rafmacalaba/datause-encoder-data huggingface dataset:
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE boreholes, while 12 percent of the wealthiest quintile of households rely on water from wells and boreholes.

Lower-income Sudanese use lower-end sanitation technologies. Around 54 percent of the poorest quintile of the population defecates in the open compared to 13 percent of the richest quintile. While none of the poorest quintile of the population uses flush toilets, almost 20 percent of the richest quintile do so. The most dominant sanitation technology across the board is pit toilets. Disparities are less glaring in the use of pit toilets—around 42 percent of the poorest quintile of the population and 56 percent of the richest quintile of the population use them.

The KWC’s poor performance, particularly due to inadequate collection of bills and network losses, also holds back service expansion. The

**Figure 15. Sanitation access is largely bimodal**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Urban Rural<br>% population<br><!-- End of picture text --> _Source:_ Access figures calculated by AICD using data 2009 DHS data.

KWC fares poorly when its performance is compared to regional benchmarks. Nonrevenue water, at 40 percent of production, is twice that of a well-performing utility. At 90 connections per employee, labor productivity is less than half the average productivity of a utility in middle-income countries, which is 200 connections per employee. On average, Sudan’s water utilities recover only 62 percent of the total billing, which is comparable to the revenue collection of utilities in resource-rich countries, but is a very poor track record in absolute terms. Finally, cost-recovery tariffs are not in place, creating a constant financial drain.

The poor performance can be quantified as a percentage of revenue, giving a sense of the hidden costs attached to such inefficiencies. Sufficient data were available to quantify these hidden costs for the KWC as well for one other provincial utility in Sudan: the South Darfur Water Corporation. The KWC emerges at the more inefficient of the two. Overall, the utilities lose between 80 and 120 percent of their revenues. The main drivers of these losses are collection inefficiencies, followed by distributional losses. The inefficiencies in Sudan’s utilities are slightly lower than several East African countries (Kenya and Ethiopia) that lose up to 150 percent of their revenues due to hidden costs (figure 16), but are still very high in absolute terms, amounting to $73 million a year.

> <sup>25</sup>
**An Alternative Estimate of School-based Management Impacts on Students’ Achievements: Evidence from the Philippines**<sup>**1**</sup>

Futoshi Yamauchi<sup>2</sup> The World Bank

**Key words** : School based management, test score, Philippines

# **JEL Classifications: I21, O53**

> 1 I would like to thank Luis Benveniste, Yuki Ikeda, William Martin, Suhas Parandekar, and Ken Vine and the reviewer of the Journal for their useful comments. AusAID and the Government of Japan provided financial support. The opinions and conclusions expressed in this paper are those of its author and do not necessarily reflect positions of the World Bank and its member governments. Any remaining errors are mine.

> 2 Correspondence: Futoshi Yamauchi, The World Bank, 1818 H Street, NW, Washington D.C. 20433; Email: <u>fyamauchi@worldbank.org, Phone: 202 458 4262</u>
(CEPII). The CEPII database contains information on bilateral distances between the major cities for each pair of countries, whether two countries share a border, as well as information on land area and whether a country is landlocked.<sup>9</sup> Population data is taken from World Bank’s World Development Indicators for the period 1970-1999. Exporter and importer population is the only variable in our gravity speci...cation that changes over time.

The data on ...nancial development comes from the database originally compiled by Beck, DemirgucKunt, and Levine (2000). We use a version that has been checked for accuracy by Loayza and Ranciere (2005). Following the standard in the literature, our preferred indicator of ...nancial development is the ratio of credit by banks and other ...nancial institutions to the private sector as a share of GDP (“private credit”). The controls in our estimation include overall trade openness (imports plus exports as a share of GDP) and PPP-adjusted GDP per capita income, both of which come from the Penn World Tables (Heston, Summers and Aten, 2002). Finally, we use information on countries’legal origin as de...ned by La Porta et al. (1998), extended to include the socialist legal system.

The ...nal sample includes 96 countries, and is an unbalanced panel of 5-year averages from 1970-74 to 1995-99. Appendix Table A1 presents the data on the external ...nancing need of exports, EFNXct, for our sample of countries for the most recent 5-year period, 1995-99. Aside from EFNXct, the table contains information on the top two export sectors, the share of the top two sectors in the overall manufacturing exports, overall trade openness, private credit, as well as the sample means of these variables. It is clear that while there is some correlation between per capita income and the ...nancial content of trade, income is far from a perfect predictor of EFNXct. The top two countries ranked according to the ...nancial content of trade in this period are Malaysia and Philippines, only then followed by Singapore, Japan, and Switzerland. Only the bottom two countries have the ...nancial content of trade that is negative in this period, Malawi and Zimbabwe. In these countries, the main export industry is Tobacco, which has a negative external ...nance dependence according to the Rajan-Zingales classi...cation.

