rafmacalaba/datause-encoder-data huggingface dataset:
symptoms (Table C.3). One possible reason is that 2013 was the first rainy season under Action for Health, and that the effects of the program simply manifested only after a longer period of exposure. By the end of 2014, a large share of children under 2 had benefitted from the program since birth or shortly after birth. Another reason may be that the children in AfH constitute only a small percentage of the population (about 20% of people in the study households, which in turn make up a fraction of the local population), so the program may not be able to significantly reduce community spread for most types of infectious illnesses, but some of the measures that the CHW teach, such as hand washing and water disinfection, are particularly effective in the prevention of diarrhea within the household.

# **5.4 Discussion: Advantages of estimating demand conditional on health status**

**Censoring and Selection Bias.** Our data make a strong case for collecting illness data in the home and analyzing spells with a hazard model, due to the censoring that occurs with spontaneous recovery: we do not observe how long parents would have waited to see a doctor in spells where the child gets better without care. Spontaneous recovery is integral – to the decision to “wait and see” during an illness spell without it, there would never be a benefit to delaying care past the first day. Of note, even care-required spells often end with unaided recovery, illustrating the challenge of improving child mortality through acute care: it is necessary to treat a large number of spells in order to prevent the small portion that may become truly dangerous to the child.

One effect of censoring is that the observed pre-care spell length systematically underestimates true time-to-care, and effects on spell length do not fully reflect changes in daily care seeking probability. For example, there will be spells that are censored (ending without care) at high _K_ , but uncensored (ending in care) at low _K_ (leading to increases in utilization). Table C.6 in the appendix correspondingly shows that the subsidy reduces (pre-care) spell length by only 12 percent (0.83 days out of 6.83 days in the control, column (1)), even though we found that it increases the care seeking probability five-fold.This problem is exacerbated in data that contains only uncensored spells that end in a consultation, such as data collected 33
in the number of hours spent searching in either the Workshop or Workshop Plus groups. This zero effect is not driven by imprecise estimates. Instead, the estimated coefficients themselves are small and never exceed 3.5% of the control mean.

**Table 3:** Effects on Job Search Intensity

||(1)<br>Search Hours|(2)<br>Search Hours|(3)<br>Applications|(4)<br>Applications|
|---|---|---|---|---|
|WS Basic|0_._225|0_._016|0_._163|0_._124|
||(0_._897)|(0_._897)|(0_._274)|(0_._273)|
|WS Plus|_−_0_._243|_−_0_._480|0_._749<sup>_∗∗∗_</sup>|0_._681<sup>_∗∗∗_</sup>|
||(0_._750)|(0_._747)|(0_._240)|(0_._235)|
|Covariates|_No_|_Y es_|_No_|_Y es_|
|Observations|1888|1886|1896|1895|
|_R_<sup>2</sup>|0_._083|0_._092|0_._308|0_._318|
|Control Mean|14_._095|14_._095|3_._835|3_._835|
|P-value|0_._595|0_._573|0_._054|0_._062|

Notes: * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01. Standard errors (reported in parentheses) are clustered at the individual level. P-value compares WS Plus to WS Basic. All regressions control for location-fixed effects and baseline value of the outcome variable. Outcome variables are winsorized at the 5% level.

In contrast, there is a significant increase in the number of applications in the Workshop Plus group, but not in the Workshop group, relative to the control mean. The number of applications is also significantly higher in the Workshop Plus group relative to the Workshop group, indicating that plan-making is driving the effect. At follow up, the Workshop Plus group submits 0.7 additional applications, which is 15% more than the Workshop group and 18% more than the Control group. While this is a sizable increase relative to the low number of applications at baseline, it only partially closes the intention-behavior gap.

We corroborate the latter results by re-estimating Equation (1) with an observed measure of application submission. We anonymously inform job seekers about an actual vacancy and 12
From the EIT center, we also obtained school averages disaggregated by gender. Note that, based on descriptive statistics, girls do better than boys, on average, on all subjects, except for Geography, with the gender gap to be most substantial on the Ukrainian exam.

