rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 3 Total, public and private expenditure as percentage GDP, 2004**

<!-- Start of picture text --> 12<br>10<br>8<br>6<br>4<br>2<br>0<br>Total Public Private<br>Percent of GDP<br>Montenegro Albania EU-12 Croatia Macedonia, FYR Kosovo Bosnia and Herzegovina Slovenia EU EU-15 Serbia<br><!-- End of picture text --> Source: WDI database 2007 for Albania, the FYR of Macedonia, Bosnia and Herzegovina, Croatia, Slovenia and the EU aggregates; World Bank estimations for Serbia, Montenegro and Kosovo. Note: Data are ordered in ascending magnitude of total health expenditure; for Kosovo, 2004 amounts are underestimated due to incomplete reporting of donor off-budget spending; the EU aggregate data are GDP-weighted.

Due to small per capita incomes, these high percentage expenditures translate into low levels of per capita expenditure (see Figure 4). In particular, in 2004 per capita expenditure in the Western Balkan countries ranged from approximately $100 (Kosovo) to $300 (Serbia) in current US dollars. Expressed in PPP-adjusted terms, this is equivalent to around $400 (for those countries for which PPP-adjusted data are available). Compared to levels in the rest of the European Union, per capita expenditure levels within the sub-region are very similar to each other.

Not only are per capita expenditures levels in the region fairly similar to each when compared to other countries, they are also very low. In 2004 dollar terms, per capita expenditure in the Western Balkans in 2004 was less than one-tenth of that in countries in the European Union and less than half of that of new EU entrants (i.e. the EU-12). While some share of the difference in expenditure levels between EU-15 countries and the Western Balkan countries can be explained by differences in purchasing power, per capita expenditure figures for the countries of the subregion remain around half of the average of the new EU entrants and about a sixth of that of the EU-15, even when the figures are adjusted for purchasing power.

Per capita expenditure levels have been increasing over time, however. With the exception of Bosnia and Herzegovina, the increase in PPP-adjusted per capita expenditure has either kept pace with or exceeded the average EU increase of 33% observed between 1999 and 2004. Still, increases are lower than the 51% average increase observed across the new EU entrants.

16
|Millennium Science Initiative|SukhdeepBrar|
|---|---|
|Millennium Science Initiative|SukhdeepBrar|
|Millennium Science Initiative|SukhdeepBrar|
|Millennium Science Initiative|SukhdeepBrar|
|Millennium Science Initiative|SukhdeepBrar|
|Millennium Science Initiative|SukhdeepBrar|
|Millennium Science Initiative|SukhdeepBrar|
|Millennium Science Initiative|SukhdeepBrar|
|Millennium Science Initiative|SukhdeepBrar|
|Third National RailwayProject|Martha B. Lawrence|
|Third National RailwayProject|Martha B. Lawrence|
|Third National RailwayProject|Martha B. Lawrence|
|Third National RailwayProject|Martha B. Lawrence|
|Third National RailwayProject|Martha B. Lawrence|
|Third National RailwayProject|Martha B. Lawrence|
|Third National RailwayProject|Martha B. Lawrence|
|Third National RailwayProject|Martha B. Lawrence|
|SME Financing& Development|NirajVerma|
|SME Financing& Development|NirajVerma|
|SME Financing& Development|NirajVerma|
|SME Financing& Development|NirajVerma|
|SME Financing& Development|NirajVerma|
|SME Financing& Development|NirajVerma|
|SME Financing& Development|NirajVerma|
|SME Financing& Development|NirajVerma|
|SME Financing& Development|NirajVerma|
|SME Financing& Development|NirajVerma|
|SME Financing& Development|NirajVerma|
|SME Financing& Development|NirajVerma|
|SME Financing& Development|NirajVerma|
|SME Financing& Development|NirajVerma|
|SME Financing& Development|NirajVerma|
|SME Financing& Development|NirajVerma|
|SME Financing& Development|NirajVerma|
|SME Financing& Development|NirajVerma|
|BR PROGRAMMATIC FISCAL REFORM - SOCIAL SECURITY REFORM|Asta Zviniene|
|BR PROGRAMMATIC FISCAL REFORM - SOCIAL SECURITY REFORM|Asta Zviniene|
|Development,Empowerment and Conservation in the Greater St L|uClaudia Sobrevila|
|Development,Empowerment and Conservation in the Greater St L|uClaudia Sobrevila|
|Development,Empowerment and Conservation in the Greater St L|uClaudia Sobrevila|
|Development,Empowerment and Conservation in the Greater St L|uClaudia Sobrevila|
|Development,Empowerment and Conservation in the Greater St L|uClaudia Sobrevila|
|Development,Empowerment and Conservation in the Greater St L|uClaudia Sobrevila|
|Development,Empowerment and Conservation in the Greater St L|uClaudia Sobrevila|
|Development,Empowerment and Conservation in the Greater St L|uClaudia Sobrevila|
|Development,Empowerment and Conservation in the Greater St L|uClaudia Sobrevila|
|Development,Empowerment and Conservation in the Greater St L|uClaudia Sobrevila|
# **Considering Labor Informality in Forecasting Poverty and Inequality: A Microsimulation Model for Latin American and Caribbean Countries**<sup>**1**</sup>

