rafmacalaba/datause-encoder-data huggingface dataset:
information and analytical capacity (in the absence of mechanic rules), need to be in place to make the system operational. Another issue with the Colombian system is that the dynamic provisions are cumulated as individual provisions, which means it is specific to a credit and cannot be allocated to cover losses of other credit. Due to these concerns, the Colombian authorities have already come up with plans to make the system more rule-based and to use dynamic provisions as general provisions.<sup>60</sup>

# **Peru**

The Peruvian system was initially introduced in 2000 and revised in 2008.<sup>61</sup> It is a rule-based system with a clear macroprudential objective, and the accumulation and release of the funds is based on GDP growth. The rule dictates two sets of general provisioning requirements according to two states of GDP growth, by specifying two sets of PDs and LGDs by type of loans (mortgage, consumer, and different types of commercial loans) [see equation (6)]. Simply put, in a ―low growth‖ period, banks are required to maintain a lower level of general provision (0.7 or 1 percent, depending on the type of loans); in a ―high growth‖ period, banks are required to increase the level of general provision (to 1.1 to 2.5 percent, depending on the type of loans). The additional funds accumulated in good times can be accessed in bad times to offset increasing specific provisioning. The advantage of the Peruvian system is its systemic perspective, but the data requirement on high-frequency and up-to-date GDP data may pose a challenge in other countries and the relevance of GDP growth vis-à-vis other indicators also may need to be evaluated.

> 60 See Fernández de Lis and Garcia-Herrero (2010) for more details.

> 61 The discussion on the Peruvian system benefits from the presentation on the subject by Michel Canta Terreros at the Workshop on Countercyclical Provisioning Systems for the Superintendencia de Bancos del Banco Central del Paraguay and Autoridad de Supervisión del Sistema Financiero de Bolivia, organized by the World Bank in April 2010 (Canta Terreros 2010). Also see Fernández de Lis and Garcia-Herrero (2010) for a discussion of the Peruvian system.

35
# **Appendices**

# **Appendix A. Descriptive Statistics, variable definitions and sources**

Table A.1: Summary statistics panel model

|**Variable**|**Mean**|**Std. Dev.**|**Min.**|**Max.**|
|---|---|---|---|---|
|CPIA cluster A and B average|3.613|0.73|1|5.850|
|CPIA cluster C|3.422|0.700|1|6|
|CPIA cluster D|3.226|0.717|1|5.5|
|EFW|6.313|1.026|2.027|8.932|
|Heritage|60.09|9.591|20.833|88.183|
|number of cumulative loans|2.86|2.763|0|16|
|number of cumulative conditions|50.368|49.784|0|210|
|year|2001.646|3.883|1995|2008|
|log GDP per capita (PPP)|7.981|1.018|5.076|10.352|
|aid over GDP|0.042|0.064|-0.019|0.806|
|Political Rights|3.777|1.989|1|7|
|log of gross IDA (current million USD)|2.31|2.26|0|8.311|
|arrangement with IMF|0.15|0.357|0|1|
|debt service (% of GNI)|5.021|6.28|0.053|138.888|
|log of population|15.926|1.756|11.515|20.854|
|election|0.194|0.396|0|1|
|election cycle|1.841|2.513|0|24|
|election frequency|5.151|2.202|1|22|

31
the house or on a stick that can be carried around. For rural households in Africa, expenditures for both traditional lighting sources and dry‐cell batteries constitute a considerable part of their total expenditures. In very remote and poor areas, people who are cash constrained generally use very little artificial lighting and sometimes even only resort to the lighting that the cooking fire emits. For this stratum, the day inevitably ends after sunset.

**Figure 1: Traditional lighting devices**

Obviously, this lighting constraint restricts people in many regards. Activities after nightfall are literally expensive but also difficult and tiring because of the low quality of the lighting (see Section II for more information on lighting quality). At the same time, it becomes evident that modern energy is not a binary situation. Rather, there are several steps between a candle and an incandescent light bulb.

