rafmacalaba/datause-encoder-data huggingface dataset:
7

# **Table 3 The Changing Role of Primary Balances–Test 1**

Dependent Variable: EMBORLAT Method: Least Squares Sample(adjusted): 1995:02 2002:01 Included observations: 84 after adjusting endpoints Newey-West HAC Standard Errors & Covariance (lag truncation=3)

|Variable|Coefficient|Std. Error|t-Statistic|Prob.|
|---|---|---|---|---|
|DEBTY(-1)<br>PRIMBAL(-1)|0.013764<br>5.95E-05|0.002046<br>0.005334|6.726813<br>0.011153|0.0000<br>0.9911|
|DSIG*PRIMBAL(-1)|-0.054382|0.012458|-4.365126|0.0000|
|C|-0.500954|0.075216|-6.660195|0.0000|
|R-squared|0.641035|Mean depende|nt var|-0.011316|
|Adjusted R-squared|0.627574|S.D. dependen|t var|0.082535|
|S.E. of regression|0.050368|Akaike info cr|iterion|-3.092458|
|Sum squared resid|0.202958|Schwarz criter|ion|-2.976705|
|Log likelihood|133.8832|F-statistic||47.62098|
|Durbin-Watson stat|0.611709|Prob(F-statisti|c)|0.000000|
|All variables defined in preceding table<br>DSIG= 1 for t> January1999; 0 otherwise|||||

**Table 4** **<u>The Changing Role of Primary Balances–Test 2</u>**

Dependent Variable: EMBORLAT Method: Least Squares Sample(adjusted): 1995:02 2002:01 Included observations: 84 after adjusting endpoints Newey-West HAC Standard Errors & Covariance (lag truncation=3)

|Variable|Coefficient|Std. Error|t-Statistic|Prob.|
|---|---|---|---|---|
|C|-0.530713|0.071481|-7.424499|0.0000|
|DEBTY(-1)|0.014446|0.001826|7.912960|0.0000|
|PRIMBAL(-1)|-0.024688|0.003926|-6.288602|0.0000|
|PRIMBAL(-1)*(DEBTDEV)|-0.002630|0.000572|-4.593340|0.0000|
|R-squared|0.632718|Mean depende|nt var|-0.011316|
|Adjusted R-squared|0.618945|S.D. dependen|t var|0.082535|
|S.E. of regression|0.050949|Akaike info cr|iterion|-3.069552|
|Sum squared resid|0.207661|Schwarz criter|ion|-2.953799|
|Log likelihood|132.9212|F-statistic||45.93872|
|Durbin-Watson stat|0.632039|Prob(F-statisti|c)|0.000000|
|DEBTDEV=Deviation of the debt ratio from t|he sample mean||||

The third and final implication of the DP model, the positive association between the primary balance and the debt ratio if the government is dependable is reflected in Table 5.
**Education and Employment I**

# **Plc,P.anning, and Research**

This study of Forn Three (ninth-grade) stludents in Nigeria indicates that single-sex schools improve girls' achievement in mathematics and engender less stereotypic ideas about mathematics, even after extensive statistical adjustments for family background and school characteristics. But adolescent Nigerian males experience lower achievement and hold a more stereotypic view of mathematics under singlesex schooling, after similar statistical controls.

Many studies of the effects of single-sex education have shown that girls benefit more than boys, but only two studies in developing countries (this one and a study in Thailand) have found that boys' schools produce negative experiences. Therefore, it should not be concluded from this study that single-sex schooling is more beneficial to students than coeducation; instead, it seems particularly effective for females.

Why are there different responses? In part, differences between the types of students attending single-sex and coeducational schools may be responsible. Girls attending single-sex schools were more likely to have advantaged backgrounds, with more professionally employed fathers and more positive attitudes.

They were younger and thus more likely to have progressed through school with few interruptions or repetitions. The girls in this sample (1,012 Form Three students in 40 government-owned schools, including four girls' schools and 16 boys' schools) also represented a very select group of students, since female secondary school attendance was very low in Nigeria in 1983 when the data were gathered. Similar advantages for boys in boys' schools were not observed.

Girls' schools also differed from coeducational and boys' schools in several important ways. The schools were smaller, operated more days per year, had a lower student-teacher ratio, and had more female teachers as role models. It is also possible that the girls' schools were boarding sclh, )ls.

While these differences between students and schools were found to contribute to differences in student achievement, a statistically significant residual effect for singlesex schools remained after adjustments were made, suggesting that other organizational or student background factors may account for the observed differences in effects.

