rafmacalaba/data-use-mentions-extended huggingface dataset:
MULTILINGUAL CHILD ASSESSMENT Up to 40% of children worldwide speak a mother tongue that is not used at school (Walter & Benson, 2012; Clegg & Simpson, 2016), which results in significant, negative consequences for hundreds of millions of children (Ball, 2011). For example, in a global study of fourth-grade students, children whose home language differed from the testing language were 10% less likely to achieve the most basic level of reading proficiency compared to students who spoke the testing language at home (UNESCO, 2018). Though mother tongue instruction is potentially complicated to implement in linguistically diverse environments, it may allow children to learn more and may better permit their parents to engage with teaching materials and monitor student performance (Konsonen, 2005; Benson, 2002; Lieberman, Posner, & Tsai, 2014). In the case of Kenya, 45% of mothers of school-aged children cannot read English at a second-grade reading level (Uwezo, 2015); in one study, 72% of parents reported not understanding how to interpret student-learning data (Lieberman et al., 2014). Thus, a country’s policy regarding language of instruction (LOI) can have significant implications for children’s development in ways that interact with poverty, parental literacy, ethnicity, and other risk factors faced by vulnerable children as they move through the formal education system.

# **Child Assessment in Multilingual Environments**

Child development assessments allow teachers to understand how and what children are learning, to diagnose learning differences or language disorders, and to benchmark achievement against national or international standards (Snilsveit et al., 2016; Armon-Lotem, de Jong, & Meir, 2015). Similarly, researchers and policymakers rely on child assessments to examine program effectiveness. In both academic and nonacademic settings, students are routinely tested in only one language, either the LOI or parents’ preferred language. It is challenging to assess 2
Financial Regulation and Government Revenue interest rate in year _t_ through Finally, we can find the data on the average annual stock of domestic debt through the Annual Reports of the NBE in 2004–2012, which we indicate for brevity as _AASDD_ . We calculate the government revenue from financial repression, as GdM shows, which can be simply thought of as the savings in government revenue emerging from the difference between international and local rates on debt. This is conceptually in line with the results of Section 4.1.

# **Financial Repression between 2004 and 2013**

Once the GdM financial repression indicator is calculated, it is interesting to show its evolution over time and whether the 27% financial regulation rule is detected in this. Figure D1 presents both the aggregate real figure and the figure standardized by GDP over the whole available period. Three interesting facts emerge:

1. the financial repression revenue in Ethiopia is very low, between 0.1% and 0.5% of GDP, compared the average across country of 2% in GdM;

2. this indicator of financial repression declines substantially between 2006 and 2010, both in level and relative to GDP; 3. there appears to be a substantial increase in revenue after 2011.

Though not conclusive, we believe that the first fact may be due to the relatively small size of the Ethiopian financial system, which records as few as one branch for 62,000 (NBE 2012), though this has been growing steadily in the last few years. The second may be due to a mild internal financial liberalization introduced between 2006 and 2010.

28
as well as more detailed customs data that allows for the measurement of market-specific spillovers. However, the focus of this literature evaluating how networks and information spillovers reduce informal trade barriers has been to explain heterogeneity in firms’ export decisions. Papers on import learning are limited. However, Bisztray, Koren, and Szeidl (2018) bridge the gap by extending a framework most similar to Koenig, Mayneris, and Poncet (2010) to a panel of Hungarian firms that allows for an evaluation of the impact of managerial networks in addition to spatial connections.

2
Belgium.

- De Loecker, J. and P. K. Goldberg (2014). Firm Performance in a Global Market. _Annual Review of Economics 6_ (1), 201–227.

- De Loecker, J., P. K. Goldberg, A. K. Khandelwal, and N. Pavcnik (2016). Prices, Markups, and Trade Reform. _Econometrica 84_ (2), 445–510.

- De Loecker, J. and F. Warzynski (2012). Markups and Firm-Level Export Status. _American Economic Review 102_ (6), 2437–71.

