rafmacalaba/datause-encoder-data huggingface dataset:
Average deficit figures are comparable across the two groups of countries. Regarding our variable of interest—primary fiscal expenditure—the figures are about a third higher for OECD industrial countries partly reflecting a larger revenue base. As defined here, there is no trend in primary expenditures for the two groups of countries over the three decades. As to the components of expenditures, developing countries spend proportionately more on general public services, on defense and on education, while OECD countries spend more on health.

|Table 1: Central fiscal expenditures: average val|ues<br>1972-1980|1981-1990|1991-2000|
|---|---|---|---|
|**High Income OECD countries (22)**||||
|_Observations(potential number in parenthesis)_|_159(198)_|_179(220)_|_129(220)_|
|Overall de<br>it in GDP<br>fic<br>%|-.90|-2.47|-1.94|
|Total public exp. in GDP %|29.21|35.48|33.77|
|Discretionary public exp. in GDP %**a/**|27.79|32.05|30.49|
|(in % of total public exp.)<br>Expenditures by function|_100_|_100_|_100_|
|hich<br>General public services of w|17.7|22.2|22.9|
|_payment_<br>_interest_|_(4.9)_|_(9.7)_|_(9.7)_|
|Defense|8.4|6.8|5.6|
|Transport and Communication|6.8|5.0|3.7|
|Housing and community ame<br>nities|2.3|2.1|2.0|
|Health|9.7|10.0|10.8|
|Recreation, culture, and religion|0.9|0.9|1.0|
|Education|9.4|7.9|7.2|
|dual)<br> Others (resi|4<br>0.0|35.5|37.1|
|**Developing countries (118)**||||
|_Observations(potential number in parenthesis)_|_360_<br>_2)_<br>_(106_|_43_<br>_0)_<br>_3(118_|_644_<br>_0)_<br> _(118_|
|Overall deficit in GDP %|-2.57|-2.38|-1.93|
|Total public exp. in GDP|23.58|26.45|25.44|
|Discretionary public exp. in GDP %**a/**|22.40|23.86|22.66|
|(in % of total public exp.)<br>Expenditures by function|_100_|_100_|_100_|
|which<br>General public services of|28.3|27.9|28.0|
|_payment_<br>_interest_|_(5.0)_|_(9.8)_|_(10.9)_|
|Defense|12.3|11.3|10.4|
|Transport and Communication|9.6|8.0|5.6|
|Housing and community amen<br>ities|2.9|3.1|3.2|
|<br>Health|6.4|6.5|6.9|
|Recreation, culture, and religion|1.5|1.2|1.4|
|Education|14.7|13.7|13.4|
|residual)<br> Others(|19.3|18.4|20.1|

Notes: See annex A.1.3, table A.3 for country classification in the sample. **a/** defined as total public expenditures less interest payments. See annexes A.1 and A.2 for details on definition and construction of variables.

_Source: Authors’ computation from GFS data. See Annex A.1._ 11
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We can not find a simple link between affordability and accessibility. Figure 16 presents the scatter-graphs of affordability and accessibility of tertiary education. The LAC countries are clustered in the upper-left corner of the graph, reflecting low access and low affordability, while the high-income countries cluster in bottom left and right of the graph indicating high affordability and a range of accessibility scores from low to high. At first glance, there appears to be a correlation between affordability and accessibility. Low affordability—indicated by a high Un-affordability score—is associated with a low accessibility score. The simple correlation coefficient is between overall affordability and affordability is - 0.68 (R<sup>2</sup> = 0.46). However, the relationship is primarily driven by the difference between the group of LAC countries compared to the group of the highincome countries. There is no correlation between affordability and accessibility among the high-income countries. The correlation coefficient is - 0.003 (R<sup>2</sup> = 0.000). The correlation between affordability and accessibility among LAC countries is weak (R<sup>2</sup> =0.26). Consequently, the preliminary data is not conclusive. Further research is required to investigate the link between affordability and accessibility.

