rafmacalaba/datause-encoder-data huggingface dataset:
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the majority of investigations related to steel and lumber products even products of everyday use have not escaped such investigations.6 The table below shows that 1789 AD investigations were launched between 1980-89 in the U.S., EU, Australia and Canada.<sup>7</sup> The number is much larger if investigations under various modalities of safeguard actions are taken into account.

6 Target Industries of U.S. AD and CVD Investigations, 1980-89

|Industry|AD|CVD|
|---|---|---|
|Chemicals|58|37|
|Food|16|45|
|Iron and Steel|201|149|
|Leather|...|6|
|Machinery|8|6|
|Nonferrous metals|16|_5_|
|Oil country tubular goods|12|8|
|Textiles and apparel|15|6|
|Lumber|...|4|
|Other|125|34|
|All products|451|300|

7 See Messerlin, Patrick A., "Antidumping" in Schott, Jeffrey J. (Editor), Completing the Uruguay Round: A Results-Oriented Approach to the GATT Trade Negotiations at 110-11 (Washington, D.C. 1990).
Table C.19: National Estimates of Effect of Years of Schooling on Labor Market Outcomes and Fertility Preference - Combined Instrument

||||Sector of W|ork||
|---|---|---|---|---|---|
||Working|Skilled /<br>Professional|Service<br>/ Sales|Agriculture /<br>Unskilled Manual|Ideal Number<br>of Children|
||(1)|(2)|(3)|(4)|(5)|
|�<br>Years of Schoolingizy|0.017|0.044|0.016|-0.039|-0.902|
||(0.043)|(0.015)|(0.028)|(0.033)|(0.493)|
||[0.692]|[0.004]|[0.573]|[0.241]|[0.068]|
|First Stage F-Statistic|19.68|20.64|20.64|20.64|19.37|
|N|24,882|24,607|24,607|24,607|24,649|

_Source_ : Author’s analysis based on data from the Ethiopian Demographic and Health Survey (in years 2005, 2011, and 2016). _Note_ : The dependent variable is described at the top of each of the five columns. In columns 1–4 it is an indicator that equals 1 if true, and in column 5 it is the ideal number of children. Skilled/Professional jobs include professional, clerical, and skilled manual job groups; the other categories exactly describe the occupation groups included. Ideal number of children is censored at 20; no women in the Demographic and Health Survey report having more than 18 children, and non-numerical responses � are assigned the maximum value. Years of Schooling _izy_ is the predicted level of schooling, instrumented with the combined intensity measure, ∆ _Izy_ . All regressions include birth year and zone fixed effects, zone-specific linear trends, and a cubic for age. Each estimate is from a unique regression, and the second-stage estimate in column 5 is generated using a tobit model. Standard errors are clustered at the zone level and shown in parentheses; _p_ -values are shown in square brackets.

A.38
the relative importance of each type of measure, whereas previous papers have mostly studied some of these types of measures in isolation. While port usage costs are measured in dollar terms and are readily available from the World Bank’s Doing Business database, the nature of other policy measures is less straightforward. This section therefore provides a brief background on such measures and how they interact.

The General Agreement on Trade in Services (GATS) defines trade as taking place through different modes of supply, two of which are most relevant to maritime trade. Cross-border trade (or mode 1) takes place when a maritime transport company from country A provides a service to a consumer resident in country B. Mode 1 is the key mode of supply for shipping services and has received greatest attention in previous research. The other relevant mode is the supply of a service through the establishment of commercial presence (or mode 3). A full commitment in mode 3 means that a country allows foreign firms to invest and establish local subsidiaries, branches or representative offices and imposes no restriction on their operation.<sup>3</sup> _Government barriers in mode 1: Cargo reservations_ The main restrictions on cross-border trade (mode 1) take the form of cargo reservations or cargo preferences. These restrictions specify that some types of cargo can only be transported by some types of vessels, in general by vessels flying the country’s flag or by vessels operated by national or domestic shipping lines. Over the past decades, most cargo reservations have disappeared (Fink _et al._ 2002) so that nowadays cargo reservations are likely to affect only a small part of world seaborne trade. For instance, in the US the volume of cargo transported under preference schemes represented around 1.5 percent of the total seaborne trade in 200507 (Bertho, 2011). In Brazil, 0.18 percent of total seaborne import tonnage was reserved for Brazilian flagged-vessels in 2009.

# _Private anti-competitive practices in mode 1_

> 3 Since today most international cargo can be transported irrespective of the vessel’s flag, and since “deflagging” has spread, the establishment of a registered company for the purpose of operating a fleet under the national flag is less and less relevant (UNCTAD, 2011).  This paper instead focuses on “the ability of international maritime transport service suppliers to undertake all activities which are necessary for the supply of a partially or fully integrated transport service, within which maritime transport constitutes a substantial element” (Draft Schedule on Maritime Transport Services mode 3b).

