rafmacalaba/datause-encoder-data huggingface dataset:
Mullis et al., 2000; Mullis et al., 2003; Mullis et al., 2004; Martin et al., 2007; Mullis et al., 2008; Mullis et al., 2009; Martin et al., 2016; Mullis et al., 2016).

**PISA.** The Organization for Economic Co-operation and Development (OECD) launched the Programme for International Student Assessment (PISA) in 1997 to provide comparable data on student performance. PISA emphasizes an extended concept of “literacy” and an emphasis on lifelong learning – with the aim of measuring pupils’ capacity to apply learnt knowledge to new settings. Since 2000, PISA has assessed the skills of 15-year-old pupils every three years. PISA concentrates on three subjects: mathematics, science and literacy. In 2000, PISA had a focus, in the form of extensive domain items, on literacy; in 2003, on mathematical skills; and in 2006 on scientific skills. The framework for evaluation remains the same across time to ensure comparability.<sup>6</sup> In 2009, 75 countries/areas participated; in 2012, 65 countries/areas participated and in 2015, 72 countries/areas participated. A main distinction between PISA and IEA surveys is that PISA assesses 15-year-old pupils, regardless of grade level, while IEA assessments assess grade 4 and 8.

# **_2.1b. Regional Standardized Achievement Tests (RSATs)_**

In addition to the above international assessments, three major regional assessments have been conducted in Africa and Latin America and the Caribbean.

**SACMEQ.** _The Southern and Eastern Africa Consortium for Monitoring Educational Quality_ (SACMEQ) grew out of a national investigation into the quality of primary education in Zimbabwe in 1991. It was supported by the UNESCO International Institute for Educational Planning (IIEP) (Ross and Postlethwaite, 1991). Several education ministers in Southern and Eastern African countries expressed an interest in a similar study. Planners from seven countries met in Paris in July 2004 and established SACMEQ. The current 15 SACMEQ-member education members are: Botswana, Kenya, Lesotho, Malawi, Mauritius, Mozambique, Namibia, Seychelles, the Republic of South Africa, Swaziland, the United Republic of Tanzania, United Republic of Tanzania (Zanzibar), Uganda, Zambia and Zimbabwe.

The first SACMEQ round took place between 1995 and 1999. SACMEQ I covered seven different countries and assessed performance in reading at grade 6. The participating countries were Kenya, Malawi, Mauritius, Namibia, United Republic of Tanzania (Zanzibar), Zambia and Zimbabwe. The studies shared common features (research issues, instruments, target

> 6As explained in the PISA 2006 technical report, this is only the case for reading between 2000-2009, for mathematics between 2003 and 2009 and for science between 2006 and 2009. See OECD (2010) for more details.

8
in underlying fundamentals to expect that large-scale increases in aid will be proportionately more effective than more moderate amounts of assistance. This does not mean that increasing aid to Africa (or other poor countries) is a bad idea. Rather, it suggests to us that we should not expect any disproportionate growth effects of large-scale aid.

We do not claim that this paper provides comprehensive coverage of the possible mechanisms of poverty traps, and the simple aggregate models we consider here are unable to capture some possible mechanisms mentioned in the literature. A significant fraction of this literature is based on highly stylized models, and on non-convexities at the micro-level, which are not easily amenable to calibration using aggregate data. Also, we face the standard trade-off of breadth versus depth. We have chosen to analyze a set of broad models that rely on aggregate mechanisms without delving deeply into the possible micro-mechanisms that lie behind our aggregate relations. A possible alternative approach would be to take seriously a specific micromechanism of poverty traps and make an effort to calibrate it using micro and macro data. Nevertheless, we consider that there are some important general lessons that can be derived from our analysis. The mechanisms that we analyze are usually mentioned in the discussion on poverty traps, and the models we use are standard in the growth literature. Also, under some conditions these models can be understood as reduced form representations of the underlying micromechanism.

It is important to notice that by using a representative-agent model in our analysis, we are implicitly assuming perfect factor markets, which leaves aside an important set of microeconomic models of poverty traps based on credit and labor market imperfections that result in apparently large TFP differences across countries.<sup>1</sup> However, despite their importance, these types of models do not speak directly to the issue of whether large across-the-board increases in aid are likely to move poor countries out of poverty. The reason is that the low aggregate productivity associated with these types of models is related to distributional problems. A redistribution of income from rich to poor people within a country could increase measured aggregate TFP without an increase in aid. Also, modest increases in aid could have large productivity results if properly targeted, while large increases could have no effect on measured aggregate TFP if they benefit those that have already moved out of the poverty trap. Therefore, in

> 1 See Banerjee and Duflo (2004) for a survey of these models, and Banerjee (2001) for a detailed analysis of credit market imperfections.

