rafmacalaba/datause-encoder-data huggingface dataset:
conduct their import policy, both outside and inside the WTO system.<sup>2</sup> And while our evidence of patterns in the exporter-received price data is admittedly weaker than our other results, partially due to the standard problems associated with reliance on price data constructed from unit values, we argue that the evidence is sufficiently suggestive to warrant treating WTO dispute settlement as a useful environment in which to further examine implications of this theory for government policy choices.

In particular, our results suggest differences across disputes depending on whether the alleged policy being challenged was a ‘global’ versus a ‘partial’ policy, in terms of how the respondent applied the policy to non-complainant trading partners. While we find little statistical difference in the levels of respondent imports from complainants in disputes between the two different sets of policies, there is some evidence of differences to changes in exports to the respondent depending on the type of policy under dispute. In particular, we find that global policies under dispute are associated with smaller reductions in import values and volumes and potentially larger reductions in the complainant’s export-received prices when compared to disputes associated with partial policies.

As a final step we use our approach to make comparisons between complainant and _noncomplainant_ exporters of the disputed product to the respondent country for the ‘global’ types of disputes in which the respondent’s policy was applied to all trading partners. We find that relative to noncomplainants, complainants have larger exports and market share. Likewise, among all exporters of the disputed product to the respondent, many of which could have alternatively initiated the dispute, complainants experience the strongest impact of the policy imposition to changes in their export values, volumes, market shares, and prices. This raises the question of whether their substantial market access at stake helps them overcome the collective active problem and free-riding (Olson, 1965) that might otherwise inhibit dispute initiation. Finally, we also find that complainants in these disputes are typically the low-priced exporter of the disputed products in the respondent’s market.

Why is it important to characterize the levels of and changes to the market access that trigger WTO disputes? One motivation is that, to our knowledge, even this relatively straightforward construction of basic facts has not yet been undertaken. And while research examining WTO dispute settlement is increasing, attempts to synthesize key insights from the prior empirical literature, especially with regard to questions related to the market access under dispute, prove elusive.

> 2 See, for example, Broda, Limão and Weinstein (2008), Bagwell and Staiger (2011), Ludema and Mayda (2013), and Bown and Crowley (2013). Bagwell and Staiger (2002) provide a book-length treatment of the terms-of-trade theory and its implication for trade agreements.

5
- Van Biesebroeck, J. (2005): “Exporting Raises Productivity in Sub-Saharan African Manufacturing Firms,” _Journal of International Economics_ , 67(2), 373–391.

- Wooldridge, J. M. (2010): _Econometric Analysis of Cross Section and Panel Data_ . MIT press.

- World Bank, . (2013): “Where Did All the Poor Go? Cambodia,” Poverty Assessment PREM Unit East Asia Pacific Washington DC.

- (2015): “Cambodia Economic Update: Maintaining High Growth,” Cambodia Economic Up-

- date, Washington DC.

- Wouterse, F., and J. E. Taylor (2008): “Migration and Income Diversification:: Evidence from Burkina Faso,” _World Development_ , 36(4), 625–640.

- Yamagata, T. (2006): “The Garment Industry in Cambodia: Its Role in Poverty Reduction through Export-oriented Development,” Discussion Paper 62, Institute of Developing Economies, JETRO.

- Yamagata, T. (2009): “Industrialization and Poverty Reduction,” in _Poverty Reduction and Beyond: Development Strategies for Low-Income Countries_ , ed. by T. Y. Takashi Shiraishi, and S. Yusuf. Palgrave McMillan.

38
Findings in the paper also highlight the important distinction between ‘low income’ and ‘low growth’ lagging regions. In ‘low growth’ regions (in Italy and Spain), firms in lagging regions perform clearly worse than firms outside lagging regions. In ‘low income’ regions (in Poland and Romania) the relationship is much less clear. In fact, firms in both types of regions appear to be impacted similarly by the business environment. A few things, however, help explain the differences. First is growth. ‘Low growth’ lagging regions, by definition, suffer from stagnation, which is likely to weaken the performance of firms in these regions. ‘Low income’ regions are just less wealthy, but are not necessarily stagnant – in fact, most of them are growing more rapidly than average. Thus, firms in these regions are likely to be operating in an environment with significant growth potential. A second reason, and the main point of this paper, is that the business environment in ‘low growth’ lagging regions tends to be clearly worse than in non‐lagging regions of these countries. In ‘low income’ regions, by contrast, this is not the case.

