rafmacalaba/data-use-mentions-extended huggingface dataset:
path algorithm. From this reference point, we run multiple “improved” scenarios that account for the planned infrastructure projects linked to the BRI and we assess the reduction in shipping times resulting from these projects. The list of projects we consider and the associated assumptions for the computation of shipment time is described in the next sub‐section.

We carry out the analysis using a GIS software which allows us to precisely map the current transportation network and then to enrich it with the planned infrastructure improvements that can be linked to the BRI. In particular, we use ArcGIS Network Analyst which provides network‐based spatial analysis tools for solving complex routing problems. A network solution involves finding the shortest path between two locations, where the length or cost of a path is the total accumulated shipping time computed along the optimal path.

In our analysis, we focus on rail and maritime links, abstracting from road and air connectivity, which allows to simplify the network analysis. Two main reasons justify this approach. First, as discussed in detail below, the large majority of BRI transport projects consist of rail and maritime infrastructure. Second, most international trade travels by sea and, to a smaller extent, by rail. Maritime shipping accounts for 80% in volume and 70% in value of global trade (OECD, 2017) and rail is the second transportation mode used, while only a small portion of international trade travels by road and air. GTAP data indicate that for BRI countries the percentage of international shipments by water and land is around 90%. According to shipping data, this fraction is even higher for China.<sup>4</sup> The nodes of our network in the global database, which serve as both origin and destination in the analysis, are all cities with population greater than 500,000,<sup>5</sup> as well as the two most populous cities in each country (data permitting). This creates a total of 1,000 cities and includes 34 cities
thus suggesting that empirical comparisons between war‐afflicted and non‐conflict economies are needed to ascertain whether debt accumulation is a central tendency around the onset of armed conflicts.

Public debt and inflation are key pillars of the macroeconomics of conflicts. However, the role they play at different stages of conflict remain blurred. This paper thus contributes new evidence to a long‐standing literature on the macroeconomics of armed conflict primarily by exploring differential trends across countries.

# **III. Data and empirical strategy**

The empirical strategy relies on two sets of variables. First, the analyses require data on the timing of the onset of armed conflicts in developing economies from 1950‐2016. Second, it requires data on the three macroeconomic variables of interest.

# **_III. A. Data on conflicts_**

To analyze the macroeconomic behavior of developing countries before, during and after the start of an armed confrontation, this paper relies on the information provided by the UCDP/PRIO Armed Conflict Dataset version 17.2.<sup>2</sup> Given that the main goal is to study the relationship between macroeconomic performance and conflicts in general, all extra‐systemic, interstate, internal and internationalized internal armed conflicts affecting developing countries reported by the database are included in the estimation samples. For conflicts that occurred between two or more states or between a state and a non‐state group outside its own territory, conflicts have been assigned to the developing countries involved in such conflicts. That is, high‐income economies are not included in the estimation samples.<sup>3</sup> We apply an event study methodology, transforming calendar years into event years.<sup>4</sup> Under this approach, year zero corresponds to the onset of a conflict. The negative and positive event years represent the periods before and after the beginning of the crisis, respectively. In total, this study covers 11 years of every conflict; five years before and after the starting date plus
**Figure 2: Employment growth and informal competition relationship**

<!-- Start of picture text --> Figure 2A<br>0.6<br>0.47<br>0.4<br>0.2<br>0<br>Low informal competition High informal competition<br>-0.2 (residuals; roughly below (residuals; roughly above<br>median) median)<br>-0.4<br>-0.6<br>-0.8 -0.7<br>Figure 2B<br>0.6<br>0.41<br>0.4<br>0.2<br>0<br>-0.2 Low informal competition High informal competition<br>(residuals; roughly below 75th (residuals; roughly above 75th<br>-0.4 percentile values) percentile values)<br>-0.6<br>-0.8<br>-1<br>-1.2<br>-1.17<br>-1.4<br>group mean values)<br>Employment growth rate (residuals,<br>group mean values)<br>Employment growth rate (residuals,<br><!-- End of picture text --> Source: Author’s own calculations based on Enterprise Surveys data. Note: 1) Employment growth rate is calculated as the difference in the level of employment (final year employment minus initial year employment) divided by the simple average (mean) of employment in the final and initial year. The ratio is then divided by the number of years between the final and initial year; the figure is multiplied by 100 to convert to percentage terms (Haltiwanger method). 2) The figures are a partial scatter plot of the residuals of employment growth rate (Y axis) plotted against residuals
# **2.3 Results at rayon level**

The 2002 Household Budget Survey allows spatial analysis of poverty only at oblast (economic region) level with urban and rural breakdown. According to the annual report of Azerbaijan’s progress toward the MDGs (SPPRED 2004), poverty rates (using the monthly per capita consumption measure of welfare) are higher in urban areas compared to rural areas, the highest poverty incidence being found in Nakhchivan AR. Poverty in the Shaki-Zagatala region is close to the average level and poverty in large cities is noticeably less than in smaller towns.