Montoya, Kelly, Olivieri, Sergio and Braga, Cicero<sup>2</sup> JEL: D39, E27, O15 _Keywords: micro-simulation, ex-ante, poverty, inequality, forecasting, Latin America and the Caribbean_

> 1 We are grateful for the multiple interactions and discussions with economists and consultants from the Poverty and Equity Practice in the Latin America and Caribbean region (ELCPV): Carolina Mejia-Mantilla, Gabriel Lara-Ibarra, Ricardo Vale, Gabriela Inchauste, Monica Robayo, Jacobus Hoost de Hoop, Agustin Arakaki, Javier Romero, Gustavo Canavire, Lourdes Rodriguez, Trinidad Saavedra, Alejandro de la Fuente, Hernan Winkler, Maria Davalos, Ana Rivadeneira, and Erika Schutt, which enriched the paper. The paper benefitted from feedback from reviewers Hugo Ñopo, and Samuel Freije-Rodriguez, additional comments from Christoph Lakner, seminar participants at the World Bank workshops, and the guidance of Ximena del Carpio and Carlos Rodriguez-Castelan.

> 2 Correspondence author: solivieri@worldbank.org
6 significantly Improved If credit subsld;es wore ollminated.<sup>3</sup> Many of the institutions established or supported for dol earing credit programs did not develop Into self-sustalned credit facliitles 4 8. The disappoInting performance of the credit supply-led approach can be attributed to two sets of Issues: (1) some of the underlying premises of this approach were frequently not valid, and (11) the Institutlons and arrangements established or utilized for kmplementing the policy were often dosigned and operated In an Inviable manner, or within a policy and social environment hindering thelr effectiveness.

Chapter 2: Issues Related to Supply-led Credit Approach 9. Perceived zhortage of credit In many Instances, It Is not clear that rural areas are slgnificantly constralned by a shortage of funds. Furthermore, In areas where effective Interest rates are kept low through subsidles or celilngs, demand tends to exceed supply and an artificlal sense of shortage of credit prevalils. Recent surveys In China and Thalland Indicated that both among borrowing and non-borrowing farmers (mostly smaliholders), only a minority had an unsatisfled dermaand for formal liquidity.6 Most farmers have access to the more flexible services provided by Informal lenders. It Is commonly claimed that Informal lenders charge very high Interest rates due to their monopoly position, and are therefore not socially desirable. This view 3~~~~~~~~~~~~~~~~~~~~~~~~~~~

> 3 R. Vog 'The Effct of Subsidizd Agrcutr Credit on ncome Diibution in Costs Rica', In Adans et al. Underminina Rural DevCoomwd _wHh_ Ch.aa Credit, Welw Prm, 1984.

