rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 5: Program implementation**||ff|||||||P-l f it|
|---|---|---|---|---|---|---|---|---|---|
||Mean [SD]|<br>Dier<br>an|ence betwe<br>d Controlgr|en [...]<br>oup||P-valu|e for differ|ence...|vaues o jon<br>test|
||in Control|||||Group 1|Group 1|Group 2|<br>group1=group2=|
||Group|Group1|Group2|Group3|N|<br>and 2|<br>and 3|<br>and 3|<br><br>group3=0|
|**PANEL A: Administrative data from CGA an**|**d Comme**|**rcial banks**|**(as of May**|**2015)**||||||
|Step 1:<br> First visit done successfully|0|0.991***|0.973***|0.976***|3600|0.065*|0.085*|0.597|0***|
||[0]|(0.009)|(0.006)|(0.005)||||||
|Step 2:<br> Second visit done successfully|0|0|0.416***|0.467***|3299|0***|0***|0.004***|0***|
||[0]|(0)|(0.018)|(0.016)||||||
|If 2nd visit conducted, time between|0|0|83.3***|87***|2068|0***|0***|0.353|0***|
|the two visits (days)|[0]|(0)|(3.4)|(3)||||||
|Step 3:<br> Business is formal acording to the|0|0|0.131***|0.148***|3299|0***|0***|0.179|0***|
|CGA<sup>β</sup>|[0]|(0)|(0.013)|(0.011)||||||
|Step 4:<br> Additional services:||||||||||
|Business registered to CGAs|0|0|0.11***|0.125***|3299|0***|0***|0.215|0***|
||[0]|(0)|(0.012)|(0.011)||||||
|Business attended to at least one group|0|0|0.058***|0.074***|3299|0***|0***|0.083*|0***|
|training at CGAs|[0]|(0)|(0.009)|(0.008)||||||
|Business opened an Entreprenant Bank<br>Account|n.a<sup>α</sup>|n.a<sup>α</sup>|n.a<sup>α</sup>|n.a<sup>α</sup>||||||
|**PANEL B: Midline survey data**||||||||||
|_Formalization process: (only formal busines_|_ses)_|||||||||
|Number of days it took to formalize<sup>η</sup>|26.5|-26**|-20.9**|-16.4**|322|0.655|0.345|0.389|0.052*|
||[27.1]|(11.6)|(8.3)|(7)||||||
|Amount paid for formalization|131276|-136062**|*-102502***|-85927***|318|0.418|0.177|0.393|0.003***|
||[162172]|(41852)|(29965)|(25206)||||||
|Share of business who paid nothing for|0|0.882***|0.845***|0.857***|318|0.864|0.897|0.905|0***|
|formalizing|[0]|(0.218)|(0.156)|(0.131)||||||
|_Program Knowledge:_||||||||||
|Ever heard of the Entreprenant status|0.064<br>[0.244]|0.18***<br>(0.035)|0.289***<br>(0.022)|0.3***<br>(0.02)|2843|0.005***|0.001***|0.628|0***|
|Ever heard of the Entreprenant status<br>|0.024|0.091***|0.191***|0.193***|2843|0.003***|0.001***|0.905|0***|
|Notes: Column 1: Standard deviations presented<br>firm owner/firm characteristic on treatment du<br>1, 5 and 10%.<br>α: Data on who in the study population opened<br>bank account. β: For the control group and grou<br>the 99th percentile.<br>and was able to describe it correctly|[0.153]<br>in brackets<br>mmies, cont<br>a bank acco<br>p 1, CGA did|(0.03)<br>. Columns 2<br>rolling for st<br>unt were no<br>not have an|(0.019)<br>-4: coefficien<br>rata dummie<br>t available in<br>y informatio|(0.017)<br>ts and stand<br>s (dummies<br>time, but w<br>n as they ar|ard err<br>for each<br>e know<br>e not fo|ors (in pare<br>triplet). *<br>that 69 bu<br>llowing up|ntheses) fr<br>**, **, * ind<br>sinesses in t<br>with these|om an OLS<br>icate stati<br>otal opene<br>businesses|regression of the<br>stical significance at<br>d an entreprenant<br>. η: Top-coded at|
**Table 3.** MP Data—prior and posterior moments of model size Properties of the chain and the best model

