rafmacalaba/datause-encoder-data huggingface dataset:
In Table 7, the pooled regression in column (1) shows that participation in the first two periods of the trading is associated with a strong and significant increase in employment. These higher estimates are likely driven by the systematic difference between regulated and unregulated firms - EU ETS firms are larger with higher production capacity. Once controlling for the fixed firm-level differences (column (2)), the parameter estimates on program participation become much smaller and are no longer statistically significant. Column (3) considers the possible heterogeneity in the time trends among participating nations. The results indicate that employment reduced by 2 and 4 percent, respectively, during the first and second phase of the program. However, these effects are statistically insignificant. Column (4) shows that controlling for the allocation and surrender of allowances does not change the results.

Table 7: Results on Employment for Cement

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Phase 1 Participation|0.420***|0.0657|-0.0220|-0.0761|
||(3.37)|(1.28)|(-0.27)|(-0.29)|
|Phase 2 Participation|0.357***|0.0549|-0.0423|-0.548|
||(2.84)|(0.93)|(-0.38)|(-1.55)|
|Phase 1×Log(Surrendered)||||0.0618*<br>(1.83)|
|Phase 2×Log(Surrendered)||||0.0756<br>(0.73)|
|Phase 1×Log(Allocated)||||-0.0585<br>(-1.61)|
|Phase 2×Log(Allocated)||||-0.0340<br>(-0.32)|
|Plant Fixed Effects|No|Yes|Yes|Yes|
|Country-by-Year Fixed Effects|No|No|Yes|Yes|
|Observations<br>|1205|1205|1205|1205|
|Adjusted_R_<sup>_2_</sup>|0.017|0.004|0.034|0.062|

Note: Dependent variable is the logarithm of number of employees. Standard errors are clustered at the firm level and t statistics are shown in the respective parentheses. *, **, and *** indicate significance at the 10, 5, and 1 percent levels.

Table 8 presents the estimation results on material costs. The OLS model suggests that EU ETS firms have lower material costs by 9-10 percent compared to their unregulated counterparts. The difference could be explained by the fact that larger firms are more efficient at production due to economies of scale. In contrast to the OLS results, the negative correlation disappears in 17
<mark>Erhan Artuc, Frederic Docquier, Caglar Ozden and Chris Parsons 2017, "Global skilled migration: Structural estimation of 2000‐2010 patterns". Mimeo, World Bank Development Research Group.</mark> <mark>Feyrer, J (2009). “Trade and Income—Exploiting Time Series in Geography,” Working Paper 14910,</mark> <u><mark>http://www.nber.org/papers/w14910</mark></u> <mark>Fleming, J. M. (1962). Domestic financial policies under fixed and under floating exchange rates. Staff Papers International Monetary Fund, 9, 369–380.</mark> <mark>Frankel, Jeffrey A., and David Romer. "Does trade cause growth?" American economic review (1999): 379‐399.</mark> <mark>Gould, David M. "Immigrant links to the home country: empirical implications for US bilateral trade flows." The Review of Economics and Statistics (1994): 302‐316.</mark> Gould, David M., and Georgi Panterov. “ Multidimensional Connectivity: Why the Interplay of International Connections Matters for Knowledge Transfers. ” Journal of Policy Modeling, 39(4), 201707 <mark>Halu, A., Mondragon, R. J., Panzarasa, P., & Bianconi, G. (2013). Multiplex pagerank. PloS one 8, e78293.</mark> <mark>Helliwell, John F. "Empirical linkages between democracy and economic growth."</mark> _<mark>British journal of political science</mark>_ <mark>24, no. 2 (1994): 225‐248.</mark> <mark>Iacovacci, J., & Bianconi, G. (2016). Extracting information from multiplex networks. Chaos 26, 065306.</mark> <mark>Kenett, Dror Y., Matjaž Perc, and Stefano Boccaletti. "Networks of networks–An introduction."</mark> _<mark>Chaos, Solitons & Fractals</mark>_ <mark>80 (2015): 1‐6.</mark> <mark>Kivelä, Mikko, Alex Arenas, Marc Barthelemy, James P. Gleeson, Yamir Moreno, and Mason A. Porter. "Multilayer networks."</mark> _<mark>Journal of complex networks</mark>_ <mark>2, no. 3 (2014): 203‐271.</mark> <mark>Kung, Howard. "Macroeconomic linkages between monetary policy and the term structure of interest rates."</mark> _<mark>Journal of Financial Economics</mark>_ <mark>115, no. 1 (2015): 42‐57.</mark> <mark>Hassan, Ibrahim Bakari, M. Azali, Lee Chin, and Wan NW Azman‐Saini. "Macroeconomic linkages and international shock transmissions in East Asia: A global vector autoregressive approach."</mark> _<mark>Cogent Economics & Finance</mark>_ <mark>5, no. 1 (2017): 1370772.</mark> <mark>Helliwell, John F. "Empirical linkages between democracy and economic growth."</mark> _<mark>British journal of political science</mark>_ <mark>24, no. 2 (1994): 225‐248.</mark> <mark>Helpman, E., 2004, The Mystery of Economic Growth. Harvard University Press, MA.</mark> 38
public schools and in urban areas, the latter to reduce logistical complexity of parcel delivery— in the Lima region population is predominantly urban, whereas in Junín and Ancash approximately 2 out of 3 households are located in urban areas. Three types of schools were excluded from the sampling frame: (a) those located in districts in the top quintile of wealth; (b) those that operated night-shifts only; and, (c) those with fewer than 15 pupils in total. These restrictions resulted in a universe of 946 schools in the three regions. To maximize statistical power, it was decided to sample 800 from these 946 schools.