4 Document JAC 88-35 of toe Joint Audit CommMit, October 5, 1988.

5 Feder at al. 'Agricultural Credit and Farm Peformance In Chins', J. of Comaarative Economics, December 1989 and unpubsd data from Woird Bank sty RPO 8733 'Land Title Security and Farm Producivity tn Thaiand'.
and weight take the form in log, giving their elasticities. The containerization and port congestion indexes are in the form of ratio, and their estimated parameters represent the percentage change of import charge / weight, with respect to a point change in the indexes. Because of the lack of data on Viet Nam, the third specification uses fewer observations.

**Table 4: Determinants of Trade Cost per Weight from Asia-Pacific Countries to the U.S.**

|_dependen_<br>_at 6-_|_t variable: import_<br>_digits commodity l_|_charge / weight_<br>_evel(log)_||
|---|---|---|---|
||(1)|(2)|(3)|
|_distance (log)_|0.2470|0.0835|0.2105|
||(10.84)***|( 3.61)***|( 9.39)***|
|_value/weight (log)_|0.4873|0.4909|0.4908|
||(161.78)***|( 163.62)***|( 159.49)***|
|_weight (log)_|-0.0294|-0.0346|-0.0320|
||(-32.32)***|(-37.56)***|(-33.66)***|
|_containerization (share)_|-0.0281|0.0169|0.0212|
||(-15.25)***|(10.96)***|( 12.71)***|
|_port congestion (index)_|0.0737<br>(18.45)***|||
|_Port Infrastructure Quality_||-0.0747||
|_(GCR) (index = 1 - 7)_||(-33.95)***||
|_Water Transportation_|||-0.0517|
|_(WCY) (Index = 1 - 10)_|||(-28.00)***|
|_Numbers of Observations_|151249|151249|145600|
|_R_<br>_2_|0.4057|0.4102|0.4111|

(Source) Authors’ estimates, using _U.S.A. Merchandise Imports DVDs_ .

(Note) 1. Estimation period is from 2001 to 2006.

2. t-values in parentheses. *** significant at 1% , ** at 5%, * at 10%.

3. _GCR_ : Global Competitiveness Report, _WCY_ : World Competitiveness Yearbook.

4. For a reference purpose, the port congestion index in the regression is multiplied by a factor of 5000. This does not affect the significance of the estimates.

The first specification, using our port congestion index, takes the values of parameters on distance, value/weight, weight and containerization ratio generally within the comparable range to the existing empirical studies. In particular, more containerization saves the trade costs, but to a modest degree. The estimated parameter, -0.029, implies a bit smaller impact than the estimates by Hummels (2008), if converted into elasticity around average.

22
Access to adequate water across emerging economies is highest across the seven adequacy dimensions followed by access to electricity. Yet, while substantial progress has been made in increasing access to clean drinking water within the last decade, many households, particularly in Sub-Saharan Africa and in rural areas still lack access. Access to water is lowest in Madagascar, where only 43 percent of HHs have access to adequate water.

# **_<mark>3.4 Aggregating to the Adequate Housing Index</mark>_**

The _Adequate Housing Index_ presents one unidimensional measure of housing adequacy. By harmonizing HCES variables and relying on one uniform definition of adequacy, the _AHI_ offers a unique metric to compare housing adequacy within and across countries. Further, the _AHI_ offers a summary picture of the multiple facets of housing adequacy, reduces complexity, and facilitates evaluation of housing adequacy. _AHI_ also allows to rank countries according to their housing adequacy, monitor progress, and incentivize institutions and governments to revisit their standards in terms of housing adequacy. Rankings have become drivers of behavioral change and are consistently applied in a variety of areas (e.g., Kelley and Simmons 2015).