This paper is a product of the Education and Employment Division, Population and Human Resources Department. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Cynthia Cristobal, S6-001, extension 33640.

The PPR Working Paper Series disseminates the findings of work under way in the Bank's Policy, Planning, and Research Complex. An objective of the series is to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.

Produced at the PPR Dissemination Center
# **Tables**

|Table 1. Trade Volumes and Balances in South Caucasus, 1995-99|6|
|---|---|
|Table 2. Share of Selected Partners in Exports, 1994, 1998|7|
|Table 3. The overall freight factors in the South Caucasus countries, 1995-99|10|
|Table 4. Main commodity flows in the South Caucasus, 1999|12|
|Table 5. Transport costs, 1999|14|
|Table 6. Average annual (1995-98) and potential exports|21|
|Table 7. Average annual (1995-98) and potential exports under growth scenario|22|
|Table 8. Comparison of Selected Wholesale Prices in Armenia, Azerbaijan and Tu<br>1998|rkey,<br>25|
|Table 9. Comparison of Selected Retail Food Prices in Armenia, Azerbaijan and||
|Turkey, 1998|26|
|Table 10. Economy on transportation of Turkish exports to Azerbaijan, 1999 data|28|
|Table 11. Selected potential effects of trade normalization in South Caucasus|34|

3
The regions of Bongolava and Sofia have the highest percentages of children weighing less than 2.5 kg, with 19% and 17%, respectively. On the other hand, the regions of Ihorombe and Anosy have the lowest percentages of low-birth-weight children, at around 8%. In other words, stunting seems prevalent in the least poor regions of Madagascar and less prevalent in the poorest regions. These findings emphasize the importance of regional context in addressing child undernutrition, highlighting the need for tailored approaches that consider socioeconomic factors, health care access, and nutritional practices to improve child health outcomes across Madagascar.

But these heterogeneities also exist within regions at the commune level. We therefore applied the SAE approach in the DHS data to produce stunting estimates at the commune level and produce the map presented under figure 3. The maps highlight higher stunting rates among communes located in high plateau of the regions in the center, also known for its better agro-ecological advantage for agricultural production. We also note the higher stunting rates among communes in the regions in the east and southeast of the country.

Linguistic, historical, and DNA research suggests that the central highlands of Madagascar were settled by people with an Austronesian background 1,200 years ago (most likely South Borneo); the coastal areas were settled later by people mainly from East Africa (Hurles et al 2005). Can this partly explain the stunting patterns? Our limited data does not allow us to verify this hypothesis.

The variables selected to predict stunting as well as the resulting coefficients from the linear mixed model is provided in table A2 of the annex. Table 4 presents a set of model diagnostics relating to the fit of the model and the consistency of the normality assumptions with the sample data. Overall, the model explains 7.5 percent of the variation in heigh for age Z score when not considering the conditional random effect, and 11.3 percent when the conditional random effect is included. The model was estimated in a subsample of 6,379 children again of approximately 3.2 million children in the census.

Regarding the distribution of the residuals, the conditional random effect has a skewness of 0.144 while the error term has a skewness of 0.004. The estimated kurtosis, which is 2.4 for the random effect and 3.05 for the idiosyncratic error term, differs moderately from the benchmark value of 3 that characterizes a perfectly normal distribution.

**Table 1: SAE diagnostics for stunting**

|**Characteristic**|**Value**|
|---|---|
|Marginal R<sup>2</sup>|0.075|
|Conditional R<sup>2</sup>|0.113|
|Random effect||
|Skewness|0.144|
|Kurtosis|2.442|
|Idiosyncratic error term||
|Skewness|-0.004|
|Kurtosis|3.050|
|Number of sample observations|6,379|

12 | P a g e
age impact is non-negligible and statistically significant, and given the virtually zero cost of the intervention this result provides further support to the cost-effectiveness of information interventions.

In many disadvantaged contexts educational choices made by girls are likely to be affected by social norms, rather than potential labor market outcomes.<sup>6</sup> Information interventions as the one included in the _Percepciones_ program can be an effective way of changing girls’ educational choices, improve their learning outcomes and, eventually their labor market outcomes.

The paper is organized as follows. Section 2 presents general information regarding the upper secondary education system in Mexico and a description of the information treatment within the _Percepciones_ project. In Section 3 we discuss a simple framework to describe how information can affect student performance together with the data used for our analysis. The econometric model and the main results are presented in Section 4. Potential explanations for the large gender heterogeneity are discussed in Section . Finally, Section 6 concludes and provides policy recommendations.

