rafmacalaba/datause-encoder-data huggingface dataset:
## **Box 1. University Information System (SIU) in Argentina**

<mark>Initiated in 1994 under the</mark> _<mark>Programa de Reforma de la Educación Superior</mark>_ <mark>(PRES) the Argentine Government has led the design and operation of an internet-based management information and statistical data system that integrates various existing modules needed for decision-making at the ministerial and decentralized level and provides adequate data and system compatibility with</mark> statistical systems of public universities.

<mark>The SIU consists of five main software modules: (i) accounting and budget; (ii) staff management; (iii) university statistics; (iv) library management and (v) student management. All modules are merged into a single platform and are available through the Internet for all participants. Close consultation with end-users in the development phase has ensured that modules are easy-to-use and compile data of high relevance.</mark> _Source: World Bank 2004 (ICR)_ Establishing structures for data collection often involves designing computer systems that standardize information and reduce the administrative burden of handling large quantities of data. Some of the elements that need to be addressed are responsibilities for data collection, reporting and evaluation of collected information. Since institutions will be funded based on collected information, systems need build in safeguards and structures for external validation of data. National auditing agencies are often well-positioned and sufficiently independent to carry out this task.

Given the complexity of getting reporting structures right it may improve changes of success if performance reporting precedes results-based funding. Requiring tertiary institutions to present periodic reports on priority indicators prior to the introduction of results-based funding familiarizes institutions with data collection and establishes a reliable baseline from which institutional performance can be assessed.

# **Selecting indicators**

The process of identifying indicators goes to the heart of designing a results-based funding system. As pointed out by Burke (2001a) “what gets measured is often what gets valued, and what gets funded is even more prized”.

Indicators are most effective when they mirror the government’s strategy for change in the higher education sector. A strategy implies the movement of the sector from its current stage to a desirable but uncertain future stage. Because the sector has never been to this future position, its intended pathway involves a series of linked hypotheses. In the Chilean Science for the Knowledge Economy Project, prioritizing outcomes and making key hypotheses explicit was an important first step in selecting indicators (Box 2). Ideally, indicators should reflect the aspects of the higher education sector that, if improved, are presumed to result in the achievement of general goals such as equity, efficiency and quality (Niven 2002).

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**-** **<u>Table 7: Heterogeneous impact for poorest of the poor (POP), poor and non poor SHG participants</u>**

||||**U**|**ntrimme**|**d sample**|**, simple**|**DD**|||
|---|---|---|---|---|---|---|---|---|---|
|||**POP**|||**Poor**|||**Non-poor**||
|Consumption p.c. (Rs/year)|615|(425)||819|(438)|*|1728|(702)|**|
|Food (Rs/year)|53|(224)||120|(231)||511|(387)|(*)|
|Non-food (Rs/year)|562|(314)|*|699|(341)|**|1217|(433)|***|
|Energy intake p.c. (Kcal/day)|176|(116)||77|(107)||18|(204)||
|Protein intake p.c. (g/day)|2.14|(2.92)||0.91|(2.49)||1.73|(3.66)||
|Non-financial asset p.c. (Rs)|558|(215)|***|428|(377)||1219|(585)|**|
|No. of obs.|4|07+73=4|80|3|08+71=37|9|2|05+47=252||
||||**Tri**|**mmed sa**|**mple, PS**<br>|**weighte**|**d DD**<br>|||
|||**POP**|||**Poor**|||**Non-poor**||
|Consumption p.c. (Rs/year)|677|(455)|(*)|1218|(524)|**|-143|(1317)||
|Food (Rs/year)|295|(281)||511|(252)|**|-289|(679)||
|Non-food (Rs/year)|382|(293)|(*)|707|(425)|*|146|(728)||
|Energy intake p.c. (Kcal/day)|286|(144)|***|321|(111)|***|-101|(326)||
|Protein intake p.c. (g/day)|6.85|(3.87)|*|7.54|(2.32)|***|4.21|(5.33)||
|Non-financial asset p.c. (Rs)|757|(257)|***|1194|(440)|***|1066|(703)|(*)|
|No. of obs.|331+|73=404|(.94)|178+65|=243(.9|0)|113+44|=157(.89|)|
|Significance of coefficient is a<br>significant at 10% for one-sided|s follow<br>test|s: * signi|ficant at|10%; **|significa|nt at 5%|; *** sign|ificant at|1% for two-sided test; (*)|

