rafmacalaba/datause-encoder-data huggingface dataset:
technology (as it relates to better logistics), regulatory environment, product standards, Technical Barriers to Trade (TBT)<sup>2</sup> etc. in order to lower cost of moving goods between destinations and across international borders.

Constraints in supply chains and trade logistics include a number of important barriers to exports for developing countries such as the lack of harmonized transport systems, frequent reloading of goods, port congestion, excessive documentation requirements, burdensome cross-border procedures, lack of automation and scarce use of IT. One study shows, for example, that for 168 out of 215 U.S. trading partners, transport cost barriers outweigh tariff barriers (World Bank, 2002). In a highly integrated economy focused on just-in-time manufacturing, seamless trade transactions can deliver the time and costs savings necessary to remain competitive. Easier movement of goods and services across borders can reduce costs for businesses, and increase predictability and transparency for small and medium enterprises (SMEs). At the same time, governments can gain through better revenue collection, and improved security and control.

The importance of TF is evident in the complexity that surrounds cross-border movement: An international trade transaction normally involves 27-30 different parties, about 200 data elements, 40 documents and costs that range between 2.5-15% of the goods value. One extra day of clearance represents up to one tariff point (Hummels, 2001). UNCTAD has estimated direct and indirect transaction costs (customs, banking, insurance, transport etc.) to be 10% of the total value of world trade (US$400 billion).

# **2. Trade Facilitation and Development**<sup>**3**</sup>

Empirical evidence indicates that trade is good for growth. Developing countries that are integrating faster with the world economy tend to have higher growth rates, and lower poverty levels (See Figure 1). High transaction costs are a huge impediment for growth of emerging economies. Factors that facilitate trade determine the extent of integration with the world economy, and therefore growth and development. Trade facilitation can also lead to better national administrative efficiency and higher competitiveness for nations.

> 2 Some have suggested including business mobility, transparency of procedures, trade information and e- business facilities in this broad definition of TF.

> 3 This section draws on presentations by Bert Hofman (World Bank) and <u>Jayanta Roy</u> (CII, India).

5
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# **3. Estimates Based on Fieldwork**

**As the data available are far from complete, it is not possible to estimate the scale of informal trade based on figures alone. For that reason, fieldwork was conducted at the Ras Ajdir crossing point on the Tunisia-Libya border and in the Kasserine governorate on the Tunisia-Algeria border.** The aim of the interviews was to gather information on the current level of informal trade between Tunisia and its two neighbors.

**The estimates were made using two different methods, which yielded roughly similar results (at least TND 1.8 bn including fuel from Algeria) (see Table 1). One estimate was based entirely on the information gathered during the fieldwork, while the second one was made using unit floor values based on data concerning modes of transport.** The second method resulted in a higher estimate of the level of informal trade, most probably because of the under-reporting of value and/or volumes in the interviews.

**Table 1: Comparison of estimates according to different methods**

||**Trade with Libya**|**Trade with Algeria**|**Total (in billion**<br>**TND)**|
|---|---|---|---|
|**Fieldwork estimates**|0.6|1.2|1.8|
|**Unit values**|1|1.4|2.4|

Source: author computations.

# **Estimates of Informal Trade with Libya**

**The border town of Ben Gardane is home to the leading wholesale and semi-wholesale market for goods arriving from Libya for distribution in the north and center of the country.** These goods are shipped from the ports of Zliten, Misrata, or Tripoli to Ben Gardane, where they are stored and distributed to the rest of the country.

**The major wholesalers based in Ben Gardane play a key role in this process. They are few in number and tend to specialize in particular product lines.**<sup>17</sup> Based on their understanding of the levels of demand in the Tunisian market, they place orders abroad (mainly in China and Turkey), arrange to receive the goods in Libyan ports, and organize convoys of transporters to bring the goods from the Libyan ports to Ben Gardane. Goods from China are subject to very different levels of customs duties in the two countries (6% in Libya compared to 33% on average in Tunisia). In addition to customs duties, goods entering the country are also subject to various consumption taxes that are normally collected by the customs authorities, and these two duties together make the disparities between Tunisia and Libya all the more marked. While the combined tax burden in the former is around 84% of the import value, for the latter, it is a little over 6% of the import value.