5
|||**Ta**<br>|**ble 6. Projec**<br>|**t Score**<br>|||||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
||Project ov|erall score|Quality|score|Quantity|score|Project no|t located|
|Training|0.135*|0.127|0.094|0.097|0.177**|0.164*|-0.003|-0.008|
||[0.071]|[0.085]|[0.074]|[0.084]|[0.074]|[0.089]|[0.012]|[0.011]|
|Training*non-livestock||-0.031||0.015||-0.053||-0.017|
|||[0.156]||[0.188]||[0.167]||[0.038]|
|Control means|-0.034|-0.034|0.012|0.012|-0.066|-0.066|0.027|0.027|
|N|859|859|867|867|863|863|895|895|
|R-squared|0.384|0.384|0.389|0.389|0.359|0.359|0.280|0.280|

Notes: This table reports the OLS regression results for the treatment effect on project-level outcomes. Odd columns are the total treatment effect, while even columns include an interaction with whether the project was livestock. The dependent variable in columns 1 and 2 is an aggregate index of columns 3 to 6. Standard errors are reported in brackets below the coefficients. Regressions include region controls. All analysis is clustered at the project level. *** p< 0.01, ** p< 0.05, *p< 0.1.

42
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biosphere reserves in Costa Rica and Bolivia. The Nature Conservancy is involved in an effort to dtvelop a conservation unit for the Ache of Paraguay and is also working on a system of Conservation Data Centres (CDC's) in Latin America.

Collectively, this effort complements the more activist campaigns against rainforest destruction that are being run by other conservation organisations. Both are essential, the one defines the problems and allocates responsibilities; the other proposes solutions and alternativet.

# 2.5.2. Indigenous Support Organisations

Three of the major international support organisations: Cultural - - Survival (CS Boston) Survival International (SI London) and the International Work Group for indigenous Affairs (IWGIA - Copenhagen) have for some time been documenting situations involving indigenous peoples and development; much less with conservationists. The usually negative outcome of past encounters has induced a circumspect attitude towards the institutions for conservation, even when these are parks dedicated to the protection of indigenous groups.

There are signs that this may be changing in response to the changes in conservation policies towards indigenous peoples and economic development. In this respect, CS has assumed a consultative role in the WWF Wildlands and Human Needs Program, as well as independently supporting indigenous projects in sustainable development and conservation.

# 2.5.3. Conservation NGOs in Developing Countries

These are playing an increasingly active role in developing country conservation, more so in Latin America than in other regions (Glick 1988). They have fast reaction times, low overhead and fewer political constraints than government conservation agencies. NGOs are often well placed to communicate directly with the clusters of organisations usually contributing to conservation projects: national indigenous organisations, local communities, government agencies, international support organisations.

Developing country conservation NGOs are most active in Latin America and the Caribbean. In Africa, such organisations tend to be dominated by expatriates and incline more towards exclusive notions of conservation area management. Baines (1984b) has suggested that NGO development in the South Pacific has been inhibited by a general reliance upon traditional political structures.

For various political and administrative reasons, NGOs are better placed than government agencies to handle funds that are raised abroad for domestic conservation projects. In addition, some NGOs have received core funding through debt-swap arrangements which enable them to provide support for independent projects.

Some organizations, such as the Caribbean Conservation Association, active in WWF sponsored projects in Dominica and St Lucia,
13 Table 4 shows the amount of legally entitled severance pay and actual payments by a set of employee and job characteristics using data from the Sakernas (2008) survey. Here, the amount actually received by women as a proportion of their legal entitlement is higher than that for men: 0.3559 and 0.2697, respectively. Generally, both legally entitled severance pay amounts and actual severance pay received increase across the age distribution up to 35-49 years: from 0.0681 at ages 15-18 years to 0.3985 at ages 35-49 years. Contrary to _a priori_ expectations, the proportions for the oldest two age groups drastically decline. These proportions compare to the all-age average of 0.2961. Except for those employees with less than primary education, the legally entitled amount increases with education. The ratio of the amount actually received, however, only increases at the highest education levels (that is, senior secondary and tertiary).

Generally, there is wide variation in both legally entitled and actual severance pay received across industries. The industry patterns differ greatly across the two surveys. The relationship between both legally entitled and actual severance pay is positive and systematic for tenure. Legally entitled severance pay amounts increase from 1.135 million IDRs for those with less than one year of tenure to 22.942 million IDRs for employees with 10+ years. Generally, the ratio of actual to legally entitled severance pay also increases with tenure, rising from 0.1897 for less than one year to 0.3055 for employees with 10+ years of tenure.

