rafmacalaba/datause-encoder-data huggingface dataset:
of goods in transit (bonded goods), but the regular carriage of goods services still fall under the control of the National Transport Safety Authority. Although the customs is the main regulator to oversee C&F agents and other transport agencies are responsible for road transport and vehicle trailer licenses, the customs has freedom to intervene in road transport operations (forwarding) in a form of inspection.

Table 3: Regulators for freight forwarding business

|**Regulators**|**Ministry of Transport**|**Ministry of Trade /**<br>**Commerce**|**Self-regulator**|**Revenue Authority**|
|---|---|---|---|---|
|Main Focus of<br>Regulations|Transport Service|Support Export-oriented<br>economy / encourage<br>integration of economy into<br>global supply chain|Maximize the<br>welfare of the<br>members of<br>associations|Revenue / Tax<br>collection / Remove<br>goods in bond|
|Countries|South Africa_(Ministry of_<br>_Transport)_<br>Japan<br>Brazil<br>Thailand<sup>23</sup><br>USA(_Federal Maritime_<br>_Commission_)<sup>24</sup><br>Netherlands (_Min. of_<br>_Infrastructure and the_<br>_Environment, Transport_<br>_Dep._)<br>Germany_(Federal office for_<br>_Goods Transport, Min. of_<br>_Transport)_<br>Sudan_(Land Transport unit,_<br>_Min. of Transport)_|China_(Min.of Commerce)_<br>Philippines_(Philippine_<br>_Shippers’ Bureau [PSB] ,_<br>_under Dep. of Trade and_<br>_Industry)_<br>Nepal_(Dep. of Commerce_<br>_under Min. of Industry )_|Singapore (_SLA_)<br>Pakistan (_PIFFA_)|South Africa_(SARS)_<br>_*for transporters /_<br>_removers of goods_<sup>_25_</sup><br>Kenya (KRA)<br>*for C&F agent<br>license<sup>26</sup>|

# _Logistics performance by different regulators_

While countries that have already thrived in trade have ministries of transport as a regulator, emerging countries in international trade have placed ministries of trade to regulate the freight forwarding business. However, with a combination of factors behind logistics services in each country, it is not appropriate to conclude with one ideal regulator for all countries. The LPI 2014 (WB, 2014) shows that countries regulated by ministries of transport slightly exceed the countries regulated by ministries of trade in the logistics competence score (Figure 3). However, the difference is trivial. Among the countries that have ministries

> 23 Thailand – Ministry of transport regulates the licensing and operational rule. In addition, there is Bureau of Logistics in the Ministry of Industry, who is responsible for the strategic agenda on “Business Logistics Improvement”

> 24 USA – Independent Federal Agency responsible for regulating the U.S. international ocean transportation system.

> 25 If a transporter removes goods in bond, the consignor bond will be required by SARS.

> 26 <u>http://www.kra.go.ke/customs/customsclearingagentsproc.html. Motor vehicle license should be issued by</u> National Transport and Safety Authority.

18
- [9] Campbell, C. M., and Kunal S. Kamlani (1997), “The Reasons for Wage Rigidity: Evidence from a Survey of Firms,”The Quarterly Journal of Economics, vol. 112(3), pages 759—89.

- [10] Canova, F. and L Gambetti. (2009), “Do Inflation Expectations Matter? The Great Moderation Revisited.”American Economic Journal: Macroeconomics 2(3), pages 183—205.

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- [12] Castellanos, S. G., R. Garcia-Verdu and D. S. Kaplan (2004), “Nominal wage rigidities in Mexico: evidence from social security records,”Journal of Development Economics, vol. 75(2), pages 507—533.

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- [15] De la Torre, A., C. Calderon, T. Didier, E. Levy Yeyati, and S. Schmukler (2010). “Globalized, Resilient, Dynamic: The New Face of Latin America and the Caribbean,”The World Bank.

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- [17] Du Caju, P., T. Kosma, M. Lawless, J. Messina and Tairi Room (2014), “Why firms avoid cutting wages: survey evidence from European firms,”Working Papers 173, Bank of Greece.

