rafmacalaba/datause-encoder-data huggingface dataset:
The results presented so far suggest that an increase in the share of ninis, or even those who fit the profile of crime perpetuators (young male adults), is not correlated with the homicide rate, unless the increase takes place within the context of an increasing market for illegal activities. Taking specification (5) in Table 2 as our preferred measure, a one percentage point increase in the share of male ninis aged 19 to 24 during the period 2007-2013 is associated, roughly, with an increase of one in the homicide rate (per 100,000 inhabitants). The same elasticity is higher (1.37) in northern Mexican states sharing a border with the United States and is even higher (2.37 = 1+1.37) in those states during the period 2007-2013. These elasticities are far from trivial. For instance, the share of male ninis aged 19 to 24 grew from 7 percent in 2006 to 10.9 percent in 2011 and this change accounted for an increase in 3.8 in the per 100,000 inhabitants homicide rate during the same period, or almost a quarter of the total increase of 16. The increase in the share of ninis during the same period was larger in states along the border dividing the United States and Mexico, rising from 6.8 percent in 2006 to 11.8 percent in 2011 and accounting for a massive increase of 11.8 homicides per 100,000 inhabitants or a third of the total increase of 35 in border states during those years.

An important result is the positive and significant effect of high school dropout rates on the rates of violence, with a one percentage point increase in dropout rates correlated with a growth of 0.6 in the homicide rate per 100,000 inhabitants. In four of the five specifications included in Table 2, higher dropout rates are positively associated with our measure of violence. This result is consistent with a vast literature, mainly from the United States, showing that time spent in school is negatively associated with crime rates. For instance, using county-level data for the United States during the period 1980-2008, Anderson (2014) shows that the minimum dropout age is negatively related to juvenile crime. Employing a regression discontinuity exploiting the exogenously determined _cut day_ for enrolling, Cook and Kang (2014) find that members of the delayed entry group are more likely to drop out and to be convicted of a serious crime. High school dropout rates decreased 2.3 percentage points during the period of violence reduction in Mexico (1995-2007), contributing a decline 20
We use the following regression to study the impacts of our treatments on patient outcomes: where _yict_ is the outcome of interest for patient _i_ visiting clinic _c_ on date _t_ , _ETc_ is a dummy variable indicating clinic _c_ was selected for the extended training intervention, _PIct_ identifies clinic-days with the patient information intervention, **x**<sup>_′_</sup> **itc**<sup>is a vector of individual and clinic-</sup> level covariates selected by double lasso, _γ_ **c** are strata (clinic-pair) fixed effects and _δ_ **t** are date fixed effects. When studying effects of the extended training intervention on provider outcomes, for which we have only one post-treatment observation, we omit the patient information dummy and the date fixed effects. In all cases, we cluster standard errors at the clinic-pair (strata) level (following de Chaisemartin and Ramirez-Cuellar, 2024).

# **4.2 Effects of Extended Training on Provider Trust in RDTs**

Table 2 evaluates the impact of the extended training intervention on provider beliefs about the diagnostic capability of RDTs and microscopy tests.

Panel A uses the knowledge test carried out with participants right after the training to assess short-term effects. As discussed in section 2, the averages from the basic-training group indicate that trust in the sensitivity of RDTs is low at the outset: participants think that an RDT will detect simple malaria only in 60 percent of cases, compared to 85 percent of cases for a microscopy test. The extended training leads to large, significant growth in confidence in RDTs: providers who received extended training estimate that 80 percent of patients with simple malaria will test positive with an RDT, a 20 percentage point increase (column 2). We also see a 7.1 percentage point increase in providers’ estimate of the sensitivity of an RDT for severe malaria patients. Column 4 shows no significant change in the estimated share of malaria negative patients who would test RDT negative. Column 1 reports treatment effects on an index that averages standardized versions of the outcomes in columns 2-4, following Kling et al. (2007). We estimate a 0.31 standard deviation unit increase in this index, significant at the 1 percent level.

Panel B uses data from the provider endline survey, carried out after in-clinic data collection, to assess whether shifts in beliefs persisted over a longer term. To make consistent comparisons vis-a-vis Panel A, we limit the sample to providers who attended the training. Control group means and treatment effects for RDT-related beliefs are similar to those in Panel A, suggesting that increased trust in RDTs was durable over the course of the experiment.

The extended training did not provide information on microscopy tests. In line with this, we find no systematic positive effects on beliefs about microscopy. Inspection of point 14
deterioration of per capita consumption levels during the recession (2008:Q4 through 2009:Q3), or any real rise subsequently. Indeed for most of the regions there is no discernible trend in these numbers.

