rafmacalaba/datause-encoder-data huggingface dataset:
Holmlund H., Lindahl M. and Plug E. (2011), “The Causal Effect of Parents’ Schooling on Children’s Schooling: A Comparison of Estimation Methods”, Journal of Economic Literature.

Huber, Martin, Lechner, Michael and Wunsch, Conny, 2013. The performance of estimators based on the propensity score, Journal of Econometrics, Elsevier, vol. 175(1), pages 1-21.

Kanbur, Ravi., and Xiaobo Zhang, “Fifty Years of Regional Inequality in China: A Journey through Revolution, Reform, and Openness, Review of Development Economics 9 (2005), 87106.

Khan, Azizur Rahman, Keith Griffin and Carl Riskin (1999). “Income Distribution in Urban China during the Period of Economic Reform and Globalization, American Economic Review v89, n2 (May 1999): 296-300. Khan, Azizur Rahman and Carl Riskin. 2001. Inequality and Poverty in China in the Age of Globalization, New York: Oxford University Press.

Knight, John and Lina Song. 1993. “The Spatial Contribution to Income Inequality in Rural China, Cambridge Journal of Economics 17: 195-213.

Knight, John and Lina Song (1999). The Rural-Urban Divide. Economic Disparities and Interactions in China, Oxford: Oxford University Press.

Knight, John, Li Shi and Deng Quheng. 2010. Education and the Poverty Trap in Rural China: Closing the Trap. Oxford Development Studies, 38:1, pp. 1-24.

Knight, John, Terry Sicular, and Yue Ximing. 2013. Educational Inequality in China: the Intergenerational Dimension. in Rising Inequality in China: Challenge to the Harmonious Society, eds. Li Shi, Luo Chuliang, and Terry Sicular, ch.4. Cambridge and New York: Cambridge University Press.

44
early.<sup>12</sup> To be conservative in our measure of absence, we exclude all school closures due to bad weather, school construction/repairs, school functions and alternative uses of school premises (for instance, elections). We also exclude all part-time teachers, teachers who were transferred or deputed elsewhere, or teachers reportedly on a different shift.

We construct a school infrastructure index by adding binary indicators for the presence of four indicators of school facilities - drinking water, toilets, electricity and a library. We construct a remoteness index by taking the average of nine normalized indicators of distance to various amenities including a paved road, bus station, train station, public health facility, private health clinic, university, bank, post-office and Ministry of Education office. A lower score on the remoteness index represents a better connected school.

During each survey visit, we record the date of the most recent school inspection. We measure the extent of monitoring and supervision as the mean probability of being inspected in the past three months across all three visits. We used a similar procedure for constructing the mean probability of a parent-teacher association (PTA) meeting. Average parental education of children in a school is computed from the basic demographic data collected for the sample of fourth-grade students chosen for assessments of learning outcomes.

For most of the analysis in this paper, we use the village as our unit of analysis and examine mean village-level indicators of both inputs and outcomes because a large number of new schools had been constructed between 2003 and 2010, including in villages that already had schools. This school construction resulted from a policy designed to improve school access by ensuring that every habitation with over 30 school-age children had a school within a distance of one kilometer. Thus, to ensure that our sample was representative in 2010, and at the same time amenable to panel data analysis relative to 2003, we constructed the panel at the village level, with a new representative sample of schools drawn in the sampled villages.<sup>13</sup> All the results reported in this paper are population weighted and are thus representative of the relevant geographic unit (state or all-India).

# **3 Summary Statistics**

# **3.1 Changes in Inputs**

The data show considerable improvements in school inputs between 2003 and 2010 along three broad categories - teacher qualifications and working conditions, school facilities, and

> 12Field teams obtained lists of state and national school holidays in advance of creating the field plans and ensured that no visits were conducted on these days.

> 13Even in the absence of school construction, the survey firm did not retain school and teacher level identifiers from the 2003 survey (complying with data protection norms), which would have made it difficult to construct a school-level panel (especially for villages with multiple schools).

7
47 except EMENA. During thls period, the share of "other welfare" expenditures were the highest in LAC and lowest in East Asia.

