rafmacalaba/datause-encoder-data huggingface dataset:
|Vietnam National CFC and Halon Phaseout Project|QingWang|
|---|---|
|Vietnam National CFC and Halon Phaseout Project|QingWang|
|Vietnam National CFC and Halon Phaseout Project|QingWang|
|Vietnam National CFC and Halon Phaseout Project|QingWang|
|Vietnam National CFC and Halon Phaseout Project|QingWang|
|Vietnam National CFC and Halon Phaseout Project|QingWang|
|Vietnam National CFC and Halon Phaseout Project|QingWang|
|Vietnam National CFC and Halon Phaseout Project|QingWang|
|Vietnam National CFC and Halon Phaseout Project|QingWang|
|Agricultural Markets and Agribusiness Development Project|Jean-Philippe Tre|
|Agricultural Markets and Agribusiness Development Project|Jean-Philippe Tre|
|Agricultural Markets and Agribusiness Development Project|Jean-Philippe Tre|
|Agricultural Markets and Agribusiness Development Project|Jean-Philippe Tre|
|Agricultural Markets and Agribusiness Development Project|Jean-Philippe Tre|
|Agricultural Markets and Agribusiness Development Project|Jean-Philippe Tre|
|Agricultural Markets and Agribusiness Development Project|Jean-Philippe Tre|
|Agricultural Markets and Agribusiness Development Project|Jean-Philippe Tre|
|Agricultural Markets and Agribusiness Development Project|Jean-Philippe Tre|
|Agricultural Markets and Agribusiness Development Project|Jean-Philippe Tre|
|Agricultural Markets and Agribusiness Development Project|Jean-Philippe Tre|
|Agricultural Markets and Agribusiness Development Project|Jean-Philippe Tre|
|Agricultural Markets and Agribusiness Development Project|Jean-Philippe Tre|
|Agricultural Markets and Agribusiness Development Project|Jean-Philippe Tre|
|Agricultural Markets and Agribusiness Development Project|Jean-Philippe Tre|
|Transport RestructuringProject|Mohammed Dalil Essakali|
|Transport RestructuringProject|Mohammed Dalil Essakali|
|Transport RestructuringProject|Mohammed Dalil Essakali|
|Transport RestructuringProject|Mohammed Dalil Essakali|
|Transport RestructuringProject|Mohammed Dalil Essakali|
|Transport RestructuringProject|Mohammed Dalil Essakali|
|Transport RestructuringProject|Mohammed Dalil Essakali|
|Transport RestructuringProject|Mohammed Dalil Essakali|
|Transport RestructuringProject|Mohammed Dalil Essakali|
|Transport RestructuringProject|Mohammed Dalil Essakali|
|Institutional Strengtheningthru Support to National Procureme|nt C Naushad A. Khan|
|Economic RecoveryCredit|Peter Siegenthaler|
|Economic RecoveryCredit|Peter Siegenthaler|
|Economic RecoveryCredit|Peter Siegenthaler|
|Economic RecoveryCredit|Peter Siegenthaler|
|Burundi IDF Procurement Reform|Prosper Nindorera|
|Burundi IDF Procurement Reform|Prosper Nindorera|
|Burundi IDF Procurement Reform|Prosper Nindorera|
|Hydropower Rehabilitation Project|Pekka Kalevi Salminen|
|Hydropower Rehabilitation Project|Pekka Kalevi Salminen|
|Hydropower Rehabilitation Project|Pekka Kalevi Salminen|
|Hydropower Rehabilitation Project|Pekka Kalevi Salminen|
|Hydropower Rehabilitation Project|Pekka Kalevi Salminen|
17 for small WSS utilities, but we cannot reject the null hypothesis of constant returns to scale for the large WSS utilities. The same kind of results is obtained for Vietnam: we find significant economies of scale for smaller water utilities.

In table 10, we report estimated returns to scale at the sample mean of each year. Estimated returns are found almost constant over the period in Brazil. The test of constant returns to scale cannot be rejected in all years. In Colombia, WSS utilities exhibit increasing returns to scale in 2003 and 2004. We find significant economies of scale in Moldova between 1996 and 2004, with returns to scale increasing over time, coinciding with a period in which the quantity and quality of water and sewerage services is steadily declining. In Vietnam, we also find significant economies of scale, but slightly lower at the end of the period than at the beginning.

