rafmacalaba/datause-encoder-data huggingface dataset:
Islamic jurisdictions would consider that matters relating to the _Shariah_ are not in their purview (e.g., the UK FSA).<sup>39</sup> Private mechanisms for the external governance of _Shariah_ compliance are equally limited. In particular, private rating agencies have not yet developed the necessary skills or found enough incentives to monitor the IIFS’ _Shariah_ compliance. “Islamic rating” has so far been the exclusive domain of government-sponsored organizations such as the IIRA and the Malaysian Rating Corporation. Likewise, other external entities with an interest in Islamic finance, such as the financial media and external auditors, are still generally less concerned with assessments of _Shariah_ compliance.

A notable exception is the multiplication of stock market Islamic indices whose major contribution is the identification of _halal_ investments.<sup>40</sup> Islamic stock market indices, like the FTSE Global Islamic Index, the Dow Jones Islamic Indexes and the Indonesian _Shariah_ Index, may contribute to better _Shariah_ governance for publicly traded IIFS. By filtering out companies with activities that are incompatible with _Shariah,_ as well as firms with unacceptable levels of debt or interest income, they contribute to reducing adverse selection in investments by IIFS, and give additional comfort on the _halal_ nature of IIFS activities.<sup>41</sup> However, current practice is not likely to meet with a full consensus of _Shariah_ scholars. Usually, these indices include companies that deal in interest because of the lack of a fully interest-free international market. While some Islamic finance scholars may find this approach acceptable, others will not, thus limiting the role of these indices in enhancing _Shariah_ governance.

> 39 In Malaysia, for instance, the judicial system has agreed to resort to the Central Bank’s _Shariah Council_ for opinions on the permissibility of Islamic financial instruments, whenever a court case brought before it may require it. It would be hard to replicate such a scheme in other countries, where regulators may not want to get involved in _Shariah_ issues.

> 40 _Halal_ conveys goodness and by extension has taken the meaning of “permissible”.

> 41 In the case of Dow Jones Islamic Indexes, excluded businesses include: alcohol, conventional financial services (banking, insurance, etc.), entertainment (casinos/gambling, cinema, pornography, hotels, etc.), tobacco manufacturers, pork-related products and defense and weapons companies. Companies classified in other industry groups may also be excluded if they are deemed to have material ownership of or revenues from the businesses mentioned above.
- 4 -

# TABLE **1:I NOMINAL** PROTECTION COEFFICIENS IN **MANUFACTURING**

|PMOdI.qE|191§-19A|1 9<br>I|9 8|Overall|Landed<br>Ilp|ort<br>Prices<br>/a|
|---|---|---|---|---|---|---|
|EdibleOils<br>|1.57|<br>2.32|---|2.11|||
|Miscellaneous Food Products||||1.13|2.26|(1989)|
|Total<br>Food Industries||||1.82|||
|Cotton<br>Textiles|1.10|||1.10|||
|Synthetic<br>Textiles|2.53|<br>2.09|1.73|<br>2.16|||
|Total<br>Textiles||||1.77|||
|Rubber<br>Products||||1.80|||
|Plastics<br>Products||||1.57|||
|Heavy<br>Chemicals|1.87|<br>1.96|1.82|1.84|<br>2.27|(1988)|
|Petrochems<br>Intermediates<br>|2.31|2.47|1.98|2.19|<br>2.30|(1989)|
|Synthetic<br>Fibers/Resins|2.17|3.38|1.68|2.32|<br>2.23||
|Other<br>Chemicals||||1.56|<br>1.47||
|Total<br>Chemical<br>Industries||||2.04|<br>2.14||
|Iron/Steel<br>Products<br>|1.51|2.08|2.01|1.74|<br>1.89|(87-88)|
|Castings/Forgings||||1.34|<br>2.17|(87-88)|
|Non-Ferrous<br>Metals<br>(Aluminum)<br> <br>||||1.23/|b<br>2.09|(1987)|
|Total<br>Basic<br>Metals||||1.54|<br>2.08||
|Machine-Tools||||1.57|||
|Non-Electrical<br>Machinery|1.72||1.97|1.80|||
|Electrical<br>Machinery<br>Total<br>Machinery||||1.35<br>1.67|||
|Electronics<br>and Parts|1.58|1.19|1.37|1.49|1.63|(1988)|
|Motor<br>Vehicles<br>and Parts|1.39|0.84||1.26|||

La Coefficient of Landed (inclusive of tariffs) to CIF prices for imported goods. **_Lb_** Mostly Aluminum products.