The aggregation of the underlying variables is the most important step within indicator creation. We construct the _Adequate Housing Index_ for every household _i_ in country _c_ to estimate if that household lives in an adequate house AHIi,c = w1*a1i + w2*a2i + ... + w7·a7i (1) where _AHI_ i,c is the adequate housing index for household _i_ in country _c_ , where (a1i, a2i, ... , a7i) are the seven housing adequacy dimensions of household _i_ coded as dummy variables as discussed above, and where (w1, w2, ... , w7) are the respective weights assigned to each adequacy dimension and sum to one (∑7 = 1 ). The _AHI_ ranges between 0 (highly inadequate housing unit) and 1 (highly adequate housing unit). ii=1 wwii The _AHI_ The aggregation of individual level _AHI_ at the national or sub-national level can be interpreted as the share of the existing housing stock that is likely to be in adequate condition. Aggregating the seven adequacy dimensions into a unidimensional _AHI_ , the principal determining factor is the weights to be assigned to each adequacy dimension. Weights assigned to the adequacy dimensions _a_ are summarized by vector _w_ = ( _w_ 1, . . . , _w_ 7). We refer to this vector of weights as a weighting scheme. The aim of weights is to reflect the relative importance of each dimension or component to the index (Drewnowski 1974).

We apply an equal weighting scheme where all variables are given the same weight. Equal weighting is the most commonly applied weighting scheme in composite indices (Bandura 2008; OECD 2008). Examples include the Human Development Index, the Doing Business Index (World
# Figure 2. EMDE growth prospects

# A. Growth

<!-- Start of picture text --> Percent<br>6<br>4<br>2<br>0<br>World Advanced EMDEs<br>economies<br>C. Growth<br>Percent<br>9<br>6<br>3<br>0<br>East Europe Latin Middle South Sub-<br>Asia and America East and Asia Saharan<br>and Central and the North Africa<br>Pacific Asia Caribbean Africa<br>E. Per capita growth differential between<br>EMDEs and advanced economies<br>Percentage points Investment per capita growth<br>GDP per capita growth<br>20<br>16<br>12<br>8<br>4<br>0<br>-4<br>2010-12 2000-08 2010-12<br>2000-082010-122013-15 2019-21 2000-082010-122013-152016-182019-21 2000-082010-122013-152016-18 2000-082010-12 2016-182019-21 2000-082010-122013-152016-182019-21 2000-082010-122013-15 2019-21<br>2000-08 2013-15 2016-18 2019-21 2010-12 2013-15 2016-18 2019-21 2000-08 2013-15 2016-18 2019-21<br>2016-18 2019-21 2013-15 2016-18<br>2000 2003 2006 2009 2012 2015 2018 2021<br><!-- End of picture text -->

# E. Per capita growth differential between EMDEs and advanced economies

# B. Growth

<!-- Start of picture text --> Percent<br>6<br>4<br>2<br>0<br>Commodity exporters Commodity importers excl.<br>China<br>2000-08 2010-12 2013-15 2016-18 2019-21 2000-08 2010-12 2013-15 016-18 2 019-21 2<br><!-- End of picture text -->

# D. Per capita growth

<!-- Start of picture text --> Percent<br>10 2000-08 2010-12 2013-15<br>2016-18 2019-21<br>8<br>6<br>4<br>2<br>0<br>-2<br>SAR EAP ECA LAC MNA SSA<br>F. Share of EMDEs with widening per<br>capita income gaps<br>Percent<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>SAR EAP ECA LAC SSA MNA<br><!-- End of picture text --> Source: World Bank.

Note: EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, and SSA = Sub-Saharan Africa A.-D. Aggregate growth rates are calculated using constant 2010 U.S. dollar GDP weights. Shaded areas indicate forecasts.

C. Unweighted average regional growth is used to ensure broad reflection of regional trends across all countries in the region.

E. Weighted based on real GDP and Investment in 2010 U.S. dollar. Investment refers to public and private real gross fixed capital formation. Sample consists of 50 EMDEs. Shaded areas indicate global recessions and slowdowns.

