rafmacalaba/data-use-mentions-extended huggingface dataset:
or a nearby location.<sup>4</sup> This question was not specific to any fertilizer type. The second source is the HFPS agriculture module which asked all respondents in crop-farming households to report the current price of inorganic fertilizer by fertilizer type.<sup>5</sup> Respondents reported on prices for either the types of fertilizer they used or, if they did not use any, for any type of fertilizer they could report on. Prices were reported in local currency units (LCU) for a certain amount of fertilizer and unit. We standardize all prices by expressing them in LCU per kilogram. If a household reported prices for

> 4 Respondents did have the option to answer that they did not know the price.

> 5 In Ethiopia (round 19) and Uganda (round 15), only those that used any inorganic fertilizer were asked to report on the price.

5
retaliation is higher for firms serving heavy AD users. Given this higher risk, firms might be more responsive to the threat of retaliation in these countries.

To investigate whether the destination country’s AD activity affects the export prices of the products of firms that are exposed to retaliation, we interact our variable of interest with another dummy variable, “Heavy,” which takes a value of one if the destination country’s total number of AD filings is higher than the world average over the previous five years. The regression estimates obtained using this variable are reported in the last column of table 6. Similar to the earlier estimates, both the retaliation-threat variable and the interaction term are statistically significant. This result suggests that the increase in the unit price of products from exporting firms that are susceptible to retaliation is higher in export destinations that are heavy AD users. In terms of economic significance, there is a 13 percent increase in the price of products that firms export to heavy AD users when these countries are defendants in Brazil’s AD investigations within the same industry.

# **_<<A>>_ CONCLUSION**

AD has become the most frequently implemented nontariff trade barrier. After the inception of the WTO in 1995 and the dramatic tariff cuts that countries experienced, the importance of AD duties increased for firms that seek alternative instruments of protection. Because AD duties are very large compared with most favored nation tariffs, they have a dramatic impact on both dumped imported varieties and domestic production. In this study, we analyze one dimension of this impact and contribute to the literature by investigating the pricing effect of AD duties in two ways. First, using rich, four-dimensional trade data, we show that AD duties imposed on Brazilian exporters significantly increase the unit value of firms’ exported products. Second, we document a significant price increase for the exported products stemming 14
documented (e.g., Bown, 2010), this China phenomenon is pervasive across policy-imposing economies. Next, in specification (2), when we substitute the macroeconomic determinants at the second lag (quarter _t-2_ ) for the first lag, there is a reduction to the estimated size differential (away from 1) of the IRRs on the US unemployment rate change and the foreign trading partner’s real GDP growth compared to the baseline. Finally, specification (3) confirms for the US a finding in the full sample of data (Table 2 column 5). Limiting the dependent variable to antidumping yields an insignificant estimate of the relationship between real bilateral US dollar fluctuations and new import protection.

Consider next the Table 3 results for the European Union, which we have noted are estimated on a shorter time series of data (1999:Q1-2008:Q3) to coincide with the introduction of the euro in 1999. Estimates of the baseline EU model in column (4) are also in line with the theory. When we substitute the second lag for the first lag of the macroeconomic determinants in specification (5), the estimated IRR for the change in EU unemployment rate drops considerably, and it is no longer statistically significant. There is no qualitative change to the other key determinants. Finally, when the dependent variable is defined as antidumping _alone_ in specification (6), there is no statistically significant evidence of a relationship between macroeconomic shocks and import protection. This specification notably omits an extensive set of import restrictions on steel products through the EU’s global safeguards policy in 2002.

