rafmacalaba/datause-encoder-data huggingface dataset:
# **3. Administrative data and dropouts in Guatemala and Honduras**

# **_3.1 Guatemala_**

Over the last several years, the Guatemalan Ministry of Education ( _Ministerio de Educación_ - MINEDUC) has substantially improved and expanded their administrative information system based on student-level records. One of the main innovations was the implementation of a unique student identifier, which started in upper secondary in 2009 and was progressively expanded until all students in primary and secondary schools were assigned unique identifiers in 2011. These identifiers are used by each school at the beginning of the school year to provide data on enrollment to the Ministry. Specifically, schools provide a list of all students who are enrolled in each grade, with their unique identifiers, and the Ministry centralizes and consolidates this information in a database that contains the annual enrollment status of all students (nearly 4 million each year) from 2011 to 2016. This data structure allows the educational trajectories of students to be followed through time, and specifically enables the identification of those who drop out and the year in which they left school. In addition to students’ unique identifiers, the data also include information on student’s gender, school year, and results of their October final examination (promoted, not promoted, withdrew), as well as school’s sector (formal public, private, cooperative, municipal), teaching modality (bilingual, monolingual), municipality, department, and school identifier.<sup>2,3</sup> These basic administrative microdata on enrollment can then be matched with additional data on student and school characteristics from the National Evaluation of Students. These assessments have been conducted in the first, third and sixth grades of primary education, the last grade of lower secondary education (ninth grade), and the last grade of upper secondary, with variable frequency.<sup>4</sup> The evaluations in secondary education are applied to all the students in the

> 2 The formal public sector is the group of schools that are under the direct administration of MINEDUC. The municipal sector is composed of public schools administered by municipalities. The cooperative sector comprises public schools with a tripartite administration that involves the participation of MINEDUC, the municipality, and student’s parents. 3 Bilingual schools are institutions where instruction is offered in both Spanish and an indigenous language. 4 The National Evaluations are conducted every year in the last grade of upper secondary, but the implementation in other grades has been less regular: 2006, 2009, and 2013 for ninth grade; 2006, 2007, 2008, 2009, 2010, 2013, and 2014 for third and sixth grade; and 2006, 2008, 2009 and 2010 for first grade. While this could limit the capacity of implementing an early warning system every year, the Government of Guatemala and the World Bank are working on replicating the analysis but using students‘ grades collected on a yearly basis.

8
increased revenue collection, by enhancing tax administration, broadening the tax base, or raising tax rates. Revenue-to-GDP ratios are particularly low in South Asia and Sub-Saharan Africa. Efforts to remove exemptions, tighten tax administration, and broaden tax bases could help generate budgetary resources to finance public investment projects. Spending on public investments could also be boosted by reallocating expenditures toward growth-enhancing investment from expenditures that are less clearly aligned with policy priorities. Such expenditures could be identified in periodic public expenditure reviews that assess all government expenditures against policy priorities.

_Facilitate private investment_ . Empirical studies show that an increase in public investment would raise private investment, but this crowding-in effect may be temporary (World Bank 2017a). A favorable business environment, including stable macroeconomic conditions and predictable policies and regulations, may not be sufficient to attract private investment. Countries may need to develop appropriate innovative fund and deal structures, such as guarantees and risk sharing, and promote blended finance instruments that can leverage private sector development financing. Public-private partnerships are a tested strategy that can be applied to numerous sectors. However, governments must establish autonomous regulatory agencies to oversee the private agents. Multilateral development institutions can help EMDEs address these issues.

