rafmacalaba/data-use-mentions-extended huggingface dataset:
The authors of these studies employed different model specifications, data, and assumptions. Therefore, variations in results across studies reflect differences in macroeconomic closures, the extent of tariff and non-tariff reforms and associated efficiency gains as well as different initial conditions due to different versions of the GTAP Database used in these analyses. Tariff cuts are largest in Yang (1996) and Wang (2007), while in Ianchovichina and Martin (2004) tariff reform is most limited because they reduce only the tariffs on imports used domestically and in the production of ordinary exports, which were _not_ produced under processing arrangements and accounted for roughly two-fifths of exports. Ianchovichina (2004) shows that failure to account for duty exemptions on imported inputs used in the production of exports leads to an overstatement of the increase in China’s exports flows by 40% and the exports of selected sectors by as much as 90%. The magnitude of this bias depends on the level of pre-accession tariffs and the size of accession-related tariff cuts; the larger the initial tariffs, the larger the bias when duty exemptions are not factored into the analysis.

The decline in tariffs and the elimination of most quantitative restrictions—both in the leadup to accession and as its direct consequence—reduced the need to use processing trade arrangements and encouraged the expansion of ordinary exports that did not involve the compliance costs associated with export processing arrangements, contributing to the increase in domestic valueadded found by Kee and Heiwai (2016). According to the estimates by Ianchovichina and Martin (2004), China’s export growth associated with accession-related reforms implemented after 2001 was projected to be one-half of the size of export growth associated with reforms implemented prior to accession between 1997 and 2001. The accession-related gains were attributed mostly to the removal of quotas on textiles and apparel in the US and the EU markets and the realization of 14
that has emerged – reducing the potential for trade diversion and negative hub-and-spoke effects associated with agreements signed by Arab countries with the EU and the United States (among others), and extending the benefits of free trade agreements between subsets of Arab countries (e.g., Agadir and GCC). PAFTA could also be critical in promoting innovation and trade diversification. A comparative analysis of dynamic exports of the Arab countries shows that for most Arab countries the number of dynamic products is higher in intra-regional trade than in exports to the EU (Zarrouk 2000). This indicates that the dynamic Arab products maintain differentiated export niches in intra-regional trade and suggests that there is scope for expanding intra-regional trade in processing activities.

# **2. The Status of Goods and Services Liberalization in the Arab World**

**Despite progress, the dismantling of intra-regional barriers to trade in goods and services remains an unfinished agenda.** PAFTA aims to enhance economic integration among its 17 Arab member countries through the establishment of a regional Free Trade Area (FTA) that

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7 between urban and rural areas since the between group component of the decomposition increased over time, especially in 1995-96 for the lower poverty lines. Over time, the increase in the within group and between group components of the decomposition were of a similar order of magnitude.

# **HI Micro determinants of consumption, poverty, growth and inequality**

_III 1. Methods_ In the previous section, we have presented a big picture as to what happened to poverty and inequality in Bangladesh from 1983 to 1996. But what are the determinants of poverty and inequality, as well as of per capita consumption and growth in average consumption over time? To answer this question, a single set of regressions can be used for each one of the HES surveys. Following standard practice, we use the semi-log specification since per capita consumption (the dependant variable) is not normally distributed. These regressions in urban (U subscript) and rural (R subscript) areas are simply:

> Urban equation: Log yUi = **u'Xi** + £Ui (5.1) Rural equation: Log YRi = 1R'Xi + gRi (5.2)