Figure 3 provides information on the magnitudes of the estimated IRRs for the US and EU samples of data. The US panel is based on specifications (1) and (2), and the EU panel is based on specifications (4) and (5). The figure follows the approach of Figure 2 by presenting the percent increase in the count of imported products per trading partner per quarter subject to new TTBs
# **4. DATA**

The data underlying this study were obtained through a panel survey of Javanese farm households conducted by the Indonesian Center for Agrosocio-economic Research (CASER) in April/May 1991 and again in June 1999. The baseline sample included rice-growing villages that had already been covered by the program, as well as villages that were not yet covered by the program, but were in areas where the program was planned to be implemented. All villages were visited in the repeat survey in 1999, but our analysis focuses only on those villages that had not yet been exposed to an FFS at the time of the baseline survey in 1991. In most of these villages, the village sample contains randomly selected farmers who participated in the training in various years throughout the period covered, as well as farmers from the same village who did not participate. Of the 268 sample households from the 22 sample villages where a field school had been implemented between 1991 and 1999, only 112 had actually participated in the training while the remaining 156 households had not attended a program, but had been potentially exposed to some of its (new knowledge) benefits through informal communications and joint activities with graduates of the program who lived in their village.

The 1991 survey collected information on households’ farm operations and characteristics for the 1990/91 wet rice season and on their household attributes, activities and assets. It also documented the farmers’ knowledge of specific aspects of pest management that were to be included in the training program, through a set of questions on specific curriculum components. The 1999 survey repeated the same questions and collected additional data regarding the household and the village participation in FFS training, and more information about the community. The farmers’ responses to the identical knowledge questions in both 1991 and 1999 were scored, and the number of correct answers relative to the total number of pest management questions serves as an indicator of pest management knowledge.<sup>2</sup> More detailed information on the construction of the
sup>|
||(0.001)|(0.000)|(0.001)|(0.000)|(0.000)|
|Observations|5,493,000|6,884,064|16,641,852|16,641,852|944,534|
|R-squared|0.062|0.001|0.069|0.034|0.008|

_Source_ : REDATAM INDEC Census Argentina 2010. _Notes_ : Standard errors clustered at the department level in parentheses. * _p <_ 0.10, ** _p <_ 0.05, *** _p <_ 0.01.

There are several reasons why differences in outcomes may survive controlling for years of education. Firstly, there may be other personal characteristics or attributes that may affect outcomes which we do not observe and may differ between Natives and nonNatives. For example, there may be differences in wealth and social capital. Secondly, there may be differences on the quality of education available to the two groups, which may make “years of education” an imperfect measure of human capital. Unfortunately, we lack a method to assess the of the education available to Natives and non- quality Natives. And thirdly, labor market discrimination may result in different returns to education for the two groups, but further research is needed to confirm this potential channel in this setting.<sup>8</sup>

# **3.4 The transmission of education**

We study the transmission of education across generations and whether this differs between Natives and non-Natives. To do so, we focus on the more than 700,000 households

> 8 On discrimination by race in Latin America, see Chong and  ̃Nopo (2008),  ̃Nopo et al. (2010), Arceo-Gomez and Campos-Vazquez (2014), and Gerard et al. (2021).

12
Figure 10: Fraction of jobs in Bamako, Dar es Salaam, Kampala, and Kigali directly located in flood zones for 10, 50, and 100 year return periods <!-- Start of picture text --> 35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>Bamako Dar es Salaam Kampala Kigali<br>50-year return period<br>100-year return period<br>Fraction of jobs affected 10-year return period<br><!-- End of picture text -->

# 4.2. Road transportation networks and flooding

In addition to the direct effect of firm locations being submerged in water, floods also indirectly affect many businesses. In fact, indirect effects of floods may be significantly larger than the immediate direct impacts. By slowing down traffic or closing off roads, flood zones disrupt transport networks that firms depend on. In the case of a flood, employees and customers are unable to reach businesses, input materials are not delivered to production sites, and finished products cannot be transported to their destination on time. Such impacts present significant disruptions to the operations of businesses of all sizes and can cause large economic damages, even for firms that are not located in flood zones.

