rafmacalaba/datause-encoder-data huggingface dataset:
Table **Al.S** Central **Sa*ing Accouits** (TL Bitlion)

|**A**|**STOCKS**|1987|L|A|STOCK 1|988|L|
|---|---|---|---|---|---|---|---|
|||H<br>|4,933.6|||H|9,100.0|
|Ee-Rese|3,313.3|CUt|3,044.1|Ee.RESc|6,662.5|aCt|4,518.4|
|CRt|2,080.2|CUp<br>|2,274.7<br>|CRt|1,394.6<br>|**CUp**|3,425.6|
|Ctb|2,062.0|CUd|769.4|CRb|1,567.6|**CUd**|1,092.1|
|CRo|501.1|Dde|1,889.5|CRo|674.9|Dde|21,743.8|
|CRd|-482.9|**Ee-Fc**|14,427.2|CRd|-847.9|**I.**|-22,786.7|
|||liO|-13,967.3|||**IVe**|-6,793.1|
|||NHe|-5,397.4|||RVTc|-15,993.6|
|||RVTc|-8,569.9|||||
|||Q8A|CAPITAL**ACCD**|**Jh.**1988|_IL_|||
|||||dN|4,166.4|||
|||d(Ee&RES0)|3,349.2|dXllt|1,474.3|||
|||**dARES**|1,257.8|**dCLip**|1,150.9|||
|||**CCRIc**|-654.8|dCLld|323.4|||
|||**cdVec**|2,746.2|dDde|2,692.1|||
|||dCRt|-685.6|**d(Ee-Fc)**|7,316.6|||
|||dCRb|-494.4|dAFGc|-2,364.0|||
|||dcRd|173.8|dFXt|165.5|||
|||dCRd|-365.0|CCR2c|-847.7|||
|||||dRV2c|10,362.7|||
|||||dlOLc|-8,819.4|||
|||||**dNIc**|-1,395.7|||
|||||CRVTC|-7,423.7|||
|||R|**OJRREN**<br>|**T**<br>ACCWNT 88|**ELS**|||
|||;,-Bbd-I|531.0|ioo-Dp-i|1,794.9|||
|||ic40od-1<br>|1,069.2|iRYCRd-1|-245.6|||
|||**i.5pdi-**|3,680.5|iF-E-FXpd-1|378.6|||
|||<br>iF*E-FXdc|-i<br>274.3|<br>iF*E*FGd-i|338.6|||
|||||**P&Ld**|-1,798.4|||
|||||**dlNd**|5,087.0|||

**Source:** See **explanation** in text.

Bank on the budget. Net worth (NWc) and its change (dNWc, savings of the Central Bank) close the stock balance sheet and _the_ capital account, respectively.

There are no data available on the current account (revenue and expenditure) of the Central Bank. We calculate a current account based on the balance sheet of the previous period by applying the corresponding interest rates to the respective assets and liabilities. Given the change in net worth of the Central Bank 20
_tice_ ) contained only the personalized information (the owner’s name and a photograph of the property) and the compliance instructions. Finally, the notice for the third treatment group (the _penalty notice_ ) had no personalized information but included the information on penalties. Appendix Figure A1 provides images of the notices.

Due to the 2014 Ebola outbreak, field activities were suspended following the property enumeration. In March 2017, using the information from the property enumeration list, the LRA prepared the different notices for property owners according to their randomized group. Delivery agents were provided with “address books” containing the photograph and location of each property to hand-deliver the notices. If the property owner was not present, the notice was handed to the resident to deliver to the property owner.

Given the three-year lag between the enumeration process and the delivery of the notices, there was some attrition in the sample. The team successfully delivered notices to 90 percent of the property owners in the sample, and this rate was not significantly different across the treatment and control groups.<sup>15</sup>

# **4.2 Data and Empirical Specification**

The data for this study come from two main sources: the baseline enumeration data and administrative records on tax payments. The baseline enumeration data include the property block and number (using a numbering system put in place by a pilot project of the postal office), the community name, the name and phone number of the resident with whom the enumeration survey was completed and his or her relationship to the property owner, the name and phone number of the property owner(s) (if they were not the survey respondents), and property characteristics, such as the number of rooms and bathrooms, the presence of a porch and/or garage, building materials, and whether the property is residential or commercial. The enumerators also measured and recorded the exterior dimensions of the property using sketches and took photographs of the exterior. The LRA determined property values using a formula that incorporates the square footage of the buildings calculated from the sketches and the construction materials observed from the images.

> 15 The reasons for nondelivery were relocation of the property owner, destruction of the property, or inability to locate the property.