We plot our estimates of the external ...nance need of exports against log of PPP-adjusted per capita income in Figure 2. It is clear that while there is a positive relationship between income and our variable of interest, it is far from close. The correlation between the two variables is less than 0.4. Figure 3 plots the external ...nance need of exports against overall trade openness. There is little relationship between the two variables, and thus, as expected, we are measuring something distinct from trade openness when we construct our measure of the external ...nancing need of exports. Finally, Figure 4 plots ...nancial development against EFNXct. There is a positive relationship

> 9 The dataset is available online at http://www.cepii.fr/anglaisgraph/bdd/distances.htm.

12
20 Table 4 Welfare Cost of Auto VIis<sup>vnderIRTS and Internatienal</sup> Capital Nobility (Izogenous Wage Distortions and Partial Rent Capture by US In **all Siealations)** _(S_ 1984 billion)

|col<br>1|Total<br>Costs<br>--2+3+4|Removal<br>Profits<br> <br>2|Rent<br>D<br>Costsf<br>3|istortion<br>Costs<br>4|Employment<br>a/<br>Change in Autos<br>5<br>|Entry<br><sup>(Ccha</sup><br>(+)<br>6b|/Exit<br><sup>nge)</sup><br>(-)<br>p|
|---|---|---|---|---|---|---|---|
|0. CRTS reference<br>case|10.08||7.77 (37.1)|2.31|-40.9|||
|1. Contestable<br>Market Pricing|9.00||7.79 (37.1)|1.21|-24.5|-|-<br>|
|2. Monopolistic<br>Competition|9.77||7.79 (37.1)|1.98|-22.1|-|-5.4|
|3. Monopolistic<br>Competition<br>and<br>Initial<br>Profits (MCIP)|10.39|-1.91|7.00 (33.4)|1.48|-35.47|33.7<br>|-6.0<br>|
|4. MCIP and International<br>Capital<br>Mobility|10.23|-1.88<br>(157.1)dl|6.85 (32.8)<br>|1.50|-34.54<br>(-1.5)el<br>|33.9<br>|-5.9<br>|
|5. MCIP and Endogenous<br>Terms of Trade|7.99|-1.91|7.00 (33.4)|-0.92|30.4|33.7|-4.5|
|6.MCIP and Intermational<br>Capital<br>Mobilityunder Endogenous<br>Terms of Trade|7.86|-1.88<br>(157.1)di|6.85 (32.8)<br>|-0.87|29.7<br>(-0.7)e/<br>|33.9<br>|-4.4<br>|
|7. MCIP with International<br>Capital<br>Mobility<br>and Endogenous<br>Conjecture|7.90<br>s|-0.06<br>(168.3)dl|6.63 (31.9)<br>|1.21|39.5<br>(-1.5)el|21.8|-3.5|
|8. MCIP with International<br>Capital<br>Mobility<br>Endogenous<br>Conjectures<br>and<br>Endogenous<br>Termsof Trade|5.66|-0.06<br>(168.3)dl|6.63 (31.9)<br> <br>|-1.03<br>|34.6<br>(-1.50)<br> <br>|21.8<br>|-2.6<br>|
|alThousand man years.<br>Employment chan<br>negative<br>of the employment<sup>change from th</sup><br>blRemove profits.|ge<sup>from r</sup><br><sup>e simula</sup>|<sup>emoving the</sup><br><sup>tion</sup><br>to elim|<sup>VER starting</sup><br>f<br>inate<br>initial|rom solution<br>profits.|<br>with no initial|profits|plus the|

cl Remove QRs from<sup>zero profits</sup> solution. dl Net capital inflow from removing profits (S million). el Net capital inflow from removing QR starting from solution with initial profits (S million). fl Figures in parentheses next to rent cost<sup>estimates</sup> are endogenous premia rates.
*. *r

