rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Fe pos ae ><br><!-- End of picture text --> <!-- Start of picture text --> ‘ ~ Additiond number of people below the extreme poverty threshdld (%aa<br>[) Less than 0.01%<br>GS 0.01% -2%<br>Bi[J6% 10%<br>No cata<br><!-- End of picture text --> <!-- Start of picture text --> og OsFis eeos Pt -<br><!-- End of picture text --> <!-- Start of picture text --> aa<br>GS 0.01% -2%<br>Bm[J]63 — 10%<br>No cata<br><!-- End of picture text -->
Table 1: Categories and dimensions of the Colombian multidimensional poverty index.

|**Category**|**Dimension**|**Deprived if**|
|---|---|---|
|**Education**|Educational achievement|Any person older than 15 years<br>has less than 9 years of schooling.|
||Literacy|Any person older than 15 years<br>and illiterate.|
|**Childhood and youth**|School attendance|Any child 6 to 16 years old does<br>not attend school.|
||Children behind grade|Any child 7 to 17 years old is<br>behind the normal grade for his<br>age.|
||Access to child care services|Any child 0 to 5 years old doesn’t<br>have access to health, nutrition or<br>education.|
||Child labour|Any child 12 to 17 years old<br>works.|
|**Employment**|Long term unemployment|Any economically active member<br>has been unemployed for 12<br>months or more.|
||Formal employment|Any employed household<br>members is not affliated to a<br>|
|||pension fund.|
|**Health**|Health insurance|Any person older than 5 years<br>does not have health insurance.|
||Health services|Any person who fell sick or ill in<br>the last 30 days did not look for<br>|
|||specialized services.|
|**Standard of living**|Water system|<br>Urban: Household not connected<br>to public water system. Rural:<br>Household obtains water used for<br>cooking from wells, rainwater,<br>spring source, water tanks, water<br>carriers or other sources.|
||Sewage|Urban: Household not connected<br>to public sewer system. Rural:<br>Household uses a toilet without a<br>sewer connection, a latrine or<br>simply does not have a sewage<br>system.|
||Floors|Households has dirt foors.|
||Walls|<br>Rural: The household’s exterior<br>walls are made of vegetable, zinc,<br>cloth, cardboard or waste<br>materials or if no exterior walls<br>exist. Urban: Walls made of rural<br>materials or untreated wood,<br>boards or planks.|
||Overcrowding|Urban: There are 3 people or more<br>per room. Rural: More than 3<br>peopleper room.|

Source: Angulo (2010). Deprivations are measured at the household level: all members of the household are considered deprived if one of the members is deprived in a dimension. 28
A Duration Analysis of CONALEP

**Table 7. Marginal Effects of Having a Certain Type of Activity after Completing CONALEP**

||**Prob[****_Activity=j_ **<br>|**|****_X, dummy=k_]**<br>|**Marginal**||**Prob[****_Activity=j_ **<br>|**|****_X, dummy=k_]**<br>|**Marginal**|
|---|---|---|---|---|---|---|---|
|**Variable**|**k=1**<br>**Survey 98**|**k=0**<br>**Survey 94**|<br>**Effect**|**Variable**|**k=1**<br>**Survey 98**|**k=0**<br>**Survey 94**|<br>**Effect**|
|_j = Work_||||_j = Searching for job_||||
|98 Survey|0.77|0.59|0.183|98 Survey|0.08|0.26|-0.174|
|Women|0.64|0.80|-0.162|Women|0.10|0.11|-0.012|
|Age|||0.004|Age|||0.000|
|South-East Area|0.78|0.74|0.040|South-East Area|0.09|0.11|-0.027|
|Center-South Area|0.81|0.74|0.076|Center-South Area|0.08|0.11|-0.030|
|North-East Area|0.85|0.73|0.122|North-East Area|0.08|0.12|-0.039|
|North-West Area|0.82|0.73|0.085|North-West Area|0.06|0.12|-0.055|
|Center-North Area|0.84|0.73|0.111|Center-North Area|0.06|0.12|-0.055|
|Pacific Area|0.84|0.73|0.116|Pacific Area|0.04|0.12|-0.081|
|92 Cohort||0.75||92 Cohort||0.10||
|93 Cohort||0.76||93 Cohort||0.09||
|94 Cohort|0.46|||94 Cohort|0.37|||
|96 Cohort|0.66|||96 Cohort|0.20|||
|97 Cohort|0.43|||97 Cohort|0.41|||
|Lower-secondary HH|0.73|0.75|-0.016|Lower-secondary HH|0.11|0.11|0.003|
|Upper-secondary HH|0.68|0.75|-0.069|Upper-secondary HH|0.11|0.11|0.001|
|University - + HH|0.73|0.75|-0.012|University - + HH|0.08|0.11|-0.035|
|Do not know HH|0.74|0.75|-0.003|Do not know HH|0.09|0.11|-0.021|
|GDP per capita|||0.062|GDP per capita|||-0.057|

Base categories for independent variables: 94 Survey, men, Center area (D.F. and Mexico), 91 cohort, none or primary school education of household head (HH). Units for GDP thousands of 1998 pesos.