23
<!-- Start of picture text --> Figure 6.2<br>Density at t= * Density at t1-<br>.275228-<br>.001558 _ _ _ _ _X<br>-8.6121 1.50382<br>Health Expenditure Shares (log)<br><!-- End of picture text --> In table 4.16 we consider the effect by quintiles of initial consumption. The bottom quintile experienced an increased in health care expenditure levels of about 4<sup>lei</sup> per capita (an increase of 158%). This quintile has no significant changes in its health care expenditure shares, however. The second and third quintiles have changes that<sup>are not</sup> statistically different than zero in either levels or shares. Meanwhile, regardless<sup>of</sup> whether we looked at it in terms of levels or budget shares, the two top quintiles experienced significant drops in health expenditures. Household in the top quintile expended on average 35.5 lei less per capita in health care after the crisis (a drop of 74%). Similarly, health expenditure shares for this top quintile drop by about<sup>11</sup> percentage points, from 18.2% to 7.1% (a drop of 61%).

Table 4.16. Health expenditures by initial total expenditure quintile

||||Shares|||Level||
|---|---|---|---|---|---|---|---|
|||Pre-crisis|Post-crisis|Change|Pre-cnsis<br>|Post-crisis<br>|Change<br>|
|_All sample_|Mean|0.104|0.075|-0.029|15641<br>|8.244<br>|-7.397<br>|
||Std.err.<br>Obs.|0.007<br>1766|0.006|0.008|1.342|0.766|1.447|
|_Quintile1 _|Mean|0.073|0.093|0.020|2.620|6.773<br>|4.152<br>|
||Std.err.<br>Obs.|0.013<br>353|0.017|0.018|0.465|1.604|1.566|
|_Quintile2 _|Mean|0.078|0.058|-0.020|5.364<br>|5.485<br>|0.121<br>|
||Std.err.<br>Obs.|0.012<br>353|0.014|0.017|0.805|1.756|1.891|
|_Quintile3 _|Mean|0.073|0.076|0.002|7.236|7.337|0.101<br>|
||Std.err.<br>Obs.|0.011<br>353|0.012|0.015|1101|1.411|1.752<br>|
|_Quintile4_|Mean|0.112|0.076|-0.036|14.930|9.161<br>|-5.769<br>|
||Std.err.<br>Obs.|0.014<br>353|0.013|0.018|1.893|1.603|2.407|
|**_QuintleS_**|Mean|0.182|0.071|-0.111|47.915|12.390|-35.524|
||Std.err.<br>Obs.|0.021<br>354|0.011|0.021|5.949|2.102|5.875|

30
firms believe that they should not refuse to pay taxes until they get better services from the national government.

Finally, as we measure different variants of tax morale for own behavior, we combine all these variables in an unweighted average of z-scores and find a statistically significant effect.

Table 3: Overall Treatment Effects - Own Paying Behaviour

||Direct|Punishable|Will Pay|Wont Refuse|INDEX|
|---|---|---|---|---|---|
||b/se/p|b/se/p|b/se/p|b/se/p|b/se/p|
|Treatment group mean|0.024|0.063|0.046|0.086<sup>_∗_</sup>|0.115<sup>_∗∗_</sup>|
||(0.03)|(0.05)|(0.05)|(0.04)|(0.05)|
||0.468|0.215|0.359|0.055|0.036|
|Control group mean|0.833|0.403|0.534|0.395|-0.072|
|Observations|1210|1210|1210|1210|1210|

Standard errors in parentheses _∗ ∗∗ ∗∗∗ p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01 Note: This Table shows heterogeneous treatment effects. Model Names (Direct Punishable Will Pay Wont Refuse INDEX) represent our outcome variables. Direct: Based on question C11 that states : My firm does not pay all the [Direct taxes] it is required to pay. Punishable: based on question D19 that asks respondent to state whether Firms not paying the taxes they owe to the government is Not wrong at all, Wrong but understandable or Wrong and punishable. Will Pay - based on question D20a, that states If I was sure I would not get caught, I would not pay all the taxes that I owe. Wont Refuse: based on question D20d that states Firms should refuse to pay taxes until they get better services from the national government. INDEX: An unweighted average of the Z-scores of all four outcome variables Our second set of outcomes are secondary outcomes on awareness of obligations, ease of tax filing process, preference for rewarding instead of punishment, compliance of suppliers, and risk of legal action for evasion (Table 4). We find that treated respondents have lower awareness, and disagreement with a rewards over punishment strategy. We believe the negative effect on awareness is due to respondents being strategically dishonest. By declaring 16
- 21 -

The results indicate that, with past achievement and socioeconomic background held constant, eighth grade students in the private schools who have the mean characteristics of a public school student have an unconditional advantage in test performance of about 3 points in the private 0-type schools and 7 points in the private F-type schools. These results are largely invariant when computed using the 0-type or F-type characteristics.