|||_m_|= 7|||_m_=_k_|_/_2 = 27||
|---|---|---|---|---|---|---|---|---|
||fxe|d_θ_|rand|om_θ_|fx|ed_θ_|rand|om_θ_|
||_g_ = 1_/n_|_g_ = 1_/k_<sup>2</sup>|_g_ = 1_/n_|_g_ = 1_/k_<sup>2</sup>|_g_ = 1_/n_|_g_ = 1_/k_<sup>2</sup>|_g_ = 1_/n_|_g_ = 1_/k_<sup>2</sup>|
|Prior mean|7|7|7|7|27|27|27|27|
|Prior st. dev.|2.47|2.47|6.57|6.57|3.67|3.67|15.88|15.88|
|Posterior mean|8.68|6.05|9.67|5.42|17.90|9.77|10.37|5.75|
|Posterior st. dev.|1.52|1.16|2.09|1.53|2.44|1.75|2.24|1.48|
|# Models visited|106,041|13,103|194,230|11,864|516,479|135,353|241,082|13,88|
|# Models covering 50%|933|31|1,549|23|3,038|1,353|2,049|28|
|Post. Prob. best model|1.65%|10.70%|1.14%|9.60%|0.49%|1.28%|0.90%|9.01%|
|_kj_ for best model|8|6|8|4|19|8|8|6|
|Corr. visits and PO|0.990|0.998|0.973|0.999|0.251|0.985|0.963|0.999|

**Table 4.** SDM Data—prior and posterior moments of model size Properties of the chain and the best model

|||_m_|= 7|||_m_=_k/_|2 = 33_._5||
|---|---|---|---|---|---|---|---|---|
||fxe|d_θ_|rand|om_θ_|fxe|d_θ_|rand|om_θ_|
||_g_ = 1_/n_|_g_ = 1_/k_<sup>2</sup>|_g_ = 1_/n_|_g_ = 1_/k_<sup>2</sup>|_g_ = 1_/n_|_g_ = 1_/k_<sup>2</sup>|_g_ = 1_/n_|_g_ = 1_/k_<sup>2</sup>|
|Prior mean|7|7|7|7|33.5|33.5|33.5|33.5|
|Prior st. dev.|2.50|2.50|6.74|6.74|4.09|4.09|19.63|19.63|
|Posterior mean|6.17|2.84|5.26|2.35|14.20|7.00|5.73|2.40|
|Posterior st. dev.|1.50|0.85|1.86|0.59|1.64|1.44|1.91|0.62|
|# Models visited|102,852|6,141|101,642|2,167|606,792|130,776|129,929|2,594|
|# Models covering 50%|590|4|248|1|485|744|383|1|
|Post. Prob. best model|6.63%|37.72%|5.74%|66.42%|1.43%|6.58%|5.22%|63.64%|
|_kj_ for best model|6|2|2|2|13|6|6|2|
|Corr. visits and PO|0.996|1.000|0.996|1.000|0.029|0.996|0.996|1.000|

_θ_ and _g_ = 1 _/k_<sup>2</sup> , the choice for _m_ has little effect. The twenty best models are the same for both values of _m_ , with very similar posterior probabilities and only slight differences in ordering. The best model (with over 60% posterior probability) in these cases as well as with two other settings, is the model with only the East Asian dummy and Malaria prevalence as regressors. The same model is also second best for the random _θ_ case with _m_ = 33 _._ 5 and _g_ = 1 _/n_ , but is well down the ordering (receiving less than 0.25% of probability) if we fix _θ_ with _m_ = 33 _._ 5. In fact, in the latter case with _g_ = 1 _/n_ , the regressors East Asian dummy and Malaria prevalence never appear together in any model with posterior probability over 0.25%. This further illustrates the huge impact of simply changing between the fixed and random _θ_ cases.

Convergence is excellent, except for the case with fixed _θ_ , _m_ = 33 _._ 5 and _g_ = 1 _/n_ , as in the other examples. The difference in convergence between the cases is even more striking than with the 15
Policy Research Working Paper 10495

# **Abstract**

This paper investigates the impacts of the Brazilian fiscal system on poverty and inequality, with a focus on the effects on vulnerable populations. Leveraging a broadly applied and accepted methodology and several household surveys and administrative data, the paper shows that Brazilian fiscal policies in 2019 were typically poverty- and inequality-reducing, but with a large heterogeneity in the effectiveness of fiscal tools. The poverty impacts of fiscal policies increased over time due to direct transfers. Income inequality reduction is among the highest in a comparable set of middle-income countries, yet the post-fiscal Gini is still high at 0.521. The results indicate that elderly people are the largest beneficiaries of the fiscal system and households with children experience a smaller decline in poverty from government transfers compared to those with no children. At the individual level, the findings also show that children and young adolescents (ages 0–15) were made poorer after taxes and transfers, which suggests that Brazilian fiscal policies in 2019 also increased poverty rates for some population groups. These findings contribute to provide a comprehensive overview of the fiscal system in Brazil and have wide-ranging consequences for the formulation of public policies.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at glaraibarra@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Source: NEA/ World Bank/ DANIDA (2002). Catch is measured in tons (t) and capacity in horse power (HP).

