In-work benefits in the context of non-reporting and underreporting 34

Ability to identify the target group is a crucial feature of policy design, which helps increase cost- effectiveness of in-work benefits. In the context of informality and under-reporting, these policies might not be effective at achieving their objectives. On the one hand, employment conditional benefits or tax credits could strengthen incentives to take up formal jobs or increase declared income by increasing pay offs to workers32_{. On the other hand, targeting of such benefits could be poor due to} underreporting, as it could even create additional incentives to under-report incomes to maintain eligibility for such benefits, unless enforcement of formal work is increased at the same time.

If underreporting is prevalent, permanent in-work benefits, such as tax credits, for example, might not be appropriate and fiscally sustainable. Instead, time-limited “back to work” benefits can be introduced which target those who are long-term unemployed and social assistance beneficiaries. This would prevent those currently working from taking advantage of the benefits by increasing share of unreported income.

The extent of underreporting is very difficult to assess, however, due to a usual lack of instruments in the survey data to identify reported earnings. Latvia Survey of Income and Living Conditions (SILC) data links respondents with administrative databases using unique national IDs and has information on the amount of social security contributions paid by employers. This registry based data allows estimating a discrepancy between wages as reported by employers and wages reported by survey respondents. The difficulty stems from the fact that survey responses could also be biased due to under-reporting, recall issues, rounding up/down and other factors which could lead to differences between the two values (measurement error). Using maximum likelihood estimation it is possible to disentangle the measurement error from the actual probability to evade33.

According to these estimates based on 2009-2010 Latvia SILC data, the average probability of evading tax among registered/formal employees34 is about 31 percent, while the average share of undeclared income is about 10 percent. Those who report earning minimum wage or less are much more likely to under-report (66 percent versus 23 percent). They also, on average, under-report a

32 _{This effect has been found for single fathers in a recent study of United States’ Earned Income Tax Credit} (EITC). Single fathers’ informal-sector participation declines by 7.3 percentage points, conditional on working in the regular sector, if a state EITC increases by 10 percent of the federal credit. Regular-sector hours worked per week increase by 4.5 and informal-sector hours per week fall by 2.2 with no significant effect on total hours. (Gunter, 2012)

33

For details on the methodology, please see Cahu and Strokova (forthcoming). 34

Informal employees, i.e. those with earnings but no social security contributions are excluded from this sample. For them the share of evaded earnings is 100 percent. For more details, please see Cahu & Strokova

significantly higher share of their wages (33 percent versus 5 percent). Among those who are more likely to under-report (probability of evasion of 70 percent or more corresponding to about 15 percent of all employees), about two-thirds report earning the minimum wage. Among those who are very likely to under-report (probability of evasion is 90 percent or more), 75 percent report earning minimum wage only, however, overall, they only represent 11 percent of all employees. Nevertheless, those who report minimum wages generally undeclare smaller amounts in absolute terms than those who report higher wages. In summary, those reporting minimum wages are more likely to be evaders, but the amounts they evade are generally smaller than those who report wages higher than the statutory minimum wage.

Inability to target in-work poor given underreporting is particularly challenging, since the problem of in- work poverty exists in Latvia, especially for households with low work intensity.35 The at-risk-of- poverty rate is almost 50 percent for low work intensity households without dependent children and more than 30 percent for household with children. Since these households have some labor market attachment, employment conditional benefits could be an appropriate policy to raise their incomes, but if it is not possible to distinguish those who have low wages due to under-reporting and those with truly low wages, the policy can be very expensive. Indeed, it has been shown that a high spike in the wage distribution at the minimum wage level is correlated with the extent of underreporting of earnings in the economy (Tonin, 2011), which is consistent also with our findings above.

Hence, minimum wage policies have to be also carefully considered. In practice, minimum wage is an important instrument that any in-work benefits are not “pocketed” by employers through lowering wages and off-setting any effects for intended beneficiaries. But it is generally considered a rather “blunt” instrument to both i) increase incomes of the low skilled and ii) increase tax compliance. The design of in-work benefits has to be calibrated with potential interactions with the minimum wage. Furthermore, given the incidence of under-reporting of wages in Latvia, policies aimed at increasing tax compliance would need to be pursued simultaneously with any policies targeting in-work poor. Some instruments which could be used to incentivize employment and provide income support to low wage earners in Latvian context will be discussed in Section 5.4.

Figure 11: In-work at-risk-of-poverty rate by work intensity of the household (population aged 18 to 59 years)

Source: Eurostat.

35

The indicator persons living in households with low work intensity is defined as the number of persons living in a household having a work intensity below a threshold set at 0.20.The work intensity of a household is the ratio of the total number of months that all working-age household members have worked during the income reference year and the total number of months the same household members theoretically could have worked in the same period. 0 10 20 30 40 50 60 Very high work intensity High work intensity Medium work intensity Low work intensity Very high work intensity High work intensity Medium work intensity Low work intensity Ho us e h o ld s   with out de p e ndent   ch ildr en Ho us e h o ld s   with de p e nd en t   childr en

5

Reform options and priorities