Note 1: Subsectoral NPCs are unweighted averages Note 2: Seven other subsectors with less than 6 observations each are not included in the table.

8. Some products groups have a sufficiently .arge number of price comparisons to permit the estimation of annual averages for 1986-87, 1988 and 1989 separately. The trends suggested by the annual averages confirm other indications derived from specific subsector knowledge: - slightly declining relative prices for synthetic textiles, synthetic fibers and resins, and petrochemical intermediates and stable relative prices for heavy chemicals and machinery. The increasing trend for iron/steel products is due to the shift in the sample's product-mix over the period 1987-1989 from mild steel to
age size in the US. Unlike the log-log specification, it does not depend on the marginal distributions of WBES and US industry-size. In other words, in a rank-rank specification _β_ is not impacted by the ratio of US to WBES industry-size variances. The rank-rank specification is thus more stable across subsamples of different development levels with widely varying WBES size-variances.<sup>17</sup> Second, the rank-rank specification appears more able to untangle technological size-differences from non-technological differences. Nondrivers such as technological availability of labor-saving instruments, labor regulations, and legal quality may differ substantially across countries and impact relative firm size, thus a wedge between WBES level-differences in size and US level-differences in size. Such non-technological drivers of size will not impact _β_ so long as they do not overturn the ranking of industries in WBES relative to the US.

Figure 2 displays non-parametrically the first stage relation between industries’ size ranks in the WBES countries and the US Census. ISIC3 industries are ranked relative to other industries in the same country-year survey. The WBES and US Census ranks are then grouped into 50 equal sized (two percentile) bins. Figure 2 plots the mean WBES size rank percentile within each 50-quantile US size rank percentile and the best-fit line. We find a steep positive slope and a linear relation between industries’ size-ranks in the US Census and their size-ranks in WBES countries. In Figure 3 we show that the rank-rank coefficients are constant by countries’ income levels: the US industry-size distribution has the same power to predict average firm size over the full size-distribution, in for example, Ethiopia, Indonesia and Mexico. In robustness analysis in Section 4.4, we show that predictive power does not hinge on using the 2001 Census, and remains almost identical when using either an earlier wave of the Census (1991) or a later wave (2015). Finally, the first stage remains strong and positive when we restrict our analysis to the manufacturing sector, a common restriction for studies using cross-industry variation.

The WBES country size-ranking predicted from the US ranking of industries is an

> 17This is also the main reason why a rank-rank specification is chosen over the log-log specification in recent studies of income mobility (Chetty et al. 2014).

12
Table 1. Top probability terms defining the topics

|“Public<br>administration”|“Social<br>policy”|“Infrastructure”|“Social<br>development”|“Education and<br>health”|“Economic<br>planning”|
|---|---|---|---|---|---|
|public|policy|national|development|education|sector|
|administration|social|public works|social|health|provinces|
|national|life|production|national|family|bank|
|services|national|construction|program|program|growth|
|interests|nation|education|policy|poverty|province|
|right|homeland|services|sector|quality|debt|
|executive|world|Plan|system|security|crisis|
|public|freedom|social|process|social|plan|
|trade|right|service|resources|right|economy|
|order|economic|development|growth|world|money|

Note: This table shows the words that have the highest probability of belonging to each topic. Because LDA defines topics as distributions over words, the lists of terms define the subject of each topic.

