rafmacalaba/datause-encoder-data huggingface dataset:
secondary levels. Examination scores take values ranging between 50 and 100. The average score is 82.0 (with standard deviation of 11.5) at the primary, 75.2 (13.2) at the preparatory, and 74.9 (12.8) at secondary levels.<sup>11</sup> We focus on scores of 12-15 year-olds at primary, 14-18 year-olds at the preparatory, and 18-21 year-olds at the secondary completion. Among these individuals, respectively, 59, 72 and 82 percent reported exam scores in the 2009 SYPE.

In addition to exam scores, the 2009 SYPE survey collected information on the background of the youth. In the analysis below, the set of variables used to capture their background include gender, governorate of birth, parental education level<sup>12</sup> , religion (Muslim vs another religion), and family wealth, obtained from information on the durables owned by households and housing conditions, and their wealth group (i.e., consumption quintiles).

We perform the analysis of inequality in exam scores using the same approach as for learning achievements evaluated using TIMSS data above. **Table 8** reports OLS estimates of the partial effects of circumstances on exam scores. Girls tend to do better at all three exams with a gap of 2 to 4 more exam points on average, increasing with attainment, than boys. The gaps associated with parental education levels are large, ranging from 6 points for father’s education at the primary completion exam to 10 points for mother’s education at the preparatory. The gaps associated with family wealth are large—about 8 points at the primary and preparatory completion exams. There are no apparent differentials in scores between Muslims and non-Muslims. However, there are large differentials associated with birthplace, with scores for some birth-governorates 13-14 points higher than others at the primary and preparatory completion levels (estimates not reported, available upon request).<sup>13</sup>

> 11 In an alternative but closely related approach, called ex-post and associated with <u>Roemer</u> (1998), equality of opportunity obtains only when individuals exerting the same degree of effort, regardless of their circumstances, receive the same reward. See Fleurbaey and Peragine (forthcoming) for a formal discussion of the relationship between the two approaches.

> 12Note that the information on parental education is missing for children who do not live with their parents. For those children, parental attainment is coded in a missing category (we do not drop those observations).

> 13Interestingly, the gaps by birth place are lower at the secondary level, suggesting that the pupils who attain that level of education are already quite selected.

24
# **Table (3a):   Augmented Dickey-Fuller Tests for Unit Roots: Second Differences of Log Levels of Real Data - Sample  1959 - 2013**

|Variable|t-adf|beta Y_lag|t-DY_lag|Maximum Lags|AIC|
|---|---|---|---|---|---|
|DDrm2|-9.329**|-0.26068||0|-5.425|
|DDrgdp|-5.181**|-1.7868|1.628|4|-6.148|
|DDINFL|-14.87**|-0.62725||0|3.749|
|DDInterestrt|-6.885**|-1.5104|1.621|2|-3.656|
|DDDisc|-5.882**|-2.0901|1.623|3|0.9403|
|DDrcons|-5.475**|-2.6611|1.525|4|-5.548|
|DDexrtend|-6.073**|-3.4492|1.739|4|-3.156|

Constant and Trend Included - Critical Values; T=55; 5%=-3.49 1%=-4.13 P-values are in brackets

*** Significance level at 1%; , ** Significance level at 5%; *Significance level at 10%

# **Table (3b):   Augmented Dickey-Fuller Tests for Unit Roots: First Differences of Log Levels of Real Data - Sample  1959 - 2013**

|Variable|t-adf|beta Y_lag|t-DY_lag|Maximum Lags|AIC|
|---|---|---|---|---|---|
|Drm2|-2.659|0.55287|-0.03925|4|-5.492|
|Drgdp|-2.977|0.38232|-0.9231|4|-6.265|
|DINFL|-3.810*|-0.85657|0.4357|4|3.169|
|DInterestrt|-3.467|-0.12022|0.3913|4|-3.838|
|DDisc<br>|-3.393|-0.18911|0.1345|4|0.7395|
|Drcons|-2.879|0.024227|0.4270|4|-5.682|
|DDexrtend|-4.909**|-1.3961|0.2195|4|-3.524|