- Dobbelaere, S. and J. Mairesse (2013). Panel Data Estimates of the Production Function and Product and Labor Market Imperfections. _Journal of Applied Econometrics 28_ (1), 1–46.

- Foster, L., J. Haltiwanger, and C. Syverson (2008). Reallocation, Firm Turnover, and Efficiency: Selection on Productivity or Profitability? _American Economic Review 98_ (1), 394–425.

- Goldberg, P. and N. Pavcnik (2016). The Effects of Trade Policy. In K. Bagwell and R. W. Staiger (Eds.), _Handbook of Commercial Policy_ , Volume 1, Chapter 3, pp. 161 – 206. North-Holland.

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), 84.-112.

Secondi, G. (1997). Private Monetary Transfers in Rural China: are Families Altruistic? _Journal of Development Studies,_ 33(4), 487-509.

Smith, J., McArdle, J. J., Willis, R. (2010). Financial Decision Making and Cognition in A Family Context. _The Economic Journal_ , 120 (November), F363-F380.

Zhao, Y., Strauss, J., Park, A., and Sun. Y. (2009). China Health and Retirement Longitudinal Study, Pilot, User ' s Guide. _National School of Development, Peking University_ .

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**Fig. 4: Aggregate Variables**

Figure 4 also shows that, with the aid of a demand shock, the model generates a large drop in consumption as in the data, which is calibrated to the relative decline of consumption and investment in the US data. The dynamics thereafter, however, are driven by the model mechanism. In particular, the quick recovery in consumption and the sluggish recovery in investment is the result of the interaction between the Non-Ricardian structure of the model and the repayment scheme of government debt. In the model, the consumption of workers is more sensitive to temporary changes in income and the profile of taxes. Fiscal policy, which fully replaces the wage income to the unemployed financed with government debt, allows constrained workers to sustain a high consumption, thus government debt crowds-out private investment from aggregate savings. Given the protracted repayment path of government debt, investment remains depressed for longer than aggregate output. As further discussed in Appendix C, increasing lump-sum taxes to balance the government budget reverses this pattern.<sup>16</sup> Figure 5 shows the behavior of factor prices. Wages and the interest rate barely move in

> 16More specifically, as is typically the case in models with heterogeneous agents and incomplete markets, the interest rate is lower than the time preferences. Workers are in the lowest income state, so their wealth converge to zero in finite time. As a results, workers with low wealth consume a large fraction of a transfer that is financed with taxes that are levied far enough into the future.

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# **Introduction**

This note serves as a guide to the calculation of weights for multiple waves of a household panel survey based on the experience of the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) project, located within the Development Research Group of the World Bank. The LSMSISA project is a multi-year project funded primarily by a grant from the Bill and Melinda Gates Foundation to collect panel data on households in Sub-Saharan Africa. The project is currently active in seven countries: Ethiopia, Malawi, Mali, Niger, Nigeria, Tanzania, and Uganda.

Although there is some variation between individual country projects, the main objective of the LSMSISA project is to provide public-use datasets to track income dynamics and changes in socioeconomic well-being with multiple rounds of data collection, while also providing as representative as possible a cross-section of households at each round. In the LSMS-ISA surveys, the initial rounds of all surveys were two-stage stratified cluster samples. Census enumeration areas (EAs) were selected in the first stage with probability proportional to size, then a household listing operation was conducted from which the second stage households were selected. As with all panel surveys, the LSMS-ISA surveys are only considered completely representative of the target population at the time of the selection of round 1. In subsequent rounds, the level of representativeness decays as the panel ages and there is sample attrition.<sup>2</sup> To minimize these effects, the LSMS-ISA surveys track both households that have changed location as well as a sub-sample of household members that have left households, surveying them in subsequent rounds in their new households. This methodology minimizes attrition by reducing the loss of shifted households, and allows new units to enter into the sample. With perfect implementation, the introduction of new households would match the natural changes in the overall cross-sectional population. In this case, the only loss of representativeness for the follow-up rounds would be from new households being formed by members not living in a particular country at the time of the round 1 survey
estimate of the impact of duration of school closures finds that for every week that schools were closed, learning levels declined by almost 1% of a standard deviation (Patrinos 2023).

