rafmacalaba/datause-encoder-data huggingface dataset:
OZIER - REPLICATION REDUX – p. 27 of 41 Davey, et al., opt for a different approach to the fourth decision, weighting the analysis by school rather than by pupil, leading some study participants (in smaller schools) to have seven times the analytical importance of others. Hicks, et al. (2015) and Davey, et al. (2014) disagree on the right approach here, and Özler (2015) and others have discussed the meaning behind the different weights, but a typical consequence of using widely varying weights is the reduction of statistical power: that is, the study is effectively driven by a smaller number of observations. There is a fifth decision that Davey, et al. (2014) face: whether to use all years of data simultaneously (as MK had), or whether to examine a single year of data at a time. Noting that the point estimate from a specification pooling across the two years is not equal to an average of the two separate within‐year estimates, they opt for the latter approach, splitting the sample into two separate tests, which also has the natural consequence of reducing the number of observations in each test. This again means further reducing statistical power—the chance of finding an effect if it is there. When all five of these decisions are made together, the result (the impact of deworming on school attendance) is finally no longer statistically significant.<sup>25</sup> Of this battery of robustness tests, Özler (2015) wrote: _“if anything, I find the findings of the original study more robust than I did before. ...a number of unconventional ways of handling the data and conducting the analysis are jointly required to obtain results that are qualitatively different than the original study.”_ The replication authors, themselves, explained much of this in their paper. In the subsequently published paper’s abstract, they wrote, _“In year‐stratified cluster‐summary analysis, there was no clear evidence for improvement in ...school attendance. In year‐stratified regression models, there was_ 25 See Davey, et al. (2014) Table 4 as well as Hicks, et al. (2015) appendix table S7.
**Table A4: Predicting Outcomes for Non-Winners Conditional on Operating a Firm**

|||Employment|||Sales|||Profits||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
||Judges|McKenzie|FaWo|Judges|McKenzie|FaWo|Judges|McKenzie|FaWo|
|Business Plan score|-0.0015|0.0019|0.0144|-0.0057|-0.0013|-0.0016|-0.0035|0.0082|0.0087|
||(0.0237)|(0.0260)|(0.0260)|(0.0137)|(0.0149)|(0.0144)|(0.0136)|(0.0152)|(0.0147)|
|Female||-0.0764|0.2189||-0.6269|-0.5693||-0.3310|-0.1299|
|||(0.8279)|(0.8664)||(0.4890)|(0.5039)||(0.4833)|(0.4891)|
|Age||0.0643|0.0482||0.0440|0.0327||0.0042|-0.0032|
|||(0.0586)|(0.0572)||(0.0361)|(0.0356)||(0.0349)|(0.0352)|
|Graduate Education||1.7434|||-1.0594|||-1.0160||
|||(1.3193)|||(0.7996)|||(0.7678)||
|Worked abroad||0.1256|||1.0070|||1.2113||
|||(1.2563)|||(0.7131)|||(0.6602)||
|Lottery choice||1.1154<sup>*</sup>|||0.4861|||0.3256||
|||(0.4963)|||(0.3387)|||(0.3306)||
|Wealth||0.1906|||0.0734|||0.0104||
|||(0.1067)|||(0.0624)|||(0.0564)||
|Grit||<br>-0.3856|||<br>0.0174|||<br>-0.1183||
|||(0.5085)|||(0.3307)|||(0.3238)||
|Ability||0.1527|||0.2689|||0.2051||
|||(0.2741)|||(0.1568)|||(0.1705)||
|Agriculture||-0.4653|||0.3281|||0.1823||
|||(0.7257)|||(0.4211)|||(0.4023)||
|IT||-1.6241<sup>*</sup>|||0.1511|||-0.3364||
|||(0.6732)|||(0.4793)|||(0.5839)||
|Manufacture||-0.9482|-0.5424||0.7329|0.6019||0.3223|0.2243|
|||(0.6473)|(0.6012)||(0.4195)|(0.3652)||(0.4075)|(0.3458)|
|Retail|||<br>0.4425|||<br>0.6653|||<br>0.3885|
||||(1.1477)|||(0.7906)|||(0.7084)|
|Self-confidence|||0.0169|||0.1383|||0.0411|
||||(0.1402)|||(0.1236)|||(0.1137)|
|Motivation|||-0.0658|||-0.2129|||-0.1952|
||||(0.1507)|||(0.1277)|||(0.1190)|
|Happiness|||0.3065|||0.2219|||0.2249|
||||(0.1757)|||(0.1193)|||(0.1213)|
|Optimism|||-0.0798|||0.1290|||0.0990|
||||(0.2457)|||(0.1960)|||(0.1752)|
|Ability|||0.1236|||0.2375|||0.1297|
||||(0.2431)|||(0.1452)|||(0.1518)|
|Credit|||<br>-0.1577|||<br>0.1267|||<br>0.1257|
||||(0.1616)|||(0.0724)|||(0.0644)|
|Existing|0.3872|0.6218|<br>-0.0783|0.8039<sup>*</sup>|0.8170<sup>*</sup>|<br>0.2834|0.6643|0.9218<sup>*</sup>|<br>0.3345|
||(0.5611)|(0.5825)|(0.7337)|(0.3335)|(0.3564)|(0.3922)|(0.3525)|(0.3893)|(0.4224)|
|Regional Indicators|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|Sample Size|630|630|630|611|611|611|611|611|611|
|P-value||0.073|0.643||0.060|0.000||0.442|0.003|
|AdjustedR<sup>2</sup>|-0.002|0.020|0.004|0.007|0.022|0.038|0.004|0.013|0.040|