This continuum has sometimes been referred to as the _energy ladder_ . In fact, SE4All has defined different tiers of modern energy access within its Global Tracking Framework (SE4All 2013) according to the electricity supply that is made available. For example,
|P069964|2001|2002|6/13/2002|3 Closed|Colombia|
|---|---|---|---|---|---|
|P069964|2001|2003|6/26/2003|5 Closed|Colombia|
|P069964|2001|2004|5/26/2004|7 Closed|Colombia|
|P069964|2001|2005|4/28/2005|10 Closed|Colombia|
|P069964|2001|2006|12/1/2005|11 Closed|Colombia|
|P069982|2001|2001|6/20/2001|1 Closed|Tanzania|
|P069982|2001|2002|5/8/2002|3 Closed|Tanzania|
|P069982|2001|2003|5/28/2003|7 Closed|Tanzania|
|P069982|2001|2004|6/30/2004|11 Closed|Tanzania|
|P069982|2001|2005|6/13/2005|13 Closed|Tanzania|
|P069982|2001|2006|12/13/2005|14 Closed|Tanzania|
|P069982|2001|2006|6/22/2006|15 Closed|Tanzania|
|P069986|2002|2002||0 Closed|West Bank and Gaza|
|P069986|2002|2003|3/20/2003|2 Closed|West Bank and Gaza|
|P069986|2002|2004|4/29/2004|4 Closed|West Bank and Gaza|
|P069986|2002|2005|4/25/2005|6 Closed|West Bank and Gaza|
|P069986|2002|2005|5/31/2005|7 Closed|West Bank and Gaza|
|P069986|2002|2006|12/28/2005|8 Closed|West Bank and Gaza|
|P069986|2002|2006|6/29/2006|9 Closed|West Bank and Gaza|
|P069986|2002|2007|12/9/2006|10 Closed|West Bank and Gaza|
|P069986|2002|2007|6/28/2007|11 Closed|West Bank and Gaza|
|P069986|2002|2008|12/13/2007|12 Closed|West Bank and Gaza|
|P069986|2002|2009|9/16/2008|13 Closed|West Bank and Gaza|
|P069996|2002|2002|6/14/2002|1 Closed|Uganda|
|P069996|2002|2003|5/30/2003|3 Closed|Uganda|
|P069996|2002|2004|5/28/2004|5 Closed|Uganda|
|P069996|2002|2005|6/9/2005|7 Closed|Uganda|
|P069996|2002|2006|12/21/2005|8 Closed|Uganda|
|P069996|2002|2006|6/30/2006|9 Closed|Uganda|
|P069996|2002|2007|12/22/2006|10 Closed|Uganda|
|P069996|2002|2007|6/26/2007|11 Closed|Uganda|
|P069996|2002|2008|12/14/2007|12 Closed|Uganda|
|P069996|2002|2008|5/30/2008|13 Closed|Uganda|
|P069996|2002|2009|10/1/2008|14 Closed|Uganda|
|P069996|2002|2020|4/19/2020|15 Closed|Uganda|
|P070016|2001|2001|6/29/2001|1 Closed|Nicaragua|
|P070016|2001|2002|5/31/2002|3 Closed|Nicaragua|
|P070016|2001|2003|6/4/2003|5 Closed|Nicaragua|
|P070016|2001|2004|5/26/2004|7 Closed|Nicaragua|
|P070016|2001|2005|4/26/2005|9 Closed|Nicaragua|
|P070016|2001|2006|11/15/2005|10 Closed|Nicaragua|
|P070016|2001|2006|6/1/2006|11 Closed|Nicaragua|
|P070016|2001|2006|6/29/2006|12 Closed|Nicaragua|
|P070038|2004|2004|6/8/2004|2 Closed|Honduras|
|P070038|2004|2005|4/25/2005|4 Closed|Honduras|
|P070038|2004|2006|9/13/2005|5 Closed|Honduras|
|P070038|2004|2006|11/21/2005|6 Closed|Honduras|
Among the provinces, the proportions of students entering the two tracks also vary largely and are further affected by migration trends. Interpreting these rates at the regional level as shown in Figure 3.15 needs to consider the large-scale internal migration in China accompanying the economic transformation in recent decades. The low transition rate in some provinces can also be a result of outward migration of students with families. There has already be a trend in big cities such as Shanghai, where children of migrant workers have constituted a large share of (vocational) school enrollment.

Figure 3.15 Lower to upper secondary promotion rate in 2015 <!-- Start of picture text --> 120%<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>academic vocational<br>普普 Tianjin 普普 Qinghai 普普 Anhui 普普 Hebei Inner Mongolia 普普 Fujian 普普 Sichuan 龙普普 Heilongjiang 辽普 Liaoning 庆普 Chongqing 普普 Xinjiang 普普 Guangxi 普普 Shanghai 普普 Hainan 普普 Zhejiang 陕普 Shaanxi 普普 Jilin 普普 Hunan 总计 Total 普普 Hubei 苏普 Jiangsu 普普 Henan 东普 Shandong 普普 Ningxia 普普 Jiangxi 普普 Shanxi 普普 Beijing 肃普 Gansu 普普 Yunnan 东普 Guangdong 贵普 Guizhou 普普 Tibet<br>普普普<br><!-- End of picture text --> _Source:_ National Bureau of Statistics of China, 2016b.

