the relationship between the share of HtM individuals, �, (x-axis), and social transfers targeting, STT , (y-axis), depicting no systematic relationship between STT (recall STT � � � �) and �.<sup>28;29</sup> In other words, when considering the universe of all types of social transfers, countries’ability to reach HtM individuals seems to mainly re‡ect the prevalence of HtM individuals, �, as opposed to a ...scal targeting e¤ort aimed to reach HtM individuals beyond their population representation. Therefore, social transfers in emerging markets are able to reach those HtM individuals mainly because a large part of its population is in the HtM group as opposed to a particularly exceptional targeting of social transfers. The same lack of evidence of high-quality social transfers targeting holds also true in developed countries.

# 5 Quantitative results from the TANK model

With the ability to measure � and �, we now proceed to solve the TANK model presented in Section 3 numerically in order to analyze the extent to which a standard calibrated TANK model is able to match key empirical regularities shown in Section 2 in terms of the size, main macroeconomic variables involved, and temporal pro...le of the STM for both the developed and the Latin American samples.<sup>30</sup> Initially, in our benchmark calibration, and in order to direct our attention to the role played by � and �, we calibrate the rest of parameters based on values frequently used in the literature for developed countries. Later, we also allow other parameter values (other than � and �) to be calibrated for Latin American countries and ...nd, indeed, very similar results to our benchmark calibration due to the actual dominant role played by � and �. Some parameter calibration values deserve special attention. A key mechanism in the literature on ...scal multipliers is the reaction of the nominal interest rate to in‡ation and output gaps by central banks (i.e., parameters �� and �Y in the Taylor rule of equation 11). We follow Iacoviello (2005) in choosing a value of �� = 1:27, �Y = 0:13, and �R = 0:73. The persistence parameter � = 0:86 is based on our own estimates shown in Section 2. The rest of parameter values are selected as follows: Frisch elasticity '<sup>�1</sup> = 1 (as in Christiano et al., 2005), discount rate � = 0:995, Calvo probabilities �p = �w = 0:75 (from Barattieri et al., 2014), sticky wage elasticity "w = 20:50 (matching 5% unemployment rate), steady-state government purchases Gss = 0:20 (from World Development Indicators), steady-state capital adjustment cost S<sup>00</sup> = 1:5 (from Altig et al., 2011) as well as depreciation rate � = 0:016, average mark-up X = 1:10, and capital share � = 0:30 (in order to match K/Y from KLEMS). Measurement and sources of data regarding parameters � and � were discussed in detail in Section 4. In particular, we take the average � and � for each sample in the Latin American and developed

> 28 Based on a simple regression of STT against �, we cannot reject the hypothesis that such a coe¢ cient equals statistically zero for the whole sample of countries (as well as for developed and emerging markets separately).

> 29 Appendix 3 shows the share of HtM individuals (�), the share of social transfers reaching HtM individuals (�), and the social transfers targeting (STT ) for each of the 99 developed and emerging countries included in our sample.

> 30 We would like to thank Steven Pennings for sharing the Dynare code from Giambattista and Pennings (2017).

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