The ‘networked’ nature of infrastructure becomes apparent when overlaying the urban road map with flood risk zones. For this purpose, we use the same flood data as in the previous section and combine it with data on roads from the OSM project. Figure 11 shows how flooding is affecting the road network in the area of this analysis in Kampala 16
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 8. Benchmarking air transport indicators for South Sudan**<sup>**1**</sup>

|**Country**|**South**<br>**Sudan**|Sudan|Ethiopia|Chad|CAR|Kenya|Egypt|
|---|---|---|---|---|---|---|---|
|**Traffic (2007)**||||||||
|Domestic travel (‘000 seats per year, excluding<br>South)|**0.7**|218|729|n.a.|n.a.|2,093|5,959|
|International travel within Africa (‘000 seats per<br>year)|**164**|302|1,837|110|21|3,145|1,886|
|Intercontinental travel (‘000 seats per year)|15|2,052|2,005|89|24|2,755|15,793|
|Seats available per 100 people|**1.6**|9.8|5.8|1.8|1|21|27.5|
|Herfindahl-Hirschmann Index—air transport<br>market(%)|**18.49**|15.29|70.61|36.35|50.26|39.47|24|
|**Quality**||||||||
|Percent of seat-km in newer aircraft|**19.7**|71.9|98.5|99.5|100|80.2|90.7|
|Percent of seat-km in medium or smaller<br>aircraft|**
coverage and improved reporting (as noted by EM-DAT), we focus our analysis on data from 2000 onwards.

We define an intensity shock, measuring the number of people affected by natural disasters using data from EM-DAT following Fomby, Ikeda, and Loayza (2013), as: where _I_ is the country and _t_ is years.<sup>2</sup> Unlike in Fomby, Ikeda, and Loayza (2013), and more recently Bayoumi, Quayyum, and Das (2021), we use the actual intensity measure as the climate shock rather than a dummy variable based on some threshold value. Table 1 provides the name, description, and source of the other variables used in this section. Loayza et al. (2012) show that the type of disaster may matter for its impact on the economy, however, we study all disasters.

We assume that any unreported event is considered to be a non-occurrence, and hence assigned an intensity value of zero. This decision is supported by several factors. First, the improved data coverage by EM-DAT from 2000 onwards significantly reduces the likelihood of major events going unreported. Also, within our sample we notice an association between missing damage values and lower climate shock intensities, suggesting that unreported events are likely to be smaller in scale. Second, we underscore the importance of establishing a counterfactual scenario without any shocks. Solely analyzing climate event datasets might not provide a complete picture of counterfactual fiscal resilience impacts. Therefore, treating all periods outside of reported events as zero allows us to approximate a counterfactual scenario devoid of shocks.

# 3.3. Results

Natural disaster shocks are associated with a statistically significant decline in output, with no empirical evidence supporting threshold effects related to the size of the shock and some evidence of compounding effects. Figure 2.A shows the impact of all available climate shocks and large climate shocks that affect 1 percent of the population. It is based on close to 3,000 natural disasters from 2000 to 2021 in 156 economies.
time: in the 1987-1995 period, only 46% of investigations had resulted in antidumping or countervailing duties.<sup>47</sup> The last column in Table 3 above shows that in 2002 34% of concluded cases had antidumping or countervailing duties in force. This percentage, far lower than the 58% previously mentioned, is due to the fact that after annual administrative or sunset reviews some duties are eliminated. Given the years gone by since the system has been implemented, a great part of its current activity focuses on proceedings other than new investigations, as shown by the UPCI’s statistics on resolutions (last two columns in Table 4).

**Table 4 - Resolutions Regarding Unfair Practices and Safeguards, 1987-2002**

|**Year**|**Rejections**|**Investigations**<br>**initiated**|**Special**<br>**proceedings**<sup>**1**</sup>|**Other**<sup>**2**</sup>|
|---|---|---|---|---|
|1987|N.A.|18|...|...|
|1988|N.A.|11|...|...|
|1989|N.A.|7|...|...|
|1990|N.A.|12|...|...|
|1991|N.A.|9|...|...|
|1992|8|26|...|...|
|1993|12|83|...|...|
|1994|16|26|...|...|
|1995|2|4|8|1|
|1996|6
ethnic differences between adults, children of different ethnicity have the potential to achieve similar levels of growth attainment in the first few years of life. Many comparative empirical studies have demonstrated the greater importance of socioeconomic factors as opposed to race and ethnicity in determining children's height. Data from India (Rao and Sastry 1977) and Guatemala (Johnston, Borden and MacVean 1973) suggest that ethnic differences in growth potential are minor prior to puberty and that it is during this stage that major differentiation between ethnic groups takes place. Clear differences surfaced during the adolescence period for both sexes:

> See Martorell and Habicht (1986) for an excellent review of the main research findings on the characteristics of children's anthropometry in developing countries.