Had we used the biased coefficients of an OLS (without correcting for selection), we would have come up with qualitatively similar results. However, the magnitudes would have been different--only about a .3 point advantage for the private 0-type schools and 1-2 points for the private F-type schools (Table 5b).

Table 5b: Pr..vate School Effects on Grade 8 Mathematics Achievement, Dominican Republic, 1982-83, Holding Background Characteristics Constant and Not Controlling for Selection Bias (OLS)

|Characteristics<br>of the Randomly|<br>Pre|dicted<br>sco|re|Differ<br>|ential<br>|
|---|---|---|---|---|---|
|Chosen Student Set at Mean of:|Public|O-Tyq;e|F-Type|O-Pub.|F-Pub.|
|Public school subsample|10.26|10.35|10.24|.09|-.02|
|Private 0-type subsample|10.99|11.25|11.66|.26|.67|
|Private F-type subsample|12.25|12.63|14.34|.38|2.10|

# THE NATURE OF THE PUBLIC-PRIVATE DIFFERENTIAL

The previous section showed that the private school students in the Dominican Republic scored higher on mathematics achievement tests at the end of the eighth grade than did their public school counterparts, after
In aggregate, we expect to observe a negative relationship between the percentage of the population that reports displacement, but at the individual level we expect that experience with hosting may, in certain circumstances, be positively associated with perceptions of social cohesion, regarding perceptions of relationships and solidarity.

# **4 Displacement and Social Cohesion: Survey Evidence**

To empirically evaluate the relationships between hosting displaced populations and social cohesion, this paper analyzes a series of surveys of civilian adults conducted in eastern DRC.<sup>8</sup> Each survey uses a multi-stage cluster sampling strategy capturing all _territoires_<sup>9</sup> in North Kivu, South Kivu and Ituri provinces. The final sampling units are randomly selected adults above the age of 18 to avoid bias toward men and/or heads of households. Multiple attempts are made over the course of one day to contact selected respondents and if necessary, appointments are made for interview. Surveys are enumerated by Congolese college students or professionals and interviews are conducted by members of the same gender and ethnicity as respondents to minimize enumeratorinduced response bias. Further methodological details have been published (Vinck, Pham, Bindu, Bedford & Nilles 2019) elsewhere and additional details and sample size calculation are detailed in Appendix.

These surveys are part of a long-term data collection effort by the research team (Vinck & Pham 2014) and were collected separately from the focus group discussions. The focus group discussions thus did not directly influence the design of the surveys, but rather directed the analysis strategy of the surveys that our team has collected at regular intervals in eastern DRC.

The survey data are analyzed in two ways. First, 11 surveys collected between 2017 and 2021

> 8Eastern DRC is a site of ongoing violence, raising a number of ethical, methodological, and practical concerns about collecting data. We discuss the ethical protections we implemented when collecting this survey data in the Appendix, Section B.

> 9 _Territoires_ are sub-provincial administrative units. Additional details on the structure of administrative units are available in the Appendix, Section A.1.

21
Table 8: Accuracy of Population Density Predictions, by Training Sample Size

|||In-sample|Out-|of-samp|le|
|---|---|---|---|---|---|
|Sample Type|Number of Villages|Pseudo _R_<sup>2</sup>|Pseudo _R_<sup>2</sup>|MAE|RMSE|
|Medium sample|1178|0.918|0.842|1490|712|
|Small sample (50% of medium)|593|0.908|0.843|1447|698|
|Very small sample (50% of small)|292|0.920|0.832|1691|738|

Note: The results are based on a Poisson regression model whose dependent variable is census village population density, and the independent variables are selected based on LASSO regularization among the following: All indicators from public and proprietary imagery sources as described in panel B in Table 1, sectoral and district fixed effects, and log village-area. The out-of sample _R_<sup>2</sup> is obtained from stata’s crossfold command using five-fold cross-validation. MAE refers to mean absolute error and RMSE refers to Root Mean Squared Error.

