rafmacalaba/datause-encoder-data huggingface dataset:
- Tarr, David G. and Natalya Volchkova  (forthcoming) ―Foreign trade and direct investment, patterns and policy issues,‖ in _Handbook of the Russian Economy_ , Michael V. Alexeev and Shlomo Weber (eds.), Oxford: Oxford University Press.

- Tarr, David G. and Natalya Volchkova  (2010), ―Foreign Economic Policy at a Crossroads,‖ in _Russia After the Global Economic Crisis_ , Anders Aslund, Sergei Guriev and Andrew Kutchins (eds.), Washington DC: Peterson Institute for International Economics and Center for Strategic and International Studies, 2010, 200-222.

- Triplett, Jack E. and Barry P. Bosworth (2004), _Productivity in the U.S. Services Sector: New Sources of Economic Growth_ , Washington D.C.: Brookings Institution.

- Tybout, J. and D. Westbrook (1995), ―Trade Liberalization and the Dimensions of Efficiency Change in Mexican Manufacturing Industries,‖ _Journal of International Economics,_ Vol. 39, 53-78.

- United Nations Conference on Trade and Development, Division on Transnational Corporations and Investment (1995), _World Investment Report 1995_ , New York and Geneva: United Nations.

- United Nations Conference on Trade and Development and World Bank (1994), _Liberalizing Trade in Services: A Handbook_ , New York and Geneva: United Nations.

- United Nations Conference on Trade and Development, Division on Transnational Corporations and Investment (1995 and 1996), World Investment Report 1995 and 1996, New York and Geneva: United Nations.

- U.S. Trade Representative (2006), Fact Sheets on the Russia-United States Bilateral Agreement on Russian WTO Accession, November. Available at http://www.ustr.gov/Document_Library/Press_Releases/2006/November/United_States,_ Russia_Sign_Bilateral_WTO_Market_Access_Agreement_Negotiations_on_WTO_Mem bership_Now_Move_to_the_Multilateral_Pha.html.

Vernon, R. (1960), Metropolis 1985, Cambridge: Harvard University Press.

- Walmsley, Terrie L. and L. Alan Winters (2005), ―Relaxing the Restrictions on the Temporary Movement of Natural Persons: A Simulation Analysis,‖ _Journal of Economic Integration_ , Vol. 20(4), 688-726.

- Warren, Tony (2000), ―The Impact on Output of Impediments  to Trade and Investment in Telecommunications Services,‖ in Findlay, Christopher and Tony Warren (eds), _Impediments to Trade in Services: Measurement and Policy Implications_ , London: Routledge.

92
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE medium term to attract a more significant volume of private finance, particularly in the energy sector (for the development of a gas-fired plant), thereby helping to liberate public funds for other pressing social needs.

**Figure 27. Angola needs to attract more private investment, in particular beyond the ICT sector**

<!-- Start of picture text --> 3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Power ICT Transport Water<br>% GDP<br><!-- End of picture text --> _Source:_ PPI Database, World Bank, 2010. _Note:_ GDP = gross domestic product; ICT = information and communication technology.

Selecting optimal technology choices could reduce the funding gap by three-quarters. Adopting lower-cost technologies could substantially reduce the cost of meeting infrastructure targets, and reduce the funding gap. If Angola could strategically expand its power trade, this would reduce the resource deficit of the power sector, lowering power needs from $785 million per year to $485 million per year, leading to savings of $300 million annually. Similarly, meeting transport connectivity standards using lower-cost road-surfacing technologies (such as single-surface treatment) could reduce the associated price tag from $423 million to $241 million. The overall savings from these measures would amount to $647 million, and would eliminate Angola’s funding gap (table 21).

**Table 21. Potential savings from innovation**

<u>$ millions</u>

||Before<br>innovation|After innovation|Savings|Savings as % of<br>sector funding<br>gap|Savings as % of<br>total<br>funding gap|
|---|---|---|---|---|---|
|Power trade|785|485|300|no gap|260|
|WSS appropriate technology|574|409|165|158|143|
|Roads appropriate technology|423|241|183|no gap|158|
|Total|1,783|1,135|647|no gap|562|

_Source:_ Derived from Carruthers and others, 2009 and Banerjee and others, 2008 _Note:_ WSS = water supply and sanitation.

It will likely be necessary for Angola to consider planning for a period longer than a decade to reach the illustrative infrastructure targets here outlined. If the huge efficiency potential could be fully captured, Angola could meet the posited targets today, assuming current level of spending. But under business-asusual assumptions of spending and efficiency, it will take up to 20 years for Angola to reach these goals.

43
region, holding everything else constant. Using the estimates in Table 4(b), Panel C, column (11). The increase in the log number of travelers is given by the expression: Note that distance ( _Dist_ ), common border ( _CB_ ), common language ( _CL_ ), and colonial relationship ( _CR_ ) are all time‐invariant variables. In computing expression (4), we use the mean values of those variables in the year 2017 for all observations occurring in the estimated regression of reference.

