rafmacalaba/data-use-mentions-extended huggingface dataset:
-- Start of picture text --> .03 a. Expenditure share 150 b. Benefits per capita<br>Djibouti Iran Djibouti Iran<br>Libya Morocco Libya Morocco<br>Tunisia Tunisia<br>.02 100<br>.01 50<br>0 0<br>0 .19 .38 .57 .76 .95 0 .19 .38 .57 .76 .95<br>Percentiles of population Percentiles of population<br>US$-PPP<br>LPG expenditure share (in%)<br><!-- End of picture text --> _Source:_ World Bank estimations from Household Budget Surveys.

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(CCWAEC). Similarly, trade costs would fall by 5.6 percent for the China‐Indochina Peninsula Economic Corridor (CICPEC) and by 21.6 percent for the China‐Central Asia‐West Asia Economic Corridor (CCWAEC). These large effects are not surprising given the importance of trade facilitation bottlenecks in BRI economies (Bartley Johns et al., 2018).

Our paper relates to several strands of literature. First, the importance of time as a trade barrier has been studied extensively in the literature and the heterogenous sensitivity of products to shipment delays has been documented in a number of papers including Hummels (2001), Hummels, Minor, Reisman, and Endean (2007), Djankov, Freund and Pham (2010), Hummels and Schaur (2013) and Baniya (2017). Second, several recent studies have analyzed the trade effects of infrastructure projects either within or across countries (Donaldson, 2018; Duranton, Morrow and Turner, 2014; Alder, 2017). Third, a subset of this literature also relies on georeferenced data and GIS analysis to assess the trade and the spatial effects of transport infrastructure (e.g. Roberts, Deichmann, Fingleton and Shi, 2010; Volpe Martincus, Carballo and Cusolito, 2017). By providing precise estimates of the potential decrease in trade costs expected from the Belt and Road Initiative, our databases are being used as inputs in papers assessing the potential implications of the BRI for trade flows, foreign investment, GDP and welfare (e.g. de Soyres, Mulabdic and Ruta, 2018; Baniya, Rocha and Ruta, 2018; Maliszewska, van der Mensbrugghe and Osorio‐Rodarte, 2018; Chen, 2018; Bird, Lebrand and Venables, 2018).

The paper is organized as follows. Section 2 presents the database on BRI transport projects and the methodology used to measure shipping times. Results of the network analysis are presented in Section 3, while the impact of the BRI on trade costs is analyzed in Section 4
led to higher R&D, patenting, innovation and productivity in the case of European countries. Iacovone et al. (2013) evaluate the effects of Chinese competition for Mexico both on the extensive (firm exit) and intensive margins, obtaining heterogeneous results depending on the size of the firm, as small firms experience losses and in some cases are forced to exit the market, bigger firms exhibit the opposite effects leading to productivity growth.

In this paper we take advantage of a very rich and novel data set regarding ICT use in Mexico in order to shed some light on the relation between ICT use and firm productivity in the case of a developing country, which has been seldom studied. Furthermore, we build upon the ICT-productivity literature and extend it by taking into consideration the strand of literature that analyzes competition as a factor that speeds up the creative destruction process. Thus, we analyze whether facing higher competition pressures can enhance the relation between ICT use and productivity by providing the incentives for firms to either pursue the organizational changes that the adoption of these new technologies requires or simply to use them in a more efficient way.

# **2 Data**

Our data are from Mexico’s National Survey on Information Technologies 2009 and 2013, which is a survey designed by the National Science and Technology Council (CONACYT) and conducted by the National Institute of Statistics and Geography (INEGI). This survey includes detailed firm-level data regarding ICT and some other firm characteristics and it is under constant revision in order to improve the quality of the information obtained.<sup>2</sup> Using ENTIC 2009 and 2013, we were able to match 1,017 firms that appeared in both waves of the survey and using the correspondences between North American Industry Classification System (NAICS) codes, which is the sectoral classification used in ENTIC, and the Harmonyzed System (HS1996) codes, we merged this information with trade data from the World Integrated Trade Statistics (WITS), World Bank. Therefore, we ended
# **Technical Note 2: Creating a poverty map using the Asset index method**

## **Data and Methods**

In order to create poverty map in this section we apply the asset index methods using data from the 1999 Census of Azerbaijan collected from the State Statistical Committee of Azerbaijan. With this technique the socio economic status of households is defined in terms of assets or wealth, rather than in terms of income or consumption. The 1999 Census included several questions regarding the ownership of consumer durables and the materials used in the construction of the household, along with basic demographic information concerning household size and composition. Table 1 presents some basic descriptive statistics of the potential components of the asset index.

