In 1988 the U.K. reformed the main in-work transfer programmes for low income households with children: the value of cash transfer entitlements were increased but conditional eligibility to associated in-kind nutrition programmes for children was removed. This was a partial cash-out of the in-work in-kind transfers while out-of-work transfer entitlements were left unchanged.
If we used difference-in-differences estimation it would be difficult to differentiate between the effects of the loss of in-kind with the gain in cash. Thus, we confine ourselves to estimating a structural model of labour supply and participation in multiple transfer programmes using a sample of single mothers drawn from repeated cross-section surveys that bridge the reform. We find that in-work cash and in-work in-kind transfers both have large positive labour supply effects. There is, however, some utility loss from transfer programme participation and this appears to be larger for cash than for nutrition programmes. This implies that the partial cash out of the in-kind benefits effectively reduced labour supply. We cannot, however, be definitive on why this occurs. We have no further information in our data concerning attitudes to the transfers. Nor does the data bridge any changes in the nature of the transfers that might allow us to make inferences. On the one hand, this would make our results quite specific to this context – where the change in cash receipt is experienced by one household member but the change in in-kind receipts is experienced by another. On the other hand, it would be quite common for in-kind transfers to be made to some household members while cash transfers are made to another – because of fear that there are agency effects associated with making transfers to one person on behalf another. There is clearly a need for qualitative research in this area to try to unpick the issues.
Our findings have several implications for public policy. First, we show that an increase in transfer entitlements available for part-time work has only a modest impact on the probability of working part-time, and has essentially no adverse effect on the probability of full-time work. Expanding transfer entitlements to full-time work has stronger participation effects. However, increasing the availability of in-work transfers to those lower down the hours distribution does cause moderate reductions in the probability of working full-time. This reflects the non-convexities in the budget constraints faced by single mothers.
Secondly, we find that nutrition transfers are actually more important for labour supply than the cash equivalent in cash transfer programmes. We interpret this as cash and in-kind having different values to recipients since our estimates imply that nutrition programmes suffer from only mild stigma/transaction/information costs. Several distinctive features of the programmes we analyse help to explain this difference. There are high transactions costs of claiming these cash transfers, whereas the additional transactions costs of claiming associated conditional in-kind transfers is relatively small. Regarding stigma: for cash transfers it is likely that the only others knowing about receipt were administrating government officials; while knowledge of in-kind transfer receipt was potentially shared with local shop assistants, in the case of Welfare Milk Tokens, and with teachers and peers at school, in the case of Free School Lunches. It seems likely that non-participation in the cash programmes by those who were eligible was largely driven by imperfect information and the transaction costs of claiming. In contrast, it seems likely that in-kind transfers may have low value for the household, in addition to any stigma, but have relatively low information/transaction costs for the claimant. In the case of Free School Lunches it seems likely that the burden of any stigma is largely borne by the child. Our results suggest that nutrition transfers may have a useful role to play in promoting work incentives. The 1988 partial cash-out of nutrition transfers in-work is thus shown to have reduced labour supply – quite the opposite to what was intended.
Third, however, we find evidence of statistically significant, and not inconsiderable, stigma/ transaction/ information costs which implies that in-work transfers are not as effective at countering the disincentive effect of out-of-work transfers, or at countering poverty amongst the working poor, as they might otherwise be. If it were possible to reduce these costs associated with transfer programmes, this would have an important impact on the labour force non-participation rate for single mothers, it would imply large savings in government expenditure on Income Support payments for those not working, and it would increase the welfare of those in receipt of transfers. Finally, we demonstrate that even though there is Pareto-inefficiency associated with in-kind transfers, such transfers may have a place in the policy portfolio because the alternative may be an even more inefficient cash transfer programme.
Of course, our estimates are conditional on unobserved attributes of the U.K. transfer system. Thus, one could not generalize from our estimates to another country. Furthermore, our analysis is not simply cash versus in-kind because the programmes we consider differ according to whether they are in-work or out-of-work transfers and in-kind nutrition versus in-kind non-nutrition But there is a general point that remains – differential costs associated with different transfers can easily exist and can make a difference to behavior. The U.S. trend of moving away from cash programmes towards in-kind may not be as inefficient as it first appears, and the estimates here suggest that such differential costs of in-work transfers might be exploited to raise labour force participation in the U.K. at no cost to the government. The results also imply that raising the costs of out-of-work welfare might also promote labour force participation. That is, a policy of cashing out the eligibility for in-kind transfers for those on out-of-work transfers, instead of those on in-work cash transfers, would have better served UK work incentives.
