Personal networks and non-agricultural employments: the case of a farming village in the Philippines*
Kei Kajisa Foundation for Advanced Studies on International Development (FASID), Tokyo Japan
Address: 7-22-1 Roppongi, Minato-ku, Tokyo 106-8677, Japan. Tel: +81(3)5413-6034, Fax: +81(3)5413-0016, email: [email protected] *Acknowledgements: The author would like to thank the International Rice Research Institute (IRRI) for providing the data of Survey on Livelihood System of Rural Households. I also wish to acknowledge the invaluable assistance in data collection by Fe Gascon, Lui Bambo, Alma B. Payra, and Florie P. Suguitan. I am indebted to Yujiro Hayami, Masaki Nakabayashi, Tetsushi Sonobe, Yasuyuki Sawada and the referees of this journal for helpful comments.
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Abstract
This paper analyzes the effects of personal networks on rural villagers’ access to
non-agricultural occupations and the terms of employment given to them, based on an
intensive village survey in the Philippines. A key finding is that personal networks are
selectively utilized to reduce transaction costs and that their impacts on employment
conditions vary by size and by location of enterprises. We find that when villagers are
employed in unskilled work at small enterprises, those who use family/relative networks
receive wage premiums. However, if we limit our sample to small enterprises located
nearby our study village, the family/relative network premiums become insignificant
presumably because of the over-riding influence of the community-wide network within
a narrow local community. Contrary to the case of small enterprises, unskilled
workers’ wages at large enterprises are not much affected by personal networks but are
largely determined by schooling years and work experience. The recent development
of large scale enterprises in the Philippines shows the diminishing importance of
personal networks at unskilled labor markets, reflecting the tendency that acquired
ability through education and training is becoming more important than nascent
characteristics like family/relative networks corresponding to economic and social
modernization.
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I. Introduction
It is widely recognized that personal networks play important roles in economic
transactions, especially in developing countries where markets are underdeveloped.
Examples include their roles in consumption smoothing (Fafchamps and Lund, 2003),
agricultural marketing (Fafchamps and Minten, 2002), capital mobilization for factory
establishment (Banerjee and Muhshi, 2000; Fafchamps, 2000), technology diffusion
(Conley and Udry, 2004), and the prevention of tenant farmers’ shirking (de Janvry,
Sadoulet, and Fukui, 1997). However, little exploration has been made on their roles
at non-agricultural labor markets in developing countries.1 This study attempts to
analyze the effects of personal networks on rural villagers’ access to non-agricultural
occupations and the terms of employment given to them, based on an intensive survey
of one village in the Philippines.
The existing literature argues that the use of personal networks facilitates a
1 A significant number of empirical studies exist which explore the roles of personal networks
in rural-urban migration (Yap, 1977; Banerjee, 1984; Caces, 1985; Lucas, 1997). Their major
focus is, however, geographical migration, rather than economic migration from the agricultural
to the non-agricultural sector. Luke and Munshi (2006) investigate personal networks and
labor market outcomes in Kenya. Their paper, however, focus on the impact of the expansion
of kinship networks by marriage, rather than the comparison of the impacts from the different
types of networks on the different types of occupations, which is the focus of this paper.
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reduction in transaction costs at labor markets by reducing two kinds of asymmetric
information problems. First, it is difficult for employers to detect job seekers’ true
abilities at the time of their application. The literature stresses that employers could
resolve this problem by obtaining information about the applicant’s true attributes
through personal networks (Saloner, 1985; Montgomery, 1991; Simon and Warner,
1992). Second, the difficulty of monitoring workers’ effort after employment is said to
be mitigated by the sense of loyalty or the fear of bad reputation among workers hired
through personal networks (Milgrom and Roberts, 1992; Putnam, 2000). Besides the
reduction in information asymmetry, well established personal networks can also reduce
search costs (Granovetter, 1974; Holzer, 1988; Mortensen and Vishwanath, 1992). The
empirical studies that support these arguments rely mainly on data from developed
countries.
This paper examines these arguments at developing countries’ non-agricultural
labor markets with two improvements on the existing empirical studies. First, by
utilizing a methodology developed in sociology, we quantify personal networks for the
statistical estimation of their impacts. Second, a wide variety of occupations covered
by our survey enables us to identify what types of occupations in which locations are
significantly affected by personal networks. Unlike many existing surveys that
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interview employees in selected sample enterprises, our survey selects a village and
interviews not only its residents but also emigrants from the village. This
village-based approach allows us to cover a wide variety of non-agricultural
occupations available to the rural village. This feature is crucial for our study because
developing countries’ labor markets are characterized as segmented markets where
different mechanisms may work at different segments. By using this approach with
quantified personal networks, we are able to quantitatively examine the differential
impacts of personal networks on terms of employment.
Our study is focused on one village in the Philippines with data collected on
agricultural workers in this village and non-farm employees who came from this village.
This study village has been covered by recurrent surveys by the International Rice
Research Institute (IRRI) as a social observatory. There are two major advantages of
focusing on this village. First, the rich accumulation of past data enables us to
construct family histories for all the households, not only those staying in the village but
also those who migrated out in the past. As cautioned by Rosenzweig (2003),
incomplete identification and tracing-out of emigrants tend to result in a serious sample
bias due to the existence of systematic differences between those who stayed and those
who emigrated. Our data are immune from this bias because on the bases of past
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survey records we were able to trace those who migrated out from the village before the
time of our survey. Another advantage is that the existence of past analyses is
invaluable for checking our data and statistical results (Hayami, 1978; Hayami and
Kikuchi, 1981, 2000; Hossain, Gascon, and Marciano, 2000).
This paper is organized as follows. In Section II, an explanation of the study
village and the characteristics of non-agricultural occupations are provided. Section III
explains the data collection method and the measurement of personal networks. The
results of the econometric analyses are presented in Sections IV and V. Finally,
Section VI summarizes the main findings and discusses their implications.
II. The study village and occupations2
The study village is located about 70 km. southeast of Manila, facing the east
coast of Laguna de Bay, the largest lake in the Philippines (Figure 1). It was first
settled in the 1880s, and continued its history as a rainfed rice monoculture village.
Major innovations in agricultural technology began in the late 1960s with the arrival of
the Green Revolution at this village, doubling the rice yield in a decade. The
2 The chronology and interpretations prior to 1997 are based on Hayami and Kikuchi (2000).
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continuous release of new modern varieties and their replacement of the older ones kept
the momentum of the Green Revolution going until the early 1980s. Throughout this
period rice farming continued to be the dominant production activity in this village.
Table 1 shows the number of households by type from 1966 to 2001, which indicates
that until 1987 few households had engaged solely in non-farm activities (only 7
households or 4%). The table also shows that, due to the closure of the land frontier
and inactive land-rental markets under the land reform regulations, the so-called
“agricultural ladder” (Spillman, 1919) for landless laborers to ascend to the status of
farm operators had been closed with the result that the number of farmers had remained
unchanged. Thus, landless laborers had to wait until non-agricultural jobs became
readily available for substantial increases in their incomes.
In the late 1970s, major improvements in highway systems, which connect the
village to Manila and other major cities, were made. The villagers were able to access
non-agricultural job opportunities available at the newly industrializing area in the west
coast of Laguna de Bay (Figure 1). The travel time from the village to Manila was
reduced from more than four hours to about two hours. Also, access to other cities
became correspondingly easier. Urban industrial activities began to spill over to local
towns, encouraging their commerce, construction, transportation, and small scale
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manufacturing activities. In the late 1980s, the number of small and medium
enterprises such as garment factories increased sharply in the vicinity of this village.
In the 1990s, following the rapid growth in neighboring ASEAN countries, relatively
cheap but high quality labor in the Philippines attracted foreign direct investment in
labor intensive manufacturing. This progress was accelerated by the establishment of
industrial parks along the coast of Laguna de Bay. Large multinational factories
absorbed educated youths from nearby villages. The initial beneficiaries in the study
village of the rising non-agricultural employment opportunities were the educated
children of affluent farmers, but later, landless laborers also began to take advantage of
the emerging opportunities. Table 1 shows the significant increase in the number of
non-farm worker households. The number of agricultural laborer households
decreased not only percentage-wise but also absolutely from 1997 to 2001. The study
village rapidly changed from a pure rice farming village to a residential area for
non-agricultural workers employed in nearby towns.
More details on the current status can be seen by reviewing the occupations the
villagers are engaged in. Conventional employment opportunities for villagers can be
classified into four categories. The first category is self-employed farming. The
second is employment as agricultural laborers by farmers within the village.
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Agricultural laborers in this category are paid for not only farm work but also casual
non-farm work requested by farmer employers. To serve as agricultural laborers,
sophisticated skills are not needed. They receive lower wages than people engaged in
non-agricultural activities outside the village, and comprise the bottom stratum of social
hierarchy.3 The third category is overseas work which is common in this village,
similar to other parts of the Philippines. The fourth category, self-employment, covers
a variety of occupations such as tricycle drivers, buy-and-sell, and metalcraft
manufacturing at the backyard of households.
Besides the occupations of these four categories, the occupations that have
been becoming more common in this village in the last decade are non-farm wage
employment other than overseas work. In this paper they are classified into three
categories by the size of enterprise and required skills: (1) unskilled labor at small
enterprises, (2) unskilled labor at large enterprises, and (3) skilled/technical labor
regardless of enterprise size. Small enterprises are defined as those employing less
than thirty workers. Examples of the first category include production line workers at
small light industries (such as garment, printing, and household articles), workers at
restaurants or retail shops, and workers at construction companies. Such enterprises,
3 The prevailing daily wage rate in 2001 was 150 pesos.
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in which people in this village work, are found throughout the area including the
municipal center of this village, nearby towns, major cities, and Metro Manila. As for
the second category, examples are mostly production line works at modern factories of
multinational and domestic companies, many of which are located inside industrial
parks or in the west coast of Laguna de Bay. The mode of management is far more
formal in the second category than in the first category in terms of personnel
management, terms of contracts, and legislative control. The third category consists of
white collar office workers, and technicians and engineers in factories as well as
professionals such as teachers, accountants, and doctors. The locations of jobs in this
category are diversified but many of them are found in Metro Manila and its
surroundings.