The bottom panel of Figure 6 shows the evolution of real per capita expenditure for a selection of deciles, with very little apparent variation over time in the bottom decile (decile 1) or the fifth decile, and some variation but no apparent trend in the top decile. The SES data for 2007-2010 are combined; the measures we used are based on a consistent set of survey questions and protocols that did not change over the period in question. Households are grouped into deciles based on the combined data, so that the deciles represent consistent levels of per capita expenditure over time.

Figure 7 presents two other series. The top line in the upper panel shows the evolution of urban expenditure per capita, which again shows no trend after the beginning of 2008. When expenditure per capita is disaggregated by household size it is clear that larger households have lower levels of per capita expenditure, but they were neither more nor less affected by the crisis than urban households.

The bottom panel of Figure 7 compares real with nominal expenditures per capita for the population as a whole; while nominal expenditures trend upwards, the level of real spending, as we have seen, has remained remarkably steady. This graph also shows nominal income per capita for 2007 and 2009, the two years for which the numbers are available. Not only is expenditure less variable than income – evidence of consumption smoothing – but there is some evidence of higher saving rates in 2009 than in 2007.

While the graphical evidence is striking, it is not completely compelling because it does not include controls. To address this problem, we ran a series of regression equations for which the dependent variable is always real per capita expenditure, and the independent variables include geographic and household variables, as well as dummy variables for each quarter (with 2007:Q1 being the reference period). This allows us to net out the effects of the recession (or at least time) per se, after controlling for other influences – such as education or family composition – that might change over time. Each observation represents a household included in a socioeconomic survey between 2007 and 2010.

Losers and Winners in Recession: Thailand 2008-09 12
usual notion derived from being below a poverty line. In this analysis, it refers to the, population that lives in households with low values of the asset index.

## _Pooling data across regions_

The analysis in this paper is undertaken for "Western and Central Africa" and "Eastern and Southern Africa" as regions-pooling the data across countries-and in order to do so the data needs to be weighted appropriately. Many DHS collect weights in order for sample averages to represent nationwide averages. For example in some countries urban areas are over sampled and weights will adjust for this. In pooling the data across countries one needs to adjust, in addition, for the fact that the sample size relative to the population varies in the different countries. Observations from a country where the sample is only a very small percentage of the population need to be inflated relative to those in a country where a relatively large percentage of the population was sampled, and _vice versa._ For example, the Nigeria 1999 sample corresponded to 0.031 percent of Nigeria's population whereas the Togo 1998 sample equaled 1.016 percent of Togo's population. Annex 2 describes the derivation of weights in more detail.

# **3. Fever and poverty: Bivariate and multivariate analysis**

This analysis uses both a bivariate and a multivariate approach to analyzing the links between poverty and fever (and subsequently its treatment). The bivariate approach allows us to investigate to what extent fever and its treatment "move" with poverty, that is investigate the proposition that the rich suffer less from illness, or seek treatment more if ill. The multivariate approach allows one to disentangle the partial association of fever and its treatment with poverty after controlling for factors which may be correlated with household wealth and have independent relationships to fever. This approach allows one to investigate the degree to which the observed association is determined by other variables-such as mother's education.

## _Geographic distribution offever and poverty_

At the global level, there is no doubt that malaria and poverty move together. Based on country-level data, Gwatkin and Guillot (2000) have recently estimated that 57.9 percent of deaths due to malaria occurred among the poorest 20 percent of the world's population in 1990. McCarthy, Wolf and Wu (2000) and Gallup and Sachs (1998) investigate the extent to which the national level association between malaria and low GNP per capita or low growth in GNP per capita is causal. Both studies conclude that malaria causes accounts for a substantial percentage reduction in GDP per capita growth. More generally, the countries where malaria is transmitted are also poor countries and while the relationship is not perfect, a focus on malaria is a focus on the world's poor.