For firms of different sizes, there is a positive and systematic relationship between both legal entitlement to and actual severance pay received. In firms with fewer than 20 employees, the ratio of actual severance pay received to the legally entitled amount is below the overall average of 0.2916; on the other hand, in the largest two firm-size categories, both ratios exceed this average.

Wage-and-salary workers employed by government agencies are the least likely to receive the severance payment to which they are legally entitled, only about 20 percent of the overall average. Employees in domestic private firms are the most likely to receive the full amount to which they are legally entitled: 0.3816. Employees in state-owned enterprises have approximately the average likelihood of recipiency while the likelihood in the remaining three firm-status categories is below-average. Across the wage
Policy Research Working Paper 7667

# **Abstract**

This paper documents new facts about the joint evolution of firm performance and prices in international markets and proposes a theory of firm dynamics emphasizing the interaction between learning about demand and quality choice to explain the observed patterns. Using data from the Portuguese manufacturing sector, the paper documents that: (1) within narrow product categories, firms with longer spells of activity in export destinations tend to ship larger quantities at similar prices, thus obtaining larger export revenue; (2) older exporters tend to import more expensive inputs; and (3) revenue growth within destinations (conditional on initial size) tends to decline with market experience. The authors develop a model of endogenous input and output quality choices in a learning environment that is able to quantitatively account for these patterns. Counterfactual simulations reveal that minimum quality standards on exports reduce welfare by lowering entry in export markets and reallocating resources from old and large toward young and small firms.

This paper is a product of the Trade and International Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at pbastos@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **_5.1 Micro and Macro Drivers of Bank Spreads in Brazil_**

Our results suggest that Institutional and Regulatory (micro) factors are the main drivers of the NIM spreads in Brazil from 1995-2009 (Figure 3a) and that a high correlation exists between Institutional and Regulatory (I&R) factors and the NIM (Figure 3b). This is contrary to what Afanasieff, Lhacer and Nakane (2002) and de la Torre, et al. (2006) find. Both studies highlight _macroeconomic_ variables as the most relevant factors in explaining interest spreads in Brazil. In particular, de la Torre, et al. find that macro-factors play an important role in the determination of spreads and a systematic relationship between spreads and the SELIC rate in the period ranging from 1994 to 2005. Our result therefore suggests a new perspective for interpreting bank spreads in Brazil—indicating a move from macro to micro drivers.

**Figure 3a. I&R factors a large part of NIM                  Figure 3b. Correlation between NIM and I&R**

<!-- Start of picture text --> 18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1995 1997 1999 2001 2003 2005 2007 2009<br>Brazil NIM<br>Percentage points<br><!-- End of picture text --> <!-- Start of picture text --> 18 I&R<br>16<br>R2 = 0.4165<br>14<br>12<br>10<br>8<br>6<br>6 11 16 21<br>I&R Linear (I&R) NIM<br><!-- End of picture text --> _Source_ : Authors’ calculations based on BankScope data _Source_ : Authors’ calculations based on BankScope data Contrary to previous studies, we find that I&R factors are the most significant factors in determining NIM spreads, when examining the period 1995-2009. On the other hand, our analysis complements the study by de la Torre, et al. (2006) which, too, suggests that micro-factors play a significant role. We differ in the magnitude of I&R factors but we agree with de la Torre, et al. (2006) in the sense that there are high administrative costs in the Brazilian banking system. The high administrative costs reflect the lack of competition in Brazil, resulting in high I&R factors. Moreover, the argument for the importance of micro-factors is supported by the low position of Brazil in the Doing Business Report (2010) in the Enforcing Contracts category ranking 98th out of 183 countries (Table 1).

De la Torre, et al. (2006) point out that as the SELIC rate decreases micro-factors become _more_ significant. We have witnessed the SELIC rate declining from 19percent in 2005 to under 10 percent in 2009, which might imply that during this period micro-factors became much more prominent. As the SELIC rate decreases, I&R factors progressively become more binding and constrain further declines in intermediation spreads. In 2009, 7.21 percentage points of the NIM were accounted for by I&R and only 1.94 percentage points by macro-factors (Figure 4). This shows that I&R factors continue to be the most significant component of the NIM.

15
23 policies include trade reform, exchange rate devaluations or revaluations, economy‐wide labor market reforms, etc.

