Increasing Migration, Diverging Communities: Changing Character of Migrant Streams in Rural Thailand


This paper studies how increasing migration changes the character of migrant streams in sending communities. Cumulative causation theory posits that past migration patterns determine future flows, as prior migrants provide resources, influence, or normative pressures that make individuals more likely to migrate. The theory implies exponentially increasing migration flows that are decreasingly selective. Recent research identifies heterogeneity in the cumulative patterns and selectivity of migration in communities. We propose that this heterogeneity may be explained by individuals’ differential access to previously accumulated migration experience. Multi-level, longitudinal data from 22 rural Thai communities allow us to measure the distribution of past experience as a proxy for its accessibility to community members. We find that migration becomes a less-selective process as migration experience accumulates, and migrants become increasingly diverse in socio-demographic characteristics. Yet, selectivity within migrant streams persists if migration experience is not uniformly distributed among, and hence not equally accessible to, all community members. The results confirm that the accumulation and distribution of prior migrants’ experiences distinctly shape future migration flows, and may lead to diverging cumulative patterns in communities over time.

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  1. 1.

    Both authors have spent significant time in the field including in 1992, 1994, 1997, 2000, 2005, 2007. In 2005, over a 3-week period we conducted in-depth and focus group interviews with current and return migrants in eight of the 22 villages, with the participation of 158 individuals. Although these qualitative data are not used directly in this study, they inform our theoretical insights and empirical analysis.

  2. 2.

    More information can be found at

  3. 3.

    We restrict our analysis to 22 villages (out of the 51 original villages) where migrants were followed-up in destination.

  4. 4.

    As with any household registration-based data collection, the Nang Rong survey may have missed individuals residing in household that were not registered during survey years, but were present in non-survey years. Based on our regular visits to the site in between survey years, and given the small size of villages, this number is very minor. Other households and individuals lost to follow-up include entire households who moved out of the village to destinations not pursued through the study in 1994 and 2000. However, these instances were relatively rare, since, more often than not, at least one member of the origin household remained in the village living in another house.

  5. 5.

    This definition is from the survey and reasonable in the Thai setting, since the majority of migrants make one trip of long duration to their destinations.

  6. 6.

    During our fieldwork, participants indicated that only few households had telephones in Nang Rong villages during the study period, and migrants typically contacted their households through return visits, and rarely via letters. Participants repeatedly noted that it was during the return visits that migrants helped potential migrants by giving them information, or taking them along to their places of destination.

  7. 7.

    A village is considered remotely located if there are three or more obstacles to traveling to the district town. The obstacles are the presence of a portion of the route to the district town that is a cart path (unpaved, rutted, and narrow), the lack of public transportation to the district town, travel to the district town takes an hour or more, that during the year there are four or months of difficult travel to leave the village, and it is 20 or more kilometers to the district town.

  8. 8.

    These statistics are compiled from various resources, such as International Labor Organization Database, Thai National Statistics Office, and reports prepared by the World Bank and Thailand Development Research Institute, and are available from the authors upon request.

  9. 9.

    Massey et al. (1994) use migration prevalence ratio to categorize villages. We performed the descriptive analysis in Tables 2 and 3 using this ratio and the results were similar. This is expected as both indices provide roughly similar, albeit not identical, classifications of villages into quintiles. Although the individual rankings of villages may differ significantly across the two indices, as shown in Table 1, the categorical assignments to quintiles are roughly consistent across the prevalence ratio and the migration history index. However, the similarity of the two indices end there. Prevalence ratio is a simple count of migrants, and cannot be decomposed into mean experience and inequality components as the migration history index. This decomposition is crucial to test our first and second hypotheses in the analysis presented in Tables 4 and 5.

  10. 10.

    Diversity index is defined as:

    $$\hbox{Diversity}={\frac{-\sum_{i=1}^np_i\times\hbox{log}(p_i)}{\hbox{log}(n)}}\times 100$$

    where n is the number of possible destinations and p is the proportion of trips to destination i. The index varies between 0 and 100. Minimum diversity occurs when all trips are concentrated in one destination and the index equals zero. Maximum diversity occurs when each destination category contains the same proportion of trips, yielding an index of 100.

  11. 11.

    Our data was collected retrospectively in 1994 and 2000, and the age distribution of the sample is not uniform across years. The 1994 data set begins with 13–35 year old individuals in 1994, and contains retrospective information on their migration patterns from 1984 to 1994. Similarly, the 2000 wave begins with those aged 18–41 in 2000 and gathers retrospective information from the period 1994–2000. The changing age distribution over time makes it difficult to evaluate the trends in migrant selectivity. We circumvent this problem by comparing migrants to the overall sample when assessing the changes in selectivity across phases of community migration history.


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This research was funded by research grants from Center for Migration and Development at Princeton University and NSF (SES-0525942). The authors thank the research team from the Carolina Population Center at the University of North Carolina and the Institute for Population and Social Research at Mahidol University for their data collection efforts and the villagers of Nang Rong district, Buriram province, Thailand for their cooperation.

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Correspondence to Filiz Garip.


Appendix 1

Descriptive Statistics

(See Table 6).

Table 6 Descriptive statistics (data collected from 13 to 41 year olds in 22 villages in Nang Rong, Thailand in 1984–2000)

Appendix 2

Robustness Checks with Alternative Samples

We perform robustness checks to address two data-related issues that might bias our results. First issue is related to the migrant follow-up rate in survey data. In the 22 Nang Rong villages, migrants who were absent at the time of the survey were followed up in four major migrant destinations. On average, 44% of migrants were successfully located. To see how the exclusion of the remainder of migrants biases our results, we use the variability among villages in migrant follow-up rates, which range from 40 to 70%. We repeat our most comprehensive analysis (Model 4 of Table 5) on a restricted sample of four villages with the highest follow-up rates (all above 65%). Comparing the coefficient estimates for the whole and restricted samples in Appendix Table 7, we find that, despite the drastic change in sample size, the estimates are mostly similar. The only major change is in the coefficient of sex, which is much higher in the restricted sample. Accordingly, the coefficients for the interaction terms including sex differ remarkably in the two samples. Other coefficients remain consistent in direction, and differ negligibly in magnitude, across samples. This evidence increases our confidence that low follow-up rates in some villages do not bias our results.

The second issue is related to the age structure in the data. The retrospective life history survey was administered to 13–35 year olds in 1994, and 18–41 year olds in 2000. Thus, we observe 13–25 year olds in 1984, 13–35 year olds in 1994 and 18–41 year olds in 2000. The changing age distribution over time may bias the results. To address this issue, we restrict our sample to 18–25 year olds (the age group present in each year), and estimate Model 4 of Table 5. The results presented in Appendix Table 7 show a number of differences from those for the overall sample. First, the coefficient of sex is higher in the age-restricted sample, and the coefficient for age is insignificant. The latter is expected due to the narrow age range of the restricted sample. Different than the overall sample, the selectivity in marital status in the age-restricted sample declines with increasing migration experience. Other coefficients remain similar in direction, but differ slightly in magnitude or significance, across the samples. Despite these minor differences, our main conclusions (regarding the differential effect of the level and inequality of migration experience on selectivity) remain unaltered.

Table 7 Random effects logistic estimation of odds of being a migrant in a year—interaction models with alternative samples (data collected from 13 to 41 year olds in 22 villages in Nang Rong, Thailand in 1984–2000)

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Garip, F., Curran, S. Increasing Migration, Diverging Communities: Changing Character of Migrant Streams in Rural Thailand. Popul Res Policy Rev 29, 659–685 (2010).

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  • Internal migration
  • Cumulative causation
  • Selectivity
  • Thailand