**Table 1: Ownership of assets and household characteristics, Azerbaijan 1999**

||_Mean_|_Standard_<br>_Deviation_|_Min_|_Max_|
|---|---|---|---|---|
|1. Gas Line|0.584|0.492|0|1|
|2. Gas Cylinder|0.209|0.407|0|1|
|3. Electric Oven|0.245|0.430|0|1|
|4. Heating system (private, public)|0.09|0.286|0|1|
|5. Stove|0.391|0.487|0|1|
|6. Water|0.588|0.492|0|1|
|7. Hot water|0.04|0.196|0|1|
|8. Sewage System|0.412|0.492|0|1|
|9. Bathroom|0.385|0.486|0|1|
|10. Telephone|0.269|0.443|0|1|
|11. Total living area (square meters)|65.24|47
.085***|0.054***|-0.026|
||(8.796)|(3.764)|(0.744)|(4.694)|(5.52)|-1.113|
|qxIV (quality of<br>institutions by legal origin)|0.662***<br>(90.41)||0.638***<br>(61.84)||0.660***<br>(46.33)||
|R-squared|0.951|0.753|0.95|0.745|0.951|0.716|
|Number of observations|27153|27153|13749|13749|7322|7322|
|Nunmber of countries||95||95||95|
|Number of industries||342||241||135|
|Widstat||8174.639||3823.676||2146.721|

Notes: The dependent variable is the natural log of exports in industry k by country i to the World. qxQ is instrumented using qxIV where the instrument (IV) is a variable that assumes the value of the average quality of institution across all countries with the same legal origin. Reported are the beta standardized coefficients of the second stage IV regression with t-values shown in brackets. * Significant at 10%; ** at 5%; *** at 1%.

# **V.  The Distance Puzzle Revisited**

Our finding that timeliness is particularly important as a determinant of trade in intermediate goods supports Harrigan and Venables (2004)'s view that fragmentation of production is a force of agglomeration and suggests that intermediate goods should be more sensitive to distance than final goods. To support our findings, in this section we also assess whether these expectations hold true using an alternative methodological approach: the standard gravity model
consumption. Instead of using a per capita monthly consumption measure, we use adult equivalence expenditure and employ an equivalence scale which gives a low weight to young children. This minimizes the impact of the drop in the number of young children in the household between the census and the time of the 2002 HBS. It is important to note that there are a number of alternative equivalence scales in use in Azerbaijan and that the choice here does not reflect any preferred measure but is simply designed to ameliorate the influence effect of recent fertility decline.

Two indicators of community welfare are computed: i) mean adult equivalent monthly consumption of the region – giving an indication of the average level of living standards the population of the region enjoys; and ii) the proportion of individuals living in poverty, where poverty is defined as living in a household with consumption in the bottom two quintiles of the population (i.e. with adult equivalent incomes below the 40<sup>th</sup> percentile or 215,235.2 Manat per month). This gives a measure of relative poverty.

The ‘imputed welfare’ poverty mapping procedure produces estimates of welfare at the rayon, administrative level and enumeration area. However, the estimates are only considered to be reliable for areas containing more than 1,000 households. Estimates at both the rayon level and administrative level could be potentially useful for geographical targeting; however there are some issues regarding the reliability of those estimates for purpose of geographical targeting in the special case of Azerbaijan. These issues are discussed in Section 2.2. Section 2.3 then shows the results at rayon level, whilst results at the administrative level are presented in Section 2.4.