# **2 Context**

The upper secondary education (EMS for its acronym in Spanish) system in Mexico consists of 4.1 million students, typically between 15 to 18 years old, in grades 10th, 11th and 12th. EMS is offered by four different providers: 1) the federal government (accounting for 26% of total enrollment), 2) the state government (43.8%), 3) publicly-financed autonomous univer– sities (12.5%), and 4) private entities. EMS offers three types of degree programs: _general_ preparing students for higher education, _technological_ – preparing students both for the labor market as well as for higher education, and _technical_ – emphasizing technical and vocational education. The typically large technological schools run by the Federal Government (930 students on average) can belong to different subsystems according to their specialization: industrial (DGETI), agricultural (DGETA) and ocean-related (DGCYTM).

According to the official statistics from SEP,<sup>7</sup> in 2013 only 61% of students graduated three years after enrolling, with this share being significantly higher among women (65%) than men (57%). Graduation rates vary across types of degree programs with _general_ schools

> 6Attanasio and Kaufmann (2012a) found for Mexico that educational choices of boys are more likely to be correlated with expectations on the labor market returns of higher education than girls’ expectations, which display a stronger correlation with their aspirations to form a family. Kaufmann et al. (2013) use data from Chile to show that being admitted to a higher ranked university has substantial returns in terms of partner quality for women.

> 7See http://www.inee.edu.mx/index.php/bases-de-datos/banco-de-indicadores-educativos

4
5) and, without accelerated progress, is unlikely to achieve the MDG targets by 2015. Although the proportion of deliveries at facility has improved, maternal mortality remains a significant challenge, particularly for disadvantaged populations. The national maternal mortality ratio in the 2011 Uganda Demographic and Health Survey (DHS) was statistically unchanged at 438 deaths per 100,000 live births compared to the 2006 DHS. Perinatal and maternal morbidity and mortality remain major causes of the high national disease burden, accounting for 20.4 percent of the burden.<sup>1</sup> Hemorrhage, abortion, sepsis, obstructed labor, pregnancy-induced hypertension, and malaria make up the major causes of maternal deaths and stillbirths. Owing to poor-quality services and limited access to skilled delivery services, compounded by the high cost of care, in the five years prior to the 2011 DHS, 43 percent of pregnant women delivered at home without the assistance of skilled health personnel with many of the home births disproportionately occurring among the poor and in rural areas. The provision of comprehensive emergency obstetric care in particular faced many challenges.

The need to reverse poor maternal health outcomes was and remains a key priority for Uganda. The four main strategies to reverse poor maternal health outcomes highlighted in the Road Map for Acceleration of Maternal and Neonatal Mortality and Morbidity (2006–15) include (1) improving and expanding the quality of maternal and newborn care, (2) improving access to family planning services, (3) providing adolescent reproductive health services, and (4) strengthening supply-chain management for reproductive health commodities in the public and private sectors. In this respect, the government, in collaboration with the World Bank and Kreditanstalt für Wiederaufbau (KfW, German Development Bank), designed the RHVP, building on the earlier STI OBA voucher scheme also financed by KfW.

The RHVP was in line with the World Bank’s Country Assistance Strategy (CAS) and Reproductive Health Action Plan (RHAP). Uganda is a priority country in the World Bank’s RHAP, which aims to address high fertility, reduce the unmet need for contraception, improve pregnancy outcomes, and reduce STIs.<sup>2</sup> The 2011–15 Uganda CAS mentions the RHVP as one of the expected results under CAS Outcome 3.2 (“Strengthened Health Care Delivery”). The project also contributed to the goal of the national Safe Motherhood Program of “ensuring that no woman or newborn dies or incurs injuries due to pregnancy and/or childbirth.” The project focused on reimbursing results and was consistent with the World Bank’s Health Nutrition and Population Strategy. The project addressed not only MDG 5—reducing maternal mortality—but also MDG 4—improving child health by reducing perinatal deaths—both being health problems of national 8
Table A2: IEC Slopes: Probabilistic Formality Assignment

|**Specifcation:**|**Ma**|**in**|**Geogr**|**aphy**||**Product**|**Codes**||**All**|
|---|---|---|---|---|---|---|---|---|---|
|Avg. of 31 Countries|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Slope|7.8|8.3|7.1|6.4|5.0|4.5|4.4|3.8|3.0|
|Confdence Interval|[ 7.3,8.4 ]|[ 7.7,8.9]|[ 6.4,7.7]|[ 5.7,7.1]|[ 4.4,5.5]|[ 4.0,4.9]<br>|[ 3.9,4.7]|[ 3.4,4.1]|[ 2.4,3.3]|
|# of p-values_<_0.05<br>|30|30|30|30|30|29|29|30|26|
|_R_<sup>2 </sup>adjusted|0.15|0.17|0.22|0.39|0.42|0.54|0.54|0.54|0.58|
|Household Characteristics||X|X|X|X|X|X|X|X|
|Urban/Rural|||X|||||||
|Survey Blocks||||X|||||X|
|Food Products|||||X|||||
|COICOP 2-dig||||||X||||
|COICOP 3-dig|||||||X|||
|COICOP 4-dig||||||||X|X|