52
Poverty and Social Policy Division Technical Department Africa Region The World Bank

**A NEW REGIONAL PRICE INDEX FOR COTE D'IVOIRE USING DATA FROM THE INTERNATIONAL COMPARISONS PROJECT**

Christiaan Grootaert Ravi Kanbur This paper is an output of the research project "Poverty and Social Dimensions of Structural Adjustment in Cote d'Ivoire, 1985-88 - A Policy-Oriented Analysis" RPO 675-26). The authors would like to thank Philippe Callier and Jean-Luc Dubois for helpful comments on an earlier draft, and Meera Venkataraman and Gi-Taik Oh for excellent computer programming and general analytic assistance.
20 could be attributed to at least two reasons. It may be because on aggregate, public investment in SSA actually crowded in rather than crowded out private investment, or because of credibility problems due to doubts on the part of the private sector regarding the adequacy and sustainability of reform (EIAL and OAL) or the prevailing policies (NAL); (vi) the increase in the import ratio in EIAL in the third period whereas it declined for the other two groups, is consistent with a relatively less binding external constraint for EIAL as reflected by the resource balance indicators; (vii) the improved growth performance in EIAL countries despite the decline in aggregate investment, could be explained by the enhanced capacity utilization (via increased imports) and investment efficiency achieved by these countries. The reduction of investment in this case could have a more significant effect in terms of allowing for higher private consumption, which should enhance the chances for sustainability of the reform; (vii) Even though our ar.alysis supports the concern regarding the ris:ng inflation in the adjusting countries of SSA (Chhibber (1991)); it nevertheless shows that countries that undertook deep reforms and stayed with it (EIAL), were able to reduce their inflation to the pre-shock levels after the steep rise associated with the initial cost of the stabilization part of the reforml'; (ix) finally and as expected, the indicators of political stability and political pluralism of table (2.5) show the EIAL countries to be more politically stable on average compared to OAL countries both in SSA and in LICs in general. This finding supports the view that reforms are successful, when the programs are owned, actively explained and campaigned for by governments that are politically stable

**10** The fact that OAL countries of SSA received a large negative external shock in the third period compared to a positive shock for the EIAL group, may account for this difference.
**<u>Table 2: Plot characteristics for different samples (only cereals, oilseeds and pulses plots)</u>**

|||**Total**|**sample**||**Ow**|**ner-cum-sha**|**recroppers**||
|---|---|---|---|---|---|---|---|---|
||**All**|**Own cult.**|**Shared in**|**Shared out**|**All**|**Own cult.**|**Share**|**d in**|
|**Relation w. partner (TP)**|||||||||
|TP relative (%)|48.16||48.55|49.83|48.95||48.95||
|TP in-law (%)|10.68||11.13|11.22|10.61||10.61||
|TP neighbor (%)|17.53||15.62|18.52|15.34||15.34||
|TP member of VA (%)|12.74||13.83|10.79|14.38||14.38||
|TP unrelated (%)|10.90||10.87|9.64|10.72||10.72||
|Harvest share received (%)|51.08||53.10|49.18|53.39||53.39||
|**Output & plot characteristics**|||||||||
|Crop output/ha (Birr)<sup>a</sup>|2753.71|2788.92|2579.31||2868.60|2973.47|2480.64||
|Crop output/ha (Birr)|2087.78|2086.07|2147.79||2043.36|2106.22|1827.98|***|
|Plot size in ha|0.26|0.26|0.29|0.28|0.26|0.26|0.28|***|
|Years of possession|15.99|17.43|3.91|17.85|13.79|16.70|3.81|***|
|Good soil quality (%)|36.94|37.81|34.92|33.27|36.08|36.43|34.88||
|Medium soil quality (%)|41.76|41.72|40.59|43.31|40.93|41.22|39.93||
|Poor soil quality (%)|21.16|20.33|24.49|23.28|22.88|22.21|25.19|***|
|Flat land (%)|3.84|3.83|2.98|4.74|68.48|67.09|73.21|***|
|Gently sloped (%)|3.45|3.41|2.80|4.42|27.51|28.58|23.86|***|
|Steeply sloped (%)|0.42|0.45|0.20|0.36|4.00|4.32|2.92|***|
|Irrigated (%)|2.47|2.67|1.39|2.26|2.42|2.75|1.29|***|
|**Variable input use**|||||||||
|<br>Used fertilizer (%)|36.95|36.99|34.33||41.88|43.45|36.49|***|
|Fertilizer used per ha (kg)|90.05|91.54|78.22||96.29|102.30|76.76|***|
|<br>Used manure (%)|25.84|28.91|6.31||23.65|28.84|5.85|***|
|Manure used per ha (kg)|2953.28|3284.99|414.29||3079.62|3803.08|294.78|***|
|<br>Used improved seed (%)|3.83|4.08|2.14||3.88|4.45|1.93|***|
|Male family labor/ha (d)|178.11|185.15|136.30||162.86|171.84|132.09|***|
|Fem. family labor/ha (d)|92.03|97.20|60.51||79.57|85.64|58.74|***|
|Total family labor/ha (d)|270.15|282.35|196.81||242.43|257.49|190.83|***|
|Used hired labor (%)|17.13|16.16|21.91||18.43|17.24|22.51|***|
|Hired labor/ha (d)|72.96|79.81|45.39||46.97|48.90|41.90||
|**Source of traction**|||||||||
|Own pair of oxen (%)|58.47|56.46|70.30||67.00|66.22|69.65|***|
|Oxen exchange for labor (%)|15.87|17.17|7.34||11.12|12.37|6.84|***|
|Oxen sharing (%)|19.28|19.39|19.80||19.39|18.98|20.82|*|
|Gift/support (oxen party) (%)|5.03|5.49|2.06||1.96|1.88|2.22||
|Oxen rental (%)<sup>b</sup>|1.12|1.20|0.50||0.54|0.56|0.47||
|Hoe and other (%)|0.40|0.48|0.00||||||
|Pair of oxen days per ha|50.38|49.74|54.36||52.27|51.83|53.79||
|**Crop choice**|||||||||
|Teff plot|32.44|31.39|40.24||34.97|32.79|42.46|***|
|Wheat/barely plot|21.13|22.05|18.08||22.06|23.16|18.30|***|
|Sorghum/maize plot|20.44|22.33|14.06||19.22|21.04|12.98|***|
|<br>Pulses plot|14.52|14.40|16.44||14.16|13.82|15.32||
|Oilseeds or other cropsplot|12.02|10.43|11.57||10.20|9.85|11.40|*|
|Number of observations|19249|14668|2013|2217|7569|5859|1710||