One further issue which need to be adressed is whether the imputed welfare map refers to the time of collection of the survey data (2002) or the time of collection of the census (1999). A key assumption of the imputation procedure is that the two datasets refer to the same population and implicitly the
The larger this distance, the weaker the upstream instrument will be as a result of pollution decay. On the other hand, monitors that are closer together are more likely to be subject to common shocks such as rainfall, which would create spurious correlation.

We thus adopt a variety of definitions of “upstream”, and check the robustness of our results to the definition used. To assign an upstream counterpart to a given pollution monitor, we use the following protocol: first, we follow the river upstream until it reaches a new district; then, we locate the nearest monitor along the river that falls within a distance range (in km) of [ _X, Y_ ] from the original monitor, where _X ∈{_ 0 _,_ 20 _,_ 50 _,_ 75 _,_ 100 _}_ and _Y ∈{_ 200 _,_ 300 _}_ . When a river splits upstream of a given monitor, so that there is an upstream monitor on each of two tributaries, we take the unweighted average pollution reading of these monitors as our upstream measure.<sup>5</sup> When there is no upstream monitor of any kind to be found, we use the river’s origin as an upstream location (subject to the distance-range requirement) and assign the sample-wide minimum value of pollution as our upstream measure.

## **2.3 Other data**

We include several types of variables as controls in many of our regression analyses. The main body of these controls consists of cross-sectional birth, mother, and child characteristics taken from RCH-2. A pair of climate controls are created using monthly, gridded rainfall totals from the University of Delaware and air temperature averages from the Indian Meteorological Institute; we use these gridded averages to interpolate monthly rainfall and temperature values at each district (in mortality-only regressions) or monitor (in regressions involving pollution). Finally, we add time-varying measures of common effluent treatment plant (CETP) capacity and the incidence
affected by the shift in the past two decades from predominantly pearls and precious stones to mineral products. Exports of mineral products (ores and metals) represented 83 percent of total exports in 2013, followed by fuel (8 percent). The importance of pearls and precious stones exports in DRC’s export basket has diminished constantly throughout the last decade from 62 percent in 2000 to just about 7 percent of total exports. Both agriculture and manufacturing remain less important (see Figure 2).

**Figure 3. DRC: Exports by Destination (percent of total exports, 2000-2013)**

<!-- Start of picture text --> 85<br>80<br>75<br>70<br>65<br>60<br>55<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>China Korea EU27 US SSA<br><!-- End of picture text --> Source **:** calculated using data from UNCTAD database.

5. **China has become the main export market for DRC in the last decade, hence replacing the European Union (EU27).** The EU has traditionally been the main market destination for DRC exports, but has lost its importance to China for most of the last decade. China alone accounted for 41 percent of DRC’s exports in 2013. DRC’s exports to China grew at an average annual rate of over 102.9 percent between 2000 and 2013 and multiplied more than 509 times their share in DRC’s exports. Meanwhile, the share of total exports going to the EU27 declined by an average annual rate of 4.6 percent, from 77.4 percent in 2000 to 23.
# **_4.2 Social Cohesion_**

Compared to the use of public spaces, the impacts on social cohesion are mixed. The impacts on trust toward outgroups, perceptions toward outgroups, and willingness to interact with outgroups are summarized in Tables 3-5, respectively, both for the full sample as well as for selected subgroups.

First, Table 3 reports that the intervention did not have a statistically significant effect on the “trust toward outgroups index” (Column 1-2) and its components, i.e. trust toward different ethnic groups and the opposite sex (Columns 3-4 and 5-6, respectively). The only exception is the urban design and diversity intervention, which had a negative impact of 0.06 standard deviation on trust toward people from the opposite sex (Columns 5 and 6). The F-tests indicate that the treatment arms are not statistically different from each other.