Figures 2 to 4 illustrate two important points that are relevant for our analysis. First, LDA detects important shifts in the political context of a country. In the case of Argentina, the advent of the Kirchner administration, as well as the arrival of the debt crisis are captured by the fact that beginning in the mid-2000s, the “social development” and “education and health” topics are quickly replaced by the “economic planning” topic. This latter topic is concerned with “debt” “crisis” and “banks,” which clearly reflects the economic realities Argentina faced at the time. Furthermore, this result is consistent with the country-specific LDA results for Argentina. LDA is able to detect an important shift in political context for the República Bolivariana de Venezuela as well. Although a topic concerned with social development prevailed until the late 1990s, it was quickly replaced by a new topic concerned with Chaves and his objectives. Clearly, the coming to power of Chaves constituted an important change in the political context of the República Bolivariana de Venezuela, and LDA captures this shift. In addition, it is worth noting that LDA also detects policy areas that are rather country-specific. For example, a topic about the oil industry is present in the República Bolivariana de Venezuela, and in Chile, a topic about the copper industry remains relevant across a long stretch of time, reflecting the economic significance the industry has had throughout Chile’s history.

The second point that the figures illustrate is that the stability or consistency of the topics—which we take as reflecting policy priorities—varies by country. In Chile, social development was an important topic from early on and it remained that way for decades. Furthermore, the education and health topic has continued to be extremely prevalent since the 1990s, much more so than in any other country. These observations capture the fact that Chile has prioritized social development and education for decades, and that these priorities have been stable over time. On the other hand, such stability was not present in Argentina 13
Outside of the group of advanced industrial societies, trends are largely consistent in emerging economies with more evolved labor markets. Estimates on a pooled sample of survey respondents from Latin America show a clear and unambiguous effect of unemployment on reported life satisfaction, even when a range of personal and household attributes are accounted for (Graham 2008, see also Graham and Pettinato 2002). However, these findings are based on simple correlations. More robust evidence has been presented for Russia by Eggers et al. (2006). They find that the effect of personal unemployment on life satisfaction was four times worse than having experienced a divorce. Because this study is based on longitudinal data, it can also account for initial levels of life-satisfaction, mitigating some concerns about unobserved personal characteristics that often plague cross-sectional survey analysis of SWB.

In spite of the relative robustness of these effects at the individual level, there are many – often little understood – contextual influences that can determine how individual experiences of work and unemployment translate into social cohesion outcomes at the aggregate level. A well-known result from the cross country literature on life satisfaction is that average SWB does not improve with marginal increases in income once a certain level of material wellbeing has been attained (see Easterlin 1974, Easterlin et al. 2010, as well as Inglehart and Welzel 2005). This suggests that the described non-economic effects of unemployment may reflect realities in countries with more formalized labor markets that do not apply in the same to lower income nations with endemic poverty and a high incidence of informal and subsistence labor. For example the aforementioned correlation analysis of World Values Survey data by Wietzke and McLeod (2012) finds that the negative wellbeing effect of unemployment drops quite considerably as one moves from high and middle income nations to lower income countries, all accounting for the usual range of controls regularly used in the literature (such as income, education, age, or family status).<sup>17</sup> These results are consistent with a large body if economic research, which suggests that the primary channel out of poverty in low income nations is a transition from informal or agricultural work into more rewarding forms of wage-employment (for overviews see WDR 2013, Azevedo et al. 2012) Beyond these economic influences social contexts also matter. For one, there is relatively strong evidence that social interactions and segregation processes described above

> 17 The authors also find that an index that summarizes people’s self-assessments of their work as involving manual or cognitive, routine or creative tasks and the degree of independence they have at work does not predict life satisfaction in low income countries.

20
54 Table 16: Crime, Inequality, Mean Expenditure and Unemployment using OLS

||Propert|yCrimes|Violent|Crimes|
|---|---|---|---|---|
||Residential<br>Burglary|Vehicle Theft|Aggravated<br>Assault|Rape|
|InequalityInPoliceStation|1.07<br>(0.11)**|-0.22<br>(0.15)|0.93<br>(0.08)**|0.61<br>(0.10)**|
|MeanExpenditureInPolice Station|1.02<br>(0.07)"*|1.95<br>(0.10)**|0.06<br>(0.05)|0.24<br>(0.06)**|
|UnemploymentInCriminalCatchmentArea|-0.08<br>(0.09)|0.10<br>(0.13)|0.02<br>(0.07)|0.03<br>(0.08)|
|ProvinceDummies|YES|YES|YES|YES|
|Constant|-11.03|-20.80|-4.40|-7.66|
||(0.44)**|(0.63)**|(0.34)**|(0.41)**|
|Adjusted-R<sup>2</sup>|<br>0.46|<br>0.50|<br>0.22|0.12|
|Observations|1066|1066|1066|1066|

Standard errors in parentheses. * significant at 5%; ** significant at 1%. Dependent variable is the log per capita crime rate. All explanatory variables are in logs. Crime rates equal to zero were replaced with the smallest value for that crime.