# **6. Conclusions and policy implications**

Using data from the IBNET database, we estimate measures of density and scale economies in four countries (Brazil, Colombia, Moldova and Vietnam) that differ substantially in economic development, piped water and sewerage coverage and characteristics of the utilities operating in the different countries. The analysis leads us to the following findings.

First, we find evidence of economies of scale in three of the four countries: Colombia, Moldova, and Vietnam. For the regional WSS utilities operating in Brazil, we cannot reject the hypothesis of constant returns to scale. The largest returns to scale at the mean (i.e., for the service with the average characteristics) are obtained for Moldova (1.26). WSS utilities operating in this country are on average small (compared to the size of utilities in the three other countries), with an average served population of 30,000 persons. The second largest returns to scale are estimated for Vietnamese water utilities (1.16), which serve on average a population of 142,000, followed by Colombian WSS utilities (1.11) which serve 229,000 inhabitants. In Brazil, we find evidence of constant returns to scale for the full set of regional WSS companies that serve on average a population of about 4 million.

The evidence seems to suggest that the very large size of the Brazilian regional WSS utilities may result in X inefficiencies. As utilities become larger, the increase in the administrative
Policy ReseaRch WoRking PaPeR 4653

# **Abstract**

This paper uses data from the 61st Round of the National Sample Survey to understand the employment outcomes of Dalit and Muslim men in India. It uses a conceptual framework developed for the US labor market that states that ethnic minorities skirt discrimination in the primary labor market to build successful self-employed ventures in the form of ethnic enclaves or ethnic labor markets. The paper uses entry into self-employment for educated minority groups as a proxy for minority enclaves. Based on multinomial logistic regression, the analysis finds that the minority enclave hypothesis does not hold for Dalits but it does overwhelmingly for Muslims. The interaction of Dalit and Muslim status with post-primary education in urban areas demonstrates that post-primary education confers almost a disadvantage for minority men: it does not seem to affect their allocation either to salaried work or to non-farm self-employment but does increase their likelihood of opting out of the labor force—and if they cannot afford to drop out, they join the casual labor market. Due to the complexity of these results and the fact that there are no earnings data for self-employment, it is difficult to say whether self-employment is a choice or compulsion and whether builders of minority enclaves fare better than those in the primary market This paper—a product of the Sustainable Development Department, South Asia Region—is part of a larger effort in the department to conduct empirical work on issues of equity and inclusion. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mdas@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
**Table 6b – Regressions with Different Estimators – Unbalanced Panel**

