rafmacalaba/datause-encoder-data huggingface dataset:
# number of years to produce a graduate and the number of years in the primary system improved, from 1.4 in 1975 to 0.7 in 1985. e -

# Zambia: education indicators

||1975|1980|1981|1982|1983|1984|1985|1986|1987 1988|
|---|---|---|---|---|---|---|---|---|---|
|Einance:<br>||||||||||
|% of public budget<br>|13.9<br>|11.4<br>|11.9<br>|15.1<br>|14.5<br>|16.0<br>|12.3<br>||8.3<br>|
|%of GDP<br>|5.9<br>|4.2<br>|4.3<br>|_5.9_<br>|4.7<br>|4.7<br>|4.3<br>|3.5<br>|2.9<br>|
|real expenditures (index)|139|100|110|145|112|112|104|86|73|
|Intrasectoral finance:<br>||||||||||
|recurrent budget share_(t)_<br>budget share to salaries_(%)_<br>|77<br>|95<br>||||94||||
|primary<br>|77<br>|88<br>|91<br>|89<br>|91<br>|||||
|secondary<br>|14|30|34|38|42|||||
|budget shares by level (%)<br>||||||||||
|primary<br>|45<br>|45<br>|43<br>|45<br>|47<br>|||||
|secondary<br>|22<br>|22<br>|20<br>|20<br>|20<br>|||||
|higher|8|11|14|14|11|||||
|Qutput (enrollment rate--%):<br>||||||||||
|gross primary<br>|97|90<br>|||93<br>|96<br>|99|97||
|net primary<br>||77<br>|||80<br>|81<br>||||
|gross secondary|15|16||17|17|17|17|||
|ouality:<br>||||||||||
|per student spending (index)<br>|143|100<br>|||||96|||
|teacher salaries (index)<br>||100<br>|83<br>|89<br>|113<br>|88<br>||||
|pnrimary repeaters_(%)_<br>|2.1<br>|1.9<br>|_1.5_<br>|1.3|_1.5_|1.8||2.1<br>||
|grade 4 completion_(t)_|96|99|100|||||100||
|Equity:<br>||||||||||
|primary enrollment rates_(%)_<br> <br>||||||||||
|male<br> <br>|105<br>|98<br>|||100<br>|103<br>|106<br>|102||
|female<br>|88<br>|82<br>|||87|89|93<br>|92||
|% primary students female|45|47|||||47|||

5.41 Within the recurrent budget, salaries were given increasing priority at the primary and secondary levels. Nevertheless, real recurrent spending per primary teacher declined by 12 percent from 1980 to 1984 (Lewin and Berstecher, 1989). Teacher salaries during the early years of adjustment were protected relative to those gf manufacturing workers: the ratio of teacher salaries to manufacturing worker salaries increased from 1.05 in 1980 to 1.56 in 1984. However, real teacher salaries in 1984 were below 1980 levels (Zymelman and DeStefano, 1989).

_50_
**Polloy,** Planning, **and** Resarch

Women In Development|

Evidence is increasing that one-dimensional policies, such as family planning and supplemental feeding programs, have little chance on their own of achieving the desired objectivesthat what is called for is a matemal and child health policy, including family planning, integrated into public policy and linked to education.

While family planning is probably more cost-effective in reducing fertility than programs for child mortality or increasing education, family planning programs are often more effective when child mortality rates are lower and levels of education are higher. In the long term, the most cost-effective approach to reducing fertfility is probably to integrate family planning with efforts to improve matemal-child health and educationL On the other hand, if the aim is increasing income per capita, educational policies dominate family planning measures, despite large cost differentials.