19

# TABLE 2

Classification of Selected Empirical Studies on Trade Regimes Group A: Summary Results of Multicountry Projects on Trade Regimes

|1) Balassa, B.<br>(1971)|<br>C|No. of<br>ountries|Period|
|---|---|---|---|
|2) Krueger, A.<br>(1978)|Liberalization<br>Attempts and Conseau-<br>ences, 1st Synthesis volume of NBER<br>project on "Trade Regimes and Economic<br>Development".|10|1950-72|
|3) Bhagwati, J.<br>(1978)|Anatomy and Conseguencas<br>of Exchange<br>Control Regimes, 2nd Synthesis volume<br>ofNBER project on "Trade Regimes and<br>Economic Development".|10|1950-72|
|4) Balassa, B.<br>(1982)|Development Strategies and Economic<br>Performance,<br>Synthesis of World Bank<br>Project on "Development<br>Strategies in<br>Semi-Industrial<br>Economies".|11|1960-73|
|4a) Balassa, B.<br>(1978)|"Exports and Economic Growth: Further<br>Evidence,fn<br>Journal of Development<br>Economics.|11|1966-73|
|5) World Bank<br>(1987)|World Development<br>Report, summarizes<br>preliminary results of World Bank project<br>on "Timing and Sequencing of Trade Liber-<br>alization Reforms".|41<br>|1963-73<br>1973-85|
|Group B: Studi<br>Neocla|es Measuring Contribution of ExRorts to Gro<br>ssical Production Function-Tyge<br>Framework|wth in LD|Cs in a|
|6) Tyler, W.<br>(1981)|"Growth and Export Expansion in<br>Developing Countries,"<br>Journal of<br>Development Economics.|55|1966-77|
|7) Feder, G.<br>(1983)|"On Exports and Economic Growth," Journal<br>of Development Economics.|19<br>31 (2|1964-73<br>groups)|
|8) Kavoussi, R.<br>(1984)|"Export Expansion and Economic Growth:<br>Further Empirical Evidence," JouXnal of<br>DeveloRment Economics.|73|1960-73|
|9) Ram, R.<br>(1985)|"Exports and Economic Growth: Some<br>Additional Evidence," Economic Develop-<br>ment and CulturalChange.|73|1960-70<br>1970-77|
**Figure 21: Investment structure by region.**

<!-- Start of picture text --> Investment structure by share of projects<br>No FDI Partnership Strictly FDI<br>South Asia<br>Latin America<br>Eastern Europe & Central<br>Asia<br>East Asia Pacific<br>Africa<br>0% 50% 100%<br>Share of projects<br><!-- End of picture text -->

**Source: Risoe (2011) and authors’ calculations.**

74
# **<u>Table 8.  Benefit-cost analysis</u>**

||**Malawi kwacha**|**US Dollars**<br>|
|---|---|---|
|||**($1 = MK 145)**|
|**Costs**|||
|**OIBM**|||
|**_One time per customer costs_**|||
|a) Account opening|1056.25|7.28|
|b) Staff time for training of farmer groups|287.50|1.98|
|c) Transportation to farmer groups (vehicle)|328.77|2.27|
|d) Transportation to farmer groups (fuel)|543.75|3.75|
|e) Management cost (risk assessment, reporting, audit)|333.33|2.30|
|**_Transaction costs_**|||
|f) Withdrawal costs|‐103.13|‐0.71|
|g) Deposit costs|937.50|6.47|
|**Customers**|||
|**_One time costs_**|||
|h) Time and transportation cost of training|333.00|2.30|
|i) Account opening|7.33|0.05|
|**_Transaction costs_**|||
|j) Withdrawal costs|150.00|1.03|
|k) Time and transport cost of going to the bank|1048.50|7.23|
|**_Other_**|||
|l) Early withdrawal|100.00|0.69|
|**Benefits**|||
|**Customers**|||
|m) Increased farm profits|19412.53|133.88|
|**n) Net benefits to OIBM -[a+b+c+d+e+f+g]**|‐3383.98|‐23.34|
|**o) Net benefits to Customers [m-(h+i+j+k+l)]**|17773.70|122.58|
|**p) Net benefits to society [n+o]**|**14389.72**|**99.24**|
|**q) Benefit-to-cost ratio [m/(a+b+c+d+e+f+g+h+i+j+k+**|**3.86**|**3.86**|
|Assumptions<br>Exchange rate|145 MK/US$1||
|<br>OIBM staff salary|<br>MK 75,000/month||
|Time for opening an account<br>Cost of OIBM smart card (to OIBM)<br>|20 minutes<br>MK 900<br>||
|Time for conducting a group training<br>Group size|1.5 hours<br>20 members||
|<br>OIBM transportation time to training|<br>2.5 hours||
|OIBM cost of transportation (vehicle)<br>OIBM cost of transportation (fuel)<br>|MK 12 million, depreciated ove<br>300 KM round trip, 8 KM/liter,<br>|r 5 years<br>MK 290/liter|
|Time for withdrawal<br>Number of withdrawals/season|2 minutes<br>3||
|<br>OIBM processing time for deposit<br>|30 minutes<br><br>||
|Number of deposits (tobacco sales)/season<br>OIBM management cost<br>|4<br>MK 500,000/season<br>||
|Cost of OIBM smart card (to customer)<br>Customer transportation cost to bank|MK 1200<br>MK 300||
|Customer waiting time at bank<br>|15 minutes<br>||
|Customer opportunity cost of time<br>Probability of early withdrawal|10%<br>MK 110/day (5 hours)||

