rafmacalaba/datause-encoder-data huggingface dataset:
Table 1: Summary Statistics: US Antidumping and Safeguard Tariff Imposition

||**Full sa**|**mple**|**Top 10**<br>**partne**|**trading**<br>**rs only**|**China**|**only**|
|---|---|---|---|---|---|---|
||Mean|St. dev.|Mean|St. dev.|Mean|St. dev.|
|**Dependent Variables**|||||||
|Antidumping (AD) tariff imposed|0.0017|0.0418|0.0046|0.0675|--|--|
|AD or safeguard tariff imposed|0.0032|0.0562|0.0060|0.0770|0.0323|0.1768|
|ln(1+AD tariff)|--|--|0.0030|0.0547|--|--|
|AD tariff conditional on a positive value|89.7|94.4|116.7|104.0|161.5|99.4|
|**Explanatory Variables**|||||||
|Growth of imports_ikt-1†|0.102|0.947|0.084|0.567|--|--|
|Change in US market share_ikt-1^|0.000|0.004|0.001|0.006|0.005|0.012|
|�<br>�<br>�<br>�_k<br>�<br>�<br>1/<br>ln<br>f<br>m<br>f<br>x <sup>�</sup><br>‡|-1.991|1.517|-1.995|1.526|-1.982|1.523|
|�<br>�_k<br>�<br>�<br>/<br>f<br>m<br>f<br>x <sup>�</sup><br>1<br>^|0.241|0.170|--|--|--|--|
|Standard deviation of import growth_ik^|0.723|0.660|0.378|0.435|0.393|0.425|
|Percent change in real exchange rate_it-1‡|0.007|0.116|0.001|0.087|0.017|0.015|
|**_Domestic industry variables_**|||||||
|ln(Four firm concentration ratio)_k*‡|3.468|0.608|--|--|--|--|
|ln(Employment)_kt-1*‡|10.377|1.029|--|--|--|--|
|Value-added/Shipments_kt-1*‡|0.513|0.118|--|--|--|--|
|Inventories/Shipments_kt-1*‡|0.129|0.063|--|--|--|--|
|Indicator for industry k is steel*|0.013|0.113|--|--|--|--|
|Indicator for industry k is chemicals*|0.021|0.144|--|--|--|--|
|**Observations**|82,341||20,775||2,075||

*These variables are based on only 81,943 observations. † Rescaled by a factor of 10-4 for estimation. ^ Rescaled by a factor of 10-2 for estimation. ‡ Rescaled by a factor of 10-3 for estimation.

21
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- 41 -

## A.2 Constraint o Implementin; Efficiency Prices

5. There are few technical constraints to implementing efficiency prices in the sector. Production is always measured and charged by the ton. Quality considerations may sometimes present a problem, in that differential prices must be set to reflect domand **as well as** supply variations. For example, it will be inappropriate to base quality differentials only on calorific value if the demand for the higher grades of coal is much larger than that for lower grades. To clear the market for both grades, differentials may need to be much larger than would be implied by relative calorific values.

6. In some countries the monopolistic position of the railroads presents problems for officiency coal pricing. Because rail transport of coal is generally so much cheaper than any alternative transport mode, railroads sometimes charge **fees** to coal producers that far **exceed** their incremental costs. This may drive up **the** coal price to consumers and stifle demand even where minesouth prices **are** appropriate. Where this occurs, it is generally better for the goverment to intervene directly with the railroads rather than to try to fix consumer coal prices.

## A.3 Adjustments to EfficiencX Prices

7. Despite the theoretical ease of calculating and implementing efficiency prices in the coal sector, actual prices tend to vary widely. In industrial countries and a few developing ones such as India, the major reason for divergence is concern about the social and employmnt implications of closing uneconomic pits. This has led to a system of financially determined prices for producers (i.e., cost-plus formula) which often amount to cross-subsidy within the sector from the lower cost to the higher cost mines. Coal prices to domestic consumers are sometimes subsidized, but they generally represent only a small part of total consumption. Few countries have tried to exploit the potential fiscal contribution that an efficient and internationally competitive coil sector could-make.

