rafmacalaba/data-use-mentions-extended huggingface dataset:
“ - ” OECD, 2020a, <u>Coronavirus (COVID 19): SME policy responses</u> (accessed on 27 July 2020). OECD, 2020b, “Statistical Insights: Small, Medium and Vulnerable” (accessed on 29 July 2020).

Osotimehin, Sophie, and Latchezar Popov. 2020. Sectoral Impact of COVID-19 : Cascading Risks Sectoral Impact of Covid-19 : Cascading Risks.

Oxford Business Group, 2020, “Is fintech the key to African SMEs' Covid-19 recovery?” (accessed on 29 July 2020).

Romei, Valentina, and Daniel Thomas. 2020. “Covid-19 Shuts down a Quarter of UK Businesses.” Financial Times. https://www.ft.com/content/f9537538-d7a0-44e3-8e86-5cb9a984aae4.

U.S. SBA. 2020. Paycheck Protection Program Report - Approvals through 12pm EST 4/16/2020. World Bank Global Economic Prospects, June 2020Added: World Bank. 2020. “Enteprise Surveys Data.” https://www.enterprisesurveys.org/en/survey-datasets. World Bank. 2020. “Map of SME-Support Measure in Response to COVID-19.” <u>https://www.worldbank.org/en/data/interactive/2020/04/14/map-of-sme-support-measures-inresponse-to-covid-19.</u>

- World Bank. 2020. Global Economic Prospects. https://www.worldbank.org/en/publication/global <u>economic-prospects.</u>

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The paper contributes to the literature in two main ways. First, the data set includes explicit information on a number of shocks hypothesized to affect vulnerability, including longer‐term, objective measures of rainfall variability and current period rainfall shocks. Many previous analyses have limited information on actual shocks faced, and as we shall see below, the standard vulnerability analyses without such information makes it difficult to disentangle which shocks are more important for explaining vulnerability using various welfare measures. Second, the paper uses a two‐period data set covering households in rural Malawi; contributing to very sparse evidence on vulnerability in Southern Africa using this particular 3
further validating the practicality of the proposed approach.

In the first stage, a certain number of primary sampling units (PSUs) will be selected in each urban and rural stratum (urban and rural areas within each administrative region). In the second stage, the ultimate sampling units or the secondary sampling units (SSUs)—households in the case of L2SC—are randomly selected within each PSU. The survey is then implemented among the selected households. Given that our focus in this paper is on the first stage of the two-stage procedure, the sampling frame used for the survey in the first stage is highlighted.

**Sampling Frame:** As mentioned, the sampling frame is based on pre-census enumeration areas (pre-EAs) providing the most accurate information on the geographic distribution of the population across Armenia. This allows for the most precise formulation of a sample design.

**Sample Size:** The objective of any sample design is to achieve the highest precision in indicators of interest given survey parameters. The sampling design aims to efficiently allocate the given PSUs across strata and

> 17 The Listening to Georgia (L2Geo) leverages the national sampling frame based on the 2014 Census with population updated by 2022 and 9,514 enumeration areas as PSUs. The preference to use comparable frames in Georgia and Armenia was one of the motivations for developing a new national sampling frame in Armenia based on pre-EAs in this paper.

> 18 A balanced sample refers to a sample where the means of covariates are equal to the population means of those covariates, ensuring that the sample is representative of the population of interest (Brown et al., 2015).

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# **Early Learning in South Punjab, Pakistan: Investigating Child Development and Classroom Quality**<sup>**1**</sup>

Jonathan Seiden<sup>2</sup> Amer Hasan<sup>3</sup> Diego Luna Bazaldua<sup>2</sup>

## **Keywords: Early Childhood Development, Classroom quality, Pakistan, pre-primary, primary**

**JEL Codes: I20, I21**

> 1 The authors would like to thank the Programme Monitoring and Implementation Unit of the School Education Department of the Government of the Punjab for sharing the microdata used in this paper. Ghulam Dastgeer provided helpful clarifications on survey protocols. The authors would also like to thank Elizabeth Hentschel for the analysis reported in Table 1. All errors are our own. This work is a product of the staff of the World Bank with external contributions. The World Bank does not guarantee the accuracy of the data included in this work and does not assume responsibility for any errors, omissions, or discrepancies in the information, or liability with respect to the use of or failure to use the information, methods, processes, or conclusions set forth. The boundaries, colors, denominations, links/footnotes, and other information shown in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. The citation of works authored by others does not mean the World Bank endorses the views expressed by those authors or the content of their works.

