rafmacalaba/data-use-mentions-extended huggingface dataset:
analysis are the most updated version of the data available at the time this paper was written.

The key question in which we are interested from the Enterprise Survey asks firms in over 100 countries between 2005 and 2009 to identify the biggest obstacle to their operation and growth. Firms have the option to choose among 25 options which we have grouped into seven categories which are displayed in Table 1.

Using the adequate weights from the survey instrument we aggregate the firm level data to country-obstacle level by calculating the percentage of firms that identified each category as their obstacle to growth.<sup>4</sup> This is our measure of firms’ perceptions; the higher the percentage the greater the obstacle to firms in a given country. Once we matched the Enterprise Survey with the OECD CRS database we are left with 67 countries. Table 2 displays all countries in the sample.

The other key ingredient of this paper is the disaggregated data on aid flows. Data on aid flows for the years 2004 to 2008 come from the OECD CRS database, which documents Official Development Aid (ODA) flows from donor to recipient countries starting in the year 1973. This database is very comprehensive and covers almost every country of the world. It includes aid extended by about 40 individual country donors as well as multilateral agencies. Each entry contains the value of the aid flow from donor to the recipient and other valuable information such as the donor and recipient’s name, the year of the aid flow, the type of aid (i.e., grant or loan), the general purpose of the grant/loan and a detailed description of the project.

> 4 Enterprise Surveys use either simple random sampling or random stratified sampling. Individual observations are weighted by the inverse of their probability of selection which is equivalent to the weighted average of the estimates for each stratum, with weights equal to the population shares of each stratum.

9
between 3 and 30 percent of FOB values. Some two thirds of these costs consist of border delays and administrative costs. Border formalities for persons and goods are slow, complex and poorly administered. A multitude of agencies are present at DRC border crossings in direct violation of presidential orders, restricting their number to four. Bribery, incivility and violence are rife.

Official passenger transport across Malebo Pool is controlled by a duopoly consisting of the two stateowned transport operators. Private operators are licensed under a quota regime and subject to steep fees payable to the duopolists. We find that the price of crossing the river at Kisangani, a comparable setting without a national border, is about 300 times lower than that of crossing Malebo Pool, with the number of passenger crossings nearly 175 times larger on a per-capita basis. The volume of official passenger travel between Kinshasa and Brazzaville corresponds to about one fifth of the volume of traffic between East and West Berlin during the times of the Berlin Wall.

We find that the high border costs and low traffic volumes are observed in parallel with significant market segmentation, suggesting a causal link. Our survey of retail prices in Kinshasa and Brazzaville shows that average prices of locally produced staple goods are largely equalized across markets within each city. When comparing prices across the river, however, we find them to be statistically significantly higher, on both sides, for goods shipped across the river. Our central estimate of this price margin is 20 percent. Despite the dominance of the Matadi-Kinshasa-Brazzaville transit route according to official statistics, prices of overseas imports appear to be somewhat higher in Kinshasa than in Brazzaville. Such price differentials point toward considerable unexploited arbitrage opportunities.

5
and are not frequently updated. Migrants in urban areas who are living in worksites, in dormitories, or even less formal housing, are less likely to be enumerated. The Rural Urban Migration in China, developed and headed by Xin Meng from Australian National University from 2008 through 2016, addressed this weakness by using geography-based survey techniques and sampling through workplaces judged to employ migrants (Akguc et al 2014; Kong 2010).

# **3.2 Long-Term Migration Trends**

With the abolition of the production team system and the establishment of the household responsibility system in agricultural production in the late 1970s, China’s farmers not only increased agricultural production and earned higher incomes (Lin, 1992), they also gained freedom to allocate their labor to higher-earning non-agricultural activities locally -- first in local township and village enterprises, and later enterprises in urban areas within their prefectures or provinces (Bao et al., 2009). The establishment of the Special Economic Zones, starting in the mid-1980s, spurred foreign investment flows into eastern and southern coastal regions, creating an increase in demand for workers in new factories. Millions of migrants responded and moved for employment, most noticeably to the Pearl River Delta region in the south and the Yangtze River Delta in the east (Liang, 1999; Shen and Chiang, 2011).