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**Land Institutions to Address New Challenges in Africa: Implications for the World Bank’s Land Policy**

# **1. Motivation and background**

How land rights are assigned, documented, and can be exchanged will affect individuals’ wealth, gender equity, incentives for investment and resource conservation, and productivity of land use. By providing price signals and facilitating the operation of land markets for land, credit, and insurance, formal registries underpin key aspects of economic activity. The information maintained by them also provides a basis for levying recurrent land taxes based on market values or capital gains taxes to generate own source revenue and provide public goods especially in settings where other tax bases are narrow; by regulating land use to reduce the scope for negative externalities; and by managing public land in line with societal objectives.

Land rights are important for Africa’s urbanization, structural transformation, and resilience to climate change. Historically, high cost of service delivery, reinforced by discriminatory legislation, often with colonial roots, limited access to documented land rights in many African countries. Equity was undermined as it was more difficult for women and other secondary right holders to enforce and exercise their rights. Agricultural productivity, adaptation capacity, and resilience suffered as output increased by expanding into marginal, risky, and often environmentally sensitive areas rather than via investment in capital and associated better factor market functioning, with shocks often triggering conflict. Urban job creation, mobility, and service access were reduced as informality limited investment in floorspace, collection of property tax revenue, and financial market development. State capacity, trust in the state, and the ability to access climate finance were often undermined by ambiguous definition and non-transparent ways of transferring rights to public land. Yet, while these challenges have been widely recognized, the cost of establishing and operating land institutions often made reform difficult.

This paper argues that digital technology can reduce the cost of and increase the benefit from documenting land rights in several ways. Shifting towards digital records, workflows, and interoperability makes it easier to document a range of rights, increase the quality of information contained in registries and the ability to access it, and allow routine dissemination of data (e.g., on prices) to provide a benchmark to help improve market outcomes. Free satellite imagery allows to consistently observe land use in near real time at a highly granular level, thus improving the ability to target and enforce land use restrictions, certify supply chains, and contract outcomes in terms of land use such as conservation. Connectivity can expand competitiveness of land markets, e.g., through electronic auctions, increase the scope for developing reputation or links to other markets (e.g., those for insurance) to improve land use, and makes protection and exercise of 2
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Table 2.6. Comparative port performance across African regions**

||**East Africa**|Southern Africa|West Africa|Global best<br>practice|
|---|---|---|---|---|
|**Performance**|||||
|Container Dwell Time (days)|**5–28**|4–8|11–30|<7|
|Truck Processing Time (hours)|**4–24**|2–12|6–24|1|
|Crane Productivity (containers per hour)|**8–20**|8–22|7–20|20–30|
|Crane Productivity (tonnes per hour)|**8–25**|10–25|7–15|>30|
|**Charges**|||||
|Container handling (US$ per TEU)|**135–275**|110–243|100–320|80–150|
|General cargo handling charge (US$ per tonne)|**6–15**|11–15|8–15|7–9|

Source: AICD ports database.

Performance of individual ports in East Africa varies (table 2.7). Mombasa (Kenya) and Dar es Salaam (Tanzania) exhibit generally good performance that is within global best practices on some indicators. On the other hand, Port Sudan (Sudan) and Djibouti (Djibouti) exhibit much lower port efficiency levels. Container dwell times in Mombasa and Dar es Salaam are within global best practice benchmarks, but the dwell

**Table 2.7. Comparative performance across East African ports**

||Djibouti|Mombasa|Port<br>Sudan|Dar es<br>Salaam|
|---|---|---|---|---|
|Container dwell time (days)|8|5|28|7|
|Truck processing time (hours)|12|5|24|5|
|Container crane productivity<br>(container per hour)|17|10|8|20|
|**Charges**|||||
|Container cargo handling charge<br>(US$ per TEU)|135|68|150|275|
|General cargo handling charge<br>(US$ per tonne)|8|7|10|14|
|Source: AICDports database.|||||

times in other ports are significantly longer, particularly in Port Sudan. Truck processing time at Port Sudan is almost five times that at Mombasa or Dar es Salaam. Crane productivity (in terms of containers handled per hour) in Dar es Salaam is twice that of Mombasa. Dar es Salaam, which performs slightly better than Mombasa in terms of productivity, levies higher port charge —four times more than Mombasa (the cheapest port in the region) for container handling. The prices for Mombasa are consistent with global best practice benchmarks.

Mombasa and Dar es Salaam are natural transshipment points on the East African coastline. While both ports have pursued transshipment, both now face severe challenges to increase their national cargo flows over the short term (box 2.2), and their limited capacity is likely to curtail their transshipment activities. Djibouti, on the other hand, may soon provide a new solution to transshipment along the East African coast. DP World is scheduled to bring a new container-terminal facility on stream at Djibouti, one intended specifically to offer significant transshipment capacity for East Africa and the Indian Ocean.

