
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Author:McDonald, James Ted; Valenzuela, Maria Rebecca
Publication:Contemporary Economic Policy
Article Type:Report
Geographic Code:1USA
Date:Jan 1, 2017
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