The policies on school admission of children of migrant workers may have a large effect on the senior secondary education enrollment dynamics recently. Children of migrant workers are not eligible to be enrolled in public regular senior secondary schools. They have the option of being enrolled in private schools or public vocational schools. In fact, for newly migrated families without temporary residence card in the destination province, while their children can be enrolled in public junior secondary schools, they cannot participate in any public national examinations in the destination provinces such as Zhongkao for a seat in regular upper secondary schools.

It appeared that almost all provinces went through a period of declining proportion of vocational enrollment recently, challenging the 50-50 tracking ratio. As Figure 3.16 shows, only Hubei and Tianjin had slight increases. However, even after these increases, the share of vocational students in Tianjin remains below 40 percent and just passes 30 percent in Hubei’s case. These recent trends have posed several important challenges to the strategies and policies at the senior secondary level. The goal of 50-50 vocational to general secondary enrollment ratio obviously has difficulty maintaining. Although loosening, the administrative control toward reaching this goal has been a strong feature at this level of education. Often, the annual enrollment plans are set by the authorities down to the county/district level. Not surprisingly, enforcing these quotas has faced increasing difficulties. Most importantly, whether it is desirable is becoming more and more debatable.

34
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that mayors become more likely to undertake formal selection processes after tax resources are conditioned upon education performance.

_−_ By formalizing the selection process, mayors may attract high quality principals and avoid hiring low _−_ quality professionals. A fast _−_ growing body of research has shown that school principals play an important role in student performance (Lavy 2008; Grissom and Bartanen 2019). It has been found that better _−_ qualified principals affect graduation rates and English test scores (Coelli and Green 2012), and performance in primary education (Dhuey 2018). In addition to formal selection, local authorities can aim to improve the general quality of principals by providing, or by adopting, the training offered by the state government. To examine the provision and quality of training, we use two outcomes of principals derived from SAEB: a dummy " _if principals enrolled in training in the last_ 2 _years_ " and a dummy " _if they considered the training useful for their daily work_ ".

According to Figure 5, school principals in _Ceará_ were significantly more likely to participate in training after 2013. The DD coefficients indicate that training participation increased by 20 _pp_ after RBF reformulation, maintaining a positive and significant impact between 20 _pp −_ 30 _pp_ in the following years. Along with training participation, principals in _Ceará_ are around 10 _pp_ more likely to report that the training was useful than principals on the other side the border. For instance, as the question used to identify participation in training refers to the last two years, such increases might reflect the availability of technical assistance since 2011 in the state of _Ceará_ . Another argument is that it may have taken some time for principals to participate in training and, consequently, evaluate its usefulness.

# **6.2 Selection and Training of Teachers**

The second part of our analysis relates to the selection and training of teachers. Table 4 presents the estimations for several teacher outcomes using Eq. (1). Panel A column (1) shows the results 31
a) Working age population: 20-59 b) Working age populations: 15+ c) Working age population: 15-74 d) 1st quarter of 2003 Source: CEE countries and EU: Eurostat (Employment in Europe 2003) Bosnia and Herzegovina: World Bank (2002b) Croatia: Central Bureau of Statistics, Labour Force Survey 2002 (second half), Bank staff calculations. Serbia and Montenegro: Republic of Serbia, Republican Statistical Office, Labor Force Survey 2002 (October), Bank staff calculations Georgia, Kazakhstan and Kyrgyzstan: ILO LABORSTA Labour Statistics Database; Bank staff calculations. Russia: Goskomstat; Bank staff calculations.

Moldova: Department of Statistics and Sociology of the Republic of Moldova; Bank staff calculations Different labor force responses to the accelerated job destruction occurring in the wake of economic restructuring have given rise to different labor market patterns, which are summarized in Table 2.