Similarly, where the aim of public health provision is to reduce mortality and improve nutrition, there is a strong consensus that integration and coordination of matemal-child health services, especialy with educational and literacy po; cy, is essential and cost-effective. Improving women's ability to work and eam outside the home improves their economic position and power (and probably expenditures - on children) although if their nutritional status is already marginal, working harder to produce more may be a mixed blessing. We must ask: how much extra income do they bring to the household, and to what extent do they maintain control over the income they eam? Also, if mothers and children enjoy more income and autonomy through mothers' employmerit outside the home, but suffer from the conditions of the work itself, or from lack of adequate childcare alternatives, public policy could alleviate these difficulties - for example, through raising the productivity of women's labor (better access to credit, extension, and markets) and through relaxing the constraints on women's welfare (giving them more schooling, which will give them access to better jobs, more money, and more control over it, as well as enable them to take better care of their children).

This paper is a product of the Women in Development Division, Population and Human Resources Department. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Johanna Klous, room S9-123, extension 33745 Me PPR Working Paper Series dissemirates the findigs of work under way in the Banks Policy, Plming, and Research Complex. An objective of the series is to get these fmdings out quickly, even if presentations are less than fiully polished. 7The findings, imntepetations, and cornclusions in these papers do not necessarily represent ofricial policy of the Bank.

Produced at the PPR Dissemination Center
# **4.2  Sampling, Randomization, and Program Description**

The school block grant (BG) program was evaluated as part of a larger education research initiative (across 500 schools) known as the Andhra Pradesh Randomized Evaluation Studies (AP RESt), with 100 schools being randomly assigned to each of four treatment and one control groups.<sup>25</sup> We sampled 5 districts across each of the 3 socio-cultural regions of AP in proportion to population. In each of the 5 districts, we randomly selected one administrative division and then randomly sampled 10 mandals (the lowest administrative tier) in the selected division. In each of the 50 mandals, we randomly sampled 10 schools using probability proportional to enrollment. Thus, the universe of 500 schools in the study was representative of the schooling conditions of the typical child attending a government-run primary school in rural AP. Experimental results in this sample can therefore be credibly extrapolated to the full state of Andhra Pradesh.

The school year in AP starts in mid June, and baseline tests were conducted in the 500 sampled schools during late June and early July, 2005.<sup>26</sup> After the baseline tests were evaluated, 2 out of the 10 project schools in each mandal were randomly allocated to one of 5 cells (four treatments and one control). Since 50 mandals were chosen across 5 districts, there were a total of 100 schools (spread out across the state) in each cell. The geographic stratification allows us to estimate the treatment impact with mandal-level fixed effects and thereby net out any common factors at the lowest administrative level of government, and also improve the efficiency of the estimates of program impact.

Since no school received more than one treatment, we can analyze the impact of each program independently with respect to the control schools without worrying about any confounding interactions. The analysis in this paper is based on the 200 schools that comprise the 100 schools randomly chosen for the school block grant program and the 100 that were randomly assigned to the comparison group. Table 6 shows summary statistics of baseline

> 25 AP RESt is a partnership between the government of AP, the Azim Premji Foundation (a leading non-profit organization in India), and the World Bank. The World Bank and the UK Department for International Development (DFID) provided financial support. The Azim Premji Foundation (APF) was the main implementing agency for the study.  See Muralidharan and Sundararaman (2010, 2011) for details of other interventions.

> 26 The selected schools were informed by the government that an external assessment of learning would take place in this period, but there was no communication to any school about any of the treatments at this time.

18
# **Dismal Science, Accounting and Newton’s Second Law:**

# **Identifying Force and Rigidity in Public Expenditure Analysis**

Dino Merotto Faya Hayati David Stephan William Battaile<sup>1</sup> JEL Classification: C53, J38, H5, H6 Keywords: Public expenditure, rigidity, discretionary, fiscal policy, Europe and Central Asia

> 1 Dino Merotto (Lead Economist), Faya Hayati (Economist), Bill Bataille (Senior Economist), David Stephan (Economist) are economists in the World Bank Group’s Macroeconomic and Fiscal Management Global Practice..
lations through development and humanitarian programs. These include public health [Stoov ́e and Pedrana, 2014, Garcia-Herranz et al., 2014], perceptions on vaccination programs [UNICEF, 2013], forecasting migration [Zagheni et al., 2014] and commuting flows [Lenormand et al., 2014], early-warning of epidemics [Garcia-Herranz et al., 2014] and information sharing during disaster response [Imran et al., 2013] and criminal violence [Monroy-Hern ́andez et al., 2013].