Figure 9 presents our projections of the number of travelers (in levels) expected to arrive in each region through the year 2047. To put the projections in perspective, in 2017 the typical MENA country received, on average, 6,732 visitors per country of origin. Holding all else constant, in Case 1, the adoption of B2C would result in an increase of 60% in that number by 2027 and 75% by 2037. For SSA, starting from average arrivals of 3,914 travelers in 2017, the projected increases are 78% and 104% in 2027 and 2037, respectively. The increase in the number of visitors is much more modest but still considerable. By 2027, average arrivals from a given country increase by 30% in MENA and 51.1% in SSA. Nevertheless, such increases rapidly reach a plateau and 10 years later the corresponding figures stand at 32% and 55%, respectively.

<!-- Start of picture text --> Figure 9<br>Travelers<br>(Mean thousands of visitors)<br>20,000<br>18,000<br>16,000<br>14,000<br>12,000<br>10,000<br>8,000<br>6,000<br>4,000<br>2,000<br>0<br>MNA (hi) MNA (low) SSA (hi)<br>SSA (low) World (hi) World (low)<br>Source: Author’s calculation.<br>2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047<br><!-- End of picture text --> Page **23** of **34**
# **Description of survey and variable definitions**

The survey used in this paper is _Encuesta Nacional de Salud 2000._ The<sup>sample of</sup> this module consists of 190,214 observations (96,958,975<sup>observationswhen weighted</sup> data are used). After cleaning the dataset 173,600 observations<sup>were leftfor analysis.</sup> To make sure that the sample was representative a comparison of<sup>somegeneral</sup> characteristics from the survey with the 2000 Census was done. By<sup>employing INEGI's</sup> definition of indigenous people, which employs the use of<sup>anindigenous languageas the</sup> main criteria, around 10 percent of the population on this survey can<sup>be considered</sup> indigenous. As mentioned, the 2000 Census classified some<sup>8.4 million peopleas</sup> indigenous (8.6 percent), which implies that our sample is largely<sup>consistent.Also</sup> according to the 2000 Census, the age average is around 20-24<sup>years,while in the sample</sup> it is around 25.4. Although there is a difference, it is not too big. The number of education years reported in our sample is a little bit below the<sup>nationalaverage.And,the</sup> percentage of men in our sample is similar to the Census estimates.<sup>Because the</sup> differences between our sample and the Census figures are<sup>small,it wasdecided to</sup> continue the analysis. Table 1 describes the variables employed.

Table 2 shows the descriptive statistics between indigenous and<sup>non-indigenous</sup> population. From this table it can be seen that the indigenous are<sup>poorer,youngerand</sup> less healthy than the non-indigenous. They have a lower level of<sup>educationand bad living</sup> conditions. Moreover, they have less health care service utilization,<sup>with the exceptionof</sup> preventive care services.

Table 3a shows the descriptive statistics between age groups, while<sup>Table3b</sup> presents the utilization rate for preventive care, medical consultations<sup>and dentalservices,</sup> and hospitalization by urban and rural residence. It can be seen from<sup>Table3a, on one</sup> hand, that children between 0-5 years old have the highest health<sup>care serviceutilization</sup> than any other age group, and in particular preventive health<sup>care utilization.From Table</sup> 3b, on the other hand, it can be gleaned that while the urban<sup>population has a higher</sup> hospitalization rate, the rural residents surprisingly have a higher preventive<sup>care</sup> utilization. Besides, there is no statistical difference between<sup>urbanand rural ratesin</sup> medical and dental consultations.

9
Despite the current negative economic downturn, Chad continues to host a high number of refugees and is among the countries that top the world’s list in this respect (Table A1). Chad is the 10th largest host country for refugees in the world and the fifth largest host in the Africa region after Ethiopia, Kenya, Uganda, and the Demographic Republic of Congo. Chad’s refugee population represents a significant portion of the national population—about 3%. The number of forcibly displaced persons increased from 474,478 in 2015 to 667,586 as of March 2019, of which about 69% were refugees or asylum seekers.<sup>3</sup> Of the 459,809 current refugees and asylum seekers, the majority are Sudanese refugees (74%) living in the eastern part of Chad; 21% are Central African Republic refugees living in southern Chad; a much smaller number of Nigerian refugees (about 2%) are living in the Lake Chad Basin. The situation is further complicated by the large population of IDPs in the Lake Chad region, estimated at 165,313 at end of 2018 (UNHCR 2018). Map 1 shows the locations of the refugee camps in Chad.

