rafmacalaba/datause-encoder-data huggingface dataset:
# **Discussion**

It is very impressive that Indonesia was able to achieve such a gain in test scores in math given the increased enrollment of disadvantaged children in the school system (see Figure 3). As enrollments in lower secondary schooling continue to increase, more and more students from families with less well-educated parents are entering the school system. For example, in 2003, the average level of education attained by the fathers of the 15-year-old students was 9.26 years, and for their mothers it was 8.30 years. By 2006, the average level of the fathers’ schooling had fallen to 9.09 years, while the mothers’ level had fallen to 8.16 years.

**Figure 3: Changes in Parental Education and Students’ Scores over Time**

<!-- Start of picture text --> 2003 2006<br>440 40 440 40<br>30 30<br>400 400<br>20 20<br>360 360<br>10 10<br>320 0 320 0<br>None Prim Low Sec Up Sec Univ None Prim Low Sec Up Sec Univ<br>PISA Mother schooling PISA Mother schooling<br><!-- End of picture text --> <!-- Start of picture text --> 2003 2006<br>440 40 440 40<br>30 30<br>400 400<br>20 20<br>360 360<br>10 10<br>320 0 320 0<br>None Prim Low Sec Up Sec Univ None Prim Low Sec Up Sec Univ<br>PISA Father schooling PISA Father schooling<br><!-- End of picture text --> Table 7 presents the variables that are listed in the 2006 dataset. The more institutional variables that we included in our analysis, the more interesting were the findings that emerged. Among other things, firing teachers, which is an indicator of school autonomy, was significant. Also, if a school was having to compete with others in the vicinity, then the effect was large, positive, and significant. Parental involvement in formulating the school budget was also positive and significant. Public schools retained their large advantage. An adequate supply of math teachers played a positive role in the determination of test scores, while grade repetition had a small negative effect. The level of the mother’s schooling was significant. Doing math work in class was also important.

16
Table 7: Effects on women’s non-agricultural activity outcomes

||(1)<br>Brigadiere has worked on non-agr<br>act 1h last 30 days|(2)<br>Brigadiere non-agr act work<br>duration in hours per month|(3)<br>Brigadiere total monthly<br>non-agr act income|(4)<br>Brigadiere total monthly<br>non-agr act income (ihs)|
|---|---|---|---|---|
|**Full sample**|||||
|ITT estimate|0.065|0.681|721.356|-0.005|
||(0.052)|(8.951)|(927.379)|(0.564)|
|Observations|1966|1967|1967|1967|
|_R_<sup>2</sup>|0.060|0.189|0.331|0.123|
|Control mean|0.548|36.259|3230.932|2.244|
|**Have child aged 0-2**|||||
|ITT estimate|0.062|0.564|875.980|0.050|
||(0.047)|(8.637)|(797.004)|(0.496)|
|Observations|1126|1127|1127|1127|
|_R_<sup>2</sup>|0.083|0.156|0.262|0.129|
|Control mean|0.543|34.192|2919.580|2.130|

_Notes:_ Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.10.

Table 8: Effects on women’s other empowerment outcomes

||(1)<br>Index of brigadieres<br>voice on decision<br>making in the<br>household (total)|(2)<br>Index of brigadieres<br>voice on decision<br>making in the<br>household (average)|(3)<br>Index of brigadieres<br>own decision<br>making in the<br>household (total)|(4)<br>Index of brigadieres<br>own decision<br>making in the<br>household (average)|(5)<br>Husband or household<br>member restrict wife<br>visiting or working<br>in last 6 months|(6)<br>Justifed that a husband<br>beats his wife if<br>she burns food or<br>neglects children|(7)<br>Work division<br>and husband<br>participation in work<br>index (total)|
|---|---|---|---|---|---|---|---|
|**Full sample**||||||||
|ITT estimate|-0.209|-0.048|-0.075|-0.023|0.000|-0.005|0.004|
||(0.185)|(0.037)|(0.344)|(0.070)|(0.013)|(0.045)|(0.222)|
|Observations|1967|1943|1967|1954|1967|1967|1967|
|_R_<sup>2</sup>|0.123|0.141|0.083|0.090|0.027|0.034|0.170|
|Control mean|4.439|0.908|3.290|0.675|0.083|0.214|4.313|
|**Have child aged 0-2**||||||||
|ITT estimate|-0.300|-0.060|-0.144|-0.031|0.012|-0.002|-0.060|
||(0.187)|(0.038)|(0.356)|(0.073)|(0.016)|(0.044)|(0.220)|
|Observations|1127|1117|1127|1120|1127|1127|1127|
|_R_<sup>2</sup>|0.186|0.201|0.108|0.110|0.044|0.062|0.146|
|Control mean|4.427|0.901|3.274|0.668|0.087|0.222|4.361|