Our findings confirmed those results and previous findings from the recent Azerbaijan Poverty Assessment (Dowsett-Coirolo 2003). However, comparing the regional poverty measures from the AHBS 2002 and the estimates of welfare obtained from the census, we also find marked geographical variation in poverty rates at the rayon level _within_ each major region, as well as at administrative areas _within_ rayons.

Figures 1 and 2 show the proportion of people below the poverty line (taken as the 40th percentile of the national distribution) in rural and urban areas. If there was no spatial variation in poverty, we would expect 40 percent of each area to be poor. Several findings stand out. First, there is a higher incidence of poverty in urban areas as compared to rural areas. Second, urban areas also show a higher degree of spatial variation in headcount poverty. Headcount poverty shows an homogeneous picture for the rural population with headcount poverty ranging around 30 to 40 per cent in most regions except higher level in rural Nakhchyvan AR and to a lesser extent in rural areas of Ganja-Gazakh and Absheron-Guba. However, headcount poverty measures in urban areas show a strong spatial heterogeneity ranging from 20 to 60 per cent. Headcount poverty is well below average in urban areas of Jalilabad , Hajigabul, Gusar , Uiar and Dashkasan rayons and poverty levels are well above average in urban areas of Fuzuli, Khyzy, Aghstafa and Gazakh rayons with the more than half of the urban population below the 10
Venezuela<br>Vietnam<br>Yemen<br>Zambia|
|---|---|---|---|---|

**Table S1.2. Data Sources for Country-level Analysis**

|**Indicator**|**Source**|
|---|---|
|Total employment (persons, millions)<br>Unemployment rate (percent)<br>Gross Domestic Product in constant prices (national currency,<br>billions)|World Economic Outlook (WEO)<br>WEO and World Development Indicators from World Bank (WDI)<br>WEO and WDI|
|Growth of Real GDP Exp. In Current Oct. Pub. (%)<br>Real effective exchange rate (2010=100)<br>Gini net mean of 100<br>Tariff rates<br>Trade balance as a share of GDP; Trade balance is computed using<br>exports of goods and services, and imports of goods and services.<br>Exports, imports and GDP are in constant prices (national currency,<br>billions)|WEO<br>Information Notice System (IMF)<br>The Standardized World Income Inequality Database (SWIID)<br>Compiled by IMF Research Department (Prati, Onorato, and Papageorgiou 2013; Giuliano, Mishra, and<br>Spilimbergo 2013). Underlying sources are the WITS, WDI, WTO, GATT, BTN (Brussels Customs Union<br>database)<br>WEO and WDI|
|Crises|Leaven and Valencia 2010|
|Wars, political regime|Polity database|
|Instruments for tariff|Author calculation usingdata from WDI and IMF Direction of Trade Statistics|

2
<!-- Start of picture text --> Today, 69 percent of adults around the world have an account<br>Adults with an account (%), 2017<br>4 55. ne<br>f ye _<br>i a oe<br>| Re =“ .<br>i, 5 *<br>mm 40-419 \ ¥ “y2. ; ‘7<br>me=:3-100 : yy<br>Mo data L<br>Source: Global Findex database.<br><!-- End of picture text -->
al. (2019) compiled data on the gross fiscal costs of shocks to SOEs that exceed 0.2 percent of GDP. They identify 29 episodes in which SOEs were the source of contingent liabilities for the government, 17 of which involved infrastructure SOEs broadly defined (15 in the four sectors we study). They find an average gross fiscal cost of 3.2 percent of GDP for SOEs (3.0 percent for the sectors we study), with a maximum gross cost of 15 percent of GDP. Most of these costs were incurred during the global financial crisis of 2008–10 (see table A.1 in the appendix).