**Figure 16. Correlation between Accessibility (Overall Score) and Affordability (Overall Score)**

<!-- Start of picture text --> 0.90<br>Peru<br>0.80<br>Brazil R 2 = 0.2642<br>0.70<br>0.60<br>Colombia<br>0.50<br>0.40<br>R 2 = 0.4597<br>0.30<br>Mexico<br>0.20<br>0.10 R 2 = 0.0000<br>0.00<br>- 10 20 30 40 50 60 70 80 90<br>Accessibility<br>Affordability<br><!-- End of picture text --> Sources: Authors’ calculation for the LAC countries and (Usher and Cervenan, 2005) for high-income countries There may not necessarily be a strong correlation between affordability and accessibility. This is an important message for policy making. High affordability, for example through tuition free public education, does not automatically lead to high access, because somebody has to foot the bill for higher education. If the families are not contributing, then investments, supply of seat and accessibility may decrease. In some countries, the government can further subsidize higher education, but in most countries governments have severe spending constraints and many competing priorities. In the context of severe spending constraints, governments could consider more nuanced ways of financing of higher education than a simple across the board subsidy to all students regardless of their 29
15 supply. For example, FSU exports of undenatured ethyl alcohol face an EC tariff of 73.8 percent which is 42 percentage points higher than the average duty that developing countries pay on this product, and more than 30 percent higher than the average duty on all exporters combined. Approximately the same adverse tariff margin (41 percentage points) applies to exports of unfermented apple juice, and differentials of 15 percent or more occur on half of the tariff line products. For all manufactured goods combined, the 6.7 percent FSU tariff applied by the EC is more than twice as high as the average duty all - porters pay on these same products, and three times the corresponding rate facing developing countries. These adverse tariff margins, resulting from preferential trading arrangements like the EC's Lome Convention, or the Community's Protocol with the EFTA for tiee trade in manufactures, allow other suppliers to displace (divert) potential FSU exports.

Several possibilities exist for rectifying this situation - including the adoption of an FTA arrangement or extension of regional preferences to put the FSU region on an equal footing with other countries. Tlis is all the more important because the match of trade and tariff data shows the adverse tariff differentials affect trade of all FSU republics. Assuming that the republics' exporte were identical with those going to the EC, they would be affected by tariffs which were at least twice as high as the world average as follows: Rusea-nine sectors accounting for at least 74 percent of its exports; Ukrainesix sectors accounting for at least 50 percent; Belarus-nine sectors accounting for at least 75 percent; Moldova-five sectors accounting for at least 1? percent; Estonia--six sectors accounting for at least 42 peret; Latvia-four sectors acounting for at least 16 percent; Lithuania-six sectors accounting for at least _55_ percent; Armenia-six sectors accounting for at least 53 percent; Azerbaijan-eight sectors accounting for at least 89 percent; Georgia--eight sectors accounting for at least 81 percent of its exports; Kazakhstan-eight sectors accounting for at least 81 percent; Kyrgyzstan-eight sectors accounting for at least 86 percent; Tajikistan-eight sectors accounting for 99 percent; Turkmenistan-8 sectors accounting
Formally, region _r_ ’s optimal demand for the _ntc_ and _fbn_ aggregates by sector and labor type takes the form: where the WO are the wage indices dual to the LO and the _σl_ are the elasticities of substitution between labor of _ntc_ and _fbn_ origin.

Region _r_ ’s economy-wide demand for these labor bundles is then given by: The LOT bundles are in turn CES composites over labor from the origin regions classified as _ntc_ or _fbn_ via a set mapping from origin regions _rt_ to _s_ for each host region _r_ denoted by maps( _r,s,rt_ ).

The optimal bottom-level demand in region _r_ for _l_ -type labor of origin _rt_ thus takes the form where WRTr,l,rt is the corresponding wage rate, _σlz_ r,l,s denotes the elasticity of substitution between type- _l_ labor from different regions of origin within nest _s_ , and the _lameff_ are labor efficiency parameters that serve to explain the ‘observed’ initial wage differentials across workers of different origin in the benchmark data set.

The model adjusts the total populations in host and origin regions arising from exogenous changes in migrant flows by assuming that the ratio of migrating people including accompanying dependents to migrating workers is the same as the baseline ratio of total migrants to migrant workers of the same origin in the host region. The model also considers remittances from households in the host region to the region of origin. Household remittances from the host region to the region of origin are fixed fractions of migrant workers’ labor income.

# **2.2 Data**

The model is updated and calibrated for use with the GTAP 8.1 and GMig2 databases of Narayanan et al. (2012) and Walmsley et al. (2013). The GTAP database provides a globally consistent database of input-output tables, allowing for the modeling of production, consumption, and trade for 134 countries, 57 sectors, and five factors of production. The GMig2 database contains the latest available model-consistent estimates of bilateral migration stocks, labor earnings, and remittance flows at GTAP 8.1 regional aggregation level as described in Walmsley et al. (2013). The GMig2 database contains economy-wide headcount estimates of skilled and unskilled workers by origin and host region in 2007 for each of the 134 GTAP 8.1 regions as well as estimates of the 6
**Table 3.6. Private Credit, Percent of GDP**

||Country|2002|2007|2009|
|---|---|---|---|---|
|New Member States|Bulgaria|17|53|84|
||Czech Republic|34|43|56|
||Estonia|40|84|119|
||Hungary|32|57|73|
||Latvia|28|82|108|
||Lithuania|14|51|75|
||Poland|27|35|48|
||Romania|8|28|49|
||Slovak Republic|37|39|48|
||Slovenia|38|69|94|
|Candidates and Potential Candidates|Albania|6|25|54|
||Bosnia and Herzegovina||||
||Croatia|44|67|79|
||Macedonia, FYR|17|33|51|
||Serbia|21|28|35|
||Turkey|13|26|36|
|Eastern Partnership Countries|Armenia|7|10|18|
||Georgia|7|22|44|
||Moldova|15|30|47|
||Ukraine||||

Source: Beck et al (2009). Note: Table reports private credit by deposit money banks and other financial institutions.

