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: kajisa@grips.ac.jp *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

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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