rafmacalaba/datause-encoder-data huggingface dataset:
**Table A2 Main results with OECD employment HGF definition**

|**Dependent:**||**Productivity (3**|**‐year growth)**||
|---|---|---|---|---|
||**Labor pro**|**ductivity**|**T**|**FP**|
|HGF Downstream share|0.039***|0.039***|0.040***|0.040***|
||(8.674)|(8.672)|(6.666)|(6.680)|
|HGF Upstream share|‐0.006|‐0.007|‐0.011*|‐0.012*|
||(‐1.359)|(‐1.450)|(‐1.755)|(‐1.902)|
|HGF Intra‐industry share||0.014***||0.025***|
|||(4.509)||(7.005)|
|Region‐Year FE|yes|yes|yes|yes|
|Sector‐Year FE|yes|yes|yes|yes|
|Firm controls|yes|yes|yes|yes|
|Observations|140,594|140,594|140,594|140,594|
|R‐squared|0.215|0.215|0.169|0.170|
|**Dependent:**|**Employ**|**Size (3‐yea**<br>**ment (ln)**|**r growth)**<br>**Sale**|**s (ln)**|
|HGF Downstream share|‐0.000|‐0.000|0.025***|0.025***|
||(‐0.070)|(‐0.068)|(4.759)|(4.790)|
|HGF Upstream share|0.002|0.002|‐0.003|‐0.003|
||(0.538)|(0.487)|(‐0.576)|(‐0.611)|
|HGF Intra‐industry share||0.009***||0.007*|
|||(2.843)||(1.926)|
|Region‐Year FE|yes|yes|yes|yes|
|Sector‐Year FE|yes|yes|yes|yes|
|Firm controls|yes|yes|yes|yes|
|Observations|140,594|140,594|140,594|140,594|
|R‐squared|0.084|0.084|0.069|0.069|
|**Dependent:**||**Income (3‐y**|**ear growth)**||
||**Average**|**wage (ln)**|**RO**|**A**|
|HGF Downstream share|0.013***|0.013***|0.023***|0.024***|
||(2.693)|(2.701)|(4.821)|(4.862)|
|HGF Upstream share|0.004|0.004|‐0.006|‐0.006|
||(0.627)|(0.682)|(‐1.187)|(‐1.308)|
|HGF Intra‐industry share||‐0.011***||0.019***|
|||(‐3.135)||(5.758)|
|Region‐Year FE|yes|yes|yes|yes|
|Sector‐Year FE|yes|yes|yes|yes|
|Firm controls|yes|yes|yes|yes|
|Observations|140,594|140,594|140,594|140,594|
|R‐squared|0.161|0.161|0.095|0.095|

Note: The reported coefficients are beta coefficients, t‐values in parentheses. The explanatory variables are measured at time t, while the dependent variables are 3‐year changes between t and t+3. Firm controls are log employment, labor productivity and log capital per labor ratio. Standard errors are clustered at the county‐industry (2‐digit NACE)‐year level. The definition of HGF is the 3‐year OECD employment definition.

25
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11 Table 1 lists the 39 data points for which changes in data sources from the 2018 to the 2019 HEFPI version led to changes in indicator values of more than 10 percent.<sup>10,11</sup> The table reports the 2018 and 2019 survey identifiers, the 2019 population mean indicator value x̅���� and the difference between the 2019 and 2018 indicator values x̅���� �x̅����.

> 10 Where we also corrected indicator computations for sources used both in the 2018 and 2019 versions of HEFPI, the difference in indicator values between the two versions is of course also affected by these corrections.

> 11 For Table 2 we did not consider differences in 2018 and 2019 HEFPI indicator values for pap smear points with changing sources as the change in the pap smear reference period between the database’s two versions confounds any change in indicator values from the change in sources.
The resulting combined data covers more than 120 countries, for which we have an uninterrupted series for both the outcome and the explanatory variables. In order to guarantee a long enough stretch of pre-treatment and post-treatment observations, set to a minimum of 5 years respectively, we focus on all PTAs signed between 1995 and 2010.<sup>11</sup> We restrict the partner countries in PTAs that include the EU, US or other OECD countries to a set of low and middle income non-EU countries.<sup>12</sup> When focusing on the EU, US, and rest-of-world (ROW) agreements, we further restrict the sample of potentially treated countries by excluding all those with socioeconomic conditions comparable to the EU or the US.<sup>13</sup> The rationale for this additional restriction is that such countries might already apply high standards in the non-trade outcomes of interest. For instance, with respect to the EU or the US, we exclude high income countries such as Canada, New Zealand and Australia. The countries that are not considered in each exercise are listed in Annex Table A-1.