2 Harvard Graduate School of Education and World Bank, Education Global Practice 3 World Bank, Education Global Practice
J. Klenow, and G. Sharma, 2013, “India’s Mysterious Manufacturing Miracle,” Review of Economic Dynamics 16, 59-85.

Bonfiglioli, A., 2008, “Financial Integration, Productivity and Capital Accumulation,” Journal of International Economics 76, 337-355.

Bradley, M., A. Brav, I.Goldstein, and W. Jiang, 2010, “Activist Arbitrage: A Study of OpenEnding Attempts of Closed-End Funds,” Journal of Financial Economics 95, 1-19.

Braun, M., 2005, “Financial Contractibility and Asset Hardness,” Working Paper.

Brenton, P., C. Saborowski, and E. von Uexkull, 2010, “What Explains the Low Survival Rate of Developing Country Export Flows?” World Bank Economic Review 24(3), 474-499. Breslow, N., 1974, “Covariance Analysis of Censored Survival Data,” Biometrics 30(1), 89-99. Broner, F., and J. Ventura, 2016,“Rethinking the Effects of Financial Globalization,”Quarterly 31
predominantly thermal power producer, the country relies on imported oil (about 400,000–450,000 tonnes annually). However, the full development of West Africa thermal capacity is some years away to materialize in its entirety.

From the demand side, despite the evident need to extend the access and availability of electricity, firms with electricity connections report good satisfaction levels compared to other West African countries. The percentage of sales lost due to power outages is comparable to what is found in other LICs, and the percentage of firms relying on their own generation is well below the Sub-Saharan African average according to the most recent enterprise survey (table 12).

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however, the study uses a carbon tax instead of ETS to meet the NDC, and the study does not explicitly represent provincial economics. To the knowledge of the authors, this is the first study that uses a recursive dynamic, multi-regional CGE model with latest available data (2012 provincial SAM) to analyze an ETS to meet China’s NDC under the Paris Agreement. The study finds that meeting China’s NDC through a national emission trading system would cause 0.4% to 1.5% reduction of economic output at the national level (GDP) in 2030 depending upon allowances allocation as well as baseline setting rules.

The study is organized as follows. Section 2 presents the details of the multi-region, recursive dynamic CGE model developed for the study, followed by a discussion on data in Section 3. Section 4 presents discussions of results from the model simulations followed by key conclusions drawn in Section 5.

# 2. **Methodology**

In this section, we present a description of the regional CGE model developed for this study.

_The Naming rules for variables in the equations:_ (1) Endogenous variables are named as uppercase letters, and exogenous variables are named as uppercase letters with a cross line above; (2) Variables are generally named as their recognized prefix, as Q is quantity, P is commodity price, W is the factor price, Y is income, E is expenditure; (3) The number of provincial regions in this model is m, and each region has n production sectors; (4) The initial value of all variables in the business-as-usual scenario are used as parameters and are named as original names with 0 added behind, while other parameters are named as lowercase letters.

_Definition of sets:_ I,J = {production sectors or commodities}; R,S = {regions}; F = {factors, including capital and labor}; E(<sup></sup> I,J) = {energy sector
These conclusions led us to propose the following policy recommendations:

- General average dwell time objective at platform level, in a not congested context, should be dropped. The objectives to be set in relation to dwell time would need to be revisited with differentiated targets according shipper and shipment characteristics (e.g. clearance in less than 4 days is a reasonable objective for some industrial companies),

- Target shippers and C&F to encourage more efficient behaviors. Reforms would indeed only be useful if shippers and C&F agents share the common objective of making dwell time in ports minimum. They need to have incentives to do that and awareness campaigns on the potential impact of fast clearance on factory prices or customer service level should be organized. The calculation of full logistics cost should replace the evaluation of demurrage and parking costs currently used to assess port transit costs. The use of pre-arrival clearance or best practices in logistics management (such as reduction of inventory level) should be encouraged.