Separate regressions are used for both sectors because the returns to household characteristics may differ between sectors. The dependent variables are the log welfare ratios (logs of nominal per capita consumption divided by the poverty line of the area in which the household lives). The independent variables are the same for both sectors. Apart from a constant, they include: (a) geographic location according to 17 districts (each household, whether urban or rural, lives in one of these districts); (b) household size variables: the number of babies, children, and adults, as well as the square of the number of babies, children, and adults; (c) other demographic and gender variables: the age of the household head and its square, the sex of the household head, and the family structure of the household, such as a
a significant increase in GVC participation in the agri-food sector, the data used for plotting panel b. of Figure 2 can be obtained by using the following syntax ``` .*ReplicatedataofWDR2020Figure1.13panelb .icio_load,iciot(eora)year(2015) Loadingtableeora2015...loaded .icio,exp(tza,4)output(gvc) ``` ``` Decompositionofgrossexports: Perspective:exporter Exporter:TZA Importer:totalTZAexports Output:GVC-relatedtrade Sectorofexport:4 ```

||`Millions of $`|`% of export`|
|---|---|---|
|`GVC`|`93.22`|_`52.74`_|

``` .icio_load,iciot(eora)year(1990) Loadingtableeora1990...loaded .icio,exp(tza,4)output(gvc) ``` ``` Decompositionofgrossexports: Perspective:exporter Exporter:TZA Importer:totalTZAexports Output:GVC-relatedtrade Sectorofexport:4 ```

||`Millions of $`|`% of export`|
|---|---|---|
|`GVC`|`38.80`|_`33.53`_|

In Figure B.2.1.1 of the WDR 2020, Vietnam’s integration in the electronics global value chain is discussed. Panel a, here reported as Figure 3, is based on the EORA MRIO database and shows the GVC-backward related trade, i.e. backward integration, in the electrical and machinery sector. Data for 2015 can be obtained running: ``` .*ReplicatedataofWDR2020FigureB.2.1.1panela .icio_load,iciot(eora)year(2015) Loadingtableeora2015...loaded .icio,exp(vnm,9)output(gvcb) Decompositionofgrossexports: Perspective:exporter Exporter:VNM Importer:totalVNMexports Output:GVC-backwardrelatedtrade Sectorofexport:9 Millionsof$%ofexport ``` 30
the nearest city on average, an additional one standard deviation in the distance to a city (i.e., considering locations that are an additional 55 km away from a city) increases the likelihood of cropland loss by .8 percent over the 15-year period. This could reflect greater investment incentives to keep land in production close to markets. Similarly, an increase by one standard deviation in the number of months of exposure to droughts (i.e., having 63 months of droughts instead of 38 over the period) increases the likelihood of cropland loss by 1.4 percent. A decrease by one standard deviation in cultivation suitability (i.e., a reduction of the suitability index from 84 to 66) increases the likelihood of cropland loss by 2.8 percent. An increase in one standard deviation in the distance to a river increases the probability of cropland loss by .3 percent. Proximity to a river also decreases the likelihood of cropland loss, which could capture the role of rivers in keeping soils moist as well as the likelihood of surface water irrigation close to rivers: A decrease by one standard deviation in the distance to a river decreases the probability of cropland loss by .6 percent. Finally, proximity to the coast is harmful to keeping land under cultivation. A one standard deviation in the distance to the coast increases the likelihood of land degradation by .3 percent. This could be potentially due to the salinization of coastal areas. We see that the factor which by far has the greatest impact on transitions out of cropland is the quality of soils (as measured by the cultivation suitability index), followed by climate events (as measured by droughts). Interestingly, the role of access to cities in keeping land cultivated is greater than that of proximity to rivers or distance to coasts. As a robustness check, we run the same regression on a sample including both cropland and cropland mosaics (53 million pixels) and consider movements out of either one of these two land use
2024), compounding the damages sustained in 2014 (Bluszcz and Valente 2022), while Russia had at most modest losses or may have gained market share (Rose et al. 2023).