The empirical work on fiscal risk has focused on contingent liability risk, usually under extreme events. But fiscal risk—defined as “the possibility of deviations in fiscal variables from what was expected at the time of the budget or other forecast” (Cebotari aet al. 2009, 4)—is not a once in a lifetime event for an SOE. As we demonstrate with systematic cross-country data, fiscal risk is more often manifested as a series of small to medium-size deviations from budgeted figures, shortfalls in performance in SOEs, and liquidity crises that may require small fiscal injections (0.1– 0.3 percent of GDP) on a regular basis. When we examine fiscal injections that increase capitalization and/or substitute liabilities (that is, net fiscal transfers) as a percentage of assets, we see that bailouts are frequent and occur more often than assumed by the literature. This finding suggests that better ex ante monitoring of SOE financials can prevent both major catastrophes and some of the smaller instances of cash flow risk, which can compound into major events (Musacchio et al. 2015; Ter-Minassian 2018).

Following the categorization of fiscal risk of Polackova Brixi and Mody (2002), we explore a variety of implicit contingent liabilities that stem from the operation of infrastructure SOEs and from their accumulation of liabilities. We examine the fiscal risk and its key
ing LSMS surveys for 18 countries after 2010.

# **4.1.1 Data challenges**

Dealing with the presence of a large number of zero-transport expenditures and including expenditures on durable goods in the total transport expenditure per household are the two main data challenges to consider before estimating the model.

**Large number of zero-transport expenditures** There is a substantial number of observations with zeros in all transport subcategories of private transport nondurables and transport services. A household is assumed not to spend on transport either when the answer is zero, or when a missing entry can be recorded as zero.<sup>8</sup>

**Include private transport durables** LSMS surveys contain information on private transport durable goods included in the durable goods section. Durable goods are a category of consumer goods that do not wear out quickly, and therefore do not have to be purchased frequently. Therefore, durables goods need a particular treatment. We follow the procedure from Deaton and Zaidi (2002) to calculate the annual value of transport durables using the following equation:

Where _StPt_ is the current value of durables ; _rt_ − _πt_ is the real interest rate; _δ_ is the depreciation rate. Because of missing values and outliers, we use the median value (12 %) of countries’ real interest rates for the survey year or the latest year available for all countries (Table A.2 in the Appendix). We then estimate depreciation rates for the countries for which the LSMS surveys contain information on purchase value, current resell value and age of durables using the following approximation: For all countries, we use the median value (8 %) to deal with missing values and outliers (Table A.2 in Appendix).

# **4.1.2 The determinants of transport expenditures**

We want to estimate the following specification:

> 8The missing expenditure entries can be recoded as zeros when there is a screening question on whether the household purchased the items over a certain period or
and Fjeldstad (2016) find no evidence for a logic of fiscal exchange among business-owners in Ethiopia, although measurement is poor and the sample is business-owners, who might behave differently from private individuals. The experiment in Castro and Scartascini (2015) finds null effects on compliance when respondents are reminded about public goods provision, although the authors attribute this to a weak treatment and the difficulty of changing perceptions of goods provision with just one message.

# **Tax morale, institutions and political legitimacy**

A related but distinct argument to the fiscal exchange hypothesis is that individuals comply with taxes because they trust the government and think that the process by which the tax was decided on is fair and legitimate.

Descriptive cross-country work based on individual-level surveys such as the World Values Survey shows that trust in government, support for democracy, and measures of the quality of institutions are positively related to survey-based measures of tax morale (Alm and Torgler 2006). Survey-based research focusing on the developing world by Daude, Gutiérrez, and Melguizo (2012) finds some support for a positive relationship between tax compliance and trust in government as well. On the other hand, the correlation between tolerance of tax evasion and 4
Policy Research Working Paper 9197