Endnotes
1 We assume that fertility and marital status are exogenous. Evidence on how responsive these are to welfare is mixed. See, for example, Joyce et al. (2003) on fertility and Bitler et al. (2004) on marital status.
2 See Currie (1996) for an exhaustive review of U.S. in-kind transfers.
3 It is difficult to use data prior to 1978 because of the absence of education information, used in the estimation of wage equations, and data beyond 1992 does not contain appropriate information about housing costs to deal with changes in the local tax system that occurred at this time. Moreover, from April 1992 the minimum hours of work requirement for Family Credit was reduced to 16.
4 The routine is based on the Institute for Fiscal Studies’ TAXBEN computer programme but deals with all of the changes that have taken place between 1978 and 1992. See Johnson et al. (1990) for details of TAXBEN. Moreover, we allow for wages to be determined differently across employment states because of the large differential between part-time and full-time wages rates that is a feature of the U.K. labour market (see Ermisch and Wright (1991)).
5 Family Credit eligibility is based on a history of hours of work at claim time and responses to the “usual hours worked” question might not match the required history. However, it seems likely to be better than using current hours of work because current receipt will depend on hours of work in the past when eligibility was established - usual hours may better capture this. Furthermore, our eligibility and programme participation measures calculated on this basis seem to fit well with published aggregates.
6 We choose 10 rather than 0 because a small number of single parents do record very low levels of hours which we think is associated with casual activity such as baby-minding. Our estimates are not sensitive to the precise definition of non-participation. The Income Support system does incorporate an “earnings disregard” that allows small amounts of income to be earned without affecting Income Support entitlement.
7 In the case of Family Credit there is a small proportion of the not entitled who are receiving (2.5%) and this arises because there is no requirement to report changes in circumstances once eligibility is established and eligibility lasts for 6 months before it needs to be re-assessed. For CH there are further measurement difficulties: the reform resulted in a delay of up to six months while those who were in receipt of Family Credit got reassessed and then lost their conditional eligibility to CH transfers; there was some local authority discretion in the provision of nutrition transfers to children at school; and disabled children may be eligible but we cannot observe this in our data. Together these factors probably account for most of the ineligible CH participants. Housing Benefit has the largest proportion of participating ineligibles (9.3%). Fry and Stark (1993) point out that this is largely because of processing delays that resulted in payments being made in arrears so that some households are currently observed to be in receipt for Housing Benefit but not entitled on the basis of current circumstances. This measurement error is unlikely to be correlated with our measures of other sources of income, but will reduce the precision of our Housing Benefit participation estimates.
8 Only 1% record receipt of Income Support and Family Credit at the same time, and just 6% record Income Support and HB. Table 3 shows small numbers on Family Credit who have a labour market status of UE or NP which is inconsistent. In our econometric analysis we respect the observed data but because we assume that choices depend on levels of entitlements we will be unlikely to predict someone choosing a point of zero entitlement. Note that we treat Income Support as equivalent to non-transfer income; but some individuals who record themselves as UE or NP receive Unemployment Benefit (sometimes plus Housing Benefit) rather than Income Support. Unemployment Benefit is conditional on searching for work but is not actually means tested. Thus, single parents with some asset income, perhaps child support (although this was rather uncommon during this period), might be better off on Unemployment Benefit and Housing Benefit than on Income Support. Since we treat Income Support as equivalent to non-transfer income, and since Income Support and Unemployment Benefit are effectively interchangeable, since there was no obligation to search for this group, we treat Unemployment Benefit and Income Support as equivalent.
9 Recall that Income Support receipt is not included in Table 3. Consequently multiple receipts are understated, for example, 80% of CH recipients also receive Income Support.