Robust Standard Errors in parentheses. *, **, *** denote significance at the 10, 5, and 1 percent levels respectively
44 explained by the growth of public sector investments from 1978 to 1982 in ttie petrochemical and fertilizer industries.

It should be borne in mind that the pollution intensity figures presented in this paper are derived from US emission data for 1987 using fixed pollution coefficients by subsector. Thus, changes in the estimates of total industrial pollution intensity are completely produced by structural shifts in the composition of manufacturing output. Changes in production technologies and other changes within subsectors that lead to differences in emissions per unit of output, ir time or between the US and Mexico are not considered. Therefore, neither the possibility of technology choice nor the influence of economic policies, e.g. trade policies, on this choice were taken into account in the estimates.

With that in mind, and in view of the fact that fuel use by subsector could give us an indication of emission-relevant changes within subsectors not captured in our estimates, a brief analysis of the energy intensity of Mexican industry in relation to energy pricing policies between 1970 and 1990 was performed. It was found that energy pricing policies led to annual implicit subsidies for energy products (i.e. petroleum fuels, gas and electricity) of between US $8 and 13 billion, or 4 to 7% of GDP, from 1980 to 1985.

Energy intensity of Mexican industry increased by 5.7% from 1970 to 1990 while industrial energy intensity in OECD countries decreased
owned mainly by foreigners. Widely-owned means that there are no majority owners, but that the shares are widely-held, possibly amongst some minority owners, who each own between 5% and 33% of shares.

Ownership is thought to be related to a financial institution's performance, because the incentives for managers to efficiently allocate resources might differ under different ownership arrangements. If owners do not have the incentive or capability to monitor the activity of management, then agency problems and subsequent costs are thought to increase. In particular it is expected that foreign-owned banks will be relatively efficient, because their corporate governance is of international standards. In addition, it is expected that company-owned, familyowned and state-owned banks are relatively inefficient. Company-owned and family-owned banks are likely to suffer from poor governance and connected lending. In particular, the company or family might be tempted to use the bank to finance their own businesses. In terms of efficiency, state-ownership is in general thought to be inferior to private ownership.

The ownership structure of banks differs substantially among the five East Asian countries (see Appendix, Tables 1 and 3). Indonesian commercial banks are predominantly owned by the state, families and companies, Korean banks are predominantly widely-held, Malaysian and Philippine banks are predominantly owned by companies, and Thai domestic banks are predominantly owned by families. However, in terms of total controlled commercial banks assets, the dominant ownership forms are state-ownership and family-ownership in Indonesia (both control 37%), dispersed ownership in Korea (56%), company-ownership in Malaysia (56%), company-ownership in the Philippines (57%), and family-ownership and dispersed ownership in Thailand (both control 36%). In East Asia, family-ownership is not unique to the banking sector. Claessens et al. (1999a) show that in most East Asian countries, ownership of corporations (including banks) is concentrated in the hands of a few families, and that there are extensive links between corporations and the governments. For all countries each of the collected variables (if averaged across banks) has increased substantially during the period 1992-96 (see Appendix, Table **4)4.** Some variables even show growth rates up to 287% for this period, such as the amount of short term borrowing by Indonesian banks. These growth rates are a sign of excessive growth and risk taking of the East Asian banks during the period 1992-96.