26
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# **Table 22: Sensitivity Analysis—Summary of Constant Returns to Scale Model Results of Our Seven Policy Scenarios. (Percentage Change from Base Year.)**

|o||**WTO**|**Accessio**|**nplus**|**Carbon Re**|**duction Po**<br>|**licies Alone**<br>|
|---|---|---|---|---|---|---|---|
||**WTO**|**carbon**|**emissions**|**energy**||**emissions**|**energy**|
||**accession **<br>**alone**|**emissions **<br>**trading**|**intensity**<br>**standards **|**intensity**<br> **standards**|**emissions**<br>**trading**|**intensity**<br>**standards**|**intensity**<br>**standards**|
|**Aggregate welfare**||||||||
|Welfare(EV as % of consumption)|1.1|0.7|0.0|-3.5|-0.4|-1.0|-4.1|
|Welfare(EV as % of GDP)|0.5|0.3|0.0|-1.6|-0.2|-0.4|-1.9|
|**Carbon Dioxide Emissions and Deco**|**mposition**|||||||
|CO2price(rubleper ton of CO2)||107.8|||103.4|||
|CO2 emissions,decomposed into:|1.0|-20.0|-20.4|-20.2|-20.0|-19.9|-19.9|
|Output effect(% of CO2)|1.1|0.4|-0.4|-4.7|-0.7|-1.4|-5.3|
|Composition effect(% of CO2)|0.4|-2.4|-0.9|-6.2|-2.5|-1.2|-6.1|
|Technique effect(% of CO2)|-0.4|-18.0|-18.6|-9.7|-17.2|-17.3|-8.8|
|**Non-Carbon Dioxide Emissions**||||||||
|Sulphur Dioxide|4.6|4.5|5.2|-4.7|-0.4|0.6|-8.9|
|Nitrogen Oxide|0.9|-2.1|3.1|-16.5|-2.9|2.1|-16.4|
|Hydrocarbons|0.1|-4.5|-3.8|-7.1|-4.3|-3.6|-6.7|
|Particulate Matter|1.1|-1.4|2.4|-12.5|-2.5|1.2|-12.9|
|Volatile Organic Components|0.1|-1.1|-1.0|-6.7|-1.1|-1.1|-6.4|
|Carbon Monoxide|1.9|0.5|0.6|-3.6|-1.4|-1.2|-5.2|
|**Aggregate trade**||||||||
|Regional terms of trade(% change)|1.0|1.1|0.7|0.1|0.1|-0.3|-0.8|
|Regional exports(% change)|0.2|-0.9|-1.0|-7.7|-1.1|-1.1|-7.4|
|Real exchange rate(% change)|0.2|0.7|0.0|0.4|0.4|-0.2|0.2|
|International exports(% change)|2.1|2.0|1.7|-0.1|-0.1|-0.4|-2.0|
|**Return toprimary factors(% change)**||||||||
|Unskilled labor|1.2|-0.3|1.7|2.7|-1.5|0.5|1.5|
|Skilled labor|1.6|-0.9|2.4|2.5|-2.4|0.8|0.9|
|National capital|1.4|-0.1|0.7|-1.2|-1.5|-0.7|-2.4|
|Regional mobile capital|1.9|0.2|1.6|-0.3|-1.6|-0.2|-1.9|
|Crude oil resources|0.2|-0.1|-1.6|-5.8|-0.3|-1.7|-5.7|
|Naturalgas resources|0.4|-35.4|-45.9|-35.2|-33.8|-43.0|-32.8|
|Coal resources|0.6|-12.2|-7.6|-0.5|-12.2|-7.3|-0.9|
|**Factor adjustments**||||||||
|Unskilled labor(% changingsectors)|0.8|0.9|1.2|2.5|0.7|0.7|2.3|
|Skilled labor(% changingsector)|0.9|1.4|1.5|3.0|1.3|1.0|2.7|
|**Source: Authors' estimates**||||||||

66
applications approved, the share of loans financing investment, the share of secured loans, the share of non-performing loans, and the loan fees and interest rates associated with SME lending. Wherever possible, we compare SME to large firm financing. Also, we contrast the behavior of banks in developed and developing countries, as well as the practices of government, private (domestic), and foreign-owned banks.