In total, 400 of the 800 schools were randomly selected to receive the ‘GYM package’. The study contains two equally powered samples: the _metropolitan_ samples with 398 schools from the Lima region, a densely populated area with over 12 million inhabitants, and the _regional_ sample which includes 402 urban and peri-urban schools in the regional towns of Ancash and Junín. To ensure balance in the experimental sample and maximize statistical power, treatment status was stratified across poverty quintiles and regional strata (Bruhn and McKenzie, 2008), making 12 strata in total.<sup>15</sup> **Table 1** provides an overview of treatment and control schools, across the pooled and individual experiments.

Treatment schools were targeted to receive the ‘GYM package’ and to implement the Grow Your Mind session during regular tutorial hours in 2015. In each institution that received the package, tutor teachers were asked to implement the session in every classroom for grades seven and eight. Control schools were not approached, and, thus, continued using the national curriculum in their tutorial sessions. The experiment was designed to assess the impact of the ‘GYM package’ using information from the students’ census evaluation ( _Evaluación Censal de Estudiantes_ , ECE), administered a few months after the intervention for those in the eighth grade, and more than a year later for those in the seventh grade (see **Section 4e** ).

15 Pre-intervention power calculations of the school-clustered randomized control trial suggested a low minimum detectable effective size (MDES) of 12% for the pooled sample, while individual experiments were powered to detect an MDES of at least 17%. To compute MDES in this level-3 cluster RCT, we assumed 2 sections per school with 25 pupils per section, an intra-class correlation at the school-level of 0.25 and within-school correlation across teachers of 0.15. In reality, pre-treatment power calculations were very conservative: not only average schools turned out to be larger, but the intra-class correlation across schools was closer to 0.15. Ex-post calculation based on the 2015 ECE national data suggests that the pooled and individual studies were powered to detect 8% and 12% MDES respectively.

9
Informal employment in manufacturing changes significantly across income groups, dropping from 77 percent in low-income to 11 percent in high-income countries.<sup>19</sup>

**Figure 1. Average Share of Informal Employment by Income Groups and Sectors.**

<!-- Start of picture text --> All Sectors Agriculture<br>1 1<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>0 0<br>High Income Upper Middle Lower Middle Low Income High Income Upper Middle Lower Middle Low Income<br>Income Income Income Income<br>Manufacturing Services<br>1 1<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>0 0<br>High Income Upper Middle Lower Middle Low Income High Income Upper Middle Lower Middle Low Income<br>Income Income Income Income<br><!-- End of picture text --> _Source: Authors’ computations using the I2D2 dataset._ Other indicators from the WDI and Penn World Tables are included as control variables in the econometric analysis. Among these variables are shares of sectoral value added, shares of import and export in GDP, human capital, dependency ratio, population, and real interest rates. The sectoral shares of the value added, import and export shares in GDP, and human capital index are obtained from the Penn World Table database (version 8). The human capital index is based on the average years of schooling from Barro and Lee (2013) and an assumed rate of return to education is based on Mincer equation estimates from Psacharopoulos (1994).<sup>20</sup> Data on dependency ratio, population, and real interest rate are taken from the WDI.

> 19 An important caveat worth mentioning is that many self-employed workers in high-income countries are not necessarily informal workers although their labor productivity is low compared to the rest of the workers. Similar patterns emerge in the service sectors.

> 20 Human Capital in PWT 9.0: https://www.rug.nl/ggdc/docs/human_capital_in_pwt_90.pdf.

