pre-RSE wage earning data (although only a minority of households earned wage income). Furthermore we know whether households tried to participate in the RSE (by having a member register for the work-ready pool, or apply directly to an employer). Finally, we also have a plausible reason why some households participated in the RSE and other households with these same characteristics did not – there was excess demand for RSE employment, and so not all households who wanted to participate were able to.

We estimate two versions of the propensity score, which we denote PS-1 and PS-2. They differ only in that PS-2 first restricts the sample to households which applied to participate in the RSE before estimating the score, eliminating the non-applicant households. This allows us to explicitly screen on demand for the RSE, although given that the reason many non-applicant households said they didn’t apply was lack of information about the program (Gibson et al, 2008; McKenzie et al, 2008), failure to apply need not imply lack of demand.

We then use six main categories of variables which we believe may influence participation in the RSE to estimate the propensity score: demographic variables (household size, number of males aged 18 to 50, number of adults, number of school age children); characteristics of the 18-50 year old males in the household, who are the individuals most likely to participate (share literate in English, share with schooling beyond grade 10, the share with self-reported health rated as very good, the share who drank alcohol in the past month, and the mean number of days of hard labor carried out in the past month); the household’s previous experience and network in New Zealand (share of adults who had previously been to New Zealand, number of relatives in New Zealand); household baseline assets and housing infrastructure (an asset index comprising the first principal component of durable goods owned, the number of pigs, cattle and chickens owned, and whether the dwelling was traditional style); geography (on Tongatapu or Efate as opposed to one of the other islands) and past household wage and salary history (household wage income for the first half of 2006 and 2007, and whether the household had any male aged 18 to 50 in each of these periods). For each variable we include both the variable and its square in estimating the propensity score.

For Tonga estimating the propensity score and restricting to the range [0.1, 0.9] reduces our sample of 448 households (197 RSE, 251 non-RSE) to 372 households using PS-1 (182 RSE and 190 non-RSE) and 284 households using PS-2 (154 RSE, 121 non-RSE). In Vanuatu the sample of 456 households (147 RSE, 309 non-RSE) reduces to 360 households using PS-1 (129 RSE, 231 non-RSE) and to 269 households using PS-2 (123 RSE, 146 non-RSE). The trimmed samples thus mainly trim out non-RSE households which are too dissimilar to RSE households to be appropriate comparators, whilst also trimming out a few of the RSE households which differ too much from any non-RSE household.

We then re-estimate (1) and (2) for households with propensity scores in the range [0.1, 0.9]. Again the differencing or fixed effects will eliminate both observed and unobserved time11