13
Policy Research Working Paper 9517

# **Abstract**

This paper employs the synthetic control method to examine the impact of using a non-native language as the medium of instruction in schools on a student’s learning. Exploiting an unanticipated policy change in Malaysia and using data from the Trends in International Mathematics and Science Studies, the results show that changing the medium of instruction negatively influenced eighth graders’ achievement in mathematics and science. The differential performance, by year and gender, suggests that using a non-native language throughout a student’s schooling may have greater negative impact on the student’s learning than switching the language of instruction in the middle of the student’s schooling does. This paper sheds light on the various manners in which a language policy can adversely affect a student’s learning outcomes. It also highlights how the transition in switching the language of instruction in schools can be implemented more effectively to mitigate its adverse effects.

This paper is a product of the Global Indicators Group, Development Economics and the Poverty and Equity Global Practice in collaboration with the Department of Economics, Monash University Australia. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ysoh@worldbank.org, xdelcarpio@worldbank.org, or liang.c.wang@monash.edu.

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

**Table 18.  Funding of Village Capital Investments**

||Mean<br>||Percentage f|inanced from|:<br>|
|---|---|---|---|---|---|
||(RMB<br>/capita)|Gov’t|Village<br>Committee|Farmers|Others<sup>b</sup>|
||||**2000**|||
|Roads & Bridges|15|_23_|_47_|_29_|_2_|
|Irrigation & Drainage|10|_42_|_31_|_21_|_5_|
|DrinkingWater Supply|4|_5_|_2_|_4_|_90_|
|Others<sup>a</sup>|19|_12_|_66_|_11_|_11_|
|**Total**|**48**|**_21_**|**_47_**|**_18_**|**_14_**|
|<br>|||**2004**<br>|||
|Roads & Bridges|124|_65_|_23_|_11_|_1_|
|Irrigation & Drainage|20|_51_|_28_|_10_|_11_|
|DrinkingWater Supply|18|_43_|_24_|_19_|_14_|
|Others<sup>a</sup>|30|_52_|_13_|_7_|_28_|
|**Total**|**191**|**_59_**|**_22_**|**_11_**|**_8_**|

a. Others include schools, clinics, electricity, office buildings, green for grain, telephones, cable television, broadcasts, etc.

b. Others includes donations from abroad, donations from enterprises, donations from universities, small group investments, private investments, investments from power supply corporations, investments from broadcast bureaus, etc. Source: 2005 village survey.

The same process has also driven up expenditures for infrastructure at the township and county levels. According to our survey of 50 townships, in year 2000, investments were only RMB 77 per capita for building roads, schools, irrigation, and other public goods infrastructure projects. By 2004, this amount had nearly tripled, to RMB 217.<sup>34</sup> At the township level a large part of the increase was for rural roads, which accounted for more than half of total investments in 2004 (Table 19).

> 34 The townships are not always aware of infrastructure investments in their villages that are financed purely by the villages themselves. Hence, the figures presented underestimate the total capital investments in a township. This also explains, at least in part, the differences between what is stated here and what is stated by the villagers (see section below) regarding the share financed by the village.

34
other closures have no differential impact across localities on wages, working days and hours. To the extent that the two new interaction terms in columns (1)-(4) capture part of the direct effect of closures on employment, the unchanged checkpoint coefficients provide further indirect confirmation that most of the labor market impact of checkpoints is not accounted for by the restrictions to workers’ mobility.

The impact of the barriers across localities could also differ according to the size of the locality’s labor market. Larger markets in a small economy like the West Bank are typically a reflection of economies of scale exploited by firms by serving the domestic market from a few locations. By breaking up the integration of this market, closures may be particularly detrimental for such larger labor markets. That is consistent with the results in column (5), where the interactions between the localities’ number of employees in 1997 (according to the population census) and the barrier variables have a negative sign. The interactions’ coefficients are not significant instead for the other labor market variables, except for working days (column 7), for which the interaction with the checkpoint is positive and significant. This result could suggest that residents in larger labor markets are less harmed by the checkpoints in reaching their workplace as they are more likely to work in the same locality.

# **6.3 Quantifying the overall marginal effects of the barriers**

The estimated coefficients of the checkpoints allow us to provide a quantification of the marginal effects of the closures for the entire West Bank labor market. We compute the costs associated with these effects for the year 2007 and note that the results would be similar taking any of the other recent years in the sample. Given the results of the road blocks and the other barriers, which are not significant for the most part, we limit this quantification to the checkpoints. The effects we capture refer to the difference in labor market outcomes between localities according to whether they are more or less surrounded by checkpoints. As explained above this difference provides a lower bound estimation of the actual total effect of the checkpoints.