**Table 3.6a. Private Credit, Percent of GDP**

||2002|2007|2009|
|---|---|---|---|
|Euro Periphery|99|149|181|
|Emerging Latin America|30|33|44|
|Emerging Asia|78|60|53|
|New Member States|28|54|75|
|Candidates and Potential Candidates|20|36|51|
|Eastern PartnershipCountries|10|21|36|

Source: Beck et al (2009).

The EBRD also reports a private credit measure. In addition, it reports the subcomponent that relates to lending to households and, within household loans, the scale of mortgage loans. These are reported in Table 3.6b. These data show that lending to households outpaced aggregate lending between 2004 and 2007 but the ratio stabilized or even fell slightly between 2007 and 2009 in most cases. In addition, mortgage loans are only important in the Baltic States and, to a lesser degree, in Hungary. In part, the declining share of the corporate sector in credit between 2004 and 2007 may reflect the role played by direct cross-border loans in the corporate sector, which relied less on the domestic banking sector for debt financing (Herzberg 2010).

In Tables 3.7 to 3.9, we look at the sectoral distribution of liabilities for the new member states. Table 3.7 shows that household debt levels grew very quickly between 2002 and 2007 in Hungary, the Baltic States, Bulgaria and Romania. However, this expansion was from quite low levels of household indebtedness in most cases. During the crisis, household debt levels expanded further, especially in Bulgaria and Slovenia. As noted by Herzberg (2010), the household debt levels have to be assessed in the context of relatively-low stocks of household financial assets in emerging Europe, compared to advanced economies.

Tables 3.8 and 3.9 report the sectoral balance sheets for non-financial corporations and financial corporations respectively. The liabilities of the non-financial sector grew rapidly between 2002 and 2007 in the Baltic States, Hungary, Bulgaria and Romania but were much 20
|Pregnancy Care|A woman who gave birth in the last 2 years did not visit a<br>clinic while pregnant or have a trained assistant during<br>delivery|1/16|
|---|---|---|
|Physical Safety|Any member feels unsafe at home or walking alone<sup>14</sup>|1/16|
|Early Marriage|A member was married before age 19|1/16|
|Living<br>Standards<br>Garbage Disposal|Main method of solid waste disposal is dumping, burying in<br>own compound, burning, or other|1/24|
|Drinking Water|Main source of drinking water is unsafe, or it takes more<br>than 20 minutes (round-trip) to get water<sup>15</sup>|1/24|
|Electricity|It does not have electricity|1/24|
|Cooking Fuel|Main energy source for cooking is solid fuels|1/24|
|Housing|It is an unimproved housing type|1/24|
|Sanitation|Main toilet facility is unimproved, or shared with other<br>households<sup>16</sup>|1/24|
|Financial<br>Security<br>Unemployment|Any member 15 or older is unemployed and looking for<br>work<sup>17</sup>|1/12|
|Legal Identification|No member has a form of legal identification|1/12|
|Bank Account|No member has a bank or mobile money account|1/12|

The MPI presented here uses equal nested weights with all four dimensions considered to be equally important, and all indicators within a dimension receiving an equal share of the total weight. The cross-dimensional poverty cut-off is defined as _k_ =50%, with those deprived in half or more of the weighted indicators identified as multidimensionally poor.

# **4. Data**

Data on forcibly displaced populations are scarce, with many household surveys excluding refugees and IDPs from the sample framework. To ensure that MPI results are representative of these communities and that they can be disaggregated for comparative analysis, an initial review of possible data sets was conducted. Feasibility was determined based on the availability of sufficient sample sizes for forcibly displaced persons for quantitative analyses, as well as inclusion of many of the indicators (on health, education, living standards, etc.)

> 14 A household is deprived if the respondent reports feeling moderately or very unsafe when alone at home, walking alone after dark, or walking around during the day. In Sudan, the indicator on the ‘feeling safe from crime and violence when at home’ was not available, and the indicator only considers answers to the questions on safety when walking alone.

