rafmacalaba/data-use-mentions-extended huggingface dataset:
data. Hence, the time-varying determinants of domestic and foreign firms’ exports _Z_ must also be considered.

The conditioning set _Z_ is composed of three categories of variables. First, following the gravity literature, the demand-side determinants of new export linkages are controlled for by the destination country’s import value, defined at the four-digit product level taken from the BACI world trade dataset<sup>5</sup> and the GDP per capita of the importing country.<sup>6</sup> Second, supplyside determinants are taken into account by introducing proxies for provincial and Chinese comparative advantages and export intensity. In the absence of firm-level data, these controls are crucial to account for the time-varying ability of different provinces to export different products to different countries. Hence, the log of the province total export sales, the provinceproduct export sales, and the China-product export sales in year _t_ are introduced. Because the regression also includes year fixed effects, which account for the evolution of total Chinese exports, controlling for these variables amounts to introducing the elements of a Balassa index of “revealed comparative advantage” at the province-product level. The total bilateral exports from province _i_ to country _j_ and the total Chinese bilateral exports to country _j_ are also introduced to control for specific relationships between the province/China and the destination country. This step is important given the use of business and trade agreements by the Chinese authorities to manage their diplomacy. Finally, the province GDP per capita is used to take into account the supply-side determinants of exports such as workers’ skills.<sup>7</sup> Third, to ensure that the decision of domestic firms to start exporting does not capture the intrinsic dynamics of exports at the product level or at the country level, the lag values of Chinese and province _i_ ’s exports at the product level and at the destination country level are included, as well as the

> 5This dataset, which is constructed using COMTRADE original data, provides bilateral trade flows at the six-digit
The remainder of the paper is organized as follows. Sections 2 and 3 introduce our data and methodology, respectively. The main empirical observations derived from our database of Twitter data on COVID vaccination are presented in Section 4. In Section 5 we contrast our Twitter-based estimates of vaccination hesitancy to estimates obtained from more conventional survey data and rationalize the divergence. Finally, Section 6 concludes.

# **2. Data**

Between December 2020 and April 2021, we used Twitter’s open API to download tweets featuring the most prominent COVID hashtag in Arabic: كورونا#. As our interest is in COVID vaccination attitudes in the region, we filtered the database for vocabulary related to vaccination. Specifically, we compiled a list of 24 vaccination-words in Arabic (shown in Table 1) and identified the sub-sample of tweets that feature at least one of these words.

|**English**|**Arabic**|
|---|---|
|Vaccine|لقاح|
|Vaccine|مصل<br>|
|Treatment|علج|
|Immunization against|تحصين ضد<br>|
|Doctors Syndicate|نقابة<br>أطباء|
|Sinopharm|سينوفارم|
|Vaccination<br>|التلقيح<br>|
|Possible side effects|والعراض الجانبية<br>المحتملة|
|Clinical trials|التجارب<br>السريرية|
|Immunity|المناعة<br>|
|Medicines|أدوية|
|G42 UAE company|شركة<br>G42الماراتية|
|G42 UAE Healthcare Company|شركة<br>G42الماراتية للرعاية<br>الصحية|
|Chinese vaccine|اللقاح الصيني|
|Pfizer vaccine|لقاح فايزر|
|Sputnik vaccine|لقاحسبوتنيك|

Table 1: Vaccination vocabulary Our data is organized into two sub-periods. The first spans from mid-December through to mid-February and contains approximately 730,000 tweets and 230,000 user accounts engaging in the vaccine discourse. Approximately 4000 influencers accounted for 95% of the discourse. The second period runs from late February to early April and
0**|**1.2**|**1.2**|**1.3**|**1.3**|**1.3**|**67.7**|
|Sri Lanka: as percent GDP|6.8|7.1|7.5|7.9|7.1|7.1||