10
|CONTEN|TS|PAGE|
|---|---|---|
|SUM|MARY|3|
|**1.**<br>INT|RODUCTION|5|
|2.<br>INV|ESTIGATION OF PATTERNS AND DETERMINANTS<br>OF HEALTH|E|
|2.1|Existing health information<br>systems in developing<br>|7|
||countries||
|2.2<br>|TheUseof surveys to investigate<br>patterns of health<br> <br> <br>|8<br>|
|2.3|Integration<br>of survey investigations<br>into existing<br>ttitil<br>t|10|
||sasca<br>sysems||
|2.4|The design of country health studies|11|
|2.5|Investigations<br>of the determinants<br>of health|13|
|3. MEA|SURING THE IMPACT OF INVESTMENTS<br>IN HEALTH|14|
|3.1|Economic evaluation|14|
|3.2|Costs|15|
|3.3<br>|Consequences<br>|16<br>|
|3.4|General problems of economic evaluation|20|
|3.5|Economic evaluation and the World Health Survey|21|
|4. SC|OPE AND LIMITATIONS<br>OF INTERVIEW-BASED<br>SURVEYS|23|
|4.1|Health interview<br>surveys- an introduction|23|
|4.2|Mortality- well established<br>methods|24|
|4.3|General morbidity, cause-specific<br>morbidity and<br>|26|
||use of services||
|4.4|Nutritional status indicators|31|
|4.5|Environmental<br>contamination|33|
|4.6|Explanatory<br>variables|33|
|4.7<br>|Comunity variables<br>|34<br>|
|4.8|Summary of core questionnaire<br>and modules|35|
|5. DE|SIGN AND ORGANISATIONAL<br>ISSUES|35|
|5.1|Selection of countries for the WHS|35|
|5.2<br>|Review of the existing health information<br>system<br>|37<br>|
|5.3|Sample sizes and design|38<br>|
|5.4|Repeat surveys|39<br>|
|5.5|In-depth studies|39|
|5.6<br>|Questionnaire<br>development and pre-testing<br>|40<br>|
|5.7|Piloting and training|41|
|<br>5.8|<br>Seasonality<br>and fieldwork operations|41|
|5.9|Validation and examinations|42|
|5.10|Data processing|42|
|5.11|Strategies for analysis|43|
|6. CO|NCLUSIONS|45|
|RE|FERENCES|47|

# REFERENCES

Trudy Harpham and Ian Timaeus, both with the Working Group on Health Impact Assessment, London School of Hygiene and Tropical Medicine, received contributions from David Bradley, Sandy Cairncross, Lucy Gilson, Wendy Graham, Allan Hill, Philip Payne, Max Price, David Ross and Andrew Tomkins.
**The Role Of Factoring In the New EU Member States**

<!-- Start of picture text --> Figure 3. EU 8 Sources of Domestic Credit<br>450000<br>400000<br>Factoring<br>350000<br>Domestic Traded<br>300000<br>Corporate Debt<br>250000<br>Leasing<br>200000<br>150000 Stock Market<br>Capitalization<br>100000<br>50000 Banking<br>0<br>1996 1997 1998 1999 2000 2001 2002<br>US$ million<br><!-- End of picture text --> Source: Bakker and Gross, 2004 followed by the Czech Republic and Estonia. Relative to GDP, however, Estonia is far ahead, followed by Latvia and Lithuania. The very high level of development observed in Estonia, however, may at least be partially explained by the unusual factoring of construction receivables (which are typically not considered to be factorable, and hence excluded from factoring statistics), which may overstates actual factoring penetration. The Czech Republic, Poland, Slovak Republic, and Hungary are next, and are grouped relatively closely. Factoring penetration is considerably lower in Slovenia. The penetration of factoring is considerably lower in the EU 8 than in Western Europe, though in the latter there also is considerable variation (Figure 6). For example, factoring is much lower in Germany than in the rest of the EU 15.

<!-- Start of picture text --> Figure 4. Factoring Volume in the EU 8<br>8,000<br>7,000<br>6,000<br>5,000<br>4,000<br>3,000<br>2,000<br>1,000<br>0<br>Czech Estonia Hungary Latvia Lithuania Poland Slovakia Slovenia EU 8<br>Republic<br>1996 1997 1998 1999 2000 2001 2002<br>Source: Factors Chain International.<br>US$ million<br><!-- End of picture text -->