# **6. Conclusions**

We estimate the global impact of COVID-19 on student learning on standardized tests over time. We model the effect of closures on achievement by predicting the deviation of the most recent results from a linear trend in mathematics and science achievement using data from all rounds.

COVID-19-induced school closures led to significant student learning losses. Math scores declined from 2018 to 2022 by an average of 12 points, or 14 percent of a standard deviation (SD), roughly equivalent to seven months of learning. Larger declines are recorded for students in schools that faced relatively longer closures and for lower-achieving students. Countries with the shortest closures experienced relatively small losses, while countries with the longest closures experienced losses of around 20 percent of a SD, or up to 12 months of learning. Depending on the closure length measure, each week of school closures was associated with 0.15-0.17 points or around 0.2 percent of a SD of additional decline in achievement for boys. At around 40 weeks of full school closures, the overall learning loss for boys is the same as for girls and increases for longer closures. Students with an immigrant background lost 0.33 points or 0.4 percent of a SD with every week of full school closures and while in countries with shorter closures their learning loss was smaller, for countries with around 25 weeks of closures their learning loss is like the learning loss for natives, and it increases for longer closures.

Distributional analyses reveal significant differences in the learning losses between students at different achievement levels, but also across countries that vary in the length of school closures. For countries with the average length of school closures the learning loss is similar for low-, average-, and high-achieving students. However, in countries with relatively short closures, the best students lost very little
individuals in the sample report being registered with M- PESA.<sup>5</sup> In terms of gender, age, and geographic distribution, there are not large differences

> 5 This figure drawn from the survey conducted October-November 2010 is substantially lower than a similar figure presented earlier in the paper: the number of mobile money subscribers in June 2011 was equivalent to roughly 75 percent of the adult population. The difference between the two figures largely reflects the large growth in mobile money subscriptions during that period. It also stems in part from differences inherent in the two sources of the data:

10
Third, we use the results to estimate the expected CO2 concentration for each city, given its geographic, demographic and economic characteristics. We explore the implications using regression residuals as performance indicators that identify cities whose CO2 emissions are less than or greater than model-based expectations. This provides the first empirical scorecard of city performance in CO2 emissions management.

Fourth, we use the econometric results to address a critical policy question on the role of public investment as a supplement to Pigouvian policy in mitigating CO2 emissions. Economists generally support emissions reduction via emissions taxation or permit trading (Stiglitz and Stern 2021; Jacobs and van der Ploeg 2019; King et al. 2019; Klenert et al. 2018). Many policy analysts who support Pigouvian pricing also argue for a non-Pigouvian supplement: coordinated public investment in low-carbon land development, energy and transport that will accelerate the transition to low-carbon economies (van der Ploeg and Venables 2020). Investments in subway systems provide an interesting test case for the non-Pigouvian approach. Reduced motor vehicle emissions and energy efficiencies associated with higher-density development are frequently cited as carbon-saving advantages of mass transit systems. By implication, cities with subways should have followed lowercarbon development paths, other things equal. We explore this proposition with our regression residuals, drawing on a recent global survey of subway systems (Turner and Gonzalez-Navarro 2018).

The remainder of the paper is organized as follows. Section 2 introduces the econometric model, while Section 3 describes the data. Section 4 performs an exploratory analysis of the data and the econometric results are presented in Section 5. Section 6 explores the implications, Section 7 offers a prospectus for future research, and Section 8 summarizes and concludes the paper.