> 17 Haddar (2013) estimates that there are around 60 such wholesalers: 15 in textiles, 10 in food, 10 in electrical and electronic goods, 5 in tobacco, 5 in carpets, 5 in clothing and footwear, 3 in tires, 3 in hardware, 3 in cosmetics, and 2 in furniture.

10
<!-- Start of picture text --> Dashboard Architecture<br>Data Use - -<br>(User Types) Legislature Executive Civil Society Academia International Bodies<br>:<br>Data Services Quality: of Richieee ke d Effectiveness: of Adviso: oh Availabilityeee and Use of<br>(ServiceService Types)Typ Data Releases OnlineOpennessAccessof andRelated Analyticalto StatisticsServices TEVA ances Saas<br>Data Prod HSS Social Economic Environmental Institutional<br>(Topics)<br>Data Sources yeaanasonlie Administrative Geospatial Private Sector Data/<br>Surveys) Data Data Citizen Generated Data<br>Legislation Finance<br>Data Infrastructure 8 and StandardsSURE EI and Skills Partnership (Domestically and<br>Governance Methods from Donors<br><!-- End of picture text -->
|Kleibergen-Paap rk Wald F|9.851|9.829|7.005|7.251|
|---|---|---|---|---|
|Root MSE|0.517|0.513|0.437|0.367|
||Panel D (<br>|Non-agricultur<br>|e self-employ<br>|ment)<br>|
|Log of refugees (50km)|0.03556<br>(0.08114)|0.03295<br>(0.07635)|0.04755<br>(0.14670)|0.04190<br>(0.15579)|
|Observations|1,068|1,067|669|667|
|Kleibergen-Paap rk Wald F|8.789|8.719|4.676|4.530|
|Root MSE|0.515|0.511|0.414|0.327|
|**_Included controls_**|||||
|Exogenous controls (age, agesq, sex “male==1”)|_No_|_Yes_|_Yes_|_Yes_|
|Other controls (HH size, marital status, education,<br>occupation, other income sources, land ownership)|_No_|_No_|_Yes_|_Yes_|
|Year fixed effects|_Yes_|_Yes_|_Yes_|_Yes_|
|Cluster fixed effects|_Yes_|_Yes_|_Yes_|_No_|
|Household fixed effects|_No_|_No_|_No_|_Yes_|

_Apart from HH size, the more endogenous controls added are as at the base year of the study. In Regression (4) where household fixed effects replace the cluster fixed effects, all the time invariant variables drop off (only age, agesq & male remain). Sampling weights are considered and Standard errors clustered at the cluster level in all regressions. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1._

**Land ownership.** The presence of refugees is potentially beneficial to the welfare of households who initially owned land smaller than or equal to the median size of 2.5 acres of land. The coefficients are relatively large and positive though only statistically different from zero when household fixed effects are controlled for (Table 7, Panel A; column 4).<sup>18</sup>

Subsistence farmers usually own smaller sizes of land and therefore it is likely that there is a correlation between the results based on initial land size and those on initial main source of income. A t-test also shows that the difference between the coefficients in corresponding panels is not statistically significant. Ownership of land as defined in this study therefore does not seem to matter for the effects of refugee presence on household welfare.

> 18 With the IV decay function which places more emphasis on the proximity between clusters and border points, we find that the coefficients remain statistically significant (Appendix 4, Table A25, Panel A; columns 5-8). Therefore, for households who initially owned less than or equal to 2.5 acres of land and are living within 50km of the refugee settlements, the likely increase in their consumption per adult equivalent is between 8% and 11% (Appendix 4, Table A25; Panel A). Similar effects are observed within the 100km buffer where the likely increase in their consumption per adult equivalent is between 12% to 19% (Appendix 4, Table A26; Panel A).

26
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Annual funding gap

Senegal’s infrastructure funding gap amounts to $578 million per year, or about 7 percent of GDP, once efficiencies are captured. The largest gap by far is in the power sector, with water and transport presenting much smaller annual funding gaps (table 23). Last, the funding gap in transport is insignificant compared to the other sectors, emphasizing the potential gains from improving efficiencies, due to the fact that investments in roads were large to start with and also because of the larger spending needs of road maintenance compared to that of other sectors.