After the mid-1990s private businesses also offered employment to migrants in closer cities (Chen and Coulson, 2002). Since the mid-1990s, rural-urban migration patterns have been dominated by intraprovincial movement (Kanbur and Zhang, 1999; Song and Liang, 2016), but inter-provincial migration increased steadily over time (Liang and Song, 2016) before stagnating since 2006, as shown in Figure 1. Recent studies on migration trends have not noted this slowdown. Most studies using 2000 and 2010 census data (Liang et al., 2014; Liang and Ma, 2004) fail to pick up the rate of change over these years. Longer distance migration is likely driven by differences in growth rates, and consequent
key mechanism for allowing entry in services is the license.<sup>5</sup> The license is defined broadly to cover any decisions or administrative actions, including authorizations or approvals, which allow a service provider to enter the market and provide a service. In this broad sense, virtually every service provider in virtually every country needs a license to operate – from an accountant and an architect to a bank and a builder. While licenses are almost always required, they are almost never issued in a non-discretionary manner. The limited evidence available from the World Bank Services Trade Restrictions Database suggests that licensing criteria are usually made public, reasons for denial are often provided, but the fulfillment of publicly stated criteria does not automatically lead to a license being issued (Figure 3).

Figure 3: Transparency, accountability and predictability

> 5 GATS Article XXVIII defines a “measure” to be “any measure by a Member, whether in the form of a law, regulation, rule, procedure, decision, administrative action, or any other form.”  It is convenient to distinguish, as the GATS does, between “measures of general application” and other measures.  Measures of general application include laws, regulations and rules (which inhabit the GATS schedules) as well as standard administrative procedures.  Other measures include decisions and administrative actions, which are one-off acts implementing measures of general application.

8
Wray and Cheruiyot, 2015, for a survey of land use modeling in South Africa).<sup>1</sup> Although the development of LUTI models for developing countries is facilitated by improved data availability, it also faces at least three major challenges: First, these models are often complex (involving hundreds of equations) and computationally intensive, which can make them cumbersome to design, difficult to operate, and intractable. Second, because of their complexity and the skills required to operate them, these models may be out of reach and unaffordable to local authorities with limited fiscal resources. Finally, the existing models overlook a key feature of land markets in developing countries: the presence of _a large informal housing sector which coexists and interacts with the formal housing sector_ (Durand-Lasserve and Selod, 2007, Napier et al. 2013). This gap precludes analyses of informal housing (which hosts the poor and often a large fraction of the middle class) and of how and the degree to which informal housing affects the whole system. More importantly, because, for many cities, housing informality is far from being a marginal phenomenon, it is possible and even likely that the predictions from models that lack an informal housing sector would not hold if the informal sector were accounted for.

In the face of these challenges, simple urban simulation models that are based in urban economic theory emerge as a less costly alternative to previously developed models (see Arnott, 2012). They are useful for urban planning as they can account for broad patterns of urban development over the long term, while remaining tractable so that users understand the forces at play. The

> 1The South African initiatives include a simple cellular-automata model (Dyna-CLUE) that was developed for Johannesburg to investigate land-use conversion processes (Le Roux and Augustijn, 2017). A conceptual framework for an agent-based model of slum evolution was introduced in Shoko and Smit (2013).

2
most recently available data for 2023. The six panels of the figure correspond to the six dimensions of governance. In each panel, we plot the baseline WGI estimate of governance on the horizontal axis, and the alternative equally weighted estimate on the vertical axis. The correlation between the two measures is very high, ranging from 0.97 (for Rule of Law) to 0.99 (for Political Stability and Absence of Violence/Terrorism). Averaging across all 25 years in the WGI dataset, the average correlations between the two measures remain very high, ranging from 0.98 to 0.99.