Assumptions Exchange rate OIBM staff salary Time for opening an account Cost of OIBM smart card (to OIBM) Time for conducting a group training Group size OIBM transportation time to training OIBM cost of transportation (vehicle) OIBM cost of transportation (fuel) Time for withdrawal Number of withdrawals/season OIBM processing time for deposit Number of deposits (tobacco sales)/season OIBM management cost Cost of OIBM smart card (to customer) Customer transportation cost to bank Customer waiting time at bank Customer opportunity cost of time Probability of early withdrawal
Policy Research Working Paper 6676

# **Abstract**

This paper proposes the first evaluation using microlevel data of the gains from the consistency of activities with a local comparative advantage. Using firm-level data from Chinese customs over the 2000–6 period, the study investigates the relationship between the export performance of firms and how their products relate to local comparative advantage. The key indicator measures the density of the links between a product and the local product space. Hence, it combines information on the intrinsic relatedness of a good with information on the local pattern of specialization. The results indicate that exports grow faster for goods that have denser links with those currently produced in the firm’s locality. The density of links between products seems to yield exportenhancing spillovers. However, this positive effect of product relatedness on export performance is mainly limited to ordinary trade activities and domestic firms. It is also stronger for more productive firms, suggesting that spillover diffusion may be hindered by insufficient absorptive capacity.

This paper is a product of the Partnerships, Capacity Building Unit, Development Economics Vice Presidency. 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://econ.worldbank.org. The authors may be contacted at sandra.poncet@univ-paris1.fr and felipe.starostade-waldemar@u-psud.fr.

_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
ABSA Group as its purchaser, NMB initially attracted no interest from buyers. As described in more detail below, private banking consultants were contracted to manage NMB over a period of years, and only in 2005 was 49 percent of NMB’s shares sold to Rabobank, a large Dutch cooperative bank with international experience in microfinance.

The original National Bank of Commerce, therefore, faced many of the difficulties associated with privatizations of large commercial banks in other countries. Entrenched interests that benefit from a state bank (e.g., through heavily subsidized loans, loans that are automatically renewed, or loans where repayment is not truly expected) have strong incentives to oppose the privatization process. Indeed, a growing body of evidence indicates that political influences help account for the under-performance of state-owned banks. Sapienza (2004) demonstrates that the interest rates charged on the loans of Italian state-owned banks are substantially lower than those charged by private banks, and are strongly negatively related to the local power of the political party that controls the bank. Dinc (2005) finds that government-owned banks increase their lending substantially in election years, significantly more than do private banks. Mian (forthcoming) shows that government largesse permits sustained under-performance on the part of state-owned banks: credit ratings data indicate that their deposits costs are lower because of the strong external support they receive from the government in times of trouble, and their balance sheets display much less sensitivity to macroeconomic shocks than those of private banks, presumably because they (and their borrowers) are insulated from such shocks by government financial support.

Moreover, there is growing evidence that political factors help determine the timing and nature of privatization, which could impact post-privatization performance. Boehmer, Nash, and Netter (2005) find that, in developing countries, bank privatization is more likely the more accountable the government is to the general electorate rather than to specific interest groups. Clarke and Cull (2002) find that higher levels of unemployment and a larger proportion of public sector employees reduced the probability that Argentine provinces privatized their banks. Clarke and Cull (2005) find that Argentine provinces with large fiscal deficits agreed to bank privatization contracts that allowed more layoffs and guaranteed a larger share of the privatized bank’s portfolio, but in return received higher sales prices.