Growth incidence curve decompositions based on economic counterfactuals have been tried for such “economy‐wide” policies as well, although the standards of empirical identification are probably lower than in the case of well‐evaluated assigned programs. Let us briefly review two approaches. In the first, exemplified by Ferreira, Leite and Wai‐Poi (2010), the treatment of general equilibrium relationships is extremely reduced‐form. These authors attempt to estimate the effect of a trade liberalization episode on the distributions of wages and household incomes – and thus on poverty and inequality – in Brazil, between 1988 and 1995.

The authors combine the two‐stage regression approach of Goldberg and Pavcnik (2005) with a parametric GIC decomposition in the style of Juhn, Murphy and Pierce (1993). They construct a panel of industries over time, and regress three industry‐level dependent variables (wage‐premia, skill‐premia and employment levels), which they obtain from estimating Stage 1 regressions (24) and (25) below, on (arguably) exogenous trade policy variables (such as changes in tariff rates and industry‐specific exchange rates).

> Equation (24) is an individual‐level earnings regression, augmented by industry (<sup>_I_</sup> _ij_<sup>) and skill (</sup><sup>_Sij_) dummy</sup> variables. The coefficients _wpj_ and _spj_ are correspondingly interpreted as industry and industry‐skill wage premia. A panel of such “premia coefficients” (as well as estimates of the constant term _λ0_ in eq. 25) across industries _j_ and years _t_ is regressed on the trade policy variables, as noted above: these are the Stage 2 regressions. Once they have been estimated, the coefficients from the second‐stage regressions are used to predict “trade‐mandated” changes in _wpj_ , _spj_ , and _λ0j_ , for actual or counterfactual values of changes in the exogenous trade policy variables. These predicted first‐stage coefficients are in turn imputed back into (24) and (25), to generate counterfactual occupation and wage distributions analogous to those in JMP or BFL.

Inequality and poverty statistics can be computed for each of these counterfactual distributions, and compared with the actual (pre‐ and post‐liberalization) distributions, in an attempt to isolate the contribution of the policy to the overall changes. Equation (13) can also be computed for each percentile, showing a full graphical comparison of the actual GIC between 1988 and 1995, and the counterfactual GIC attributed only to the trade‐mandated effects of the liberalization. This decomposition is shown in Figure 3, drawn from Ferreira et al. (2010). In this figure, the thick upper line is the actual wage growth incidence curve for Brazil between 1988 and 1995, and the dashed line that matches it closely from the first quintile upwards is the counterfactual GIC obtained from the simulation exercise just described. The line is interpreted in the paper as a lower‐bound for the effects of trade
arrangements are mostly industry-specific and less influenced by the experience and networks of exporters.

Another related limitation involves gaps in monthly export sequences (e.g. firms exporting once every 3 months, or only twice per year): depending on the product, the sector, or even the agreement between firms, there can be considerable heterogeneity in timing (e.g. the seasonality of produce in agriculture<sup>4</sup> ). It therefore cannot be concluded that an export contract has been terminated simply because there is a gap. Taking this heterogeneity into account, the importance of the sunk costs related to exporting and the presumed depreciation of know-how and business connections in time, the article follows the literature (Sabuhoro, Larue and Gervais 2006) and assumes that an export spell ceases after a gap of more than 12 months (the robustness of this assumption is tested). Subsequent re-entries are treated as new spells<sup>5</sup> .

In general, export transactions tend to receive less attention than imports because they do not generate revenue. Comparisons with international trade statistics are therefore problematic, especially regarding trade with China and the number of trade partners. Lao PDR does not report directly to UN’s Comtrade, and neither does Vietnam, so mirror data from destination countries can be very different. Moreover, improperly encoded re-exports via Thailand can lead to a considerable inflation of trade because so much of Lao’s exports are routed through Bangkok. There may also be significant unreported border trade (e.g. smuggling, traditional trade, illegal logging). While product codes are recorded with accuracy by Customs, errors are sometimes evident in the data with regard to the destination country. Even with care, the twoletter code for destination on customs forms leaves room for confusions between China (CN), Switzerland (CH) and Swaziland (SZ) or between Germany (DE) and Georgia (GE).

In order to allay some of these concerns, the data cleaning process accounts for the following irregularities: product codes (missing or non-standard), destinations (excluding exports with non-standard codes or to Laos), firm licenses (missing or with generic codes), and USD transaction values (those with a zero, missing or showing abnormal currency exchange rates). Re-exports have also been excluded (those recorded as such in the data), as have transactions under $500<sup>6</sup> , which the literature considers samples or gifts of no trade value.

> 4 In fact, the mean gap is highest in agriculture while the shortest is in minerals (3.6 months compared to 2.6 months, statistically different at the 99% level).

> 5 This affects only ~3.2% of transactions or ~8% of FPDs.

> 6 The USD value of all transactions has been deflated using the monthly United States Consumer Price Index (October 2005=100).

9