From a policy perspective, the results from the paper support addressing the business regulatory environment as part of a wider strategy to facilitate investment in lagging regions. While there may be some benefits from targeting support on the business environment to lagging regions, perhaps integrated with skills development and institutional strengthening, the weak results on the interaction effect in Section 6 suggest that the biggest impact would come from region‐independent impacts on the overall business environment, which means intervention for business environment reform at the national level may be most effective. This is perhaps good news, as it suggests that addressing the business environment can have a direct effect on the private sector’s potential to invest, grow, and create jobs, without necessarily having to solve what may be deeper and more pervasive structural issues.

Further research should extend this analysis to additional countries that are covered under the SDB series, including Bulgaria and Hungary (recently completed). It will also be valuable to extend the analysis to wider measures of the regional business environment, for example addressing issues of infrastructure quality, access to skilled labor, access to suppliers, and additional measures of regional quality of governance. One potential source for this is the World Bank’s Enterprise Surveys, which measure a wide range of business environment factors, although they are perception based and (at present) results in the EU are not representative at the regional level. Further exploration of sector‐ specific relationships may also provide a better understanding of the impact of the business environment on firms. Last, while we retained ten‐or‐more‐employee firms, smaller firms may be affected in a different fashion.

18
||Poverty (NSO)|Poverty (Public)|
|---|---|---|
|(Intercept)|-0.45205 **|-0.58123 **|
||(0.21987)|(0.22611)|
|ln<br>~~l~~pop12|-0.03781 ***|-0.02969 ***|
||(0.00734)|(0.00848)|
|ln<br>~~r~~ugged|0.02051 **|0.02977 ***|
||(0.01005)|(0.01076)|
|ln<br>~~s~~lope|-0.06148 ***|-0.05727 ***|
||(0.01745)|(0.01836)|
|ln<br>~~f~~orloss|0.07558 ***|0.04426 ***|
||(0.01169)|(0.01122)|
|agr<br>opp<br>cst|0.0085 ***|0.01517 ***|
||(0.00328)|(0.00357)|
|rain<br>~~c~~rop<br>~~s~~h|-0.02675|-0.02488|
||(0.01844)|(0.02009)|
|dist2major<br>~~r~~iver|-0.00131 ***|-0.00131 ***|
||(0.00016)|(0.00017)|
|ln<br>~~t~~t50k2010|0.02608 **|0.02195 *|
||(0.01045)|(0.01145)|
|ln<br>~~n~~tl<br>~~2~~010|-0.02033 **|-0.01452|
||(0.00968)|(0.01016)|
|ln<br>~~n~~tl<br>~~1~~996|-0.03566 ***|-0.03638 ***|
||(0.01279)|(0.01248)|
|bio1|-0.00406 ***|-0.00401 ***|
||(0.00077)|(0.00079)|
|bio4|-0.00021 ***|-0.00025 ***|
||(4e-05)|(4e-05)|
|pet|0.00147 ***|0.00153 ***|
||(0.00016)|(0.00017)|
|s2|0.02357|0.02622|
||(0.24747)|(0.3463)|
|rho|0.75524 ***|0.6478 ***|
||(0.00165)|(0.00181)|
|Observations|765|738|
|adj. _R_<sup>2</sup>|0.558|0.514|

Table 6: Parameter estimates of SEM model with _τ_ = 0 _._ 54 t-statistics in parentheses: * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01 difference is small, and (b) as expected, the correlation between the two SEM estimates is high. Figures 8 and 9 confirm the close correspondence by comparing the respective poverty maps.