|**Column**|**`1`**|**`2`**|**`3`**|**`4`**|**`5`**|**`6`**|
|---|---|---|---|---|---|---|
|**Dep. Var.**|**`y`**|**`y`**|**`y`**|**poor**|**poor**|**poor**|
|**Model**|**fe**|**re**|**be**|**fe**|**re**|**be**|
|`b/sbd`|`0.038`|`0.103`|`0.144`|`-0.053`|`-0.007`|`0.005`|
||`-0.52`|`(2.24)*`|`(2.39)*`|`(2.74)**`|`-0.74`|`-0.48`|
|`hfem`|`0.118`|`0.108`|`0.095`|`-0.017`|`-0.03`|`-0.031`|
||`(2.88)**`|`(6.77)**`|`(5.45)**`|`-0.86`|`(4.53)**`|`(4.33)**`|
|`hage30_45`|`-0.008`|`-0.351`|`-0.419`|`0.005`|`0.042`|`0.044`|
||`-0.12`|`(12.10)**`|`(13.05)**`|`-0.16`|`(3.46)**`|`(3.40)**`|
|`hage46_60`|`-0.085`|`-0.441`|`-0.506`|`-0.018`|`0.07`|`0.076`|
||`-1.07`|`(15.14)**`|`(15.96)**`|`-0.47`|`(5.84)**`|`(5.92)**`|
|`hage60plus`|`-0.094`|`-0.586`|`-0.675`|`0.022`|`0.09`|`0.088`|
||`-1.04`|`(17.12)**`|`(17.88)**`|`-0.49`|`(6.37)**`|`(5.80)**`|
|`hprim`|`0.253`|`0.152`|`0.138`|`-0.129`|`-0.092`|`-0.088`|
||`(2.09)*`|`(3.69)**`|`(3.11)**`|`(2.18)*`|`(5.44)**`|`(4.91)**`|
|`hsec`|`0.161`|`0.162`|`0.154`|`-0.126`|`-0.071`|`-0.067`|
||`-1.33`|`(4.20)**`|`(3.73)**`|`(2.12)*`|`(4.52)**`|`(4.04)**`|
|`htert`|`0.446`|`0.766`|`0.786`|`-0.205`|`-0.258`|`-0.261`|
||`(3.09)**`|`(17.62)**`|`(17.06)**`|`(2.91)**`|`(14.59)**`|`(14.07)**`|
|`nchild`|`-0.23`|`-0.293`|`-0.312`|`0.153`|`0.138`|`0.138`|
||`(7.97)**`|`(25.18)**`|`(23.76)**`|`(10.80)**`|`(28.26)**`|`(25.81)**`|
|`dep3_4`|`-0.086`|`0.006`|`0.063`|`0.02`|`-0.002`|`-0.013`|
||`-1.68`|`-0.23`|`-1.85`|`-0.77`|`-0.13`|`-0.92`|
|`dep5plus`|`0.068`|`0.239`|`0.314`|`-0.067`|`-0.095`|`-0.099`|
||`-0.83`|`(5.73)**`|`(6.44)**`|`-1.66`|`(5.42)**`|`(5.01)**`|
|`terremprate`|`0.132`|`0.297`|`0.357`|`-0.06`|`-0.12`|`-0.129`|
||`-1.55`|`(6.83)**`|`(7.00)**`|`-1.43`|`(6.49)**`|`(6.22)**`|
|`growth`|`4.353`|`5.022`|`5.751`|`-2.409`|`-2.994`|`-3.35`|
||`(16.14)**`|`(25.80)**`|`(20.71)**`|`(17.95)**`|`(34.10)**`|`(29.69)**`|
|`Constant`|`-3.286`|`-3.771`|`-4.516`|`3.038`|`3.609`|`3.984`|
||`(10.34)**`|`(17.60)**`|`(14.95)**`|`(19.34)**`|`(37.66)**`|`(32.57)**`|
|`Observations`|`24597`|`24597`|`24597`|`24597`|`24597`|`24597`|
|`Number of hhid`|`18011`|`18011`|`18011`|`18011`|`18011`|`18011`|
|`R-squared`|`0.06`||`0.12`|`0.08`||`0.12`|
|`Hausman test (fe/re)`|||||||
|Chi2|93.53|||57.62|||
|Prob>chi2|0.0000|||0.0000|||

z-stat or t-stat under coefficients 27
from models estimated on past data generate credible estimates, in a variety of country contexts, of poverty headcount and gaps.

The preferred model generates an estimate of 10.0% in urban areas and 16.8% in rural areas, which implies an overall poverty level of 14.6% for 2014-15 (Table 1 and Figure 1). The estimate is consistent with poverty reduction from 2004-05 to 201112 in rural areas and suggests a slight deceleration in poverty reduction in urban areas (Table 2). This in turn implies that the elasticity of poverty with respect to growth of per capita GDP was -2.11, which is within the range of past experience. Reassuringly, when looking at the state level, predicted poverty reduction is greater in states with higher rates of GDP growth.

We use two main tests to validate the survey-to-survey imputation models. The first estimates a model with the same specification, using data from 2004-05 and 2009-10, to predict into 2011-12. These predictions are then compared with actual measured levels of poverty from the national sample survey. In a similar, vein, the second validation test uses data from 2009-10 and 2011-12 to predict backwards to 2004-05. This is a much more challenging exercise because the data from two years apart are being used to extrapolate five years in the past.