12
one‐day decrease in bilateral time to trade leads to an increase in the bilateral export pattern by: �β� ∗t� �β� ∗t̃�� %.

The results presented in Table 6 show that the impact of trading times is relatively higher for products that are time sensitive to their consumers or end users and that use time sensitive inputs in production. In addition, the indirect effect (impact of trading times on products that highly use time sensitive inputs in production) is higher than the direct effect (impact of trading times on products that are time sensitive to their consumers), suggesting that improving trading times is particularly important in a world of global value chains, where time sensitive inputs are imported for production.

To gauge the magnitude of the estimates on the interaction between time reductions and direct time sensitiveness, we evaluate the coefficients for two sets of industries: textiles, leather and apparel, and motor, parts and transport equipment. These industries fall respectively in the 25<sup>th</sup> and 75<sup>th</sup> percentile of the _direct_ time sensitivity distribution. The increase in average trade from a one‐day reduction in trading time is 0.17 percentage points higher for products within the motor, parts and equipment sector compared to products in the textile, leather and apparel industries. Conversely, the impact of reduction in timeliness on exports of products that use time sensitive inputs is 0.23 percentage points higher for products in the paper and metals industries, which fall in the 75<sup>th</sup> percentile of the _indirect_ time sensitivity distribution, compared to products in the textiles, leather and apparel industries, which are in the 25<sup>th</sup> percentile of the distribution.

_Table 6: Time sensitive products and products using time sensitive inputs_

||**OLS**|**PPML**|
|---|---|---|
||(1)|(2)|
|GIS Time * t0 (Direct Effect)|‐0.00925***|‐0.00886***|
||(0.00105)|(0.000701)|
|GIS Time * t1 (Indirect Effect)|‐0.0261***|‐0.0388***|
||(0.00333)|(0.00181)|
|Ln(PRF+1)|‐0.509***|‐0.220**|
||(0.151)|(0.0910)|

17
Mexico iFA 10

# **2. Testing tor Rent-Sharing**

# **2.1 The Data**

**Thc** data used for this study are similar to those used by Erzan, Krishna and Tan (EKT). Because of conflicting classification systems between U.S. data and MFA import data the categories used were aggregated in the same fashion as the EKT data, choosing groups of apparel that minimized **the** differences between U.S. production groupings and aggregate MFA category groupings. Six industry groups were examined: sweaters, trousers, men's coats, women's coats, woven shirts, and underwear.8 The data include observations for the following variables from 1981 to 1990, where j indicates the apparel group, and t indicates the year:

**pitUS=** the unit value of U.S. production.

- pjt<sup>m</sup> = F.O.B. price of apparel imports from Mexico.

- tjt = Ad valorem tariff in the U.S.

- Tit = Unit transport cost from Mexico to the U.S.

- **pitm** = Adjusted Mexico price, where pjtm = pjtm (1 +tj ) + Tjt _9_

- **QjUS** = U.S. sales of U.S. production.

- **Qitm** e Mexican exports to the U.S. market.

- Hjt = Numbers equivalent of the Herfindahl index of concentration among Mexican exporters.

- Vit = Quota level for Mexican exports to the U.S.

- Uit = Quota utilization rate defined as Ui, = Qjtm /Vit

The sources and composition of these data are found in the data appendix.

tThe composition of these groupings in terms of MFA categories is explained in the appendix. 9A further adjustment was made to the unit value of Mexican exports to take into account the lower tariff rates levied on imports with a high proportion of U.S. content under the production sharing arrangements codified in HTS 9802.00.
**Table S19. Relationship between the UN Human Development Index and ODIN, Open Data Barometer, and Global Data Barometer scores, 2013-2022**