Inter-regional comparison of trends in social sector expenditure **s** hows that relative to the other regions, the six low income countries of South Asia as a group (i.e., Bangladesh, India, Myanmar, Nepal, Pakistan and Sri Lanka), had among the lowest shares of health and education expenditures. On the other hand the group of East Asian countries (i.e., Indonesia, Korea, Papua New Guinea, Philippines, Singapore, and Thailand had relatively larger public expenditure allocations to the education sector. Relative to the other regions, central government expenditure allocatlons to health and "other welfare" programs are larger in the LAC region. Central government expenditure allocations in Africa compared to the overall average for all developing countries, is higher for education, lower for "other welfare programs," and about equal for health.

In summary, the relative share of education expenditure in total central government expenditusce has tended to be more than<sup>twice as large as that of</sup> health. In 1975, 1980, and 1985 health expenditure as a share of central government expenditure was about 6 per cent, and that of education expenditure was around 13 per cent. The share of expenditures on "other welfare" was also around 13 per cent. On the whole there is better expenditure data on education than health. Other than the consolidated central government expenditure data from IMFGFS, there is currently little data on government expenditure by function suitable for cross-country comparison.
China’s iron and steel industry has reduced its energy intensity by 60% from 1994–2003, while variation in energy intensity across firms in China’s iron and steel sector has become larger during the same period. However, unlike our present study, these past studies employ industrynot firm-level data and are therefore unable to examine what is happening at the firm-level.

In this paper, we utilize a unique set of firm-level data from China’s most energy-intensive largeand medium-size industrial enterprises in each of these four industries over a six-year period, 1999–2004. We empirically examine to what extent China’s energy-saving programs, liberalization of domestic markets, and openness to the world economy contributed to the decline in energy intensity within these industries. We estimate firm-level energy intensity on factors such as energy prices, technology development expenditures, region, and ownership type, expecting that higher energy prices, technology development (including process innovation and product innovation), more openness to world markets (including regional location), and ownership reform have all contributed to the decline in energy intensity in these four industries.

We find rising energy prices to be one of the main factors explaining the decline in energy intensity in these industries. Scale economies, encouraged by policies such as “grasping the large, letting go off the small” are another important factor explaining the decline within each industry. However, technology development, trade openness, and regional and ownership differences are only contributors in one or two of the four industries. Additionally, in the case of Pulp and Paper, firms in the Northern and Eastern regions of China have lower energy intensity than firms in the South. In the Cement industry, the energy intensity of firms in the North, East, and South is less than firms in the Southwest. In the Iron and Steel industry, energy intensity of firms in the South and Southwest is less than firms in the North and East.

5
# Annex 5. Correspondence between template 11 and TAFIRE

Sixteen African countries belonging to the Organization for the Harmonization of Business Law in Africa (OHADA) use the statement of sources and application of funds (tafire) instead of the statement of cash flows: Benin, Burkina Faso, Cameroon, Central African Republic, Chad, Comoros, the Republic of Congo, Côte d‘Ivoire, Equatorial Guinea, Gabon, Guinea, Guinea-Bissau, Mali, Niger, Senegal, and Togo, with the Democratic Republic of Congo joining soon. The template, capturing cash flow entries, is designed to facilitate data collection from TAFIRE.