# **2.  Modeling Urban CO2 Concentrations**

An extensive empirical literature has explored the determinants of CO2 emissions growth. Most of the attention has focused on drivers of CO2 emissions from fossil fuel combustion and cement production (e.g. Raupach et al., 2007; Jotzo et al., 2012). Land-use change also produces
)|(0.000)|
|Former cadre|0.003|0.001|0.002|0.002|
||(0.003)|(0.002)|(0.001)|(0.002)|
|Former|0.001|0.000|-0.001|-0.002*|
|manager|(0.002)|(0.002)|(0.001)|(0.001)|
|In Congress or|0.004**|0.001|-0.000|-0.001|
|Consultative Conf.|(0.002)|(0.001)|(0.001)|(0.001)|
|Party member|0.000|-0.001|-0.002*|-0.001|
||(0.002)|(0.001)|(0.001)|(0.001)|
|Prov., Sector, year|Yes|Yes|Yes|Yes|
|Observations|11,166|9,184|7,545|9,244|
|R-squared|0.034|0.050|0.031|0.048|

_Data sources: National Surveys of Privately Owned Enterprises in China._ Notes: Standard errors are clustered at provincial level and reported in parentheses. Significance levels 0.1, 0.05 and 0.01 are noted by *, **, and ***, respectively.

39
* second advantage of using **GDP** weights is that one does not need to obtain the full set of bilateral trade data and recompute a new set of weights for each country. But using bilateral trade weights is a possible extension for future research.

> **17** See Appendix 2 for a detailed description of the construction of the numeraire.

> **'** Data on major currencies and some of the emerging countries was extracted from Bloomberg and Datastream. Data for the case studies of Chile and Israel was downloaded from the respective Central Banks Web pages.

**19**
about the demand for the Bank’s research among the organization’s operational staff. How familiar are they with Bank research? How much do they rely on it for their work? How do the answers to these questions vary across units and sectors of

> 4 On the (broadly similar) challenges facing private firms in deciding whether to invest in basic research see the discussion in Rosenberg (1990).

> 5 A precise count is not available. My count excludes research assistants.

> 6 It has been argued that the heavy reliance on economics reflects an undesirable lack of ―ideological pluralism‖ at the Bank (Weaver, 2009, p. 505). While multiple ideologies might well balance out, Bank research (in common with mainstream economics and other social sciences) aims to be non-ideological. It is unlikely that the work of DECRG would attain its current high level of academic credibility if it failed in that aim.

> 7 This is based on the IDEAS rankings using a composite index of 20 variables measuring publications, downloads and citations based on the RePEc data; the rankings and details can be found here. If one confines attention to the Bank’s research department then the University of Chicago alone does better. Also see the data on citations assembled in Ravallion and Wagstaff (2010).

> 8 External audiences are also important but are not the focus of this paper. For data and analysis on the impacts of World Bank research on external clients see Ravallion and Wagstaff (2010).

3
that are present in both the GDSP and PovcalNet in total account for approximately 800 million of the poor population and 5.1 billion of the total population,<sup>6</sup> both of which

> 2 Available at: <u>http://www.worldbank.org/en/topic/poverty/brief/global‐database‐of‐shared‐prosperity</u> 3 Most of the increase is due to the addition of high income countries in Europe and Central Asia as well as North America.

> 4 For a more detailed description on how the Shared Prosperity indicator is calculated, please refer to Box 1 in the annex.

> 5 PovcalNet is the primary source of the World Bank’s international poverty estimates, available at: <u>http://iresearch.worldbank.org/PovcalNet/</u>

> 6 Based on figures in the World Development Indicators as of the year of the second survey for each country.

3
shifted to bank accounts in havens when political institutions are weak (Andersen et al., 2017). Last, our results contribute to the broader literature on political corruption (Olken and Pande, 2012).

The remainder of the paper is organized as follows. Section 2 describes our key variables. Section 3 explains our empirical strategy. Section 4 presents the results. A final section concludes.

# **2 Data**

## **2.1 Cross-border bank deposits**

We use data on foreign bank deposits from the Locational Banking Statistics of the Bank for International Settlements (BIS). This quarterly dataset has information on the value of bank deposits in 43 financial centers owned by residents of around 200 countries. The deposit information is at the bilateral level, e.g. the value of deposits in Swiss banks owned by residents of Tanzania, and builds on confidential reports from individual banks on their foreign positions. Importantly, deposits are assigned to countries based on immediate ownership rather than beneficial ownership; hence, if a Tanzanian firm has a subsidiary in Bermuda, which holds a Swiss bank account, the account is assigned to Bermuda in the BIS statistics.