Table 17: Crime and the Distribution of Welfare in Catchment Area using OLS

||Propert|yCrimes|Violent|Crimes|
|---|---|---|---|---|
||Residential<br>Burglary|VehicleThieft|Aggravated<br>Assauit|Rape|
|InequalityInPolice Station|1.06<br>(0.11_)-_|-0.22<br>(0.15)|0.93<br>(0.08)"|0.61<br>(0. 10)**|
|MeanExpenditureInPoliceStation|(0.976)|(010)**|(0106)|(0.275)|
|UnemploymentInCriminalCatchmentArea|(0.09)|(0.13)|(0.07)|(0.09)|
|RichestJurisdictionInCriminalCatchment|0.27|0.24|0.12|-0.02|
|Area|(0.11)*|(0.16)|(0.09)|(0.10)|
|ProvinceDummies|YES|YES|YES|YES|
|Constant|-10.70<br>(0.46)**|-20.51<br>(0.66)**|-4.25<br>(0.35)**|-7.68<br>(0.42)**|
|Adjusted-R<sup>2</sup>|0.46|0.50|0.22|0.12|
|Observations|1066|1066|1066|1066|

Standard errors in parentheses. * significant at 5%; significant at 1%. Dependent variable is the log per capita crime rate. All explanatory variables are in logs. Crime rates equal to zero were replaced with the smallest value for that crime.
statistically significant. Figures in parenthesis represent t-values.

As we add variables to expand the group of covariates for Mathematics (Table 10), Reading (Table 11) and Science (Table 12) we follow a trial and error method, depending on whether or not, within each group of variables (students, parents, teachers and schools), the inclusion of the specific variable leads to a reduction in the Vietnam dummy coefficient. We retain the variable if it leads to a reduction in the Vietnam dummy coefficient, even if the variable itself may or may not be statistically significant. In this approach, we are not interested in accurately capturing the size of the coefficients other than the one for the Vietnam dummy. Neither are we seeking to maximize the explanatory power of the regression.<sup>10</sup> After considering student related variables, the second set of variables relates to the home background and parents of students. Mean comparisons showed that PISA indices on family wealth or parental education are much lower for Vietnam compared to the other seven countries. Clearly, Vietnam’s higher PISA scores cannot be explained by higher parental wealth or prental education. Inclusion of these variables would increase the coefficient on the Vietnam dummy and would take us away from our objective. If Vietnam had enjoyed the Dev7 levels of those variables, the Vietnam gap would have turned to be larger than 128 points. Hence, the parent variables that are retained and presented in column (3) are only those variables that reduce the Vietnam dummy. The reduction amounts to only 11 points compared to the nearly 20 point reduction for student variables. Only the variable PARPRESSURE is statistically significant in this group and indeed has a sizeable impact on the score. This variable reports on principals claiming that “there is _constant pressure_ from many parents, who expect our school to set very high academic standards and to have our students achieve them.”

> 10The R code for all statistical analysis undertaken in this research paper is freely available for download at http://github.com/zagamog/PISA ~~P~~ APER

21
This paper uses individual-level data on 41,873 respondents from 17 Sub-Saharan African countries to examine gender differences in ten socio-emotional skills and their relationship with education and earnings. It makes four main contributions to the literature.