3
<!-- Start of picture text --> accumulators, diversifiers, and dependents. While the accumulator households are able to capitalize<br>on a crisis, they are not entirely immune to shocks. At the same time, highly vulnerable households<br>may be able to recover from one shock, often through help from informal social networks, but a<br>series of shocks can knock them out and make them entirely dependent on governmental support.<br>Diversifiers fluctuate between periods of vulnerability and more resilient states.<br><!-- End of picture text --> accumulators, diversifiers, and dependents. While the accumulator households are able to capitalize on a crisis, they are not entirely immune to shocks. At the same time, highly vulnerable households may be able to recover from one shock, often through help from informal social networks, but a series of shocks can knock them out and make them entirely dependent on governmental support. Diversifiers fluctuate between periods of vulnerability and more resilient states.

Figure 8: Illustrative representation of the livelihood dynamics for the three categories of households in Botswana’s drylands, based on a case study by Sallu et al. (2010). The red boxes depict critical moments during which climatic, environmental, and social stressors converge, threatening livelihoods and well‐being among the poor and the poorest. (Source: Olsson et al. 2014) These trajectories do not occur in isolated bubbles. A relational poverty lens reveals inter‐ dependencies between individual households and between people and their places. While one may argue that environmental changes such as droughts affect everybody in a community, power differentials and policy decisions result in flourishing of some, to the detriment of others. For 34
modest increase in productivity growth. Key factors cited in various studies to explain why Mexico has not grown as fast as other countries (see, for example, OECD, 2009) include relative weaknesses in education, infrastructure, financial development, and the rule of law, as well as anticompetitive and restrictive regulation of product and labor markets (Loayza and Palacios, 1997; RodríguezOreggia, 2010).

The Mexican economy was hard hit by the financial crisis and the increase in international food prices in 2008. The inflation rate has been on declining trajectory since the end of 2008 while the exchange rate has experienced a slight increase (figure 2.1). The collapse of external demand, particularly in durable consumer goods, in the last quarter of 2008 and the first half of 2009 led to an almost immediate and severe downturn in economic activity. The loss of employment and the high level of uncertainty and risk brought by the economic crisis contributed to a fall in private consumption and investment, further reducing aggregate demand and inflationary pressure.

**Figure 2.1: Annual Inflation and Exchange Rate**

<!-- Start of picture text --> Inflation and Exchange Rate<br>16.00 7<br>14.00<br>6<br>12.00<br>5<br>10.00<br>4<br>8.00<br>3<br>6.00<br>2<br>4.00<br>2.00 1<br>0.00 0<br>I-07 II-07 III-07IV-07 I-08 II-08 III-08IV-08 I-09 II-09 III-09IV-09 I-10 II-10 III-10IV-10<br>Quarter<br>Exchange Rate Inflation<br>Inflation %<br>Exchange Rate (dolars per peso)<br><!-- End of picture text --> _Source:_ Data from Central Bank of Mexico.

A subsequent rebound in external demand as of the second half of 2009 gave rise to a recovery, even though private consumption and investment are trailing behind and have not yet contributed significantly to the upturn of economic activity. Figure 2.2 depicts levels of economic activity and the main components of aggregate demand, showing that by the second quarter of 2010 GDP was still slightly below its pre-crisis level. A similar scenario occurred for private consumption and 3
by equation (6) above. We denote this rank as _R_ 25<sup>_β_toconveythat</sup><sup>_β_ˆ0and</sup><sup>_β_ˆ1wereused</sup> to derive this rank estimate. Note that the expected normalized schooling of children (i.e., normalized by the standard deviation) and expected years of schooling of children are monotonically related to each other, and thus the rank remains the same. This implies that if we calculate the rank _R_ 25<sup>_ρ_,basedontheexpectednormalizedschoolingusingOLS</sup> estimates of parameters of equation (2), i.e., _ρ_ ˆ0 and _ρ_ ˆ1, it will be identical to _R_ 25<sup>_β_,i.e.,</sup> _R_ 25<sup>_β_=</sup><sup>_R_</sup> 25<sup>_ρ_.</sup>