_Source: World Bank (2005)_ Significantly, remittances are larger<sup>_1_</sup> and more stable<sup>_2_</sup> than foreign direct investment (FDI), and on a per capita basis, workers’ remittances to Sri Lanka are the highest in South Asia (Figure 1). In recent times, remittances to Sri Lanka have exceeded FDI inflows by 2 to 3 times; more than doubled net receipts of foreign assistance; and reached close to 25 percent of export earnings, second only to the garment industry and ahead of tourism and tea -- the island’s traditional export industry. They account for 7 percent of gross domestic product. At the microeconomic level, about 10 percent of households are recipient of overseas and domestic remittances. Overseas and domestic remittances constitute as much as 20.7 percent and 9.1 percent, respectively, of total income of recipient households, according to the 2001/2002 household survey.<sup>3</sup> 1
This index is defined by ICRG so that higher values mean less risk, but in our estimations we reverse the sign on this variable to give it the more intuitive interpretation that higher values mean more risk.

> 15 Although the ICRG Economic Risk index includes the host-country GDP per capita as a subcomponent, the correlation coefficient between the ICRG Economic Risk index and host-country GDP per capita variables is very low in our sample.

12
have a survey estimate.

## **3.2 Estimating the counterfactual 2020 welfare distribution**

To isolate the impact of the pandemic on poverty and inequality, we construct a counterfactual 2020 welfare distribution that approximates what the income distribution in a country would have looked like in the absence of the COVID-19 pandemic. To that end, we use the January 2020 per capita GDP growth forecasts for 2020 to grow each country’s 2019 welfare distributions to 2020. We assume all households grow with the same growth rate within a country, so inequality within a country is held fixed at the 2019 level. Evidence suggests that the pass-through from growth in per capita GDP to growth in the mean consumption in household surveys is less than

> 12 The authors show that more complicated methods that try to predict growth in the survey mean and/or changes to inequality yield only slight increases in predictive accuracy.

> 13 Note that while there are 169 countries with microdata in PIP, there is no national accounts data for the República Bolivariana de Venezuela after 2014. The República Bolivariana de Venezuela is assigned the regional average distribution between 2014 and 2019.

9
presents the empirical strategy and how food assistance and food access changed following the 2017 IPC announcement; section 6 discusses potential reasons why the increase in the share of households receiving assistance might not have had a larger or more immediate impact on food access; and section 7 concludes.

# **2. The 2017 Integrated Food Security and Phase Classification in Yemen**

The official 2017 Integrated Food Security and Phase Classification (IPC) was released in March 2017 by the Yemen IPC Technical Working Group. The announcement classified each governorate and the municipality of the capital- 22 regions in total- on the IPC scale, which is a system that has been adopted by the United Nations and their humanitarian partners to identify food emergencies. To determine the classification, the technical committee combined information on food security, nutrition, and other indicators to best identify each region of the country on the IPC scale (IPC 2017).

The classification differentiates between five progressively worse food insecurity phases- generally food secure (IPC 1), borderline food insecure (IPC 2), acute food and livelihood crisis (IPC 3), humanitarian emergency (IPC 4), and famine/humanitarian crisis (IPC 5). The classifications respectively correspond to more than 80 percent of households can meet their food needs in absence of coping strategies, at least 20 percent of households have consumption that is lessened but still adequate, at least 20 percent of households have significant intake shortfalls that cannot be met without irreversible coping strategies, at least 20 percent of households have 4
for each household group available income is computed in terms of dollars per day. Three of the household groups fall in between the one and two dollar per day poverty lines. The notable exception is the household group with an outside link and lacking draught power with just over two dollars per day. This is in line with the rural-urban income differences since this household group specializes in outside province migration.

To get a clear view of differences in household endowments that affect household response, the middle part of Table 2 details income sources. Specific features of the village SAM and equilibrium model arise in this table. For example, income from labor and irrigated land is split between shadow income<sup>4</sup> and above shadow income. The household survey data reveal imperfect labor and land markets. Households are involved in a variety of off-farm activities with different wages. These wages are well above the estimated shadow wage, indicating restricted access to off-farm employment and suggesting a situation of labor surplus at the local level. This is not unexpected, given the high population density in rural China and similar findings in a study by Bowlus (2003).

In the SAM and village equilibrium model the demand-constrained labor market is accounted for by valuing labor against household-specific shadow wages, estimated using the household survey data. In case of off-farm activities, labor then earns revenue above the shadow wage which is tracked in a separate account of the SAM. For example, for the unlinked households with no draught power labor is the most important endowment, contributing 74.9 percent to its income, broken down into 57.4 percent coming from shadow wages and 17.5 percent from above shadow wages.