While the traditional line-up method performs slightly better than our preferred specification in 2011-12, it gives wildly implausible estimates when projecting back to 2004-05. To be specific, the standard line-up method is off by only a percentage point in 2011-12 but exaggerates poverty by 25 percentage points when predicting back to 2004-05. Our preferred model performs much better on average, as the discrepancy is about 2 percentage points in 2011-12 and about 3 percentage points in 2004-05.

The traditional line-up method happens to do well when projecting forward into 2011-12 because growth in mean reported consumption in the survey was unusually high between 2009-10 and 2011-12 and nearly matched growth in HFCE. This is a sharp break from the pattern observed during the previous period, and in most other contexts. It remains to be seen whether this correspondence between HFCE and survey mean growth will be sustained, but there are at least two reasons to speculate it may not be. The first is that survey-based consumption may be more responsive than national account data to weather shocks. India experienced significant droughts during 2004 and 2009, followed by above-average rainfall in the 2011 season. To the extent that survey-based consumption is more sensitive than national accounts to agricultural income, favorable rainfall in 2011 would cause a larger jump in survey 5
**Table 11: Regression results: Investment and Not Investment Grade** Dependent <u>variable: Log of spread-to-benchmark</u>

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Investment<br>Grade<br>pre-crisis|Investment<br>Grade<br>post-crisis|Not<br>Investment<br>Grade<br>pre-crisis|Not<br>Investment<br>Grade<br>post-crisis|
|S&P Sovereign rating|0.153***|0.038*|0.088***|0.143***|
||(4.66)|(1.72)|(3.77)|(3.12)|
|Log (size)|0.169|-0.005|0.083**|0.095|
||(1.17)|(-0.07)|(2.57)|(1.54)|
|Years to Maturity|0.018*|0.004|0.007**|-0.015**|
||(1.93)|(1.10)|(2.42)|(-2.15)|
|U.K. Law|-0.120|0.125|0.037|0.188*|
||(-0.44)|(1.21)|(0.49)|(1.74)|
|VIX|0.037***|0.039***|0.008|0.016**|
||(3.24)|(5.41)|(1.56)|(2.46)|
|GDP growth (_launch-1_)|-0.026*|-0.031**|-0.010|0.003|
||(-1.74)|(-2.12)|(-1.20)|(0.18)|
|EAP|-0.518***|-0.429**|0.149|0.123|
||(-3.01)|(-2.54)|(1.21)|(0.55)|
|ECA|-0.634**|0.143|0.263***|0.325|
||(-2.46)|(1.00)|(3.29)|(1.15)|
|LAC|-0.229|-0.293*|0.295***|0.424|
||(-1.26)|(-1.99)|(3.41)|(1.66)|
|SAS|||0.436**|0.313**|
||||(2.39)|(2.15)|
|SSA|-0.046|0.060|0.449|0.368*|
||(-0.19)|(0.41)|(1.24)|(1.87)|
|Observations|76|76|252|86|
|R-squared|0.749|0.744|0.545|0.562|

Robust t-statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% Year dummies and a constant term were included in all specifications, but not reported. The default region is Middle East & North Africa (MNA); the regional dummies represent East Asia & Pacific (EAP), Europe & Central Asia (ECA), Latin America & Caribbean (LAC), South Asia (SAS) and Sub-Saharan Africa (SSA).

We explore whether the governing law impact extended to dollar-denominated bond issuance of investment grade ratings. In the case of investment grade rated bonds, we see no impact of U.K. governing law on spreads both before and after the crisis. In the case of sub-investment grade bonds, while the U.K. law effect is not statistically significant prior to the crisis, it is significant after the crisis (Table 11).<sup>12</sup>

> 12 During the period under consideration (1990-2015), some supranational entities, for instance, the African Development Bank, Asian Development Bank, IBRD (World Bank), and IFC issued bonds under both U.S. and U.K. law. Regression results indicate that choice of jurisdiction had no statistically significant impact on spreads of supranational bonds. This adds to the argument that the higher spreads of U.K. law issuances relates to reputation effects and risk-aversion among investors post-crisis. For the World Bank Group sub-sample (IBRD+IFC), the U.K. law effect is not statistically significant.