# **(4) Data and Variables: India and Bangladesh**

To estimate the bias caused by truncation arising from the coresidency rules in surveys, we need data sets that include all of the children of the household head (and spouse) irrespective of their residency status, and also need to identify which members of the household were coresident at the time of the survey implementation. We take advantage of two exceptionally rich data sets particularly suited for our analysis. The data on India come from the 1999 Rural Economic and Demographic Survey done by the National Council for Applied Economic Research, and the data on Bangladesh are from the 1996 Matlab Health and Socioeconomic Survey (MHSS).<sup>10</sup> The Bangladesh survey collected information on all children of the household head and spouse (including from past marriages) irrespective of their residency status from 4,538 households in Matlab thana of Chandpur district. The India survey also collected information on all of household head’s children from the current marriage, but did not gather information on non-coresident mothers of children from earlier marriage(s). Both of these surveys focus on rural areas in the respective countries. The bias from censoring due to possible non-completion of younger children may not be as important in rural areas, because the proportion of children who go on to have more than middle school (or high school) education is not likely to be large. The children who go for more than high school

> 10The MHSS 1996 is a collaborative effort of RAND, the Harvard School of Public Health, the University of Pennsylvania, the University of Colorado at Boulder, Brown University, Mitra and Associates and the International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B).

9
|**Table 16: SkillG**|**aps of Work**<br>|**ers by Cate**<br>|**gory in For**<br>|**mal Enterprises**<br>||
|---|---|---|---|---|---|
|**Type of Skill Missing Among**<br>**Current Employees**<sup>9</sup>|Manual<br>workers|Service<br>workers|Admin<br>workers|Technical and<br>professional<br>workers|Managerial<br>workers|
|Numeracyskills|15.2|15.6|8.5|4.1|8.1|
|Literacyskills|10.7|12.2|2.2|1.2|4.1|
|Language(English/Portugese)|46.6|57.7|33.4|21.5|57.9|
|Technical andpractical|21.3|14.4|3.9|14.4|6.5|
|Customer handlingskills|16.2|34.8|8.5|2.4|12.6|
|Management skills|7.7|11.1|14.8|2.0|28.5|
|Administrative & Financial|12.0|20.9|39.3|4.9|27.1|
|Drivingskills|11.1|10.1|3.9|3.2|5.1|
|Basic computer literacyskills|10.9|14.2|14.2|7.7|17.6|
|Advanced IT or software skills|7.5|10.3|10.3|8.9|13.6|
|Marketingskills|7.5|13.8|9.3|3.6|22.7|
|Negotiatingskills|6.1|12.4|6.9|3.0|25.9|
|No need to acquire further skills|3.8|3.9|1.6|2.2|8.1|

Note: More than one skill reported. Source: UES, 2004 61. From a public policy viewpoint, the above results need to be assessed carefully. Not all the needed skills listed are ones that need to be provided by the public sector. Some skills (e.g. driving) are generally provided through private channels quite effectively. There is clearly a need for assessing where the market failures on skills training are most pronounced in order to assess implications for future training policies.

|**Table 17: Skills to be b**|**ecome important in next 2years**|
|---|---|
|**Skill**|**Percent Enterprises**|
|Customer handling|61.9|
|Driving|57.9|
|Problem solving|44.5|
|Advanced IT or software|41.9|
|Technical andpractical|38.5|
|Literacy|32.2|
|Numeracy|30.4|
|Communication|29.8|
|Foreign Language|24.9|
|Basic computer literacy|23.8|
|Teamwork|18.6|
|Other|5.3|

Note: More than one skill reported. Source: UES, 2004.

62. (b) <u>Training provision:</u> Almost 40 percent of the formal enterprises provide some training to their workers. A large part of this training is in-house and on-the-job, but a good 40 percent of the enterprises use external providers as well, either solely or in combination with other methods. The findings are presented in Figure 21 below.

> 9 See Annex 4 for description of skills.