16
conflicting national standards are withdrawn. CEN had issued 12,357 standards and approved documents by the end of 2006, with another 3,510 in preparation (see <u>http://www.cen.eu/cenorm/aboutus/information/statistics/index.asp, accessed on 2 February</u> 2007). By contrast, the European Commission has issued to date less than _two dozen_ New Approach Directives (see Table 2).

What are the key points that emerge from the complexities of standards at the WTO and in the EU? For present purposes, there are at least two. First, it is not just technical regulations that potentially matter for trade. As we have shown, both technical regulations and product standards play a simultaneous and sometimes symbiotic role in the EU standardization context. Thus, both are relevant to African manufacturers interested in exporting to the EU. Both are also connected, though in very different ways, with the WTO _Agreement on TBTs_ .

Second, standardization in Europe is not limited to New Approach sectors. CEN’s own work— independently of EU Directives—leads to EU standards in other areas. And as the figures in the previous paragraph suggest, CEN has been most active in performing this role. It is therefore important to have an idea of the economic impact of CEN’s standards, in particular from a development point of view.

# **3 The World Bank EU Standards Database**

The empirical literature referred to in Section 1 makes clear the difficulty of assembling reliable and consistent data on product standards. (See WTO, 2005, for a review of the various data sources that are available.) In order to investigate the impact of EU standards on African textiles and clothing exports, we have collected original data on the extent of European standardization

- 7 -
# **Box 4: Financing Education Development in the Republic of Korea**

The Republic of Korea has taken a progressive approach to universalizing primary and secondary education. In 1954, the government launched its first plan for free compulsory primary education financed by the Education Tax Act of 1958 and foreign aid. Between 1954 and 1959, public education spending as a share of the overall government budget increased threefold from 4.2 percent to 14.9 percent. Nearly 80 percent of this increase was devoted to primary education, and by the late 1950s and early 1960s, primary school enrollment was nearly universal (Kim 2002).

After the expansion of primary schooling was complete, the government shifted the focus of its funding to secondary education (see figure below). Secondary education spending as a percentage of total public education spending surged from 21 to 39 percent between 1970 and 1987. This funding provided schools with enough resources to increase the secondary school gross enrollment rate from 39 percent in 1971 to 101 percent by 1996.<sup>a</sup> While primary public education spending as a proportion of GDP fell, absolute levels of public education spending at the primary level continued to rise in real terms over the same period. Since the universalization of secondary schooling in Korea in the late 1990s, the government’s focus has shifted, to expanding higher education.

<!-- Start of picture text --> As Universal Primary Enrollment Was Achieved, Government Spending Shifted toward Secondary<br>Education<br>a. Education spending as a percentage of GDP b. Secondary school learning outcomes<br>(Harmonized Learning Outcomes (HLO) scale)<br>7<br>Total 600<br>6 Primary<br>Secondary 500<br>5<br>400<br>4<br>300<br>3<br>200<br>2<br>1 100 Mathematics<br>Science<br>0 0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text --> _Sources:_ World Bank calculations using UIS, IMF online databases; Altinok, Angrist, and Patrinos (2018).

Learning outcomes have improved alongside the continual increase in public education spending as a share of GDP. While information on secondary school learning outcomes only goes back to 1985, the data show an upward trend that corresponds to increases in spending on secondary education. More recently, average learning outcomes have remained relatively unchanged, even while public education spending on secondary education has continued to increase rapidly. While learning outcomes only measure one objective of the education system, this situation points to a potential lack of efficiency in the education system.

a. The secondary gross enrollment rate is the number of children enrolled in secondary school divided by the number of children of official secondary school going age.

12
the endogenous variable, and the average bribery incidence for the whole sample as the instrumental variable.