> 15 Unprotected dug well, unprotected spring, carts with tank, tanker-truck, surface water, or other are considered as unsafe water sources according to international guidelines. See https://washdata.org/monitoring/drinking-water. 16 Pit latrine without slab, bucket, hanging toilet, and no facility (open defecation) are considered as unimproved sanitation facilities according to international guidelines. See https://washdata.org/monitoring/sanitation.

> 17 According to the ILO definition, those who did not participate in employment in the last four weeks (and have no work to return to) are actively looking for work and are available to start, or those currently waiting to start work are - classed as unemployed. See https://www.ilo.org/ilostat <u>files/Documents/description_UR_EN.pdf.</u>

10
2012; Ravallion 2012; Ravallion 2013; Edward and Sumner 2013; Chandy, Ledlie, and Penciakova 2013; Yoshida, Uematsu, and Sobrado 2014; and Poverty Research Report, The World Bank 2000). In contrast, given that poverty nowcasting relies on actual GDP data, its accuracy depends only on the features of each nowcasting method.

The purpose of this paper is to provide a comprehensive analysis of several methods for poverty nowcasting to get real-time poverty estimates in Latin America and the Caribbean (LAC). To this end, the paper first documents existing methodologies for poverty nowcasting by describing in detail their main assumptions, their information requirements, and their main limitations. Then, it validates the performance of these methods by comparing nowcasted poverty numbers with actual poverty estimates. In the particular case of the LAC region, National Statistical Offices (NSOs) usually release poverty numbers with one year of lag. Thus, this paper validates these poverty nowcasting methods one year ahead.

Three contributions to the literature on poverty nowcasting—and forecasting—stem from this paper. First, this paper develops a methodological framework to understand the errors of these techniques for poverty nowcasting. Second, this analysis validates the methods using a set of about 180 harmonized micro household datasets available for 15 countries in LAC for the 2003–14 period. By using this comprehensive database, the paper minimizes the possibility of arriving to conclusions based on biased samples of countries or spurious results. Finally, the paper assesses the robustness of the results and quantifies the accuracy of each method by empirically testing the magnitude of the errors in each methodology.

Our results suggest that the three methods are promising for nowcasting both large poverty reductions as well as poverty stagnation in the region one year ahead. In general, the two bottomup approaches that simulate the performance of each agent in the economy to nowcast poverty perform better than the top-down approach that uses welfare estimates to explain the performance of poverty at an aggregate level over time. The paper also provides evidence that the results are in general stable to changes in the underlying parameters used for producing poverty nowcasts.

The paper is organized as follows. Section 2 introduces the three approaches for nowcasting poverty and the theoretical framework of the nowcasting errors. Section 3 presents the data used and the empirical approach. Section 4 presents the validation results of the poverty nowcasting in LAC. Section 5 shows the stress and sensitivity analysis of the methods. Finally, section 6 concludes.

3
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The two countries that are found to impact our results most are Vietnam, whose omission from the sample would raise the estimate to 47.9 million people, and Pakistan, whose exclusion would lower the final count to 38.2 million people.

As a second robustness check, we calculate global poverty implications of higher food prices without using country-specific information on domestic price changes for selected commodities. Redoing our calculations using only the global price changes scaled by the domestic import shares, we arrive at a significantly lower estimate of poverty change of 30.9 million people, which suggests that our assumption for pass-through parameters may be conservative, and that domestic food price changes in developing countries may, in fact, be more responsive to global price changes than assumed.

As an additional check on our results, we calculate their responsiveness of our estimates to poverty lines higher or lower than the internationally standard $1.25 per day measure for extreme poverty. We repeat our calculations for four additional poverty lines in the range of the standard definition of extreme poverty. Our results (Table 6) show that while the estimates of global poverty vary depending on the poverty line chosen—the poverty estimates decline as the poverty line rises—the results are not vastly different within the range from $1 to 2 that we consider.

The observed negative relationship between the poverty impacts and the poverty line appears to arise primarily from the declining share of expenditure on food as incomes rise. While the shares of net food sales rises with income, this appears to be a less important determinant of the overall resul than the decline in the share of food in total consumption. These findings are reflected in the last two columns of Table 6 which show little relationship between gross reductions in poverty and poverty line, and a much stronger negative relationship between the poverty line and gross increases in poverty.

# **Comparison with the impacts of the 2008 food price crisis**

The recent surge in food prices, which has raised the World Bank food price index above its 2008 peak, raises an important question of how the poverty impacts of this surge might relate to those of the earlier food crisis in 2008. The poverty impacts of the 2008 food crisis were extensively analyzed by Ivanic and Martin (2008), who using the price change from 2005–08 found its average impact to be an increase in extreme poverty of about 105 million additional 12