4These figures include the annual inflation rates which were on average 9% in Indonesia, 5% in South Korea, 4% in Malaysia, 8% in the Philippines and 5% in Thailand during 1992-96 (Source: IFS, IMF).

14
To further illustrate, we plot in Figure 1 poverty and inequality against temperature at the subnational level for India, a populous country with a major share of the global poor. This figure shows large degrees of subnational variation in both poverty and inequality. Poverty, as measured by the headcount poverty rate at US$ 1.90 a day, ranges from being relatively low in the Northern regions (lowest rate of 0.5 percent) to quite considerable in the Central and Eastern regions (highest rate of 52.8 percent) (Panel C). A similar pattern is seen with inequality, as measured by the Gini index, which ranges between 18.9 percent and 47 percent (Panel D). Within the country, average temperature also widely varies between 4.3<sup>◦</sup> C and 28.7<sup>◦</sup> C (Panel E). Such wide-ranging subnational variations are not revealed by simply looking at India’s country-level averages of poverty, inequality, and temperature (16 percent, 31 percent, and 22<sup>◦</sup> C, respectively), suggesting that an accurate assessment of the effects of global warming on poverty and inequality would require data analysis at the subnational level.

In this study, we find strong and statistically significant global effects of both higher and lower temperature on poverty and inequality, employing different identification strategies on a novel global database of subnational poverty and inequality. Our (preferred) subnational fixed effects model shows that a one-degree Celsius (i.e., 1<sup>◦</sup> C) annual increase in temperature causes headcount poverty increases of 0.9, 1.8, and 2.3 percentage points, respectively, using the daily poverty lines of $1.90, $3.20, and $5.50 (which correspond to 9.1 percent, 9.0 percent, and 6.8 percent increases). The corresponding estimated effects using the long differences model are less pronounced at 0.5, 1.2, and 2.0 percentage point increases in poverty (which correspond to 5.3 percent, 6.1 percent, and 5.9 percent increases), suggesting household adaptation to gradual warmer temperature over time. Analysis of subnational inequality data suggests that a 1<sup>◦</sup> C rise in temperature leads to 0.8 and 1.4 percent increases in the Gini and Theil indices, respectively.

3
Mozambique and $504 in Ghana (see Table 1). The Future of Business Survey is the only study in the data set with no data on earnings.

Apart from the STEP surveys, samples used for this analysis are typically not representative of the general population but come from selected subpopulations. This implies that results are not always externally valid, but representative surveys very rarely contain detailed measures of socio-emotional skills.

Table 2 shows summary statistics on the 13 socio-emotional skills measures for the pooled sample (Panel A) and at the study-level (Panel B). Overall, 323 items were used to construct the socio-emotional skills measures. Importantly, more than 70% of socio-emotional skills items refer to intrapersonal skills, which reflects the larger effort made to collect data on intrapersonal skills (mostly positive self-concept, personal initiative and PSDM). In comparison, interpersonal skills, and especially empathy and teamwork, suffer from a thinner information base. This translates into a lower number of observations being used for interpersonal skills and a lower reliability in these indices as indicated by systematically lower Cronbach’s alpha across studies compared to intrapersonal skills. Panel B indicates for each study which socioemotional skills measures are available and the number of items they are based upon with summary statistics when available. Note that each study measures between 2 and 9 specific socio-emotional skills (excluding aggregate measures), the average study measuring 5.7 specific socio-emotional skills: 3.8 intrapersonal skills (out of 6) and 1.8 interpersonal skills (out of 4).

# **3.3 Estimating gender differences in socio-emotional skill levels**

To measure gender differences in socio-emotional skill levels, we regress each of the 13 measures of socioemotional skills on the gender of the respondent controlling for a set of project dummies, using the pooled sample described above.

# Where:

- Skillis is the socio-emotional skill measure for individual _i_ , either skill-specific or aggregated.

- Womanis is a dummy equal to one if the individual _i_ in study _s_ is a woman.