- An alternative position is to adopt a demand-driven approach where short dwell time is no longer an objective as such but rather a mean to meet expectations of those shippers that want to keep cargo dwell time in ports minimal. Setting an overambitious and unattainable dwell time for all shippers seems indeed pointless and setting differentiated targets to maximize port user satisfaction appears a more sensible objective. Main industrial companies for example are more likely to adopt short dwell time objectives because of the high regularity of their imports (same materials, fixed replenishment intervals) while small commercial companies will probably need support to keep dwell time in ports minimal for the few containers they order every year.

- Available statistical tools can help customs profile shippers’ behaviors and help terminal operators improve yard productivity therefore delaying capacity investments. However, the latter is only possible if data on cargo characteristics is made available to terminal operating companies and used to segregate full containers according expected dwell time. In that respect this study has only used very partially the potential of data available in ASYCUDA and Cameroon
. The expected dropout rate of primary school age children is substantially smaller than that of secondary school age children (35% of the total Primary and Secondary drop out), given the greater income generating opportunities of secondary school age children and far greater supply constraints in the developing world. The dropout estimates are only affected by income shock, and ignore the potential effects of concerns about school safety concerns and of school disengagement, which are likely to be relevant but are extremely hard to measure at this stage. If anything, these further effects have a clear upward bias, so the out-of-school numbers presented here can be seen as lower-bound estimates. In any case, this crisis is not over, and our understanding of the ramifications to the economy and household welfare are being updated daily.  Since March 2020 global growth projections have been frequently revised, and the recently released Global Economic Prospects indicates that growth projections are likely to continue to go down. In each of these revisions, our expected number of student’s dropping out due to the household income shock is revised upwards. Our initial estimate, based on the March MPO suggested that approximately 1.4 million primary students would drop out of the education system; by October, this number had already been revised to 3.8 million, and is likely to be revised further upward based on revisions to the magnitude of the economic recession (Figure 1).

> 37 For details in the methodology, see Azevedo et al. (2020).

> 38 Kakwani 1980.

> 39 Villasenor and Arnold 1989.

> 40 Lorenz parameters were estimated using the user written Stata ADO function GROUPDATA, available at <u>https://github.com/jpazvd/groupdata.</u>

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There is not much variation in earnings in Azerbaijan until the tertiary level. The earnings advantage of secondary education is minimal. But those with higher education earn 1.4 times more than those with primary education (Figure 1).

**Figure 1: Earnings Ratio by Educational Level (No education = 100)**

<!-- Start of picture text --> 143<br>112<br>100 99<br>None Primary Secondary Higher<br><!-- End of picture text --> Source: AMSSW 2015 Figure 2 shows the age-earnings profile. Only the curve for tertiary education is well-behaved. For the lower levels of education, age-earning profiles are substantially lower and mostly flat. The ageearnings profile for individuals with no education has the lowest earnings and a decreasing trajectory.

10
births, deaths, marriage and migration had combined to alter the character of many families in the interim. The associated summary statistics are reported in Table 2.

The re-survey of households was supplemented by so-called focus group interviews. Dr. Behera personally assembled a diverse group of villagers in each community and put to them a set of structured questions aimed at cross-checking and extending the answers to those in the household questionnaire. Important disagreements among the participants were duly noted. As time had become very short in relation to the preparation of the loan proposal, such interviews could be conducted in only 12 of the 30 villages. Included in these 12 were the 9 villages that had received a new road no earlier than 2007. Of the remaining three, two had had an all-weather road at the time of the original survey; the other still awaited such a connection.