# B. THE PRACTICE

# B.l. Project S-mnle

8. Our **sample** in the coal sector consisted of all five projects (in 4 countries: Morocco, China, India and Colombia) approved by **the** Bank during FY84 and FY85. One project **was** for coking coal production, one **was** for coal exploration, and the others were for steam coal production. Coverage of pricing issues was extensive, except in the exploration project for which only a President's Report was available.
addition, a developing country may provide further amber box support on a non-product specific basis and have it be defined as de minimis provided the non-product specific support is not above ten percent of the gross value of aggregate agricultural production. Countries can self-classify their support between product and non-product specific support, subject only to dispute settlement, which is rarely used in this area.<sup>14</sup> As part of their accession commitments, however, the countries of the former Soviet Union that have acceded to the WTO have had to agree to accept developed country de minimus limits (sometimes with an adjustment period). That is, Estonia, Lativa, Lithuania, Georgia, Armenia, Moldova and the Kyrgyz Republic have all accepted five percent limits on product specific agricultural subsidies plus and 5 percent limits on non-product specific support. Without specific knowledge of the Russian negotiations, it is likely that Russia is being pressured to accept the same smaller de minimus limits on Amber Box subsidies. If Russia were to agree to this, it would mean that independent of Russia’s declaration as a developed or developing country postaccession, the de minimis level of agricultural subsidies would be developed country de minimis levels.

# _The Aggregate Measure of Support (AMS) and the Russian support level_

Incumbent members of the WTO, like the European Union, Canada, the United States and Norway, have a base period for trade-distorting agricultural subsidies that allows more substantial trade-distorting subsidies than the de minimis levels. The precedent among acceding countries, however, is that the three year period prior to accession forms the base period for permitted tradedistorting subsidies, and trade-distorting subsidies are negotiated down from that base. Russia failed in the bilateral discussions to achieve its objective of defining 1992-1994 as the base period for trade-distorting agricultural subsidies. Russia now hopes that it will be able to negotiate about $9 billion in trade distorting subsidies.

The total value of state support to Russian agriculture in 2008 was about 163 billion rubles (or about 6.5 billion US dollars).<sup>15</sup> The Ministry of Finance data includes all support to agriculture, including many items that would be considered Green Box support, i.e., unconstrained support. The total value of Russian agricultural output in 2008 was 1776 billion rubles (including agriculture, hunting and, fishing).<sup>16</sup> Subsidies of 163 billion rubles are about nine percent of the value of agricultural output. The $9 billion in Amber Box subsidies sought by Russia is about 12-13 percent of the aggregate value of Russian agricultural output.

14 The de minimis levels of support for developed countries are one-half the allowed levels for developing countries. Post-accession, countries self declare whether they are developed or developing.

> 15 <u>http://www1.minfin.ru/ru/budget/federal_budget/08-10/.</u>

16 See the Rosstat website for this information at: <u>http://www.gks.ru/bgd/free/b00_25/IssWWW.exe/Stg/dvvp/i000610r.htm</u> 10
During the 19th century, the largest part of the border between Cˆote d’Ivoire and Ghana was under the domination of the Ashanti Empire, whose capital city, Kumasi, was located in central present-day Ghana. In localities that lie no further than 75 km from this border and in the years 1986–8, surveys indicate that more than 50% of household heads belonged to the Akan ethno-linguistic grouping, which includes the Ashanti people: 56% on the Cˆote d’Ivoire side and 59% on the Ghana side. At the end of the 19th century, the French and British began to extend their domination from trade posts located on the coast toward the North by signing protectorate treaties with local kingdoms. Negotiations between the two colonial powers finally resulted in partitions of pre-colonial political entities in the middle part of the border (Gyaman, Indenie, Sefwi). In its southern part (Sanwi), a rebellion unsuccessfully challenged the border alignment after independence. The layout of the last demarcation on the field, with teak trees, beacons, and pillars, was achieved in 1988.

The two other borders of Cˆote d’Ivoire that we examine are less clearly demarcated on the field (Brownlie 1979). In surveys, the great majority of household heads belong to the Mande-Voltaic ethno-linguistic grouping. In particular, the boundary between Cˆote d’Ivoire and Mali lies across the Senoufo (Gur/Voltaic) area in its eastern part and the Malinke (Mande) area in its western part. The hazards of French conquest and of the wars against the Almami Samori Toure at the end of the 19th century reflected the boundaries’ alignment within the French Empire and resulted in partitions of some former political entities (Kenedougou and Kong kingdoms). These borders were only stabilized after World War I.