**Table 2  Patterns of labor force adjustments**

|**Labor force participation**|**Unempl**<br>|**oyment**<br>|
|---|---|---|
||High|Low|
|High|_Limited job opportunities. But_<br>_workers look for jobs._<br>Georgia,<br>Lithuania<br>(late<br>1990s), Poland (late 1990s),<br>Serbia, Slovakia, Ukraine|_High employment. But are the_<br>_jobs sustainable?_<br>Czech R., Estonia, Lithuania<br>(2000s), Moldova, Romania,<br>Russia, Slovenia|
|Low|_Scarcity of jobs. Some workers_<br>_get discouraged while others_<br>_continue searching for jobs._<br>Bulgaria, BiH, Croatia, Latvia,<br>Macedonia,<br>Poland<br>(early<br>2000s)|_Limited_<br>_job_<br>_opportunities._<br>_Most workers cease looking_<br>_for jobs._<br>Hungary|

The four basic patterns of adjustment are as follows.

_High employment._ The category combining high labor force participation rate and low unemployment rate implies a high employment-to-population ration, and thus is apparently the most desirable one. However, it comprises two distinctly different sub categories: countries such as Estonia or Slovenia, where high employment is a consequence of successful transition, and countries, such as Moldova or Romania, where high employment is associated with slow restructuring and a high share of low productivity jobs (e.g. in subsistence agriculture, or in nonprofitable enterprises). To some extent, the delayed restructuring can be tracked back to the nature of the privatization process in this countries (mass privatization schemes with dispersed ownership structures and thus weaker governance).

_Limited job opportunities: in search of jobs._ This category comprises countries such as Georgia or Slovakia, where labor force participation rates are high, but so is unemployment. This combination suggests that unemployed workers still see employment opportunities and thus actively search for jobs. Relatively few workers are discouraged and gave up job search. As a 8
**Table 1: Baseline summary statistics by experimental group**

||Control|No motiv,<br>no enroll|No motiv,<br>pre-enroll|Motiv,<br>no enroll|Motiv,<br>pre-enroll|
|---|---|---|---|---|---|
||Mean/SD<br>(1)|Mean/SD<br>(2)|Mean/SD<br>(3)|Mean/SD<br>(4)|Mean/SD<br>(5)|
|_Panel A: SEBRAE data (2017/2018)_||||||
|Used SEBRAE service during the year b|efore the interve|ntion||||
|Any type of service|0.202|0.145|0.212|0.192|0.197|
||[0.403]|[0.353]|[0.410]|[0.395]|[0.399]|
|Material|0.155|0.139|0.165|0.158|0.174|
||[0.363]|[0.347]|[0.372]|[0.366]|[0.380]|
|Training|0.089|0.023***|0.124|0.079|0.045*|
||[0.286]|[0.151]|[0.330]|[0.271]|[0.208]|
|Business practices used (%)|0.493|0.450|0.505|0.499|0.518|
||[0.255]|[0.273]|[0.272]|[0.266]|[0.259]|
|_Panel B: RAIS and RFB data_||||||
|Number of employees (2015)|3.869|4.098|3.412|4.124|3.798|
||[3.525]|[5.871]|[3.324]|[7.177]|[5.016]|
|Average wage (minimum wages, 2015)|1.596|1.624|1.631|1.656|1.606|
||[0.489]|[0.551]|[0.560]|[0.595]|[0.486]|
|Average employee age (2015)|33.967|34.206|33.484|33.97|33.682|
||[8.673]|[8.852]|[8.441]|[8.708]|[8.372]|
|Share of male employees (2015)|0.446|0.458|0.486|0.477|0.497|
||[0.405]|[0.409]|[0.412]|[0.401]|[0.407]|
|Share of employees with college or|0.072|0.07|0.081|0.083|0.084|
|higher (2015)|[0.189]|[0.182]|[0.220]|[0.208]|[0.209]|
|Firm age (2018)|12.577|13.509|12.476|12.983|11.185|
||[9.984]|[11.078]|[9.228]|[8.610]|[8.557]|
|Commerce sector|0.482|0.48|0.447|0.497|0.427|
||[0.501]|[0.501]|[0.499]|[0.501]|[0.496]|
|Service sector|0.387|0.416|0.412|0.339|0.416|
||[0.488]|[0.494]|[0.494]|[0.475]|[0.494]|
|Starting capital (USD)|9771.951<br>[20061.252]|7140.508<br>[13389.208]|10526.56<br>[42617.959]|9947.013<br>[22146.332]|9573.12<br>[20069.107]|
|N|168|173|170|177|178|

Notes: This table shows baseline summary statistics using administrative data from SEBRAE-PR, for 2017 and 2018, and RAIS, for 2015, the latest available year at the start of the study. Starting capital comes from the Federal Tax Authority (RFB). Column 1 displays means and standard deviations for the control group. Columns 2 through 5 display means and standard deviations for each treatment group. Material denotes receipt of written or recorded material on business practices, either during a visit to a SEBRAE service center, or to SEBRAE's website. Training includes classes, consulting, and networking events. Business practices are only available for 846 observations, since recording errors led to missing observations for firms in one week. Stars refer to differences in means of the respective treatment group to the control group. ***, **, and * indicate significance at the 1, 5, and 10 percent level.