_Notes:_ Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.10.

Table 9: Effects on intra-household dynamics

||(1)<br>Brigadiere takes care<br>of children alone|(2)<br>Brigadiere often takes care<br>of children|(3)<br>Brigadiere takes care of children<br>as well as her partner|(4)<br>Brigadieres partner often takes<br>care of children|
|---|---|---|---|---|
|**Full sample**|||||
|ITT estimate|-0.031|-0.035|0.063|-0.001|
||(0.048)|(0.060)|(0.041)|(0.026)|
|Observations|1788|1788|1788|1788|
|_R_<sup>2</sup>|0.088|0.055|0.072|0.092|
|Control mean|0.307|0.505|0.153|0.034|
|**Have child aged 0-2**|||||
|ITT estimate|-0.055|-0.020|0.077*|-0.003|
||(0.043)|(0.058)|(0.038)|(0.027)|
|Observations|1035|1035|1035|1035|
|_R_<sup>2</sup>|0.100|0.079|0.102|0.093|
|Control mean|0.325|0.495|0.145|0.034|

_Notes:_ Robust standard errors in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.10.

38
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-6-

# Previous Real Product Estimates for Manufacturing

The present study is not thc' first to use the industry of origin approach to derive PPPs and measure real output levels in manufacturing. Table 1.3 lists 14 other international comparisons of levels of output or productivity in manufacturing which have appeared over the past four decades. They all use information derived from production censuses, and they are all restricted to two or three countries. This type of study was given its initial impetus by Rostas (1948), and the studies of Maddison (1Q52), Galenson (1955), Frankel (1957) and Yukizawa (1978) more or less replicated his method for measuring real output, which concentrates on comparisons of "physical" gross output of different countries (with or without coverage adjustments for non-sampled products within an industry). Paige and Bombach called this the "single indicator" approach, and they themselves devoted considerable space to discussing an alternative "double indicator" method, which would involve separate estimation of inputs across countries as well as gross output. This approach, if fully implemented. involved a double deflazion procedure, i.e. separate calculation of PPPs for output and inputs, to arsive at a true comparison of value added in real terms. In fact, Paige and Bombach did not achieve their goal of double deflation for manufacturing (though they did it for agriculture and part of transport), and were able only to make a very partial input PPP for fuel use. Subsequent researchers have also failed to achieve the goal of double deflation even for countries which provide a substantial amount of detailed information on inputs, because the structure of inputs is so heterogeneous.

The most ambitious studies in terms of sample size were those by Paige and Bombach (1959), Kudrov (1969), West (1971), Smith, Hitchens and Davies (1982), Smith (1985)1, and Davies and Caves (1987). Table 1.3 shows their coverage so far as we could determine. Another incicator of the adequacy of their sample is the number of items matched (first column of table 1.3). On the latter criterion, our study is among&s the most comprehensive.

Some of the studies cited in table 1.3 used a mixed methodology, in the sense that they combined independently determined PPPs by industry of origin with some proxy PPPs derived from expenditure studies. This was true in particular of Paige and Bombach (1959), to a smaller extent of Smith, Hitchens and Davies (1982) and Davies and Caves (1987). In our study we stuck strictly to the industry of origin approach, without using proxy PPPs. The different studies vary in the way they summarize their results for manufacturing as a whole. In most cases, the sample results themselves are presented as representative. Paige and Bombach, the Czech-French study, West and our study are the only ones to adjust the sample in order to present a blown-up estimate for manufacturing as a whole (see end of chapter III for deb;ails).

