Information, Mobile Communication, and Referral Effects: Barwick, Liu, Patacchini & Wu (2023)

What this is. The paper’s core results, the identification strategy, and the estimating equations: enough to know what it found and how, without reading all 38 pages. To replicate or extend, read the full source at the original.

TL;DR

The paper exploits geocoded cellphone records from a major Chinese telecom provider to study whether social contacts (referrers) transmit job-relevant information to job seekers. It documents (i) an inverted U-shaped spike in call frequency between switchers and their referrers in the months before a job change, with no corresponding pattern for non-referrer friends; (ii) a referral effect of 0.35 on job location choice - having a social contact working at a location nearly triples the probability of switching there; and (iii) referral jobs are of higher quality: they pay more, involve shorter commutes, are more likely to be full-time, and lead to faster firm growth. Effect heterogeneity shows referrals matter especially when information asymmetry is more severe, as for young workers, rural-to-urban movers, and sector-changers. Topa (2001) provides the foundational social-interactions framework; Bayer, Ross, and Topa (2008) introduced the residential-neighbor proxy approach that this paper extends using direct communication data.

Core results

Magnitudes and significance are as reported; */**/*** = 10%/5%/1%. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Referral effect on job location choice: having a friend at location l increases probability of switching there by 0.35	Table 3, col 2, p. 1186	Coeff = 0.35 (SE 0.01); mean baseline probability = 0.09; N = 915,251 switcher-location pairs
R2	Inverted U-shape in referrer call frequency: calls between switchers and referrers peak at the job switch month; nonreferrer calls are flat throughout	Figure 3, p. 1188	Referrer-pair coefficient at month 0 approx +8 above baseline; nonreferrer pairs approx 0 throughout; 238,092 referrer-month obs vs 4,759,176 nonreferrer obs
R3	Referral effect amplified for high information-asymmetry groups: rural-to-urban movers and sector-changers show substantially larger referral effects	Table 4, cols 5-6, p. 1191	Friend x rural-to-urban = +0.32 (SE 0.05); Friend x changing sector = +0.21 (SE 0.02); baseline Friend 0.34 (col 5) / 0.32 (col 6)
R4	Referral wage premium: referral jobs pay RMB 620 more per year, about 2 percent above the mean wage	Table 7, col 1, p. 1199	0.62 thousand RMB (SE 0.31); mean wage approx 31 thousand RMB/year; N = 17,615
R5	Referral jobs are more likely to be full-time: having a referrer at the new workplace raises the probability of a part-time to full-time transition by 1.4 percentage points	Table 7, col 3, p. 1199	0.014 (SE 0.007); approx 2% relative increase (p. 1198); N = 19,431
R6	Referral jobs have shorter commutes: referral raises probability of a shorter commute by 9 percentage points	Table 7, col 4, p. 1199	0.09 (SE 0.01); approx one-third of job changes involve a shorter commute; N = 29,117
R7	Firm benefit: net labor inflow: firms hiring through referrals gain 63 percent more workers (log net inflow) in the most saturated specification	Table 8, Panel A col 4, p. 1201	gamma = 0.63 (SE 0.14); R-squared = 0.66
R8	Firm benefit: matching rate: firms hiring through referrals achieve an 84 percent higher job matching rate (log hires over vacancies)	Table 8, Panel B col 8, p. 1201	gamma = 0.84 (SE 0.27); average matching rate = 1.53 for large firms

Overall (paper’s conclusion). Information provided by social contacts mitigates information asymmetry in labor markets and facilitates better worker-firm matching. The inverted U-shape in referrer communication around job changes, absent for non-referrer friends, provides direct evidence that referrers pass job-relevant information and rules out homophily and sorting as the sole explanation. Both workers and firms benefit: referred employees earn more, commute less, and are more likely to hold full-time positions, while firms that hire through referrals grow faster and fill vacancies at higher rates.

Theory / model

This paper has no formal economic model. It tests three linked hypotheses derived from the theoretical literature on information transmission in labor markets (Topa 2001):

1. Information channel hypothesis. Referrers pass job-relevant information to job seekers, generating an increase in communication intensity in the months before the job change. The prediction is an inverted U-shape in call frequency between referrer pairs centered on the event month, with no such spike for non-referrer friends.