F. Economies with a widening income gap are those with per capita GDP growth of at least 0.1 percentage point lower than advanced economy per capita GDP growth in 2019.
13 recorded under HD 1.2 covers spending incurred in CHCs and polyclinics, as well as schools. In Table 2 spending on school health is assumed to account for 25% of the total, and the rest is allocated between CHCs and polyclinics pro rata according to utilization volumes. Neither assumption is particularly satisfactory.

The out‐of‐pocket spending figure reported in the health ministry’s NHA data in Table 2 is much less than the grossed‐up figures from the household survey in Table 1. The gap between the health ministry’s out‐of‐pocket figures and the survey figures is particularly pronounced in the case of inpatient care, where the official figure captures only 19.6% of the spending recorded in the survey. This discrepancy has been noted in previous work (Knowles et al. 2005 p12), where it was suggested that informal payments may account for part of the discrepancy. Transport costs are another likely source of discrepancy, since respondents in the VHLSS are asked to include “payments for medical service and treatment”, but also “other related costs (e.g. bonus for doctors, transport)”. Unfortunately there is no way to exclude transport costs from the household data.

BIA studies (those for Vietnam have followed this practice) typically believe the aggregate figure reported in the health ministry’s statistics and assume that the survey data are wrong, due to inaccurate recall, or because they include payments for things other than health care, such as transport. Researchers then scale down the fees reported in the household survey by the ratio of reported aggregate user fees as a fraction of the NHA aggregates, with the result that the scaled user fees in the survey gross up to the NHA figure. Then the _Fki_ are scaled fees and _Fk_ is the fee revenue reported in the NHA. An alternative approach, and not apparently used to date, is to believe the household survey data, and to assume that the ministry data may omit payments made by households to public providers. Government providers may hide fee
years old, more education is associated with a lower likelihood of being _Ninis_ (Appendix Table A2). In all cases, the associations are consistently larger (more negative) for men than they are for women.

Education is more associated with an increased likelihood of _studying only_ for men than for women in the younger group. As expected, for the younger men, aged 15 to 18, education is associated with a reduced probability of _working only_ for those with at least complete secondary education and an increased probability of _working only_ if they have incomplete primary education (see Appendix Table A1).

Figure 8. Proportional Change in the Probability of Being Nini to Changes in Youth’s Education, Base Group is No Education 1, Youth Aged 15-18 y.o., by Gender <!-- Start of picture text --> (a) Male (b) Female<br>0.20 0.20<br>0.10 0.10<br>0.00 0.00<br>-0.10 -0.10<br>-0.20 -0.20<br>-0.30 -0.30<br>-0.40 -0.40<br>Incomplete Complete Complete Incomplete Complete Complete<br>Primary Primary Secondary + Primary Primary Secondary +<br><!-- End of picture text --> _Notes_ : The value of the bars represent the marginal effects on the probability of being Nini for the corresponding variables from the underlying estimation of multinomial logit models (one for each gender). The dependent variable in the multinomial models takes three states (Nini, Work Only, and Study Only). Only marginal effect for the outcome of Nini is presented in this graph. The models include controls for history of past decision indices, youth’s own education level, parental education level, household income quintiles, a set of demographic variables, and state economic condition variables. Complete results are presented in Appendix Table A1. _Source_ : Authors’ calculations based on model estimation using data from the _Encuesta Nacional de Ocupación y Empleo_ (ENOE), 2005:Q1 - 2013:Q4.