When running regressions by gender, we use the average score of the female students, respectively male students, of a school and regress it on the same set of explanatory variables, with the exception that our language variable is now the percentage of exams (other than the Ukrainian exam) taken in Ukrainian by the female students of a school rather than by all students.

We find that for the Ukrainian exam (table 9a and 9b)

- Like for the total sample, school size has a positive effect on the school‟s mean Ukrainian exam score for both males and female students. Again, the effect is clearly stronger for urban schools.

- Class size has a negative effect for male and female students and this effect is statistically significant for male students and almost significant for female students. The size of these effects is bigger for males than for females but in both cases moving from the 25<sup>th</sup> to 75<sup>th</sup> percentile does not change the mean by more than 1 point.

We also find that

- Student-teacher ratio has a consistently negative effect but like for the whole sample, for females when dis-aggregating the sample into urban and rural schools, this effect is not significant. For boys there is a significant negative non-linear effect, but the size of this effect is very small.

- Evening schools perform substantially less well (more so for girls than for boys), urban schools do better than rural schools and there are substantial oblast specific effects.

- Gymnasia and Lycea score 5 to 10 points higher than other school types.

- The gender composition matters little for girls but is significant for boys: more boys among the test takers reduces the average school scores of boys somewhat: going from the 25<sup>th</sup> percentile to the 75<sup>th</sup> percentile in terms of share of boys will decrease the average score by 0.3 to 0.6 points. The fact that we found a negative effect of the share of

39
Table 2.3

||**Gr**|**ossDomesti**|**cProduct- 1**|**999**|||
|---|---|---|---|---|---|---|
||GDP|GDPPer<br>Capita|Percentof<br>WorldGDP|Percentof<br>APEC GDP|Percentof<br>ASEANGDP|Percentot<br>ASEAN+3<br>GDP|
||(US$nmillions)|USS|_<br>_|_|_<br>_%||
|BruneiDarussalam|||||||
|Cambodia|3,117|260|0.01||0.58|0.05|
|Indonesia|142,511|688|0.46|0.79|26.36|2.27|
|LaoPDR|1,432|286|0.00||0.26|0.02|
|Malaysia|79,039|3,436|0.26|0.44|14.62|1.26|
|Myanmar|||__||||
|Philippines|76,559|1,035|0.25|0.43|14.16|1.22|
|Singapore|84,945|21,236|0.28|0.47|15.71|1.35|
|Thailand|124,369|2,073|0.40|0.69|23.00|1.98|
|Vietnam|28,682|368|0.09|0.16|5.31|0.46|
|China|989,465|789|3.20|5.50||15.75|
|Hong KongSAR|158,943|22,706|0.51|0.88|||
|Taiwan(1993&1999)'|287,881|13,086|0.93|1.60|||
|Japan|4,346,922|34,228|14.08|24.18||69.17|
|Korea|406,940|8,658|1.32|2.26||6.48|
|Australia|404,033|21,265|1.31|2.25|||
|New Zealand|54,651|13,663|0.18|0.30|||
|Canada|634,898|21,163|2.06|3.53|||
|Chile|67,469|4,498|0.22|0.38|||
|Mexico|483,737|4,987|1.57|2.69|||
|PapuaNew Guinea|3.586|717|0.01|0.02|||
|Peru|51,933|2,077|0.17|0.29|||
|Russia|401,442|2,750|1.30|2.23|||
|UnitedStates|9,152,098|32,921|29.64|50.90|||
|ASEAN|540,654|1,168|1.75|3.01|100.00|8.60|
|ASEAN+3|65,283.981|3,323|20.35|34.95||100.00|
|APEC|17,980,103|7,129|58.23|100.00|||
|NAFTA|10,270,733|25,360|33.26|_||1|
|EU|8,478,054|22,524|27.461||||
|World|30,876,254|5,165|100.00||||

1 Taiwan data from Taiwan Govemment National Statistics. May not be directly comparable to others. Source: World Bank's World Development Indicators 2001

> GDP, another measure of economic size, is presented in Table 2.3 above. GDP tells a story that is consistent in terms of economic size and diversity with that of previously discussed measures. Among the ASEAN countries, Singapore's $21,236 (1999) is over 80 times that of Cambodia's $260. If the grouping is extended to include all ASEAN+3 countries, then Japan's per capita GDP ($34,228) becomes the highest and

> is about a hundred times greater than that of the newest members of ASEAN.