As previously mentioned, non-agricultural wage work is the new path for
landless laborers in the village to increase their income levels and living standards.
Hayami and Kikuchi (2000), using data from 1966 to 1997, argue that “for those who
want to find stable, non-farm employment, the only way is to invest in education” (p.62).
Figure 2 shows the distribution of schooling years by occupation in 2001. Note that
the completion years of primary, secondary, and tertiary levels of education are six, ten,
and fourteen, respectively. It is obvious from the figure that most of the tertiary school
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graduates are found only in technical/skilled work, implying that the completion of that
level of education is a necessary and probably sufficient condition to acquire such jobs.
It is interesting to observe that although we see that the secondary school graduates are
most common in unskilled labor positions in both small and large enterprises, we can
still observe significant proportion of people having the same level of education in the
agricultural laborer group; in fact the proportion is about as large as that in the entire
population. This implies that secondary level education is necessary but not sufficient
to move away from agricultural labor to non-farm wage employment.
Existing studies on the labor market in the Philippines show that in addition to
education, the use of personal networks is another important condition to access
non-agricultural wage employment. Pinches (1989) shows that most workers are
employed at an elevator industry in Manila through family networks. Stretton (1981)
reveals that most of the construction workers in Manila are collected through the
foremen’s network. A labor market report in 1993 shows that 40 % of the respondents
obtained their current jobs through relatives or friends while only 10% did so from
public employment agencies (The Japan Institute of Labour, 1993).
The major questions that we address in this paper are: what kind of personal
networks have influenced rural villagers’ access to different kinds of non-agricultural
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employment and what mechanisms may have underlain their influences? After
explaining the data set, we will analyze the first question in Section IV, and
subsequently the second question in Section V.
III. The data
Data collection
A household survey was conducted in 2001 in this village by the International
Rice Research Institute (IRRI) as an update of their recurrent surveys. This survey
covered all the households in the village. With the help of past records, we generated a
list of households and their members (1) residing in the village (2) living outside of the
village but still considered as members of village households, and (3) those who became
independent while living outside of the village. Using this list, we tried to interview
all the relevant individuals living not only in the village but also outside of the village.
Of 1,432 individuals living inside and outside of the village in 2001, our targets
were 746 individuals, after excluding full-time homemakers, students, children aged
below six, and retired persons (column (1) in Table 2). Of those 746 targets, 611
individuals were interviewed, and the complete information for our analyses was
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obtained from 444 individuals (column (3)). The information we collected is about job
related data, personal networks, and individual characteristics not only at the time of
interview but also at the time she/he entered into the current job. Those who could not
give the complete information are mostly farmers or agricultural laborers because they
had no clear idea about the timing of job entry and the personal network at that time.
Besides, we could not fully cover overseas workers who are nearly impossible to track.
Therefore, farmers, agricultural laborers, and overseas workers could be
underrepresented in our analyses. However, except for these three occupations, the
occupational compositions of the sample (column (4)) are still similar to those of the
entire population (column (2)). Moreover, the sub-samples of unskilled laborers
employed at small or large enterprises cover almost all the relevant observations (54 of
59 and 78 of 79 respectively), and thus the analyses based on these sub-samples in
Section V are little affected.
Measurement of personal networks
Similar to human capital, personal networks are measured as the respondent’s
stock that affect her/his employment conditions. Among the major methods developed
in the area of sociology, the method we use is the one called the “position generator
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method” (Lin, 2001; Lin, Fu, and Hsung, 2001).4 It uses a list of positions salient in
the context of research and asks if a respondent knows anybody at each position. If the
respondent knows somebody at a certain position, she/he selects the most important
person and we ask about the respondent’s closeness to that person. The closeness is
measured by whether the respondent can ask a favor from that person. We judge that
an effective network channel to that position exists if the answer is yes. In the case of
an affirmative answer, we further identify the respondent’s relationship with that person
from the three types of relationships: (1) family/relative, (2) friend, or (3)
acquaintance.5 Therefore, the interview reveals, of the positions listed in the
4 The other major method is the “name generator method” which asks a respondent to name a
name in response to questions concerning, for example, the people with whom one had
important consultations in the past six months, or the people important to one’s job. Lin, Fu,
and Hsung (2001, chapter 3) extensively discuss the pros and cons of these two methods. In
short, the name generator method tends to miss distant relationships which are supposed to play
significant roles in conveying job opportunities to job seekers, whereas the position generator
method tends not to do so. Since the role of networks in job acquisition is our main focus, we
use the latter method in this paper. 5 We classify family members, close relatives, and distant relatives into the family/relative
category, friends in the same village and friends in other villages into the friend category, and
business partners other than friends and acquaintances into the acquaintance category. The
difference between friend and acquaintance is a respondent’s subjective judgment. It is argued
that ritual relationships such as godparents and godchildren are important personal connections
in a Christian society like the Philippines. Our questionnaire has those options, but no
respondent chose them presumably because the ritual relationship usually derives from
preexisting one (e.g. friend) to strengthen the preexisting one.
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questionnaire, to which positions a respondent has connections through which type of
relationships.
In our questionnaire, we made a list of sixteen non-agricultural positions. We
classify these positions into four categories as the existing literature suggests that more
prestigious positions in society may be associated with greater influence (Lin, 2001).
The four categories are (1) politicians/bureaucrats, (2) manager level positions, (3)
professional positions, and (4) regular employee positions, where each category consists
of four specific positions.6 Using these four categories together with the information
about the three types of relationship, we construct twelve variables that measure the size
of personal networks in the non-agricultural sector: family/relative network in each of
the four categories, friend network in each, and acquaintance network in each. Each of
these twelve variables can take a value from 0 to 4 such that the sum of the twelve
variables must be less than or equal to 16.
6 The first category consists of national level politicians, municipal level politicians, national
level government officials, and municipal level government officials (4 positions). The second
category includes the managers or an equivalent level of the following four occupations:
financing/crediting/banking, manufacturing, service, and transportation (4 positions). The
third one includes polices, lawyers, medical doctors, and school teachers (4 positions). The
last one consists of regular employees of the same four occupations used for manager level
positions (4 positions).
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IV. Personal networks and acquisition of non-agricultural jobs
Descriptive statistics
To investigate whether the personal networks explain occupational choice, we
use 427 of the 444 observations as we exclude two occupations of small sample sizes
(i.e. farmers and overseas workers) which did not produce relevant and significant
statistical results. The descriptive statistics of the 427 individuals by occupation are
shown in Table 3. In addition to the personal networks, the possible determinants of
occupational choice include (1) a measure of organizational network that is a dummy
indicating whether a respondent is a member of any organizations, (2) other individual
characteristics such as schooling years, age, and male dummy, and (3) household
characteristics such as the number of household members, values of land assets,
agricultural assets, and non-agricultural assets. All the variables measure the values at
the time of the respondent’s job entry.
The table shows that agricultural laborers are the least (or the second least in a
few cases) equipped individuals in terms not only of human capital (schooling years)
and physical capital (asset values) but also of the sizes of the personal networks over the
non-agriculture sector. The table also shows that the levels of average schooling years
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are not so different across occupations except for technical/skilled work, implying that
some other factors could affect occupational choice. Since the sizes of the personal
network variables by relationship and by position category vary between occupations,
some of them may explain occupational differences.
Regression results
In order to obtain more rigorous conclusions, we conduct the multinomial logit
regression analyses in which agricultural labor is used as the base category. Table 4
reports the marginal effects evaluated at the mean values of the explanatory variables.
Before interpreting the results, it is worth discussing the endogeneity of network
variables which has been an issue of this kind of literature. For the sake of discussion,
let us assume the size of network is expressed as a network formation function. First
of all, we would like to point out that since our variables measure the sizes of networks
at the time of job entry, the type of current occupation is not an element of our network
formation function; we do not have to worry about simultaneous equation bias.
However, if there exist omitted factors which influence both the network formation and
the occupational choice, the covariance of errors in these two functions becomes
non-zero, leading to biased estimators. Munshi (2003) uses fixed effect and
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instrumental variable (IV) treatments, and Luke and Munshi (2006) use IV treatment.
Given that we are running multinomial regression with cross section data, we
cannot simply follow the treatments used by our predecessors. Rather, we use two
alternative approaches akin to the IV treatment to check the endogeneity of our network
variables. First, using father’s schooling years, mother’s schooling years and their
squared terms together with all the explanatory variables, the network formation
functions are estimated and the residuals are computed. Then, we run a multinomial
regression model with the residuals, observing no significant coefficients of the
residuals at any conventional levels. This indicates the zero covariance between the
error terms. Second, we construct a new dichotomous dependent variable for each
occupation. Then, by occupation, we run OLS regressions of the linear probability
models that explain the choice of a particular occupation against all the others. In each
of these models, we include the same residuals and find no significant coefficients of
the residuals at any conventional levels. Based on these two test results, we infer that
our results in Table 4 do not seriously suffer bias.
We now turn to the interpretation of the results. The results of the z-tests for
each network variable show that some particular types of networks selectively affect
occupational choice. In order to start self-employment occupation, having networks of
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all kinds of relationship (i.e. family/relative, friend, and acquaintance) is important. To
enter into unskilled work at small enterprises, an increase of the family/relative network
more effectively raises the probability than the other types of relationships do, and
among family/relative networks, knowing those who are in regular employee positions
is by far the most effective. The impacts of personal networks in entering unskilled
work at large enterprises are not as significant as those at small enterprises (see the
F-test at the lower portion of the table). Nevertheless, we can still observe that having
friends in a regular employee position helps in obtaining jobs at large enterprises. As
we will see later, personal networks are used to reduce information asymmetry or
transaction costs; the network in regular employee positions seems to serve these
purposes sufficiently both at small and large enterprises.