|**Item**|**Category**|**French equivalent or comment**|
|---|---|---|
|**Incom**|**e statement**||
|1|Revenues from sales|Chiffre d’affaires|
|2|Total employee compensation|Charges du personnel.|
|3|Purchase of goods and services directly used in<br>production|Achats de matières premières et fournitures liées (+/- Variation de stocks) +<br>Autres achats (+/- Variation de stocks) + Achats de marchandises (+/-<br>Variationde stock)|
|_3a_|_... fuel_||
|_3b_|_... power purchase agreement (PPA) fees (if_<br>_applicable)_||
|4|Other purchase of goods and services (that is, those<br>not included in line 3 above )|Transports + Services extérieurs|
|5|Rent||
|6|Depreciation and amortization|Dotations aux amortissements et aux provisions (exploitation)|
|7|<br>Miscellaneous taxes/fees (property and so on)|<br>Impôts et taxes+Autres charges|
|8|Other operating expenditures|If any.|
|9|Income (loss) from operations|Total des produits exploitation–total des charges d’exploitation|
|9a|<br>Income (loss) from operations as reported by<br>enterprise|<br>Resultat d’explotation|
|10|Interest paid|Total des charges financieres|
|10a|_... foreign _||
|11|Interest earned|Total des produits financiers|
|12|Direct foreign grants||
|13|<br>Transfers/subsidies from government|Subventions d’exploitation|
|14|Revenue from irregular activities|Produits HAO or produits hors activités ordinaires|
|14a|_... fixed assets’ selling price_|Prix de cession des immobilisations|
|15|Other nonoperating revenue|Production immobilisée + Autre produits + Reprises de provisions +<br>Transferts de charges|
|16|Irregular activities expenditures|Total des charges HAO|
|16a|_... book value of fixed assets sold_|Valeurs comptables des cessions des immobilisations|
|17|Other nonoperating expenditures|If any|
|18|Profit (loss) before tax|Total des produits annuels–total des charges annuels|
|19|<br>Corporate income tax|<br>Impôt sur les bénéfices (ou TSS)|
|_19a_|_... tax exemptions_||
|20|Net income, as reported by enterprise|Resultat net de l’exercice|
|**Statem**|<br>**ent of cash flows**||
|21|Net cash from operating activities|Excédent de trésorerie d’exploitation (for consistency, should be negative if<br>there is cash surplus, and positive in case of cash shortage)|
|22|Net cash flow from investing activities|In TAFIRE, it is_investissement total_, which is the difference between<br>applications and resources(or_emplois and ressources_)|
|22a|Capitalized rehabilitation costs (increase in the|TAFIRE specific: Charges immobilisées (augmentation dans l’exercice)|

66
employees participate in these economic sectors, compared with 8 percent to 10 percent in the case of men wage employees. Presumably, those women who self-select into these sectors do so because they expect to have good jobs there given either their ability or their training. In other services, the average JQM is 16 percent higher among men than among women. Women and men may be choosing different occupations in this case because other services encompass a wide range of occupations.

Figure 7. The Job Quality Measure among the Wage Employed, by Industry and Sex <!-- Start of picture text --> 4.00 100%<br>90%<br>80%<br>3.00<br>70%<br>60%<br>2.00 50%<br>40%<br>30%<br>1.00<br>20%<br>10%<br>0.00 0%<br>Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male<br>Agriculture Mining Manufacturing Public utilities Construction Commerce Transport and Financial and Public Other Services<br>Communications Business Services Administration<br>Income Benefits Stability Working Conditions Share of Wage Employees<br>JQM<br>Share of Wage Employees<br><!-- End of picture text --> _Source:_ Estimates based on multiple labor force and household surveys (see annex table A1). _Note:_ The sample includes 37 countries with information on all dimensions. The figure shows JQMs by industry, disaggregated by sex, for the wage employed only.

# **b. The main correlates of job quality**

Given the high correlation across various JQM dimensions, the paper follows Brummund, Mann, and Rodríguez-Castelán (2018) and runs the following regression to identify conditional correlations and to understand more clearly the characteristics that may be the main predictors of job quality: where JQjcs is the level of job quality for an individual _j_ , working in sector _s_ , in country _c_ ; Xjcs is a vector of covariates that may help inform policy interventions; Sectors is a sector of employment; and Countryc is country fixed effects.

The results from a linear regression of equation 2 are presented in table 3. Column 1 shows the results, including all countries with available information, and columns 2–5 show the results for countries in particular regions. (The Europe and Central Asia and the Middle East and North Africa regions are excluded because only two countries have adequate data in those regions.) There are some notable differences across regions. In particular, the impact of being male on job quality is statistically significant only in the Latin America and Caribbean region. Education and urban location are statistically significant in all regions, but to a lesser extent in the East Asia and the Pacific and in the South Asia regions. Industry sectors have a generally positive impact on job quality compared with agriculture, the omitted category. However, the statistical significance of the effects is much lower in the South Asia region, and sometimes they are not statistically significant.