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endeavor to train SME account managers and raise their risk awareness, so that the credit approval process is streamlined and the loan application has a higher likelihood of not being rejected later on by risk analysts.

The bank surveys suggest that large banks, particularly in Chile and Argentina and much less so in Colombia, use well-developed screening tools to sort out “good” debtors from the loan applicant pool. These screening tools are differentiated by firm or loan size. The size threshold for the applicability of a given screening tool is typically determined by the effectiveness of the tool itself, as gauged by repeated experience. Thus, automatic scoring methods are usually applied to small companies with small loans, for which the owner and SME information is combined (see also Marquez 2008). Moreover, back testing or statistical analyses of the effectiveness of automated scoring is used to determine the threshold size beyond which it is deemed to lose potency, although efforts are continuously made to improve the scoring technique to apply it to incrementally larger loans or firms. Streamlined and substantially standardized rating tools are used to screen larger SMEs applying for larger loans, for which automatic scoring is deemed to be not effective. Such tools include quantitative and qualitative information and are typically developed by adapting (simplifying, streamlining, and standardizing) to the SME business the rating methods applied to large corporations. SME ratings do not lead to the automatic approval of loans, but they rather provide the basis for the risk analyst to evaluate loans and decide on their approval. After loans are approved, banks continuously monitor the loans and the SMEs (the larger ones in particular), plus they have an early warning system, with triggers, to anticipate and detect potential problems.

33
representative sources is focused exclusively on corruption undertaken by bureaucrats, meaning that the skew towards unelected officials may be slightly greater vis-à-vis the CPI. Furthermore, the CC uses fewer representative sources (five versus eight), meaning that it may be slightly less likely to be composed of representative sources regarding the level of ‘political’ corruption vis-à-vis the CPI.

The index is available from 1996 and was published bi-annually until 2002, after which annual scores became available when countries were scored. Since then it has been updated yearly. Table 2 shows the component parts of the CC. Like the CPI, albeit to a lesser extent, the indicator is focused on surveys of experts, some of which are the same as the CPI, for example the Economist Intelligence Unit. However, it does, vis-à-vis the CPI, focus on both grand political and more petty corruption (e.g. level of petty, large-scale and political corruption).

# _Table 2: Representative Components of the CC_

|**Source**|**Who Was**<br>**surveyed/asked?**|**Question/Assessment of**<br>**(Bureaucratic/Both/Political)**|**Source Type**|
|---|---|---|---|
|Economist<br>Intelligence Unit<br>Risk-wire &<br>Democracy Index|Expert Staff|‘Pervasiveness of Corruption’|Commercial<br>Business<br>Information<br>Provider|
|World Economic<br>Forum Global<br>Competitiveness<br>Report|Survey- Senior<br>business leaders;<br>domestic and<br>international<br>companies|‘Public trust in financial<br>honesty of politicians<br>Diversion of public funds due to<br>corruption is common’<br>‘Frequent for firms to make<br>extra payments connected: (1)<br>trade permits, (2) public<br>utilities, (3) tax payments, (4)<br>loan applications, (5) awarding<br>of public contracts, (6)<br>influence laws, policies<br>regulations, decrees, (7) to get<br>favourable judicial decisions.’|Non-Government<br>Organization|
|Gallup World Poll|Survey- general<br>public|‘Is corruption in government<br>widespread? ‘|Commercial<br>Business<br>Information<br>Provider|
|Institutional<br>Profiles Database|Expert Staff|‘Level of petty, large-scale and<br>political corruption‘|Government|
|Global Insight<br>Business<br>Conditions and<br>Risk Indicators|Expert Staff|‘An assessment of the<br>intrusiveness of the country’s<br>bureaucracy. The amount of red<br>tape likely to countered is|Commercial<br>Business<br>Information<br>Provider|

15
# **<mark>4. MNE STATUS AND THE PROBABILITY OF DISCLOSURE BY INDICATOR</mark>**

In this section, we assess if multinational enterprises are different from non‐multinationals with regard to their reporting patterns on economic, labor and social, environmental, and governance indicators at the detailed indicator level. In a first step, we show the percentage of reporting companies by indicator differentiating between MNEs, large companies and SMEs. In a second step, we show the correlation between MNE status and the likelihood of reporting by indicator, controlling for firm size and country‐ sector fixed effects in all regressions as specified in section 2.2 equation 1. All variables refer to the 2015 GRI indicators.

