rafmacalaba/datause-encoder-data huggingface dataset:
defines its own regulatory code to grant operating licenses, issue permits, define sector-specific technical standards, and define regulatory fees and charges.

Second, Peru recently implemented a worldwide unique ex post control mechanism governed by the competition authority to enforce the elimination of subnational barriers to market entry that are inconsistent with national law. Before 2013, the competition authority’s legal mandate to contest regulatory barriers imposed by subnational governments that are inconsistent with national law had been very limited. But in 2013, it became much easier to eliminate similar regulatory barriers to market entry across the country once they had been confirmed as inconsistent with national law in one municipality. Therefore, the complaint of a single firm in a random municipality, once confirmed, triggered the elimination of similar regulatory barriers across the country. The dependence on such precedents led to a ‘roll-out’ of eliminated, often sector-specific regulatory barriers across municipalities which can be regarded as exogenous to other contemporaneous municipal policy changes.

The legislative changes produced a database of eliminated regulatory barriers to market entry and competition across municipalities in 2013 and 2014 which we match with establishment census data from 2008-15. We follow the integrated approach of De Loecker and Warzynski (2012) to derive establishments’ (revenue) productivity and markups and estimate how they are impacted by the subnational regulatory changes in a difference-in-differences specification. The integrated approach is critical for our purpose as it allows to control for unobserved establishmentlevel productivity shocks, permits technologies to differ across establishments within the same industry, and is valid under imperfect competition.

Moreover, estimating the reform impact on establishments’ productivity and markups makes it possible to infer their impact on physical productivity. That is, physical capital can increase because of increases in revenue productivity or increases in establishment-specific prices from higher markups.<sup>1</sup> Markups thus drive a wedge between the observed revenue and unobserved physical productivity, potentially biasing the measured reform impact. Specifically, the elimination of regulatory barriers to access domestic markets may increase establishments’ markups since it raises the demand for their products and their ability to differentiate products across markets. On the other hand, the reforms may also reduce markups by reducing local rents from the regulatory protection from (price) competition.

> 1 De Loecker and Goldberg (2014).

3
**Figure 1: The long‐term trend of ERR presence in PAD and in ICR, respectively**

<!-- Start of picture text --> SAR Other regions<br>100% 100%<br>80%<br>80%<br>60%<br>60%<br>40%<br>40%<br>20%<br>0% 20%<br>0%<br>% ERR at appraisal % ERR at completion<br>% ERR at appraisal % ERR at completion<br>1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014<br>1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014<br><!-- End of picture text --> Sources: IEG database and staff calculation.

**Table 3: Trends in Reporting of ERR in PAD for “High‐CBA” and “Low‐CBA” sectors**

||||||South|Asia||||||Other|Region|s|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||1975~|1980~|1985~|1990~|1995~|2000~|2005~|2010~|1975~|1980~|1985~|1990~|1995~|2000~|2005~|2010~|
||79|84|89|94|00|04|09|15|79|84|89|94|00|04|09|15|
|Globle Practice||||||||High|CBA G|P|||||||
|Agriculture|84.6|80.0|73.1|75.0|85.0|70.4|47.1|48.0|90.7|91.8|82.2|68.2|58.8|43.1|43.1|55.1|
|Energyand Extractives|69.2|85.7|67.6|68.2|82.4|80.0|60.0|50.0|79.7|74.8|50.3|58.0|69.3|68.2|52.5|58.7|
|Social and Urban Development|100.0|100.0|0.0|50.0|70.0|0.0|14.3|0.0|40.0|80.6|85.7|65.3|56.4|29.4|18.4|17.3|
|Transport & ICT|92.3|88.9|86.7|100.0|82.4|72.7|61.5|62.5|94.7|95.0|85.4|81.4|85.6|76.7|66.1|69.9|
|Water|..|60.0|66.7|100.0|77.8|44.4|0.0|71.4|78.9|70.0|52.7|37.5|71.2|55.8|52.3|60.7|
|Total for High CBA GP|83.0|81.6|71.7|74.2|81.8|67.2|43.5|47.7|89.0|86.6|73.0|66.2|68.1|55.4|47.0|52.7|
|||||||||Low|CBA G|P|||||||
|Education|0.0|0.0|0.0|0.0|0.0|0.0|5.6|53.8|0.0|0.0|0.0|0.0|1.1|1.7|6.7|18.9|
|Environment & Natural Resources|..|..|..|..|80.0|0.0|..|0.0|..|0.0|..|..|31.6|13.6|22.2|30.4|
|Finace,Markets,Trade & Competitiveness|0.0|0.0|14.3|33.3|11.1|0.0|9.1|28.6|0.0|4.1|1.7|9.1|4.7|7.1|10.5|24.6|
|Governance|..|0.0|0.0|0.0|..|..|0.0|16.7|0.0|0.0|0.0|2.7|5.9|4.5|3.7|6.9|
|Health Nutriton & Population|..|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|0.0|3.0|5.7|17.0|
|Macroeconoimcs & Fiscal Management|..|..|..|0.0|0.0|0.0|0.0|..|0.0|0.0|0.0|0.0|0.0|0.0|0.0|6.7|
|Social Protection & Labor|..|..|..|..|0.0|..|..|0.0|..|..|0.0|0.0|12.8|2.8|5.7|7.1|
|Total for Low CBA GP|0.0|0.0|1.9|1.6|7.5|0.0|3.3|19.7|0.0|1.0|0.3|1.5|4.2|4.1|6.8|14.4|
|Total|69.8|66.0|63.3|66.0|64.2|39.4|21.6|36.1|69.2|66.7|58.3|51.6|44.4|29.8|29.2|36.0|