In these regressions, we include a dummy variable ( _Applied_ ) to control for whether a firm conducted a business transaction. This dummy captures the direct effect of a business transaction on sales growth. Firms who apply for business transactions may do so because they have plans to expand; those who applied for a business transaction grew about nine percent more than firms who did not apply for a business transaction. Comparing these results to those obtained in Table 7, we observe a stronger negative effect for bribery on sales growth, firms who were bribed have growth rates that are 34.7 percentage points lower, or lower by two standard deviations. However, in column (1), we cannot reject the instrument is weak. In column (2), the total effect of corruption for firms with median sales is 35 percent, almost identical to estimates in column (1) without an interaction term. Comparison of the total impact of bribe payments shows that among firms who were bribed, firms with sales in the 25<sup>th</sup> percentile saw growth rates that were 57 percentage points lower than the rate for firms that are in the 75<sup>th</sup> percentile which is more severe than the result obtained from Table 7.

## **4.2 Robustness Tests**

We perform a variety of sensitivity checks. First we repeat the estimation by different country groupings, and for only SMEs (Table 9). Second, we use an alternative definition of the Bribery Indicator by coding refusals to bribery questions as non-responses (Table 10). Thirdly, we create clusters based on finer categories using industries, and larger categories by location only (Table 12 and Table 13). Lastly, we estimate the effects of bribery using a panel of firms surveyed in both 2006 and 2010.

# <u>A. Firm & Country Groupings</u>

First we analyze bribery distortions for only small and medium-size enterprises (SMEs). SMEs form the backbone of economic activity in most of the developing countries and they suffer more from unfavorable business environment. In order to understand how stringent bribery is for their evolution, we restrict the sample to firms with employment levels less than 100 workers. Since sales data varies substantially across countries, we use employment levels to define SME. Excluding firms with more than 100 workers drops the sample size to 4,826 firms.

19
<!-- Start of picture text --> Figure 4.1. Employment Embodied in Vietnam's Industrial Exports to the World<br>LT —CCCTCTCTCTCOTCTTTC*'TM!<br>LOCC<br>_8 oo500.0 es |)el O.CCCCCOCCTMS RR»<br>= Td Pe<br>= 300.0 — a eee<br>-lool | | l | F | | will |<br>> jcmccl | al | | reel | |<br>om = mw 7 2 _ w p i o a=] _a2<br>£ Po f§sSEu BBG ea, E = Bia $5re<br>= mS 2o5 g fe 5 SEs | oa S825 8.56<br>= ft ESete ens Tes o mo e235 Bae<br>a+ a ao = 2a 2 aad a mo 7a LU PeP@e<br>we E ts a aBe a a2 FSF FERS<br>ge 8 s O25 FRFs<br>E mH E C E<br><!-- End of picture text -->

Figure 4.2, Employment Embodied in Vietnam's Industnal Exports to the U.S.

<!-- Start of picture text --> Figure 4.5. Employment Embodied in Vietnam's Industrial Exports to the Rest of the World<br>_. 300.0<br>Sooo Ln La<br>= o o | a eee<br>oo omc |_| al | eee il ee eet |<br>== =BP 2 cok; og wi S- 5 cD»z 3gw —£a aE2 nw a_2<br>£ a: #03 go Bes a £& +. | o 2 o 2s eres<br>= a ESEc $a 8 Fes o nm oO e458 Cae<br>eH a oo Boe = uD ra oo pk PEO@oees<br>ue oe GS G28 = a3 FS Beppe<br>=a 3 ia Sa" Dato<br>= i E fa =<br>2000 92001 02002 02003 92004 ©2005 ©2006 O 2007<br><!-- End of picture text -->
As shown empirically in Section 3, both features result in interesting differences between the results of Alvaredo and Londoño (2013), based on Alvaredo (2011), and the ones we obtain in this paper for the same country, Colombia. A final note concerns the term _F_ ( _b_ ) that appears in the equation of Propositions 1 and 2. This term represents the fraction of the population with incomes below _b_ , and needs to be estimated once the preferred threshold _b_ is chosen. We estimate _F_ ( _b_ ) using the same methodology employed by Atkinson (2007) and Alvaredo (2011) to estimate the share of income that accrues to the top 1 percent of the income distribution. In particular, _F_ ( _b_ ) = 1 _− C_<sup>_<u>A</u>_,where</sup><sup>_A_isthe</sup> total population above the income tax threshold _b_ (computed using the tax records), while _C_ is the control for population (an estimate of the total population of the country).<sup>3</sup>