**Source.** Own computation from AAU/UG Amhara Panel Survey **Note.** Unit of observation is a plot in a given year. Thus the same plot could provide multiple observations.

20
# **Table 1.9. Fields’ Mobility Index and** **<u>Short-Term and Long-Term Equality, RLMS 1994-2015</u>**

|||**Gini Index **<br>|||**Theil index **<br>|||
|---|---|---|---|---|---|---|---|
|**Period**|**_M_**<sup>**_E_**</sup>|**Short-term**<br>**inequality**|**Long-term**<br>**inequality**|**_M_**<sup>**_E_**</sup>|**Short-term**<br>**inequality**|**Long-term**<br>**inequality**|**No. of individuals**|
|1994-1998|0.12|0.46|0.41|0.27|0.41|0.30|6 303|
|1998-2004|0.19|0.45|0.37|0.37|0.37|0.24|5 527|
|2004-2009|0.21|0.41|0.32|0.40|0.30|0.18|5 200|
|2009-2015|0.14|0.34|0.29|0.28|0.19|0.14|5 001|
|1994-2004|0.20|0.45|0.36|0.40|0.38|0.23|4 942|
|2004-2015|0.27|0.41|0.30|0.49|0.29|0.15|3719|
|1994-2015|0.34|0.45|0.30|0.59|0.37|0.15|2 478|

**Note** : Estimation results are obtained based on total household income per capita. All numbers are deflated with December to December regional CPIs and weighted with population weights. The mobility index _M_<sup>_E_</sup> is calculated as defined in Fields (2010).

57
- 19 -

Overall, the estimated production function exhibits constant returns to scale: The sum of the coefficients of the six production factors is estimated at 1.081 with a standard error of 0.155. The null hypothesis of constant return cannot be rejected.

**<u>Table 6. Instrumental variable estimation: OPRC effect</u>**

||Battese's sp<br>Coef.|ecification<br>Std.Err.|Small-posi<br>Coef.|tive-number model<br>Std.Err.|
|---|---|---|---|---|
|_OPRC_*_t_|4.574|<br>(2.777) *|6.902|<br>(3.248) **|
|_OPRC_|-1.595|<br>(1.356)|-2.323|<br>(1.573)|
|_t_|-2.590|<br>(1.536) *|-3.953|<br>(1.776) **|
|ln_POT_|0.014|<br>(0.042)|-0.552|<br>(0.217) **|
|ln_L_|0.021|<br>(0.082)|0.022|<br>(0.114)|
|ln_H_|0.418|<br>(0.084) ***|1.058|<br>(0.275) ***|
|ln_S_|0.334|<br>(0.090) ***|0.009|<br>(0.037)|
|ln_F_|0.180|<br>(0.065) ***|0.020|<br>(0.006) ***|
|ln_P_|0.119|<br>(0.096)|0.051|<br>(0.013) ***|
|ln_TR_|0.009|<br>(0.031)|-0.004|<br>(0.013)|
|d (_S_)|1.657|<br>(0.437) ***|||
|d (_F_)|0.540|<br>(0.310) *|||
|d (_P_)|0.402|<br>(1.058)|||
|d (_TR_)|0.164|<br>(0.308)|||
|ln_SIZE_|0.050|<br>(0.083)|0.124|<br>(0.111)|
|d (_MALE_)|0.033|<br>(0.082)|0.056|<br>(0.115)|
|ln_AGE_|0.145|<br>(0.103)|0.210|<br>(0.142)|
|ln_EDU_|0.070|<br>(0.037) *|0.008|<br>(0.006)|
|_CONC_|0.047|<br>(0.101)|0.175|<br>(0.138)|
|_TOIL_|0.167|<br>(0.081) **|0.279|<br>(0.106) ***|
|_MOTO_|0.436|<br>(0.219) **|0.636|<br>(0.309) **|
|_BIKE_|0.043|<br>(0.075)|0.051|<br>(0.105)|
|_CHIPATA_|0.276|<br>(0.133) **|0.336|<br>(0.170) **|
|Constant|3.068|(1.264)**|10.544|<br>(1.662)***|
|Obs|2049||2049||
|R-squared|-0.456||-2.127||
|Wald chi2|845.43||338.76||
|Exogeneity test:|||||
|C statistic (chi2)|30.541|***|50.977|***|
|Overidentifying res|triction:||||
|Hansen's J|2.098||0.490||
|statistic (chi2)|||||
|Marginal effect of<br>_OPRC_|1.301|<br>(0.790) *|1.964|<br>(0.924) **|