28
25 Last, one could argue that there are important differences in the role market orientation plays in fostering mineral versus hydrocarbon discoveries. In particular, minerals might be seen as more appropriable than hydrocarbons because mining output does not move through pipelines and takes place exclusively onshore. Instead our results suggest that in fact the effect of market orientation is driven as much by hydrocarbon as mineral discoveries (columns 2 and 3 of Table S11).<sup>29</sup>

# **D.   A look at what the SW Openness Indicator might be proxying?**

As explained in the data section, market orientation is defined as the absence of the following features: (i) average tariff rate on imports above 40%; (ii) non-tariff barriers covering more than 40% of imports; (iii) a socialist economy; (iv) the state holds a monopoly of the major exports; and (v) a black market premium above 20%. In essence, countries defined as market oriented thus allow for relatively free trade and do not exercise state control over the main exports. One might see it as quite intuitive that such a situation should encourage more exploration investment and thus discoveries than the contrary one. One might also wonder, however, what more detailed features of the economy market orientation might be proxying for. To get a sense of the effects of unbundling market orientation, Table 7 shows partial correlations between the SW/Wacziarg indicator and various International Country Risk Guide (ICRG) Political Risk Rating sub-indices which are available since 1984.

**Table 7: Partial correlations of SW/Wacziarg Openness Indicator with ICRG Indices**

|Variable|Partial<br>Corr.|Semipa<br>Corr.|rtial<br>Partial<br>Corr.^2|Semi<br>Corr.^|partial<br>Significan<br>2<br>Value|ce|
|---|---|---|---|---|---|---|
|ICRG Investment Profile||0.2379|0.2061|0.0566|0.0425|0|
|ICRG Corruption||0.0686|0.0579|0.0047|0.0034|0|
|ICRG Law and Order||0.0554|0.0467|0.0031|0.0022|0|
|ICRG Government Stability||0.0859|0.0726|0.0074|0.0053|0|
|ICRG Internal Stability||0.0837|0.0707|0.007|0.005|0|
|ICRG Bureaucratic Quality||0.0531|0.0448|0.0028|0.002|0|

Note: The table reports partial correlation coefficients between the SW/Wacziarg Openness indicator and ICRG political risk sub-indices. The overlap between the data series is 1984-2004.

> 29 We lose some power when splitting the sample, hence the reduced significance levels.
**APPENDIX D GUIDELINES AND PROTOCOLS FOR LESSON OBSERVATION USING VIDEO IN GUANGZHOU**

# **D.1 Purpose**

The goal of this pilot study is to demonstrate how videotaping of lessons can be used to better understand what takes place in Guangzhou’s classrooms and what practices teachers use. This document provides brief guidelines and protocols for the filming of classrooms. It includes general steps to take in the school visit as well as protocols on how to conduct the actual filming. The protocols are adapted from the TIMSS-R Video Study.<sup>9</sup>

# **D.2 General Steps for the School Visit**

## **_Arrival at the school_**

_Arrive at the school at least one hour before the scheduled shooting._ Late arrival can create difficulties in the preparations for the filming. The teacher will have a set time for his or her lesson and the set-up should not alter the lesson time.

_First meet with school officials._ You should never go directly to the teacher's classroom. Always go to the main office first and meet with the principal or the person who has been assigned as your official contact person.

## **_Once in the Classroom_**

As soon as you get to the classroom where you will shoot the lesson, two factors will help you determine where to position the camera: 1) information about what will happen during the lesson, and 2) the physical arrangement of the classroom.

### Ask the Teacher about the Lesson

_Try to find out from the teacher about what will happen in the lesson._ Often there will be little time for you to talk to the teacher because even though you arrive early, he or she might be busy teaching. However, if you have a chance, ask the teacher and find out as much as possible about the lesson.

The information you want to find out is:

- Roughly how long the lesson will last (longer than 60 minutes?)

- General outline of the activities of the teacher and students that will take place during the lesson

- Whether the chalkboard will be used

- Which chalkboard, if there are more than one, will be used

- Whether AV materials will be used and where they will be placed.

### Choose Camera Position

Camera positioning is documented in detail later in the section _Placement of the_ Camera. General rules include the following:

- Try to set up the cameras with the windows at your back, thereby avoiding back light problems.

- Close windows, doors, and blinds as needed to adjust the light and reduce noise. _Make sure the teacher does not mind before you do any of this._

- If your video equipment requires electricity, find the location of the nearest electrical outlet. If at all possible you will want to plug your camera into this outlet so that in the unlikely event of a battery failure you can use electricity as a backup while you replace the battery.