> 4  Nonseparability results in household-specific shadow prices that balance households’ unobservable demand and supply. We therefore estimated an agricultural production function, explaining the total value of household output in terms labor-, land-, manure-, feed- and external inputs. The shadow prices are derived from this estimated production function as the marginal value product
of “basic opportunities” that is improperly allocated, relative to equal access across all types (Barros et al. 2011). In other words, it is a measure of how much re-distribution in access to a particular service would be required to move from the observed allocation to one in which average access was the same across types. Subject to the caveat in footnote 17, this is a perfectly plausible measure of between-type inequality in a particular dimension (that of service _j_ ). IEO-R, on the other hand, measures inequality of opportunity as the between-type share of income (or consumption) inequality. How do these two measures correlate? Do they yield essentially the same country ranking, even though their information bases are quite different, as appears to be the case with the HDI and the HOI?

It is probably too early to answer this question in cross-country terms. The overlap between the country samples in Table 1 (for which we have estimates of IEO-R) and in Table 3 (for which we have estimates of the dissimilarity index) is only ten countries, six in Latin America and four in Africa. Very little can be said, even about descriptive correlations, on the basis of such a small and unrepresentative sample. Nevertheless, for what it is worth, Figure 9 plots the IEO-R index against the dissimilarity index, averaged across its five dimensions. The correlation is -0.6989 (p-value: 0.0245), suggesting that the two alternative approaches to measuring inequality of opportunity can yield very different country rankings. It is true, of course, that in this sample the negative correlation is driven primarily by a dichotomy between Africa and Latin America, where the latter has lower dissimilarity in access to services, but a higher share of income inequality driven by unequal opportunities. Given that the IEO-R data for Africa in our sample is based on coarser partitions than in most other cases, one really should not read too much into this correlation. Nevertheless, it equally cannot be taken for granted that
, how this distribution of exposure falls upon regions and socioeconomic groups, and how climate change may influence these trends. Employing flood hazard maps and spatial socioeconomic data, this paper examines these questions in the context of Vietnam:

1. How many people are exposed currently? How might this change under climate change?

2. Where is exposure highest currently? How might this change under climate change?

3. How many poor people are exposed currently? How might this change under climate change?

Furthermore, given that the dynamics of poverty and natural disasters (and particularly, floods) occur at the local level, analyses at the national scale (or even at the province or district level) may miss important mechanisms and small‐scale differences, from one city block to the next. To complement the country‐ level analysis, we also focus at the local level within Ho Chi Minh City (HCMC), a city with high flood exposure. Here, we combine high‐resolution flood hazard data with spatial data on slum location, urban expansion, and migration, to examine the distribution of exposure across poor and non‐poor locations. While many studies have examined flood risk in Vietnam, many have only focused on hazard mapping. The contribution of this paper is to include the socioeconomic dimensions and examine how flood exposure is distributed across poor and non‐poor locations, at the country and city levels.

The national‐level analysis finds that a third (33%) of today’s population is already exposed to a 25 year event (an event with a probability of occurrence of 0.04), assuming no protection. For the same return 2
Table 7: The World Bank’s IEG Project Ratings and Missions

||(1)|IEG <br>(2)|rating: sat<br>(3)|isfactory <br>(4)|(0/1)<br>(5)|(6)|
|---|---|---|---|---|---|---|
|Fraction of locations with missions|0.02|0.02|_−_0.02|0.01|0.02|_−_0.01|
||(0.07)|(0.07)|(0.09)|(0.07)|(0.07)|(0.10)|
|Sector dummies|No|No|No|Yes|Yes|Yes|
|Mean dependent variable|0.67|0.67|0.66|0.67|0.67|0.66|
|_R_-squared|0.00|0.05|0.16|0.06|0.11|0.20|
|Observations|324|324|188|324|324|188|

