rafmacalaba/data-use-mentions-extended huggingface dataset:
� in the second formula), which are returns on productive capital. This approach introduces a potential bias to the extent that public sector fixed capital may grow at a rate different from productive fixed capital (that is, g� may have two components, one for productive fixed capital and another for public sector fixed capital). We currently have no independent measure of public sector fixed capital, but there is recent work on this at the IMF (IMF 2017).

# _Comparison with OECD estimates of TFP_

We have compared the results of measuring TFP growth using the above methodology with the OECD results published in OECD (2016) for specific countries.<sup>7</sup> In some cases these estimates are similar, in most cases they are not. The reasons for the disparity are many: OECD weights fixed capital at the user cost of capital, rather than surplus less resource rents; OECD has a more precise measure of labor input, based on hours worked; and the OECD adjustment for natural capital is based on an earlier and more limited version of the Wealth of Nations database. As a result, we do not publish the comparisons in this paper, but it will be an active point of 5 This approach is a heuristic which has the virtue of ensuring that the labor share does not change when constructing the ‘with natural resource’ and ‘excluding natural resource’ measures of TFP growth. The approach implicitly treats land rents in agriculture as part of the compensation of employees in agriculture-dependent economies, however, and should be revisited in the next version of the _Wealth of Nations_ database.

> 6 See the online supplementary material from Feenstra et al. (2015) for details.

> 7 We eliminate the adjustment made by OECD for pollution abatement expenditures to increase comparability of the estimates.

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added content—that is if measured in terms of processing of imported components into final products for export (Hubert Escaith 2008). Adding the sales of services by foreign affiliates of multinational firms, then the value of trade in services rises further. Data for fifteen OECD countries puts the value of such sales at some $1.5 trillion in 2007 (WTO 2009, Hoekman et al., 2010).

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42</sup> , agreeing with the government to undertake regular surveys of citizens‘ opinion on satisfaction with service delivery, and narrowing the scope of the project to what was deemed feasible based on lessons learned from earlier projects and the current analysis of reform context. Both operations that involved explicit political economy analysis _ex ante_ are currently rated as ̳satisfactory‘.

While not hard evidence, it supports the idea that taking political economy factors into account can be associated with better performance. This is also consistent with evidence from a sample of 17 public sector operations that were assessed in 2009 with regards to their relative attention to governance and political economy factors, and that were revisited with regards to their current performance for this paper.

In 2009, the Bank‘s internal Quality Assurance Group (QAG) undertook an initial review of how key aspects of governance and anticorruption are integrated into lending operations.<sup>43</sup> Three dimensions were covered: attention to Governance and Political Economy (GPE); Fiduciary Aspects (GFA); and the demand side of governance (DSG). Operations were rated on a six point scale ranging from (1) _very responsive_ to (6) _highly unresponsive_ . The GPE dimension was assessed by examining the adequateness of analysis of stakeholder interests and their underlying incentives; of formal institutions and informal rules; of the project‘s vulnerability to rent-

> 42 One team made an effort to understand and manage PE dynamics, but new political forces emerged unexpectedly. In another country, in contrast, the potential alternative party was readily identifiable.

> 43 In the context of the Bank‘s _Governance and Anti-corruption Strategy_ and _Implementation Plan_ launched in 2007.

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barriers to access should be classified into _price_ barriers (service is available but too expensive), _information_ barriers (e.g. poor household’s creditworthiness cannot easily be established) and _product and service design_ (banks fail to offer the kind of services that would be most useul for poor households). Data on these aspects would be both essential for research purposes and a natural frame for the purpose of national summary statistics on barriers. Behind each of these barriers may be technological, regulatory or market factors (such as lack of competition).

_Price barriers:_ Evidently the all-in cost of accessing any service is an important determinant of the degree to which the service will be used. This will include both the price charged by the service provider and the additional costs incurred by the household in accessing this service. For the price charged by the service provider, technical efficiency, regulatory and tax factors and competition (or lack of it) are all relevant drivers. For the additional costs incurred by the household, cost and availability of transport and communications infrastructure, given branch location, will be relevant.