2. Referral effect hypothesis. Having a social contact working at a given location raises the probability of switching there. The coefficient captures both information provision (the referrer informs the job seeker of an opening) and endorsement (the referrer vouches for the candidate to the employer). Under either channel, the referral should increase location choice probability.

3. Information-asymmetry amplification. The referral effect should be larger when information between workers and firms is more asymmetric: for young workers with limited labor market experience, rural-to-urban movers unfamiliar with urban job markets, and sector-changers whose skills are less observable. If the mechanism is pure preference (working near friends), no such heterogeneity would be predicted.

Identification strategy. The key threats are homophily (friends share unobserved location preferences) and sorting (friends cluster in locations with unobserved job opportunities). The paper addresses these via:

• Origin-destination neighborhood-pair fixed effects (equation 1, p. 1184): the referral coefficient beta is identified from within-pair variation, comparing job switchers who move between the same old-new neighborhood pair but have different social networks. This controls for all aggregate pair-specific attributes including industry composition, labor demand, and amenities.

• Falsification tests using friend type (Table 3, cols 3-4, p. 1186): friends who recently moved away from location l have a coefficient of 0.07 (far below the 0.35 for current referrers). Friends who live in the new location’s neighborhood but do not work there have a coefficient of 0.15. If homophily or the specific neighborhood drove the result, these coefficients would be similar to the baseline. Current employment at the destination is what matters, consistent with information about job openings being the active ingredient.

• Vacancy restriction (Table 3, col 2, p. 1186): the baseline sample restricts to switchers with at least one alternative location in the same neighborhood offering the same occupation and salary range, ruling out the concern that the friend dummy proxies for the only available matching job.

• Homophily controls (Table 6, p. 1195): adding same-gender, same-age-group, same-birth-county, same-housing-price controls and k-means cluster dummies for switcher-friend pairs leaves the estimate stable at 0.33-0.34, showing the baseline controls adequately capture sorting.

The paper also compares its call-based referral measure with proxy-based approaches common in the literature: residential neighbors in the spirit of Bayer, Ross, and Topa (2008) (coefficient 0.21) and same-birth-county coworkers (0.10). The call-based measure dominates both proxies by a margin that is statistically significant at the one-percent level (Table 5, col 3-4, p. 1192).

Method

The paper applies OLS panel regression with fixed effects and an event study. The builds on panel-regression and event-study technique primitives. The four estimating equations are introduced on pp. 1181-1184 and 1197-1200.

Preliminary correlation (Table 2, p. 1182). The relationship between information flow (call volume) and worker flows across neighborhood pairs is established via OLS with origin and destination fixed effects. Adding call volume as a regressor raises the R-squared from 0.037 to 0.17, and doubling call volume is associated with a 16 percent increase in worker flows (inverse-hyperbolic-sine specification). This motivates using communication intensity as a proxy for information provision.

Main referral regression (eq. 1, p. 1183-1184). Let $\text{M}_{il} = 1$ if job switcher $i$ moves to location $l$ within the new workplace neighborhood. The specification restricts individual $i$‘s choice set to locations within the destination neighborhood to absorb heterogeneity across neighborhoods:

M_{il} = \beta \text{Friend}_{il} + \mathbf{X}_i \mathbf{Z}_l \gamma + \lambda_{\tilde{c},c} + \epsilon_{il} \tag{1}

where $\text{Friend}_{il} = 1$ if at least one of $i$‘s social contacts works at location $l$ three months before the job switch; $\mathbf{X}_i$ = individual demographics (gender, age groups, migration status, total social contacts); $\mathbf{Z}_l$ = location amenities (restaurants, roads and parking lots, schools within 500 m radius); $\lambda_{\tilde{c},c}$ = old-by-new neighborhood-pair fixed effects (20,811 total pairs in unrestricted sample; 16,468 in the baseline vacancy-restricted sample). Standard errors are clustered at the neighborhood-pair level.