29
Table 3. Mexico's Imports, 1981-1998 (US$ billion)

|Yr||AllR&DIntensit<br>Imports|yIndustries|NAFTA<br>Total|
|---|---|---|---|---|
|ea|NAFTA|OtherOECD'|Total|(%)|
|1981|13|5.309|18.309|71|
|1982|6.961|3.321|10.282|68|
|1983|3.588|1.661|5.249|68|
|1984|5.117|2.265|7.382|69|
|1985|7.248|2.117|9.365|77|
|1986|9.723|2.728|12.451|78|
|1987|7.404|2.933|10.337|72|
|1988|11|3.762|14.762|75|
|1989|13.7|3.653|17.353|79|
|1990|18.2|5.949|24.149|75|
|1991|20.3|5.949|26.249|77|
|1992|43.7|9.604|531304|82|
|1993|46.7|10|56.7|82|
|1994|47.9|11.2|59.1|81|
|1995|49|9.257|58.257|84|
|1996|63.8|10.5|74.3|86|
|1997|79|13.6|92.6|85|
|1998|89.5|14.9|104.4|86|

1. Other OECD does not include the USA and Canada.

18
# The Curricular Content of Primary Education in Developing Countries

# by

# Aaron Benavot

# and

# David Kamens

# Table of Contents

|1.|Introduction .................................................<br>3|
|---|---|
|ll.|Background<br>and Issues ................................................<br>4|
|**Im.**|Data: Nature and Limitations ................................................<br>10|
|IV.|The Organization<br>of Analysis ................................................<br>14|
|V.|Subjects in the Curriculum, the 1980s ............................................<br>16|
|VI.|Curricular Priorities in the 1980s ................................................<br>23|
|VII.|Changes in Curricular Offerings, 1960-1980 .................................... 30|
|yVI.|Changes in Curricular Priorities, 1960-1980 ..................................... 35|
|IX.|Cross-National Variation in Annual Instructional Time ......................... 43|
|X.|Conclusion .................................................<br>47|
|Appe|ndix<br>1<br>..<br>49|
|Refe|rences<br>..<br>50|
established rigorous selection procedures to admit the most suitable people into the teaching profession. There are essentially two models of selection. One is to select the people before the teacher training starts and limit the places in the training program; the other is to leave the training process until the prospective teachers have graduated and then select the best graduates to become teachers. Most top-performing systems follow the first selective-entry approach, as the second may lead to over-supply of potential teachers and make the teaching profession less attractive. Figure 4 shows the procedure used in Finland.

**Figure 4: Procedure of Teacher Selection in Finland**

<!-- Start of picture text --> Check for strong intrinsics: 300 question multiple choice<br>National Screening assessment testing numeracy, literacy and problem<br>solving<br>Assessment Tests Top 20% of the cohort from last round. Check overall<br>academic ability and literacy: tests evaluate ability to<br>(University)<br>process information, think critically, and synthesize data<br>Interview Check suitability for teaching: interview look for<br>motivation to teach, motivation to learn, communication<br>(University) skills and emotional intelligence<br>Check suitability for teaching group exercise and teaching<br>Group Work<br>demonstrations test communications and interpersonal<br>(University) skills.<br>Recruitment by On completion of their teacher training, candidates are<br>recruited by individual schools.<br>School<br><!-- End of picture text --> Source: McKinsey & Company 2007 Second, apart from the selection mechanism, these successful systems entail good starting compensation and salary structure. The top performing systems all pay higher than the OECD average starting salary, relative to GDP per capita. As Figure 5 shows, among the countries with available data that ranked highest in PISA 2008, the ratio of salary after 15 years of experience (minimum training) to GDP per capita is mostly higher than 1. Not only is compensation a critical factor for maintaining the quality of teaching, but also improvement of salary structure which can lead to higher student learning outcomes (OECD 2010: 392; Vegas 2005). Good salary structure can provide salary incentives and rewards to attract high-quality teachers and increase their job satisfaction and performance. Salary increases can be concentrated at different points in the salary structure, for example, early in the career or for more experienced employees, or a more linear structure with gradual salary increases throughout a teacher’s career (OECD 2010).