We can also use the margins of error associated with the baseline estimates in the WGI to assess the statistical significance of the differences between the baseline and alternative equally weighted 24
.44|19.46|7.70|1.16|0.26|8.83|2.18|7.81|1.63|2.23|

Source: Authors’ calculations using data on tariffs from UNCTAD and data on trade from UN Comtrade accessed through WITS (2012).

If we consider the case of China, we observe that countries such as Turkmenistan, Azerbaijan, and Belarus face the highest levels of protection (see Appendix). In the case of Turkmenistan, exports of guts, bladders, and stomachs of animals, which represent 34.1% of the exports of group 1, have a tariff of 88.57%. Trade policy barriers faced by Azerbaijan are associated to tariffs of 70% on hazelnuts, tomatoes, and potatoes, which account for 40% of the exports of group 1. Belarus’s average tariff, 72.9%, reflects trade protection on exports of truffles (tariff of 75%), which explain 27% of the external sales of food. Tariffs on products that belong to group 3 are lower than that corresponding to group 1, but higher than the tariffs related to petroleum. Uzbekistan and Turkmenistan are the countries that face a higher level of protection. In both cases, exports of cotton, not carded or combed, mostly explain the average tariffs of 78.23% and 76.62%, respectively. Exports of petroleum are associated to tariffs of 20%, approximately, for most of the non-WTO members.

The conclusions regarding Korea’s level of protection are similar to the ones drawn from China. However, contrary to the case of China, trade policy barriers do not seem to generate substantial differences between the average tariffs applied to products that belong to group 2 and those imposed on group 3. This fact is important, as it suggests the potential benefits of diversifying towards goods that are neither food nor petroleum. Countries such as Azerbaijan, Uzbekistan, and Belarus face average tariffs
_ANDR EE_<sup>_ ́_</sup> _SEPTEMBER 28, 2022_ _2_ Zero Hunger is the second of the Sustainable Development Goals (SDGs) adopted by all United Nations (UN) Member States in 2015. Despite progress in reducing poverty in recent decades (World Bank Group, 2018), FAO et al. (2022) conclude that there is no longer any doubt that the world is moving backwards in its efforts to end hunger, food insecurity and malnutrition in all its forms. Close to 195 million people were estimated to be in food crisis in 2021 (Food Security Information Network, 2022), while FAO et al. (2022) estimate that the number of severely food insecure people reached 924 million in 2021.

Against this backdrop, the effects of the war in Ukraine are rippling through the global commodities markets (World Bank Group, 2022), fueling the multiple already existing humanitarian crises. The anticipated economic damages from the conflict have resulted in downward revisions of growth expectations, and upward revisions in inflation expectations (World Bank, 2022b; International Monetary Fund, 2022). Food prices have increased sharply globally, hitting vulnerable populations in low-income countries hardest and adding directly humanitarian aid costs. This occurs at a time in which low-income countries have not yet recovered from the economic blow dealt by the pandemic (World Bank, 2022a).

The wide deterioration in food security conditions calls for global long-term food insecurity outlook, along with a mechanism to update and revise projections systematically. This paper proposes a parsimonious machine learning model to derive a multi-year projection of global severe food insecurity from available macroeconomic outlooks. The model-based approach allows producing counterfactual scenarios, which can help investigate whether reaching certain macro-economic targets can be expected to sufficiently address food insecurity risks or whether more targeted interventions are needed.

The application builds the model using 2015-2019 country-level data on food insecurity conditions available for around 80 of 144 countries classified in the World Bank’s IDA and IBRD cohorts (59 IDA, 15 Blend, 70 IBRD)
**Table 3 Random effects matching regressions for performance, size, and subsidies variables**