The difference in economic development among countries is even more apparent if a country's share of global GDP is measured. None of the ASEAN countries' shares of global GDP is greater than 0.50 percent, and in the aggregate, ASEAN's GDP is only 1.75 percent of worldwide GDP. If China, Japan and Korea are included, the share rises 35
Figure 5 (a): Cambodia Skill Premiums – basic controls <!-- Start of picture text --> Cambodia: Skill Premium and Skilled Workforce - Basic<br>controls only<br>1<br>Skilled-1: At least<br>0.9 Lower Secondary<br>0.8 Skilled-2: At least<br>0.7 Upper Secondary<br>0.6 Skilled-3: Tertiary and<br>above<br>0.5<br>Skill Premium-1<br>0.4<br>0.3 Skill Premium-2<br>0.2<br>0.1 Skill Premium-3<br>0<br>1997 2003-4 2007<br>Year<br>workers<br>Skill premium and<br>proportion of skilled<br><!-- End of picture text --> Figure 5 (b): Cambodia Skill Premiums – industry controls <!-- Start of picture text --> Cambodia: Skill Premium and Skilled Workforce - With Industry<br>controls<br>0.8<br>Skilled-1: At least<br>0.7<br>Lower Secondary<br>0.6 Skilled-2: At least<br>Upper Secondary<br>0.5 Skilled-3: Tertiary and<br>above<br>0.4<br>Skill Premium-1<br>0.3<br>Skill Premium-2<br>0.2<br>Skill Premium-3<br>0.1<br>0<br>1997 2003-4 2007<br>Year<br>of skilled workers<br>Skill premium and proportion<br><!-- End of picture text --> 26
**Table 8. Student's change in performance, 1994.** Dependent variable: Probability of testing above the median in **6h** grade.

||**Coefficient**|**Std. Error**|**t-Statistic**|**Prob.**|
|---|---|---|---|---|
|**Native schools**|||||
|Constant|-0.991|0.217|-4.572|0.0%|
|Control|1.272|0.246|5.162|0.0%|
|Factor - Characteristics of community & family|0.054|0.103|0.528|59.8%|
|Factor - Characteristics of school & system|0.630|0.104|6.056|0.0%|
|N||481|||
|Log likelihood||-295.562|||
|F-statistic|15.024|||0.0%|
|Chi-square|60.095|||0.0%|
|Obs with Dep= 1|237||||
|Obs with Dep=0|244||||
|Ex-ante probability|49%||||
|Estimated probability (at means)|49%||||
|Estimated probability without PARE (control)|27%||||
|PARE contribution - percentage gain probability||45%|||
|**Rural schools**|||||
|Constant|-0.396|0.183|-2.161|3.1%|
|Control|0.495|0.209|2.372|1.8%|
|Factor - Characteristics of community & family|-0.233|0.086|-2.713|0.7%|
|Factor - Characteristics of school & system|0.107|0.083|1.279|20.1%|
|N||549|||
|Log likelihood||-374.073|||
|F-statistic|3.112|||1.5%|
|Chi-square|12.448|||1.4%|
|Obs with Dep= 1|271||||
|Obs with Dep=0|278||||
|Ex-ante probability|49%||||
|Estimated probability (at means)|49%||||
|Estimated probability without PARE (control)|40%||||
|PARE contribution - percentage gain probability||19%|||
|Source: Own calculations.|||||

11
Macro Prudential Policies from a Micro Prudential Angle<sup>∗</sup> Tito Cordella<sup>†</sup> and Samuel Pienknagura<sup>‡</sup> JEL Classification: G21, G28. Keywords: Macroprudential Regulation, Microprudential Regulation, Bank Risk Taking.