- **1** In fact Smith (1985) is derived from the same data set as Davies and Caves (1987).
Bibliography:

- Aguiar, Angel, Maksym Chepeliev, Erwin L. Corong, Robert McDougall, and Dominique van der Mensbrugghe. 2019. “The GTAP Data Base: Version 10.” Journal of Global Economic Analysis 4 (1): 1–27. https://doi.org/10.21642/jgea.040101af.

- Brahmbhatt, Milan, and Arindam Datta. 2008.  On SARS type economic effects during infectious disease outbreaks. World Bank Policy Research Paper No. 4466, World Bank, Washington, D.C. http://documents.worldbank.org/curated/en/101511468028867410/On-SARS-type-economiceffects-during-infectious-disease-outbreaks.

- Bloom, E., V. de Wit, and M. J. Carangal-San Jose. 2005. “Potential Economic Impact of an Avian Flu Pandemic on Asia”, ERD Policy Brief No.42.

- Burns, Andrew; Mensbrugghe, Dominique van der; Timmer, Hans. 2006. Evaluating the economic consequences of avian influenza (English). Washington, DC: World Bank.

- Evans, David, Marcio Cruz, Francisco Ferreira, Hans Lofgren, Maryla Maliszewska, and Mead Over. 2014. “Estimating the Economic Impact of the Ebola Epidemic: Evidence from Computable General Equilibrium Models.” https://doi.org/91219.

- Lee, J.-W. and W. J. McKibbin. 2004.  “Globalization and Disease: The Case of SARS,” Brookings Discussion Papers in International Economics, No. 156, February.

- McKibbin, Warwick, and Roshen Fernando. 2020. “The Global Macroeconomic Impacts of COVID-19.” Brookings Institute, no. March: 1–43. https://www.brookings.edu/wpcontent/uploads/2020/03/20200302_COVID19.pdf.

- McKibbin, Warwick J. and Alexandra A. Sidorenko. 2006. “Global Macroeconomic Consequences of Pandemic Influenza,” Lowy Institute Analyses, Lowy Institute for International Policy, February, Sydney.

- OECD. 2020. “Coronavirus: The World Economy at Risk.” OECD Interim Economic Assessment, 1–18. http://www.oecd.org/berlin/publikationen/Interim-Economic-Assessment-2-March-2020.pdf.

- Orlik, Tom, Jamie Rush, Maeva Cousin, and Jinshan Hong. 2020. “Coronavirus Could Cost the Global Economy $2.7 Trillion. Here’s How.” Bloomberg, 2020.

S&P. 2020. “Global Credit Conditions : Coronavirus Casts Shadow Over Credit Outlook,” 1–15.

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**Figure 5:  Number of small and large exporters (by sector)**

<!-- Start of picture text --> (a) Small and medium-sized exporters (b) Large exporters<br>40 20<br>30 15<br>20 10<br>10 5<br>0 0<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>Agriculture Mining Manufacturing Agriculture Mining Manufacturing<br><!-- End of picture text --> (tungsten), and tantalum, for which total production is counted in a few thousand tons.<sup>7</sup> In Rwanda’s case, it is more likely that the industry concentration stems from government regulation and interventions. The number of small exporters of mining products did increase rather rapidly from 2 in 2012 to 12 in 2016.

Note that the scale on the vertical axis for the left panel of Figure 5 (for small exporters) is twice as large as on the right (for large exporters). As a result, the figure obscures the fact that the agriculture sector at the end of the sample period counts twice as many small exporters than large exporters. While a more natural, and arguably more economically healthy situation, even that ratio implies a rather strong concentration. In manufacturing, there were 4 large exporters for each small exporter in 2016.

It might be that phyto-sanitary standards impose barriers to exports of agricultural products, but we doubt this is the main reason that we observe so few small-scale agriculture exporters. Standards are certainly present for exports to Europe, but only large firms target those markets. They could play a role for exports to Uganda or Tanzania, but we would expect the smallest agricultural exporters to target Burundi or the DRC, where product standards are non-existent or weaker than in Rwanda.