_Source_ : Authors’ analysis based on mission locations from Beach (1903); aid projects from AidData, World Bank geocoded research release, version 1.3; and control variables from multiple sources, detailed in appendix A _Note_ : Robust standard errors are given in parentheses. The dependent variable equals 1 if the IEG rating is at least moderately satisfactory and equals 0 otherwise. Regressions are done without controls in columns 1 and 4.<sup>_∗_</sup> _p < ._ 1 _∗∗ p < ._ 05 _∗∗∗ p < ._ 01 In terms of project-level variables, the controls include project length (in years), the log of total
developing countries is the dual labor market framework. The labor market is expected to be segmented into a primary and a secondary market where the primary market is found in urban and industrialized areas and the secondary market is found in rural and agricultural areas. Segmentation is generated by entry barriers in the urban sector, skills mismatch, distances and other frictions and imperfections that prevent the two markets from functioning as a single market and wages from equalizing. One test of dual labor markets is precisely the existing wage differentials between the two sectors which is evident and significant in all the CIS-7.<sup>12</sup>

# **Dual performance**

Given that the CIS-7 have been able to grow almost continuously since 1997, a number of questions arise as to the structure and consequences of growth in a dual labor market context. Is growth occurring as a result of a process of re-industrialization? Are workers being pulled out of agriculture and into urban and industrial areas and are their wages improving? What are the other key development aspects characterizing this growth phase?

A **first** aspect to notice is that GDP growth in industry and agriculture has been very uneven. Industry has undergone a much deeper recession than agriculture and has recovered slowly during the growth period. In 2002, almost all the CIS-7 had an industrial output in between 40% and 60% below the 1991 level. Only Uzbekistan seems to have embarked into a real industrialization process having managed to increase industrial output by over 40% since 1991. On the contrary, agricultural output decline was contained within 40% of the 1991 value and in 2002 output was

> 12 National Statistical agency websites and CIS-Stat (1998 and 2004)

13
concentration of the export sector. This explanation is also akin to that advanced by Klapper, Laeven and Rajan (2006) for firms’ dwarfism in countries like Italy. Overall, our findings for the effect of financial structures on export concentration may then offer more support to the SBC hypothesis than to the banks’ long-termism hypothesis.

## **6.2 Further robustness tests**

In Table 7, we carry out several robustness checks for our baseline results. In Panel A, we experiment with instrumenting the relative presence of foreign banks with the indicator of “banking sector entry barriers” constructed by Abiad et al. (2008). This indicator is an inverse measure of the extent to which the country imposes entry restrictions in the domestic banking market, especially on foreign banks: it takes values from 0 to 3, with 0 representing the highest barriers and 3 the lowest.<sup>22</sup> We lag this indicator by five years to allow for delays in the impact of the entry regulation on the operations of foreign banks in the country. The first-stage estimates show that bank entry regulation significantly affects the relative presence of foreign banks (as expected, lower entry barriers imply larger presence of foreign banks). The second-stage estimates confirm the baseline results: foreign banks appear to promote entry into, and exit from, the export sector, boosting the turnover of exporters.

In Panel B of Table 7, we experiment by considering alternative indicators of institutional quality of the country in the analysis of the mechanisms of influence. We use credit registry coverage of adults as an alternative measure of the information available to banks. Similar to the results in Table 5a, Panel A, adding this control for the quality of information has no impact on the coefficients on the interaction of external finance dependence with the relative importance of foreign banks.

Further, we interact a measure of government effectiveness with the indicator of information complexity in Panel C, and with the indicator of contractual complexity in Panel D. These estimates
%|1%|
|Ecuador|49%|8%|43%|29%|9%|7%|
|Guatemala|35%|3%|59%|26%|6%|6%|
|Guyana|5%|2%|26%|8%|69%|52%|
|Honduras|33%|4%|62%|43%|5%|5%|
|Mexico|47%|5%|42%|28%|10%|10%|
|Nicaragua|35%|5%|47%|34%|17%|17%|
|Paraguay|45%|1%|20%|14%|35%|32%|
|Peru|43%|2%|22%|16%|36%|30%|
|Panama|26%|1%|26%|13%|48%|42%|
|Uruguay|37%|1%|23%|18%|40%|40%|
|Venezuela|51%|5%|40%|26%|10%|8%|
|_LCR Totals_|_43%_|_4%_|_40%_|_27%_|_18%_|_15%_|

Source: Population and density are from CIESIN Gridded Population of the World 3.