Constant and Trend Included - Critical Values; T=55; 5%=-3.49 1%=-4.13 P-values are in brackets

*** Significance level at 1%; , ** Significance level at 5%; *Significance level at 10%

42
# **Table A2: List of selected accidents in oil and gas wells and refineries and storage facilities** <u>Only large accidents are included</u>

|**Date & Accident**|**Description**|**Impacts**|
|---|---|---|
|1982:<br>Sinking of Ocean Ranger,<br>Canada<sup>a</sup>|On 15 February 1982, Ocean Ranger, a semi-submersible mobile<br>offshore drilling and exploration platform sank on the Grand<br>Banks ofNewfoundland, Canada due to ahightide|All 84 crew members on board died|
|1984: Oil refinery explosion in<br>Romeoville,Illinois<sup>b</sup>|On July 23, 1984, an explosion and fire took place at a Union Oil<br>petroleum refineryin Romeoville,Illinois, outside Chicago|Deaths of 17 people|
|1984: San Juan Ixhuatepec,<br>LPG storage disaster nearby<br>Mexico City<sup>c</sup>|On 19 November 1984, a large LPG storage and distribution<br>center in San Juan Ixhuatepec, 20 km north of Mexico City,<br>exploded due to a fire ignited in LPG vapor cloud created due to<br>leakage|500–600 people killed, and 5000–7000<br>others suffered with severe burns|
|1988: Norco, oil refinery,<br>Louisiana, USA<sup>d</sup>|Fire and explosion due to escaped gas from a corroded pipe in<br>one of the facility's catalytic cracking units|7 fatalities, 42 injured|
|2005:<br>Texas refinery explosion<sup>e</sup>|On March 23, 2005, a hydrocarbon vapor cloud was ignited and<br>exploded at the ISOM isomerization process unit at BP's Texas<br>Cityrefinery, which is US’s thirdlargestrefinery|Deaths of 5 workers and 180 others<br>injured|
|2010: Gulf of Mexico, drilling<br>rig explosion<sup>f</sup>|On April 20, 2010 where a deep-water horizontal drilling rig,<br>owned and operated by British Petroleum, exploded. It caused a<br>fire onthemobile offshore drilling unit|Deaths of 11 workers and seriously<br>injured 17 others|

a The New York Times (16 February 1982). “84 Feared Dead as Oil Drilling Rig Reportedly Sinks in North Atlantic". <u>http://www.nytimes.com/1982/02/16/world/84-feared-dead-as-oil-drilling-rig-reportedly-sinks-in-northatlantic.html?scp=7&sq=sinks%20in%20Atlantic&st=cse</u> b Chicago Tribune (2 November 1985). "Welding Cracks Caused Union Oil Blast Fatal To 17" by Dave Schneidman. http://articles.chicagotribune.com/1985-1102/news/8503150149_1_cracks-weld-fire-marshal cArturson, G. (1987). "The tragedy of San Juanico--the most severe LPG disaster in history". Burns Incl Therm Inj. 13 (2): 87–102.

d Tim Haïdar (August 27, 2012), Five Deadliest Onshore Oil & Gas Blasts. Oil & Gas IQ, https://www.oilandgasiq.com/integrity-hse-maintenance/articles/fivedeadliest-onshore-oil-gas-accidents e BP Fatal Accident Investigation Report -Isomerization Unit Explosion Final Report, Texas City, Texas, USA. December 9, 2005.