20
|**Code**|**Division/group/class**|**GFSM 2001 definition**|
|---|---|---|
|||_and so on)._|
|_70452_|_Water transport_|_Administration of affairs and services concerning operation, use, construction, and maintenance_<br>_of inland, coastal, and ocean water transport systems and facilities (harbors, docks, navigation_<br>_aids and equipment, canals, bridges, tunnels, channels, breakwaters, piers, wharves, terminals,_<br>_and so on), as well as of water transport facilities._|
|_70453_|_Railway transport_|_Administration of affairs and services concerning operation, use, construction, and maintenance_<br>_of railway transport systems and facilities (railway roadbeds, terminals, tunnels, bridges,_<br>_embankments, cuttings, and so on)._|
|_70454_|_Air transport_|_Administration of affairs and services concerning operation, use, construction, and maintenance_<br>_of air transport systems and facilities (airports, runways, terminals, hangars, navigation aids and_<br>_equipment, air control amenities, and so on)._|
|7046|Communication||
|_70460_|_Communication_|_Administration of affairs and services concerning construction, extension, improvement,_<br>_operation, and maintenance of communication systems (postal, telephone, telegraph, wireless,_<br>_and satellite communication systems)._|
|**705**|**Environmental protection**||
|7052|Wastewater management||
|_70520_|_Wastewater management_|_Administration, supervision, inspection, operation, or support of sewage systems and wastewater_<br>_treatment._|
|**706**|**Housing and community ame**|**nities**|
|7063|Water supply||
|_70630_|_Water supply_|_Administration of water supply affairs; supervision and regulation of all facets of potable water_<br>_supply including water purity, price, and quantity controls._|

_Source: Adapted from IMF (2001)_ . _Note_ : See annex 2 for a more detailed description of the content of each category.

# **Economic classification of expenditures**

The economic classification provides the desirable breakdown of outlays in order to differentiate the nature of expenditures (tables 3.3 and 3.4).

38
Ravallion Martin (1992). “Poverty Comparisons: A Guide to Concepts and Methods.” Living Standards Measurement Study Working Paper No. 88.

Republic of Latvia (2001). “Labor Force Survey Report.” Central Statistical Bureau, Ministry of Finance, Republic of Latvia.

Republic of Latvia (2003). “Ministry of Welfare Social Report.” Government of Latvia, Republic of Latvia.

World Bank (1998). “African Development Indicators.” The World Bank, Washington D. C.

World Bank (2000a). “The Republic of Latvia Poverty Assessment.” Poverty Reduction and Economic Management Unit (ECSPE) Eastern Europe and Central Asia Region. Report No. 20707-LV, The World Bank.

World Bank (200b). “Making Transition Work for Everyone: Poverty and Inequality in Europe and Central Asia.” The World Bank, Washington D.C.

World Bank (2002). “Country Assistance Strategy for The Republic of Latvia.” Poverty Reduction and Economic Management Unit (ECSPE) Eastern Europe and Central Asia Region. Report No. 23610-LV, The World Bank, Washington D.C.

World Bank (1998). “World Bank African Development Indicators.” The World Bank, Washington D.C.

Zile Roberts and Inna Steinbuka (2001). “Latvia on the way to the European Union.” _Finance and Development_ , Volume 38, Number 2. International Monetary Fund.

35
P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. Standards errors allow for clustering at the country level. All models are estimated via two methods: ordinary least squares (OLS) model and robust regression model. In dropping outlier OLS model, top 5% samples on each dependent variable are dropped except % of Women Borrowers. Though we do not present the coefficients to conserve space, all models include all of the following controls; annual % of inflation rate (GDP deflator), annual % of GDP growth, Rule of law , Bank overhead costs / Total assets, Ease of doing business index, Bank Credit / GDP, regional dummies (Europe and Central Asia, Latin America and Asia, Sub-Saharan Africa, South Asia and East Asia and Pacific, omitting Middle East and North Africa), lending methodology dummies (individual and solidarity lending, omitting village banking) and log of MFI's total asset values. Combined group dummy is 1 if one of the three status dummies (commercial bank, credit union / coop or rural bank) equals 1. NGO dummy is omitted in these models.