The schooling years significantly increase the chance to obtain unskilled
positions with a greater impact for large enterprises. In comparison with the impact of
the personal network, it is worth noting that in acquiring unskilled positions at small
enterprises, having one more year of schooling has a smaller impact than having one
more family/relative network in a regular employee position, indicating the comparable
importance of the family/relative network at the labor market of this type of occupation.
The same applies to the friend network in regular position for the large enterprises.
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Regarding technical/skilled work, the personal networks have no significant
influence. The marginal effect of schooling years becomes highly significant if we
evaluate it at the completion year of tertiary education (the coefficient of 0.29 with the
z-statistic of 4.95), indicating the predominant importance of advanced level educational
attainment.
We now summarize the findings of this section. First, to acquire
self-employment occupation, the personal networks of any of the three types of
relationships play significant roles. Second, in addition to education, having the
family/relative network particularly in regular employee positions is important in
increasing the chance to move to unskilled non-farm work at small enterprises, and
having the friend network in regular employee positions helps to obtain unskilled work
at large enterprises. Third, having tertiary level education is crucial to acquire
technical/skilled work.
V. Personal networks and the terms of employment
Realizing the importance of personal networks, we investigate through what
mechanisms they affect labor market outcomes. Selective use and the differential
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impacts of the personal networks are the keys to answer this question. Hence, we
focus on two occupations in which these features are salient, i.e. unskilled work at small
enterprises and that at large enterprises.
Literature review and hypotheses
The theoretical literature shows that because of the reduction of uncertainty
(e.g. uncertainty about true ability) and the determent of opportunistic behaviors (e.g.
shirking and unexpected turnover), workers employed through personal networks are
likely to possess the attributes that employers prefer, thus labor market outcomes are
characterized as (1) higher wage rates, (2) lower turnover rates, and (3) lower wage
growth rates (Simon and Warner, 1992) (Figure 3). 7 The last feature is derived from
the proposition that although the employees not utilizing networks are hired under
greater initial uncertainty and start at lower wage rates, those who prove to be good can
7 Based on the model by Simon and Warner (1992), we clarify the difference between the
reduction of uncertainty and the determent of opportunistic behaviors. Let θ be a random
variable indicating the attribute of an applicant capturing not only ability but also
trustworthiness. Assume it follows distribution . What an employer can observe
is the applicants’ attribute with noise: where . The reduction of
uncertainty can be expressed as the reduction in in this setup. The determent of
opportunistic behavior can be expressed as the increase in
),( 2θσμN
εθθ +=ˆ ),0(~ 2εσε N
2εσ
μ . In either case, the model shows
that employers offer wages with premiums.
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stay and catch up to the wage level of employees hired through networks.8 In this
paper let us call these three features as “network premiums” paid for the reduction in
information asymmetry.
One condition regarding this proposition is worth mentioning here. Another
advantage of the use of personal networks is the reduction in job search costs.
Obviously, this would increase the probability of entering into occupations that job
seekers can easily get opening information on. At the same time, the use of personal
networks for this purpose may result in discounted starting wage rates (Bentolila,
Michelacci, and Suárez, 2004). The rationale is that networks help workers to find
jobs more easily at certain occupations and thus for some workers, entering into jobs in
which workers cannot fully exploit their abilities becomes more attractive than
continuing searching through formal channels and being unemployed for a while.
Hence, we should keep it in mind that the network premiums of higher starting wage
rates could be nullified by this effect if the advantages of the use of personal networks
for the resolution of information asymmetry are not so large.
In this regard, depending on the magnitudes of the advantages, the realization
8 If the use of personal networks has only one effect, that is the determent of opportunistic
behaviors, the uncertainty about applicants’ attributes does not change regardless of the use of
personal networks. Hence in this case the same wage growth rate is observed.
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of network premiums differs. More specifically, we would claim that the network
premiums are realized particularly in small enterprises with the use of family/relative
network in which the advantage of the use becomes the largest. The size of enterprises
matters because for large enterprises, the use of networks is an inefficient device to hire
workers in large number because the number of would-be-applicants is limited by the
size of the networks. Moreover, large enterprises are equipped with formal
management systems that are effective in preventing production line workers from
shirking and from quitting unexpectedly. Hence, the advantages from the use of
networks are limited. In contrast, in employing unskilled workers at small enterprises
where personnel management is informal, workers’ trustworthiness is highly
appreciated. In order to find a sufficient number of such workers for a small enterprise,
the use of personal networks and the provision of network premiums is an effective
method. Hence, the network premiums tend to be observed more explicitly in the case
of small enterprises.
Of different types of personal networks, the use of family/relative network is
considered to facilitate providing trustworthy workers to small enterprises most
effectively. Literature in sociology argue that the network formed on the bases of
some inherent common factors is affluent in a kind of social capital that strengthen the
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senses of loyalty, solidarity, and trust among the members; family/relative network is a
typical example of such a network (Gittell and Vidal, 1998; Putnam, 2000; Woolcock
and Narayan, 2000).9 The literature on the value system in the Philippines argues that
in the lowland Philippines these senses are strongly and unchangeably recognized in the
group of family and close relative members (Lynch, 1967; Hollnsteiner, 1967;
Hollnsteiner, 1972; Tamaki; 1982).10 This argument leads us to claim that the
family/relative network most effectively reduces information asymmetry not only in
conveying information about job applicants’ attributes but also in increasing workers’
loyalty and suppressing their opportunistic behaviors.
Summarizing the arguments above, we hypothesize:
Hypothesis-1: In hiring unskilled workers at small enterprises, the network premiums
are realized most strongly among the workers who obtained the jobs through the
9 This social capital is called “bonding social capital.” Putnam (2000, pp. 22-23) explains its
feature: “Bonding social capital is good for undergirding specific reciprocity and mobilizing
solidarity. Dense networks in ethnic enclaves, for example, provide crucial social and
psychological support for less fortunate members of the community, while furnishing start-up
financing, markets, and reliable labor for local entrepreneurs (emphasis mine).” 10 Kinships in the Philippines is bilateral, meaning that both father’s and mother’s relatives are
considered as kin. There is no clear rule that determines which relatives are included and
which are excluded from one’s social networks. Kaut (1965) argues that they are determined
by contingent factors. In this regard, the strength of relationships outside of family and close
relatives may be quite uncertain and changeable and thus people outside of this circle tend not to
share strongly the senses of solidarity, loyalty and trust.
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family/relative network, while the network premiums are not realized in hiring unskilled
workers at large enterprises.
One may argue that the locations matter for the importance of personal
networks. Within a narrow local community, the true attributes of applicants are
known by all the community members including employers to a large extent.
Moreover, regardless of the types of networks used at the time of job application,
locally hired workers must find it difficult to behave opportunistically for fear of having
a bad reputation in the local community to which they belong. In this regard, the
community-wide network serves, to some extent, as a substitute for the family/relative
network within the narrow local community. If so, all the workers who were employed
in enterprises located nearby our study village while living inside the village would be
receiving the network premiums. On the other hand, when villagers move to distant
enterprises, asymmetric information problems are resolved most effectively when the
family/relative network is used. Therefore, only the family/relative network users
receive the significant network premiums in this case. Our second hypothesis is:
Hypothesis-2: Among workers from our study village who are employed in unskilled
positions at small enterprises, those who are employed by the enterprises located nearby
the village receive no significant difference in the network premiums for the use of the
25
family/relative network, whereas among villagers working in distant enterprises,
family/relative network users receive the premiums.
Descriptive statistics
Table 5 reports the descriptive statistics by occupation and by channel to the
current job. In interviewing regarding the job history, a detailed question about the
channels used by a respondent was asked.11 For the hypothesis testing, we classify the
channels into four categories: (1) family/relative, (2) friend, (3) acquaintance, (4)
advertisements(ads) /employment agencies. The last category represents the case that
no personal network is used. Note that there is no individual who has entered into
unskilled jobs at small enterprises through ads or employment agencies, while at large
enterprises the four channels are used almost equally. This seems to indicate that at
large enterprises, the use of personal networks does not entail much advantage as
expected in the literature review section.
The terms of employment look consistent with Hypothesis-1 at small
enterprises where those who entered through the family/relative channel receive higher
starting wage rates, stay longer in that occupation, and experience lower wage growth 11 One shortcoming of the questionnaire design is that in cases where a personal network was
used, the relationship with that person was asked but the position of that person was not asked.
26
than those who entered through the other channels.12 Although the growth rates are
lower for them, the current wage rates are still higher than for others. Their schooling
years are slightly greater than others. For hypothesis testing, we have to show that the
workers hired through the family/relative channel receive higher wages even after
controlling for this effect. The table also shows that those who entered through the
family/relative channel have larger family/relative networks at the time of the job entry
(2.46) than do those who entered through the other channels (1.44 and 1.10). The
same applies for either channel (friend or acquaintance), indicating a covariate
relationship between the relationship used by a job seeker and the size of her/his
personal network in that relationship.
At large enterprises, starting wages become higher when Ad./Agent is used,
which may imply that the magnitude of Bentolila, et al. (2004)’s discounted starting
wage effect is larger than that of the network premiums at large enterprises. However,
the differences are small. We test whether these numerical differences are statistically
insignificant at large enterprises as postulated in Hypothesis 1. As expected, those
who used ads or agencies are least equipped with personal networks. Comparing the 12 This does not necessarily mean that different wages are offered for the same positions.
Applicants applying through personal networks may be offered a better position than the lowest
position which she/he would be offered otherwise. Actually, we observed many examples of
this kind in our study site.