||_(1)_|_(2)_|_(3)_|_(4)_|
|---|---|---|---|---|
|_Variable_|_ROAA_<br>_A_|_djusted ROAA_|_EBITDA to_<br>_revenues_|_EBITDA to_<br>_average_<br>_assets_|
|SOEs that do not receive|–2.101**|–7.268***|–34.87***|–7.062***|
|subsidies|(1.015)|(1.084)|(4.806)|(1.211)|
|SOEs that receive subsidies|0.416|–3.844***|–25.84***|–3.476***|
||(0.753)|(0.790)|(2.927)|(0.758)|
|Leverage|–0.0491***|–0.0576***|0.0588*|–0.0146*|
||(0.00790)|(0.00833)|(0.0321)|(0.00837)|
|Size (ln(assets))|–0.0174|0.0366|3.660***|0.675***|
||(0.109)|(0.116)|(0.513)|(0.115)|
|World Bank region dummies|Y|Y|Y|Y|
|Year dummies|Y|Y|Y|Y|
|Sector dummies|Y|Y|Y|Y|
|World Bank income level|Y|Y|Y|
# **Figure 9. Investment growth slowdown in Europe and Central Asia, 2010-15**

_Regional investment growth declined from 10.2 percent in 2011 to 0.4 percent in 2015. Initially, the decline was concentrated in the western part of the region and reflected spillovers from the Euro Area crisis. The recovery of investment growth in the western part of the region in 2014-15 was outweighed by a contraction in oil-exporting economies in the eastern part of the region, which suffered a major terms-of-trade shock after the oil price drop. Recession in Russia was exacerbated by international sanctions_ .

## **A. Investment growth by region**

## **B. Five-year-ahead investment growth expectations**

<!-- Start of picture text --> 20Percent 1995-2008 avg 2003-08 avg Percent9 Commodity exportersCommodity importers<br>15 8<br>10 7<br>5 6<br>0 5<br>-5 4<br>3<br>2<br>Western Eastern Overall<br>1<br>Region Region<br>0<br>Europe and Central Asia EMDE 2010 2011 2012 2013 2014 2015<br>C. EMDEs with weak investment growth D. FDI inflows<br>Percent of ECA economies Percent of GDP<br>75 Below long-term average Contracting 19 2015<br>Difference from long-term average<br>14<br>50<br>9<br>4<br>25<br>-1<br>0 -6<br>2010 2011 2012 2013 2014 2015<br>E. Terms of trade change F. Political stability<br>Percent ICRG index (0-100, 100=best)<br>30 Commodity exporters 68<br>20 Commodity importers 66<br>64<br>10 62<br>0 60<br>58<br>-10<br>56 Commodity exporters<br>-20 54<br>52 Commodity importers<
As control variables we use a group of economic and political variables previously analyzed in the literature of successful fiscal consolidations.

In the economic variables we include lagged real GDP growth, lagged inflation rate, and international interest rate (long-term U.S. government bond yield: 10 years). These variables are taken from the IMF World Economic Outlook (October 2018).

The information from political variables (majority government and election year) come from the Database of Political Institutions 2017, which is hosted at the Inter-American Development Bank and has coverage of 180 countries from 1975-2017. Rule of law comes from the PRS Group in their International Country Risk Guide. The scores are based on evaluations from experts surveyed and the data are available since 1984 for a group of 140 countries. The Gini coefficient comes from the data set “All the Ginis” created by Branko Milanovic. Table 2 shows some summary statistics of the control variables for the sample with available data to identify need of fiscal adjustment.

**Table 2: Descriptive statistics of the control variables**

||<br>|a) Sample|with pr<br>|imary ga<br>|p|b) Sample wit<br>|h debt/<br>|revenue<br>|ratio|
|---|---|---|---|---|---|---|---|---|---|
||N|mean|SD|min|max|N<br>mean|SD|min|max|
|GDP growth||0.03|0.04|-0.16|0.30|0.04|0.05|-0.46|0.92|
|Inflation||0.06|0.10|-0.09|2.02|0.06|0.10|-1.30|
a country-level indicator of government performance, the World Bank Governance indicator of government effectiveness, in the model. This indicator measures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies (Kaufmann, Kraay and Mastruzzi, 2006, 4). This variable is not significant at the p _<_ .05 level.