Event study (p. 1187). Call frequency between switcher $i$ and friend $j$ in month $t$ is regressed on event-time dummies interacted with referrer vs non-referrer status, covering an event window from 11 months before to 9 months after the job switch (month $s = -1$ is the reference category):

$\text{Freq}_{ijt} = \sum_{s=-11}^{9} \gamma_s \text{Referral}_{ij} \cdot \mathbf{1}\{t = s\} + \sum_{\substack{s=-11 \\ s \neq -1}}^{9} b_s \text{Nonreferral}_{ij} \cdot \mathbf{1}\{t = s\} + \lambda_i + \tau_t + \epsilon_{ijt}$

where $\lambda_i$ are individual fixed effects and $\tau_t$ are calendar month fixed effects. The coefficients $\{\gamma_s, b_s\}$ capture changes in call frequency relative to the individual’s own baseline rate of talking to non-referrer friends. Standard errors are clustered at the individual level.

Worker outcome regression (eq. 2, p. 1197). Labor market outcomes of the referral job:

Y_{ilr} = \beta \text{Friend}_{ilr} + \mathbf{X}_i \mathbf{Z}_l \gamma + \lambda_c + \alpha_r + \epsilon_{ilr} \tag{2}

where $Y_{ilr}$ is a labor outcome for worker $i$ at new work location $l$ in residential neighborhood $r$; $\lambda_c$ = new work neighborhood fixed effect; $\alpha_r$ = residential neighborhood fixed effect; controls $\mathbf{X}_i$ include gender, age groups, migration status, and log number of social contacts. Standard errors are two-way clustered by residential and new work neighborhood.

Firm performance regression (eq. 3, p. 1200). Log firm outcomes:

Y_i = \gamma \text{Referral}_i + \mathbf{Z}_i \beta + \lambda_c + \epsilon_i \tag{3}

where $\text{Referral}_i = 1$ if at least one new hire at firm $i$ has a social contact already working there; $Y_i \in \{\log(\text{net inflow}),\, \log(\text{matching rate}),\, \log(\text{growth rate})\}$; $\lambda_c$ = neighborhood fixed effect; $\mathbf{Z}_i$ includes firm age, 18 industry dummies, SOE dummy, average employees 2010-2015, average capital stock 2010-2015, previous employment growth rate, share of female workers, share of migrants, average employee age, average employee housing price, and the firm’s referral network size. Standard errors are clustered at the neighborhood level.

Empirical specifications

Baseline specification (Table 3, p. 1186). Equation (1) estimated on 915,251 switcher-location pairs (restricted to individuals facing at least one alternative same-neighborhood, same-occupation, same-salary-range opening). Old-by-new neighborhood-pair fixed effects (16,468 pairs). The mean within-neighborhood switching probability is 0.09. The baseline referral coefficient is 0.35 (col 2). Columns 3-4 add falsification friend types (moved-away: 0.07; lives-but-not-works: 0.15); column 5 replaces direct friends with friends-of-friends (coefficient 0.14, confirming these second-degree links carry less job information); column 6 adds extensive local labor market controls (0.33, similar to baseline).

Effect heterogeneity (Table 4, p. 1191). Equation (1) augmented with interaction terms $\text{Friend}_{il} \times X_i$, one per column: (i) distance between old and new workplaces, coefficient 0.002 (SE 0.0004); (ii) distance between home and new workplace, 0.002 (SE 0.0003); (iii) young dummy (ages 25-34), +0.04 (SE 0.01); (iv) rural-to-urban switch, +0.32 (SE 0.05); (v) sector-change dummy, +0.21 (SE 0.02). For rural-to-urban movers and sector-changers the point estimates of the total referral effect are 0.66 and 0.53 respectively, substantially above the base estimate.

Event study (Figure 3, p. 1188). 238,092 switcher-referrer-month observations and 4,759,176 switcher-nonreferrer-month observations. The referrer-pair call frequency coefficient rises from near zero at month -9 to a peak of approximately +8 above baseline at month 0, then remains elevated post-switch as referrers become coworkers. Nonreferrer friends show coefficients near zero throughout. Falsification event studies (Figure 4, p. 1190) show that moved-away and lives-at-new-location friends display flat or mildly elevated patterns with no inverted U-shape, confirming the information spike is specific to current employment at the destination.