2
The surprise of the small island states is their moderately competitive cargo transport market. What explains the level of competition is the intra-island system of routes whereby ship lines follow routes with big vessels that touch every island every time they do the route. Interestingly, Vieux Fort, the only transshipment port of the small island states shows the lowest level of competitions among the small island states.

Finally, Belize is an outlier of the analysis. The country has a highly concentrated market, likely because it is a very small market that does not piggy-back onto regional shipping routes (like the small island states).

# **d. Freight Tariffs**

A final consideration is freight tariffs and what factors might be driving them.

In absolute terms, the freight rate for shipping a 20-foot container from Miami to St Kitts and Nevis, at about US$3,500/container, is more than two times the tariff of shipping the same container all the way through to Hong Kong SAR, China (US$1,270), Argentina (US$1,360) or France (US$1,100) (Figure 37). The distances to Miami from Hong Kong SAR, China; Buenos Aires; and France are 7, 4 and 3 times longer than from Miami to St. Kitts and Nevis.

**Figure 37: Average Freight per TEU Shipped from Miami to Different Destinations**

<!-- Start of picture text --> Average Freight Rate from Miami to<br>Various Destination<br>Basseterre, St Kitts &...<br>Roseau, Dominica<br>St Georges, Grenada<br>Kingstown, St Vincent<br>Castries, St Lucia<br>St John's,...<br>Port au Prince, Haiti<br>Paramaribo, Suriname<br>Georgetown, Guyana<br>Port of Spain, Trinidad...<br>Caucedo, DR<br>Bridgetown, Barbados<br>Kingston, Jamaica<br>Freeport, Bahamas<br>Cartegena, Colombia<br>Colon, Panama<br>Buenos Aires, Argentina<br>Hong Kong, China<br>Le Havre, France<br>0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000<br>US$/TEU<br><!-- End of picture text --> _Source: Based on data from Shipping Lines in World Bank (2012b)._ The first consideration is how distances affect tariffs. This is a complex manner that involves the appropriation and transfer of the economies of scale, the trajectory of trading routes, and the overall 88
Following Graetz and Michaels (2018), we constructed an industry-level measure of replaceability using data from IFR on robot applications, the US census of occupational classifications and the distribution of hours worked across occupations and industries from the 1980 US Census. The IFR distinguishes between different applications of robots, including (among others) welding, processing, and assembling (International Federation of Robotics, 2012). Using the 2000 Census three-digit occupations, we assign a replaceability value of one to a three-digit occupation if the name and/or description of at least one of the five-digit occupations included in it contains at least one of the IFR application categories and zero otherwise. We assume that services occupations are not replaceable by industrial robots and remove occupations that our text analysis algorithm identifies as being potentially replaceable by robots but we believe are not, such as “agricultural inspectors” and “artillery and missile officers”. We then map our replaceability measure into the 1990 Census occupational classification, which is available for the 1980 and 2000 censuses. If several 2000 occupations map into one 1990 occupation, then we assign the 1990 occupation a replaceability value of one if and only if at least one of the corresponding 2000 occupations has a value of one. To measure replaceability at the industry level, we first assign these variables to each individual in the 1980 IPUMS Census based on their reported 1990 occupation. We then assign to each individual one of our 28 EU KLEMS industries based on a crosswalk to the 1990 Census industry classification. We compute the fraction of replaceable hours for each of the 16 industries by dividing the sum product of replaceability and annual hours worked by the total sum of hours worked (applying person weights both when computing the numerator and the denominator). The replaceability values represent an upper bound to the share of hours that may be performed by robots—occupations are classified as replaceable even if only a small proportion of their work can be replaced by robots.

We combined this information with product-level bilateral data on trade and import tariffs. Trade data come from BACI, the World trade database developed by CEPII, building on original data provided by the COMTRADE database of the United Nations Statistical Division. These data contain annual information on bilateral exports and imports at the HS 6-digit product disaggregation for 224 countries. In our final estimation sample we focus on trade between 26 OECD countries for which we have both EU KLEMS and IFR data (Northern countries), and 181 non-OECD (Southern countries). We exclude from our analysis OECD countries for which we do not have either IFR or EU KLEMS data. Annual data on bilateral applied tariffs by country and product (SITC 2-dig., Rev. 4) come from the TRAINS database of the United Nations Conference 15