rafmacalaba/datause-encoder-data huggingface dataset:
the US at the 10-digit HTS level are available from the US International Trade Commission's DataWeb data base for the years 1989-2003.<sup>24</sup> Given the need to instrument with ln(miht-2 ) , this allows us to estimate equation (2) on a dynamic panel of yearly, bilateral, product-level US import data from 19912003. As stated earlier, we have an unbalanced panel of 1471 different 10-digit HTS products in chapters 72 or 73 over the life of the sample.

# **3.4.2  Policy data**

This paper investigates the potential for differential effects of various instruments of import protection on steel US imports during the 1989-2003 period. We have collected detailed product-level changes to trade policies that were associated with antidumping and countervailing duty investigations, the removal of AD and CVD after revocation orders or sunset reviews, and the imposition and removal of acts of safeguard protection. All of these basic trade policy data are now available electronically through the World Bank’s _Temporary Trade Barriers Database_ (Bown, 2012). The information regarding the implementation and removal of AD or CVD is directly from the US _Federal Register_ , as well as from public documents that are available at either the USITC or the Department of Commerce's official websites.

Much of the data for the safeguard cases are also from either the _Federal Register_ or the ITC's official website. The US President imposed safeguard protection over various 10-digit HTS products in the steel industry on three occasions during the 1989-2003 period: circular welded pipe and steel wire rod in 2000 (five different 10-digit HTS products), as well as the 272 different HTS-products that were targeted by the March 2002 policy application that we have referred to as the “2002 steel safeguard.” In each case, the 10-digit HTS products as well as any excluded countries were made publicly available in the Presidential Proclamation that announced the safeguard policy.

The one important piece of policy information that was not readily available from electronic sources for the 2002 steel safeguard and compiled into Bown (2012) is the 10-digit HTS categories of the foreign firm-specific _product exclusions_ that were granted by the USTR. Descriptions of the physical characteristics of the products that were excluded from the safeguard were made public on the USTR's website; however, this information was not sufficiently detailed for systematic efforts to match excluded products to the 10-digit HTS coded import data that were used in the estimation. However, information on the 10-digit HTS codes of the excluded products was available in the actual surveys that petitioners filled out to request that their product be excluded. Such petitions are publicly available, though the data had to be transcribed by hand from hard copy surveys that were available in the International Trade

> 24 See http://dataweb.usitc.gov/, last access date of 16 December 2012.

15
## **Figure 1 - Income and Expenditure Distributions**

<!-- Start of picture text -->
-2 0 2 4 6 8<br>x<br>kdensity Wexp_lncap kdensity Winc_lncap<br>kdensity Wexp_unhcr_lncap<br>.8<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text -->

Source: Authors’ estimations based on Jordan UNHCR data. On the x-axis is the log of income or expenditure per capita. On the y-axis is the density (percentage of population).

# 4. Models

The underlying model of the predictions and simulations that will follow is a simple poverty model described as follows: where _Pi_ =1 if the case is under the poverty line and _Pi_ =0 if the case is on the poverty line or above; _Xi_ =vector of case characteristics that derive from merging the UNHCR proGres registry data and the home visits data; ε� = normally distributed error term with zero mean; and _i_ =case.

The model is estimated with a probit function and the main objective is to maximize its poverty prediction capacity. In order to achieve this objective, we followed a systematic procedure to optimize the construction of the independent variables and the explanatory power of the model. For the variables 8
seem to not only affect the poor or near-poor. Some weather shocks induce income declines (or increases) on average, also in traditionally more stable sectors, like the tertiary, and to some extent the secondary sector. This may be driven by the fact that richer households have a larger asset base and may as such incur in larger financial losses, for instance business or farm owners involved in soy production (Wesz Junior, 2022). Secondly, the poorest may find themselves at welfare levels that are so low, that (weather) shocks create limited financial havoc on a living standard that is already at subsistence level. However, on top of that, there also seems to be a vulnerable middle class that remains exposed to the negative effects of precipitation shocks in urban areas.

# _Cumulative and overlapping effects of weather shocks_

We conclude that the effects of different temperature and precipitation shocks on income and poverty in Paraguay are not to be interpreted in isolation. Within a year, or across years, there may be multiple and overlapping weather shocks with which households must cope. More importantly, the interaction between such shocks and the rate at which they re-occur may have cumulative effects on poverty. A preliminary analysis using the data from this paper suggests that in urban areas, the combined effect of heat and flood shocks on poverty is significant and would increase poverty levels, though a deeper exploration of these joint effects remains for future research. Moreover, as the EPH data does not cover the departments Boquerón and Alto Paraguay, which are in the north of the country and may exhibit the most extreme weather conditions and anomalies (Figure 1), the effects of weather shocks on poverty could be even higher than our estimates suggest. In light of this and the increasing frequency and intensity of weather shocks in the future, we believe that our results are a lower-bound estimation of the impacts of weather shocks on poverty, and more severe effects are to be expected without long-term adaptation and climate risk mitigation strategies.

The long-term effects of weather shocks can be profound and complex, not only having an immediate impact on income and poverty, but also exacerbating structural vulnerability over time. Historically, Paraguay has experienced high rates of deforestation. However, over the past decade, this trend has shown signs of improvement, with deforestation rates gradually declining.<sup>8</sup> Despite this positive development, the country still faces significant challenges. Climate change poses a particular threat, as it is expected to exacerbate deforestation through increased droughts and wildfires if preventive measures are not implemented (World Bank, 2024, _Forthcoming_ ). In addition, repeated weather shocks may further degrade the country’s natural resources and

> 8Between 2000 and 2020 alone, 30 percent of the country’s forest area was lost (OECD, 2022; World Bank, 2021).