21
|Married|-0.026|-0.026|-0.026|-0.030|-0.027|-0.029|
|---|---|---|---|---|---|---|
|Children|-0.025|-0.020|-0.022|-0.016|-0.011|-0.004|
|No education|-0.055|-0.104|-0.087|-0.115|-0.121|-0.132|
|Prime education|0.076|0.059|0.111|0.119|0.117|0.132|
|General education|0.031|0.032|0.035|0.038|0.042|0.043|
|Vocational education|0.039|0.037|0.042*|0.050**|0.054**|0.049*|
|Higher education|-0.029|-0.035|-0.029|-0.029|-0.028|-0.031|
|Northern Europe|-0.070**|-0.060**|-0.056**|-0.055*|-0.055**|-0.069**|
|Non-Rom South<br>Europe|-0.014|-0.013|-0.013|-0.014|-0.010|-0.003|
|Eastern Europe|-0.432***|-0.407***|-0.395***|-0.394***|-0.388***|-0.444***|
|Non-European<br>Anglophone|-0.084|-0.025|-0.005|0.000|-0.015|-0.015|
|Other countries|0.258|0.250|0.194|0.207|0.218|0.204|
|Observations|1,956|1,953|1,952|1,946|1,923|1,921|

Notes: Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1. Reference groups are not fluent and no language training abroad, worse occupation, construction, high-school education, Southern Europe.

24
Sources: Household Budget Survey 2012-2015. Eliminating 3SE outliers.

The declared prices paid by households and the declared quantities of cigarettes purchased are applied, thereby rendering possible a measure of the effective price paid for cigarettes that reflects changes in brand and quality. In Moldova, tobacco prices are affected by government action as it affects the specific and ad valorem tax. Tobacco tax policy has had many changes during the last 10 years. In 2007 filtered cigarettes had a specific tax of 6.5 Moldovan Lei (MDL), per 1,000 cigarettes and a 3% _ad valorem_ excise tax. Between 2008 and 2015 the Government of Moldova tested different changes for the specific tax and the _ad valorem_ tax. One problem it faced is that inexpensive cigarettes would pay a very small _ad valorem_ tax. Since 2016, there is a new minimum excise tax per 1,000 cigarettes, of 400 MDL, the specific tax was of 300, and the _ad valorem_ tax was 12%. In 2016, the excise share for cigarettes would vary from 56% to 25%, depending on the price of the cigarettes[29]. A detailed analysis of the tobacco taxation policy in Moldova can be found in Krasovsky (2016). Figure 1 shows the estimated average price paid for cigarettes per income decile based on household survey data adjusted for inflation as well as national statistical data.

**Figure 1 – Tobacco Price index, by Year and Decile**

After obtaining a measure of the prices paid for cigarettes in Moldova, the study calculates real prices per year and then the elasticity for each income decile of the population (figure 2; table 4).<sup>6</sup> It estimates an average tobacco price elasticity of −0.33, which, in absolute terms, is slightly lower than the elasticities found in the literature on other countries in the region, but within the confidence interval of the estimates of Gallus et al. (2006) on European countries.[37] The Standard error of this estimate is approximately 0.10, generating a 95% confidence interval of -0.20,+0.20. To show the effect of different scenarios, we simulate a lower bound elasticity and an upper bound elasticity. These estimates have differences of -0.2 and +0.2 with the elasticity

> 6  The use of three different elasticities helps to test the robustness of our results. We tried different specifications with subgroups of tobacco products to calculate elasticities, showing a variety of results, that are represented in the three possible scenarios. As an example: for packages of 20 cigarettes, filtered and not-filtered, sold in authorized establishments, for personal consumption and after eliminating outliers, the estimated elasticity is similar to the lower-bound elasticity presented in this paper.

8
Finally, to account for day of the week effects, we tested the significance of dummy variables. The associated coefficients turned out to be individually and jointly not different from zero.<sup>15</sup>

# **5. Data Description and Estimation Results**

# **5.1 Data Analysis**<sup>**16**</sup>

Daily exchange rate returns are calculated by taking the log difference of the US dollar/ Mexican peso ($US/MXP) exchange rate from August 1, 1996 to June 29, 2001 and of the US dollar/Turkish lira ($US/TL) from February 22, 2001 to May 30, 2002, respectively. For Mexico, we use the exchange rate determined in the 48 hour inter-bank foreign exchange market.<sup>17</sup> In the case of Turkey, we employ the selling spot rate.

Table 1 shows descriptive statistics for the exchange rate log returns, the first difference of the Brady bond yields in Mexico, the target for cumulative balances (or _short_ ) in millions of pesos, and the first difference of the overnight interest rate in Turkey.

# **Table 1. Descriptive statistics on exchange rate log-returns and money market.**

> 15 We do not report such estimators on the grounds of parsimony, though the results can be obtained from the authors upon request.