The Guardian (November 28, 2012). "BP suspended from new US federal contracts over Deepwater disaster" by Suzanne Goldenberg and Terry MacAlister. https://www.theguardian.com/environment/2012/nov/28/epa-suspends-bp-oil-spill 36
Table 3: Estimated Subsidy Rates for Energy Sources in Tunisia (valid May 2014)

||Nonsubsidized<br>price (NPi),<br>TND|Subsidy (Si),<br>TND|Subsidy rate<br>(SRi=Si/NPi), %|Market price, (DPi=NPi-<br>Si), TND|
|---|---|---|---|---|
|Gasoline|1.856|0.186|10|1.670|
|LPG|1.790|1.220|68|0.570|
|Diesel|1.584|0.334|21|1.250|
|Electricity: Household|s consuming less tha|n 200 kWh per m|onth||
|Electricity 0–50|0.268|0.193|72|0.075|
|Electricity 0–100|0.268|0.160|60|0.108|
|Electricity0–200|0.268|0.128|47|0.140|
|Electricity: Household|consuming more th|an 200 kWh per m|onth||
|Electricity 0–200|0.268|0.117|43|0.151|
|Electricity 201–300|0.268|0.084|31|0.184|
|Electricity 301–500|0.268|-0.012|-4|0.280|
|Electricity> 500|0.268|-0.082|-31|0.350|

_Source:_ World Bank staff calculations.

In the case of LPG, the latest available numbers are from 2013<sup>6</sup> (Table 4) and show a subsidy rate of 68%. In fiscal terms, these subsidies amounted to TND 749 million, 15% of all energy subsidies publicly transferred and 1% of GDP. Diesels (containing either 0.005 or 0.2 percent of sulfur) have subsidies between 16 and 26% of final prices (respectively), representing 1.5% of GDP, 23% of energy subsidies, and TND 1,146 million in 2013. The energy source most highly subsidized in terms of public spending was electricity, with 3.4% of GDP, 51% of all energy subsidies, and over TND 2.5 billion a year (in 2013). Its subsidized price share oscillated between 27 and 50%. The remaining 12% of energy subsidies were distributed among gasoline, kerosene, and heavy fuel.

Interestingly, at the level of residential spending on energy, other forms commonly utilized by households such as charcoal, natural gas, or solid biofuels are not subsidized.<sup>7</sup> Within the household sector, solid biofuels constitute the main source of energy expenditure (42%), followed by LPG (18%), electricity (15%), and natural gas (10%). Diesels and gasoline are very low sources of energy expenditures. Also, as the next section shows, there are marked differences in consumption and spending by socioeconomic groups.

> 6 Nonetheless, LPG prices have remained unchanged since February 2010.

> 7 In addition, the consumption of each energy source and, therefore, the ultimate beneficiaries of the subsidized prices vary substantially by sector, as shown in appendix 3. Figures reported in appendix 3 refer to 2012, the latest available for the composition of consumption within each sector, residential and nonresidential.

11
on firms and has to resort to imperfect proxies such as size-dependent polices (Best et al. 2015, Bachas and Soto 2016). As a country’s fiscal capacity grows (Kleven et al. 2016, Jensen 2016), the tax authority’s reliance on size-dependent policies can be weakened. We therefore hypothesize that the size gradient in tax inspection decreases with a country’s income level. To test this hypothesis, we estimate the tax inspection size gradient across subsamples of countries at five different levels of development.<sup>22</sup> Figure 6 shows the reduced form and IV coefficients for the likelihood of tax inspection at each income level.<sup>23</sup> The magnitude of the tax inspection size-gradient is decreasing over levels of development, both in the reduced form and IV specifications.<sup>24</sup> While we cannot reject that lower-middle, middle and higher-middle income groups have different sizegradients,<sup>25</sup> we can reject that their size-gradient is equal to that of high-income countries. In high-income countries both the reduced form and IV coefficients are centered at zero. The absence of size-dependent policies at high income levels is consistent with the theory that countries with strong fiscal capacity decrease their reliance on production-inefficient tax instruments. It also provides some support to the identifying assumption that tax inspection is orthogonal to size in a high tax capacity environment.