In both the agriculture and manufacturing sectors we had, a priori, expected to observe many more small exporters. Family farms are the predominant economic activity in the country. In the informal manufacturing sector there are many tailors or seamstresses working out of their homes. However, the majority of small-scale agricultural exports to neighboring countries are unlikely to be captured in the Customs data that we work with and the same is likely to be true for small manufacturing exports.

In our dataset there are additional export transactions conducted by firms with less than 10,000 USD total exports in a given year. In many instance, when such export transactions are

> 7 A few basic production and export statistics are provided at https://en.wikipedia.org/wiki/Mining_in_Rwanda.

11
dimensions. Thus, this approach assumes two poverty standards uˆM<sup>anduˆ</sup> OD<sup>,respec-</sup> tively for the monetary dimension and the other (non-monetary) dimensions. These two standards uˆM<sup>anduˆ</sup> OD<sup>areconsidered“equivalent”,asintheWorldBank’sMPM.43The</sup> comprehensive survey allows computing the “true” fraction of multidimensionally poor individuals, denoted by H<sup>ˆ</sup> .

We propose a method to construct an estimate H<sup>ˆˆ</sup> of H<sup>ˆ</sup> when the joint distribution between monetary and non-monetary dimensions is missing. That is, there is no comprehensive survey but rather two separate surveys, say one LSMS household consumption survey and one DHS or MICS household survey. The former survey allows computing the fraction of monetary poor individuals, which we denote by H<sup>ˆ</sup> M . The latter survey allows computing the fraction of other-dimensions poor individuals, which we denote by ˆ HOD. Some individuals are simultaneously monetary poor and other-dimensions poor. The only information missing to compute the “true” fraction H<sup>ˆ</sup> can be called the coefficient of overlap (c) between the two types of poverty. This coefficient captures “by how much” monetary poor individuals are more likely than those who are not monetary poor to be other-dimensions poor. Formally, c is the computed as the fraction of monetary poor individuals who are other-dimensions poor divided by the fraction of individuals who are not monetary poor but are other-dimensions poor In this setting, coefficient c summarizes all the relevant information on the missing joint distribution. If we knew the value for c, we could compute the “true” value for H<sup>ˆ</sup> through The proposed method consists in assuming a value cˆ for the unknown coefficient c. This allows estimating the “true” value for H<sup>ˆ</sup> through an indicator H<sup>ˆˆ</sup> (ˆc), computed by ˆ replacing c with c in last equation. By definition, we know that c ≥ 0. We have c = 0 when no monetary poor individual is simultaneously other-dimensions poor. We have c = 1 when monetary poverty status is uncorrelated with other-dimensions poverty status. Finally, we have c →∞ when all other-dimensions poor individuals are simultaneously monetary poor. If other-dimensions poverty status is positively correlated with monetary poverty status, we have c > 1. For instance, the coefficients of overlap between the extreme IPL and the global MPI can be computed for the six developing countries studied

> 43Indeed, in the WB’s MPM, individuals below the extreme IPL receive a weight of 1/3, which is the same identification threshold (k = 1/3) used to identify multidimensionally poor individuals who are not below the extreme IPL.