> 16 All data are obtained from the Banco de Mexico and the Central Bank of the Republic of Turkey except for Brady par yield, which is taken from Datastream (mnemonics MXBSYLD). 17 We also used the spot floating exchange rate and the results are basically equivalent.

15
This paper aims to identify, review and analyze the existing evidence on the effect of the COVID19 pandemic and school closures on learning outcomes progress among school-age children and youth. The specific research questions are:

1. What is the impact of the COVID-19 pandemic and school closures on learning progress among school children and youth?

2. How does the impact differ across socioeconomic background, age, boys and girls, subjects, sub-national contexts, ethnicity, etc. (data permitting)?

# II. Search strategy and identification methods

We searched the following scholarly sources:

- ECONLIT, Google Scholar, PubMed, Education Resources Information Center, and Cochrane Library

- Coronavirus Research Database, Education Database (ProQuest), Web of Science

- Preprint and working paper repositories: SSRN, MPRA, IZA, NBER, OSF Preprints, PsyArXiv, SocArXiv, EdArXiv

- Reach out to researchers.

To conduct the search, the keywords “COVID-19”, “coronavirus”, “2019-ncov”, “sars-cov-2”, or “cov-19”, were used in combination with “learning loss”, “learning slide”, “education gap”, or “achievement gap”. We focused on learning loss among school-age children across all subjects, with special attention to the most reported subjects of math and reading. School-age children and youth are defined as being between ages 5 and 18, thus excluding both kindergarten and higher education.

4
Our measure of policy uncertainty is the Economic Policy Uncertainty (EPU) index compiled by Baker, Bloom and Davis (2016) and available with monthly frequency from <u>www.PolicyUncertainty.com. Baker, Bloom and Davis (2016) have developed the EPU index for</u> 22 countries using frequency counts of newspaper articles that contain terms pertaining to the trio of economy, policy, and uncertainty. The 22 countries are: Australia, Brazil, Canada, Chile, China, Colombia, France, Germany, Greece, India, Ireland, Italy, Japan, Mexico, Netherlands, Russian Federation, Republic of Korea, Singapore, Spain, Sweden, United Kingdom, and United States. The EPU index is strongly related to other measures of economic uncertainty, such as stock market volatility, and economic variables such as industrial production and unemployment rates.

**Figure 2.** **_Import volume growth and policy uncertainty, by country and year: for goods and services imports and for goods imports and using level or change of policy uncertainty_**

<!-- Start of picture text --> 0.4 0.4<br>0.3 y = ‐0.0691x + 0.3787 0.3 y = ‐0.0155x + 0.0597<br>R2 = 0.0839 R2 = 0.0026<br>0.2 0.2<br>0.1 0.1<br>0 0<br>‐0.1 ‐0.1<br>‐0.2 ‐0.2<br>‐0.3 ‐0.3<br>‐0.4 ‐0.4<br>‐1.5 ‐1 ‐0.5 0 0.5 1 1.5<br>3 3.5 4 4.5 5 5.5 6<br>Log‐difference of Economic Policy Uncertainty (EPU)<br>Log of Economic Policy Uncertainty (EPU) index index<br>0.4 0.4<br>0.3 y = ‐0.0729x + 0.3948 0.3 y = ‐0.0208x + 0.058<br>R2 = 0.0876 R2 = 0.0044<br>0.2 0.2<br>0.1 0.1<br>0<br>0<br>‐0.1<br>‐0.1<br>‐0.2<br>‐0.2<br>‐0.3<br>‐0.3<br>‐0.4<br>‐0.4<br>‐0.5<br>‐0.5 ‐1.5 ‐1 ‐0.5 0 0.5 1 1.5<br>3 3.5 4 4.5 5 5.5 6 Log‐difference of Economic Policy Uncertainty (EPU)<br>Log of Economic Policy Uncertainty (EPU) index index<br>Source: International Monetary Fund World Economic Outlook, www.PolicyUncertainty.com (Baker et al. 2016), and<br>authors’ calculations.<br>)‐difference<br>g<br>(log‐difference) (lo<br>Goods and services import volume Goods and services import volume<br>)‐difference<br>g<br>(loort volume<br>p<br>Goods im<br>Goods import volume (log‐difference)<br><!-- End of picture text --> The sources used for the other variables depend on the specification and are listed in Table 1. All estimation samples cover the period from 1995 through 2018. Years prior to 1995 are excluded 7