> 22The groups correspond to the World Bank income classification, except for the top income group (above USD 21,000) which corresponds to the 90th percentile of income in the WBES and was chosen such that countries are comparable to the US in terms of fiscal capacity.

> 23The first stage coefficients are constant across income levels and the 1st stage F-statistic is well above 10 at each income level.

> 24The coefficients for the lowest income countries (GDP per capita below $1,100) are the largest but are only significantly different from zero at the 10% level. There are two explanations for this. First, this group contains fewer country-year observations than lower-middle, middle and higher-middle income groups. Second we weight ISIC-3 industries by their number of observations. Surveys in the poorest countries tend to be smaller in terms of number of firms and have fewer ISIC-3 industries represented.

> 25These levels of development include the countries often studied in the misallocation literature such as China, India and Mexico.

19
6. EPRs In India change dramatically over time because of the pricing policies of the Government for major intermediate products. By the nature of world markets, international prices of intermediates (metals, chemicals, etc.) a-e substantiallc **y** clical. In the name of price stability, the Government adjusts tariffs on these products such that the International price fluctuations are not passed on to the domestic prices. Since ERP estimates are based on comparisons of domestic and international prices, certain sectors would show widely varying ERP estimates depending on when they are made.

7. Nominal Protection. The overall effects of trade, protection and regulatory policies for industry are ultimately reflected in the comparative prices of industrial products (domestic ex-factory prices relative to international prices, before taxes charged to the users, i.e., realized protection). Some 500 price ratios between domestic ex-factory prices and CIF import prices, (nominal protection coefficients NPC) covering the period 19871989, assembled from a multiplicity of sources (BICP reports, Bank subsectoral analyses, DFI-financed sub-projects) are presented in Table 1. These price ratios provide a reasonably comprehensive picture of the level of competitiveness of Indian industry in the late 1980s.
- 34 -

expected from a public school without adding to the financial burden of the government. As public schools are normally geared to the average child, private schools in a pluralistic society offer alternatives for parents who want more for their children.

# CONCLUSIONS

The paper uses data from the Dominican Republic to extend the evidence on the relative cost and effectiveness of public and private schools. It also increases our understanding of how improvements in effectiveness and economic efficiency are being achieved by exploiting the opportunities afforded by several unique characteristics of the data set -- unlike in previous studies, the Dominican Republic survey contains information on two types of private schools. Furthermore, useful (albeit limited) information on costs, tuition and other fees were obtained. This information enabled us to understand the market for education a little better and to confront some of the prevailing myths about private education with real evidence.

Several conclusions are worth highlighting here. First, selectivity bias is important, and failure to deal with it effectively can lead to misleading conclusions. For example, when selection bias is not controlled for the estimates suggest the effectiveness of private schools is negligible. Our study, however, reveals that the selectivity bias disappears when peer group characteristics are included in the regression equation. This finding, if generelized to other situations, may provide a credible approach to the
World Bank. 2023g. “Republic of Congo Country Climate and Development Report - Diversifying Congo’s Economy: Making the Most of Climate Change.” World Bank, Washington, DC.

World Bank. 2023h. “Romania Country Climate and Development Report.” World Bank, Washington, DC.

World Bank. 2023i. “Côte d’Ivoire Country Climate and Development Report.” World Bank, Washington, DC.

World Bank. 2023j. “Democratic Republic of Congo (DRC) Country Climate and Development Report.” World Bank, Washington, DC.

World Bank. 2023k. “Kenya Country Climate and Development Report.” World Bank, Washington, DC.

World Bank. 2023l. “Uzbekistan Country Climate and Development Report.” World Bank, Washington, DC.

World Bank. 2023m. “Azerbaijan Country Climate and Development Report.” World Bank, Washington, DC.

World Bank. 2023n. “Tunisia Country Climate and Development Report.” World Bank, Washington, DC.

World Bank. 2023o. “Dominican Republic Country Climate and Development Report.” World Bank, Washington, DC.