The dependent variable is the log of total crop production value, _V_ . Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at the 10, 5 and 1 percent, respectively.
# **4 Data**

## **4.1 Quality of education data**

Our measure of education quality is the Colombian equivalent to the SAT, the SABER 11 test administered by the ICFES (Colombian Institute for Evaluation of Education) which is part of the Ministry of Education. It includes a comprehensive evaluation of different areas of knowledge, specifically mathematics, verbal and sciences (biology, physics and chemistry). The test is carried out twice per year due to the existence of two main school calendars, and, though it is not compulsory for graduation, it is an entry requirement by universities in order to use it as a common filter for selecting their new students. In order to ensure comparability, test results are standardized by wave at the Bogot ́a level in each one of the described subject areas and an average is taken of the scores (called here the general result).

Tables 3 and 2 show average, standardized test scores of schools according to their characteristics including only the universe of schools used in the estimation, specifically, Bogota schools located within a 3.5 Km range around the libraries as shown in Figure 1. Table 3 shows that students attending schools with a full-day schedule score higher, on average, than students attending double-shift schools. Among the latter, the students attending school in the morning score higher, on average, than those attending schools in the afternoon. This is related to the management of the school: students attending those managed by the government typically do worse than those managed by the private sector, which are normally private institutions. These relationships are stable over time and a common factor in the Colombian quality of education literature (Gaviria and Barrientos, 2001). Table 2 shows that there are also differences in test scores between students who attend different types of schools in terms of school size, the teacher-student ratio, the female-male student ratio, and teacher education level. These are all traditional inputs of education that we will discuss further in the next section Table 4 shows a U-shape relationship between school quality and distance to the libraries. Schools close to the libraries are normally better than those at a medium-range distance (1 Km - 2.5 Km), but worse than or similar to those far away (2.5 Km - 3.5 Km). As this relationship might be driven by the allocation of inputs, our next section 11
_“China and India are seen by many as two mighty giants threatening the jobs of the manufacturing industry.”_ La Nación Newspaper, Buenos Aires March 2005 _“[We] must not repeat the mistakes of the nineties, when an ‘invasion’ of Chinese products destroyed entire sectors of our industry [...]”_ Communiqué of CAME (Medium Enterprises Association of Argentina) 6 April 2004

# **1. Introduction**

For many in Latin America, the increasing participation of China and India in international markets is seen as a looming shadow of two ‘mighty giants’ on the region’s industrial sector, and one of the major causes behind the significant reduction of employment in the manufacturing industry in the last decade. Are these claims justified? Are China and India the ‘mighty giants’ driving the secular fall in manufacturing jobs in Latin America?

This paper attempts to provide answers to these questions with a focus on Argentina, which experienced a 31 percent decline in industrial employment over the last decade, while the share of imports from China and India increased six-fold. We apply a dynamic econometric model where labor demand in each industry is a function of wages, the capital stock, prices and productivity. The last two (prices and productivity) are a function of import and export penetration, and will allow us to identify the impact that trade with China and India is having through these two channels on labor demand in Argentina’s manufacturing sector.

In principle, trade should affect the level of employment across and within sectors. Empirical research on the impact of trade on employment has found little evidence either way, particularly in developing countries.<sup>1</sup> Using plant level data for Morocco, Currie and Harrison (1997) find only a small impact of trade liberalization on the

> 1 See Hoekman and Winters (2005) for a comprehensive survey on the recent empirical evidence on the effects of trade on employment.