27
sums over family/relative, friend, and acquaintance networks, we see that the total
non-agricultural network is smallest (5.94 persons) for those who used ads or
agencies.13 This is further support for the possible relationship between the channel
used and the network size.
The descriptive statistics for Hypothesis-2 are shown in Table 6. The
locations are classified into four categories: (1) local community, (2) vicinity, (3) distant
major cities, (4) Metro Manila. Category (1) includes enterprises located in the study
village’s municipality and its adjacent municipalities. Category (2) includes those
located in cities along highways up to Metro Manila. Category (3) includes enterprises
located in other cities except for Metro Manila. The existence of the network
premiums of family/relative channel users is most clearly observed among enterprises in
the vicinity. Although the network premiums of family/relative channel users can also
be detected in the local community, the differences between family/relative network and
acquaintance channel users are not as large as in the vicinity. For example, the
difference in starting wage between family/relative network users and acquaintance
network users is 27 pesos (189.3-162.3) or 14 % in the local community, whereas it is
116.7 pesos (249.4-132.7) or 47 % in the vicinity, indicating that acquaintance channel
13 The sum of 0.39, 2.11, and 3.44.
28
users in the vicinity are hired at much more discounted wage rates than those in the
local community. Likewise, the network premiums among family/relative network
users are more clearly observed in the vicinity than in the local community for other
variables in Table 6. Due to missing observations in the acquaintance channel in
distant and Metro Manila enterprises, the same comparison is infeasible.
Regression results
For testing Hypothesis 1 by means of regression analysis, the dependent
variables we use are (1) log of starting daily wage rate, (2) years in current job, (3) wage
growth rate (annual), and (4) log of current daily wage rate. To control for different
starting years, starting wage rate is converted into the real term with the whole sale price
index. The explanatory variables are the dummies indicating the channels to current
job where the base category is the family/relative channel. We also control other
factors usually employed: schooling years and its squared term, age and its squared term,
male dummy, and years of experience before current job.14
Two econometric issues are addressed. First, the sample selection problem
14 One may be concerned about the multicollinearity problem between schooling years and
network channel dummies. However, the correlation coefficients are not statistically
significant at any conventional levels.
29
may arise as we run the regression by occupation. To control for this, we include the
selection correction term which is defined as ( )[ ])(
)(1
jyprobjyprob
i
i
==Φ−φ , where [.]φ and
are standard normal density and distribution functions and is the
predicted probability to enter into the occupation j of our interest which is obtained
from the multinomial logit model in the previous section (Lee, 1983). Second, the
endogeneity problem may arise as the job channel is workers’ choice variables. We
apply the instrumental variable (IV) method using the identifying instruments such as
the sizes of the three types of personal network (i.e., family/relative, friend, and
acquaintance network sizes at the time of job entry), father’s and mother’s schooling
years, and the value of agricultural assets. As we observe a covariate relationship
between the sizes of networks and the job channels, we expect them to serve as relevant
instruments.
(.)Φ )( jyprob i =
The regression results for unskilled workers at small enterprises are reported in
Table 7. Table A1 in Appendix shows the first stage regression results of instrumented
variables. The IV relevance tests in Table 7 as well as the first stage F tests in Table
A1 are highly significant, indicating that the instrumented variables are properly
identified. The over-identification tests show the difficulty of rejection of the
exogeneity of the instruments at any conventional levels, adding confidence to the
30
validity of our IV specifications. Hence, the following discussion relies on our IV
results, although Hausman tests do not strongly suggest the necessity of IV treatment.
A key finding is that those who used the friend or acquaintance channel started
their work with significantly lower wage rates than those who used the family/relative
channel. Although the signs are correct, the regressions of years in current job and
wage growth rate do not have significant personal network coefficients. Nevertheless,
as we hypothesized, the gap in current wage between the friend network users and the
family/relative network users becomes insignificant. Although it is still significant, the
same gap between the acquaintance network users and the family/relative network users
becomes smaller than the gap having existed at the starting time. The gaps become
more subtle and insignificant if we limit our sample to workers who stay longer than
five years (the last column of Table 7). Another key finding is that the schooling years
do not significantly determine the labor market outcomes, implying that, once one
enters into this category of occupation, what is appreciated is not ability indicated by
schooling years but the trustworthiness of the applicant that is assured through the
family/relative network. Interestingly, however, compared with the full sample current
wage regression, the results in the last column tell us that the coefficients of schooling
years improve their significance, while job channel dummies become less significant.
31
This implies that once the workers can prove to be trustworthy and thus can stay long,
their wage level tends to reflect their ability indicated by schooling years. In this
regard, we would argue that, holding workers’ ability equal, the difference in long run
earnings is not significant across the channels used, once people can enter into this
category of occupation and can stay long.
Table 8 shows that the results for the sample of unskilled workers at large
enterprises. The test statistics in the lower portion of Table 8 as well as those in Table
A1 show the validity of our IV specification.15 The coefficients reported in Table 8 are
in clear contrast to those in Table 7. First, the job channel dummies have no
significant impact on any dependent variables (F-test at the lower portion of the
table).16 Second, in wage functions, the schooling years and its squared term become
15 In these IV models, two identifying instruments (father’s schooling years and mother’s
schooling years) are not used in the first stage regression as the inclusion of them deteriorates
the result of over-identification tests. The reason for this may be the influence from parents’
education to children’s wage through children’s education. Meanwhile, in small enterprises,
children’s education is not statistically significant and thus the path of this influence does not
exist there, allowing us to use parents’ education as identifying instruments. 16 There is a concern that this result is partly due to restrictions under the labor law in the
Philippines. The positions of regular employees are highly protected by the law in the
Philippines, and thus companies cannot easily dismiss them. Hence, the salaries of regular
employee are regarded as fixed costs. Meanwhile, the law allows companies to dismiss
probationers if it is before six months of their employment period. In six months, companies
have to decide whether they will promote probationers to regular employees. Ohno (1997)
reports that the companies that want to keep fixed costs low tend to replace probationers every
32
highly significant with a conventional sign to each. Summarizing the results in Tables
7 and 8, we conclude that Hypothesis 1 holds.
In order to test Hypothesis 2, new interaction terms between network channels
and location dummies are introduced, with the results reported in Appendix Table A2.
The signs and magnitudes of the network variables are consistent with Hypothesis 2.
However, as summarized in Table 9, over-identification tests for the wage growth
function and the currant wage function doubt on the exogeneity of identifying
instruments. Besides, the regression of years in current job has no significant
coefficients of the network variables in vicinity. What we can claim from Table 9 with
statistical confidence is that in vicinity the starting wage premium is given to the
family/relative network users while it is not so in local community. Hypothesis 2
holds at least regarding the starting wage rate.17
six months. If this is the prevalent case, unskilled workers are hired as probationers regardless
of the personal networks. Ohno (1997) shows that this feature is likely to be observed at
garment factories. We removed garment factories from our sample and checked whether the
network premiums appear. Our results (not shown here) shows qualitatively similar results.
Besides, our data shows that years in current job have a range from 1.2 to 5.0 years (Table 5).
Hence, we conclude that the frequent replacement of probationers is not prevalent, and thus the
impact from that practice is not materially large. 17 To provide additional evidence, we have also examined between-location differences. As
argued, the users of any kind of personal networks in the local community receive network
premiums because information asymmetry is resolved there. At the same time, the users of the
family/relative network in the other locations also receive the network premiums as information
33
VI. Summary and implications
Using data of both the residents of and the emigrants from a village in the
Philippines, this paper shows the differential impacts of personal networks on rural
peoples’ move from agricultural to non-agricultural occupations. Our regression
results show that having the family/relative network especially in regular employee
positions in non-agricultural sectors appreciably increases the probability of obtaining
unskilled positions at small enterprises. We also find that among those who participate
in that occupation, the ones actually using the family/relative network channel are
characterized by a higher starting wage. These findings imply that the family/relative
network especially in regular employee positions has a referral function, and thus its use
results in the realization of network premiums in return for the resolution of asymmetric
information problems. Because this is the root of the existence of network premiums,
asymmetry is resolved among them. Hence, no significant difference should be observed
between any kind of network users in the local community and the family/relative network users
in other locations, if no location fixed effect exists on wage dynamics. However, we have to
admit that this is a big if because Table 6 shows that the wage rate increases as we move from
the local community to Metro Manila. In fact, our F test results (now shown here) indicate a
marginally significant difference in starting wages and a significant difference in current wages.
Meanwhile, no significant difference is detected in years in current job and in wage growth rate.
Taking into account the highly possible existence of location fixed effects, we judge that the
indicated between-location differences are not strong enough to reject Hypothesis 2.
34
these premiums become insignificant if we limit our sample to the small enterprises
located nearby our study village where the community-wide network substitutes for the
family/relative network within the narrow local community. Our regression results
also imply that even if the friend or the acquaintance network users start with lower
wage rates, the disadvantages may not be so large in the long run because, among them,
workers who can prove to be trustworthy and thus stay long can catch up to the wage
level of the family/relative channel users.
Our results also show that the probability of obtaining unskilled positions at
large enterprises increases slightly with the size of the friend network in regular
employee positions. The reason is not the same as the case for small enterprises,
because the network premiums are not realized with any kinds of network channels, but
the wage dynamics are determined mainly by schooling years and experience.
Presumably, the positive effect of the friend network on job acquisition stems mainly
from the reduction in search costs by sharing job opening information among friends in
regular employee positions. Nevertheless, the impact of the friend network on the
employment at large enterprises is much smaller than that of the family/relative network
on the employment at small enterprises; thus, we conclude that labor market
imperfections are resolved at large enterprises not mainly by the personal networks but
35
by other institutional and non-institutional devices. The diminishing impact of the
personal network becomes a more salient feature in the acquisition of technical/skilled
jobs. Our regression results show that the completion of tertiary level education is
crucial for the acquisition of skilled/technical jobs, whereas the personal networks have
little impact. Yet, it is worth reminding that such little impact may be attributed to the
small sample size of this occupation in our study village. We leave more detailed
analyses on that occupation for future research.