We also use French data for the colonial period (Huillery 2009) and explore differences in initial conditions between the border areas lying inside the French 13
33 Table 9. Amnual Population increaso

*nd Growth Rate by Geographic Region **and** Income Group, 1985-2100

||Amual p4|pmlatio|n irerea|se (mi|tlions)|Armua|t growt|h rate|(percen|t)|
|---|---|---|---|---|---|---|---|---|---|---|
|Region|1985-90|2000|2025|2050|2100|1985-90|2000|2025|2050|2100|
|World|87.6|90.6|74.4|40.5|6.9|1.73|1.46|0.90|0.42|0.07|
|Less developed (a)|80.8|86.4|72.7|40.7|6.9|2.09|1.75|1.05|0.49|0.08|
|More devetoped(a)|6.8|4.6|1.2|-0.5|0.1|0.57|0.36|0.09|-0.04|0.01|
|Africa|18.5|23.9|25.0|18.4|2.3|3.06|2.74|1.66|0.90|0.10|
|East Africa|7.9|10.3|11.2|8.2|1.0|3.15|2.83|1.74|0.93|0.10|
|West Africa|7.1|9.6|10.4|7.9|1.0|3.22|2.93|1.78|0.97|0.10|
|North Africa|3.6|4.1|3.5|2.3|0.4|2.66|2.24|1.27|0.64|0.09|
|America|10.8|10.0|6.8|2.6|0.7|1.55|1.21|0.66|0.23|0.06|
|Latin America and Caribbe|an<br>8.6|8.7|6.6|2.8|0.7|2.04|1.64|0.91|0.34|0.08|
|Northern America|2.2|1.3|0.4|-0.2|0.0|0.80|0.45|0.11|-0.07|0.00|
|Asia|54.4|53.8|40.4|19.4|3.6|1.84|1.47|0.84|0.35|0.06|
|EastandSoutheast Asia|26.0|26.4|17.0|5.0|1.4|1.52|1.29|0.67|0.18|0.05|
|South Asia|24.9|25.1|21.4|11.9|2.4|2.22|1.75|1.06|0.49|0.09|
|Southwest Asia|3.4|3.8|3.3|2.0|0.3|2.73|2.26|1.26|0.62|0.09|
|Europe and U.S.S.R.|3.7|2.7|1.0|0.0|0.0|0.48|0.33|0.11|0.00|0.00|
|Oceania|0.4|0.3|0.2|0.1|0.0|1.41|0.98|0.51|0.17|0.04|
|Isnome group(b)|||||||||||
|Low|51.2|53.6|45.7|25.6|4.7|1.99|1.65|i.02|0.48|0.08|
|Lower-middle|17.2|19.3|16.9|10.0|1.8|2.38|2.03|1.20|0.57|0.09|
|Upper-middLe|11.4|11.8|8.9|4.4|1.0|1.84|1.54|0.86|0.37|0.08|
|High (oil<br>exporters)|'0.7|0.9|0.7|0.5|0.1|3.27|2.94|1.35|0.76|0.09|
|Industrial<br>market|3.6|2.0|-0.4|-1.4|0.0|0.48|0.26|-0.05|-0.18|0.00|
|Nonreporting normember|3.5|3.1|- 2.2|1.2|0.2|0.93|0.74|0.46|0.24|0.05|

(a) "More developedP comprises Europe, U.S.S.R., Northern America (United States and Canada), Austratia, New Zealand, and Japan. "Less developed" coprises the rest of the world.

(b) Criteria for income groups are given in World Bank 1988. See listing in Table 15 of this paper.
self-reporting by all household members, the household’s primary respondent will then report on behalf of other members as well, “proxying” their responses.<sup>3</sup> Recently, the global penetration of cell phone access has introduced phone surveys as a method of data collection that could allow for more frequent interview intervals along with shorter recall periods at a lower cost. While weekly interviews have been shown to be less vulnerable to recall loss than longer windows (Arthi et al. 2018), weekly interviews across an entire year or season would be prohibitively expensive for most research budgets and highly fatiguing for respondents. In this paper we use a survey experiment to test an intermediary option, comparing quarterly (90day) with an annual recall question using a series of phone surveys.

Labor outcomes measured using the long recall responses result in much lower recorded levels of any work involvement (22%), number of unique activities reported (24%), and number of months worked (20%) compared to those based on the quarterly measures, although we do not find significant losses in total hours worked. These gaps increase with the amount of time that has passed. Proportional losses from long recall (relative to short recall averages) are, depending on the outcome variable, between two and ten times larger for proxied individuals than for households’ primary respondents who self-report. Splitting the sample by self and proxy reported individuals, we explore heterogeneity by gender and age of the respondent as well as of the proxied household member. We do not see clear patterns of heterogeneity for self-reports or by the primary respondent’s characteristics when reporting for others. We do, however, see that younger household members have greater relative losses from long recall when their responses are proxied than older proxied household members. The profile of primary respondents (generally household heads) further biases annual recall data against women and younger household members, as they will be exposed to larger losses from long recall as a consequence of having their labor contributions more frequently reported by proxy.