## **Table 23. Funding gaps by sector**

### <u>$ millions</u>

||ICT|Power|Transport|WSS|Total|
|---|---|---|---|---|---|
|Spending needs|(248)|(1,062)|(141)|(342)|(1,792)|
|Existing spending*|212|396|95|170|873|
|Reallocation potential within sectors|0|0|38|0|38|
|Efficiency gains|0|219|15|78|313|
|Funding gap|**(36)**|**(447)**|**No gap**|**(94)**|**(578)**|

_Source:_ Derived from Foster and Briceño-Garmendia (2009).

_Note:_ Potential overspending across sectors is not included in the calculation of the funding gap, because it cannot be assumed that it would be applied toward other infrastructure sectors. WSS = water supply and sanitation; ICT = information and communication technology. * traced to needs. — = Not available.

# What else can be done?

The funding gap can be addressed only by raising additional finance or, alternatively, using lower-cost technologies and/or adopting less ambitious targets for infrastructure development.

In the case of Senegal, the country could very likely increase the flow of resources and technical and managerial expertise by attracting private sector investment in the various infrastructure sectors, and by continuing to emphasize infrastructural projects with a regional dimension that facilitate access to larger, regional markets through Senegal’s unique geographical location.

Senegal already has a long history of private participation in infrastructure (PPI), and among the West African countries, it has been one of the main destinations of private sector participation. One of the first projects was the concession of the Port of Dakar awarded to Bollore in 1987, and since 1990, total investment commitment in private infrastructure projects has amounted to more than $2.6 billion. Most of this, $2 billion, was directed to telecommunications (figure 28).

42
# **1 Introduction**

Vietnam participated in the Programme for International Student Assessment (PISA) for the first time in 2012 and its performance has been much higher than other developing countries that take part in this OECD led initiative. PISA scores of 15 year-olds in Mathematics, Reading and Science are calibrated to an OECD mean of 500 and standard deviation of 100 points. Only a few developing countries take part in PISA, perhaps because most of them have results much lower than the OECD countries. In the OECD-PISA 2012 database, there are seven countries other than Vietnam with a per capita GDP (in 2010 PPP dollars) below US$ 10,000 - Albania, Colombia, Indonesia, Jordan, Peru, Thailand and Tunisia. At US$ 4,098, Vietnam’s GDP per capita is the lowest of this group. Figure 1 indicates a positive, albeit non-linear correlation between GDP per capita and PISA test scores. Vietnam, represented by a red star, lies much above the other developing countries clustered in the lower left hand corner of Figure 1. With a mathematics mean score of 511, Vietnam is more aligned to Finland (519) and Switzerland (531), rather than Peru (368) and Colombia (376).

**Figure 1: PISA 2012 results compared with GDP per capita**

<!-- Start of picture text --> Shanghai−China<br>Finland<br>Vietnam (511) (519) Switzerland<br>(531)<br>Colombia (376)<br>Peru (368)<br>0 10000 20000 30000 40000 50000 60000<br>GDP per Capita in PPP 2010<br>700<br>600<br>500<br>400<br>PISA Math Average Score 2012<br>300<br><!-- End of picture text --> Source: OECD-PISA database The weighted average mathematics score of the seven developing countries is 383. It is helpful to understand the significance of the 128 point difference of the seven countries as compared with Vietnam. According to a recent OECD publication [OECD, 2013a], _“an entire proficiency level in mathematics spans about 70 score points –a large difference in the_ 2
-7-

# THE DATA

## The Sample

Since both types of private schools in the sample are<sup>located</sup> exclusively in urban areas, we compare them to<sup>urban public schools only.</sup> This paper analyzes data from 2,472 students<sup>in 76 schools out of the national</sup> sample of students and schools included in<sup>the 1982-83 study of mathematics</sup> achievement in the Dominican Republic<sup>(Luna and Gonzalez 198E). The sampling</sup> frame for the original study stratified schools<sup>by type (three types of public</sup> schools and two types of private schools)<sup>and by location (five types of urban</sup> and three types of rural settings); a random<sup>sample of schools from each cell</sup> in the frame was drawn, and, within each school,<sup>one or two classrooms were</sup> sampled.