Notes: 1) LCR totals include all Latin American and Caribbean countries, including those that do not appear in the table.

2) There were a small number of coastal gridcells that were missing country codes, and were therefore omitted from the above table.

3) The denominator for the percentage was total population as given by the tabulated cells, and excluding those few cells with omitted country codes.
approach to validating the model using out-of-sample testing.

Finally, is the error term; , and are unknown parameters to be estimated.

To test for the robustness of the above demand estimation, I also allow household income to enter the interaction term as a continuous variable: (2) Furthermore, households may not have noticed price changes until after receiving the electricity bills. To account for delayed response to tariff increase, I also estimate the model using the electricity prices of the previous month. The results are robust to both alternative specifications. This is discussed in detail in Section V.

# _Sample Selection_

Using disaggregated data to estimate residential electricity demand is desirable as we avoid the confounding effect of misspecification arising from aggregation bias. (Blundell, 1988; Barnes, Gillingham, and Hagemann, 1981; George 1980; McFadden and Dubin 1980). However, using 8
**Table 11: Identifying the $3.20 poor based on covariates of poverty**

_<u>(Pooled data across countries with each country weighted by millions of poor)</u>_

|**Category**<br>**(1)**|**Per capita**<br>**poor only**<br>**(2)**|**Root N**<br>**poor only**<br>**(3)**|**Diff.**<br>**p-value**<br>**(4)**|**Per capita**<br>**poor**<br>**(5)**|**Root N**<br>**poor**<br>**(6)**|**Diff.**<br>**p-value**<br>**(7)**|**Obs**<br>**(8)**|
|---|---|---|---|---|---|---|---|
|Years of schooling|0.021***|-0.088***|0|-0.462***|-0.571***|0|683,533|
|Asset index|-0.005*|0.038***|0|-0.802***|-0.759***|0|431,436|
|Asset ownership|-0.021***|-0.085***|0|-0.284***|-0.347***|0
Table A10: Workers who are more idle at baseline do not benefit more from employment

||(1)<br>PS Index|(2)<br>PHQ|
|---|---|---|
|Work|0.212|0.242|
||(0.041)|(0.075)|
|Cash|0.060|0.013|
||(0.050)|(0.089)|
|Work * Baseline|-0.002|0.039|
|Idleness|(0.023)|(0.049)|
|Cash * Baseline|-0.021|0.035|
|Idleness|(0.022)|(0.049)|
|Test: Work X Idle = Cash X Idle|0.346|0.925|
|Observations|726|726|

**Notes:** This table reports the treatment effect of receiving employment, and the interaction effect of receiving employment and baseline hours reported idle, relative to the control group. The columns show OLS estimates of a regression of the outcome on a dummy for assignment to the work treatment, baseline hours idle, the interaction of the two, the parallel for the cash group, as well as camp and enumerator fixed effects, the baseline value of the independent variable, and sociodemographic controls as determined through a double-selection LASSO procedure Belloni, Chernozhukov, and Hansen (2014).

54
said to have better design than those that had not. Lending thus appeared as an important channel through which knowledge products informed developed policies and strengthened institutions at the country level. The effect of ESW products on informing World Bank strategies was significant as well, with “core” analytical products being particularly important in this respect.

In another study, a survey of senior World Bank operational staff was used to assess their awareness about research products, such as those produced by DEC (Ravallion and Wagstaff 2011). There were 2,900 recipients of the anonymous survey instrument used, and 555 respondents. Views were similar across regional units, but markedly different across sectors, with staff in areas of economic management and human development being more familiar with research products than their colleagues elsewhere.

10
. While this is ad hoc, it is coherent with expected positive returns from migration. All results are similar with the lower and upper poverty lines.

What happened to inequality? It increased in both sectors over time (with a small drop in 1991 - 92, see Table 1), but more so in urban than in rural areas. The group decomposition of the Gini index in Table 3 indicates that the increase in the national Gini was due in part to the increase in inequality measure of stratification or overlap between groups (see also Formby et al., 1997).