The dataset covers the vast majority of the world’s cross-border bank deposits: all significant banking centers contribute to the dataset and within each banking center coverage is rarely below 90% (BIS, 2011). This is one of the most reliable sources for information about foreign assets and is therefore frequently used by central banks to construct capital accounts; by macroeconomists to gauge net wealth positions (Lane and Milesi-Ferretti, 2007; Zucman, 2013); and by public finance economists to study offshore tax evasion (Johannesen, 2014; Johannesen and Zucman, 2014).

While the BIS generally makes deposit information publicly available at the country level (e.g. deposits held by Tanzanians in all foreign banking centers combined and deposits held in Cayman banks by all foreigners combined), it has traditionally restricted access to deposit information at the bilateral level (
149 districts. The sample captures an impressive amount of Indonesia’s remarkable ethnic and geographic diversity.

We analyze the national _Ebtanas_ test scores of former junior secondary school students. Data on test scores was collected in 1997 and 2000 from all household members between the ages of 14 and 25 at the time of the interview. The survey asked respondents to state their score on the test, if they took the exam, for the elementary, junior, and senior secondary school levels. The survey also ascertained the type of school attended at each level.

The sample consists of all students who reported, in either 1997 or 2000, taking the junior secondary school test between 1990 and 2000. Of the 5,608 respondents that reported taking the national junior secondary school exam between 1990 and 2000, 4,498 reported scores from both elementary and junior secondary schools. An additional 115 respondents were not included in the sample because they did not report the type or district of the junior secondary school they attended, leaving a final sample consisting of 4,383 respondents.

This study also uses data on the presence of private schools, at both the district and the village level, to identify the effect of school type on student’s test score. Districtlevel data on the presence of schools come from the 1998 round of annual census of schools conducted by the Indonesian Ministry of Education. Eighty percent of the 42,000 secondary schools in Indonesia responded to this survey. Unfortunately, because of a budgetary shortfall during the 1998 financial crisis, the education census did not record detailed data on the characteristics of private schools. The only information recorded for private schools were their private status and location. This information is used to 10
feeling that the social order is somewhat fragile, although, the government has recently undertaken steps to address these sentiments.

_Table A3_ provides detailed institutional comparisons. Transparency International‘s Corruption Perception Index, which relates corruption perceptions of various countries‘ government, indicates considerable differences between Botswana and South Africa: Botswana‘s score is some 20 percent higher (better) than South Africa‘s. These results are consistent with the Quality of Governance Index provided by Kaufmann et al. (2004). Botswana has higher control of corruption values than South Africa. Similarly, the Rule of Law Index, which measures the degree of agents‘ confidence in and compliance with social rules, is more than three times larger in Botswana. Consequently, the respect of citizens for the state and the institutions that govern economic and social interactions is higher in Botswana. The capacity of the government to effectively formulate and implement sound policies (represented as the government effectiveness and regulatory quality) is higher in Botswana, which also has a higher level of political stability and absence of violence. Only voice and accountability are higher in South Africa, referring to the process by which governments are selected, monitored and replaced. Overall, the values of these six governance dimensions for the periods 1998 and 2000 clearly indicate a higher level of institutional quality in Botswana compared to South Africa. These results are also supported by the _International Country Risk Guide_ (ICRG). _Table A3_ also shows that political rights and the level of civil liberty are similar in both countries. To measure the variable income inequality – we use the newest available data set, Estimated Household Income Inequality (EHII), constructed by Galbraith and Kum (2005)<sup>7</sup> . The GINI coefficients indicate that income inequality is slightly greater in Botswana. The Index of Economic Freedom clearly indicates a higher fiscal burden<sup>8</sup> for South Africa, but also less government interventions in South Africa. The Polity IV data set shows, in line with