What implications can be drawn for poverty reduction? Taking a pessimistic
view first, we may say that due to imperfections in the labor markets the possibility that
people can move out from the lowest strata still depends on nascent factors like the size
of the family/relative network over the non-agricultural sector. However, our analysis
shows that the role of personal networks is changing along with economic and social
modernization. The recent development of large scale enterprises in the Philippines is
widening the path for rural people to ascend to the upper income strata by improving
their acquired attributes like education. Such development is preparing a condition to
achieve more equal occupational opportunities. However, to the extent that high
education is disproportionately borne by the rich, there is a danger that education may
become a greater source of inequality.
36
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Table 1: Number of households by type from 1966 to 2001 survey years
Year Farmer a Agricultural laborer b Non-farm worker c Total 1966 46
(70)d 20
(30) 0
(0) 66
(100) 1976 54
(50) 55
(50) 0
(0) 109
(100) 1987 53
(34) 98
(62) 7
(4) 158
(100) 1997 41
(16) 163 (61)
62 (23)
266 (100)
2001 40 (12)
148 (45)
142 (43)
330 (100)
Sources: Data from 1996 to 1997 are from Hayami and Kikuchi (2000). The data on 2001 are from IRRI
Livelihood Survey.
Notes: a Households cultivating paddy fields b Households having no land to cultivate and engaging in hired farm work c Households having no land to cultivate and engaging only in non-farm activities d Percentages in totals are shown in parentheses
Table 2: Structure of data
Occupation Village residents and their
household members living
outside of the village
Sample w/ current and
past information.
(1)
Number
(2)
%
(3)
Number
(4)
%
Farming 49 (6.6) 4 (0.9)
Agricultural Labor a 259 (34.7) 106 (23.9)
Overseas work 42 (5.6) 13 (2.9)
Self-employment 191 (25.6) 154 (34.8)
Unskilled labor-Small enterprise 59 (7.9) 54 (12.0)
Unskilled labor-Large enterprise 79 (10.6) 78 (17.6)
Technical/skilled work 67 (9.0) 35 (7.9)
Total 746 (100) 444 (100)
Note: The figure excludes homemakers (311 persons), students or children aged below 6 (364 persons), retired
old persons (11 persons) from the total number of 1,432 persons. a This category includes within-village casual work
Table 3: Descriptive statistics by occupation (at the time of the job entry)
Ag. Laba Self-emp Unskilled
Small Unskilled
Large Tech/Skille
d All
Personal networks in non-ag Family/Relative networks in politics/bureaucracy 0.08
(0.29) 0.16
(0.43) 0.39
(0.59) 0.18
(0.44) 0.47
(0.81) 0.20
(0.49) in manager level positions 0.05
(0.21) 0.15
(0.37) 0.16
(0.42) 0.06
(0.24) 0.06
(0.23) 0.10
(0.31) in professions 0.33
(0.68) 0.77
(0.95) 1.15
(0.90) 0.61
(0.88) 1.69
(1.11) 0.77
(0.96) in regular employee positions 0.07
(0.24) 0.15
(0.37) 0.26
(0.52) 0.10
(0.30) 0.03
(0.16) 0.12
(0.35) Friend networks in politics/bureaucracy 0.05
(0.21) 0.18
(0.47) 0.30
(0.69) 0.10
(0.38) 0.63
(1.12) 0.19
(0.55) in manager level positions 0.42
(0.58) 0.33
(0.57) 0.21
(0.53) 0.18
(0.41) 0.33
(0.53) 0.31
(0.54) in professions 1.01
(0.79) 0.92
(0.80) 0.96
(0.80) 1.00
(0.82) 0.94
(0.95) 0.96
(0.81) in regular employee positions 0.60
(0.51) 0.42
(0.58) 0.60
(0.63) 0.58
(0.63) 0.58
(0.55) 0.54
(0.58) Acquaintance networks in politics/bureaucracy 0.88
(0.82) 1.39
(1.11) 1.22
(0.91) 1.26
(0.98) 1.50
(0.87) 1.23
(0.99) in manager level positions 0.51
(0.65) 0.87
(0.83) 0.91
(0.71) 1.01
(0.72) 1.16
(0.87) 0.84
(0.78) in professions 0.98
(0.76) 1.20
(0.91) 1.01
(0.82) 1.16
(10.9) 0.94
(1.01) 1.10
(0.91) in regular employee positions 0.10
(0.33) 0.64
(0.76) 0.28
(0.53) 0.41
(0.59) 0.61
(0.90) 0.42
(0.67) Organizational networks
Member of organizations (dummy)
0.53
0.39
0.39
0.44
0.6
0.46
Other individual characteristics Schooling years 8.1
(2.8) 7.7
(2.9) 9.9
(2.7) 9.9
(2.1) 13.7 (1.9)
9.0 (3.1)
Age 25.4 (10.0)
34.9 (13.1)
32.3 (9.5)
28.3 (7.8)
36.2 (10.4)
31.1 (11.6)
Male (dummy) 0.7
0.7
0.5
0.5
0.6
0.6
HH characteristics HH size 6.8
(2.2) 5.8
(2.4) 6.5
(2.3) 6.3
(2.6) 6.4
(2.2) 6.3
(2.38) Land assets 4.8
(25.1) 6.3
(33.5) 27.9
(92.7) 16.9
(77.5) 44.8
(171.7) 13.7
(72.0) Ag assets 19.0
(50.4) 30.0
(62.8) 30.0
(51.7) 32.1
(62.1) 95.4
(116.3) 33.0
(67.1) Non-ag assets 68.2
(148.0) 53.6
(103.7) 100.1
(203.4) 73.7
(80.2) 337.5
(489.2) 89.8
(200.7) Obs. 106 154 54 78 35 427
Standard deviations in parentheses a This category includes within-village casual work
Table 4: Marginal effects from multinomial logit regression of an occupational choice model (1) (2) (3) (4) Self-employment Unskilled labor
Small enterprises Unskilled labor
Large enterprises Technical/Skilled
work Personal networks in non-ag
Family/Relative in politics/bureaucracy -0.069 0.081 0.035 0.002
(0.82) (2.03)** (0.53) (0.96) in manager level positions 0.290 -0.054 -0.149 0.001
(2.06)** (0.71) (1.18) (0.48) in professions 0.090 0.064 -0.055 -0.000
(1.69)* (1.95)* (1.27) (0.03) in regular employee positions -0.097 0.157 0.085 -0.002
(0.74) (2.36)** (0.79) (0.52) Friends
in politics/bureaucracy 0.097 0.069 0.001 0.002 (1.21) (1.64) (0.01) (1.02)
in manager level positions 0.176 -0.072 -0.112 -0.000 (2.16)** (1.36) (1.61) (0.28)
in professions 0.013 0.049 -0.011 -0.000 (0.23) (1.36) (0.26) (0.03)
in regular employee positions -0.097 0.056 0.106 0.001 (1.23) (1.25) (1.67)* (0.71) Acquaintances
in politics/bureaucracy -0.019 0.011 0.023 0.002 (0.51) (0.45) (0.73) (1.09)
in manager level positions -0.042 0.005 0.060 0.001 (0.72) (0.16) (1.29) (0.86)
in professions 0.028 0.022 -0.005 -0.001 (0.61) (0.71) (0.12) (0.77)
in regular employee positions 0.240 -0.052 0.043 -0.001 (3.30)*** (1.13) (0.70) (0.80) Organizational network Member of organizations (dummy)a -0.038 -0.064 -0.007 -0.001 (0.59) (1.63) (0.13) (0.74) Other individual chara
Schooling years -0.060 0.026 0.046 0.003 (4.66)*** (3.10)*** (4.13)*** (1.21)
Age 0.008 0.002 -0.004 0.000 (2.49)** (1.20) (1.52) (0.20) Male (dummy)a -0.006 -0.050 -0.049 0.001 (0.09) (1.15) (0.86) (0.78) HH chara
HH size -0.027 0.006 0.006 -0.00 (1.94)* (0.73) (0.57) (0.19)
Land asset 0.000 0.000 0.000 -0.00 (0.51) (0.34) (0.35) (0.85)
Ag asset 0.000 -0.001 -0.000 -0.00 (0.44) (1.65)* (0.19) (0.19)
Non-ag asset -0.000 0.000 -0.000 0.00 (1.29) 0.37 (0.22) (0.59) Constant 0.532 -0.528 -0.373 -0.038 (2.61)*** (4.24)*** (2.17)** (1.19) F-test (H0: no network variables has effect)
37.98 [0.00]
30.95 [0.00]
17.27 [0.13]
1.41 [0.99]
Log-likelihood -445.8 LR test for zero slope 176.6
[0.00] Observations 427 Base category: Agricultural labor (including within-village casual work) The marginal effects are evaluated at the mean values of explanatory variables. Absolute value of z-statistics in parentheses; P-values in brackets * significant at 10%; ** significant at 5%; *** significant at 1% a The marginal effect of dummy variable is the change of dummy from 0 to 1.