At both the beginning and end of the school year,<sup>students were</sup> administered an IEA mathematics test and completed detailed<sup>background</sup> questionnaires. Teachers completed several instruments at the pretest, the posttest and during the school year, including<sup>a background questionnaire</sup> and a general classroom process questionnaire, and provided information about teaching practices and the characteristics of their randomly selected "target" class. A school administrator provided data about the school at the time of the original data collection; we obtained additional<sup>data on costs and</sup> enrollment in conjunction with the preparation of this paper.
While indirect taxes, as expected, increase the incidence of poverty when moving from disposable income to consumable income, the effect is not large enough to nullify the gains from direct transfers —and also from subsidies, which exhibit a positive marginal contribution to poverty (Figure 8)—, therefore placing the consumable income-based poverty still below the incidence measured with market income plus pensions: 1.8 percentage points (or 24 percent) below by using the $4/day poverty line; 1.9 points (or 27 percent) below by using the official extreme line; and half a point (or 3 percent) below by using the official moderate line.<sup>27</sup> Moreover, after the intervention of taxes, subsidies and direct transfers not only the incidence of poverty declines, but also the depth of poverty (intensity) and the magnitude of inequality among the poor (severity) fall remarkably (Figure 7.B).

**Figure 7: Effects of fiscal interventions on poverty**

<!-- Start of picture text --> A. Percentages of population in poverty, by income concept<br>18%<br>16% 17.0%<br>16.4%<br>15.9%<br>14%<br>12%<br>12.5%<br>10%<br>7.5% 7.7%<br>8%<br>5.7%<br>6% 6.9% 7.1%<br>4.4%<br>4% 5.0%<br>3.9%<br>2%<br>0%<br>Market Income plus pensions Net market income Disposable income Consumable income<br>$4/day poverty line Extreme poverty line (official) Moderate poverty line (official)<br>B. Percent changes in poverty indices from market income plus pensions to<br>consumable income<br>0%<br>-3%<br>-10%<br>-20%<br>-22%<br>-24%<br>-30% -27%<br>-36%<br>-40%<br>-41%<br>-45%<br>-50%<br>-51%<br>-55%<br>-60%<br>Incidence Intensity Severity Incidence Intensity Severity Incidence Intensity Severity<br>$4/day poverty line Extreme poverty line (official) Moderate poverty line (official)<br><!-- End of picture text --> _Source_ : Author’s elaboration based on Martinez-Aguilar and Ortiz-Juarez (2016). _Notes_ : The indices measuring the incidence, intensity, and severity of poverty correspond to the FGT family of poverty indices (Foster, Greer and Thorbecke, 1984). The incidence represents 27 The official extreme and moderate poverty rates in 2013 are, respectively, 4.5 and 14.4 percent, and these figures are conceptually comparable with the poverty rates resulting from the disposable income concept in this paper: 3.9 and 12.5 percent, respectively. The differences occur because the methodology implemented in this paper includes the near-cash transfers as part of direct transfers, whereas near-cash transfers are not considered in the income used by the Ministry of Social Development in the estimation of national poverty rates.

16
# Does E-Government Improve Government Capacity? Evidence from Tax Administration and Public Procurement<sup></sup>

Anna Kochanova<sup>†</sup> , Zahid Hasnain<sup>‡</sup> , and Bradley Larson<sup>‡</sup> JEL Codes: H11, H26, H57, O38 Keywords: E-government; E-filing; E-procurement; Tax administration; Public procurement; Corruption.

>  Acknowledgement: We thank Christoph Engel, Hanjo Hamann, Vahagn Jerbashian, Philip Keefer, and Bob Rijkers for their helpful comments during the preparation of this paper. We are grateful for <mark>funding from the World Bank's Knowledge for Change Program (KCP). This paper builds on a background paper for the World Development Report 2016: Digital Dividends”.</mark> † Corresponding author. Max Planck Institute on Research for Collective Goods. Kurt-Schumacher-Str. 10. 53113, Bonn, Germany. Phone: +49(0)2289141634. E-mail: kochanova@coll.mpg.de.