||ODIN -|ODIN -|ODIN -|ODB -|ODB -|ODB -|GDB -|
|---|---|---|---|---|---|---|---|
||Model 1|Model 2|Model 3|Model 1|Model 2|Model 3|Model 1|
|ODIN Score|0.006***<br>(0.00)|0.000<br>(0.00)|0.000<br>(0.00)|||||
|Open Data Barometer<br>Score||||0.005***|0.000|0.000||
|||||(0.00)|(0.00)|(0.00)||
|Global Data Barometer|||||||***|
|Score|||||||0.006|
||||||||(0.00)|
|Trade (% of GDP)|||0.000|||0.000*||
||||(0.00)|||(0.00)||
|Agriculture, forestry,||||||||
|<br>fishing value added (%<br>of GDP)|||0.000|||-0.001||
||||(0.00)|||(0.00)||
|Manufacturing value<br>added (% of GDP)|||0.000|||0.001||
||||(0.00)|||(0.00)||
|School Enrollment,|||0000***|||0000||
|Primary (% gross)|||.|||.||
||||(0.00)|||(0.00)||
|Year 2014|||||0.005***|0.004***||
||||||(0.00)|(0.00)||
|Year 2015|||||0.009***|0.007***||
||||||(0.00)|(0.00)||
|Year 2016|||||0.013***|0.010***||
||||||(0.00)|(0.00)||
|Year 2017||0.004***|0.003***||0.016***|0.012***||
|||(0.00)|(0.00)||(0.00)|(0.00)||
|Year 2018||0.007***|0.006***|||||
|||(0.00)|(0.00)|||||
|Year 2019||0.011***<br>|0.009***<br>|||||
|||(0.00)|(0.00)|||||
|Year 2020||0.006***|0.005***|||||
|||(0.00)|(0.00)|||||
|Year 2021||0.005***|0.003***|||||
|||(0.00)|(0.00)|||||
|Year 2022||0.005***<br>(0.00)|0.002**<br>(0.00)|||||
|Constant|0.440***|||0.579***|||0.550***|
||(0.02)|||(0.02)|||(0.02)|
|Sigmau||0.1|0.04||0.09|0.04||
|_<br>Sigmae||0.01|0.01||0.01|0||
|_<br>R2|0.470|0.999|0.999|0.569|0.999|1.000|0.519|
|<br>Country FE|<br>No|<br>Yes|<br>Yes|<br>No|<br>Yes|<br>Yes|<br>No|
|<br>No of countries|160|160|160|101|101|101|95|
|No of observations<br>Note: * p<0.10, ** p<0.<br>Development Indicators|1083<br>05, *** p<0.<br>(WDI), Open|1083<br>01. Standard er<br>Data Watch (|1083<br>rors are cluster<br>ODIN), Global|362<br>ed at the count<br>Data Baromete|362<br>ry level. Data<br>r (GDB), and|362<br>from the Worl<br>Open Data Ba|95<br>d Bank's World<br>rometer (ODB).|

Note: * p<0.10, ** p<0.05, *** p<0.01. Standard errors are clustered at the country level. Data from the World Bank's World Development Indicators (WDI), Open Data Watch (ODIN), Global Data Barometer (GDB), and Open Data Barometer (ODB). In cases where data are missing for a particular covariate, the data are imputed forward using the nearest available value. Estimates with country fixed effects not available for the Global Data Barometer, because the indicator contains only one time period.

68
transport, telecommunication, and finance. Such efficiency improvements are crucial for the competitiveness of other sectors in the economy (via reductions in production costs) and their integration into global markets but also to facilitate the participation of ECA countries in the global fragmentation of services production.

**_Figure 11: Average Annual Growth in KLEMS Labor Productivity in Services Sub-Sectors_**

<!-- Start of picture text --> Wholesale Trade Retail Trade Inland Transport<br>15% 15% 16%<br>10% 10% 12%<br>5% 5% 8%<br>0% 0% 4%<br>-5% -5% 0%<br>-10% -10% -4%<br>-15% -15% -8%<br>1997-2000 2000-2004 1997-2000 2000-2004 1997-2000 2000-2004<br>Post & Telecommunications Financial Intermediation Real Estate Activities<br>20% 30% 20%<br>15%<br>15% 20%<br>10%<br>10% 10%<br>5%<br>5% 0%<br>0%<br>0% -10%<br>-5%<br>-5% -20% -10%<br>-10% -30% -15%<br>1997-2000 2000-2004 1997-2000 2000-2004 1997-2000 2000-2004<br>Computer & Related Activities R&D Legal, Technical & Advertising Activities<br>25% 40% 16%<br>20%<br>30% 12%<br>15%<br>10% 20% 8%<br>5% 10% 4%<br>0%<br>0% 0%<br>-5%<br>-10% -10% -4%<br>-15% -20% -8%<br>1997-2000 2000-2004 1997-2000 2000-2004 1997-2000 2000-2004<br>EU-15 Poland SloveniaCzech Rep. Hungary Latvia LithuaniaSlovak Rep. Estonia<br>Slovenia LatviaSlovak Rep. Estonia EU-15Czech Rep. Lithuania Poland Hungary Slovak Rep. SloveniaCzech Rep. EU-15 Poland Lithuania Latvia Hungary Estonia<br>Slovak Rep. HungaryCzech Rep. Slovenia Poland Lithuania EU-15 Estonia Latvia Hungary Latvia Poland EU-15 Slovenia Czech Rep. Lithuania Estonia<br>Latvia Poland Lithuania EU-15Czech Rep. Slovenia Estonia HungarySlovak Rep.<br>LithuaniaSlovak Rep. Czech Rep. EU-15 Slovenia Hungary Poland Latvia Estonia<br>Slovak Rep. Latvia EU-15 Poland SloveniaCzech Rep. Lithuania Hungary Estonia<br>Slovenia EU-15 HungarySlovak Rep. Poland Estonia Latvia LithuaniaCzech Rep.<br><!-- End of picture text --> Source: KLEMS database 2007.