2
low and high productivity firms. Looking at their averages, internet users are 3.7 more productive than nonusers in terms of labor productivity and 34.7%, in terms of TFP.

_Figure 1 Computer Users versus Non-Computer Users_ <!-- Start of picture text --> Relative Sales per Worker distribution by quintiles<br>Relative TFP by Sector distribution by quintiles<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Non-Computer User Non-Computer User<br>Source: Author’s own elaboration from enterprise surveys<br>Figure 2: Internet Users versus Non-Internet Users<br>Relative Sales per Worker distribution by quintiles<br>Relative TFP by Sector distribution by quintiles<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Non-Internet User Non-Internet User<br>1<br>1<br>.8<br>.8<br>.6 .6<br>.4 .4<br>Computer User Computer User<br>.2 .2<br>0 0<br>1<br>1<br>.8<br>.8<br>.6 .6<br>Internet User .4 Internet User .4<br>.2 .2<br>0 0<br><!-- End of picture text --> Source: Author’s own elaboration from enterprise surveys Source: Author’s own elaboration from enterprise surveys Therefore, the graphs above suggest that while ICT adopters tend to be more productive than non-adopters, there is no statistical dominance, at least for TFP. This implies that although it is likely that there is some self-selection of more productive firms into ICT adoption, there could also be some impact of ICT on productivity. This issue is explored in detail in the next section.

# **6
15 developed in MENA yet. Therefore, while many SME units seem to be developing more sophisticated lending techniques, in most cases they are still conducting relationship lending, although in a more effective way.

We also look at how the quality of financial infrastructure in a country affects SME lending. _Cover_ is a variable that represents the volume of credit information provided to lenders by public credit registries or private credit bureaus. This variable is based on information from the World Bank‟s Doing Business Database and is defined by the highest coverage ratio of either the public registry or the private bureau.<sup>11</sup> The _Legal_ variable measures the quality of the legal and contractual framework, and is represented by several alternative legal indicators from the World Bank‟s Doing Business database. We use: (i) the legal rights index that measures the degree to which collateral and bankruptcy laws protect the rights of lenders, (ii) the time to register a property, (iii) the time to enforce a contract and (iv) the time to close a business. All these indicators capture the strength of creditor rights and the risks of SME lending.

Finally, we look at the impact of a particular type of government intervention – partial credit guarantee schemes (PCGs). These schemes have become a popular tool to stimulate SME lending in many countries, and 10 MENA countries have already introduced such schemes.<sup>12</sup> _Guar_ is a variable that measures the size of PCGs through the ratio of outstanding guarantees to GDP. Table 7 provides information on the stock of outstanding guarantees (in % of GDP) of MENA PCGs. Some schemes are still very young and have not yet accumulated a significant volume of guarantees.

Our analysis includes all the countries listed in Table 1 except for Libya (where we could not verify the data). Most banks reported information for at least two years in the 2007-2009 period, allowing us to build a panel of about 240 observations. The appendix
<br>Burkhauser<br>et al. (2018),<br>Czajka<br>(2017), Van<br>der Weide et<br>al. (2018)|
|Reweighting and<br>Replacing|Various|survey and admin<br>data|above which use admin data<br>to reweight survey data and<br>then replace survey with tax<br>data to allow representivity<br>at the very top|Blanchet et<br>al. (2022)|