13
Table A6: Balance check. Soft Skills

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Cont|rol|Offe|red||
||Mean|S.D.|Mean|S.D.|p-value|
|**Panel A. Likert Scales**||||||
|Emotional Awareness|-0.01|0.66|0.01|0.66|0.56|
|Self Awareness|0.01|0.64|-0.01|0.64|0.73|
|Emotional Regulation|0.01|0.61|-0.01|0.58|0.69|
|<br>Self Control|-0.01|0.68|0.01|0.63|0.53|
|Perseverance|0.01|0.66|-0.01|0.66|0.55|
|Personal Initiative|0.00|0.61|-0.00|0.66|0.93|
|Problem Solving|-0.03|0.63|0.03|0.58|0.17|
|Listening|-0.00|0.61|0.00|0.60|0.84|
|Empathy|-0.01|0.57|0.01|0.61|0.53|
|Expressiveness|0.01|0.72|-0.01|0.71|0.74|
|Relatedness|0.01|0.63|-0.01|0.64|0.67|
|Infuence|-0.00|0.74|0.00|0.66|0.99|
|Negotiation|-0.01|0.68|0.01|0.70|0.69|
|Collaboration|-0.02|0.63|0.02|0.66|0.35|
|**Panel B. Alternative Measures**||||||
|Self Awareness|-0.03|0.51|0.03|0.54|0.10|
|Emotional Regulation|0.00|0.40|-0.02|0.48|0.46|
|<br>Self Control|0.03|0.57|-0.04|0.73|0.15|
|Perseverance|0.00|0.55|-0.02|0.63|0.61|
|Personal Initiative|-0.01|0.57|-0.01|0.68|0.95|
|Problem Solving|-0.02|0.63|-0.02|0.70|0.90|
|Networking|-0.00|0.68|-0.07|0.79|0.18|
|Empathy|-0.04|0.61|-0.04|0.72|0.85|
|Expressiveness|0.01|0.53|-0.05|0.67|0.13|
|Relatedness|0.01|0.58|-0.04|0.70|0.20|
|Infuence|-0.01|0.58|-0.07|0.92|0.21|
|Negotiation|-0.00|0.64|-0.05|0.63|0.20|
|Collaboration|-0.03|0.60|0.02|0.56|0.25|
|**Panel B. Joint test for panels A-B**||||||
|p-value (joint F-test)|||||0.23|
|<br>Observation|447||448||894|

Notes: the p-value reported in Column 5 is obtained from a regression of each variable on an assigned to treatment dummy with standard errors robust to heteroskedasticity, controlling for lottery dummies. **p-value (joint F-test)** : corresponds to the orthogonality test in a regression of assigned to treatment dummy on covariates, the regression also controls for lottery dummies.

31
TABLE 4. Cuasifiscal Expenditures of the Central Bank (% GDP)

|Year\Item|A|B|C|D|Total|
|---|---|---|---|---|---|
|60|-0.32|||2.09|1.77|
|61<br>|1.82<br>|||-2.26|-0.44|
|62|0.34|||-3.08|-2.74|
|63|-0.85|||0.92|0.07|
|64|-0.52|||-0.81|-1.33|
|65|0.11|||0.28|0.39|
|66|-0.39|||-0.16|-0.55|
|67|-1.24|||2.66|1.42|
|68|-0.03|||-0.14|-0.17|
|69|0.94|||-1.33|-0.39|
|70|-0.54|||0.48|-0.06|
|71|0.66|||-1.75|-1.09|
|72|-0.51|||0.69|0.18|
|73|-1.83|||0.77|-1.06|
|74|-2.79|||-1.14|-3.93|
|75|-2.27||0.16|-1.3|-3.41|
|76|-6.21||5|3.46|2.25|
|77|-5.86|1.28|0.57|3.59|-0.42|
|78|-2.36|2.72||1.98|2.34|
|79|-0.82|0.86||2.8|2.83|
|80|3.17|-0.61||-3.43|-0.87|
|81|2.94|-1.38|3.99|-5.33|0.22|
|82|7.0?|4.96|16.63|-3.74|25.45|
|83|-3.69|1.04|8.59|-1.06|4.87|
|84|3.8|4.06|10.01|-0.3|17.56|
|85|-1.78|1.56|4.63|2.99|7.0|

A. Annual changes in loans to the financial system minus annual changes in reserve requirements.

- B. The Interest Equalization Account (Cuenta de Regulaci6n Monetaria) introduced in 1977 and the cost of remunerated reserve requirements and other remunerated

- liabilities introduced in its place in 1985.

C. Losses for Swaps and other "exchange insurance" mechanisms.

D. Accumulation of International Reserves.

Source: FIEL (1986) "El Gasto Publico en la Argentina 1960-1985." 14
3 in part our analysis of new surveys (or adaptations thereof) since releasing the 2018 database, but also refinements we have made to out-of-pocket expenditure estimates for some surveys included in the 2018 database.