- <u>Panel B: Regulatory Framework and Practices (0 100)</u>

||V|<br>ersion 1(Ful sampl|<br>Depvar: Financial S<br>es)|elf-Sufficiency<br>Version 2|<br> (DroppingRural b|<br>ank samples)|
|---|---|---|---|---|---|---|
||OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Regulatory Framework and Practices (0-100)|0.0011|0.0016|0.0011|0.0014|0.0019|0.0012|
||[0.631]|[0.150]|[0.592]|[0.530]|[0.104]|[0.538]|
|NBFI dummy * Regulatory Framework and Practices (0-100)|-0.0013|-0.0018|-0.0013|-0.0017|-0.0023|-0.0016|
||[0.481]|[0.265]|[0.448]|[0.355]|[0.167]|[0.351]|
|Combined group dummy * Regulatory Framework and Practices (0-100)|-0.0044|-0.0045***|-0.004|-0.0060*|-0.0064***|-0.0049*|
||[0.176]|[0.005]|[0.133]|[0.082]|[0.004]|[0.059]|
|NBFI dummy|0.0749|0.1214|0.0884|0.0846|0.1382|0.0872|
||[0.510]|[0.181]|[0.411]|[0.459]|[0.145]|[0.409]|
|Combined group dummy|0.2115|0.2426***|0.2075|0.2533|0.2953**|0.2098|
||[0.241]|[0.010]|[0.145]|[0.200]|[0.016]|[0.150]|
|Observations|636|636|611|579|579|556|
|Number of countries|39|39|39|39|39|39|
|R-squared|0.217|0.231|0.255|0.226|0.242|0.258|
|Adjusted R-squared|0.193|0.208|0.231|0.2|0.216|0.232|
|_Microscope index+ interaction term (P-value in brackets)_|||||||
|NBFI dummy|-0.000228|-0.000189|-0.000262|-0.000311|-0.000437|-0.000342|
||[0.900]|[0.891]|[0.881]|[0.865]|[0.763]|[0.850]|
|Combined group dummy|-0.00327|-0.00295**|-0.00288|-0.00459*|-0.00452**|-0.00368|
||[0.128]|[0.041]|[0.153]|[0.092]|[0.027]|[0.172]|

42
Table 4.1: Poverty and Indigence Rates in Dispersed Rural Areas of Argentina, 2003 (percent)

|Mendoza<sup>Santiago del</sup><br>Estero<br>Poor and indigent HOUSEHOLDS mea<br>|Chaco<br>sured by CO|Santa Fe<br>NSUMPTIO|Total<br>N:|
|---|---|---|---|
|Indigent<br>26.6<br>29.1|20.7|7.6|21.6|
|Poor<br>60.8<br>67.7|42.3|18.6|48.7|
|Poor and indigent PEOPLE measur|ed by CONS|UMPTION:||
|Indigent<br>38.5<br>36.6|31.4|11.2|30.9|
|Poor<br>70.1<br>80.6|54.9|25.1|60.6|
|Poor and indigent HOUSEHOLDS<br> <br> <br>|measured b<br>|y INCOME:<br>||
|Indigent<br>38.3<br>31.2|46.7|15.4|33.2|
|Poor<br>57.5<br>60.4|65.3|34.1|54.3|
|Poor and indigent PEOPLE me|asured by IN|COME:||
|Indigent (%)<br>43.8<br>34.9|56.2|18.8|38.8|
|Poor(%)<br>67.3<br>69.6|75.2|42.7|64.3|

Note: Poverty line AR$118.61 per adult equivalent. Indigence line AR$69.65 per adult equivalent. See Section 3 for information on poverty measurement. Source: Own calculation based on RHS 2003.

<!-- Start of picture text --> Figure 4.2: Infant Mortality Rate in Argentina and Selected Provinces, 1991–2002<br>35<br>30<br>25<br>20<br>15<br>10<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>Total Argentina Chaco Misiones Santiago del Estero Tucumán Formosa Mendoza<br>Source: Ministry of Health, Argentina.<br><!-- End of picture text --> 18