17
**Table S2.S: Results for the baseline specification with interaction terms (same sector x network variables) added**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|VARIABLES|newprod|invdum|anyrd|it|pcf_tech|suprat|htrain|ftrain|
|supbuy|0.0838|-0.210|-0.302**|0.418|-0.258|-0.101|-0.124|0.378|
||(0.363)|(0.328)|(0.134)|(0.586)|(0.231)|(0.0894)|(0.265)|(0.346)|
|samesup|-0.0210|-0.160*|-0.150*|-0.303**|-0.274*|-0.0141|0.0298|-0.0122|
||(0.0842)|(0.0820)|(0.0804)|(0.131)|(0.142)|(0.0468)|(0.0921)|(0.0586)|
|samecus|0.0415|0.00296|0.132***|0.220***|0.187*|-0.0443|0.0826**|0.0219|
||(0.0263)|(0.0355)|(0.0363)|(0.0623)|(0.0988)|(0.0293)|(0.0396)|(0.0704)|
|same_sec|-0.00501|-0.0138|-0.0238|-0.109**|-0.0577|-0.00136|-0.0280|-0.0251|
||(0.0199)|(0.0269)|(0.0188)|(0.0486)|(0.0388)|(0.0117)|(0.0245)|(0.0218)|
|ldist|4.00e-05|-0.000943|-0.0126***|-0.0224***|-0.0184***|0.00312|-0.00786**|-0.000860|
||(0.00172)|(0.00240)|(0.00302)|(0.00545)|(0.00614)|(0.00201)|(0.00308)|(0.00438)|
|ad_fmage|0.000122|0.000256|0.000360|-0.00318***|-0.00102|-0.000523*|-0.000422|0.000879|
||(0.000283)|(0.000368)|(0.00103)|(0.00120)|(0.00133)|(0.000299)|(0.000662)|(0.000992)|
|ad_meduc|0.00570|0.0409***|0.00758|0.0189|0.0664***|0.00381|-0.000344|-0.0144***|
||(0.00616)|(0.0144)|(0.00926)|(0.0167)|(0.0236)|(0.00339)|(0.00577)|(0.00474)|
|ad_mexper|-1.72e-06|-0.000301|-0.000409|-2.83e-05|-0.000121|0.000258|-0.00155***|0.000637|
||(0.000287)|(0.000265)|(0.00116)|(0.00218)|(0.00240)|(0.000598)|(0.000501)|(0.00108)|
|ad_mfemale|0.00121|0.0355*|0.0978**|0.435***|0.241***|0.0298*|0.0446|0.0848|
||(0.00629)|(0.0187)|(0.0396)|(0.0909)|(0.0751)|(0.0176)|(0.0375)|(0.0542)|
|ad_lemp|0.0231**|0.0524***|0.0394**|0.167***|0.197***|0.0182***|0.0567***|0.0780***|
||(0.00911)|(0.0143)|(0.0175)|(0.0421)|(0.0457)|(0.00569)|(0.0170)|(0.0205)|
|secdist|0.00264|0.00283|0.00262|0.00285|0.00657|0.000701|-0.00142|0.00385|
||(0.00423)|(0.00470)|(0.00397)|(0.00836)|(0.00808)|(0.00224)|(0.00439)|(0.00423)|
|seclink|-0.130|0.0200|-0.0662|0.0773|-0.0660|0.0322|-0.000292|-0.00623|
||(0.0960)|(0.0816)|(0.0833)|(0.120)|(0.130)|(0.0428)|(0.0753)|(0.0703)|
|Constant|0.461***|0.375***|0.332***|0.507***|0.812***|0.292***|0.377***|0.0977**|
||(0.0172)|(0.0308)|(0.0360)|(0.0654)|(0.0686)|(0.0160)|(0.0346)|(0.0381)|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 29