Table 1 summarizes the number of PTAs with binding or non-binding NTPs in our sample, as well as the number of countries making such commitments. While EU PTAs have both binding and non-binding provisions (except in civil and human rights, where provisions are non-binding), the US only has binding NTPs in its PTAs, and does not include provisions on civil and human rights. Most provisions entail environmental commitments, with some 80 countries signing binding or non-binding environmental provisions. Annex Table A-2 provides further information on signatories of PTAs with NTPs, including the number of “overlapping provisions” – instances where a country has accepted NTPs in more than one PTA.

> 11In a few cases we partially impute some covariates and outcome indicators to preserve a minimum sample size.

> 12We exclude EU accession countries from the sample, given the special nature of their agreements with the EU, which included extensive financial transfers and much deeper cooperation than observed in other trade agreements.

> 13 _ROW_ refers to all agreements signed with all possible non-EU, non-US partners. This list includes but is not limited to other OECD countries such as Australia, Japan, Switzerland and Korea, and all partners in South-South PTAs involving non-OECD countries.

10
Harrison, Glenn W., and H.D. Vinod, "The Sensitivity Analysis of Applied General Equilibrium Models: Completely Randomized Factorial Sampling Designs", _The Review of Economics and Statistics,_ 74, May 1992, 357-362.

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21
this case the benchmark (no‐crisis) and crisis states for the same point in time. A GIC in this case will plot the percentage change in per capita consumption between 2010 benchmark and crisis scenarios for every centile in the benchmark distribution of per capita consumption/income.<sup>28</sup> Taking the country as a whole, the reduction in per capita expenditures due to the crisis (as a percentage of benchmark expenditures) rises as one moves up the distribution, with the largest difference (a drop of 4‐5% in per capita consumption) occurring for households above the 70<sup>th</sup> percentile of the benchmark distribution (Figure 7a). The drop in per capita consumption is less than 1% for households in the bottom 10% of the distribution, but 3‐4% for households in the 50<sup>th</sup> to 70<sup>th</sup> percentile.

There are interesting differences between how the impact of the crisis would be distributed _within_ urban and rural areas (Figure 7b). Three key differences stand out: (i) in rural areas, the largest impact is seen among the richest (above the 80<sup>th</sup> percentile), while in urban areas those between the 50<sup>th</sup> and 80<sup>th</sup> percentiles suffer the largest loss; (ii) the rural rich suffer a much steeper drop in consumption than the urban rich; (iii) the moderately poor and near‐poor in urban areas (10<sup>th</sup> to 40<sup>th</sup> percentiles) are affected more than the corresponding group in the rural areas. These differences are mainly explained by high dependence of the rural rich on external remittances, and the fact that the moderately poor and the middle‐class in urban areas depend more on income from the industry sector than do their rural counterparts.<sup>29</sup>

**Figure 7: Growth incidence curves (crisis vs. benchmark, 2010)**

<!-- Start of picture text --> (a) Per capita expenditure: Bangladesh (b) Per capita expenditure: urban and rural<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Percentile Percentile<br>Urban Rural<br>0.00 0.00<br>-1.00 -1.00<br>-2.00 -2.00<br>-3.00 -3.00<br>% Change % Change<br>-4.00 -4.00<br>-5.00<br>-5.00<br>-6.00<br>-6.00<br><!-- End of picture text --> _Source_ : Own simulations based on HIES (2005) _Note_ : 1) GICs have been smoothed to eliminate sharp kinks in the original curves 2) Percentiles on the X‐axis are applicable to the relevant group <u>(national, urban, rural, etc.)</u> Thus the impact of the crisis is higher for the better‐off and particularly for the top 30% of the distribution. The loss in welfare among the richest 10% of the rural population is larger than that for the 28 A “centile” refers to 1% of the population, when the households are ordered by per capita consumption/income. 29 It is important to note that the percentiles of expenditures are defined with reference to each area (urban or rural). Since urban areas are better off on the average than rural areas, the k‐th percentile of urban households is better off than (and thus not strictly comparable with) the same percentile of rural households.