# **3 Application: The Case of Colombia**

Over the past 20 years, the countries of Latin America and the Caribbean have made notable improvements in reducing poverty and income inequality. Income concentration, measured by the Gini coefficient, has been steadily falling in the region, going from 0.58 in 1996 to 0.52 in 2011 (World Bank 2013). However, in the case of Colombia, the Gini coefficient remained practically stagnant from 2002 to 2012, and declined only marginally during the second part of the decade. Moreover, in 2012 Colombia’s Gini coefficient remained higher than the regional average, placing Colombia among the three most unequal countries in LAC and one of the most unequal in the world (World Bank 2013). Furthermore, these numbers do not take into account the potential bias associated with the top of the income distribution, which, if properly accounted for, could result in a higher Gini In recent years, an increasing number of governments have been granting public access to administrative records and other information. The use of administrative records as a statistical tool is a recent trend that increases transparency and makes available to citizens,

> 3The control for population is computed as the total number of individuals over a certain age (here, 20 years) in the country. The threshold depends on the age at which young people enter the labor force. See Atkinson (2007).

10
# Table 14: MONTHLY EARNINGS BY EMPLOYMENT STATUS

|||Abidjan||Ot:|her<br>Citie|s|R|ural Are|as|
|---|---|---|---|---|---|---|---|---|---|
|Employment Status|WI|W2|W2/W1|WI|W2|W2/W1|WI|W2|W2/W1|
|Government Employee|<br>167,699|197,93|0 118.03|-<br>115,508|-CFAF-<br>142,91|7 123.73|110,447|118,345|107.15|
|Private Sector<br>Employee-Formal|111,606|133,52|9 119.64|141,103|1 193,94|9 137.45|70,578|78,981|111.91|
|Private<br>Sector<br>Employee-Informal|31,242|31,599|101.14|30,081]|30,870|102.62|23,701|25,490|107.55|
|Self-Employed|96,489|96,489|100.00|51,544|51,544|100.00|26,036|26,036|100.00|
|All|111,009|123,418|111.1|8 70,946|81,120|114.34|27,497|27,720|100.81|

Index

|Government Employee|100|100|-<br>68.88|72.21|-<br>65.86|59.79|-|
|---|---|---|---|---|---|---|---|
|Private Sector<br>Employee-Formal|100|100|-<br>126.4'1|145.25|-<br>63.24|59.15|-|
|Private Sector<br>Employee-Informal|100|100|-<br>96.28|97.691|-<br>75.86|80.67|-|
|Self-Employed|100|100|-<br>53.42|53.42|-<br>26.98|26.98|-|
|All|100|100|-<br>63.9]|<br>65.73|-<br>24.77|22.46|_|

See text for definitions of WI and W2. Index is calculated separately for each type of worker and for W1 and W2.
22

# Education

The student loan program, the third of the government credit programs, also initially addressed perceived market imperfections. Since a student's future income cannot be repossessed and sold by a lender, students frequently have no collateral. The future return from education, even if the student's future income could be attached, is risky. The loans are small and have a high potential of default since students are highly mobile with no credit history. Other goals of the programs include promoting education with its positive effects on society and improving equity by providing opportunities for low income students.

The empirical work again provides mixed results. Gale computes a more than doubling of the share of credit going to students. The elasticity he uses comes from Bosworth's study. Bosworth cites a Manski-Wise study which shows that a 1 percent net drop in education costs would increase enrollment rates by .1 to .3 percentage points, resulting in an elasticity of between .1 and .3. McPherson (1978)21 cites 9 studies which show increases in enrollment rates ranging from .05 to 1.46. Since the costs of education to the student have fallen due to the availability of subsidized student loans, Bosworth calculates that anywhere from .3 to 1.4 million more students were enrolled in higher education in 1984 than would have been enrolled without a subsidized program. The higher of the two numbers comes from using the higher of the two enrollment changes in the Manski-Wise study and assuming that none of the loans would have been available through the private sector. Since 3.3 million students received loans in 1984, less than a third of the loans actually increased borrowing in the best of all possible scenarios, the assumptions of which are unlikely. Another study cited by McPherson which uses time series data rather than 2"Michael S. McPherson, "The Demand for Higher Education," in Public Policy and Private Higher Education, edited by David W. Breneman and Chester E. Finn, Jr. (Washington, D.C.: Brookings Institution, 1978), pp. 143-96.
**_Energy demand models for policy_** **_<u>formulation</u>_**