We use the checkpoint coefficient column (2) in Table 6 (-0.0041), which represents our most robust estimation, in order to compute the marginal effect of the checkpoints on employment for the entire West Bank. As the average value of _CP_<sup>_30min_</sup> in 2007 was 1.59 and the labor force was 1.05 million, the checkpoints were responsible for approximately 6,900 more 29
Wacziarg, Romain and Karen H. Welch (2008), “Trade liberalization and growth: The new evidence,” The World Bank Economic Review, 22(2), pp.187-231.

Wilson, John S., Catherine L. Mann, and Tsunehiro Otsuki (2003), “Trade facilitation, and economic development: A new approach to quantifying the impact,” The World Bank Economic Review, 17(3), pp. 367-389.

# **8. Appendix**

**Table 1 List of Countries**

|1|Albania|36|Djibouti|71|Kyrgyz Republic|106|Russian Federation|
|---|---|---|---|---|---|---|---|
|2|Algeria|37|Dominica|72|Lao PDR|107|Senegal|
|3|Argentina|38|Dominican Rep.|73|Latvia|108|Serbia|
|4|Armenia|39|Ecuador|74|Lebanon|109|Seychelles|
|5|Australia|40|Egypt, Arab Rep.|75|Lesotho|110|Slovak Republic|
|6|Austria|41|El Salvador|76|Lithuania|111|Slovenia|
|7|Azerbaijan|42|Eritrea|77|Luxembourg|112|South Africa|
|8|Bangladesh|43|Estonia|78|Macao, China|113|Spain|
|9|Belarus|44|Ethiopia|79|Macedonia, FYR|114|Sudan|
|10|Belgium|45|Finland|80|Madagascar|115|Swaziland|
|11|Belize|46|France|81|Malawi|116|Sweden|
|12|Benin|47|Gabon|82|Malaysia|117|Switzerland|
|13|Bolivia|48|Gambia, The|83|Mali|118|Syrian Arab Republic|
|14|Bosnia and Herz.|49|Georgia|84|Mauritania|119|Tajikistan|
|15|Botswana|50|Germany|85|Mauritius|120|Tanzania|
|16|Brazil|51|Ghana|86|Mexico|121|Thailand|
|17|Brunei Darussalam|52|Greece|87|Moldova|122|Togo|
|18|Bulgaria|53|Guatemala|88|Montenegro|123|Trinidad and Tobago|
|19|Burkina Faso|54|Guinea|89|Morocco|124|Tunisia|
|20|Cambodia|55|Guinea Bissau|90|Mozambique|125|Turkey|
|21|Cameroon|56|Honduras|91|Namibia|126|Turkmenistan|
|22|Canada|57|Hong Kong|92|Netherlands|127|Uganda|
|23|Cape Verde|58|Hungary|93|New Zealand|128|Ukraine|
|24|Central Afr. Rep.|59|Iceland|94|Nicaragua|129|United Kingdom|
|25|Chad|60|India|95|North America|130|United States|
|26|Chile|61|Indonesia|96|Norway|131|Uruguay|
|27|China|62|Iran, Islamic Rep.|97|Pakistan|132|Uzbekistan|
|28|Colombia|63|Ireland|98|Panama|133|Venezuela, RB|
|29|Comoros|64|Israel|99|Papua New Guinea|134|Vietnam|
|30|Congo, Dem. Rep.|65|Italy|100|Paraguay|135|West Bank and Gaza|
|31|Costa Rica|66|Japan|101|Peru|136|Zambia|
|32|Croatia|67|Jordan|102|Philippines|137|Zimbabwe|

14
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semester of 2011 (2011-2), as this was the only semester for which the scores on the general component were comparable across programs.

**_Student-level higher education and labor market outcomes_** . To determine student-level higher education and labor market outcomes, we relied on individual-level records from the Ministry of Education’s System for the Prevention of College Dropout ( _Sistema para la Prevención de la Deserción de la Educación Superior,_ SPADIES) and the Labor Observatory for Education ( _Observatorio Laboral para la Educacion,_ OLE). SPADIES contains biannual information on all higher education students. It identifies the program and institution attended as well as the entry cohort, and tracks students until they drop out or graduate. We obtained biannual information spanning 2007 to 2015. OLE includes annual labor market information (formal employment and wages) for higher education graduates who made social security contributions between 2010 and 2013 and were therefore employed in the formal sector of the economy as paid employees or selfemployed entrepreneurs. OLE records include salaries for paid employees but not earnings for self-employed individuals.

