and poultry raising activities increase by 47.3 and 46.6 percent respectively when remittances are doubled. Although garment transfers do not seem to have an effect on expenditures for hiring draft power (Col. 4), they do increase the amount spent on chemical fertilizers and pesticides: a 100 percent rise in transfers translates into a 58.4 percent increase in this expenditure post. In addition, a twofold increase in remittances boosts agricultural labor productivity by 0.28 kilogram per worker. Overall, these findings are consistent with the literature that underlines how remittances relax the liquidity constraints of agricultural households (Minot, Kherallah, and Berry, 2000; Abdoulaye and Sanders, 2005). For instance, they allow overcoming imperfect credit and insurance markets in Burkina Faso (Wouterse and Taylor, 2008) and China (Rozelle, Taylor, and DeBrauw, 1999). In the specific case of the textile and apparel sector, our IV results also broadly echo findings from qualitative studies reporting that remittance-recipient households living in rural areas in Cambodia are able to increase their food consumption, cover health and education expenses, and invest in crop production and durable assets (Cooperation Committee for Cambodia, 2005; Dahlberg, 2006).

# **7 Conclusion**

The garment industry is one the key engines of Cambodia’s impressive economic growth in recent years, as well as the largest source of jobs in the manufacturing sector. Nonetheless, it is also associated with poor labor conditions and suboptimal health and hazard regulations. This paper seeks to contribute to the debate on the socio-economic advantages of the industry for Cambodia by exploring the benefits of participating in the textile and apparel sector. Using a rigorous econometric approach, it explores the relationship between garment participation and both monetary and non-monetary welfare indicators at the household level. More specifically, we use PSM estimators to compare households with at least one member employed in the textile and apparel sector (treatment group), with those with no participation (control group). Our preferred specification uses kernel weights and estimates the standard errors using the bootstrap method.

When considering the entire sample of households, we find that garment households have lower food insecurity and a higher proportion of children aged 6-14 attending school. However, they also have lower consumption per capita and accumulate fewer assets. But the sign of the latter point estimates is reversed when we restrict the sample to the bottom 40 percent of the consumption distribution. Among these households, participation in the garment sector raises per capita consumption by 3 percent and enhances asset ownership by 2.6 percentage points. The benefits in terms of food insufficiency and increased school enrollment appear even larger for this sample, at 1.8 and 9.3 percentage points respectively. Similarly, it decreases the poverty incidence, the extreme poverty incidence and the poverty gap index by 8, 3 and 1.7 percentage points respectively. Although the magnitude of the coefficients is modest (except for school enrollment), overall the results are robust to different specifications of the selection model and to the use of alternative PSM techniques, such as nearest neighbor, radius matching and caliper. Moreover, the quality of the matching is good, as the covariates are balanced for both the full sample and the sub-sample of the bottom 40 percent of the distribution, and the results are robust to high levels of unobserved heterogeneity.

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