Comparison with literature proxies (Table 5, p. 1192). Equation (1) replaces the call-based Friend dummy with (i) residential neighbor dummy (0.21, SE 0.01) and (ii) same-birth-county coworker dummy (0.10, SE 0.01). Columns 3 and 4 each include one proxy alongside the call-based measure. In column 3, the residential neighbor coefficient falls to 0.18 (SE 0.01) while the call-based Friend (not neighbor) coefficient is 0.25 (SE 0.01). In column 4, the same-birth-county coefficient falls to 0.09 (SE 0.01) while the call-based Friend (not same birth county) coefficient is 0.35 (SE 0.03). Both proxy estimates decline when the direct communication measure is included, confirming the proxies capture genuine but attenuated social interactions, consistent with Granovetter (1973) and the approach of Gee, Jones, and Burke (2017).

Worker benefits (Table 7, p. 1199). Equation (2) estimated separately for five outcome variables. Sample sizes vary by outcome due to data availability. All columns include residential and new-work neighborhood fixed effects. Key results: wage = 0.62 thousand RMB (SE 0.31, N=17,615); coworker housing price difference = 0.07 thousand RMB/m$^2$ (SE 0.04, N=23,323); PT-to-FT = 0.014 (SE 0.007, N=19,431); shorter commute = 0.09 (SE 0.01, N=29,117); non-SOE to SOE = 0.012 (SE 0.005, N=15,881).

Firm benefits (Table 8, p. 1201). Equation (3) estimated on large-firm locations (firms with more than 100 employees) to reduce spurious worker-firm linking. In the most saturated specification (column 4/8/12 for each panel), gamma = 0.63 (SE 0.14) for log net inflow, 0.84 (SE 0.27) for log matching rate, and 0.45 (SE 0.11) for log firm growth rate. The estimates are stable across specifications with progressively richer firm and employee controls, arguing against upward bias from fast-growing firms being more likely to use referrals.

Datasets used

Dataset	Role in paper	Wiki page
Geocoded cellphone records, Company A (anonymous northern Chinese city)	Main analysis: social network construction, information-flow measures, work and home location histories for 456,000 users, Nov 2016-Oct 2017	No page yet (proprietary; provider anonymous under data-sharing agreement)
Administrative firm-level records (merged by location)	Industry composition, average payroll, number of employees, capital stock; used for firm-performance regressions and location controls	No page yet (Chinese administrative data; not publicly available)
Job postings data (unnamed online platform)	Occupation and salary range at each location; used to restrict baseline sample to switchers facing comparable alternative opportunities	No page yet
Residential housing price data	Proxy for coworker socioeconomic status; used as a nonwage benefit measure (delta coworker HP)	No page yet
China Family Panel Studies (CFPS, 2014)	Descriptive only: national average demographics and job-search method frequencies for comparison with sample (Figure 1, Table 1)	No page yet
US Current Population Survey (2014)	Descriptive only: US job-search method frequencies for cross-country comparison (Figure 1)	No page yet

Sample: November 2016 to October 2017 (12 months). Final analysis sample: 456,000 individuals with stable work locations for at least 45 weeks and at most two work locations; 38,102 job switchers (8 percent of sample). Social contacts defined as anyone with at least one call to or from individual i in the three months prior to the job switch; on average 50 percent of a user’s friends are Company A customers.

When to read the full paper

Use the original if you are: measuring information transmission in labor markets with mobile phone data or other digital footprints; designing or evaluating employee referral programs and need evidence on the heterogeneity of referral effects by worker type; working on urban labor mobility in developing economies where formal job-search institutions are weak; extending the event-study design to other communication technologies (WeChat, messaging apps) or other information channels; or benchmarking referral-effect magnitudes for structural job-search models. The online appendix (referenced in the paper and in the replication package at 10.3886/E183161V1) contains detailed robustness tables and the event study for unemployed job-seekers.

Attribution and rights

Source: peer-reviewed, American Economic Review 113(5), May 2023. Replication data available at 10.3886/E183161V1. This distillation was extracted by an LLM on 2026-06-24 and is not human-verified or independently reproduced. No CC licence was found in Crossref metadata; standard AEA copyright applies; extract-only.

Barwick, Panle Jia, Yanyan Liu, Eleonora Patacchini, and Qi Wu. “Information, Mobile Communication, and Referral Effects.” American Economic Review 113, no. 5 (May 2023): 1170-1207. DOI: 10.1257/aer.20200187. Copyright 2023 American Economic Association. This page is an extract only; it is not a substitute for the original.