World Bank. 2023p. “West Bank and Gaza Country Climate and Development Report.” World Bank, Washington, DC.

World Bank. 2023q. “Benin Country Climate and Development Report.” World Bank, Washington, DC.

Yuan, B., Y. Cui, X. Wang, and H. Xu. 2022. “Study on the Heterogeneity of China’s Agricultural Economic Growth in the Context of Temperature Shocks.” _Scientific Reports_ 12 (June): 10511.

# **References**

Anttila-Hughes, J. K., and S. M. Hsiang. 2013. “Destruction, Disinvestment, and Death: Economic and Human Losses Following Environmental Disaster.” _SSRN Electronic Journal_ .

Baez, J. E. ; de la F., Alejandro; Santos, Indhira. 2010. “Do Natural Disasters Affect Human Capital? An Assessment Based on Existing Empirical Evidence _Research Papers in Economics_ Bowen, T., C. Del Ninno, C. Andrews, S. Coll-Black, U. Gentilini, K. Johnson, Y. Kawasoe, A. Kryeziu, B. Maher, and A. Williams. 2020. _Adaptive Social Protection: Building Resilience to Shocks_ . Washington, DC: World Bank.

Bozzoli, C., A. Deaton, and C. Quintana-Domeque. 2009. “Adult Height and Childhood Disease.” _Demography_ 46 (4): 647–69.

24
Table 1b: Role of one-person plants in unorganized sector trend

||1989|1994|2000|2005|2010|
|---|---|---|---|---|---|
|||A. Raw count o|f data observatio|ns after basic prun|ing|
|Unorganized sector|94,430|155,343|218,768|81,074|102,193|
|More than one-person plant|65,019|111,736|144,857|50,062|56,963|
|One-person plant|29,411|43,607|73,911|31,012|45,230|
||B. Estima|ted number of p|lants in India usin|g sample weights|from Panel A|
|Unorganized sector|13,274,862|12,027,015|16,879,067|16,833,852|17,679,808|
|More than one-person plant|8,276,138|8,177,452|9,763,850|9,056,699|7,644,606|
|One-person plant|4,998,723|3,849,563|7,115,217|7,777,153|10,035,202|
||C. Share|of raw data obse|rvations from Pa<br>of financial lo|nel A that report p<br>ans|ositive levels|
|Unorganized sector|0.13|0.11|0.10|0.16|0.09|
|More than one-person plant|0.16|0.13|0.14|0.22|0.13|
|One-person plant|0.07|0.05|0.03|0.06|0.03|
||D. Sh|are of weighted<br>posit|manufacturing pla<br>ive levels of finan|nts from Panel B<br>cial loans|that report|
|Unorganized sector|0.10|0.09|0.06|0.08|0.06|
|More than one-person plant|0.12|0.11|0.09|0.11|0.10|
|One-person plant|0.06|0.05|0.03|0.04|0.02|

Notes: Descriptive statistics taken from the National Sample Survey (NSS). Positive levels of financial loans are taken to be where loans and/or interest is reported. A one-person plant is an establishment with only one employee, irrespective of whether the employee is full time or part time.
Carlo simulation technique provided by Elbers et al. (2002, 2003), imputation and aggregation are done repeatedly to develop point estimates of poverty and inequality measures as well as their associated standard errors.

The SAE estimates of poverty rates are often plotted on a map, and conventionally named a poverty map. The poverty map is visually immediate and popular among policy-makers and other stakeholders. The SAE estimates can support geographic targeting policies to focus assistance on the neediest people. Such estimates can also be used to analyze the spatial relationship between poverty and geographic variables. In Vietnam, Minot (2000) created a poverty map using the VLSS 1992–93 and the Agricultural Census for 1994 with the probit model. Minot et al. (2003) have produced consumption-based small-area estimates of poverty and inequality using the VLSS 1997–98 and the Population Census for 1999.