# _(iv) Parents’ education to proxy for socioeconomic context,_ �

Socioeconomic context, as measured by parental education is associated with the decision to be _Ninis_ for young women but less so for young men (Figure 9) _._ Compared to the base group (household head with no education), young women aged 15-18 coming from households where the head has completed lower secondary school or has at least some upper secondary (or tertiary) education, are 6 p.p. and 12.2 p.p. less likely to be _Ninis_ , respectively (Figure 9; Appendix Table A1). For young men, parental education is only associated with being _Nini_ 25
10 Figure 4: The incidence of catastrophic out-of-pocket payments in 59 countries <!-- Start of picture text --> 12<br>10<br>8<br>6<br>4<br>2<br>0<br>% experiencing catastrophic out-of-pocket payments<br>Vietnam Brazil Azerbaijan Colombia Argentina Lebanon Cambodia Ukraine Paraguay Peru Egypt Latvia Portugal Panama Zambia Greece Nicaragua Bulgaria Jamaica Rep. Korea Yemen Mexico Lithuania Ghana Mauritius Indonesia Sri Lanka ngladeshBa Thailand hilippinesP Kyrgyzstan Guyana Switzerland Senegal USA Spain Finland Israel Djibouti Estonia Iceland Norway Croatia Hungary Sweden Morocco Costa Rica Namibia Belgium Canada Romania Denmark Slovenia UK Germany South France Czech Slovakia<br><!-- End of picture text --> Source: Xu et al.<sup>5</sup> .

Van Doorslaer et al.<sup>7</sup> look at catastrophic spending in 10 Asian territories. They find relatively low rates in Malaysia, Sri Lanka and Thailand, and relatively high rates in China, Vietnam and Bangladesh. This study also looks at the distribution by pre-payment income of those experiencing catastrophic payments. For the most part, they find that catastrophic spending is concentrated among the better off, though this depends to some degree on the threshold chosen. Taiwan (China) is the exception: catastrophic spending is concentrated among the poor whatever the threshold. A different picture emerges in the study by Waters et al.<sup>4</sup> of the United States: they find a higher incidence of catastrophic spending among poor families, as well as those with multiple chronic conditions.
<!-- Start of picture text --> fo}<br>So<br>fo}<br>2S<br>~ s 8 y\ IS a<br>& |." | ~ y\ VN \<br>3 | yr o\t NN awe;<br>me} 3 / \ / \ / \ / \<br>ra 3 v \ r\7T\<br>2 le 1M \ oe<br>2 G \ v 2oN<br>8 @ 2 \ 7 \y \<br>23 8 S \// ‘<br>R5 |e |2<br>Vs =<br>8 |g?| s<br>3oO 2a<br>2oO 15<br>=<br>Oo oO fo}<br>1990 1995 2000 2005 2010 2015<br>{-)_ Number of migrants leaving in the year<br>————_ International crude oil price<br>—-—-—- Wage abroad in the middle of stay<br><!-- End of picture text -->
|Chisinau|13.4|33.6|40.5|21.2|15.2|20.5|33.4|41.2|2.7|12.4|35.8|55.7|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Tighina|7.4|3.6|1.0|0.6|4.4|4.2|3.6|2.8|7.5|9.4|8.0|6.4|
|Ungheni|12.2|9.0|5.2|0.2|7.3|5.6|7.4|9.7|13.4|14.5|14.3|12.3|
|Chisinau county|11.5|6.6|3.4|0.6|5.6|11.8|12.8|9.1|7.3|8.6|6.7|3.6|
|Balti|7.0|15.2|7.8|0.9|14.8|14.4|13.4|11.0|6.7|10.6|7.2|4.1|
|Cahul|5.9|2.9|13.0|25.6|6.8|9.0|5.3|4.5|4.4|2.9|2.8|1.5|
|Edinet|17.7|9.8|8.8|25.7|5.6|5.5|5.1|2.9|10.9|7.9|4.6|2.9|
|Gagauzia|5.9|3.3|9.6|23.6|3.5|2.8|2.5|1.7|22.6|12.8|7.4|3.5|
|Lapusna|6.2|1.5|0.5|0.0|14.1|9.5|4.6|5.2|5.7|6.3|5.3|4.3|
|Orhei|7.8|6.6|5.4|1.2|13.8|8.6|6.3|4.6|6.2|4.6|2.4|1.8|
|Soroca|5.1|7.9|4.8|0.4|9.1|8.0|5.6|7.2|12.7|9.9|5.7|4.0|

Source: Calculations based on HBS data.

31