> 9 TIMSS-R Video Study Data Collection Manual, Lesson Lab Inc. of Los Angeles, California

44
Policy Research Working Paper 7353

# **Abstract**

How many and which firms issue equity and bonds in domestic and international markets, how do these firms grow relative to non-issuing firms, and how does firm performance vary along the firm size distribution? To evaluate these questions, a new data set is constructed by matching data on firm-level capital raising activity with balance sheet data for 45,527 listed firms in 51 countries. Three main patterns emerge from the analysis. (1) Only a few large firms issue equity or bonds, and among them a small subset has raised a large proportion of the funds raised during the 1990s and 2000s. (2) Issuers grow faster than non-issuers in assets, sales, and employment, that is, firms do not simply use securities markets to adjust their financial accounts. (3) The firm size distribution of issuers evolves differently from that of non-issuers, tightening among issuers and widening among non-issuers.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group; and the Operations and Strategy Unit, Development Economics Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at tdidier@ worldbank.org, rosslevine@berkeley.edu, and sschmukler@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Table 2. Daily Cases

# Panel A: Restrictions on Personal Liberties

||**DUMMY**<br>**VARIABLE**|**EFFICIENT**<br>**INSTRUMENT**|**DUMMY**<br>**VARIABLE**|**EFFICIENT**<br>**INSTRUMENT**|**DUMMY**<br>**VARIABLE**|**EFFICIENT**<br>**INSTRUMENT**|**DUMMY**<br>**VARIABLE**|**EFFICIENT**<br>**INSTRUMENT**|
|---|---|---|---|---|---|---|---|---|
||||**_Addition_**|<br>**_al Controls Om_**<br>|**_itted_**||||
|**STAY ATHOME**<br>|-0.0147***<br>(0.00387)|-0.0445***<br>(0.00941)|||||||
|**BANMEETINGS**|||-0.0133**<br>(0.00406)|-0.0478***<br>(0.0104)|||||
|**BAN ONPUBLIC**|||||-0.0219***<br>|-0.0511***<br>|||
|**EVENTS**|||||(0.00472)|(0.00934)|||
|**RESTRICTIONS**<br>**ONMOVEMENTS **|||||||-0.0121***<br>(0.00337)|-0.0515***<br>(0.0115)|
|**OBSERVATIONS**<br>|26,973<br>|25,359<br>|26,973<br>|25,359<br>|26,973<br>|25,359<br>|26,973<br>|25,359<br>|
|**COUNTRIES**<br>|158<br>|158<br>|158<br>|158<br>|158<br>|158<br>|158<br>|158<br>|
|**FIXEDEFFECTS**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

## Panel B: Restrictions on Activities

||**DUMMY**<br>**VARIABLE**|**EFFICIENT**<br>**INSTRUMENT**|**DUMMY**<br>**VARIABLE**|**EFFICIENT**<br>**INSTRUMENT**|**DUMMY**<br>**VARIABLE**|**EFFICIENT**<br>**INSTRUMENT**|**DUMMY**<br>**VARIABLE**|**EFFICIENT**<br>**INSTRUMENT**|
|---|---|---|---|---|---|---|---|---|
||||**_Addition_**|<br>**_al Controls Om_**|**_itted_**||||
|**CLOSESCHOOLS**|-0.0290***<br>(0.00543)|-0.0791***<br>(0.0131)|||||||
|**CLOSE**<br>**WORKPLACES**|||-0.0206***<br>(0.0038)|-0.0543***<br>(0.0102)|||||
|<br>**CLOSEPUBLIC**|||||-0.00887|-0.00997|||
|**TRANSPORT**|||||(0.00516)|(0.0122)|||
|**CLOSEBORDERS **|||||||-0.0137**<br>(0.00473)|-0.0810***<br>(0.0224)|
|**OBSERVATIONS**|26973|25359|26973|25359|26973|25359|26973|25359|
|**COUNTRIES**|158|158|158|158|158|158|158|158|
|**FIXEDEFFECTS**|Yes|Yes|Yes|Yes|Yes|Yes|Yes|158|

Note: Standard errors in parentheses *p<0.05; ** p<0.01, *** p<0.001

**Table 3 replicates these exercises for the case of the daily death rate** . Again, we report only the estimated parameters of the CCMs. Contrary to the case of daily cases, the results for daily casualties are notoriously different: when using the naïve dummy variable approach, all parameters are statistically insignificant and of negligible magnitude. On the contrary, estimates using the efficient instrumental variable approach indicate that all but one of the CCMs are effective in reducing the daily number of deaths.

19