_Information barriers_ : An extensive literature on credit rationing due to adverse selection points to the importance of information aspects. Evidently these can effect the cost of credit or even make it unprofitable to extend credit to certain groups at any price. A range of policies is available to alleviate this whether by improving information or reducing the need for it. For example, the establishment of an improved credit information infrastructure can be eased by enabling laws and by steps to overcome the collective action problems that are often involved. Improved procedures and laws relating to secured lending, leasing and factoring help overcome information gaps by reducing the need for information. (Of course, information gaps are not external to the system, but can also be affected by behavior of market participants, for example, through improved financial product design.) _Product and service design barriers_ : Surveys often reveal that the products and services offered by financial intermediaries are not well-adapted
<mark>methodologies. In the first method, using the eligibility thresholds presented in Phadera et al. (2022) as hard cutoffs, inclusion errors of the PPMT models corresponding to the SEVAT and Legacy-VA models would be 15 and 21 percent, respectively, under the high confidence level. While these errors are well below the levels observed in practice, PPMT exclusion errors would be extremely high at 95 percent for both the models. This translates into referring only 2 percent of the extremely vulnerable humanitarian beneficiaries to the government program. However, exclusion errors under the second (ranking) approach, which is likely to overcome the differences arising from survey design and other data related disparities (further discussed in analytical strategy section 3.2), would be within the acceptable level of 30 and 15 percent for the PPMT corresponding to the SEVAT and Legacy-VA models. Both models would refer about 36 percent of the extremely vulnerable households to the MoLSA program.</mark> <mark>The choice between electing the PPMT models and collecting new data will depend on</mark> capacity, budget, and appetite for the uncertainty of eligibility but the choice comes with important trade-offs. <mark>Besides identifying the cross-eligibility with certainty, collecting new data may elicit greater insight into the possible transition. For instance, the field-test data showed that a smaller but a meaningful share of households (3 and 16 percent for SEVAT and Lgacy-VA models respectively) that are deemed nonvulnerable and, hence, ineligible for any humanitarian assistance, in fact, would be eligible for the MoLSA program when applying the full-PMT. This group, otherwise, will not even be considered for the crosseligibly assessment when relying purely on the PPMT models and the existing humanitarian database. Moreover, perceived ISIS affiliation and fear of persecution may mean some households do not want to be referred to the formal government system. One-third of the h</mark> ouseholds that are eligible for the humanitarian assistance in the field-test data are unwilling to be referred to the government support.
<!-- Start of picture text --> 14 9 8<br>12 8 7<br>7<br>6<br>10<br>6<br>5<br>8 5<br>4<br>6 4<br>3<br>3<br>4<br>2<br>2<br>2<br>4 1<br>0 —— 0 )<br>Allocation to nontraditional asset classes Number of eligible currencies Number of eligible asset classes<br>mam |\Vididle office reports fo the board<br>mum Middle office does not report to the board<br>==—=Total average<br>N=71, 100, and 97 respectively.<br>Source: Second RAMP Survey on Central Bank Reserve Management Practices<br><!-- End of picture text -->
acceleration.

# **4.2 Results**

Considering first the raw and de-seasonalized data (Figure 1), it is clear that the number of card transactions jumps sharply in August 2014, precisely when the VAT rebates first become available. This immediate and large response is not surprising, as the VAT rebates were large in size, were introduced with great media fanfare, and were very salient to consumers (Figures A.2-A.5).<sup>14</sup> The second outcome of interest, the volume of card transactions, also increases with the reform, but the increase here is less pronounced. The increase in the number of transactions is hence driven by smaller transactions. This is consistent with the fact that the VAT rebates were proportionally smaller for larger transaction amounts, and that a larger share of large transactions was likely already carried out through electronic payment methods before the

> 13Here, _t_ can be a week or a month. For weeks that stretch across two months, we consider that each week falls into the month in which it has more days.

> 14We do not observe prices or the incidence of the VAT rebate, but the strong consumer response suggests that a substantial share of the rebate was passed through to consumers.

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# **_5.2. Econometric Analysis of MWRs_**

Most empirical studies examine the differences of MWRs across different classes of annuities without testing whether these differences are significant. The large dataset of individual annuities in Chile enables a more formal examination of the main determinants of MWRs, and the testing of whether the relationships identified above are significant. For this purpose we specify the MWR as a function of individual annuitant characteristics, as in equation (3): (3) MWRi,t = f (genderi,t, agei,t , premiumi,t , guaranteei,t , defermenti,t) Where MWRi is the money’s worth ratio of the annuity bought by individual i at time t, regressed against the gender and age of the individual annuitant, the size of the annuity premium expressed in logs, and the guaranteed and deferment periods. Since the bulk of the market is constituted by joint annuities, the equation was estimated using this type of annuity as the base variable and dummies included for single male and single female annuities. Likewise, 1999 was considered as the base year and dummies were included for 2002, 2003, 2004, and 2005. Table 6 shows the results obtained through least squares with robust standard errors. This specification was selected after conducting a number of specification tests, including the White test for heteroskedasticity of the residuals.