> 8I included two additional measures in the model neither of which were significant at the p _<_ 0.05 level. One is a measure of citizens’ approval of how well their local government council provides citizens with the information about the councils budget (i.e. revenues and expenditures). The other, the World Bank governance indicator, control of corruption, measures the extent to which public power is exercised for private gain, as well as capture of the state by elites and private interest (Kaufmann, Kraay and Mastruzzi, 2006, 4).

13
change in school attendance following the ceasefire was small in magnitude and precludes a large decline. Using the upper bound of the 95 percent confidence interval, one can reject the hypothesis that the decline in school attendance was larger than 4.3 percentage points at standard significance levels.

The lack of a significant change in school attendance following the ceasefire also survives a number of different specifications. Column (5) demonstrates the estimates are qualitatively identical when using a logit specification; and column (6) demonstrates that when breaking up the pre- and post-periods by month, school attendance was not trending prior to the ceasefire, did not change during the ceasefire, and also did not change after the ceasefire fell apart.34 And third, we demonstrate a qualitatively identical pattern with the capture of Al Jawf from the IRG by DFA forces in column (7)-(9).35 Despite the rapid increase in violence in the region described in figure 2, there is little evidence suggesting that there was a large impact on school attendance. Column (7) demonstrates that there was little difference in school attendance following the capture. Although the estimate in column (7) is small in magnitude, it is also imprecisely estimated and cannot rule out larger impacts on school attendance.

The lack of a significant change in school attendance in Al Jawf also survives different specifications. Column (8) demonstrates the estimates are qualitatively identical when using a logit specification with an imprecisely estimated but positive impact; and column (9) demonstrates that when breaking up the pre- and post-periods by month, school attendance was not trending prior to the capture and there was little change either during the capture or in the month-and-a-half following the transition back to the IRG. Importantly, estimates in column (9) also demonstrate that there was little change in school attendance following a limited ceasefire signed in late September that led to significantly less violence in the governorate between October and January, which further corroborate estimates in columns (4)-(6).

Although
1.643|21.234|0.292|
|Observations|1589|17|43|3332|
|Number of months|7||7|7|
|Number ofphone numbers|227|2|49|476|

Notes: This table displays data for 476 Airtel phone numbers reported by our survey respondents. We do not include other phone numbers in the Airtel transactions data since we do not have the necessary information to map these to our study locations. Column 5 shows the p-value of the difference in treatment and control group means, clustered at the EA cluster level and conditional on strata and month dummies.

30
# **6.2 Determinants of Variations in Impacts under AGOA**

External trade barriers continue to be vital in understanding trade flows among countries. Even with the easing of trade barriers through preferential access such as AGOA; there are fundamental factors that limit a country’s capacity to robustly engage in international trade and exports. In SSA countries, supply-side constraints are receiving increasing attention on improving their trade performance. Analyses of African trade flows indicate that the relatively low performance is largely due to poor infrastructure, particularly transport and poor trade facilitation (Limao and Venables, 2001; Wilson et al., 2005). Wilson et al. (2005) find that raising capacity in four areas of trade facilitation, namely port infrastructure (air and maritime), customs environment, regulatory environments and communication infrastructure would significantly improve trade performance, especially exports. Physical infrastructure, soundness of the macroeconomic framework and quality of institutions appear to be major determinants of export performance (UNCTAD, 2007).

In addition, various forms of institutional quality are essential determinants of trade performance (Levchenko, 2007; Nunn, 2007; Francois and Manchin, 2013). These include contract enforcement, property rights protection, judicial quality, ease of regulations, transparency and corruption. Nunn (2007) suggests that contract enforcement explains more of the pattern of trade than physical capital and skilled labor combined. Francois and Manchin (2013) and Anderson and Marcouiller (2002) argue that hard physical infrastructure is much more important than the rest. Both the institutional framework and the quality and quantity of physical infrastructure may be essential in the push for greater trade and exports. Trade facilitation either in the form of hard or soft infrastructure is found to have improved export performance for developing countries (Portugal-Perez and Wilson, 2012). For SSA in particular, issues of security and fragility are often considered significant determinants 34