# **Abstract**

The past decades witnessed big changes in international trade with the rise of global value chains. Some countries, such as China, Poland, and Vietnam, rode the tide, while other countries, many in the Africa region, faltered. This paper studies the determinants of participation in global value chains, based on empirical evidence from a panel data set covering more than 100 countries over the past three decades. The evidence shows that factor endowments, geography, political stability, liberal trade policies, foreign direct investment inflows, and domestic industrial capacity are very important in determining participation in global value chains. These factors affect participation in global value chains more than traditional exports.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at afernandes@worldbank.org, hlkee@worldbank.org, and dwinkler2@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
a lower price in local markets if water access is good and thus productive (US$299 in Doho and US$302 in Chokwe). This implies that, under proper management, large-scale irrigation can provide good returns as emphasized by Inocencio et al. (2007). Although we cannot perform a similar exercise for Sahelian countries, noting relatively high net returns in all schemes except Bagré, their competitiveness should also be high.<sup>10</sup> In summary, descriptive analyses indicate that irrigated rice of large-scale irrigation schemes has potential to achieve high yield and thus to be competitive if farmers have good access to irrigation water and use adequate crop management practices. Note that such efficient rice farming is achieved by small farmers, as in the case of Asia (see Table 3 for farm size). In the following section, using data from Doho and Chokwe, we conduct more detailed statistical analyses, to explore what kind of constraints hinder adequate management for high yield and how they are related to water access.

# **4. Regression analyses**

# _Methodology_

In order to examine the conditions to achieve high yield at our study sites, we estimate the yield function and input use functions. In a structural form, yield per ha can be expressed as a function of inputs per ha, given technology and the management ability of farmers: , where y is yield per ha, X is a vector of inputs, and H is a vector of household and farming characteristics. Our econometric concern, however, is that inputs are endogenous variables and OLS is not an appropriate approach. To circumvent this problem, we apply

> 10 Bagre‟s low income (US$166) stems from the much lower paddy price in local markets (92 Fcfa) than the other schemes (128 and 119 Fcfa). Meanwhile, excessively high income in Mali (US$1,000 and $983) is due to the high paddy price (208 and 192 Fcfa).

12
Since long-run marginal cost could be expected to lie above historic average cost, the resulting costrecovery figures are therefore likely to overestimate the extent to which marginal costs are covered by tariffs.

The concept of affordability (or vertical equity) is not very rigorously defined in the literature but refers to the fact that the expenditure burden of meeting basic energy needs should be kept within reasonable levels to ensure that households have adequate budgetary space to meet other basic needs. While there is no objective definition of what constitutes a reasonable level of expenditure, 5-10 percent of the household budget is a widely used rule of thumb in the literature (Lampietti et al. 2007 and Komives et al., 2005). Broadly, the 5 percent threshold is more applicable to tropical environments where electricity is primarily used for lighting and appliances, while the 10 percent threshold is more relevant to temperate environments where electricity may also be used in thermal applications. To gauge the affordability of electricity from the perspective of poor households, an affordability indicator is used, capturing the share of the average electricity bill in the household budget of the poorest 40 percent of the household income distribution (see Box 3). The average budget of the poorest 40 percent of each country’s population is obtained from the World Bank World Development Indicators and converted into household terms through dividing by the population of the bottom 40 percent and multiplying by the household size.

Cross-subsidization is widely practiced in the electricity sector, and entails charging some customer categories above the cost of service provision with a view to charging others below the cost of service provision. Cross-subsidization can be used to meet a variety of economic and social goals, such as promoting affordability for residential customers or enhancing the competitiveness of non-residential customers.

6
capabilities are complementary factors for other investments, such as the adoption and use of Information Technologies (IT) (see, for example Bloom et al., 2012, 2016).<sup>18</sup> Figure 4: Informality according to normalized polychoric **PCA 5** vs. Management practices and digital readiness <!-- Start of picture text --> (a) Management vs. Informality index (b) Digital readiness vs. Informality<br>.7 2<br>1.8<br>.6<br>1.6<br>.5<br>1.4<br>.4 1.2<br>1<br>.3<br>-.2 0 .2 .4 .6 .8 -.2 0 .2 .4 .6 .8<br>PCA Informality PCA Informality<br>(c) Management score by informality type (d) Digital readiness by informality type<br>0.7 1.5<br>1.4 1.34<br>0.6 1.3<br>0 .5 4 1.2<br>0.5 1.1<br>1.0<br>0.4 0.37 0 .4 0 0.9 0.78 0 .8 8<br>0 .3 3 0.8 0 .6 9<br>0.7<br>0.3<br>0.6<br>0.5<br>0.2 0.4<br>0.3<br>0.1 0.2<br>0.1<br>0.0 0.0<br>Necessity De Soto Parasite Fully formal Necessity De Soto Parasite Fully formal<br>Normalized Management score Initial Digital Readiness Score<br>Mean management score<br>Mean digital readiness
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