**Table 6: Main Determinants of MWRs**

**Dependent Variable: 100*MWR;  Least Squares with Robust Standard Errors Pooled Data for 1999, 2002, 2003, 2004, and 2005; Observations: 6526**

|**Variable**|**Coefficient**|**Std. Error**|**t-Statistic**|**Prob.**|
|---|---|---|---|---|
|C|62.39024|0.722912|86.30404|0.0000|
|AGE
Minister of Finance. Other ministers may want their own source of funds and power. Therefore the minister in charge of an infrastructure area may have an incentive to create and maintain exclusive arrangements, even if this is not the most efficient way to generate rents from the point of view of the government as a whole.<sup>7 9</sup> Perceptions and information costs have similar effects. The citizens in many countries are alert to the risk that governments may reduce overall welfare by redistributing rents for personal or narrowly political objectives. Citizens often resist these tendencies. Popular action is much more likely against a few gross or obvious abuses than against a myriad of small and complex schemes. For example, citizens are usually strongly opposed to tax increases. The cost imposed on citizens by inefficient infrastructure is usually much harder to see, and therefore inspires less opposition. In some cases this may provide an additional reason for governments to maintain restrictions on entry in infrastructure.

78 McCarthy (1994), Kerf & Smith (1996) Box 1.4 citing ESMAP (1993).

79 For a game-theoretic view of this dynamic, see Mauro (1995) p.705-<sup>7</sup> 06.

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A striking result in these figures is the very large share of human capital (more than 100% of total wealth) and the negative intangible residual wealth for Korea. There could be several explanations for this. First, Korea has historically enjoyed relatively high annual real income growth, which is reflected in the 3.98% forecast growth rate shown in Table 2.<sup>13</sup> A similar argument may hold for Poland and Spain as well, since their projected income growth rates are also relatively high. Second, and in contrast to all the other countries covered in the OECD project, the educational attainment data for Korea combine educational Level 4 (post-secondary, non-tertiary education) with Level 5B (first stage of tertiary education), while earnings’ data for Level 5B are applied to this combined group, which results in an upward bias to the human capital estimates for Korea.

The intangible wealth residual is small but positive in New Zealand and Norway, 1% and 4% of total wealth respectively. There are no obvious explanations for this low share – both countries have a high proportion of natural capital (which will decrease calculated intangible capital in total, given the wealth accounting methodology), but other countries such as Romania, Canada and Poland also have considerably high shares of natural capital. New Zealand stands out in Figure 2 as having the highest share of human capital after Korea, in spite of a projected real income growth of only 0.77% as shown in Table 2. The other notable feature for New Zealand is the large negative figure for net foreign assets, more than 5% of total wealth. In contrast, Norway’s share of human capital in total wealth is near the sample mean, while it enjoys a positive 5% share of net foreign assets in total wealth.<sup>14</sup>

# **6. Concluding remarks**

We began this analysis by presenting evidence that the SNA balance sheet accounts are substantially incomplete and hypothesizing
over time in log points of the instrument can be expressed as The same intuitions from Section 2.2 carry over to our comprehensive formulation of the SNAP formula, although they are more difficult to see here. As in Section 2.2, changes in the numerator no longer depend on realized trends either in rents or in incomes of program participants. The simulated variable is also free from the influence of participants joining the program after 2006. Again, note that we find it necessary to include a housing-price control to net out the influence of these new participants on the actual benefits, which crucially affects the first stage. The simulated benefits depend only on the intensive-margin changes based on the levels of demographic characteristics of participants _Xi_ 0 realized prior to the Great Recession.

# **C Adjustments to the SNAP Formula Made by Individual States**

Although the SNAP formula is set at the federal level as described above, a number of adjustments to the formula occur at the state-level during implementation that introduce cross-state idiosyncrasies in final disbursement levels. These include, most importantly, the standard utility allowance program (SUA).

## **C.1 Standard Utility Allowance**

First, all states participate in the SUA. States set their own utility-deduction standards, which are designed to reflect the average within-state utility costs of households. According to Holleyman et al. (2017), states’ methodologies fall into two categories: (1) methodologies that rely on state-specific recent utility data and (2) methodologies that adjust a base number using an inflation measure such as the Consumer Price Index (CPI) of utility costs. Some states use a methodology that combines both approaches. The first set of states recalculate the SUA yearly while the second set of states update the SUA based on the change in fuel CPI. Considerable variation exists within these methodologies. For example, some states use only data for low-income households, whereas others gather data for all households. States incorporate a variety of fuel types, and some assign weights to
2014e). Some programs have been costed and are known to be relatively inexpensive with light implementation requirements, such as information campaigns, while others are much more costly and demanding in terms of implementation, such as extending school hours (Holland, Evans, and Alfaro 2014). However, we are still far from having systematically comparable cost data across different types of programs and contexts, which would facilitate decision-making for policy makers (Evans and Popova 2014).