Notes: For each ECA country and sub-sector, geometric averages of labor productivity growth for 1997-2000 and 2000-2004 are shown. For EU-15 countries the growth rate of each sub-sector shown is the simple average across all EU-15 countries of their sub-sector’s geometric average annual labor productivity growth rates in 1997-2000 and 2000-2004. In this figure, financial intermediation is NACE 65 category.

25
<!-- Start of picture text --> a<br>rural urban<br>o<br>y<br>/ \\<br>/<br>/// \\\<br>sy / \<br>nN ~L +--+!/ \ \<br>ire)<br>2015 2016 2017 2018 20192015>a2016 _._.22a2017 20181 2019<br>year<br>—--—-- a-2011 (based on b-2011) ———_ a-2017 (based on b-2017)<br><!-- End of picture text --> <!-- Start of picture text --> b<br>rural urban<br>2<br>8<br>aed<br>8<br>2015 2016 2017 2018 20192015> 2016 ._20170a 2018274.2019<br>year<br>b-2011 (third moment: NSS-2011)——_ b-2017 (third moment: NSS-201/7)<br><!-- End of picture text --> <!-- Start of picture text --> s2<br>rural urban<br>©; TaN<br>7 \<br>/ \<br>/ \<br>/ \<br>/ \<br>ae / \<br>4 yo \<br>7<br>7<br>4<br>_ _7<br>8<br>a<br>2015 2016 2017 2018 20192015 2016 2017 2018 2019<br>year<br>—-—--- s2-2017 (second moment: imputed NSS)<br>———_ s2-2011 (second moment: imputed NSS)<br><!-- End of picture text -->
countries, as developing countries have continued to use WTO dispute settlement (including against other developing countries) to regain lost export access to foreign markets.<sup>24</sup> And while Turkey has little experience as a complainant in WTO disputes as it has only filed two cases on behalf of its exporters since 1995, Table 8 documents that it is relatively experienced overall given its extensive participation as an interested third party in thirty different disputes during this period, covering many different policy and topic areas, including antidumping and safeguards. Thus, it would be somewhat unexpected if Turkey’s use of TTBs _continued_ to escape increased legal challenges through trading partners filing formal WTO dispute settlement proceedings.

# **5 Conclusions**

Since the worldwide financial crisis began in 2008, Turkey has joined almost all major economies by going through a sustained period of uncertainty and volatility with regards to its trade and trade policy. Turkey’s economy managed a remarkable recovery after the major initial negative shock to real GDP and increase in unemployment. Turkey’s policymakers have largely withstood protectionist pressure to make comprehensive change to the relatively liberal import regime during the Great Recession. Nevertheless, there are a number of policy concerns arising based on trends in the data from 2008-11 regarding Turkey’s exercising trade policy flexibilities through changes to its applied tariffs and use of antidumping, safeguards, and other TTB policies.

With respect to trade policy toward its imports, Turkey has accessed various institutional “flexibilities” during 2008-11 to implement _new_ trade barriers. Recent increases to applied tariffs in the textiles and steel industry alone may affect up to 9 percent of Turkey’s manufacturing imports. Newly available data from the World Bank’s _Temporary Trade Barriers Database_ indicate Turkey’s antidumping and safeguards in effect by 2011 are estimated to impact another 4.4 percent of Turkey’s imports. While implementing new import restrictions through the flexibilities permitted by the WTO and preferential trade agreements may be in line with international rules, the economic impact is that they do contravene some of the effectiveness of relatively low applied import tariffs that Turkey’s policymakers had previously implemented.

> 24 A number of Turkey’s applied TTBs affect developing economy exports covering tens of millions of dollars (Table 3). Bown and McCulloch (2010, Table 6, p. 57) documents many examples, and covering much smaller amounts of trade, of formal WTO disputes that developing countries have initiated by using the Advisory Centre on WTO Law.

22