10 The specification for the determination of wages is where h stands for part time (both higher and lower) and full time work. We estimate the wage equations by including the Mills Ratios from a Bivariate Probit model of participation vs. non-participation and full-time vs. ( part-time work conditional on participation. We include the level of unearned income in the reduced form labour force status equations but not in the wage equations to achieve identification. Other covariates included in the reduced from selection and wage equations are education, a quadratic in experience, numbers and ages of children, region and year dummies. MaCurdy et al.1990) show that inconsistent estimates may result from using predicted gross wages in a non-linear second stage labour supply equation. One solution is to integrate out the prediction error in the wage equations, at the cost of increasing the dimensionality of the estimation problem. Van Soest (1995) does this for the Netherlands, on top of a much simpler logit structure, and finds labour supply elasticities to be unchanged.
11 We compute their incomes at 6, 16, 26, and 36 hours.
12 The Family expenditure data we have access to contains hours of work, and for those with zero hours of work, whether or not they are unemployed (i.e. seeking work). We model hours of work as a choice between four discrete alternatives: non-participation, low hours part-time, high hours part-time and full time work. For those who are not unemployed, we assume that they are observed in their most preferred labour market state. For those with zero hours who are unemployed, we assume they reveal themselves to not be in their most preferred labour market state. Note that we are not introducing a new discrete alternative. We use the information on unemployment to distinguish among the preference orderings of those working zero hours: that the unemployed would rather be working at the going wage rate, so zero hours is not the most preferred labour market state for the unemployed. In doing this we follow Blundell et al. (1987) and others who use this information to discriminate between non-participation and unemployment. This is important because women who are unemployed are not observed to be in their most preferred state, and must be classified appropriately in a choice model. For the purposes of labour supply modeling this group is assumed to reveal, by stating that they are searching for work, that some positive hours state is preferred to zero. Furthermore, individuals observed in any positive hours labour market state are assumed to prefer their observed state to all alternatives and are not rationed in exercising this preference. They are distinguished by the following reduced form latent and observed unemployment rationing equations , where R* is the latent variable describing the rationing process, and R
i
is the observed outcome, which we define to be unity if i is observed to be not working and searching for work and zero otherwise. Z is a vector that includes demand side variables, τ is a corresponding vector of parameters, and υ
i
is a random error. While this is an extension that has not previously been considered in the literature concerned with joint labour supply and programme participation, we consider it important here because we would otherwise understate the extent of programme non-participation.
13 In common with the literature, we do not take into account errors in classification which may arise through mis-measurement of transfer receipt or errors in calculating eligibility. We appeal to our good match with aggregate data and our adoption of precise entitlement calculations to support this. See Poterba and Summers (1995) for a treatment of errors in classification in the context of unemployment transitions.
14 Ideally we would unbundle Income Support receipt from other sources of unearned income at zero hours in order to distinguish programme participation costs. However we lack variation in Income Support programme participation in our data in order to identify such a distinction.
15 The choice of g(.) is arbitrary. Brewer et al.2007) use a quadratic utility function in their analysis of the successor programme to Family Credit, but here we find that a linear local approximation can be accepted.
16 This would not be the case if programme participation impacted directly on labour supply apart from through the budget constraint – for example, if claiming a transfer took a lot of time and this reduced labour supply. We ignore this possibility here because once a claim is made the eligibility lasts for 6 months unless circumstances change. So claiming and reclaiming is infrequent and will have little effect on an average week.
17 Non-separability means that programme incomes directly affect labour market status in addition to its effect through income levels at each state. Note from Equation (5) that although the terms in individual characteristics cancel out, the error terms do not. These terms carry through into the variance of the labour supply function (see the Appendix available online, or from the authors on request).
18 The sensitivity of the labour supply model estimates to the hours grouping was tested. Parameters were not significantly affected by altering the lower part time and higher part time criteria, until higher part time reaches 35 hours, which effectively brought the fulltime hours of work peak into the higher part time range.
19 The rationing equation contains year and region effects and the regional unemployment rate data features relatively little relative variation so it is not very surprising that it does not appear significant in the rationing equation. Nonetheless, we feel it appropriate to allow for the distinction in the model because whether individuals declare themselves as UE or NP will still depend on the financial attractiveness of working – which we demonstrate in a later simulation.