# 2.1. <u>New indicators</u>

The HEFPI 2018 database included five financial protection indicators: the incidence of (i.e. fraction of households experiencing) ‘catastrophic’ health expenditures, defined alternately as expenditures exceeding 10% of consumption or income, or as expenditures exceeding 25% of consumption or income; and the incidence of (i.e. fraction of households experiencing) impoverishing expenditures, defined as expenditures without which the household would have been above the poverty line, but because of the expenditures is below the poverty line, with two absolute poverty lines ($1.90-a-day and $3.20-a-day in 2011 purchasing power parity (PPP) dollars) and one relative poverty line (50% of median consumption or income).

The HEFPI 2019 database includes these five indicators plus nine new financial protection indicators:

1. The incidence of impoverishment for two other international poverty lines proposed by Joliffe and Prydz (2016), namely $5.50-a-day (relevant to upper middle-income countries) and $21.70-a-day (relevant to high-income countries).

2. The incidence of impoverishment using the societal poverty line (SPL) proposed by Joliffe and Prydz (2017), equal to the higher of two poverty lines: the $1.90-a-day line or 50% of median consumption.

3. For all four $-a-day poverty lines, the HEFPI 2019 database also includes not just the incidence of impoverishing expenditures but also the addition to the per capita poverty gap (in $ terms) attributable to out-of-pocket expenditures.<sup>1</sup>

> 1 This is computed (for the $1.90-a-day poverty line) as 1.9 times the difference between the per capita poverty gap for consumption (or income) net of out-of-pocket expenditures and the per capita poverty gap for consumption (or income) gross of out-of-pocket expenditures.
<!-- Start of picture text --> o 5 19 15 20 km ‘See ee bas<br>OO —— | Set, and<br>naaa fT Tt ‘ened<br>MATAR Es os ges<br>PeEr i ie AT 7.9,<br>irajceauncaeSSpe Be Ge OE CS eeeTn pe Sg! i arot<br>AH EH 7 WE Tigi > a a<br>PtPig ical teTSS on Oe<br>sagen sesh teeteb2NStp p e epste<br>eepe AO cecnmncspee:A OkEbambeScsameeese.sce cs iak ace ain<br>“sO etd oO COTE EEP retainec py €eae7<br>. 4 cA Loss of accessibility (% point) :<br>\$ - 4a —aiasroad networkGB) 25.0-300-35030.0 MlBl 65.9-70.0709-750<br>f oa pentae C 0.0-0.000-50 DBi 40.0-35.0- 45.040.0 MBMl 75.0-80.0- 85.090.0<br>yw ek. - CF 56-100 GB 45.0-50.0 MM 85.0-90.0<br>Sacf ?OPE.A nlySy— ES¢EB 20.015.0-100-150- 20.025.0 =TMMM 500-55060,0-55.0- 60.065.0 MlMM 90.0-95.095.0- 100.0<br><!-- End of picture text -->

<!-- Start of picture text --> 0 5 19 15 20km ‘ane YO bard<br>==) H Ps eo<br>a PTT EY<br>PeAPES ge<br>[Tee err ia a a<br>; COREBEESRMEMRBE? 250100000 ASeee SSE002500 nf i eerieepit<br>ET Scam ame ase aan MTT a igs . re a<br>CPR cat aTSSATitan Oy?<br>ret / ee FRO AR,Te2 ee<br>(00 i NO Seas Occ sammencetsce _s<br>Ys RRpas EN went)Sang,) § MeVE ARTeaein EESSe is LH<br>. / “A toss of accessibility (% point) “<br>i% “é —droad14160 network [7]DD 25.0-300-35.0 30.0 HEMM 65.070.0-75.0- 70.0<br>fi amie Setaalie Co 0.0-oe-s0 0.0 DHBa 35.0-40.0-45.040.0 MMMM 80.075.0--85.0 90.0<br>VB - “4 50-100 @ 45.0-50.0 MM 85.0- 90.0<br>|)aw) Aye ~SL,4a ¥ i“<(() 20.9-25.0159-20.0100-150 TiHMMM 500-55.060.0-65.055.0- 60.0 MlMl 95.090.0-- 95.0 190.0<br><!-- End of picture text -->
Although we include a comprehensive list of variables, it is possible that there are other variables that might matter but are not available in our data set. Thus, as a robustness check for our results, we employ the test for omitted variable bias described in Oster (2019). This method allows us to estimate identified sets for the parameter of interest in the presence of omitted variable bias, under the assumption that selection on observable controls is proportional to selection of unobservable controls. In Appendix tables A1, A2, and A3 we report these “Oster bounds” for the coefficients estimated in Tables 2, 3 and 4 respectively.<sup>6</sup> First, we observe that these bounding sets never include the value zero, providing a robustness check for our estimated results. Second, in addition to excluding zero, they are also tightly bound around the estimated coefficient of interest. These results further support our findings and help us in rejecting the hypothesis that there are unobservable factors that are driving the observed relationship between violence and joint decision making.