<!-- Start of picture text --> Access to drinking water is crucial for reaching a number of VDGs. Sustainable access to safe<br>drinking water is part of the Government’s traditional emphasis on the role that investments in<br>infrastructure can play in reducing poverty (see below). Infrastructure forms the core of key targeted<br>programs to address poverty and these include the provision of clean drinking water systems and seven<br>other components. In 2000, 56 percent of the population had access to safe water. This is a commendable<br>increase from 48 percent in 1990. However, Vietnam is on the lower end regionally and globally for<br>access to safe water (Figure 17). Progress in this area will be essential for further success in reducing<br>infant and child mortality and malnutrition.<br>Figure 17. Country Comparison of Access to Safe Water, 2000 (percentage of population)<br>Mon<br>7}= nea me me me<br>oy, __ 2 oo 2 se oy my i<br>oo, ____ =a@eieig®®=s& &<br>—EemmnHHREHhHhtihmiim &i<br>y S § £& S§ FSF $F SS SC<br>e a?= ars rdry ws eo& > vA<br>Source: UNDP (2002a)<br>Urban slums have poor infrastructure. The urban poor in Vietnam live in areas with poor<br>infrastructure and the access to basic services (safe water, sanitation, water drainage, electricity,<br>garbage collection) is limited. Most slum dwellers have unstable jobs and unstable incomes and<br>their difficulties in securing permanent registration makes things worse. The VLSS data show an<br>urban poverty rate of 9 percent percent, but this is likely to be an underestimation, as many urban<br>slum dwellers are not registered.<br><!-- End of picture text -->

# Targets andfuture challenges

The CPRGS recognizes the importance of environmental protection for sustainable poverty reduction and growth and proposes a number of key measures. These include strengthening conservation of natural forests and protection of watersheds through the involvement of the people that depend on them for their livelihood, enhancing access of the poor to clean water, and halting urban pollution. To monitor progress towards environmental sustainability, six specific targets are identified (Box 9).

34
from World Bank, 2006c). Because aid that is expressed as a % of GDP is likely to be correlated with GDP (per capita), excluding the latter may induce a spurious relationship between aid and expenditure. Growth is included to capture the reaction of expenditure to short-term shocks in GDP per capita. If government education and health expenditure do not immediately adjust to a higher (lower) level in the event of a positive (negative) growth shock, then a negative coefficient is expected. The effect of trade is a priori ambiguous (e.g., Rodrik, 1998). Greater openness may erode a government’s capacity to finance expenditure as tax bases become more mobile. Moreover, tariff reductions may increase trade openness while starving the government of revenue, which again suggests a negative association between trade and public education or health expenditure. However, openness to trade may also increase the demand for social spending to insure against increased external risk and to redistribute gains from trade, and public education and health expenditure may play a role in these effects. Urbanization may also have a positive or negative effect. Some services should be easier to administer in a more urbanized society (Hepp, 2005), and urbanization may create more opportunities for economies of scale. However, lower transportation costs and easier lobbying for government services in urbanized societies may increase the demand for education and health services (Hepp, 2005; Baqir, 2002). For health spending, the risk of contagion and pollution may be higher in cities (Gerdtham and J ̈onsson, 2000).

Table 1 shows summary statistics for the education and health regression samples. Education aid constitutes approximately 28% of public spending in the education sector, whereas health aid accounts for approximately 22% of public health spending. Slightly less than one-fifth of aid (excluding actions relating to debt and donor administrative costs) is targeted toward education or health.

# **Hypothesis tests for no fungibility and full fungibility**

As discussed in section I, the presence of off-budget aid in the donor-based measure of sectoral aid ( _SAIDit_ ) decreases the estimate of _β_ , thereby overstating the true degree of fungibility. For a correct assessment of fungibility, it is necessary to distinguish between on- and off-budget sectoral aid. Consequently, I also estimate models that partition education and health disbursements into the four prefix codes: where _IP_ represents investment projects, _SP_ denotes sector program aid, _TC_ represents technical cooperation, and _ONM_ denotes other (no mark) aid.

SP aid should primarily be on-budget aid because, by definition, program aid involves a government-togovernment transfer of resources. In contrast, TC is a good proxy for off-budget aid. The costs of providing 11
_EXR_ , clear the balance of payments, making sure that the level of foreign savings matches the level that is financed on the right‐hand side of the capital account.