25
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18
-25-

# **REERECES,**

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<!-- Start of picture text --> / Vable 4: Decline in Infant and Child Mortality: International Comparison<br><!-- End of picture text -->

|l<br>|Rate ofDecline(1<br>|990-98)<br>|
|---|---|---|
|Region||MR|<sup>USM</sup>|
|Lowandmiddleincomecountries|<br>10.6||66||

# Source: ADB and WHO (2001)

This recent progress gives Vietnam an impressively low starting point (by international comparisons) for infant and child mortality. If Vietnam followed a pattern more typical of Asia, it would have an IMR approximately double its current level, given current per capita income levels. Given these low starting points, achieving the MDG — which calls for a two-thirds reduction in the infant and child mortality rates between 1990 and 2015 — is unrealistic.

But regional differences persist- The impressive national averages of infant and child mortality in Vietnam conceal wide variations at the regional levels. For example, the national average infant mortality rate is more than two and half times the rates observed in Red River Delta and Southeast region. The IMR in rural areas is about 150 percent greater than that in urban areas. In general, these indicators are high in the Central Highlands and Northern Uplands, closely followed by the North Central and Central Coastal regions. These significant differences in child and infant mortality reflect the large regional disparities in socio-economic levels and access to health services.

As Table 5 shows, it is more alarming that these regional disparities have increased. In the 1990s, infant mortality declined in urban areas by 32 percent, while rural areas experienced less than half of that rate of decline. In 1990, the rural IMR was about 80 percent greater than that in urban areas. This differential has grown to about 125 percent in 1998. These increasing inequities have serious implications for policy and need urgent attention.

22
<!-- Start of picture text --> Disclosure questions Non-disclosure questions<br>wo oO<br>NN<br>NN<br>__oO©)£<br>82 82<br>cTM cTM<br>2 &<br><!-- End of picture text --> <!-- Start of picture text --> = E<br>fo}ou fe)ou<br>c c<br>[e} [e)<br>Zz Zz<br>lo ite}<br>3 3<br>Oo lo}<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>Year Year<br><!-- End of picture text -->
the type of event (conflictive or cooperative) and its intensity using the BAR scale, which ranges from –7, the most conflictive (war), through 0 (neutral events), and up to +7, the most cooperative (voluntary merging of countries).

To extract the events examined in this study, we categorized the events data as they relate to country-basin dyads with no treaties and those country-basin dyads with treaties. For those country-basin dyads with treaties, we also categorized the events data paying close attention to the period before and after a treaty governed the basin in order to compare the two time-periods. The data processing produced a database listing the number and intensity (BAR values) of all the events relevant to each country-basin dyad. This is in line with previous studies using BAR events data (e.g. Yoffe and Larson, 2001; Brochmann, 2012). ). There were no examples of extreme scores (- 7 or 7). As one of the primary goals of this study was to investigate the impact of treaties, in cases with a BAR score of positive 6 (the signing of a treaty), we subtracted one observation from the total number so as not to attribute the signing of the treaty as an outcome of the treaty. The distribution of events is shown in Figure A1 in the Appendix. In conducting our regression analysis, we used ( **_AvgBAR_ )** as a dependent variable, which was the mean of the anti-logged values of conflictive and cooperative relations between the countries. Anti-logs were used instead of actual BAR scores in order to amplify the increasing differences in scale for more extreme events, as is common in previous work using BAR values (e.g., Yoffe et al., 2003).