The advantages of such public social media signals are clear. Large quantities of passively produced data may be collected in real-time or near real-time. Often social media content is augmented with user meta-data such as geographic location and demographic information such as gender and ethnicity may be inferred [Mislove et al., 2011, Pennacchiotti and Popescu, 2011, Sakaki et al., 2014]. Although such novel signals are not without their shortcomings, such as a bias towards young and urban populations, the potential for these streams to augment traditional information collecting processes such as individual or household level surveys is clear.

# **2 Objective**

In this study, we ask whether we can replicate the results of the more traditional _La Prensa Grafica_ household surveys in El Salvador and use social media data over the same period to provide a deeper analysis of public sentiment. To accomplish this we obtain a number of Spanish language tweets containing certain keywords of interest and filter these tweets to those originating in El Salvador–a process known as _geolocation_ . We then perform exploratory analysis of the data and refine these results until we are satisfied that we have captured much of the available relevant discourse. We will then classify these tweets by subject and by sentiment. This is done in two stages. First, domain experts identify the subset and sentiment of a subset of the tweets manually. Then appropriate statistical classifiers are used to estimate the content matter and sentiment of the remaining tweets. This workflow is described in Figure 1. The following sections describe this process in greater detail. The data gathering process, geolocation, and manual tagging are described in Section 3. Section 4 details how we identified the ground truth for the topic and sentiment of a subset of tweets. Section 5 describes the classification process. We present the results in Section 6. We provide some avenues for further study in Section 7, and Section 8 concludes.

3
# **Unequal Laws and the Disempowerment of Women in the Labor Market: Evidence from Firm-Level Data**

Asif Islam<sup>1</sup> , Silvia Muzi<sup>2</sup> and Mohammad Amin<sup>3</sup> JEL: K10, J16, J21 Keywords: Gender and Economic Empowerment; Financial Inclusion; Female Workers’ Gender and Employment; Gender Inequality Under the Law

> 1 Enterprise Analysis Unit, Development Economics Vice Presidency, World Bank, email: aislam@worldbank.org

> 2 Enterprise Analysis Unit, Development Economics Vice Presidency, World Bank, email: smuzi@worldbank.org

> 3 Enterprise Analysis Unit and Malaysia Country Office, Development Economics Vice Presidency, World Bank, email: mamin@worldbank.org
gradual increase in the retirement age and its convergence for men and women<sup>9</sup> , transfer of the responsibility for early retirement to professional pension funds, and adoption of universal mandatory pension contributions based on salary levels. On the front of other social security programs, government particularly aims at restricting access to short-term social security payments.

**Table 3 Projected Social Security Expenditures**

||Projecte<br>deficits: w|d pension<br>/o reform|Projecte<br>deficits:|d pension<br>w/reform|Total soci<br>spending w|al security<br>/o reform|Total soci<br>Spendin|al security<br>g w/reform|
|---|---|---|---|---|---|---|---|---|
||BGN,mil|_%_of GDP|BGN,mil|%ofGDP|BGN,_mil_|_%_ofGDP|BGN,_mil_|_%of_GDP|
|2000|<br>489|<br>2.1|<br>492.9|<br>2.1|<br>2081|<br>8.9|<br>2041|<br>8.7|
|2001|<br>648|<br>2.6|<br>476.2|1<br>1.9|<br>2405|<br>9.5|<br>2176|<br>8.6|
|2002|<br>744|<br>2.7|<br>524.8|1<br>1.9|<br>2700|<br>9.9|<br>2408|<br>8.8|

Source: NSSI Under the reform scenario, projections of government pension expenditures look more reassuring, with incurred cash deficits around 2 percent of GDP. Projections shown in table 4 assume the envisaged increases in the retirement age, reductions in pay-as-you-go pension benefits, but also higher compliance, resulting in a cash deficit of around 1 percent of GDP annually, for the next five years.