# _Sparseness_

One solution to the lack of sampled households or individuals at the very top of the distribution is to oversample such households. The US Survey of Consumer Finances aims to obtain estimates of the wealth of households in the US. The survey has two independent samples – one that has a positive probability of selection for all US households and a second that includes only the rich in the sample frame, which is derived from tax filings (Kennickel, 2019). Although such a sample design does improve the sample size at the top of the distribution and thus potentially solves the sparseness problem, the response rates for the survey drawn from the rich sample frame survey are still very low, less than 10% for the wealthiest (Kennickel, 2019). The very low response rates implies that sparseness could 12
the efficacy of propensity score-based weights in a web marketing survey that asks questions on hygiene products and attitude towards local banks in a northern European country. They find that estimates from the web survey and the phone surveys (which they use as a comparison) are quite different, and various weighting schemes did not make a difference regarding this. Some studies find that the type of measure matters. Schonlau et al (2004) found among propensity score adjusted web-survey estimates, only 8 of 37 estimates were not significantly different from phone survey estimates. Web survey estimates were significantly more likely to agree with RDD phone survey estimates for found factual questions, and those regarding personal health, and when the questions involved two as opposed to multiple categories of responses. Lee and Valliant (2009) show that weighting adjustment that combines propensity score adjustment and calibration adjustment has the potential to reduce bias for volunteer panel web survey estimates that are contaminated by sample selection bias. However, their results are for model variables used for propensity score adjustment or in the calibration exercise, and not for non-model variables.

Malhotra and Krosnick (2007) compare the 2000 and 2004 American National Election Study (ANES) data collected via using probability sampling with simultaneous Internet surveys of volunteer samples. The non-probabilistic samples were weighted using the raking method using the US CPS so as to match sample proportions with the population proportions on age and education. A comparison of the results yielded many differences in the distributions of variables and in the associations between variables. Applying the weights did almost nothing to reduce the differences between modes/sampling methods in the distributions of the political variables. Chang and Krosnick (2009) simultaneously fielded a probabilistic telephone survey, a probabilistic Internet survey, and a non-probabilistic internet survey. The non-probabilistic Internet survey used a weighting procedure to adjust for variable propensity of individuals to have regular access to email and the Internet. The comparisons showed that th <mark>e probability samples were more representative than the nonprobability sample in terms of demographics and electoral participation, even after
% 70% 70%<br>60%50% 60%50% 60%<br>40% 40% 50%<br>30% 30% 40%<br>20% 20% 30%<br>10% 10% 20%<br>0% 0% 10%<br>Benue FCT Abuja Kogi Kwara Nasarawa Niger Plateau Adamawa Bauchi Borno Gombe Taraba Yobe 0%<br>Jigawa Kaduna Kano Katsina Kebbi Sokoto Zamfara<br>Urban Boy 5-17 Urban Girl 5-17 Rural Boy 5-17 Rural Girl 5-17 Urban Boy 5-17 Urban Girl 5-17 Rural Boy 5-17 Rural Girl 5-17 Urban Boy 5-17 Urban Girl 5-17 Rural Boy 5-17 Rural Girl 5-17<br><!-- End of picture text --> Source: Authors’ estimates based on MICS 2016/17 data Note: Individuals with missing age were dropped from the analysis. Total refers to all individuals with known age.

28
**Table 6: Relationship between temperature, humidity and observed demand**

|Dependent var: Log (consumptionper dayover billingcycle)||
|---|---|
|Log (average temperature over billing cycle)|1.219***<br>(0.134)|
|Log (average rainfall over billing cycle)|-0.0268**<br>(0.0119)|
|Constant|-3.002***<br>(0.462)|
|Observations|7,613|
|R-squared|0.041|
|Robust standard errors in parentheses||
|*** p<0.01, ** p<0.05, * p<0.1||

Notes: Averages of temperature and rainfall are calculated at the household level for each billing cycle in the data set. The regression is weighted by sampling probabilities and clustered at the town and village level.

# **Table 7: Relationship between temperature, humidity and demand for energy services based on appliance ownership and usage data**

|Dependent var: log(predicted dailyconsumption)<br>||
|---|---|
|Log (average temperature over billing cycle)|0.262***<br>(0.0624)|
|Log (average rainfall over billing cycle)|-0.0554***<br>(0.0134)|
|Constant|-0.0154<br>(0.233)|
|Observations<br>R-squared|3,828<br>0.010|
|Robust standard errors in parentheses<br>*** p<0.01, ** p<0.05, * p<0.1||

Notes: Averages of temperature and rainfall are calculated at the household level for each billing cycle in the data set. The regression is weighted by