Our paper focuses on banks as opposed to other financial institutions because studies have shown that banks are the main source of external finance for SMEs across countries (see Beck, Demirguc-Kunt, and Maskimovic, 2008). Furthermore, we concentrate on large banks due to their systemic importance and their significance as potential SME financiers. Until recently, a large literature had argued that small banks are more prone to finance SMEs because they are better suited to engage in “relationship lending”, a type of financing based primarily on “soft” information gathered by the loan officer through continuous, personalized, direct contacts with SMEs, their owners and managers, and the local community in which they operate, to mitigate opacity problems (see Berger, Kayshap, and Scalise, 1995; Keeton, 1995; Berger and Udell, 1996; and Strahan and Weston, 1996). However, some recent studies (see Berger and Udell, 2006; Berger, Rosen and Udell, 2007; and de la Torre, Martinez Peria and Schmukler, 2008) have disputed this conventional wisdom and argued that large banks, relative to other institutions, can have a comparative advantage at financing SMEs through arms-length lending technologies (eg., asset-based lending, factoring, leasing, fixed-asset lending, credit scoring, etc.) instead of relationship lending.<sup>2</sup> Hence, we believe our focus on large banks is also justified by this recent literature.

This paper is closely linked to and builds on a couple of previous International Finance Corporation (IFC) and World Bank initiatives to better understand banks’ involvement with SMEs. The IFC (2007) study was conceived to identify “best practices” in bank involvement with SMEs, including key factors and links among business models, processes, tools, as well as the actual performance in SME banking. As part of this effort, the IFC developed its own questionnaire and in 2006 surveyed 11 banks assumed to be leaders in the SME business segment operating in Australia, Brazil, India, the Netherlands, Poland, Thailand, the UK, and the

> 2 Large banks are also the ones with the most extensive branch networks and, hence, the ones most accessible to SMEs, at least in terms of location.

2
Policy Research Working Paper 7382

# **Abstract**

This paper studies how self-help groups—village-based organizations designed to encourage savings, household production and social cohesion among the poor—can promote economic and social capital. The paper uses survey data and a wide array of social capital measures to assess the impact of a pilot program that was randomly rolled out in rural villages in Cambodia. The study finds that the program encouraged savings and associations via self-help groups. However it did not improve social capital measured by household and network surveys and lab activities that gauge trust, trustworthiness and the willingness to contribute to public goods. The findings contradict recent work that has found significant positive impacts of such groups on social capital. This paper evaluates community-wide impacts while most previous studies focus on program participants. In addition, the empirical strategy is based on a broader array of social capital measures, including behavioral indicators, suggesting that finding impacts of such programs on social capital is sensitive to the measurement strategy.

This paper is a product of the Impact Evaluation Team, Development Research Group. 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://econ.worldbank.org. The corresponding author may be contacted at mg5@nyu.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
of neighboring Namibia and South Africa (World Bank 2012b). Primary education expenditures are relatively steady at about 2 percent of GDP or 8-9 percent of total government spending. Secondary education expenditures fluctuate slightly at around 0.4 percent of GDP or 2 percent of total government spending. See Table 3.

Table 3: Government expenditures on education, 2006-2009

||2006|2007|2008|2009|
|---|---|---|---|---|
|Total educ. exp. as % of GDP|3.8|3.8|4.2|4.2|
|o/w primary school/GDP|1.7|1.5|2.0|2.0|
|o/w secondary school/GDP|0.4|0.3|0.4|0.5|
|Educ. exp. as % of total exp.|19.1|18.0|18.5|19.7|
|o/w primary/total exp.|8.3|7.0|8.8|9.3|
|o/w secondary/ total exp.|2.2|1.5|1.8|2.2|

_Source: Authors’ compilation from MoFNP financial reports_ As shown in Figure 2, primary education receives the largest share of education spending, followed by administration at the various levels. However, in terms of expenditure per student, the tertiary level has the highest average followed by the secondary level, with primary education having the lowest average expenditure per student.<sup>7</sup> In 2009, expenditure per student at the secondary-school level was 3 times that of the primary level, while spending per student at the tertiary level was a full 35 times that of the primary level. These averages –although falling over time– place Zambia right up in the middle of the African distribution: the averages of these two ratios for all 18 African countries for which data are available are 4 and 36 times in 2003, respectively.

> 7 Expenditure per student is calculated by dividing public expenditure in each educational level by the number of students enrolled in the corresponding level as indicated in Table 2.

9