Before embarking on an analysis of the survey data, it should be noted that this task is somewhat complicated by a feature common to many surveys, namely, that households were sampled in clusters (villages). All members of the same village share a common socio-economic and natural environment, and the great majority have some familial and other social ties; so that the observations in the whole sample are unlikely 5
5.20%|$1,166.00|2.56%|<br>State|
|93<br>80|SVA Electron|Electronics|$562.50|$12.60|2.24%|$2,054.40|0.61%|<br>Joint-Venture|
|96<br>93|Shanghai Founder Yanzhong Sci.|Computers|$545.20|$15.60|2.86%|$364.80|4.28%|State|
|97<br>—|Amoisonic Electronics|Electronics|$542.00|$73.30|13.52%|$584.20|12.55%|Joint-Venture|

SOURCE: The statistics are derived from "China's 100 Largest Companies," _FORTUNE_ , Monday, August 25, 2003

- a. Controlling ownership is based on a phone inquiry into China Enterprises Confederation and online research.

- b. State-owned enterprises include enterprises with controlling shares hold by the state.

- c. Joint-Venture enterprises may also include enterprises with shares by state or collective or private.

8
<!-- Start of picture text --> Figure 7. International Remittances<br>25<br>Initial<br>Final<br>20<br>15<br>10<br>5<br>0<br>percent of GDP<br><!-- End of picture text -->

**_Source:_** World Development Indicators, 2012

Figure 8. Change in the Share of Transfers in Total Household Income <!-- Start of picture text --> 35%<br>30% All<br>25% Poor < US$1.25<br>20% Poor <US$2.5<br>15%<br>10%<br>5%<br>0%<br>-5%<br>-10%<br>-15%<br>average annual percentage change<br><!-- End of picture text -->

**_Source:_** Authors’ calculations with data from SEDLAC, RIGA, and National Household Surveys.

23
(1999).<sup>9</sup> In our benchmark specification, a country’s quality of institutions _Qi_ is measured by the "rule of law index" from the World Bank Worldwide Governance Indicators (2009).In the robustness checks, we also use the time and the cost to enforce contracts<sup>10</sup> from the World Bank Doing Business Indicators as alternative measures for the quality of institutions.A country’s ability to export timely is measured by the quality of transport infrastructure<sup>_T_</sup> _i_<sup>as captured by the infrastructure component of the World Bank</sup> Logistics Performance Index.<sup>11</sup> Quality of infrastructure matters because it is an important determinant of the length of time to export and of the certainty of delivery, beyond being an important determinant of the financial dimension of trade costs. As additional measures for a country’s ability to deliver on time, we use the time to export from World Bank Doing Business Indicators<sup>12</sup> and an index of the quality of transport infrastructure constructed as in Limão and Venables (2001). The latter is calculated as the average of the deviations from the sample mean of four variables: (i) the percentage of paved road; (ii) the density of the rail network, both taken from the World Development Indicators 2008; (iii) the number of airports with paved runways over 3,047 meters – obtained from the CIA Factbook; and (iv) a port efficiency index (ranging between 1 and 10) taken from the IMD World Competitiveness year book.

Summary statistics of factor endowments and factor intensities are provided in Tables 2 and 3. These statistics show that timeliness and institutional intensity are on average higher for intermediate than for consumption goods. In particular, 22 percent of U.S. intermediate imports are shipped by air compared to 18 percent of consumption goods, and 65 percent
household head is constructed fr|453<br>om the answers t|620<br>o hypothetical survey q|1073<br>uestions that ask|

Note: Risk preference of the household head is constructed from the answers to hypothetical survey questions that ask people to choose between receiving an amount of cash for certainty versus playing a game with uncertainty. The seven survey questions are formulated as the following: “Would you like to receive X yuan for certainty or receiving 2000 yuan with 50% possibility and zero yuan with 50% possibility?” Value X takes values 400, 600, 800, 1000, 1200, 1400, and 1600. We construct a binary variable for each of these seven questions, which takes value one if the answer is to receive with certainty, and zero if the answer is to play the game. We calculate a proxy of risk premium from each question. For example, when X is 400, and the person would like to receive 400 yuan with certainty. This means that the person would like to pay at least 600 yuan to avoid playing the game with uncertainty, which further means that the risk premium in this case is at least 600 yuan. We then add up all positive and negative “risk premiums” from the seven questions and normalize it to a range from 0 to 1, with 0 being the most risk-loving and 1 being the most risk-averse. * p-value <0.1 ** p-value<0.05 *** p-value<0.01