> ∗We thank Pierluigi Bologna, Giovanni Dell’Ariccia, Augusto de la Torre, Jose Luis Diaz Sanchez, Alain Ize, Carlos Vegh and participants at the Conference at the Central Bank of Turkey and CEMLA (Mexico City) for helpful comments and suggestions. The usual disclaimers apply.

> †The World Bank, 1818 H St. NW, Washington, DC 20433, USA; E-Mail:tcordella@worldbank.org.

> ‡The World Bank, 1818 H St. NW, Washington, DC 20433, USA; E-Mail:spienknagura@worldbank.org.
25

# **Another application: Using new price distortion measures to estimate their global effects on agricultural versus non-agricultural markets as of 2004**

The above estimates of distortions to agricultural prices for China, and similar ones for other developing countries, can be used in global economy wide computable general equilibrium (CGE) models. Most such CGE models depend on just applied tariffs supplemented by agricultural subsidies in high-income countries as measured by the OECD. These are collated and distributed as the GTAP Protection Database by the GTAP Center in Purdue University (www.gtap.org). The newly estimated average of distortions for China in 2004 is less than two-thirds the average of those in the latest version (the pre-release of Version 7.0) of the GTAP Protection Database. In particular, the production-weighted average tariff on imports of all agricultural and processed food products that year is 10.9 percent in the GTAP database, compared with just 6.8 percent based on the new estimates reported above (plus an average export subsidy for those products of 0.2 percent, compared with 0.0 percent in the GTAP database).

These new numbers based on price comparisons for China, and similar ones for other developing countries in the project, are inevitably going to generate different national and global results than those based mainly on trade-weighted average tariff rates. When used in a global CGE model they are going to provide a better indication of what impact policies are having on output, trade and agricultural relative to non-agricultural value added than can be inferred simply from nominal and relative rates of assistance. A small sample of estimates of the effects of those price distortions, using the Linkage Model (see van der Mensbrugghe 2005) of the global economy, is provided in what follows, details of which are available in Anderson, Valenzuela and van der Mensbrugghe (2008).

With China (like much of Asia, Africa and Europe) having less agricultural comparative advantage than Latin America, and tending to protect its farmers more from import competition, it is not surprising that the model suggests an own-country reform that removes distortions to all goods markets causes farm output to decline in China. It would fall by US$4.8 billion per year, or 1.3 percent. However, China’s agricultural and food exports would expand (by $1.2 billion or 12 percent), while its imports of those
hypothetical full size of total enrollment. This would be the full size of enrollment if there were no dropouts or repetitions in subsequent years. Table 5.5 compares the hypothetical number of students per academic classroom in high schools in school years 2002/03 and 2007/08 by our sample divisions. It is important to note that large numbers of new classrooms were added in Neuva Vizcaya (non-TEEP) and Antique and Leyte (both TEEP). Interestingly, we observe that this measure of school quality has converged between adjacent TEEP and non-TEEP divisions.

# **6. Results**

In this section, we discuss the empirical results.

# **_Results on Schooling Attainment_**

To provide appropriate weights for the analysis, we estimate a logit selection function of TEEP schools in which we control for initial school quality and the poverty level of the community of the school location. School quality is proxied by pupil–teacher ratio, total student enrollment in grade 6, and total classes provided by the school. These variables are based on the 2002/03 Basic Education Information System (BEIS) data. Community-level poverty is proxied by the mean of the logarithm of the value of the consumer durables of the sampled households with students at the school. We also include school location dummies and their interactions with pupil–teacher ratio and community poverty. The results are reported in Table A.1. Although only three variables are statistically significant, the overall explanatory power is favorable (the pseudo R-squared is 0.144).

Tables 6.1, 6.2, and 6.3 report the regression results of the three schooling outcomes: years of schooling, number of repetitions in high school, and college entry, respectively.<sup>24</sup> In each table, we report the result for the untrimmed and unweighted sample (full sample) and the trimmed and weighted sample. In both samples, we estimate two specifications: with and without cohort-specific 24In all the estimations below, we restrict the sample to those whose ages were within plus or minus 2 from the standard age, that is, age 12 at the respective grade-6 year. This process reduced the sample to 3,028. This is an important step in order to make each cohort as homogeneous as possible. Since all the equations include the log of the total value of consumer durables, the estimations also do not include those households that have missing values on consumer durables in 2000. The total effective sample size of students becomes 2,962. We estimate a linear probability model for college entry though it is a binary variable. This is because the trimming and weighting method is typically used in linear regressions. The percentage of predicted values that lie outside the range from 0 to 1 varies from 5.1 percent to 6.6 percent, depending on the model (see Table 6.3). This suggests that the linear probability model should be a reasonable approximation.