# Table A1.3: Comparison of bottom-up models

|Criteria|RESGEN|EFOM|MARKAL|TIMES|MESAP|LEAP|
|---|---|---|---|---|---|---|
|Approach|Optimisation|Linear<br>Optimisation|Linear<br>Optimisation|Optimisation|Optimisation|Accounting|
|Geographical coverage|Country|regional<br>and<br>national|Country or multi-<br>country|Local,<br>regional,<br>national or multi-<br>country|National|Local to national to<br>global|
|Activity coverage|Energy system|Energy System|Energy System|Energy System and<br>energy trading|Energy System|Energy system and<br>environment|
|Level of disaggregation|Pre-defined|User defined|User defined|User defined|Pre-defined sector<br>structure|Sector structure pre-<br>defined|
|Technology coverage|Good|Extensive|Extensive|Extensive|Extensive|Menu ofoptions|
|Data need|Variable, Limited to<br>extensive|Extensive|Extensive|Extensive|Extensive|Extensive<br>but<br>can<br>work<br>with<br>limited<br>data|
|Skill requirement|Limited|High|Highto veryhigh|Veryhigh|Highto veryhigh|Limited|
|Portability to anothercountry|Difficult|Difficult|Difficult|Difficult|Difficult|Difficult|
|Documentation|Limited|Good|Extensive|Good|Good|Extensive|
|Capability to analyse price-<br>induced policies|Exists|Exists|Exists|Exists|Exists|Does not exist|
|Capability to analyse non-price<br>policies|Good|Very good|Very good|Very good|Good|Very Good|
|Ruralenergy|Possible|Possible|Possible|Possible|Notknown|Possible|
|Informalsector|Not possible|Not possible|Not possible|Not possible|Not possible|Possible|
|New technology addition|Difficult|Possible|Possible|Possible|Possible|Possible|
|Energy shortage|Not explicitly|Not explicitly|Not explicitly|Not explicitly|Notknown|Possible explicitly|
|Subsidies|Difficult|Possible<br>but<br>often ignored|Possible<br>but<br>normallyignored|Possible<br>but<br>normallyignored|Not known|Not<br>considered<br>explicitly|
|Rural –urban divide|Possible<br>but<br>not<br>covered usually|Possible but not<br>covered usually|Possible<br>and<br>covered|Possible<br>and<br>covered|Not known|Possible and covered<br>usually|
|Economic transition|Not covered|Not covered|Not covered|Can be covered|Not known|Usually<br>covered<br>throughscenarios|

148
put significantly more faith in RDTs to diagnose severe malaria, reporting that 89 out of 100 such patients will test positive. These results coincide with our qualitative observation that doctors believe RDTs become more effective as the parasite load in the blood increases. While this belief is founded in fact, RDTs perform very well when presented with parasite loads typical of simple malaria (World Health Organization, 2014b).

Finally, Panel C shows that 70 percent of providers use microscopy tests to detect malaria. They trust this method more; on average, they think 84 and 95 out of 100 patients with simple and severe malaria respectively will have a positive microscopy result. Consequently, only 33 percent of providers would treat a relative with a negative microscopy test for malaria.

**Malaria Testing and Treatment in the Study Sample.** Health workers’ beliefs indicate widespread skepticism of tests – especially RDTs – coupled with inflated beliefs regarding malaria prevalence. To understand how these beliefs translate into patient care, we use data from surveys conducted with patients visiting our study clinics. We focus on two sets of outcomes: patients’ reports of whether they received a malaria test at the clinic and whether they were prescribed an antimalarial, and patients’ “true” malaria status, which we measure via follow-up RDTs conducted by our research team at patients’ homes, 1-2 days after they visited the clinic (see subsection 3.3 for more detail). To capture status quo outcomes, we limit attention to patients visiting clinics that were not trained on RDT accuracy.

Figure 1 studies how the match between the underlying cause of illness (malaria positivity per our home follow-up tests) and treatment (antimalarial prescriptions) varies with inclinic testing. Panel A shows that compared to those who received RDTs at the clinic, patients tested via microscopy are less likely to be malaria positive yet more likely to receive antimalarials. As a result, the darkest bar in Panel B shows that the match between malaria status and malaria treatment is worst in the microscopy group and best in the group of patients who received an RDT only. Yet even in this group, the match remains under 50 percent. The lighter bars in Figure 1, Panel B report the match rate for malaria negative patients (middle gray bar) and malaria positive patients (light gray bar). Here we see that – the poorer performance of microscopy patients is largely driven by overtreatment roughly a third of malaria negative patients in the RDT-only group do not receive an antimalarial prescription, while just 17 percent of malaria negative patients in the microscopy group receive the same. These patterns suggest two issues are at play: first, providers may ignore test results when writing prescriptions, either due to patient pressure (Lopez et al., 2022) – or due to their own misgivings regarding the test results especially in the case of RDTs. Second, lab technician errors (unintended or due to poor infrastructure/supplies) may lead to false positives (poor specificity) and, therefore, overtreatment among malaria negative patients tested via microscopy.

7