As a manifestation of Walras’ law, in a CGE model like the one presented above, one equation should be removed to assure equality between the number of variables and independent equations; it is possible to check that the omitted equation holds in a post‐ calculation. Here we opt for the alternative of instead inserting one variable, named WALRAS, into one equation. Hence, the presence of _WALRAS_ in the capital account of the balance of payments. In the absence of errors, the solution value for _WALRAS_ should be (very close to) zero.

Finally, a well‐specified CGE model like GEM‐Core is homogeneous of degree zero in prices, meaning that only relative prices matter and that, if one set of relative prices solves the model, then any multiple of this set of prices would also solve the model (scaling all domestic prices and payments) without any influence on quantities. To anchor the price level, a price or price index, referred to as the numéraire, needs to be fixed, with the consequence that all other prices are measured relative to this numéraire. In this mathematical statement, the consumer price index, _CPI_ , is the numéraire. Here, equation INV16 defines the CPI, which is fixed, on the basis of the base‐year weights of household consumption payments by commodity and household type in total household consumption.<sup>20</sup>

**Table A.2.4. Equations for investment, system constraints, and numéraire**

|INV‐1|<br><br>,<br>_t_<br>_t_<br>_t_<br>_t_<br>_t_<br>_gov t_<br>_t_<br>_INVG_<br>_YG_<br>_EG_<br>_ndfg_<br>_CPI_<br>_nff_<br>_EXR_<br><br><br><br><br><br>|_t_<br>_T_<br>|Gov’t primary<br>deficit,<br>investment<br>value, and<br>financing|
|---|---|---|---|
|INV‐2|<br><br>,<br>,<br>,<br>,<br>',<br>'<br>_i t_<br>_i t_<br>_i t_<br>_t_<br>_i t_<br>_t_<br>_t_<br>_t_<br>_t_<br>_i t_<br>_i_<br>_INSDNG_<br>_INV_<br>_SAV_<br>_nff_<br>_EXR_<br>_SAV_<br>_ndfg_<br>_CPI_<br>_drf_<br>_EXR_<br>_SAV_<br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br><br>|_i_<br>_INSDN_<br>_t_<br>_T_<br><br>|_G_<br>Non‐gov’t<br>investment<br>value and its<br>financing|

20 As an alternative to CPI, the domestic producer price index (DPI) may serve as numéraire. In addition, it is often used as the denominator in the definition of the price‐level‐deflated (PLD) real exchange rate (REXR). Algebraically, with time subscripts omitted, _DPI_   _PDSc_  _dwtsc_ and _REXR_  _EXR DPI_ . _c_  _C_ ‐51‐
This paper has empirically examined the key determinants of migrant remittances at the individual level using a recently conducted survey on internal migration within Vietnam. Our empirical model incorporated, in an eclectic manner, variables assumed to capture some of the underlying motives for remitting suggested by existing theories. The paper also tried to uncover some factors unique to Vietnam that determine remittance behavior in that country.

The empirical analysis yielded a number of key findings and suggests that no one theory is likely to be sufficient to explain the remittance phenomenon in Vietnam. The study found that the education of migrants has a well-defined positive effect on the level of remittances. This seems to provide some support for the theory of contractual arrangement (Lucas and Stark, 1985), where remittances are seen as a repayment for the money and resources that the migrant’s family originally invested in the migrant’s education. The negative coefficient on the variable for the presence of the migrant’s parents at the destination also provides some support for this notion. However, the negative coefficient can also be seen as supportive of altruistic motives (Becker, 1974). Unfortunately, given the absence of any information regarding the recipient household, such as the origin household’s income or assets, we are unable to interrogate empirically this issue in a more systematic fashion. On the other hand, the observed negative coefficient relating to the presence of school age children at the destination suggests that remittances can also be seen as an investment in the education of the migrant’s family.

The empirical estimates also provide support for the co-insurance theory (AmuedoDorantes and Pozo, 2006; Stark, 1991). For instance, we have found that having a family 24
eliminated by setting everyone’s income to group average income. If there are no differences in mean income across groups, this term also is equal to one. Overall inequality simply is the product of these two terms.