Table 5: Descriptive statistics by occupation and by job channel among wage workers
Occupation Unskilled-small enterprises Unskilled-large enterprises
Channel to current job Fa/Re Fr Acq Fa/Re Fr Acq Ad/ Agent
Starting wage adj. to present value
(P./day)
266.3 (130.4)
200.2 (131.5)
159.6 (114.8)
210.3 (98.9)
220.8 (83.4)
238.7 (104.0)
266.3 (59.8)
Years in current job. (Years)
8.5 (8.9)
7.2 (6.7)
4.0 (7.1)
1.2 (2.2)
4.8 (6.0)
5.0 (6.4)
2.3 (2.4)
Wage growth* (annual rates)
0.04 (0.05)
0.08 (0.10)
0.14 (0.14)
0.06 (0.06)
0.01 (0.46)
0.09 (0.12)
0.08 (0.07)
Current wage (P./day)
304.9 (123.2)
253.4 (117.1)
188.4 (97.4)
229.3 (111.1)
261.9 (87.9)
284.9 (99.1)
304.6 (79.3)
Years of experience before current job (Years)
9.9 (9.1)
11.8 (7.2)
6.9 (7.8)
3.1 (3.1)
7.7 (8.0)
6.9 (6.3)
3.6 (7.7)
Schooling years (Years)
10.3 (2.2)
9.7 (2.9)
9.3 (3.3)
9.6 (2.1)
9.4 (2.6)
10.7 (2.1)
10.2 (1.1)
Age (Years)
30.4 (8.8)
34.3 (7.9)
34.2 (13.9)
26.4 (5.6)
30.6 (8.7)
30.3 (8.9)
25.8 (6.5)
Family/Relative non-ag net (Persons)
2.46 (1.24)
1.44 (1.20)
1.10 (1.45)
1.65 (1.79)
1.09 (1.38)
0.61 (0.92)
0.39 (0.78)
Friend non-ag net (Persons)
1.88 (1.70)
2.39 (1.29)
2.00 (1.56)
2.05 (1.47)
1.73 (1.75)
1.61 (1.37)
2.11 (0.83)
Acq/Colleague non-ag net (Persons)
3.15 (1.82)
3.78 (1.31)
4.00 (1.94)
3.55 (2.11)
3.86 (2.59)
4.61 (1.85)
3.44 (1.33)
Obs 26
18 10 20 22 18 18
Obs ( by occup) 54 78
Standard deviations in parentheses
* Sub-sample excluding workers working less than one year.
Ex. Agricultural labor wage=P150/day
Table 6: Descriptive statistics by job channel and by enterprise location among unskilled labors at small
enterprises
Unskilled labor at small enterprises
Local Community Vicinity Distant Major Cities Metro Manila
Channel to current job
Fa/Re Fr Acq Fa/Re Fr Acq Fa/Re Fr Acq Fa/Re Fr Acq
Starting wage adj. to present value (P./day)
189.3 (87.6)
118.7 (64.9)
162.3 (113.5)
249.4 (129.4)
270.6 (147.0)
132.7 (108.9)
288.5 (93.5)
133.6 (55.8)
na 312.2 (164.8)
280.9 (163.1)
253.8
Years in current occup. (Years)
8.2 (9.8)
6.6 (1.8)
5.8 (9.3)
12.2 (12.6)
1.2 (1.3)
2.8 (4.9)
9.3 (7.0)
14.3 (5.6)
na 6.2 (7.7)
8.3 (9.6)
na
Wage growth* (annual rates)
0.04 (0.05)
0.07 (0.07)
0.06 (0.08)
0.03 (0.02)
0.12 (0.21)
0.29 (0.01)
0.06 (0.02)
0.06 (0.02)
na 0.02 (0.07)
0.08 (0.07)
na
Current wage (P./day)
208.2 (47.4)
147.2 (36.7)
176.4 (121.6)
257.7 (132.5)
302.0 (110.7)
187.0 (84.2)
375.1 (63.7)
211.1 (104.8)
na 348.9 (143.1)
367.5 (77.8)
253.8
Obs 6 5 5 5 5 4 6 4 0 9 4 1 Obs ( by occup)
15 15 10 14
Standard deviations in parentheses
* Sub-sample excluding workers working less than one year.
Ex. Agricultural labor wage=P150/day
Table 7: Estimation of starting wage, years in current job, wage growth rate, and current wage functions for unskilled work at
small enterprises
ln(Starting wage) Years in current job Wage growth rate ln(Current wage) ln(Current wage) (years in current
job > 5 years) OLS IV OLS IV OLS IV OLS IV OLS IV Channel to current joba
Friendb -0.392 -0.465 -2.847 -2.697 0.013 0.023 -0.336 -0.277 -0.350 -0.171
(1.64) (1.68)* (1.94)* (1.62) (0.49) (0.77) (1.92)* (1.31) (1.12) (0.62)
Acquaintanceb -0.698 -1.175 -3.160 -2.538 0.056 0.120 -0.529 -1.041 -0.294 -0.166
(2.01)* (2.25)** (1.48) (0.81) (1.23) (1.55) (2.07)** (2.61)*** (0.38) (0.16)
Ad./Agentb
Other individual chara.
Schooling years 0.403 0.278 -2.069 -1.885 -0.018 -0.000 0.115 0.032 0.465 0.506
(1.14) (0.82) (0.95) (0.92) (0.45) (0.01) (0.44) (0.12) (0.93) (1.30)
Schooling years sq. -0.020 -0.014 0.087 0.078 0.001 0.000 -0.005 -0.001 -0.023 -0.024
(1.15) (0.87) (0.82) (0.79) (0.58) (0.13) (0.36) (0.07) (0.90) (1.23)
Age 0.037 0.029 -0.601 -0.596 0.018 0.021 0.073 0.046 0.154 0.131
(0.46) (0.35) (1.19) (1.22) (1.90)* (2.15)** (1.21) (0.75) (1.21) (1.14)
Age sq -0.001 -0.001 0.008 0.007 -0.000 -0.000 -0.001 -0.001 -0.002 -0.002
(1.19) (0.91) (1.19) (1.15) (1.34) (1.63) (1.45) (0.76) (1.40) (1.31)
Male (dummy) 0.214 0.248 -1.552 -1.611 -0.023 -0.027 0.208 0.206 -0.255 -0.291
(0.85) (1.05) (1.00) (1.13) (0.75) (0.96) (1.12) (1.14) (0.69) (1.01)
0.077 0.063 0.844 0.863 -0.004 -0.003 -0.001 -0.014 0.036 0.048 Years of experience before current job (2.66)** (2.19)** (4.72)*** (4.96)*** (1.20) (0.92) (0.06) (0.65) (0.92) (1.45)
0.342 0.454 2.086 1.953 0.007 -0.011 -0.100 0.054 0.099 0.066 Selection correction term (0.99) (1.32) (0.98) (0.94) (0.18) (0.26) (0.39) (0.20) (0.14) (0.12)
Const 3.092 3.888 22.134 21.093 -0.250 -0.376 3.914 4.757 0.589 0.685
(1.53) (1.96)** (1.78)* (1.77)* (1.08) (1.53) (2.64)** (3.15)*** (0.19) (0.26)
F-test (H0: no job channel has effect)
2.49* [0.09]
7.61** [0.02]
2.22 [0.12]
3.18 [0.20]
0.76 [0.47]
2.70 [0.25]
2.92* [0.06]
8.31** [0.02]
0.62 [0.54]
0.38 [0.83]
IV relevance test (chi-sq) c
18.3*** [0.00]
18.3*** [0.00]
14.36** [0.04]
18.3*** [0.00]
9.76* [0.08]
Overidentification test (chi-sq) d
5.40 [0.25]
1.66 [0.80]
9.84 [0.13]
6.25 [0.18]
2.49 [0.64]
Hausman test (chi-sq) e 2.08 [0.35]
0.12 [0.94]
1.33 [0.51]
2.97 [0.23]
1.88 [0.38]
obs 54 54 54 54 49 49 54 54 24 24
Absolute value of t-statistics in parentheses; P-values in brackets * significant at 10%; ** significant at 5%; *** significant at 1% a Dummy variable. Base is Family/Relative channel. b Instrumented variable. See Appendix Table A1 for first stage regression results. c Anderson canonical correlations likelihood ratio test d Sargan’s over-identification test e Durbin-Wu-Hausman endogeneity test
Table 8: Estimation of starting wage, years in current job, wage growth rate, and current wage functions for unskilled work at
large enterprises
ln(Starting wage) Years in current job Wage growth rate ln(Current wage) OLS IV OLS IV OLS IV OLS IV Channel to current joba
Friendb 0.232 0.304 0.394 0.670 -0.057 0.234 0.123 0.371
(1.43) (0.96) (0.41) (0.36) (0.49) (0.68) (0.87) (1.31)
Acquaintanceb 0.094 0.280 0.983 -0.457 0.077 0.264 0.162 0.251
(0.56) (1.10) (0.98) (0.30) (0.68) (1.23) (1.10) (1.10)
Ad./Agentb 0.267 -0.053 0.677 2.126 0.011 -0.276 0.309 0.064
(1.53) (0.21) (0.65) (1.43) (0.08) (1.05) (2.02)** (0.29)
Other individual chara.