# **2.2 Measuring data on the conflict that are relevant for agricultural production**

Studies have traditionally used self-reported household survey data on conflict experience (Muriuki et al. 2023) or physical proximity to war zones (Akresh et al. 2022) to measure conflict exposure, although the subjective and static nature of these measures has long been recognized as a drawback. An important source of <mark>independent,</mark> time-varying data on <mark>conflict is the ACLED project (</mark> Raleigh et al. 2010), which relies on media reports on different types of violent events, together with the number of fatalities as a measure of severity, georeferenced to the nearest locality. Although it is not specific to the agriculture sector, the data it provides, combined with georeferenced household data, have been widely used to draw inferences on the household-level impacts of conflict. For example, in Ethiopia, exposure to an additional battle was found to increase food insecurity, mainly through reduced scope for nonfarm activities (Abay et al. 2023b). In Nigeria, more intense conflicts, measured by the number of fatalities, reduced the quantity and variety of foods eaten (George et al. 2020) and impeded medium-scale farmers’ area expansion (Adelaja et al. 2023). In Mali, conflict significantly reduced the likelihood of using inputs, and cash transfers failed to reverse this (Sessou and Henning 2024).

<mark>To create a measure of time-varying conflict exposure that is relevant for agricultural production, we use</mark> p <mark>ublicly available Sentinel-2 imagery with medium spatial (10-meter pixel size) and temporal (five-day revisit cycle) resolution to identify conflict-induced damage to agricultural fields. We constructed a measure of the area of fields damaged by military actions such as
figure shows that more than 80 percent of the households report complete destruction of their homes within the immediate vicinity of the earthquake. Since quality of the pre-earthquake housing as reported in the 1998 population census and in our own survey is uncorrelated with the fault-line, this is a causal effect of the earthquake shock and not due to the differential housing quality closer to the fault-line. The housing collapse tapers off slowly with distance from the fault-line and even households living twenty kilometers away show a 40 percent rate of destruction. Combining partially and fully destroyed properties, almost every single house within 5 kilometers of the fault-line was affected. Predictably, the percentage of houses that are partially damaged increases farther from the fault-line; consequently, the percentage of houses that were either fully destroyed or damaged decreases very slowly with distance from the faultline. Confirming the destruction at the household level, Figure 1b shows the collapse of facilities, both public and private due to the earthquake. More than 65 percent of all facilities were completely destroyed in villages closest to the fault -line.

<u>Aid</u> The sudden and immediate destruction following from the earthquake resulted in an outpouring of aid and assistance, both from within Pakistan and from the global community. As part of our census, every household (28,000 in all) was asked to name as many organizations/groups of people that they directly witnessed providing aid in their village within the first six months of the earthquake. The time period was specifically restricted to focus (a) on the rapid assistance provided as part of the “race-against-winter” and (b) the rescue, relief and rehabilitation phase of the recovery rather than reconstruction. For instance, between October 2005 and March 2006, U.S. aircraft flew more than 5,000 sorties, delivering over 9 million kilograms (20 million pounds) of aid. Medical units treated 30,000 patients and crews cleared 19
an analytical perspective of its determinants drawing on new survey data from Bangladesh.

We randomly selected and interviewed over 821 farmers across Bangladesh with structured questionnaires containing information on their input use & yield; farmers’ characteristics; knowledge of pesticides and sources of information, precautions and damage-averting behavior along with any associated health effects. From their responses, we developed a simultaneous three-equation model of farmers’ overuse, risk perception and health effects of pesticides. Our analysis indicates that the toxicity of pesticides, crop composition, location and other socio-demographic factors determine the incidence of overuse. To the best of our knowledge, such in-depth studies of overuse of pesticides are non-existent in the literature, especially in the context of developing countries. The current study did not permit us to investigate issues such as the use of faulty equipment leading to overuse, mis-labeling or content switching, nor the role of pesticide subsidies in pesticide overuse. These issues remain as important research issues that should be the focus of further studies into the farmers’ behavior of pesticide overuse.