> ‡ The World Bank 1818 H St, Washington DC 20433, USA. Zahid Hasnain: Phone: +1 202 458 8616. Email: zhasnain@worldbank.org. Bradley Larson: Phone: +1 202 458 9445. Email: blarson@worldbank.org.
The use of country fixed-effects has usually not been the practice in previous studies. For example, in their widely cited study examined below, Spilimbergo et al.(1999) do not control for country-specific features that could account for differences in inequality such as labor market specificities emphasized by Rama (2002)), productivity differences (see Easterly (2004)), or institutions (Barro 2000). Nor do more recent studies (e.g. Milanovic (2005)) typically use such controls for heterogeneity.<sup>7</sup> Insofar as omitted factors do not change over time, the inclusion of fixed effects controls for such idiosyncratic factors. Since our data set covers a rather long period, and inevitably some of the relevant omitted variables will change over time, this needs to be kept in mind when interpreting results. Likewise, the validity of the results rests on the assumption that the data reflect a sufficiently stable relationship (this is why we exclude all transition economies from our samples) and that the same dynamics can be imposed on all countries, an assumption that is less likely to hold, but about which little can be done. We use two data sets. The first set of results are based on five-year average data spanning the 1980-2000 period relying on the extensively used Deininger and Squire (D-S) data set (augmented to include the year 2000 by the availability of the WIDER (2004) data). The second is the more recent high-quality data set World Income Distribution (WYD) also at approximately five-year intervals which covers the 1988-1998 period. Using two data sets provides further robustness checks, and the second data set is helpful when trying to quantify effects of trade liberalization on poverty. Table 1 shows that our sample

> 7 Among the studies that control for heterogeneity, Edwards (1997, 43 countries, 70s and 80s) finds no evidence that openness or trade liberalization increases inequality. When including fixed effects, Barro (2000, 84 countries for 1960-90, table 6) finds no correlation between inequality and openness, echoing Ravallion’s correlations between average household incomes and inequality across 117 growth spells (Ravallion (2001), table 1).

8
**Table 3: Cross-Country Branch and ATM Penetration Comparisons (2002-2003)**

||**Mexico**|**Chile**|**Brazil**|**Argentina**|**United**<br>**States**|
|---|---|---|---|---|---|
|Branches per 1,000 sq. km|4.1|2.0|3.1|1.4|9.8|
|Number of branches per 100,000 people|7.6|9.4|14.6|10.0|30.9|
|ATMs per 1,000 sq. km|8.9|5.1|3.7|2.1|38.4|
|Number of ATMs per 100,000 people|16.6|24.0|17.8|14.9|120.9|

Source: World Bank (Access database).

Rates and terms of household financing appear to have improved since 2000, especially for mortgages. As can be seen in figure 35, there is limited availability of historical interest rate time series by credit product, while the reliability of existing ones is questionable<sup>51</sup> , thereby limiting any conclusive evaluation of affordability. However, anecdotal evidence strongly suggests that rates and terms of household financing have improved since the early part of the period – for example, there is strong market consensus that mortgage interest rates and commissions have fallen considerably, although an official interest rate series has only recently began to be published. The terms of mortgage credit have also improved (maturities now extend up to 20 years) while fixed-rate mortgages are becoming increasingly pervasive as banks compete for clients<sup>52</sup> .

Figure 35: Evolution of Nominal Interest Rates (2000-2005) <!-- Start of picture text --> 40<br>35 Credit cards<br>30<br>25 Commercial loans<br>20<br>15<br>Mortgage loans<br>10<br>5<br>TIIE<br>0<br>Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05<br>Interest rate (%)<br><!-- End of picture text --> Source: BOM and Infosel.

Note: The mortgage loan rate is the average of the Total Annual Cost (CAT)<sup>53</sup> . The commercial loan rate is the simple average of nominal interest rates in local currency loans granted during each period. The credit card rate is the simple average of bank credit card rates in the market.

51 For example, after an initial decline in early 2001, the interest rate on credit cards (according to the Infosel series) has remained fairly stable and relatively high. However, in addition to the fact that the ‘headline’ rate does not capture the evolution in credit card commissions over this period, it is questionable whether it is representative of the diversity in credit card types and terms that currently exist in the market. 52 According to a BBVA Bancomer Index of Mortgage Accessibility, the cost of paying a 15-year mortgage for a median priced house (80 percent loan-to-value ratio) has fallen from a level of 2 times the median family income in 2000 to 1.1 times in 2004; see BBVA Bancomer (July 2005).

> 53 CAT ( _Costo Anual Total_ ) is the effective interest rate used for comparative purposes that includes all direct annualized costs of a loan (e.g. bank commissions and insurance) excluding taxes and third party expenses (e.g. notary fees).

34
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