20
Page 9 to the region relative to other non-concessional lenders, despite the subsidies available to the export credit agencies and the substantial profit margins on commercial transactions which motivate the suppliers, in those rare cases where they lend.

# 5. ShoTerm Lendig

According to the WDTs, Sub-Saharan Africa's short-term debt stocks, defined as loans with original maturities of less than one year and including only nonrescheduled interest arrears on this short-term debt ( i.e., non-rescheduled interest arrears on long-term debt not included), increased sharply between 1980 and 1983 but have remained fairly stable at around $13 billion since that time.

For lack of accurate information, the WDTs do not distinguish between types of short-term lenders and borrowers. However, it is believed that a major portion of the outstanding short-term debt is owed to the commercial banks.

The primary short-term debtors are Sudan, Congo, Nigeria and Zambia (all with stocks in excess of $1.3 billion), followed by a second tier of countries owing more than $500 million: Cameroon, Congo, Kenya, Tanzania and Zaire.

In general, short-term debt stocks are more evenly distributed than long-term debt stocks. Only 9 countries are shown to have stocks of less than $30 million in 1988, while 22 of the 44 countries had stocks of over $100 million.

The variations in short-term debt stocks over the 1980-88 period are due to actual increases or decreases in new lending and to performance on short and long-term debt.

Thus in many countries, such as Sudan, Congo, C6te d'Ivoire, Gabon, Liberia, Mala*i, Zaire, Uganda and Benin, the sharp increases were due to non-rescheduled interest arrears, some of which were reduced in 1988. The major drop in shortterm debt in Nigeria in 1987 is due to thie consolidation and long-term rescheduling of short-term debt in the London Club, preceded by an actual decline in short-term credit facilities in 1984-86.

In other countries, such as Guinea, Tanzania, Ethiopia and Burundi, the increase in short-term debt represents actual new lending.

# 6. Performance Record

According to World Bank statistics, of the approximately $10 billion outstanding in the London Club reschedulings of 18 Sub-Saharan African countries, about $8 billion, representing 35% of Sub-Saharan Africa's long-term commercial bank debt stocks, are owed by public and publicly-guaranteed borrowers in Nigeria and C6te d'Ivoire. Both countries have been attempting to renegotiate their London Club agreements on concessional terms and are starting to pursue buy-back and debt conversion options.
Table 6: Impact of Civil Conflict on the Stock of Refugees: Country-level Analysis

||(1)<br>|(2)<br>|(3)<br>|(4)<br>|(5)<br>|(6)<br>|
|---|---|---|---|---|---|---|
|VARIABLES|Refugees|Refugees|Refugees|Refugees|Refugees|Refugees|
|25+ battle deaths|143,789***<br>(11,963)||||||
|1000+ battle deaths<br>25-1000 battle deaths||337,498***<br>(31,004)<br>66,436***|487,968***<br>(45,835)<br>66,873***|424,115***<br>(41,811)<br>57,496***|463,596***<br>(43,736)<br>41,575***|466,418***<br>(43,790)<br>38,586***|
|||(7,018)|(7,118)|(6,759)|(6,406)|(6,419)|
|year before war|||-5,246<br>(20,679)||||
|frst year of war|||-384,851***<br>(52,151)||||
|last year of war||||-259,338***<br>(53,001)|-256,393***<br>(52,639)|-255,965***<br>(52,618)|
|1<sup>_st_ </sup>recovery year||||103,277***<br>(27,380)|156,552***<br>(28,291)|161,668***<br>(28,274)|
|2<sup>_nd_ </sup>recovery year|||||123,404***<br>(20,514)|128,224***<br>(20,493)|
|3<sup>_rd_ </sup>recovery year|||||113,294***<br>(20,348)|117,929***<br>(20,339)|
|4<sup>_th_ </sup>recovery year|||||102,866***<br>(19,990)|107,279***<br>(19,991)|
|5<sup>_th_ </sup>recovery year on|||||76,984***<br>(8,624)||
|5+ recovery years||||||116,326***<br>(22,593)|
|6<sup>_th_ </sup>recovery year||||||109,938***<br>(23,690)|
|7<sup>_th_ </sup>recovery year||||||109,283***<br>(24,808)|
|8<sup>_th_ </sup>recovery year||||||88,871***<br>(15,669)|
|9<sup>_th_ </sup>recovery year||||||84,465***<br>(16,136)|
|10+ recovery years||||||73,528***<br>(8,684)|
|Observations|12,784|12,784|12,763|12,784|12,784|12,784|
|R-squared|0.435|0.467|0.489|0.478|0.483|0.485|
|Country FE|YES|YES|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|YES|YES|