25
– **Table 1** Depth and duration, by income level and region

||Mean<br>Depth|Median<br>Depth|Mean<br>Duration|Median<br>Duration|Number<br>of Spells|Censored<br>Spells|Number of<br>Countries|
|---|---|---|---|---|---|---|---|
|||_Panel _|_(a) Structur_|_al breaks est_|_imated with _|_size = 0.10_||
|_Income Level_||||||||
|High Income (OECD)|-10.9|-11.1|2.4|2|7|1|29|
|High Income (Other)|-37.5|-46.0|10.0|11|4|2|12|
|Upper Middle Income|-26.3|-20.8|8.4|3|13|2|33|
|Lower Middle Income|-23.6|-19.0|6.3|2|17|2|39|
|Low Income|-31.9|-32.1|14.0|4|16|4|34|
|_Geographical Region_||||||||
|Africa|-30.9|-28.1|15.6|8|22|6|44|
|_Northern_|-16.1|-18.9|17.1|9|3|0|5|
|_Sub-Saharan_|-33.3|-36.2|4.5|2|19|6|39|
|Americas|-20.7|-18.7|4.9|3|12|1|23|
|Asia|-28.4|-22.6|4.4|1|14|3|43|
|Europe|-17.0|-14.5|2.9|2|9|1|35|
|_Total_|-26.0|-21.0|10.3|3|57|11|145|
|||_Panel _|_(b) Structur_|_al breaks est_|_imated with _|_size = 0.20_||
|_Income Level_||||||||
|High Income (OECD)|-7.5|-4.9|1.9|2|13|1|29|
|High Income (Other)|-37.5|-46.0|10.0|11|4|2|12|
|Upper Middle Income|-23.3|-18.9|7.3|2|17|2|33|
|Lower Middle Income|-23.5|-20.0|7.0|3|22|3|39|
|Low Income|-31.0|-36.2|14.1|5|21|5|34|
|_Geographical Region_||||||||
|Africa|-29.2|-28.0|13.9|8|29|7|44|
|_Northern_|-16.1|-18.9|14.9|5|3|0|5|
|_Sub-Saharan_|-30.7|-32.1|5.7|2|26|7|39|
|Americas|-22.5|-20.0|7.7|4|14|2|23|
|Asia|-24.5|-16.6|4.0|1|18|3|43|
|Europe|-13.1|-9.9|3.0|2|16|1|35|
|_Total_|-23.5|-18.9|9.4|3|77|13|145|

_Notes_ : Depth is defined as the percent decrease in GDP per capita at the trough relative to GDP per capita before the slump (not log difference). Mean and median duration are expressed in years. As a result of some spells being censored, both mean duration and depth are underestimated. The number of countries refers to countries with more than one million inhabitants and more than 20 observations of GDP per capita in a particular income group or region. _Source_ : Authors’ analysis based on data from the Penn World Tables version 9.0 (series _rgdpna/pop_ ).

et al., 1993, Rodrik, 1999, Pritchett, 2000, Jones and Olken, 2008). Several collapses follow the post-communist transitions of 1989–1990. We find no slumps starting in the period of the early and mid-2000s, but a handful of slumps begin in 2008.<sup>9</sup> Note that the global recession of 2008–2009 is still a bit too close to the end of the sample to permit reliable

> 9Supplementary Appendix S4 reports the annual frequency of starting dates for both samples.

13
# **III. Empirical Analysis**

Apart from providing information to bank supervisors, the standard _raison d’être_ of a PCR, is to share information across financial institutions. However, there has been no empirical work published assessing the value of this information sharing that we are aware of, neither at the national or international level. Moreover, there remains the important question as to what information to share. In particular, banks are more likely to voluntarily share negative information. Hence some private credit bureaus essentially work with negative information only. Some PCRs also only contain negative information. However, there are certainly advantages to sharing both positive and negative information. In this section we employ data from the three PCRs of Argentina Brazil and Mexico to illustrate the potential value of these databases. In particular, we conduct two analyses. First, we show the importance of a PCR having both positive and negative information. Second we attempt to assess the importance of information sharing generally.

# **_Positive versus negative information_**

The first part of the empirical exercise tests the importance of including both positive and negative information in empirical models estimating borrower default. The term “negative information” refers to data on late payments and defaults. “Positive information” refers to information on borrowers that have paid their obligations on time and other descriptive data including loan amounts, interest rates, and loan maturities.

Following Barron and Staten (2003), a credit scoring model is developed to test the importance of using both positive and negative data to predict loan defaults. The following equation is then estimated separately for Argentina, Brazil and Mexico.<sup>7</sup>

> 7 We use the probit procedure for estimations in Argentina and Mexico and a logit model for Brazil.