**Table 4. Projected Public Pension Expenditures After Reform**

|I<br>||Total Ou|tlays|Annual Cash De|ficit/Surplus|
|---|---|---|---|---|
|<br>Years|BGN,_mi_|% of GDP|BGN,_mil_|% of GDP|
|1999|2173|9.5|-114.|0.5|
|2000|2315|10.1|-296|1.3|
|2001|2432|10.0|-262|1.1|
|-2002|2550|9.8|-240|0.9|
|2003|2667|9.7|-238|0.9|
|2004|2743|9.4|-341|1.2i|
|2005|2780|9.0|-261|0.9|
|2006|2802|8.6|-130|0.4|
|2007|2798|8.2|45|0.1|
|2008|2798|7.9|169|0.5|
|2009|2802|7.5|309|0.8|
|2010|2848|7.4|420|1.1|
|2011|2877|7.1|796|2.0|

Source: Agency for Economic Analysis and Forecasting Hence, according to the above calculations provided by two different sources in Bulgaria, deficit from pension and other social security benefits could range between 1 and 2 percent of GDP per year in the next 12 years. The state has provided no guarantee or commitment under 9 The present retirement age (55 for women and 60 for men) will be preserved until 2003. After the year 2003, the retirement age for men will grow by 4 months each year until it reaches the age of 65. The retirement age for women will grow by 6 months each year until it reaches the age of 63.

19
- 23 -

# Table 2 Sources of venture caRital (1985-86)

|EUROPE||UNITED STATES||
|---|---|---|---|
|Banks|23|Pension funds|34|
|Governments|21|Foreign investors|18|
|Insurance companies|15|Individuals|15|
|Pension funds|14|Corporations|14|
|Other|10|Insurance Co.|13|
|Corporations|9|Foundations|6|
|Individuals|8|||
||100||100|

Sources: Etrope: European Venture Capital Association; USA: OECD. Includes only private VCCs, not SBICs.

Table 3 Venture capital investments in selected countries. by sectors (sample of 957 enterprises, 1985) Sect3r

|Sect3r<br>No.|of firms|
|---|---|
|Agroindustry|14|
|Biotechnology|53|
|Chemical products|13|
|Communications|152|
|Computer hardware|14|
|Computer software & services|104|
|Diversified industries|495|
|Electronic components|84|
|Automation|22|
|Health & medical services|136|
|Real estate|34|
|Retailing|29|
|Services|22|
|Sample covers:<br>USA, Canada, UR, Europe and Asia||

Source: Guide to International Venture Capital.
15 to spend more time with students, and differences across classrooms may be striking, particularly for children with low initial reading scores. For example, in OECD countries, some first-grade teachers spent as little as 43 minutes on language arts while others devoted over 104 minutes per day. Extrapolating to an academic year, some children received as much as 180 more hours of instruction in language arts compared to other children.<sup>74</sup> Similar results were seen in a Peruvian study, where some low-income schools taught much more reading than others.<sup>75</sup> Clearly, engaging students as much as possible on learning tasks is a hallmark of teacher quality. And engaging students in activities that are more conducive to subsequent recall and to the formation of linkages among pieces of information is another important feature of quality.

U.S. research in the 1980s found that _corrective and immediate feedback_ , attention to and transfer of prior learning, and the teacher interaction with students were important factors affecting achievement.<sup>76</sup> Specifically, the following relationships were found between time use and classroom activities:<sup>77</sup>

- more time spent on discussion and review (r = +0.40);

- more time spent reading aloud (r = +0.59); and

- more supportive, corrective feedback from the teacher (r = +0.50).

Activities found to be negatively associated with reading gains were:

- more time spent in silent reading (r = -0.23);

- more time spent in sustained silent reading (r = -0.52);

- more time on classroom management (r = -0.24);

- more time spent by the teacher on organizing activities (r = -0.52);

- more time spent by the teacher on social interactions (r = -0.52); and

- more time spent on negative interactions (r = -0.29).