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Country-level data on yearly stocks of refugees are gathered from UNCHR database. In column (1) “25+ battle deaths” is the definition from PRIO dataset of at least 25 battle related deaths in a given year. In column (2)-(6) internal conflict, i.e. variable “1000+ battle deaths”, is defined as a year in which violence involves more than 1000 fatalities. Variable “25-1000 battle deaths” controls for years in which fightings cause a number of deaths between 25 and 1000. “Year before war” is a dummy that controls for years with no violence or violence that involves between 25 and 1000 fatalities, followed by a year with at least 1000 battle related deaths. Variables “first year of war” and “last year of war” are dummies that take a value of one during the first and the last year of civil war respectively. Dummies “1<sup>_st_</sup> recovery year”, “2<sup>_nd_</sup> recovery year”, etc. take a value of 1 in the _x_<sup>_th_</sup> year after the war ends.
**Table 2: Number of workers by single digit ISIC Industry‐ Uganda Business Registry 2001 and 2010**

|**One Digit Industry (ISIC Rev. 3.1)**||**Number**|**of workers**|**% shar**|**e of total**|
|---|---|---|---|---|---|
||**2001**|**2010**|**Avg. Annual % Growth**|**2001**|**2010**|
|**Traded sector:**||||||
|A - Agriculture, hunting and forestry|25,023|31,579|2.9|5.1|2.9|
|B - Fishing|1,358|13,887|102.5|0.3|1.3|
|C - Mining and quarrying|1,494|3,161|12.4|0.3|0.3|
|D - Manufacturing|88,277|135,548|5.9|18.0|12.6|
|_Traded sector total_|116,152|184,175|6.5|23.7|17.2|
|**Non-traded sector:**||||||
|**Other industry:**<br>||||||
|E - Electricity, gas and water supply|2,631|1,464|(4.9)|0.5|0.1|
|F - Construction|7,491|14,362|10.2|1.5|1.3|
|**Services:**||||||
|G - Wholesale/retail trade; repair of motor vehicles, etc.|179,680|462,687|17.5|36.6|43.1|
|H - Hotels and restaurants|54,277|153,490|20.3|11.1|14.3|
|I - Transport, storage and communications|12,071|18,272|5.7|2.5|1.7|
|J - Financial intermediation|8,879|27,060|22.8|1.8|2.5|
|K - Real estate, renting and business activities|19,690|54,997|19.9|4.0|5.1|
|L - Public administration and defense; compulsory social security|7,292|229|(10.8)|1.5|0.0|
|M - Education|37,010|26,754|(3.1)|7.5|2.5|
|N - Health and social work|18,095|55,095|22.7|3.7|5.1|
|O - Other community, social and personal service activities|26,527|72,265|19.2|5.4|6.7|
|P - Activities//undifferentiated activities of private households||1,842|||0.2|
|Q - Extraterritorial organizations and bodies|1,212|293|(8.4)|0.2|0.0|
|_Non-traded sector total_|374,855|888,810|15.2|76.3|82.8|
|**Grand total**|491,007|1,072,985|13.2|100.0|100.0|

37