This table shows the average slope of the Informal Engel curve across countries for different specifications under the probabilsitic assignment of places of purchase to formality. The Probabilistic scenario uses the observed probability of VAT registration by store type in the Mexican census as the formality probability of each store across countries (see Figure 1). The slopes are estimated from: _Share In f ormali_ = _β_ . _ln_ ( _expenditure pc_ ) _i_ + Γ _Xi_ + _εi_ . The dependent variable is informal expenditure share and the main explanatory variable is log expenditure per capita. Controls include household characteristics (household size, age, gender, education of head), location indicators (urban/rural, survey enumeration blocks), and product codes for food vs all other purchases, 2nd, 3rd and 4th level of the COICOP classification.

68
Table 3. Determinants of credit take-up

|Sample: TreatedHowseholas|Householdtooka|warantageloan|
|---|---|---|
||(1)|(2)|
|HoweholdHead Characteristics|||
|Has no formal schooling|-0.161*|-0.102*|
||(0.084)|(0.033)|
|Has completed primary|-0.194*|-0.032|
||(0.093)|(0.093)|
|Has completed secondary|O.30444+|0.388*|
||(0.161)|(0.213)|
|OtherHousehold Characteristics|||
|Household size|0.007|0.006|
||(0.009)|(0.008)|
|Numberofcrops|0.012|O.028*|
||(0.018)|(0.014)|
|Totalvalueofcrops (inUSD)|-0.000|-0.000|
||(0.000)|(0.000)|
|Total expenditures atbaseline|0.000%|0.000|
||(0.000)|(0.000)|
|Has received transfers during the past 12months|0 22783*|-0.134|
||(0.083)|(0.087)|
|Land owned|-0.003|-0.004|
||(0.002)|(0.002)|
|Trust, confidence andneighboring village relations|||
|Interactswithneighboringvillagemonthly|0.046|-0.056|
||(0.064)|(0.053)|
|Confident storing inneighboring village|-O.047|0.004|
||(0.060)|(0.049)|
|Neighboring village memberwouldreturnwallet|-0.216%**|-0.178*#|
||(0.060)|(0.078)|
|Confidence in institutions index|-0.013|-0.006|
||(0.012)|(0.012)|
|Haraniagespecific|||
|# of bags to store iftakingpart inwarrantage|O.O0g***|O.008***|
||(0.003)|(0.002)|
|Scoreoffamiliaritywithwarrantage|0.021*|0.019%|
||(0.012)|(0.010)|
|Numberofloansaskedduringthepast12months|0.13274|0.051|
||(0.053)|(0.050)|
|Observations|245|245|
|Village FE|NO|YES|
|R-squared|O.217|0.168|

Notes: The sample is restricted te lottery winners who stored their crops. Dependent variable measured from the epdline survey. All other variables measured from the baseline survey. Standard errors are clustered by village and presented in parentheses. *, **, *** indicate significance at the 1, 5, and 10 percent respectively.
The coefficients on the dummy variables for low SES, low levels of parental education, and girls living in more remote areas all suggest that, in the absence of the scholarship program, girls in these households were less likely to be enrolled in and attending school, as one would expect. However, the difference in enrollment and attendance by SES, parental education and distance to school is dampened or disappears entirely among girls who receive the JFPR scholarships. For example, the first column suggests that, in the absence of the scholarship, there is a difference of 24.3 percentage points in enrollment between girls with high and low SES; however, among girls who receive the scholarship, there is no difference in enrollment between girls with high and low SES. Put differently, because program effects are larger among girls with lower SES, the scholarship program eliminated the gradient between SES and school enrollment. The regression results in Table 4 therefore confirm the patterns observed in Figure 2. The scholarship program also reduced the gradient by parental education, although the compensatory effect of the program is not large enough to bring girls with low levels of parental education to the same enrollment and attendance levels as those with higher levels of parental education.