Source: IEG database and Staff calculations.

16
<!-- Start of picture text --> Qatar: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>B-<br>Jordon: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>Lebanon: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram-2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br><!-- End of picture text -->

# **B- Oil importers**

<!-- Start of picture text --> Morocco: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br><!-- End of picture text --> <!-- Start of picture text --> Tunisia: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br><!-- End of picture text --> 31
# **4.2 Robustness and Treatment Heterogeneity**

In our gravity analysis, we cannot confirm the robust trade-creating role for non-tariff provisions in TAs that has been reported by the literature so far, while tariffs—once properly measured—are robustly shown to be potent inhibitors of trade, yielding plausible trade elasticities in the estimation. We show next that this result is very robust to alternative empirical specifications and is not driven by treatment heterogeneity.

We test if the results might be driven by the definition of depth. To do so, we use alternative measures that are standard in the literature. The results remain virtually unchanged in columns (8) to (14), for which we use the number of legally enforceable provisions to determine the depth of trade agreements. When using DESTA (results can be found in Table A1, Panel (A)) to determine the depth, the NTB effects of the “no tariffs” and “WITS” columns are much smaller than when using the World Bank’s DTA data—the coefficients almost halve. This result is most likely due to the more precise measure of depth in the DTA data compared to DESTA; nevertheless, the NTB effect of deep trade agreements does not survive when controlling adequately for tariff cuts while the discriminatory changes in tariffs remain to be a significant driver for trade.

The results also hold for a longer time horizon. The second release of the ITPDE extends the number of available years and starts as early as 1989. The advantage of the longer sample is that we can cover more trade agreements entering into force. However, data on services are only available starting in 2000. With this extended data at hand, we redo the baseline analysis including the years 1989 to 2015 for the two broad sectors manufacturing and agriculture. The coefficients, which are shown in Panel (B) of Table 1, report the same pattern as in the baseline and are also robust to alternative definitions of depth (c.f., columns (8) to (14) and Table A1, Panel (B)).

We follow recent methodological developments of the gravity literature and cluster standard errors three-way by importer, exporter, and year (Larch et al. 2019). The three-way clustered standard errors are the most conservative approach. Hence, they work against finding significant NTB effects because the standard errors are relatively large. To make sure that we are not only reporting insignificant effects of non-tariff provisions in deep trade agreements due to conservative clustering, we also cluster standard errors by country-pair, which can be considered as the approach yielding the smallest standard errors and corresponds to the level of the treatment. Despite pushing standard errors down, we still find that the non-tariff provisions mostly matter when non-discriminatory provisions are included, while discriminatory tariffs still have a significant effect. Also clustering by importer-sector, exporter-sector, and year does not alter the results.

18
# **Table A3: Regression results for currently attending school, boys and girls**

||(1)<br>|(2)<br>|(3)<br>|
|---|---|---|---|
|VARIABLES|Currently<br>Attending<br>School|Currently<br>Attending<br>School|Currently<br>Attending<br>School|
|Survey year: 2016|0.067|0.0739**|0.003|
||(0.125)|(0.032)|(0.127)|
|Survey year: 2017|0.005|0.099|-0.041|
||(0.106)|(0.071)|(0.078)|
|Inflation*Post|-0.035|||
||(0.071)|||
|Inflation*distance to school more than 5 hours*Post|0.0664*|||
||(0.035)|||
|Food Inflation*Post||-0.0865**<br>||
|||(0.038)||
|Food inflation* distance to school more than 5<br>||||
|hours*Post||0.186**||
|||(0.085)||
|Non-food Inflation*Post|||0.001|
||||(0.053)|
|Non-food inflation* distance to school more than 5||||
|hours*Post|||0.0447*|
||||(0.024)|
|Distance to school: More than 5 hours (Ref: Less than||||
|<br>30 minutes)|-0.276***|-0.279***|-0.272***|
||(0.020)|(0.018)|(0.020)|
|Gender of school child: Girl|-0.116***|-0.115***|-0.116***|
||(0.019)|(0.019)|(0.019)|
|Female household head|0.031|0.029|0.031|
||(0.037)|(0.037)|(0.037)|
|Education level of household head_Primary|-0.036|-0.035|-0.036|
||(0.103)|(0.103)|(0.104)|
|Education level of household head_Secondary|0.111**|0.108**|0.110**|
||(0.045)|(0.044)|(0.045)|
|Education level of household head_University|0.117*|0.120**|0.116*|
||(0.059)|(0.060)|(0.058)|
|Education level of household_Other|-0.504***|-0.478***|-0.507***|
||(0.046)|(0.043)|(0.049)|
|Constant|0.480***|0.482***|0.481***|
||(0.055)|(0.055)|(0.054)|
|Observations|6435|6435|6435|
|R-squared|0.060|0.062|0.060|
|*** p<0.01, ** p<0.05, * p<0.1.||||
|Notes: Robust standard errors in parentheses.||||