Finally, the index I(y, y̅) generalizes to a class of inequality indices, whose members only differ by the definition given to z. Each member in the class defines z to be equal to the mean income among the X% richest individuals: where y̅<sup>X</sup> ≥0 denotes mean income among the X% richest individuals. The index I(y, y̅<sup>X</sup> ) is a measure of inequality because it is homogeneous of degree zero and it satisfies the transfer axiom. The index I(y, y̅) corresponds to the extreme case where X=100%, i.e., I(y, y̅ ) = I(y, y̅<sup>100%</sup> ). The other extreme member in the class defines z to be the largest income in the distribution. This latter measure can be interpreted as the factor by which incomes need to be multiplied to attain the income of the richest person in the distribution. Any member of this class admits an interpretation that is related to a Kuznets ratio (Kuznets, 1955). Indeed, a Kuznets ratio measures the _ratio of average income_ among the top X% to average income among the bottom 1-X% of the distribution.<sup>17</sup> In contrast, index I(y, y̅<sup>X</sup> ) measures the _average of income ratios_ , namely those of the average income among the top X% to each individual income. This illustrates that index I(y, y̅<sup>X</sup> ) is more distribution sensitive than the corresponding Kuznets ratio.

# **3. Empirical Applications**

## **3.1 Data**

We use data from the World Bank’s Poverty and Inequality Platform (PIP) (World Bank, 2022a) to illustrate the empirical properties of the new welfare measures proposed in this paper and contrast them with existing ones. The PIP is a large compendium of over 2000 household surveys covering 168 countries and over 97 percent of the world’s population.<sup>18</sup> For most of the analysis in this section, we rely on the “lined-up” version of the PIP data which interpolates/extrapolates between/beyond survey years assuming distribution-neutral growth to create a continuous annual dataset covering 1990-2019.<sup>19</sup> This is the same dataset used by the

> 17 A closely related measure is the Palma ratio, defined as the ratio of the top 10 percent income share to the bottom 40 percent share (Palma, 2011). Setting X= 10% in I(y, y̅<sup>X</sup> ) so that it measures the average factor by which all incomes must increase to reach the mean income of the top 10% of the distribution is similar in spirit to the Palma ratio and moreover gives greater weight to the shortfall of the poorest.

> 18 For documentation on PIP, see pip.worldbank.org and for technical details on the data and estimates used in PIP, see the Global Poverty Monitoring Technical Notes at https://pip.worldbank.org/publication.

> 19 For details on the methodology used for the distribution-neutral shifts to the survey distribution to line up surveys - to a common reporting year, see the PIP methodological handbook (https://datanalytics.worldbank.org/PIP

15
accounts).<sup>29</sup> This would give a poverty line of $2.38 for use with HFCE and $3.55 for use with GDP.<sup>30</sup> Figure 5, Panel B illustrates the poverty estimate for each country using these poverty lines. Although poverty estimates with the adjusted lines are more closely aligned to survey estimates than in Panel A, there is still substantial variation, resulting from the variation in the gaps across countries combined with the uniformly adjusted poverty lines.

Just as it does not make much sense to scale up survey means and not change the $1.90 poverty line, it is also not reasonable to increase the poverty line by the same proportion as the survey means. Part of the poverty line is based on estimating the cost of obtaining minimum nutrition needs and this estimate need not necessarily be affected by underreporting of consumption (or income). Chen and Ravallion (2010) clarify this point better by noting that typical methods for setting national poverty lines will underestimate the poverty line if non-food spending is underestimated in surveys, and thus any correction for the underestimation of nonfood spending would also lead to higher poverty lines. But this adjustment, by construction, would almost certainly be less than the entire gap between survey means and national accounts.

A different way of adjusting the IPL for poverty measurement based on national accounts means, is to set a country-specific IPL is based on the country-specific survey-national accounts gap observed in the data. Alternatively (and equivalently), one could use the national accounts series, scaled down by the gap, and use the $1.90 line. Such an approach could be justified from a perspective that national accounts may be a more comparable measure of changes in living standards over time, but that surveys, on average, do a better job at measuring both inequality and the level of poverty. Under this approach, large changes to survey methodology that affect the survey mean (but smaller changes to inequality), would have a much smaller effect on the evolution of poverty which would be based on a more stable series of national accounts means.

> 29 Since a proportion of the national poverty lines for many poor countries is based on the pricing a basket of caloric assumption it can be argued that this proportion is not underestimated to the same extent as non-food consumption or income. Thus, simply adjusting the poverty line by the average gap may be too drastic, as it would imply that both food and non-food is underestimated in surveys used to define the poverty line. Moreover, we are using the average the gap, which larger than the gaps typically found in low-income countries.

> 30 These are simply conversions using the overall ratio observed in Table 1. For HFCE, the gap is -0.202, so adjusting the poverty line of 1.90, would give 1/(1-.202)*1.9=2.38. For GDP the gap is .465, resulting in the poverty line being 1/(1-.465)*1.9 = 3.55.

23