**24**

**THE WORLD BANK**
# _Table 3: Representative Components of the GE_

|**Source**|**Who Was**<br>**surveyed/asked?**|**Question/Assessment of**<br>**(Bureaucratic/Both/Political)**|**Source Type**|
|---|---|---|---|
|Economist<br>Intelligence Unit|Expert Staff|‘Quality of bureaucracy /<br>institutional effectiveness‘|Commercial Business<br>Information Provider|
|World Economic<br>Forum Global<br>Competitiveness<br>Report|Survey- Senior<br>business leaders;<br>domestic and<br>international<br>companies|‘Quality of general<br>infrastructure Quality of public<br>schools Time spent by senior<br>management dealing with<br>government officials‘|Non-Government<br>Organization|
|Gallup World Poll|Survey- general<br>public|Satisfaction with public<br>transportation system<br>Satisfaction with roads and<br>highways Satisfaction with<br>education system|Commercial Business<br>Information Provider|
|Institutional Profiles<br>Database|Expert Staff|‘Quality of the supply of public<br>goods: education and basic<br>health Capacity of political<br>authorities to implement<br>reforms’|Government|
|Political Risk<br>Services<br>International Country<br>Risk Guide|Expert Staff|‘Bureaucratic Quality ‘|Commercial Business<br>Information Provider|
|Global Insight<br>Business Conditions<br>and Risk Indicators|Expert Staff|‘An assessment of the quality<br>of the country’s bureaucracy.<br>The better the bureaucracy the<br>quicker decisions are made and<br>the more easily foreign<br>investors can go about their<br>business. Policy consistency<br>and forward planning How<br>confident businesses can be of<br>the continuity of economic<br>policy stance - whether a<br>change of government will<br>entail major policy disruption,<br>and whether the current<br>government has pursued a<br>coherent strategy. This factor<br>also looks at the extent to<br>which policy-making is far-<br>sighted, or conversely aimed at<br>short-term economic<br>advantage.’|Commercial Business<br>Information Provider|

_Source: The World Bank, 2012_ 17
**A3 Brazil**

**Table A3: Regional** disparity trends in **Brazil**

|Year|Max/Min<br>(MMR)|Simple<br>coefficient<br>of variation<br>(CVu)|Weighted<br>coefficient<br>of variation<br>(CVw)|Relative<br>mean<br>Deviation<br>(Rw)|Unweighte<br>gini index<br>(G<br>.)|d Weighted<br>gini index<br>(Gw)|Theil<br>index (T)|
|---|---|---|---|---|---|---|---|
|1985|7.679|0.491|0.478|0,411|0.281|0.270|0.123|
|1986|6.995|0.479|_0.462_|0.399|0.275|0.261|0.115|
|1987|8.287|0.510|0.498|0.423|0.287|0.280|0.132|
|1988|8.457|0.525|0.509|0.434|0.296|0.286|0.137|
|1989|8.314|0.550|0.510|0.441|0.340|0.290|0.137|
|1990|7.254|_<br>0.525 _|0.490|0.432|0.322|0.279|0.126|
|1991|8.775|0.571|0.475|0.416|0.334|0.271|0.118|
|1992|7.771|0.567|0.485|0.432|0.340|0.276|0.125|
|1993|7.409|0.551|0.470|0.421|0.335|0.268|0.118|
|1994|6.968|0.532|0.452|0.399|0.326|0.258|0.110|
|1995|7.576|0.551|0.473|0.419|0.333|0.269|0.119|
|1996|6.881|0.550|0.458|0.405|0.330|0.261|0.112|
|1997|7.567|0.563|0.468|0.409|0.334|0.267|0.116|

**Figure A3:** Reg tnal **disparity trends** in **Brazil**

<!-- Start of picture text --> 0.600<br>- , . - , r, ,,- >,, -. ,,,,,<<<br>0.500 - . ..<br>E 0 3 00 ,<br>I n 0.20 - --. . - . . ,<br>. 0.200 .<br>0.100 . ' ~.<br>0.000<br>1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997<br>Year<br>Simple coefficient of variation (CVu) -- Weighted coefficient of variation (CVw) Relative mean Deviation (Rw)<br>~Unweighted gini index (Gu) +Weighted gini index (Gw) -Theil index (T)<br><!-- End of picture text --> 37
- Psacharopoulos, G. and R. Mattson. 1998. "Estimating the Returns to Education: A Sensitivity $ll' alysis of Methods and Sample Size." _Journal of Educational Development and Administration_ 12(3): 271-287.

- Psacharopoulos, G. 1973. _Returns to Education: An International Comparison._ Amsterdam: Elsevier.

- Psacharopoulos, G. 1985. "Returns to Education: A Further International Update and Implications." _Journal of Human Resources_ 20(4): 583-604.

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Psacharopoulos, G. 1996. "A Reply to Bennell." _World Development_ 24(1): 201.