7
Table 3. Output implications of amplified global pandemic – cumulative impacts (% deviations from the benchmark)

||**Agriculture**|**Natural**<br>**resources**|**Manufacturing**|**Services**|**Domestic**<br>**services**<br>**affected by**<br>**pandemic**|**Traded**<br>**tourist**<br>**services**|**Total**|
|---|---|---|---|---|---|---|---|
|China|-3.12|-1.08|-3.61|-3.67|-4.85|-4.64|-3.54|
|Developing EAP excluding<br>China|-2.70|-1.04|-3.21|-5.40|-9.45|-11.28|-4.12|
|Cambodia|-2.87|-3.98|-2.69|-9.66|-14.96|-19.00|-5.11|
|Lao PDR|-2.41|-3.89|-2.60|-5.85|-12.18|-15.02|-3.57|
|Malaysia|-4.19|-0.79|-4.11|-4.34|-7.30|-9.73|-4.03|
|Thailand|-3.06|-2.91|-4.43|-6.84|-11.53|-14.64|-5.29|
|Vietnam|-3.06|-0.72|-3.34|-3.93|-8.52|-8.99|-3.37|
|Philippines|-2.51|-2.65|-3.93|-5.16|-11.10|-13.30|-4.44|
|Indonesia|-2.70|-0.61|-3.03|-3.67|-7.65|-8.84|-3.15|
|HongKongSAR,China|-1.29|-3.24|-1.33|-6.06|-8.46|-9.23|-5.35|
|Korea,Rep.|-3.91|-4.25|-3.68|-4.53|-6.87|-6.15|-4.10|
|Singapore|-2.61|-3.47|-4.32|-4.01|-7.18|-6.28|-4.11|
|Taiwan,China|-1.04|-7.75|-1.80|-6.84|-7.82|-7.17|-4.15|
|Canada|-4.30|-1.10|-3.25|-3.02|-8.95|-9.16|-2.96|
|Europe|-3.00|-1.02|-2.89|-4.02|-9.04|-9.06|-3.65|
|Japan|-4.71|-2.85|-2.77|-4.62|-8.75|-8.35|-3.98|
|United States|-3.60|-0.21|-2.45|-3.80|-9.99|-11.27|-3.38|
|Middle East & North Africa|-2.76|-1.65|-2.67|-3.02|-9.11|-10.03|-2.65|
|Sub-Saharan Africa|-2.51|-1.72|-2.95|-3.02|-6.35|-8.13|-2.79|
|Brazil|-3.40|-1.20|-2.86|-3.14|-8.55|-9.28|-2.99|
|Rest of Latin America &<br>Caribbean|-2.64|-1.21|-2.94|-4.05|-10.51|-11.87|-3.49|
|Russian Federation|-3.00|-2.19|-3.73|-3.86|-8.72|-9.62|-3.58|
|Rest of Europe & Central<br>Asia|-2.20|-0.59|-3.53|-5.07|-10.20|-11.36|-4.20|
|India|-3.36|-0.84|-3.98|-4.35|-8.23|-8.76|-4.03|
|Rest of South Asia|-2.62|-2.64|-3.25|-5.23|-8.04|-9.28|-4.14|
|Oceania|-3.93|-1.89|-3.10|-3.20|-8.21|-8.07|-3.11|
|Developingcountries|-2.90|-1.42|-3.47|-3.87|-7.98|-8.63|-3.51|
|High-income countries|-3.49|-0.95|-2.78|-4.00|-9.20|-9.60|-3.59|
|World Total|-3.04|-1.29|-3.13|-3.95|-8.77|-9.26|-3.56|

Note: Agriculture – Crops, Livestock; Manufacturing - Meat products (incl. fisheries), Other food, Textiles, Wearing apparel, Leather products, Wood and paper products, Refined oil, Chemical products (incl. rubber and plastics), Non-metallic minerals, Metals, Computer, electronic and optical products, Machinery and equipment nec, Motor vehicles and parts, Transport equipment nec, Other manufacturing; Services – Electricity, Construction, Trade incl. warehousing, Accommodation, food and service activities, Water transport, Air transport, Other transport, Communications, Recreational and other services, Other services; Natural resources - Natural resource products, Fossil fuel extraction; Domestic services affected by pandemic - Trade, Accommodation, food and service activities, Water transport, Air transport, Other transport, Recreational and other services; Traded tourist services - Accommodation, food and service activities, Water transport, Air transport, Other transport, Recreational and other services.

_Source:_ Envisage simulations

- 16 -