Across all six Central American countries, there are common policy priorities which would likely contribute to reducing dropout, including improving the quality of education at all levels, addressing remaining gaps in access at the preprimary and secondary levels, and strengthening the coverage, targeting, and coherence of existing programs aimed at improving education outcomes. Beyond this broad set, each country’s particular circumstances can inform specific priorities – initial ideas are discussed below in the concluding section.

# **5. Conclusions**

While substantial progress in educational attainment has been made in Central America over the last several decades, dropout remains a major challenge to building the skills of the workforce and human capital of the population. As detailed in Section 1, each country has a unique history of education progress, but today the sub-region faces some similar dropout problems. In Panama and Costa Rica, most youth remain in school through US ages, but many are over age for their grade, and most drop out before completing US. In Honduras, Guatemala, and Nicaragua, only a minority 27
open sun. Nobody is likely to enjoy this work, and that is undoubtedly part of the reason that relatively rich people appear to rarely turn up for work on the scheme. Yet, while this “selftargeting” mechanism is a key aspect of the rationale for such schemes in fighting poverty, the fact that the type of work is unpleasant has never (to our knowledge) been used in assessing the welfare of actual or potential participants.

This paper has offered a simple correction for this deficiency in past assessments of the performance of such programs. Our adjustment to measured household consumption to allow for the disutility of casual manual labor involves a single preference parameter that can be readily calibrated to available data assuming that labor supply choices are utility maximizing. We have also proposed a robustness test given the possibility of involuntary unemployment in casual labor markets. The empirical implementation has been for India’s NREGS.

The proposed adjustment of the welfare measure for the disutility of casual manual labor entails a marked difference to standard measures of poverty and inequality. The distributional change implies a lower incidence of poverty, but higher measures of its depth and severity; for example, the squared poverty gap index roughly doubles. For rural India as a whole, the Gini index of inequality rises from 0.31 to 0.43.

The adjustment also affects the assessment of targeting performance of NREGS. Whereas the choice between the unadjusted consumption and our adjusted version does not make a significant difference among the 30% richest, the assessed targeting performance is changed appreciably among the poorest half of the distribution when we allow for the disutility of doing casual manual work.

However, allowing for the disutility of work also devalues the benefits to participants. Using survey data for Bihar, we find that this effect dominates the better targeting performance implied by allowing for the disutility of the type of work done. The scheme reaches even poorer people, but that it does less to raise their welfare. On balance the poverty impact of the scheme is lower, representing a decline
18 _period_ that would make agents indifferent between experiencing and not experiencing the shock. If _c_<sup>_∗_</sup> is steady state consumption, _ct_ is steady state consumption with the shock and _r_ is the discount rate, then the welfare measure is the value of ∆ such that We assume a discount rate of 4 percent (Web Appx. Section E discusses why). In the most severe scenario, welfare decreases in the LICs by 1 _._ 9 percent in perpetuity, 3 _._ 0 percent in the MIC and 4 _._ 3 percent in the HICs (see Figure 4(d)). Given the discount rate, this is equivalent to a one-time loss of 74 percent, 89 percent and 111 percent of GDP respectively.<sup>36</sup> This adds up to 102 _._ 4 percent of World GDP in 2019 ($86 _._ 7 trillion). The cost of human capital decumulation is high also. In year 10, once the shock is over, ∆= 2 _._ 0 percent in the LIC, 3 _._ 0 percent in the MIC and 3 _._ 4 percent in the HICs.<sup>37</sup> If the shock is to schooling only, then welfare decreases by 1 _._ 3 percent in the LIC, 1 _._ 9 percent in the MICs and 1 _._ 8 percent in the HICs (Web Appx. Fig. A.5). The experience shock reduces welfare by 1 _._ 6 percent in the LICs, 1 _._ 6 percent in the MICs, and 2 _._ 6 percent in the HICs (Ibid.). Fifty-six percent of the impact on the LICs is from the shock to employment/experience, whereas it is 45 and 57 in the MICs and HICs respectively.

That said, much of this has to do with the short run impact of the shock,
estimates. Furthermore, the quality concerns over the 2017/18 survey require further investigation. Across a wide range of publicly available data sources, the paper finds no evidence of an increase in poverty between 2011/12 and 2017/18.

The lack of publicly available data creates doubts among the general public, obstructs scientific debate, and hinders the implementation of sound, empirically based development policies. The imputation methods adopted in this paper are more appropriate to extrapolate poverty across shorter periods when data are not available, or for geographic areas where survey data is not appropriate. They are imperfect substitutes for actual data on standards of living. There is no alternative to timely, quality assured, and transparent data for poverty measurement and for the design and monitoring of anti-poverty policies.

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