Schooling years 0.616 0.750 -1.630 -2.080 0.109 0.311 0.350 0.502
(3.31)*** (3.52)*** (1.47) (1.65)* (0.93) (1.67)* (2.15)** (2.65)***
Schooling years sq. -0.029 -0.037 0.086 0.114 -0.008 -0.018 -0.016 -0.024
(3.05)*** (3.34)*** (1.51) (1.76)* (1.29) (1.92)* (1.95)* (2.47)**
Age -0.057 -0.059 -0.902 -0.897 0.009 0.003 0.043 0.038
(1.07) (1.13) (2.86)*** (2.89)*** (0.24) (0.08) (0.93) (0.81)
Age sq 0.001 0.001 0.015 0.015 -0.000 -0.000 -0.001 -0.001
(0.92) (1.00) (3.26)*** (3.30)*** (0.42) (0.25) (1.46) (1.31)
Male (dummy) 0.080 -0.032 -0.086 0.541 0.005 -0.150 -0.045 -0.110
(0.59) (0.22) (0.11) (0.62) (0.04) (0.87) (0.38) (0.84)
0.032 0.028 0.529 0.539 0.013 0.009 0.041 0.034 Years of experience before current job (2.12)** (1.71)* (5.87)*** (5.61)*** (1.29) (0.74) (3.10)*** (2.36)**
0.105 0.102 0.635 0.691 -0.101 -0.106 -0.019 -0.017 Selection correction term (1.73)* (1.67)* (1.75)* (1.92)* (2.66)** (2.41)** (0.35) (0.31)
Const 2.923 2.491 19.278 20.226 -0.252 -1.031 3.047 2.438
(2.29)** (1.87)* (2.54)** (2.57)** (0.29) (0.91) (2.73)*** (2.06)**
F-test (H0: no job channel has effect)
1.07 [0.36]
1.45 [0.69]
0.35 [0.79]
4.55 [0.21]
0.65 [0.58]
2.61 [0.46]
1.39 [0.25]
1.99 [0.57]
IV relevance test 13.4*** [0.00]
13.4*** [0.00]
5.20* [0.07]
13.4*** [0.00]
Overidentification test 0.23 [0.62]
0.00 [0.99]
1.80 [0.18]
0.74 [0.38]
Hausman test (chi-sq) 3.99 [0.26]
4.55 [0.20]
4.62 [0.20]
3.25 [0.35]
obs 78 78 78 78 54 54 78 78
Absolute value of t-statistics in parentheses; P-values in brackets * significant at 10%; ** significant at 5%; *** significant at 1% a Dummy variable. Base is Family/Relative channel. b Instrumented variable. See Appendix Table A1 for first stage regression results. c Anderson canonical correlations likelihood ratio test d Sargan’s over-identification test e Durbin-Wu-Hausman endogeneity test
Table 9: Examination of the differential network effects by enterprise location for unskilled work at small enterprises (F-test
results)
ln(Starting wage) Years in current job Wage growth rate ln(Current wage) OLS IV OLS IV OLS IV OLS IV (1) Local Community
H0:Family=Friend
1.90 [0.18]
1.97 [0.16]
0.69 [0.41]
0.08 [0.78]
0.04 [0.84]
0.03 [0.85]
0.97 [0.41]
2.10 [0.15]
H0:Family=Acquaintance
1.07 [0.31]
0.73 [0.39]
0.69 [0.41]
4.04** [0.04]
0.02 [0.88]
0.02 [0.87]
0.33 [0.56]
0.13 [0.71]
(2) Vicinity H0:Family=Friend
1.29 [0.26]
3.56* [0.06]
3.62* [0.07]
3.75 [0.05]
0.49 [0.49]
0.34 [0.56]
0.63 [0.43]
0.20 [0.65]
H0:Family=Acquaintance
6.66*** [0.01]
3.32* [0.07]
1.93 [0.17]
1.64 [0.20]
4.34** [0.04]
9.71*** [0.00]
2.04 [0.15]
1.43 [0.23]
(3) Distant Major Cities H0:Family=Friend
0.24 [0.63]
0.13 [0.71]
0.00 [0.96]
0.02 [0.89]
0.03 [0.86]
0.01 [0.91]
3.99* [0.05]
4.95* [0.03]
(4) Metro Manila H0:Family=Friend
0.05 [0.82]
0.04 [0.84]
0.29 [0.60]
0.03 [0.85]
0.07 [0.79]
0.09 [0.76]
0.01 [0.90]
0.01 [0.92]
IV relevance test (chi-sq) a 33.4*** [0.00]
33.4*** [0.00]
42.8*** [0.00]
33.4*** [0.00]
Overidentification test (chi-sq) b
18.8 [0.14]
18.0 [0.16]
33.5*** [0.00]
24.9** [0.02]
Hausman test (chi-sq) c 8.57 [0.47]
12.5 [0.18]
5.12 [0.82]
9.91 [0.35]
Statistical results based on regression analyses reported in Table A2 in Appendix P-values in brackets * significant at 10%; ** significant at 5%; *** significant at 1% a Anderson canonical correlations likelihood ratio test b Sargan’s over-identification test c Durbin-Wu-Hausman endogeneity test
Appendix
Table A1: First stage regression result of instrumented variables in Tables 7 and 8
For Unskilled Small Enterprise Regression For Unskilled Large Enterprise Regression Friend Acq Friend Acq Ad/Agent Excluded Instruments
Fa/Re network -0.139 -0.057 -0.009 -0.069 -0.137
(3.30)*** (1.62) (0.27) (2.43)** (4.76)***
Fr network 0.064 -0.051 0.124 -0.050 -0.071
(1.83)* (1.75)* (3.82)*** (1.87)* (2.62)**
Acq. network -0.058 0.057 -0.016 0.091 -0.072
(1.63) (1.90)* (0.72) (4.86)*** (3.79)***
Ag. Asset value 0.002 0.001 -0.000 0.002 0.001
(1.36) (0.57) (0.01) (1.69)* (0.91)
0.028 -0.031 Father’s schooling years (1.18) (1.56)
-0.030 0.004 Mother”s schooling years (1.22) (0.19)
Included Instruments
Schooling years -0.245 -0.152 -0.160 0.089 0.128
(1.25) (0.93) (0.97) (0.65) (0.93)
Schooling years sq. 0.011 0.008 0.007 -0.003 -0.008
(1.10) (1.01) (0.85) (0.41) (1.09)
Age 0.065 -0.063 0.041 0.013 -0.003
(1.41) (1.63) (0.88) (0.34) (0.07)
Age sq -0.001 0.001 -0.001 -0.000 -0.000
(1.52) (1.87)* (0.99) (0.31) (0.03)
Male 0.210 -0.063 -0.037 0.115 -0.059
(1.58) (0.57) (0.31) (1.17) (0.59)
-0.004 -0.006 0.019 -0.007 0.002 Experience before current job (0.30) (0.51) (1.53) (0.70) (0.19)
-0.294 0.096 0.017 -0.050 -0.119 Selection correction term (1.57) (0.61) (0.25) (0.90) (2.13)**
Const 1.061 2.091 0.201 -0.731 0.673 (0.88) (2.07)** (0.17) (0.73) (0.67)
First stage F test 7.82*** [0.00]
3.12** [0.01]
4.99*** [0.00]
11.3*** [0.00]
7.94*** [0.00]
Obs 54 54 78 78 78
Absolute value of t-statistics in parentheses; P-values in brackets * significant at 10%; ** significant at 5%; *** significant at 1% For wage growth function in Table 7, a sub-sample (49 obs.) of the entire sample (54 obs.) is used. For current wage function (year>5) in Table 7, a sub-samples (24 obs.) is used. For wage growth function in Table 8, a sub-sample (54 obs.) of the entire sample (78 obs.) is used. The results (now shown here) are essentially the same.
Table A2: Results of regressions to explain the differential network effects by enterprise location for unskilled work at small enterprises. ln(Starting wage) Years in current job Wage growth rate ln(Current wage) OLS IV OLS IV OLS IV OLS IV Enterprise Location and Channelsa
(1) Local Community Local & Familyb 0.623 0.620 2.454 -0.835 0.010 0.007 0.270 0.455
(1.38) (1.40) (0.83) (0.28) (0.20) (0.18) (0.82) (1.45) Local & Acquaintanceb 0.038 0.161 -0.633 -8.127 0.019 0.016 0.032 0.316
(0.07) (0.26) (0.18) (1.92)* (0.29) (0.26) (0.08) (0.71) (2) Vicinity
Vicinity & Familyb 1.128 1.167 3.487 0.665 0.023 0.015 0.815 0.866 (2.20)** (2.36)** (1.04) (0.20) (0.41) (0.32) (2.18)** (2.47)**
Vicinity & Friendb 0.526 0.142 -3.108 -6.455 0.066 0.045 0.510 0.695 (1.13) (0.29) (1.02) (1.93)* (1.19) (0.93) (1.51) (1.98)**
Vicinity & Acqb -0.345 0.137 -1.708 -4.232 0.201 0.256 0.221 0.387 (0.70) (0.28) (0.53) (1.27) (2.69)** (3.85)*** (0.61) (1.11) (3) Distant major cities Dist & Familyb 1.248 1.196 2.600 -0.094 0.022 0.013 0.976 1.060 (2.68)** (2.74)*** (0.85) (0.03) (0.44) (0.32) (2.88)*** (3.43)*** Dist & Friendb 1.016 1.051 2.451 -0.468 0.013 0.009 0.281 0.423 (2.04)* (2.26)** (0.75) (0.15) (0.25) (0.21) (0.77) (1.28) (4) Metro Manila MM & Familyb 1.029 1.000 2.806 -1.049 0.005 -0.000 0.842 0.992 (2.44)** (2.41)** (1.02) (0.37) (0.11) (0.00) (2.75)*** (3.36)*** MM & Friendb 1.150 1.102 0.906 -0.414 0.022 0.016 0.887 0.958 (2.07)** (2.05)** (0.25) (0.11) (0.33) (0.28) (2.19)** (2.51)** Other Individual Chara.