This paper contributes to a growing body of work on the effects of recall periods in the survey methodology literature. A considerable body of evidence suggests that longer recall periods tend to undercount household consumption, provision of agricultural inputs, and negative health events

> 3 The Living Standards Measurement Study (LSMS) surveys follow a similar structure and have a similarly stated preference for self-reporting whenever possible. In a review of six LSMS surveys, Desiere and Costa (2019) find that the use of proxy reporting is still widespread, ranging between 24% in Nigeria up to 85% in Mali.
# Table **II.** Exporting Firms (Cont.)

||Supply|Side**Only**|Joint**Supp**|**ly**and Demand|
|---|---|---|---|---|
|**Paranieter**|CRTS|Non-CRTS|CRTS|Non-CRTS|
|_bet_|1. 66*<br>(0.47)|2.13*<br>(0.82)|-12.86*<br>(1.30)|-7.73*<br>(1.06)|
|_b,_||-0.86||-2.33E+2|
|||(20.9)||(2.OOE+2)|
|||5.65<br>(4.39)||1.57E+2*<br>(80.29)|
|1p,||<br>2.62E-4||-9.80E-7|
|||(3.27E-4)||(3.81E-7)|
|78||-0.09<br>(0.09)||0.0014<br>(0.00036)|
|_bd._|||-0.0012*<br>(0.0003)|-4.5E-4*<br>(0.0002)|
|_bdt_|||0.00185<br>(0.0015)|0.0030*<br>(0.0008)|
|_bd_|||-6.83E+4|-5.12E+4'|
||||(4.36E+4)|(1.98E+4)|
|_ad_|||-5.50E+1<br>(1.38E+2)|-3.02E+2*<br>(80.29)|
|_Pd_|||-1.31E-6|-9.80E-7*|
||||(8.42E-7)|(3.81E-7)|
|I_d_|||0.00014<br>(0.00062)|0.0014*<br>(0.00036)|
|_bmt_|||-0.22*<br>(0.04)|-0.80*<br>(0.046)|
|_bm_|||7.58E+t,*|3.23E+6*|
||||<br>(2.71E+6)|(1.49E+6)|
|am|||1.68E+4*<br>(4.07E+3)|6.61E+3<br>(5.04E+3)|
|,Om|||1.46E-4*<br>(5.23E-5)|6.17E-5*<br>(2.86E-5)|
|Tm|||-0.063*<br>(0.019)|-0.027<br>(0.022)|
|Objective*N<br>N|530.00<br>134|510.00<br>134|276.42<br>134|202.60<br>134|

Standard errors are in parentheses. * Significant at the 5 percent level. ** Significant at the 10 percent level.

-18-
their perceptions are not consistent with at least short-term evidence, or that the intensity of previous shocks is not sufficient to modify their risk perceptions. This is consistent among labor groups.

**Table 3: Incidence of Shocks and Perception of Risks Among Occupational Categories**

||**Past 12-mo**|**th shocks**|<br>**2008**|||**Past 12-mo**|**th shocks**|**2009**|||
|---|---|---|---|---|---|---|---|---|---|---|
||<br>**(% of househ**|<br>**olds with at**||**Risk perception**||<br>**(% of househ**|<br>**olds with at**||**Risk percepti**|**on**|
||<br>**least one**|<br>**shock)**|**(1 = Not p**|**robable. 4 = Ver**|**y probable)**|<br>**least one**|<br>**shock)**|**(1 = Not p**|**robable. 4 = V**|**ery probable)**|
||||**Loss**|**Loss**||||**Loss**|**Loss**||
|**Ocupation**|**Economic**|**Health**|<br>**employment**|<br>**goods/money **|<sup>**Risk of death**</sup>|**Economic**|**Health**|<br>**employment**|<br>**goods/mone**|**y **<sup>**Risk of death**</sup>|
|**Formal Salaried**|10.9|14.2|1.6|1.6|1.5|17.1|12.1|1.8|1.8|1.6|
|**Formal Self-employed**|11.7|13.1|1.5|1.5|1.4|20.2|11.1|1.8|1.6|1.5|
|**Informal Salaried**|16.8|15.8|1.7|1.7|1.5|22.6|13.3|1.8|1.9|1.6|
|**Informal Self-employed**|14.0|15.7|1.6|1.6|1.6|17.8|12.6|1.8|1.7|1.6|
|**Unemployed**|28.7|17.1|1.6|1.8|1.6|35.9|13.6|1.8|1.9|1.6|
|**Out of labor force**|13.3|17.8|1.7|1.7|1.5|17.2|16.4|1.8|1.7|1.6|
|**TOTAL**|**14.3**|**16.5**|**1.6**|**1.7**|**1.5**|**19.1**|**14.5**|**1.8**|**1.8**|**1.6**|

Source: Authors’ estimates from Fedesarrollo (2008, 2009) FSLS.