**_Program- and institution-level information_** _._ This dataset comes from the National System of Higher Education Information ( _Sistema Nacional de Información de la Educación Superior, SNIES_ ) and includes information on higher education programs and institutions. For each SCP, it includes program length (two or three years); field of study (Agronomy and Veterinary Medicine, Arts, Economics and Business, Engineering and Architecture, Health, Social Sciences, and Math and Natural Sciences); mode of delivery (distance or traditional); location; enrollment; tuition; and institution. For each institution, the data includes the location and institution type (universities, technological schools, T&Ts, and SENA). If a single institution has multiple locations, then each one receives a different code and is treated separately. For example, SENA in Medellín and SENA

11
extra moving cost paid by the unobserved type II workers, _C_<sup>3</sup> .

First, we run the regression without types, hence imposing that _C_<sup>3</sup> = 0. The results are reported in the first row of Table 4. We find that _C_<sup>1</sup><sup>_,s_</sup> _/ν_ is equal to 2.68 for young workers who are less than 30 years old, similar to what we find with CPS data. We find that _C_<sup>1</sup><sup>_,s_</sup> _/ν_ is equal to 3.09 for the workers between 30 and 36, and with less than seven years of sectoral experience. We find that ( _C_<sup>1</sup><sup>_,s_</sup> + _C_<sup>2</sup> ) _/ν_ is equal to 4.38 for the workers who are older than 30 years and with more than 7 years experience. All estimates are significant at the 99% level.

The workers are divided into two unobserved types. In reality, if there is a continuum of types with different moving costs, then allowing two types of workers is equivalent to discretizing a continuous distribution into two pieces. Ideally, we would like to discretize into finer grids but it is not possible given the available data. Therefore, we consider two types, and impose the restriction that _C_<sup>3</sup> _/ν_ = 1, then estimate _C_<sup>1</sup><sup>_,s_</sup> _/ν_ and _C_<sup>2</sup> _/ν_ , while we calculate the mass of each type with an EM loop. Although it is theoretically possible with very detailed data, we can not estimate _C_<sup>1</sup><sup>_,s_</sup> _/ν_ and _C_<sup>3</sup> _/ν_ separately without a restriction, since we do not have enough observations of workers with different work histories. It is possible to try different values for _C_<sup>3</sup> _/ν_ and change the distance between the centers of mass of the grids; we find that the weighted average of moving costs _C_<sup>1</sup><sup>_,s_</sup> _/ν_ and _C_<sup>2</sup> _/ν_ is robust to different specifications.

The EM loop converges to a distribution with 50% type I and 50% type II. We report the estimated moving costs for type I workers in the second row of Table 4. Since we impose _C_<sup>3</sup> _/ν_ = 1, the moving costs for type II workers are exactly one unit larger than type I workers, which are reported in the third row.

# 6 Simulations

In the previous section, we estimated parameters of the workers’ optimization problem. In order to simulate the model, we need to parametrize wage equations, or in other words the labor demand functions. Thanks to the Cobb-Douglass nature of the production functions, it 21
That variable, _ethcon_ , is coded, reflecting our ―double requirement,‖ equal to 1 if 70% or more of a national minority group lives in the region and makes up the regional majority.

A complication arises from the fact that there could be cases where the same national minority group is spread over several regions, and is concentrated in an area larger than the administrative region that we use as our units. This could occur where large groups have historical homelands that do not map onto administrative boundaries. If our data extended back to the American civil war, for example, we would code ―Southerners‖ as having a sovereignty movement in several adjoining regions, but _ethcon_ would be coded 0 for each of those regions. We deal with this problem by coding another binary variable ( _minregmaj_ ) that identifies all regions where a national minority makes up more than 50% of the regional population without considering the concentration dimension. We also code a binary variable identifying all regions in which the same group is seeking sovereignty ( _same_group_ ) and another variable identifying all adjoining regions with active sovereignty movements ( _sd_adjoin_ ). These allow us to check if cases do not fit our model because ethnic group boundaries spill over administrative boundaries.

# **_Democracy and Political Institutions_**

Our study abstracts from country-wide regime characteristics, such as the level of democracy.<sup>40</sup> It is plausible that sudden regime change can induce sovereignty demand, especially following the collapse of an authoritarian regime.<sup>41</sup> Democratization amounts to a renegotiation of the terms on which the polity has operated up to that point, creating expectations of greater freedom. Indeed, instability at the center implies higher effective sovereignty in the

> 40 That said, there is no significant difference in the mean level of sovereignty across regime types in our data and no significant correlation of regime type and conflict over sovereignty (see results in the supplement).  The effect of other covariates (e.g. population) on sovereignty is slightly different across regime types.  Regime type is coded on the basis the Polity IV database (Marshall and Jaggers 2000), taking a five-year average for the period from 1995-2000.

> 41 The destabilizing effects of bursts of democracy have been discussed extensively since Tocqueville (1856). Gurr (2000) links stable democracies to lower levels of ethnopolitical conflict.

25
**37**

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