- ρs is a study fixed effect.

14
4 persistent complicated internal procedures and controls for approving budgets for specific activities; and (c) prolonged postponement of decisions regarding important studies for evaluation.

# **3. Previous studies**

From its inception PARE’s performance was monitored through statistical comparisons between the target, or experimental, population (schools in the states of Chiapas, Guerrero, Hidalgo and Oaxaca) and a control group formed by students in comparable schools in the state of Michoacan which falls outside the scope of the program. Special surveys were conducted yearly between 1992 and 1995. In addition, all students were given standardized achievement tests in Spanish and mathematics. PARE also provided the resources to evaluate the success of this program. To this end, two studies were conducted by two different research institutions. One study was done by the C.E.E (Centro de Estudios Educativos), which focused mainly on measuring quantitative variables regarding school inputs and the academic and socioeconomic characteristics of students, parents, community, teachers, principals and supervisors. The other study was undertaken by the D.I.E (Departamento de Investigaciones Educativas), which examine qualitative variables. The database was developed to evaluate the effects of PARE on student achievement.

During program implementation, several standardized tests on Mathematics and Spanish were given to the students in three consecutive years, when they were in fourth, fifth and sixth grades. The test scores provide analysts outcome indicators and allow estimation of the value-added of PARE.

Two previous Bank analyses of these data reveal that PARE has a significant impact on student learning. Table 1 below, which has been drawn from the PAREIB Project Appraisal Document<sup>1</sup> , shows before and after comparison of Spanish test scores of the control and experimental groups of students in indigenous, rural (regular) and urban schools. Table 2 shows the per student intervention costs. Table 1 reveals that in indigenous and rural schools where PARE was fully implemented as designed, the project was able to increase by 42.3 percent the Spanish test scores of indigenous students, and by 16.5 percent the corresponding scores of students in regular rural schools. With the cost of these improvements estimated at about 38.1 and 24.2 percent of routine expenditure per student, respectively, the elasticity of student learning achievement in Spanish with respect to cost would appear to be about 1.11 and 0.66 in that order.

1 PAREI PAD
Policy Research Working Paper 5999

# **Abstract**

Optimism about the use of laws, constitutions, and rights to achieve social change has never been higher among practitioners. But the academic literature is skeptical that courts can direct resources toward the poor. This paper develops a nuanced account in which not all courts are the same. Countries and policy areas characterized by judicial decisions with broader applicability tend to avoid the potential anti-poor bias of courts, whereas areas dominated by individual litigation and individualized effects are less likely to have pro-poor outcomes. Using data on social and economic rights cases in five countries, the authors estimate the potential distributive impact of litigation by examining whether the poor are over or under-represented among the beneficiaries of litigation, relative to their share of the population. They find that the impact of courts varies considerably across the cases, but is positive and pro-poor in two of the five countries (India and South Africa), distribution-neutral in two others (Indonesia and Brazil), and sharply anti-poor in Nigeria. Overall, the results of litigation are much more positive for the poor than conventional wisdom would suggest.

This paper is a product of the Human Development and Public Services 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 authors may be contacted at vgauri@worldbank.org or danbrinks@austin.utexas.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
low<sup>26</sup> . It should be noted that the share of government support – which is among the highest in Latin America – is likely to be marginally greater than shown below due to the existence of financing programs in the agricultural sector via Finagro and FAG that also cover SMEs, although specific figures are unavailable<sup>27</sup> .