# **3. Research methodology**

This section presents the methodology to examine the role of e-commerce participation in household income growth and the distributional aspects of the benefits of e-commerce participation. The descriptive statistics show the average income per capita of e-commerce households is higher than that of non-e-commerce households. Empirically, we aim to answer two questions: 1) Are more-wealthy/better-endowed households more likely to participate in e-commerce than less wealthy households? and 2) Do more-wealthy/better-endowed households benefit more from
Source: Own calculations based on PARE's database.

Table 6 shows a simple ordinary linear square model that captures only about 6% of the variance in the difference of scores (between 4th and 6th grades) amongst students in rural schools and 12% amongst students in native schools. No doubt this reflects an inadequate specification of the model be it in its functional form or inclusion of relevant explanatory factors. To the extent that the measured test scores fail to capture the true level of performance in the sample, much of the influence of variables such as parental background, the quality of teaching, etc., is lost in the model. The point to note, however, is that, even so, the explanatory variables behave as expected.<sup>4</sup> More importantly, the coefficient of the experimental variable is large and significant. The PARE program has a large positive impact on student achievement in this counterfactual experiment by all means in the scenarios and specifications.

The impact is larger for the native schools, a result that is consistent with the orientation of the program. As reported in Table 6, the marginal contribution of each explanatory variable is measured in terms of standard deviations of the dependent variable; i.e., of the percentage change in performance between 4th and 6th grades. This is in order to control for possible demand driven effects and hence simplify the analysis. For the average student at native schools, attendance at a school fully served by the program would, on average, increase the percentage change by 25%. The comparable percentage change for students attending rural schools is half as large. The variables- of school ".supply" (the performance of teachers, principals and supervisors) are partly an _outcome_ of the program. Thus, the program, at its _maximum_ effect estimated with the results of Table 6, could increase the performance of the average student by one-half of the standard deviation of the percentage change in test scores for the respective sub-ample.

It
1 if the respondent listed “hosting Syrian refugees” as one of the most important challenges facing Jordan (options not read); an outcome with higher values for responses associated with beliefs that Syrian refugees pay more in taxes than Jordanians; an outcome with higher values for responses associated with refugees having a positive effect on the economy.Policy preferences in a standardized index of the following outcomes: less belief that Syrian refugees should be forced to live in camps; more belief that refugees should have the right to work outside camps; more belief that refugees should be able to attain Jordanian citizenship; more support for unrestricted work permits for refugees; more support for integrated classrooms with Syrians and Jordanians; more support for refugee right to enter/exit camps freely; support for housing assistance for Syrian refugees; belief that the international community should spend more money supporting refugees.Altruism to Syrians reports how many JD out of 5 the respondent allocated to a charity supporting syrian refugees. The other two options were allocating to a charity supporting low-income Jordanians or keeping for self. Panel B reports the two significant subcomponents of the social attitudes and perceptions index. Panel C reports other statistically significant treatment effects on neighbors. Subjective health is the response to “Would you describe your general health as good, fair, poor, or very poor? ” Life satisfaction is the response to “All things considered, how satisfied are you with your life as a whole these days on a scale of 1 to 10? ” Days of media consumption is the response to “In the past 7 days, how many days did you read or listen to the news from any source, including newspapers, online, WhatsApp, etc.?” Neighborhood perceptions of refugee aid reciept is the response to a question asking “Of the refugee households in your neighborhood who receive assistance, what do you think is the average value in Dinar of the assistance (in cash or in kind) that they receive from organizations in a typical month?” The sample size for