Table 3. Treatment Effects on Trust Toward Outgroups

|VARIABLES|(1)<br>Index Trust<br>(z-sc|(2)<br>Outgroups<br>ore)|(3)<br>Trust Dif<br>Gr|(4)<br>ferent Ethnic<br>oup (=1)|(5)<br>Trust Op|(6)<br>posite Gender<br>(=1)|
|---|---|---|---|---|---|---|
|[T1] Urban Design|-0.07|-0.06|0.00|0.01|-0.04|-0.04|
||(0.064)|(0.063)|(0.032)|(0.032)|(0.032)|(0.032)|
|[T2] Diversity|-0.06|-0.06|0.02|
4|25.6|0.3|0.7|4.9|
|2012|Poverty Rate|41.1|21.3|37.0|18.4|7.0|13.2|
||S.E.|0.7|5.1|22.5|0.3|0.7|4.5|
|2013|Poverty Rate|40.3|17.7|46.7|17.9|6.6|16.1|
||S.E.|0.8|5.8|7.9|0.3|0.5|6.7|

_Notes_ : Regionally deflated, OLS imputation model, with time-invariant characteristics. The Near Syrian Border region (NSB) includes the NUTS2 areas TRC3, TCR2, TCR1, TR63, TR62. Time in Turkey is based on the date of arrival of the head of household. _Source_ : LFS 2009-2014. World Bank author’s calculations.

Figure 2 illustrates poverty trends in 2009 and 2013, by region and migrant status. Regions are divided into the near Syrian border region, and the rest of the country. The NSB includes the NUTS2 areas TRC3, TCR2, TCR1, TR63, and TR62. The population is also divided into host communities, recent migrant households, and settled migrant households.

Poverty is higher in regions near the Syrian border, for both host communities and migrants. The South Eastern region of Turkey has been historically the poorest area of the country. However, between 2009 and 2013, poverty has declined for host communities and settled migrant households. On the other hand, poverty of recent migrant households in the NSB region has been increasing, and significantly so in 2013 (Figure 3). The sharp increase in the poverty rates of recent migrant households is evident. Near the Syrian border, recent migrant households had a poverty rate of 15.6 percent (caveat on
the largest firms by the revenue quartile (above 75th percentile) are the most likely to file for business closure.

# **2.3 The Labor Force Survey**

The Labor Force Survey (LFS) is a nationally representative survey that is conducted on a quarterly basis by the General Statistical Office of Vietnam. The survey collects detailed employment information on a representative sample of workers in the country. In this paper, we utilize monthly data collected from three LFS surveys, covering the period between January 2018 and September 2020. Our sample is truncated to include only working-age individuals (15 years or older), who were identified as either being self-employed or working in a household business.<sup>16</sup> For abbreviation, we refer to this group uniformly as household business workers, or “HB workers”. We focus on two indicators that capture the intensive margins of COVID-19 impact: worker’s reported working hours during the week prior to the survey, and worker’s reported income during the month prior to the survey. To correct for idiosyncratic interference and measurement biases, we exclude individuals who reported to have reduced workload due to sickness, natural disaster, or holidays. Our analysis sample includes 419,323 individuals interviewed in 2018, 2019 and the first three quarters in 2020.

> 16“Self-employment” and “working in a household business” belongs to occupational categories 2 and 3 in the LFS.

6
in Vietnam recovered quickly.

The rest of the paper proceeds as follows. Section 2 introduces the background of household businesses in Vietnam and the main data sets employed in this paper. Section 3 presents our methodology to estimate the impacts of COVID-19 on household business survival. Section 4 discusses the results and Section 5 concludes.

# **2 Household Businesses in Vietnam**

In Vietnam, a household business (HB) is a firm run by a household or family that employs fewer than ten workers and operates in a fixed location (usually at their own residential premises). All HBs must register with the Department of Taxation, which operates under the Ministry of Finance, for a tax identification (tax ID) and pay taxes if their annual revenue exceeds 100 million Vietnam dong (VND), or approximately 4,300 USD.<sup>6</sup> Household businesses do not face stringent financial oversight like enterprises which operate at a larger scale and are required to house an accountant as well as to prepare official accounting documents. Its annual lump-sum tax payment, usually remitted at the beginning of a fiscal year in January, is often calculated as a percentage of annual revenue, and comprises annual license tax, value added tax (VAT), and personal income tax (PIT).<sup>7</sup>