Figure 3.2: FNG and Bancoldex Support for SME Loans and Leases (2004-2006) <!-- Start of picture text --> 20% 4,000<br>18%<br>3,500<br>16%<br>3,000<br>14%<br>2,500<br>12%<br>10% 2,000<br>8%<br>1,500<br>6%<br>1,000<br>4%<br>500<br>2%<br>0% 0<br>2004 2005 2006<br>Year<br>FNG Guarantees Bancoldex Funding FNG & Bancoldex Loan Volume (annual flow)<br>by FNG & Bancoldex<br>FNG & Bancoldex (COP Billion)<br>Annual Volume of SME Loans & Leases Supported by<br>% of Total Outstanding SME Loans & Leases Supported<br><!-- End of picture text --> _Source: Authors’ analysis based on figures provided by Asobancaria, FNG and Bancóldex._ _Note: The legal (as opposed to the business) definition of SMEs is used in the above Figure. Since the average FNG guarantee is around 50 percent of a loan’s value, only one-half of the ‘mobilized’ FNG-related loan volume shown above is actually backed up by an FNG guarantee. In order to convert annual flows to end-of-year stocks, the average duration of an FNG-guaranteed SME loan and of a Bancóldex-funded SME loan are assumed to be 1.1 and 1.5 years respectively. A few SME credits funded by Bancóldex are also supported by an FNG guarantee, but this potential double-counting issue is not significant in size._ SMEs represent a sizeable portion of the lending business of most Colombian credit institutions, particularly leasing companies. As can be seen in Figures 3.3 and 3.4 below, SME exposures (using the legal definition of SMEs that is based on assets) represent around 23 percent of the total loan and lease portfolio across all Colombian credit institutions. This market segment is particularly important for a few finance companies and almost all leasing companies, although there are virtually no banks that currently specialize exclusively, or even predominantly, in SME lending.

> 26 The average net-of-recoveries loss rate on FNG’s guarantees portfolio has averaged around 2 percent in the last few years, while the exposure of Bancóldex in its rediscounting facilities is to the credit institutions themselves and not to the ultimate borrowers; both institutions have been profitable in recent years. 27 Finagro’s total outstanding credit portfolio amounted to COP 3.3 trillion as of end-2006. Around COP 0.3 trillion and COP 1.6 trillion were disbursed during 2006 – via credit institutions (primarily _Banco Agrario_ ) – to small/medium-sized agricultural companies and to individual and ‘associative’ producers respectively, some of which can be classified as SME credits. FAG reports outstanding guarantees on agricultural loans totaling COP 1.7 trillion that were mostly funded by Finagro.

16
**Table 1: Ratings, outlooks and numerical scores**

|**S&P**|**Fitch**|**Moody's**|**Outlook**|**Outlook adjusted score**|
|---|---|---|---|---|
|AAA|AAA|Aaa|P<br>S|Does not exist<br>1|
||||N|2|
|AA+|AA+|Aa1|P<br>|3<br>|
||||S|4<br>|
|AA|AA|Aa2|N<br>P<br>S|5<br>6<br>7|
||||N|8|
|AA-|AA-|Aa3|P|9|
||||S<br>|10<br>|
||||N|11|
|A+|A+|A1|P|12|
||||S<br>N|13<br>14|
|A|A|A2|<br>P<br>S|<br>15<br>16|
||||<br>N|<br>17|
|A-|A-|A3|<br>P|18|
||||S|19|
||||N|20|
|BBB+|BBB+|Baa1|P|21|
||||S<br>|22<br>|
||||N|23|
|BBB|BBB|Baa2|P<br>|24<br>|
||||S<br>N|25<br>26|
|BBB-|BBB-|Baa3|P|27|
||||S|28|
||||N|29|
|BB+|BB+|Ba1|P|30|
||||S|31|
||||N|32|
|BB|BB|Ba2|P|33|
||||S<br>|34<br>|
|BB-|BB-|Ba3|N<br>P|35<br>36|
||||S|37|
||||<br>N|<br>38|
|B+|B+|B1|P<br>|39<br>|
||||S|40|
||||N|41|
|B|B|B2|P<br>|42<br>|
||||S|43|
||||N|44|
|B-|B-|B3|<br>P|<br>45|
||||S|46|
||||N|47|
|CCC+|CCC+|Caa1|P|48|
||||S<br>N|49<br>50|
|CCC|CCC|Caa2|P|51|
||||S|52|
||||N|53|
|CCC-|CCC-|Caa3|P<br>|54<br>|
|CC|CC|Ca|S<br>N|55<br>56|
||||P|57|
||||S|58|
||||N|59|
|C|C|C||60|

Notes: Based on Gaillard (2009), Ratha, De and Mohapatra (2011) and Basu, et al. (2012), as adapted by the authors. ‘P’ signifies Positive, ‘S’ Stable and ‘N’ Negative.

The numerical values of the ratings for the high-income and developing countries are shown for the pre-crisis period (July 2008) and the post-crisis period (December 2012) in Figures 1–4. By and large there is a high degree of correlation among the ratings for the three agencies.

6