In addition to questions about their valuation and beneficiary status, individuals are also asked to report their knowledge of public social programs. Valuation is high on average—close to four out of five possible points—a finding common across all social programs, with little difference across occupational status (Table 4). Nor are large differences found between social security (health care and pension, whether contributive, non-contributive or private) and other programs of social protection and assistance. These results are consistent across individuals reporting to know either a lot or little about the programs they are asked to value and whether or not they are beneficiaries of such programs (results not shown).

11
balance wage adjustments with economic realities, even amid the uncertainties brought about by the pandemic. By mitigating the rise in labor costs for already financially vulnerable firms, the decision aimed to pave the way for a more rapid economic recovery.

**In summary, Romania's labor market demonstrated some resilience during the pandemic-triggered crisis, with unemployment rates recovering in 2021 after a moderate increase in 2020.** The wage landscape exhibited positive movement, underscored by a gradual rise in the mean gross wage and measured adjustments in the minimum wage to align with prevailing economic conditions. These factors ultimately contributed to the maintenance of stability and progress within the labor market.

**How high is the statutory minimum wage relative to the formal wage distribution? Analyzing the wage distribution in January 2020 suggests that the minimum wage's value aligns with the 25th percentile of the entire wage distribution** . Table 3 illustrates Romania's gross monthly wage

**Table 3. Distribution of gross monthly salaries in Romania, January 2020.**

|**Statistics**|**Value (RON)**|
|---|---|
|Mean|4,385|
|P1|129|
|P5|515|
|P10|1,054|
|P25|2,231|
|P50 (Median)|3,096|
|P75|5,202|
|P90|8,310|
|P95|10,928|
|P99|22,034|

_Source: Own estimates based on administrative tax data._ distribution as of January 2020. Our analysis encompasses solely those observations that possess complete information concerning gross salary and working hours, constituting approximately 89% of the total observations. Notably, the value of the minimum wage for full-time workers (2,231 RON) is approximately aligned with the 25th percentile of the overall distribution. This means a large proportion of the population earns an income below the minimum wage designated for full-time workers. However, a considerable fraction of these individuals may be employed part-time, which could account for the divergence from the full-time minimum wage threshold. Another explanation is that, in specific sectors (e.g., manufacturing), receiving the minimum wage is conditioned by a specific level of productivity (a certain quantity produced per day).

**Further analysis reveals spikes in the minimum wage in the wage distribution, which could indicate a binding minimum wage** . To evaluate how the statutory minimum wage more effectively influences the wage distribution, we provide the histogram depicted in Figure 3. For clarity, we limit the distribution's representation to the 99th percentile. Evident spikes in the distribution occur precisely at the points corresponding to the minimum wage values.

**However, the spikes can also be driven by wage underreporting** . Gavoille and Zasova (2023) found evidence that tax evaders are overrepresented among minimum wage earners in Latvia. Robayo-Abril, Balaban

25
point the process reverses itself and specialization occurs. It is worth noting that these estimates are within countries; the estimations rely on panel data covering 11 years of data for each country and include country-specific effects. This happens in the export basket at a higher transition point than that found by Imbs and Wacziarg with domestic production data ($22,500 in 2000 US dollars, PPP adjusted, compared to $14,600 in 1996 US dollars), but these results support the view that the pattern of economic diversification is in fact related to patterns of international trade.

# _<Insert Table 1 here>_

This robust pattern in both domestic production and export data suggests a particular relationship between discovery and levels of development, given the relationship between diversification and new products.<sup>4</sup> We expect countries at relatively low levels of development to have more export discoveries, as they are in the process of diversifying their economies. As income rises, the frequency of these events declines, particularly at high levels of development when economies experience rising specialization.

To examine these two effects in our data, we estimate the relationships between both export discoveries and patent counts from Lederman and Saenz (2005), on the one hand, and the level of development on the other hand. Table 2 shows the results from Negative Binomial estimators, which are appropriate for count data afflicted by over dispersion.<sup>5</sup> As expected, the frequency of discovery falls as countries develop, after peaking at the lower-middle income level. Although low among the world’s poorest

> 4 The connections between Imbs and Wacziarg (2003) and the process of discovery were first suggested by Hausmann and Rodrik (2003b).

> 5 The null of no over-dispersion was rejected with a probability value of 0 for both models. The same is true for all models discussed in the rest of this paper.

8