To our knowledge, ours is the first paper to analyze gender differences in several dimensions of socioemotional skills drawing on a large sample of data from multiple African settings, where limited evidence exists. Only a few key studies have extended analysis of gender differences to developing countries, including some in Sub-Saharan Africa (Lynn & Martin, 1997; Costa et al., 2001; Schmitt et al., 2008), and these studies are mostly confined to measures of the Big Five personality traits<sup>3</sup> . Unexpectedly, these studies find that a higher human development index ranking is associated with larger gender differences in personality traits; the magnitude of gender differences is higher in European and American contexts and is small to nonexistent in Asian and African countries. Based on Cohen’s D, the overall magnitude of the gender difference in our results is similar to results for personality differences in Sub-Saharan Africa (Costa et al., 2001; Schmitt et al., 2008). Our results indicate that men on average have an aggregate socioemotional skills level that is 0.151 standard deviations higher than the average level among women, equivalent to 5.6 years of education or a 5 percentile increase in socio-emotional skills levels. This male advantage persists after controlling for education, age, marital status and even measures associated with confidence, such as self-esteem and self-efficacy. However, the range of gender differences across skills and studies is considerable, and most would be categorized as _small_ or _moderate_ based on a meta-analysis of gender differences in skills (Hyde, 2005).

A large literature from developed countries has examined whether gender stereotypes are reflected in specific socio-emotional skills gender differences, which we summarize in Appendix Table A1. Eagly and Wood’s (2012) cross-disciplinary social role theory predicts that men have an advantage in _agentic_ skills while women have an advantage in _communal_ skills, though this may vary with local norms and occupational segregation. Focusing on concepts associated with the ten specific socio-emotional skills used in this study, trends in the literature suggest that women score lower than men on self-reported measures of positive self-concept, emotional regulation and higher on measures of self-control, empathy, and interpersonal relatedness. Gender differences are mixed, small in magnitude, or weak in evidence for perseverance, personal initiative, problem solving, expressiveness, and collaboration (see Appendix Table

> 3 These are five factors that often arise in the analysis of personality traits. They include Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism.

3
**<mark>Table 9 – FYR Macedonia: Composition of Foreign Currency Debt and Reserves</mark>** <mark>as of end December 2016, millions EUR</mark>

|||USD|EUR|JPY|GBP|SDR|Other|Total|
|---|---|---|---|---|---|---|---|---|
||EUR mil. eq.|1,173|1,434|-|-|264|0.7|2,872|
|Reserves|Percent of total|40.8|49.9|-|-|9.2|0.02|100.0|
||Percent of GDP|11.5|14.0|-|-|2.6|0.01|28.1|
||EUR mil. eq.|124|2,707|71|-|-|49|2,952|
|External Debt|Percent of total|4.2|91.7|2.4|-|-|1.7|100.0|
||Percent of GDP|1.2|26.5|0.7|-|-|0.5|28.9|
|Concentration|Ratio FX debt/reserves|0.11|1.89|n/a|n/a|n/a|70.0|1.03|

Source: Ministry of Finance and National Bank of the Republic of Macedonia _Applied FX Rate: as of end December 2016 61.518 dinar/EUR_ Almost all of FYR Macedonia’s external debt is denominated in EUR (92 percent), while only half of its reserves are in EUR (53 percent after SDR decomposition) with nearly the other half (44 percent) in USD. Accordingly, the concentration ratio is almost double in EUR while around 1/10 in USD.

**Figure 7 – FYR Macedonia: FX Currency Composition of:**

<!-- Start of picture text --> Reserves (SDR Decomposed) Debt<br><!-- End of picture text --> <!-- Start of picture text --> JPY GBP CNY Other JPY Other USD<br>1% 1% 1% 0% 3% 0% 4%<br>USD<br>44%<br>EUR<br>53%<br>EUR<br>93%<br><!-- End of picture text --> FYR Macedonian currency, the dinar, has an exchange rate that is closely tied to the EUR and FYR Macedonia has also been awarded candidate status by the European Union. Despite being a de jure floating exchange rate regime, the National Bank of the Republic of Macedonia maintains a de facto stabilized arrangement _,_ participating in the foreign exchange market to maintain a stable exchange rate within a narrow fluctuation band against the EUR.

# **C. Uruguay**

Uruguay has made strides in developing its local currency debt market and decreasing dependence on USD-denominated funding from external sources. While Uruguay continues to issue in the international market, the external debt share of total debt has fallen from 85 percent 19