27
12 A second simple defense is that pure measurement error cannot make a positive coefficient negative. So while pure measurement error could account for a small, or statistically insignificant estimate, it cannot account for a negative estimate.

The third defense is the econometric solution for pure measurement error, instrumental variables (IV) estimates. In this case I have one legitimate instrumen because of the repeated measurements on the growth of the educational capital stock. I can use the N-S-D schooling estimates as an instrument in the regression which uses the B-L data and vice versa<sup>21</sup> . Table 2 presents the IV estimates. The coefficient on schooling becomes slightly more negative when instrumented. The IV estimates using the two measures in identical samples are very similar, -. 12 (B-L) and -. 13 (N-S-D), and both are borderline statistically significant. Of course, the IV estimates will only be consistent if the measurement errors are uncorrelated, but even if the correlation were substntial the IV estimates should move substantially towards the true value.= <!-- Start of picture text --> OLS bias = S<br>p.<br><!-- End of picture text --> Whilc the correlation (rho) of two measures is; 21 Instrumental variables always seem a little like magic, but this case, in which two potentially bad measures are used to correct for each other the magic seems particularly egregious. Ile iruuition is that since measurement error creates a bias in OLS because the effect of the real variable and the effect of the measurement error are constrained to be the same (because they canmt be identified). Using another measurement, even one that is itself measured with error, allows idricaon of the variabe because of the information from the differing covariance of the new measure with the components of the original measurement between the 'truthW and the neasurement error.

2 For instance. assume enough pure measurement error in both vanables to drive the coefficient from a presumed "te value of .3 to .10, and assumed that the correlation of the measurement errors (in growth rates, not levels) is .5. Then the IV estimate should be about 40 percent than larger OLS.
anes (2005) presents several studies that evaluate the 1990s experience of privatization in Latin American countries. All in all, they found that privatization brought about substantial gains in productivity but its results on employment and income distribution were not as desirable.

# Case Study: Bolivia and Hydrocarbons

Replete with natural resources, including minerals and hydrocarbons, Bolivia has experienced waves of privatization and nationalization that date back to the 1900s. While under Spanish colonial rule, Bolivian silver mines were widely exploited. In the 20th century, these mines were superseded by tin mines which played a signi...cant role in the country’s economy for almost an entire century. Bolivia’s ...rst oil well was drilled in 1922 and today the hydrocarbon industry dominates the economy. Bolivia’s abundant natural resources have resulted in an economy whose health is subject to world price ‡uctuations in the commodities it produces. These ‡uctuations have been accompanied by political instability and repeated nationalization and privatization cycles.

Bolivia’s ...rst oil well was built by the Standard Oil Company in 1922 and its ...rst oil ...eld began production just two years later. Standard Oil’s operations in Bolivia proved to be quite pro...table. The Chaco War between Bolivia and Paraguay (1932-35) showed the Bolivian government and its military the importance of natural resource ownership for both economic and geopolitical considerations. It became quite clear that Standard Oil was bene...tting greatly from the oil concessions it had obtained.

In 1936 Colonel David Toro founded the state-owned petroleum company, Yacimientos Petrolíferos Fiscales Bolivianos (YPFB), and the next year the government con...scated all of the Standard Oil Company’s holdings. Standard Oil’s expulsion from Bolivia was the ...rstever nationalization in Latin America, and e¤ectively nationalized Bolivia’s entire petroleum industry. The next decade was a dynamic political period in Bolivia. In 1952, the Movimiento Nacionalista Revolucionario (MNR) overthrew a military regime and conducted a revolutionary program that granted universal su¤rage, implemented agrarian and educational reform, and 9
**Figure 1. The share of agriculture in world merchandise trade, %**

<!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br><!-- End of picture text --> Source: UN COMTRADE downloaded through WITS, January 2011.