## **2.1 Household business tax data**

We analyze a high-frequency tax census that comprises all tax-registered household businesses in Vietnam since January 2018. We retrieved historical tax filing records of household businesses from the official online portal of the Department of Taxation.<sup>8</sup> Pending reporting delays, this information from the Tax Department allows us to tabulate a complete set of tax filings in the country at the commune-month level, between January 2018 and August 2020.<sup>9</sup> Each household business is responsible to file and pay one annual lump-sum tax within a fiscal year. We use the (change in) tax filing
these drop-out rates are consistent with other similar entrepreneurship training program analyzed in the literature, such as Groh et al. (2012) or de Mel, McKenzie and Woodruff (2012).

Anecdotal evidence from qualitative interviews with graduates, coaches, and professors points to a number of reasons for students dropping out of the entrepreneurship track, including some lack of information and implementation challenges. The program was being implemented for the first time, and in some universities, students were informed late or insufficiently of the modalities of their participation in the entrepreneurship track.<sup>15</sup> The quality of the training and coaching sessions also varied both by training location and by coach or facilitator.<sup>16</sup> Table 3 presents marginal effects from the estimation of a logit model to describe the profile of students who complied with their assignment to the entrepreneurship track and completed it. Students who had a project idea at the time of application were more likely to comply with their assignment to the treatment group by completing the business training and personalized coaching sessions. In contrast, male students were more likely to drop out, and so were students in an Economics, Finance, or Business stream.<sup>17</sup> These students may have been more familiar with the entrepreneurship training content and as such may have perceived a relatively lower value-added of the entrepreneurship track compared to students from other subjects. Variations in drop-out rates across regions are also observed: compared to Tunis, compliance with treatment was lower in Sfax, but higher in Sousse, Monastir, Kairouan, Gafsa, and Jendouba, which are also the regions with relatively higher unemployment rates.

Administrative data reveal high compliance for students assigned to the control group. The twenty days of CEFE training was regularly provided by employment offices so that some control students may also have been able to take the training after graduating, although personalized coaching would not have been available to them. Administrative and survey data show that take-up of the CEFE training was low
_What has been the marginal impact on African countries of one additional dollar in_ **_ODA_** _debt relief?_ Table 6 reports the results of panel data regressions of import **capacity on** total grants (pure grants plus ODA debt relief), for the sample three groups of countries. The results include a more restrictive model (A), that forces the relationship to **be the** same for the entire sample period, and less restrictive ones (B and C) that allow for a break-point in 1989, when ODA debt forgiveness began increasing.<sup>2 1</sup> It is important to note that rather than implying any causality running from the independent variable (grants) to the dependent variable (import capacity), as is the case in standard regression analysis, our results are intended to be interpreted just as simple correlations between the variables (for a methodological discussion see Appendix 2). Constants for all countries are not reported because they are of no interest to our analysis. Similarly, results for the less restrictive models are usually reported only when they differ from the results obtained under the more restrictive one.

Table **6. Impact of incremental total grants on import bill capacity**

||_(A)_<br>_Total period_|_(B)_<br>_First period_<br>_(1984-88)_|_(C)_<br>_Second period_<br> <br>_(1989-93)_|
|---|---|---|---|
|Low group (16 countries)|1.285<br>(4.916)******|stable|stable|
|Middle group (8 countries)|0.964|0.997|0.974|
||(5.913)******|(4.296)****
713|100|764|100|18,273|100|

_Source:_ 2010 Zimbabwe Road Condition Survey.

Based on the physical configuration and condition of Zimbabwe‘s infrastructure network, it is possible to estimate the resources that would be needed to clear the current rehabilitation backlog and maintain the network in good condition. Comparing recent spending against these norms indicates that Zimbabwe has not succeeded in securing adequate resources for road-network preservation and maintenance (figure 12). There is a shortfall of about 50 percent between recent spending on road maintenance and rehabilitation and the requisite amounts. As the benchmarking shows, this situation is quite commonplace in Sub-Saharan Africa.

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