The remainder of the paper is organized as follows. In Section 2 we describe the trend of pesticide use and the associated problems in Bangladesh. In Section 3 we present the survey, and in Section 4 we describe the dataset. The models, along with our econometric findings, are documented in Section 5. In Section 6 we conclude and offer some implications of our findings.

# **2.  Pesticide Use in Bangladesh at a Glance**

As in many developing countries, Bangladesh<sup>2</sup> has promoted the use of pesticides to expand agricultural land and increase output per acre. Promotional activities have included extension services and significant subsidies (Rasul and Thapa, 2003; Hossain 1988). As a consequence of this expansive policy, pesticide use has more than doubled since 1992, rising from 7,350 metric tons to 16,200 metric tons in 2001 (See Figure 1). A

> 2 Approximately 84% of Bangladesh’s people are directly or indirectly dependent
a listing exercise was conducted for both host and Rohingya displace in the selected enumeration areas. In this section we explain how the listing exercise was conducted and highlight some challenges that arose when implementing the listing and their solutions.

The resources available for refugee camps and host communities were significantly different, but the proposed listing methodologies for both samples were based on the respective maps available for the enumeration areas. Field teams were instructed to identify the enumeration areas with assistance from local authorities and using landmarks from the maps. Once at the enumeration area, the listing instructions were divided into three categories:

- For EAs with less than 400 households in both camps and hosts, the entire EA was to be listed.

- For larger EAs with more than 400 reported households in camps (based on NPM population counts), the block was divided into sub-blocks. The segmentation methodology yielded subblocks of 100-150 households each based on the number of buildings and geographical

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<!-- Start of picture text --> 130 Ethiopia Kenya 125 Bangladesh Cambodia<br>Tanzania Uganda Ethiopia Vietnam<br>Ghana Mozambique<br>120 120<br>Zambia<br>110 115<br>100 110<br>90 105<br>100<br>80<br>95<br>70<br>Source: Data from NBE.<br>Jan-14 Mar-14 May-14 Jul-14 Sep-14 Nov-14 Jan-15 Mar-15 May-15 Jan.-14 Feb.-14 Mar.-14 Apr.-14 May.-14 Jun.-14 Jul.-14 Aug.-14 Sep.-14 Oct.-14 Nov.-14 Dec.-14 Jan.-15 Feb.-15 Mar.-15 Apr.-15 Mar.-15<br><!-- End of picture text -->

**Figure 9. Official and Parallel Exchange Rates, and Parallel Market Premium**

<!-- Start of picture text --> 35 Panel A. Official and parallel exchange rates (2002/03-2017/18)<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Source: Data from NBE.<br>2002/03_Q1 Q3 2003/04_Q1 Q3 2004/05_Q1 Q3 2005/06_Q1 Q3 2006/07_Q1 Q3 2007/08_Q1 Q3 2008/09_Q1 Q3 2009/10_Q1 Q3 2010/11_Q1 Q3 2011/12_Q1 Q3 2012/13_Q1 Q3 2013/14_Q1 Q3 2014/15_Q1 Q3 2015/16_Q1 Q3 2016/17_Q1 Q3 2017/18_Q1 Q3<br><!-- End of picture text --> <!-- Start of picture text --> 40 Panel B. Parallel Market Premium, % (2005/06-14/15)<br>35<br>30 Official exchange rate<br>Parallel exchange rate<br>25<br
on RTCs is a recognized and unmet challenge <mark>[18]</mark> . The objective is to improve RTC data for urban planners so they can contribute to addressing the high toll of road deaths, estimated globally at 1.35 million a year [3]. Our case study is Kenya, a country with high road mortality, where the official figures are said to underestimate the number of fatalities by a factor of 4.5 [3].

The United Nations’ Sustainable Development Goal (SDG) 3 sets a target to halve road mortality by 2020; progress has been slow, and the target moved to 2030. The Stockholm Declaration by the Third Global Ministerial Conference on Road Safety “Achieving Global Goals 2030” reiterated the call for country investments in road safety–from legislation and regulation, safe urban and transport design, safe modes of transport and vehicles, to modern technologies for crash prevention, trauma care, and urban management. However, resource constraints make it unlikely that countries will be able to meet all of these goals. Instead, countries should strategically invest for the greatest impact. This requires knowing where and when crashes happen, so that resources can be targeted to risky locations and times.

Social media data, with all their biases, can contribute to filling some of the data gaps for urban analysis, planning and management [19]. In this study, we create an algorithm that classifies transport-related tweets into geolocated RTCs for Nairobi. This is done by building on existing literature to test two natural language 2