With such a small share of world trade, it might seem reasonable for the WTO to pass on this particularly fractious sector. Such a conclusion cannot, however, be reached without careful consideration, including the level and variability of protection in each sector. A widely-held view of agricultural trade policy has been that poor countries typically tax their agricultural sectors. Historically, part of the motivation for this policy has been seen as due to the political weakness of an agricultural sector composed of numerous, widely-dispersed small farmers, many of whom are net buyers of food, and hence not supportive of agricultural protection; and part the need for easy-to-collect revenues from sources such as export taxes.

8
2000s, I increase the labor shares for the decades of 1980s and 1990s, and decrease them for the decade of 2010s.

To access the importance of changes in factor shares, I repeat the first counterfactual exercise in presence of declining decennial labor shares. Figure 12 plots the implied productivity growth from the repeated exercise against the benchmark case in which labor shares were time invariant. Panel 12a corresponds to the case in which labor shares in all sectors decline by 2% every decade while panel 12b corresponds to a higher decennial decline rate of 8%.

As expected, the larger deviations from earlier estimates are realized when I use a higher decennial decline rate of 8%. However, the difference in productivity gains relative to the benchmark case of time invariant labor shares is small even when a higher rate of decline is used. Median productivity gain is around 3.8 percentage points higher for the benchmark case for the longest period under consideration and is around 1.4 percentage points higher for all country-periods. Estimated productivity gains from the two approaches differ by more than 5 percentage points in less than 3% of the 263 country-periods and never surpass 8 percentage points. When using the lower decennial decline of 2%, the difference in productivity change never exceeds 2 percentagepoints barring a single country-period.

Columns (5) – (7) of table 5 show the percentage of growth accounted for by changes in distortions for country-periods in which growth was more than 10% when labor shares decline by 8% per decade. Compared to the benchmark where labor shares were time invariant, the ability of distortions in explaining growth reduces with proportionate decline in labor shares. In fact, for all periods the direction of implied growth is opposite to the direction of actual growth. Contribution of changing distortions at the 90<sup>th</sup> percentile also diminished and ranges over 3 – 17% of actual growth compared to 7 – 22% in the benchmark.

# **5.2 Disproportionate Changes in Factor Shares across Sectors**

As discussed earlier, the distortions are unchanged if the changes in factor shares are proportionate across sectors. As such, the differences in productivity estimates from the benchmark case are to an extent insulated against such changes in factor shares. In this exercise, I carry out the first counterfactual exercise with disproportionate factor shares changes across sectors that lead to a divergence in estimated distortions from the benchmark case.

Once again I need reasonable estimates of sector-specific changes in labor shares and in this case also I use the findings in Karabarbounis & Neiman (2014). Using the EU KLEMS data starting from 1975 to 2007, Karabarbounis & Neiman (2014) estimate the trends in labor share for 10 non20
the current Lome convention (beyond 2002) may also be important. This way producers will be encouraged to at least begin thinking about how to adjust to lower prices.

# _Consideration should be given to supporting research_

Under all variants research into banana production and qualiy should be undertaken, possibly on a region-wide basis. This will support banana production in those countries where it is economic. As other agricultural activities develop, research and extension should be undertaken to support the new activities. Initial studies could be carried out at an early stage to see which agricultural or other activities are likely to develop. However, it is difficul **t** o forecast which activities will develop under different real exchange rates.

Other infrastructural support should also be underaken as new activities develop, both on and off the farm, including transport and marketing.

# Policy considerations for Latin American exporters

Decoupling aid from banana prices will improve the trading environment of efficient Latin American exporters. Therefore these countries also face incentive to try and persuade the European Union to pay aid direcdy or allow transferability of quotas. Latin American countries gain most by seeing distortions to the world market and reduced access to the EU market elminated. It appears that a major policy objective for these countries should be to coordine and reinforce their efforts to fuel the debate on reform of EU policies. Further development of arguments alenging EU policy are important for keeping up pressure for change. This would require furhier analysis, measurement and debate to help inform many other groups indirectly hurt by EU policy.

The recent EU attempts to persuade some Latin American countries to drop their GA,T action against the Union **in** exchange for larger preferentil access suggest another policy priority for this group of countries. They need to closely research the long-term problems of foregoing competitive trading conditions for the promise of short-term yet small concessions. The fact that the European Union is wiling to compromise, in response to the GAIT panel finding, reflects how sensitive it is to GATE actions and the publicity it creates. This helps emphasize the need to keep up pressures on the European Union tbrough GATT as a means of stimllating debate. And the inefficiencies in production and marketing in ACP countries and other protected agricultral producers around the world helps to emphasize the senous long-term problems which can arise by responding to distorted price signals and having to allocate preferential access using quotas.

29