# **5.2 Qualitative Evidence on Shared Responsibility as an Alternative Mechanism**

Our results show that couples where the wife and husband both report making decisions jointly have the lowest levels of physical, sexual and emotional violence perpetration by the husband. Given that this result is not explained by attitudes towards violence, similarity of preferences, marital capital in the couple, or women’s bargaining power, what can explain it? Recent qualitative work has suggested the importance of joint decision making between spouses and supports the view coming from the management, political science and conflict resolution literature of its value as a way of reducing conflict.

> 6 To calculate the bounds we use the Stata package psacalc using Rmax, that is 1.3 times the R-squared in specifications that control for observables, the lower bound is estimated using delta=0 and upper bound is estimated using delta=1.

24
8 Table 1. Explicit trading costs

|**Economy**|**Price**|**Commissions**|**Fees**|**Market**|**Total**|
|---|---|---|---|---|---|
|**averages**|($)|(bp)|(bp)|**impact**<br>|(bp)|
|||||(bp)||
|Indonesia|0.3|47.7|10.6|9.9|68.1|
|Korea|34.3|29.3|12.4|19.2|60.9|
|Malaysia|1.6|34.2|6.1|15.7|55.9|
|Philippines|0.4|48.5|34.4|11.3|94.1|
|<br>Thailand|1.0|43.7|1.8|11.4|56.9|
|Hong Kong|1.5|22.3|10.7|11.3|44.3|
|Singapore|3.1|25.7|2.0|13.8|14.5|
|_Memorandum:_||||||
|Japan|14.7|12.9|0.3|6.3|19.5|
|<br>Canada|17.5|20.3|0.2|6.5|27.0|
|France|31.0|17.9|0.4|7.7|36.0|
|Germany|33.8|17.6|0.6|14.1|32.3|
|<br>UK||||||
|Buys|5.3|14.2|49.8|7.4|71.4|
|Sells|5.2|13.4|0.8|13.3|27.5|
|USA (NYSE)|25.0|18.2|0.3|7.8|26.3|
|Chile|0.7|29.1|10.2|15.7|55.0|
|Greece|12.9|28.7|19.2|10.9|58.8|
|Mexico|2.1|26.3|0.1|8.2|34.6|
|Peru|3.3|20.5|0.5|25.2|46.2|
|Spain|14.1|17.5|0.5|11.8|29.7|
|Turkey|0.0|31.4|2.7|20.6|54.7|

Source: Elkins McSherry trading costs (2004).

Implicit trading costs represent the opportunity costs of delaying a trade if conditions are not appropriate, the expected cost of failure to complete the trade because of physical infrastructure failure, and any premium due to the illiquidity of the market. These costs are of course hard to measure.

In this paper we rely on a methodology developed by Lesmond et al (1999) that captures both explicit and implicit costs. Their method exploits the fact that a trade only happens if the benefit of the transaction exceeds the cost. Hence, by using the fraction of zero-return days (i.e. when trades do not happen), one is able to disentangle econometrically an estimate of transaction costs. The underlying assumption is that the marginal trader will trade on new information not already incorporated in the price of a security only if the trade yields a profit net of transaction costs. Hence there will be an observed difference between the “real” and the perceived return. To extract a measure of transaction costs, a Limited Dependent Variable (LDV) model is estimated.