# **5. Conclusion**

Raising the schooling levels of girls is often regarded as an important priority for developing countries (World Bank 2001, Schultz 2002). In many developing countries the Mincerian rate of return to schooling for women is at least as large as that for men (for examples, see Deolalikar 1993 for Indonesia; Schultz 1993 for Thailand, Cote d’Ivoire and Ghana). There is also a large literature documenting associations between female education and a variety of social outcomes, including lower fertility, decreases in child mortality, improvements in child health and nutrition, and better education and child cognitive development (see reviews in Strauss and Thomas 1995; World Bank 2001).

In this paper we estimate the impact of a scholarship program for girls on school enrollment and attendance in Cambodia. OLS estimates of program impact indicate that the program increased enrollment and attendance at eligible schools by approximately 30 percentage points, and enrollment at any school by about 22 percentage points; estimates of program effects based on propensity score matching are larger. The program effects we estimate are robust to a variety of concerns: Regressiondiscontinuity specifications suggest that our estimates are not driven by unobservable differences between scholarship recipients and non-recipients, and an application of bounds shows that selective attrition cannot account for the pattern of program effects we observe. Moreover, we find that program impacts are largest among girls who come from poorer households, have parents with less education, and live farther away from a secondary school. As a result, the JFPR program appears to have dramatically reduced socioeconomic gradients in enrollment and attendance.

13
**Figure 16: A Multipronged approach was used during the capital surge in mid 2000s which included an increase in CRR and policy rate but also resulted in some increase in money supply**

|**Narrow and broad money supply increased**<br>**and CRR and Repo Rate increased as well**<br>**during the surge **|
|---|
|2<br>4<br>6<br>8<br>10<br>12<br>Percent<br>CRR<br>Repo Rate<br>Reverse Repo<br>2<br>7<br>12<br>17<br>22<br>27<br>0<br>20,000<br>40,000<br>60,000<br>80,000<br>100,000<br>120,000<br>y-o-y % change<br>mln, $ Total Capital Flows<br>% change in RM with public<br>(RHS)<br>% change in M3 (RHS)|
|Source: RBI Handbook of Statistics on the Indian Economy<br>The increase in money supply is also evident in quarterly data. In Table 2, using data from<br>2000-2007, we regress measures of money supply for a dummy for surge years, alternatively<br>takes value 1 for years 2005-2007, or 2006 and 2007. Results show an acceleration in money<br>supply, which picked up in the later years of the surge. It was most pronounced in reserve<br>money supply but less so in the reserve money in circulation and in broad money supply,<br>due to the increase in the CRR. The acceleration is evident even after controlling for GDP<br>growth in the regressions in Table 2.<br>**Table 2: Money Supply during Capital Flow Surge**<br>**(Quarterly data from 2000-2007)**<br>0<br>1997-98<br>1998-99<br>1999-00<br>2000-01<br>2001-02<br>2002-03<br>2003-04<br>2004-05<br>2005-06<br>2006-07<br>2007-08<br>2008-09<br>2009-10<br>2010-11<br>2011-12<br>2012-13<br>2013-14<br>2001-02<br>2002-03<br>2003-04<br>2004-05<br>2005-06<br>2006-07<br>2007-08<br>2008-09<br>2009-10<br>2010-11<br>2011-12<br>2012-13<br>2013-14|
|(1)<br>(2)<br>(3)<br>(4)<br>(5)<br>(6)<br>% growth in<br>% growth in<br>% growth|
|Dependent<br>Variables<br>% growth in<br>reserve<br>money<br><br>% growth<br>in reserve<br>money<br><br>reserve money<br>supply in<br>circulation with<br> <br>reserve<br>money supply<br>in circulation<br> <br>in broad<br>money<br>supply<br><br>% growth<br>in broad<br>money<br>|
|supply<br>supply<br>public<br>withpublic<br>(M3)<br>supply (M3)|
|Surge dummy =1 for|
|<br>2005, 2006, 2007<br>9.15***<br>2.31***<br>3.36***|
|[6.80]<br>[3.03]<br>[3.91]|
|Sure dumm =1 for|
|g y<br>2006 and 2007<br>10.17***<br>3.02***<br>5.20***|
|[6.70]<br>[3.76]<br>[7.38]|
|Constant<br>10.72***<br>11.60***<br>12.82***<br>12.93***<br>15.12***<br>15.1***|
|[13.0]<br>[15.3]<br>[27.5]<br>[32.2]<br>[28.7]<br>[42.8]|
|Observations<br>32<br>32<br>32<br>32<br>32<br>32|
|Adj. R-squared<br>0.59<br>0.59<br>0.21<br>0.30<br>0.32<br>0.63|

24