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INTRODUCTION Ix'xXi Table 20 Stationafy Population, Momentum, and Year When Net Reproduction Rate Reaches 1 for **All** Countries and Economies

|Statio|nary||||Station|ary|||
|---|---|---|---|---|---|---|---|---|
|popul|ation||||popula|tion|||
|||Popula-|||||Popula-||
|||ti|Y||||i|Y|
|Country or economy<br>Millions|Ratio<br>to 1985|on<br>momen<br> <br>tun|ear<br>when<br>NRR=1|Country or economy|Miltions|Ratio<br>to 1985|ton<br>=oxe<br> <br>turn|ear<br>nwhen<br>NRR-1|
|Wo'ld<br>11514|2.4|1.4|2060|Gabon|6|6.2|1.4|2045|
|||||Gambia, The|5|6.9|1.4|2050|
|Africa<br>3049|5.5|1.6|2060|Ghana|61|4.8|1.6|2035|
|||||Guinea|33|5.4|1.3|2045|
|EastAfrica<br>1424|6.2|1.6|2055|Guinea-Bissau|4|4.8|1.3|2045|
|Botswana<br>3|2.7|1.8|2010|Liberia|11|4.9|1.5|2035|
|Burundi<br>29|6.2|1.5|2045|Mali|57|7.7|1.5|2050|
|Comoros<br>3|6.5|1.6|2040|Mauritan'a|13|7.1|1.4|2050|
|Djibouti<br>2|5.8|1.5|2040|Niger|72|10.9|1.5|2060|
|Ethiopia<br>246|5.8|1.5|2045|Nigeria|509|5.1|1.6|2035|
|Kenya<br>200|9.8|1.8|2050|SaoTom6and Principe|0|3 4|1.5|2025|
|Lesotho<br>6|3.7|1.5|2030|Senegal|41|6.3|1.5|2045|
|Madagascar<br>49|4.8|1.6|2035|Sierra Leone|23|6.2|<br>1.3|2050|
|Halawi<br>75|10.2|1.5|2055|Togo|16|5.1|1.6|2035|
|Mauritius<br>2|1.7|1.5|1985|Other West Africa(a)|0.01|1.9|1.6|2000|
|Mozambique<br>86|6.3|1.5|2045||||||
|Namibia<br>5|4.4|1.6|2030|North Africa|435|3.5|1.6|2050|
|Rlunion<br>1|1.?|<br>1.5|1995|Algeria|84|3.9|1.7|2025|
|Rwanda<br>64|10.6|1.6|2055|Egypt, Arab Rep. of|132|2.8|1.5|2020|
|Seychelles<br>0|1.8|1.6|2005|<br>Libya|32|8.5|1.6|2050|
|Somalia<br>38|7.0|1.5|2050|Morocco|66|3.0|1.6|2020|
|South Africa<br>91|2.9|1.5|2020|Sudan|102|4.7|1.4|2035|
|Swaziland<br>4|5.3|1.7|2035|Tunisia|17|2.3|1.6|2010|
|Tanzania<br>160|7.2|1.6|2045|Other North Africa (a)|1|6.3|1.4|2050|
|Uganda<br>87|5.9|1.6|2040||||||
|Zaire<br>196|<br>6.<br>4|1.5|2045|America|1201|1.8|1.4|2030|
|Zambia<br>51|7.6|1.6|2045||||||
|Zimbabwe<br>27|3.3|1.7|2015|Latin America and Caribbea|n<br>872|2.2|1.6|2030|
|||||Antigua and Barbuda|0.1|1.8|1.8|2030|
|West Africa<br>1190|5.8|1.5|2060|Argentina|49|1.6|1.3|2005|
|AngoLa<br>61|7.0|1.4|2050|Bahamas|0.5|2.0|1.6|2000|
|Benin<br>20|4.9|1.6|2035|Belize|1|<br>3.3|1.8|2020|
|Burkina Faso<br>48|6.1|1.4|2045|Bolivia|25|<br>4.0|1.5|2030|
|Cameroon<br>68|6.7|1.6|2045|Brazil|280|2.1|1.6|2005|
|Cape Verde<br>1|3.5|1.9|2025|Chile|21|1.7|1.5|2000|
|Central African Rep.<br>11|4.4|1.5|2035|Colombia|57|2.0|1.6|2005|
|Chad<br>27|5.3|1.4|2045|Costa Rica|5|2.1|1.7|2005|
|Congo, People's Rep. of the<br>17|8.8|1.6|2050|Cuba|13|1.3|<br>1.4|2C30|
|C6tedlivoire<br>85|8.3|1.5|2050|Dominica|0|1.8|1.8|2000|
|Equatorial Guinea<br>1|3.8|1.3|2035|Dominican Rep.|14|<br>2.2|1.7|2010|