2
this switch, the overall crime rate dropped by almost 10 percent over the next 20 years (Wright et al., 2014).

Shifting cash payments into accounts can also increase transparency and ensure that people receive wage or government transfer payments in full. Cash is easily pilfered by middlemen, but digital payments curb opportunities for theft by reducing the number of intermediaries between senders and recipients. Digital payments also are easier to track than cash, and when recipients have records of the amount of payments they are entitled to, it is more difficult for middlemen to seek bribes. In Argentina, moving cash payments for a national anti-poverty program into accounts was found to reduce corruption. When the payments were made in cash, 4 percent of recipients reported paying kickbacks to people or organizations that helped enroll them in the program; when the payments were made directly into accounts, that number dropped to just 0.03 percent (Duryea and Schargrodsky, 2008). In India, bribe demands for receiving social security pension payments were cut by 1.8 percentage points (or 47 percent) when the payments were made via smart cards instead of being handed out in cash by government officials (Muralidharan et al., 2014). At the same time, shifting cash payment into accounts can also help governments and businesses reduce the incidence of “ghost” or fake recipients. Payments into accounts generally require more stringent identification documentation, making it harder for ghost recipients to remain undetected. Incidence of ghost recipients fell by 1.1 percentage points when India’s social security pension payments were made digitally via smart cards rather than cash (Muralidharan et al., 2014).

Shifting payments, especially regular bill payments, from cash into accounts can also help people build a payments data history which can then be leveraged for better access to credit. Access to credit often depends on lenders being able to assess the credit risk of potential borrowers based on their credit history. However, many low-income adults lack a documented credit history, which might reduce their ability to secure a loan. Including payment data on regular bill payments such as utility or telephone payments can help adults build credit history and qualify for better loan terms. In the United States, for example, the inclusion of utility and telecom payment data into credit files reduced the share of adults for whom no credit score could be calculated from 12 percent to 2 percent. The greatest benefits accrued to lower-income adults, members of minority communities, young adults, and the elderly (Turner et al., 2012; Turner and 9
**Box 2.1** Bringing Market Discipline to Public Sector Use of the Radio Spectrum

- Inventory of public sector spectrum holdings

- Valuation of holdings

- Incorporation of spectrum valuation in public sector expenditure and investment decisions

- Application of administrative incentive prices based on opportunity cost

- Development of opportunities and incentives to lease or share spectrum with private sector

- Full participation of public sector bodies in spectrum market

_Source:_ Cave 2006 rating spectrum value in budget and investment decisions.<sup>25</sup> One step up, administrative incentive prices can be applied to spectrum use to promote more efficient use. Eventually public sector bodies could participate fully in spectrum markets, once these are established (Box 2.1).

Administrative incentive pricing has been introduced recently by some authorities to promote more efficient use of the spectrum within the framework of traditional government administration. Pricing the use of spectrum at levels that somehow reflect the economic value of specific frequencies can induce users to adopt spectrum-saving new technologies and return unused spectrum. It can also signal the cost of reserving large parts of the spectrum for government use, including the military, or for uncertain future commercial applications at the expense of meeting immediate spectrum demand. Administrative incentive prices can be set in terms of the opportunity cost of the specific frequencies or by reference to prices observed in market transactions in the same or related frequencies (Ofcom 2005a).<sup>26</sup>

# **2.4 Scope of Application**

Despite these partial improvements, the trend is toward introducing market-oriented alternative solutions (Wellenius and Neto 2005). Wholesale replacement of government administration is, however, unlikely in the foreseeable future. Governments will continue playing important roles managing parts of the spectrum and discharging government responsibilities:

- Manage spectrum use as necessary to accomplish compelling public interest objectives. For example, public safety and critical infrastructure may require dedicated spectrum at particular times to ensure priority access for emergency communications. Radio

> 25In the absence of a market mechanism, taking the opportunity cost of spectrum into account in investment decisions is consistent with calculating economic (as distinct from private) returns of public sector projects. This includes pricing resources so as to reflect real scarcities for the economy as a whole rather than the prices actually paid for them. See, for example, Belli etal. 2001.

> 26The opportunity cost of a particular frequency can be calculated as the increase in cost which would occur in producing the same service with another input, such as a different frequency or non-radio technologies. In imperfectly competitive markets, observed market prices may include excess profits from exclusivity or market power.

**11**