if Venezuelans see that many Brazilians are downgraded in the firm, they might not be too willing to work there but once Venezuelans are present in the firm, they care about the relative Venezuelans’ performance and not directly the Brazilians’.<sup>25</sup> The model below shows the second stage estimated in the Heckman model and for the OLS model, we include the same variables except the _λ_ s (Inverse Mills ratio).

where _Ff,ijz_ is the F-index of integration at the firm and all the other covariates are as described before except _λijz_ , which is the inverse mills ratio, which is estimated using the first stage selection model of the Heckman model and _Firmijz_ includes a dummy which is 1 if the Venezuelans in the firm are more likely to be downgraded than Brazilians .

To answer the third question, a Mincer wage regression is estimated using both ordinary least square estimation and a two-stage Heckman selection model. The selection problem in the Mincer regression arises from the fact that we only observe the wages of workers, who are in wage employment as of December 2019, consequently in the RAIS dataset, this paper only observes wages of about 80% of the workers in the formal sector. In line with previous estimation strategies, this paper estimates the Heckman model with and without using exclusion restrictions. The exclusion restriction that

> 24The Probit model is used as a robustness check.

> 25The paper admits that the exclusion restriction may be weak but in the result section it shows statistical evidence of proportion of Brazilian downgraded a↵ecting the selection model but not the extent of integration.

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- Elbers, C. and van der Weide, R. (2014). Estimation of normal mixtures in a nested error model with an application to small area estimation of poverty and inequality. Policy Research Working Paper 6962. The World Bank.

- Elvidge, C.C., Hsu, F. C., Baugh, K. E. and Ghosh, T. (2013). _National Trends in Satellite Observed Lighting: 1992-2012_ , O. Weng (ed), Global Urban Monitoring and Assessment Through Earth Observation edn. CRC Press.

- Engstrom, R., Hersh, J. and Newhouse, D. (2021). Poverty from space: Using highresolution satellite imagery for estimating economic well-being. _World Bank Economic Review_ .

- Ferreira, F. H. G., Chen, S., Dabalen, A. L., Dikhanov, Y. M., Hamadeh, N., Jolliffe, D. M., Narayan, A., Prydz, E. B., Revenga, A. L., Sangraula, P., Serajuddin, U. and Yoshida, N. (2015). A global count of the extreme poor in 2012 : data issues, methodology and initial results. Policy Research Working Paper 7432. The World Bank.

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# **2.1.1 Technology grid**

A critical feature of the survey is how technology is measured. To design modules B and C, the FAT survey relies on a group of technology experts to determine the business functions relevant to the firm and the list of technologies that can be used to implement the key tasks in each function, as described by Cirera et al. (2020). We call the resulting structure the _Technology Grid_ . The grid in FAT has three characteristics. First, it is comprehensive. It includes the main business functions and the full array of technologies in each function, from the most basic to the most advanced technologies available. Second, the business functions and technologies in the grid are relevant to all firms within any given sector. In addition to identifying the key business functions and relevant technologies, technology experts also provided a ranking of the technologies in each business function based on their sophistication. Overall, the FAT survey covers about 300 technologies split into almost 60 business functions, including general business functions (GBF) that apply to all firms, regardless of the sector, and sector-specific business functions (SBFs) applied to agriculture (crops and livestock), manufacturing (food processing, wearing apparel, leather, pharmaceutical, and automotive), and services (retail, accommodation, land transport, banking, and health). Appendix A shows the grid for GBFs and an example of SBFs for the food processing sector.

# **2.1.2 Technology questions**

The survey contains three types of questions about the technologies used by the firm. First, FAT asks whether the firm uses each of the technologies in the grid to conduct the tasks of the particular business function. After determining the technologies that are used by the firm in a business function, the survey asks which of these technologies is the most widely used in the business function. Third, when a firm uses an advanced technology in a given business function, the survey asks how many years