25
The relocation and integration of production processes across national borders has been reinforced by increasing flows of private capital, especially in the form of foreign direct investment (FDI), which is often associated with global production strategies of transnational corporations (see Chart 3). Technological progress that reduces **the** cost of transportation, communications, and financial transactions, coupled with declining trade barriers, has enlarged opportunity for anyone searching for less costly production bases for exports and for spot production for local markets. From the point of view of the recipients, capital inflows enlarge import capacity above and beyond export earnings for a certain period. If inflows are used to increase domestic supply capacity and augment international competitiveness, countries are rewarded with higher productivity growth and export earnings, which preserves their import capacity in the longer run, thus creating a virtuous cycle of high growth and trade integration.

Chart 3. Aggregate Net Resource Flows to LMIC, 1970-1994 <!-- Start of picture text --> US$ billion<br>250-<br>200<br>150-<br>100 -<br>50 -<br>0<br>70 72 74 76 78 80 82 84 86 88 90 92 94<br>Official Grants Foreign Direct Investment<br>- Official Loans li Portfolio Equity Flow<br>Private Loans<br>Source: intemational Economics Department, Wofid Bank.<br><!-- End of picture text --> 3
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# **Coffee Prices, Growth and Poverty: How Different Household Groups Fared**

At the aggregate level, Uganda’s performance over the period under consideration has been remarkable: yearly growth rates of per capita consumption (estimated from the household surveys) were 4.4 percent between 1992 and 1995, and 8.7 percent between 1995 and 1999. These growth rates were accompanied by a substantial poverty reduction during the 1990s, which accelerated in the second half of the decade. The poverty headcount decreased from 56 to 49 percent during the first period, and from 49 to 34 percent in the second period.

To assess how these aggregate gains were distributed across households and whether their pattern was correlated with coffee price fluctuations, we disaggregate households in five separate groups according to their degree of dependency on coffee farming. The five groups are: coffee farmers, other rural as well as other urban households in coffee regions and noncoffee regions. Coffee farmers are defined as those who report some coffee production<sup>5</sup> , and coffee regions are those districts with actual _per capita_ production of coffee of more than 20 bags (of 60kg each) or production potential of more than 100,000 bags.<sup>6</sup> To improve comparability across these five groups and among different time periods, we constructed a more homogenous sub-sample by dropping some districts that were not covered in all the surveys and some specific non-coffee districts, in particular from the North.<sup>7</sup> The latter region has been shown to suffer from adverse agricultural conditions and to be largely de-linked from the rest of the economy.

Based on this sub-sample and for each of the five household groups, Table 1 shows their respective sizes in terms of shares of total population and per capita consumption values. The population share of coffee farmers increased considerably in the first period, starting from just 22 percent in 1992 and reaching 38 percent by 1995. Not much change is recorded in the

> 5 Later, we will analyse coffee farmer performance based on different degrees of dependency/specialization in coffee production.

> 6 Coffee districts by this definition are (according to 1992 district definition): Kalangala, Kapchorwa, Kiboga, Luwero, Masaka, Mpigi, Mubende, Mukono, Rakai, Mbale, Kamuli, Iganga, Bushenyi, and Jinja. 7 The sub-sample includes observations from the following districts: Kalangala, Kiboga, Luwero, Masaka Mpigi, Mubende, Mukono, Rakai, Iganga, Jinja, Kamuli, Tororo, Bushenyi, Kabarole, Kibaale, and Mbarara. The subsample sizes are 4994 households in 1992/93, 2241 in 1995/96, and 5637 in 1999/2000.

11
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For example, an increase in the wages paid a machinist relative to other trades, a rising number of unfilled jot vacancies for computer aiialysts, or the inability of trainees in a carpentry prograrn to find employment are each signals provided by labor markets concerning the balance of skills demand. They reflect, respectivr!y, a shortage of qualified machinists and computer analysts and a possible sur. lus of carpenters. The informaticin provided is important to weighing the benefits and costs of alternative t.aining options, both on the part of prospective trairses and the managers of training programs.

# Focusing on Education and Tralnhna QuallficatIons

Labor market signalling calls for focusing zn education and training qualifications and not on occupational classifications (Godfrey, 1991, Psacharopoulos, forthcoming). The r6asons relate to the quality of occupational statistics, the impact of technology on the concept of an occupation, and the practical link between academic specialization and occupational placements. In the first case, the quality of labor foice statistics is generally poor in developing countries, and as suggested in the discussion of manpower forecasting, occupational statistics are of even poorer quality. In many situations, these statistics are available only in highly aggregated lorm, e.g., managers, professionals, technical workers, etc. They are poorly suited to the planner's needs when attempting to capture the balance of skills demand and supply. Technological change is by itself undermining the very concept of an c ccupation. In Japan, for example, workers' roles are rapidly changed as needs arise (ILO APSDEP, 1988). Skills '-ecome the central issue in this context rather than occupations.

Perhaps the most important reason to focus on education and training qualifications in manpower planning is that the link between skill specialization and occupational placement in weak. A reverse tracer study of higher education and the Philippine labor market found that the proportion of employed graduates of the previous h -: years working in their field of academic specialization was only 7b, ircent for applied scientists and 47 percent for liberai arts graduates (Arcelo and Sanyal, 1987). The important issue for the allocation of resources on training is the impact of a training program on productivity rather than occupational placement. The emphasis on occupations is a feature of manpower 8