Indicators that show a statistically significant difference between MNEs and non‐MNEs are highlighted in bold. A positive coefficient sign indicates that MNEs are more likely to report a GRI metric than non‐MNEs, while a negative coefficient sign means their reporting probability is significantly smaller. Throughout the regressions firm size ( _large_ ) explains a large share of the reporting differences, while only some of the indicators show statistically significant differences between MNEs and non‐MNEs.

## **4.1 ECONOMIC INDICATORS**

Figure 4 shows that across all economic indicators SMEs tend to underreport compared to large firms and MNEs. Large companies in some cases seem to report more on specific metrics, including the impact of infrastructure and indirect economic indicators (EC7). Firms across the board are more likely to report their value added than other economic indicators, especially indirect impacts. While 84% of MNEs report information on their value added creation and distribution (EC1), only half of MNEs indicate how much they spend on local suppliers (EC9), and 47.8% reveal their impact of infrastructure and services (EC7). Only 41.4% are willing to deliver information on indirect economic impacts (EC8) which is only marginally higher than for SMEs (37.4%), but much lower than for large firms (54.2%).

The regression analysis in Table 13, however, suggests that MNEs do not differ from non‐MNEs in their likelihood to report on economic indicators, while firm size explains differences in reporting for two indicators. In particular, firm size shows a significant correlation with the probability of reporting on the impact of infrastructure and services (EC7) and on indirect economic impacts (EC8).

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send a larger share of highly skilled migrants receive less or more remittances than countries which send relatively fewer skilled migrants.

We pull together the most comprehensive database on migrants currently available, comprising over 33,000 migrants in 11 OECD countries. Using this database we examine exactly the decision between remittance decisions and education. Combining both the extensive margin (the decision to remit at all) and the intensive margin (the decision how much to remit), the fact is that more educated migrants do remit significantly more – migrants with an university degree remit $300 more yearly than migrants without an university degree.

We are able to analyze several competing theoretical channels to understand this result. We find that differences in household composition between high and low skilled migrants do not explain the observed remittance behavior. One explanation may be that although low skilled migrants are more likely to have a spouse and children left in the home country, they have larger families in general than high skilled migrants and tend to live in larger households in the host country. In contrast, we find considerable support that an income effect is the dominant channel through which education operates. More educated migrants earn more money and for this reason remit more than low skilled migrants.

This paper has important implications for migration policy. There is much concern about the negative effects of the ‘brain drain’ on developing countries. However, our main finding that remittances increase with education, illustrates one beneficial dimension of high-skilled migration for developing countries. High skilled migrants work better jobs and earn more money than low skilled migrants, and in turn, send more money back home in remittance flows. This suggests that sending highly skilled migrants who are able to earn higher income is one way to increase remittance flows.

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Table 5 shows the incorporation intensity by industry for both Mexico and the United States. A lower number indicates that proprietorships are a more typical legal form of organization in the given industry. A comparison of the U.S. and Mexican data confirms that the distribution of legal forms is sector-specific. The corporate form tends to be more common in the mining and manufacturing industries, while businesses with unlimited liability are common in the services and retail sectors.<sup>17</sup>

# _Other Variables_

Our regressions also control for the effect of market size, scarcity of entrepreneurial talent, and the presence of dominant firms. Market size is measured with the log of total employment in the sector and state, along with the log of state per capita GDP. The log of population is used as an instrument for sectoral employment in the IV regressions.

The availability of entrepreneurial talent is measured by educational attainment levels at the state level. We use the percentage of the population aged 25 to 40 with at least 9 years of schooling, representing completion of lower secondary schooling, measured using the 1990 population census data. Results using upper secondary (high school) or university attainment are similar. Finally, in some regressions we control for the presence of large firms and foreign firms in the sector. For these regressions, the dependent variable is the level of employment in firms with 10 or fewer employees as a percentage of employment in firms with fewer than 250 employees. The presence of large firms is then measured as the level of employment in firms with more than 250

> 17 Incorporation intensity is likely to depend on industry characteristics such as capital intensity, the ease of monitoring worker or manager effort, and so on. At the industry level, the correlation between incorporation intensity in the US and Mexico is 0.34.

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