Time use in class is partly related to subject matter and need to pay attention. Yair (2000) gave students in the US watches that beeped at random times, during which students reported what they were doing. He found that the gap between allocated and productive instructional time is significantly larger among minority students. Students reported engagement in lessons only 55.4% of the time; 62% in grade 6, and 47% in grade 12. Disengagement increased with age and absorption in other activities. Math held their attention 63% of the time, reading 55%, lab work 74% of the time, group work 70%. Boys were engaged 21% more than girls and blacks were engaged 29% less. Relevant and challenging materials held the attention more than boring materials. Teacher-centered methods that demand greater 'pan-optical' control got less engagement. Thus, research suggests that students may be more engaged in hierarchically oriented courses (such as science) that demand prior knowledge than in courses where prior knowledge is not needed. Thus, the preponderance of continuous lecturing that requires little response from students may result in low rates of instructional time.

> 74. Kelly 2003.  OECD is the Organization for Economic Cooperation and Development. 75. Crouch et al. 2005

> 76. Quartarola 1984

> 77. Stallings 1980
vis developing countries, we uncover fewer and smaller differences when we compare banking to SMEs relative to large firms within countries. The differences between developed and developing countries are likely to reflect deficiencies in the contractual and informational frameworks in developing countries and less stable macroeconomic environment. Differences across banks of different ownership types are also less pronounced than differences across countries and are mostly in line with the literature. Specifically, foreign-owned banks rely more on hard verifiable information for lending decisions and real estate as collateral. On the other hand, contrary to conventional wisdom, foreign-owned banks do not seem to lend less to small and medium-sized enterprises than private domestic banks.

While a large literature has used firm-level data to analyze differences in access to finance by firm size, this paper is a first step in better understanding SME financing from the supply side. Going forward, it would be interesting to expand the number of banks and countries surveyed in order to see if we can corroborate our findings in a larger sample. Also, it would be useful to explore in greater depth what drives differences across developed and developing countries and between different bank ownership types. Finally, combining demand-side data, such as firm-level surveys, and supply data, as presented in this paper, might allow identifying the remaining bottlenecks in SME lending and thus help policy makers develop priorities.

16
for cohort-specific measures of high school quality in the analysis.

The sources of bias discussed above are common to all outcome variables. In estimating the wage equation, we also need to consider the sample selection bias, that is, salaries can be observed only for individuals who participated in the labor force. For example, females who cannot be paid well in the labor market (or who can study more effectively) tend to delay entering the labor force.<sup>21</sup> Furthermore, our sample students are relatively young (especially for the TEEP cohort). Therefore, students attaining higher education may not have participated in the labor force, though it is common that students work and study at the same time in the Philippines. To check robustness of our findings, we also take a two-stage procedure using the inverse Mills ratio (Heckman, 1979) in <u>the appendix.</u>

# **5. Data**

In this section we describe the data we use in our analysis.

# **_Sampling Framework_**

The data come from the survey conducted in eight education divisions (four intervention and four nonintervention divisions) in the Philippines from July 2010 through April 2011. The eight divisions were chosen to represent three major metropolitan areas: Manila (National Capital Region), Cebu, and Baguio. We sampled in such a way as to pair an intervention division with an adjacent nonintervention division in the same area so that the pair shares similar socioeconomic conditions. Our sample was grouped into four areas: (a) Ifugao and Neuva Vizcaya, (b) Antique and Iloilo, (c) Negros Oriental and Cebu, and (d) Leyte and Western Samar. Figure 5.1 maps our sampled provinces.

In each division, relatively poor municipalities (school districts) were chosen first. Municipalities of census-2000 income classes ranked 3 to 5 (the highest income is rank 1 and the poorest is rank 6) were chosen from the adjacent area (near the division border) of an intervention and a nonintervention division (see Philippines, Department of Finance 2001). In Ifugao division,

> 21As described in Section 5, we use wages received at the latest job held. This mitigates the above selection problem since we have wage observations for those who are currently studying (or are unemployed) but recently had work experience.

15