# _<u>Water Variability</u>_

While water variability is an inherent characteristic across river basins, climate change is predicted to intensify such variability. All else being equal, higher water variability has been shown to lead to political tensions between states sharing river basins (Yoffe et al., 2004; Stahl, 2005).<sup>6</sup> Results presented in Wolf, Stahl and Macomber (2003: 1), for example, demonstrate that “extreme events of conflict were more frequent in marginal climates with highly variable hydrologic conditions, while the riparians of rivers with less extreme natural conditions have been more moderate in their conflict/cooperation relationship.”

> 6 According to the National Intelligence Assessment (2008) “climate change could threaten domestic stability in some states, potentially contributing to intra- or, less likely, inter-state conflict, particularly over access to increasingly scarce water resources.

7
stronger in countries with better institutions, but generously designed deposit insurance can still curtail it, resulting in fragility.

The issue of how bank regulation and supervision affects banking crises is very important, since ensuring bank safety and soundness is a major goal of bank regulators. Barth, Caprio and Levine (2004), having developed a comprehensive survey database on measures of regulation and supervision, are able to investigate this issue empirically for the first time. Their results indicate that regulatory and supervisory practices that force accurate information disclosure, empower private sector monitoring of banks, and foster incentives for private agents to exert corporate control work best to promote bank performance and stability. In a cross-country setting they show that regulatory and supervisory regimes with these features have suffered fewer crises in the past two decades. Barth, Caprio and Levine (2004) also confirm that poorly designed explicit deposit insurance leads to greater 13 probability of banking crises, even after controlling for regulation and supervision.

# _The political system and crises_

Political considerations may play a very important role in government decisions to deal with insolvent institutions. Based on a rigorous examination of the US Savings and Loan crisis, Kroszner (1997) argues that disseminating information about the costs of inefficient government policy, ensuring competition among interest groups, increasing the transparency of government decisions, improving the structure of legislative oversight of the regulatory process, and allowing entry of foreign banks are all measures that can potentially improve government financial sector policy and reduce the cost of crises. These recommendations place great importance on the disciplining role of information and the existence of competitive elections.

21
<!-- Start of picture text --> Figure 2: PISA and the Reform Cohorts<br>XII<br>XI<br>X<br>IX PPI I SSA A PPI I SSA A PPI I SSA A<br>VIII<br>VII<br>VI<br>V<br>IV<br>III<br>II<br>I<br>School year 97/98 98/99 99/00 00/01 01/02 02/03 03/04 04/05 05/06 06/07<br>The age group covered by PISA 2000 old 8-grade primary school<br>The age group covered by PISA 2003 old secondary schools incl. VET<br>The age group covered by PISA 2006 new 6-grade primary school<br>Final exams in primary and gymnasium new lower secondary school<br>New matura new upper secondary school<br><!-- End of picture text --> The group covered by PISA 2000 consisted of the first grade students of the pre-reform secondary schools: general lyceum (one had to pass an entrance exam to enter), secondary vocational school and basic vocational school (with very low prestige). The results of PISA 2000 in Poland showed huge variations among schools, which was not surprising at all, as the pre-reform system was based on a strong selection of primary school leavers. However, the group covered by PISA 2003 (and PISA 2006) consisted of students of the last (third) grade of compulsory gymnasium. Not surprisingly, the results showed smaller variations among schools and larger ones among students within schools.

Among the PISA 2000 participants, only students of lyceums and some secondary vocational schools had a previous experience of taking a written entrance exam. The others had no experience at all. Actually, the lyceum entrance exam was not a test. It consisted of two parts: Polish language (written essay) and mathematics (solving five slightly complicated but standard problems). The first national final tests after primary school and gymnasium were carried out in 2002. At that time, the group of PISA 2003 were in the second grade of gymnasium, so they did not take the final primary school test; however, the PISA 2006 group were then still in primary school (the fifth grade) so they took the full set of the new external exams. PISA 2000 was, for most Polish students covered by the survey, the first experience in writing a test-item exam. PISA 2003 participants, although not having written an actual test-item exam, had had some previous experience due to the preparation (mock exams introduced by their teachers) for their upcoming final gymnasium exams. PISA 2006 participants were very well acquainted with doing tests. They took the final primary school test and had three years of preparation for the gymnasium exam. Konarzewski (2004) shows that teachers considered seriously the results of the 2002 final exams (the first ones ever). In fact, one-third of a 7