Although the SAE estimates are useful, limitations remain. Since existing SAE techniques can only generate static maps, they do not reveal how the poverty map will change as a result of changing macroeconomic environment. Hence, the geographic targeting policy based on the static SAE estimates may be inappropriate after Vietnam’s accession to the WTO. To overcome the static nature of poverty mapping, this study combines the SAE method with an integrated microsimulation-CGE model.

This paper uses a CGE approach to elucidating linkages between trade and poverty, and joins a large and growing literature on this subject. Beginning with Adelman and Robinson’s work on Korean growth in the 1970s, CGEs have found application to trade, growth, and poverty issues in scores of developing countries. A complete survey of these contributions is outside the scope of this paper, but readers can find an extensive set of applications as well as literature synthesis in a recent volume by Hertel and Winters (2006).

The present approach represents a recent line of CGE techniques that integrate traditional economywide models with microeconomic simulation menthods calibrated to household survey and census data. This significantly increases the resolution of economic analysis and captures essential structural heterogeneity. Integrated 11
Table A.2. Heterogeneous effects on the Prevalence of Viral Diseases

|(1)|(2)|(3)|
|---|---|---|
|_A. Heterogeneous Effects by Wealth Index_|||
|Dih|F|Ch|
|Indicator Variables<br>arrea<br>last month|ever<br>last month|oug<br>last month|
|SID_×_I( High Wealth)<br>0.000|0.012|0.010|
|(0.021)|(0.015)|(0.016)|
|SID<br>0.021|0.029|0.021|
|(0.025)|(0.018)|(0.015)|
|I(High Wealth)<br>-0.016*|-0.051**|-0.006|
|(0.007)|(0.017)|(0.018)|
|R-squared<br>0.014|0.018|0.025|
|Observations<br>1,817|3,320|2,174|
|_B. Heterogeneous Effects by Gender_|||
|SID_×_I(Male)<br>0.010|-0.008|0.012|
|(0.008)|(0.012)|(0.011)|
|SID<br>0.016|0.042***|0.021**|
|(0.011)|(0.009)|(0.008)|
|I(Male)<br>0.008|-0.020|-0.035|
|(0.014)|(0.025)|(0.018)|
|R-squared<br>0.013|0.017|0.025|
|Observations<br>1,831|3,341|2,187|
|_C. Heterogeneous Effects by Age_|||
|SID_×_I(Young)<br>-0.000|-0.001*|-0.001|
|(0.000)|(0.000)|(0.001)|
|SID<br>0.024|0.056***|0.052**|
|(0.016)|(0.011)|(0.018)|
|I(Young)<br>-0.001|-0.038|-0.058|
|(0.024)|(0.041)|(0.036)|
|R-squared<br>0.013|0.017|0.021|
|Observations<br>1,831|3,341|2,187|

_Notes:_ The table presents results from equation 3 with estimates examining the interaction between SID and indicator variables of wealth, gender, and age. SID represents standardized values of the inverse distance from surveyor households to refugee camps. The estimates incorporate population weights. Dependent variables are available for the 2019 wave, and detailed explanations for their construction are provided in Appendix B. Panel A illustrates the heterogeneous effects for high wealth, where _I_ ( _High Wealth_ ) is an indicator set to one if the respondent’s wealth asset index exceeds the median of the overall sample. Panel B presents the heterogeneous effects for male respondents, where _I_ ( _Male_ ) is an indicator variable set to one if the respondent’s gender is male. Panel C showcases the heterogeneous effects for age, with _I_ ( _Y oung_ ) as an indicator set to one if the respondent is between 0 and 23 years old. Estimates control for individual characteristics including male=1, age, household size in 2019, household head=1, married=1, and household income sufficiency in 2017. Wave fixed effects are included and clustered standard errors at the upazila level (i.e., subdistrict) are presented in parentheses. Significance levels:<sup>_∗∗∗_</sup> at 1%,<sup>_∗∗_</sup> at 5%,<sup>_∗_</sup> at 10%.

53