Schooling years -0.081 -0.147 -3.339 -5.296 -0.035 -0.039 -0.052 0.032 (0.19) (0.39) (1.18) (2.05)** (0.74) (1.01) (0.17) (0.12)
Schooling years sq. 0.003 0.007 0.152 0.242 0.002 0.002 0.004 -0.000 (0.15) (0.39) (1.12) (1.94)* (0.82) (1.10) (0.26) (0.02)
Age 0.102 0.118 -0.263 -0.584 0.016 0.016 0.045 0.055 (1.12) (1.49) (0.44) (1.09) (1.50) (1.88)* (0.68) (0.97)
Age sq -0.002 -0.002 0.005 0.009 -0.000 -0.000 -0.001 -0.001 (1.82)* (2.42)** (0.65) (1.36) (1.12) (1.52) (1.06) (1.44)
Male (dummy) 0.264 0.337 -0.527 -0.336 0.007 0.010 0.112 0.091 (0.93) (1.37) (0.28) (0.20) (0.19) (0.35) (0.54) (0.52)
0.038 0.045 0.598 0.537 -0.004 -0.004 0.010 0.012 Years of experience before current job (1.05) (1.43) (2.50)** (2.50)** (0.97) (1.06) (0.37) (0.55)
Selection correction term 0.421 0.254 0.433 -0.397 -0.032 -0.050 0.112 0.125 (1.04) (0.71) (0.16) (0.16) (0.63) (1.18) (0.38) (0.49) Constant 3.587 3.670 20.338 39.811 -0.104 -0.067 4.394 3.665 (1.37) (1.51) (1.19) (2.43)** (0.36) (0.27) (2.31)** (2.13)** Observationsc 54 54 54 54 49 49 54 54
Absolute value of t-statistics in parentheses; P-values in brackets
* significant at 10%; ** significant at 5%; *** significant at 1% a Dummy variable. Base is Local & Friend. b Instrumented variable. First stage regression results are shown in Table A3. c The observation in MM & Acq category is removed as there is only one case in that category.
Table A3: First stage regression result of instrumented variables in Table A2 (1) (2) (3) (4) (5) (6) (7) (8) (9) Local &
Family Local &
Acq. Vicinity
& Family Vicinity & Friend
Vicinity & Acq.
Dist. & Family
Dist & Friend
MM & Family
MM & Friend
Excluded instruments Local*Fa/Re network 0.157 -0.018 -0.017 0.038 -0.018 -0.011 -0.004 0.001 -0.038 (7.88)*** (0.46) (0.52) (0.76) (0.44) (0.98) (0.55) (0.03) (1.21) Vicinity* Fa/Re network -0.037 0.022 0.219 -0.093 -0.039 -0.013 -0.003 0.005 -0.052 (1.53) (0.47) (5.50)*** (1.55) (0.79) (0.90) (0.35) (0.15) (1.35) Dist* Fa/Re network -0.024 0.017 -0.018 0.024 -0.002 0.199 -0.159 0.011 -0.043 (1.09) (0.41) (0.49) (0.44) (0.04) (15.3)*** (18.1)*** (0.36) (1.23) MM* Fa/Re network 0.037 -0.022 -0.021 0.004 0.023 -0.020 -0.007 -0.001 0.035 (1.20) (0.37) (0.41) (0.05) (0.37) (1.10) (0.56) (0.01) (0.72) Local*Fr network 0.011 -0.050 -0.001 0.006 -0.008 -0.004 -0.003 -0.022 -0.034 (0.74) (1.73) (0.04) (0.17) (0.27) (0.45) (0.58) (1.02) (1.44) Vicinity* Fr network 0.008 0.007 -0.070 0.142 0.025 -0.012 -0.005 -0.013 -0.056 (0.34) (0.15) (1.73) (2.31)** (0.50) (0.82) (0.54) (0.36) (1.43) Dist* Fr network -0.018 0.009 -0.024 0.030 0.001 -0.096 0.114 0.008 -0.038 (0.66) (0.18) (0.55) (0.44) (0.02) (6.07)*** (10.6)*** (0.20) (0.88) MM* Fr network 0.017 -0.003 -0.021 0.004 0.025 -0.015 -0.010 -0.149 0.191 (0.68) (0.05) (0.50) (0.06) (0.47) (0.98) (0.96) (4.06)*** (4.66)*** Local*Acq. network -0.046 0.126 0.003 -0.000 -0.004 -0.023 -0.007 -0.011 -0.029 (2.72)** (3.86)*** (0.11) (0.01) (0.10) (2.30)** (1.08) (0.47) (1.08) Vicinity*Acq. network -0.007 0.008 0.049 -0.074 0.101 -0.009 -0.006 -0.020 -0.021 (0.44) (0.27) (1.97)* (1.98)* (3.28)*** (0.99) (1.03) (0.92) (0.90) Local*Father’s school 0.047 -0.012 0.001 -0.040 0.041 0.015 0.007 -0.002 0.021 (2.03)* (0.28) (0.03) (0.69) (0.87) (1.13) (0.73) (0.06) (0.57)
0.004 -0.024 0.015 0.010 -0.034 -0.012 -0.005 -0.012 -0.017 Local*Mother’s schooling years (0.21) (0.67) (0.49) (0.22) (0.88) (1.08) (0.68) (0.45) (0.58)
0.030 -0.006 -0.056 0.114 -0.055 0.004 -0.000 -0.019 0.005 Vicinity*Father’s schooling years (2.05)* (0.22) (2.34)** (3.14)*** (1.83)* (0.42) (0.08) (0.90) (0.22)
-0.007 -0.002 0.032 -0.020 0.012 -0.009 -0.001 -0.001 -0.002 Vicinity*Mother’s schooling years (0.60) (0.09) (1.62) (0.67) (0.49) (1.29) (0.28) (0.07) (0.12)
0.001 0.005 0.006 -0.013 0.005 -0.010 0.036 -0.009 -0.007 Dist*Father’s schooling years (0.05) (0.18) (0.29) (0.40) (0.17) (1.25) (7.00)*** (0.49) (0.35)
0.020 -0.008 0.020 -0.017 -0.011 0.007 0.047 -0.023 -0.005 Dist*Mother’s schooling years (0.84) (0.18) (0.51) (0.29) (0.23) (0.48) (5.01)*** (0.66) (0.14)
-0.029 0.028 0.015 -0.010 -0.012 -0.003 0.002 0.131 -0.109 MM*Father’s schooling years (1.09) (0.54) (0.34) (0.15) (0.22) (0.16) (0.20) (3.44)*** (2.57)**
0.005 -0.010 0.010 0.001 -0.015 -0.003 -0.004 -0.022 0.030 MM*Mother’s schooling years (0.25) (0.25) (0.29) (0.02) (0.35) (0.21) (0.42) (0.74) (0.88)
Local*Ag. asset -0.004 0.005 0.001 0.000 -0.001 -0.000 -0.000 -0.001 0.000 (4.95)*** (3.49)*** (0.54) (0.24) (0.95) (1.11) (0.49) (0.49) (0.08) Vicinity*Ag. asset 0.000 -0.000 -0.001 0.000 -0.000 -0.000 0.000 0.000 0.001 (0.38) (0.12) (1.21) (0.03) (0.08) (0.01) (0.53) (0.02) (0.89) Dist*Ag. asset -0.002 0.003 0.013 -0.013 -0.005 0.049 -0.017 -0.012 -0.008 (0.14) (0.12) (0.51) (0.35) (0.15) (5.55)*** (2.93)*** (0.55) (0.35) MM*Ag. asset 0.002 -0.001 -0.000 0.001 -0.000 0.001 0.000 -0.004 0.002 (1.69) (0.32) (0.18) (0.22) (0.06) (0.86) (0.48) (2.32)** (1.05) Included instruments Schooling years 0.290 -0.161 0.017 -0.013 -0.010 -0.035 -0.002 -0.118 0.138 (3.69)*** (1.05) (0.13) (0.07) (0.06) (0.77) (0.07) (1.05) (1.10) Schooling years sq. -0.015 0.007 -0.001 0.000 0.001 0.001 0.000 0.007 -0.007 (3.77)*** (0.97) (0.15) (0.03) (0.14) (0.61) (0.03) (1.19) (1.08) Age -0.018 -0.016 0.008 0.022 -0.037 -0.013 -0.001 0.005 -0.009 (0.85) (0.40) (0.23) (0.42) (0.84) (1.01) (0.09) (0.17) (0.27) Age sq. 0.000 0.000 -0.000 -0.000 0.001 0.000 0.000 -0.000 0.000 (0.75) (0.57) (0.11) (0.63) (0.95) (1.10) (0.41) (0.30) (0.33) Male -0.129 0.092 0.056 0.004 -0.090 -0.021 0.023 0.043 -0.008 (2.76)** (1.01) (0.72) (0.03) (0.92) (0.75) (1.24) (0.64) (0.11)
0.009 -0.010 -0.001 0.003 -0.000 -0.001 -0.003 0.004 0.007 Experience before current job (1.63) (0.89) (0.09) (0.21) (0.03) (0.20) (1.54) (0.48) (0.73)
0.051 -0.067 -0.171 0.157 0.083 -0.014 -0.019 0.112 -0.086 Selection correction term (0.69) (0.47) (1.40) (0.85) (0.55) (0.32) (0.63) (1.07) (0.73)
Constant -1.053 1.048 -0.289 -0.224 0.669 0.587 0.078 0.513 -0.184 (2.08)* (1.07) (0.34) (0.18) (0.64) (1.98)* (0.39) (0.71) (0.23) First stage F test 24.02
[0.00] 2.17
[0.04] 5.24
[0.00] 2.19
[0.04] 2.35
[0.03] 64.6
[0.00] 102.4 [0.00]
14.5 [0.00]
3.78 [0.00]
Observations 54 54 54 54 54 54 54 54 54 Absolute value of t-statistics in parentheses; P-values in brackets; * significant at 10%; ** significant at 5%; *** significant at 1% For wage growth function in Table A2, a sub-sample (49 obs.) of the entire sample (54 obs.) is used. The results (not shown here) are essentially the same.
Figure 1: Map of the Province of Laguna, Philippines
0
.51
0.5
10
.51
0 5 10 15 20 0 5 10 15 20
0 5 10 15 20
1 3 16
30 41 42
43
Den
sity
Schooling yearsGraphs by typoccup22
Farming Ag. labor (incl. casual work) Overseas work
Self employment Unskilled work at small ent Unskilled work at large ent
Technical/skilled work
Figure 2: Distribution of schooling years by occupation
ln(wage) w/ PN
experience
w/o PN
Figure 3: Personal networks and wage dynamics