# **III. Methodology and Data**

# **<mark>Methodology</mark>**

<mark>The methodology used in this paper relies on the cross-survey imputation framework, which was first introduced by Elbers</mark> et al. <mark>(2003).</mark><sup>4</sup> <mark>Most recently, Dang</mark> et al. <mark>(2017) build on this literature to propose a model that imposes fewer restrictive assumptions and offers an explicit formula for estimating the poverty rate and its variance. Three advantages of the modifications introduced by Dang</mark> et al. <mark>(2017) are (i) the variance formula, which is simple and in line with the recent statistical literature; (ii) it can accommodate complex design sampling; and (iii) the framework remains applicable to two surveys with different designs. Finally, the approach allows for different modeling methods, including the standard linear regression model, its variant with a flexible specification of the empirical distribution of error terms, a logit model, and/or a probit model.</mark>

> 3 UNHCR uses the term “people of concern” to describe those who are forcibly uprooted from their homes, including asylumseekers, refugees, stateless persons, the internally displaced, and returnees.

> 4 See also <mark>Tarozzi (2007) and Mathiassen (</mark> 2009) <mark>for further improvements and adaptation of this approach (e.g., by estimating the standard errors in a different way).</mark>

5
# **2.2 Two Field Experiments**

In this section we describe the field experiments that we use to evaluate the impact of two different modalities of the API program. A full description of the different data sources used throughout the empirical analysis is provided in Appendix A. Because both experiments were run at scale within the infrastructure of the existing program, the recruitment process and the assignment mechanism of the mentors are the same between experimental and nonexperimental schools.

**First Experiment.** As part of an effort to evaluate a broader set of interventions targeted to families and schools in disadvantaged communities (Mart ́ınez, 2012), in 2010, the government undertook the first impact evaluation of the API program. Eighty primary schools are selected among those that met the eligibility criteria for the program across four Mexican states (72 in Chiapas). Assignment to the API program was randomized at the school level using a block design, with the strata represented by the Mexican states where schools are located. Forty schools were assigned to receive the API program starting from the 2011– 2012 school year while the remaining half of the schools were assigned to the control group without mentors.

A mid-line survey collected after one year of the API assignment recorded parental behaviors and investments for 208 parents in 73 schools (the enumerators were not able to reach the parents in seven schools). Student outcomes were measured two years after treatment assignment through the results in the national standardized test for students in grades three through six. Due to the incomplete take-up of the test—mainly due to the opposition from the teachers’ unions in some states—we were able to match 70 schools with 599 test score records out of the subsample of 73 schools with parental outcomes. Both sources of sample attrition are orthogonal to treatment assignment. Five of the unmatched schools were in the treatment group and five were in the control group. Table B-1 shows the balance for the original sample as well as the nested samples with parental outcomes and student outcomes between the treatment and the control groups across mean community-level and school-level characteristics measured in the year before the start of the first experiment.

**Second Experiment.** In 2014, as part of a World Bank project, we designed and evaluated

7
known to have lower levels of corruption and regulation, but they also have better physical and financial infrastructure which could have direct impacts on productivity. Thus, in the absence of country fixed effects, our corruption and regulation variables could easily spuriously pick up the effects of physical and financial infrastructure on productivity.

Second, productivity differences can also arise across industries and sectors. If industries or sectors that have high productivity also happen to be with low or high corruption and/or low or high regulation, then our main results could suffer from omitted variable bias problem. To guard against this possibility, we control for industry and sector fixed effects. Our data have 9 one-digit industries and 3 sectors, including manufacturing, retail and services. We also include region dummies and year trend in the analysis to control for region specific characteristics that may affect productivity and potential effects of global shocks that took place in the years when the data were collected.

The next set of controls includes size, age, export status and foreign ownership. There is a fair amount of literature on the importance of these variables for the productivity of firms. Among other factors, the positive relationship between firm-size and productivity is often attributed to greater allocative efficiency for the relatively larger firms (Tybout 2000; Bartelsman et al. 2013). Firm-size is also considered an important proxy measure for several firm attributes such as access to finance, access to raw materials and product markets, tendency to innovate, exporting activity, firm-efficiency, and growth (e.g. Acs and Audretsch 1988; Cohen and Klepper 1996; Pagano and Schivardi 2003; Söderbom and Teal 2004). For firm-size, we use (log of) number of full-time permanent workers at the firm at the end of the last fiscal year ( _Firm size_ ).

Substantial work has also been done linking productivity and age of the firm. Age related effects among surviving firms may be due to a number of reasons, including scale economies 13
Figure 2.1: Colombia – Evolution of Domestic Credit to Private Sector (1960-2006) <!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Colombia Argentina Chile Middle Income<br>1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004<br>% of GDP<br><!-- End of picture text --> Figure 2.2: Cross-Country Comparisons of Domestic Credit to Private Sector (2006) <!-- Start of picture text --> 200<br>180<br>160<br>140<br>120<br>100<br>C HL<br>80<br>60<br>40 BR A<br>C OL<br>20 M EX<br>AR G<br>0<br>6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5 11.0<br>Ln GDP per Capita, PPP (current international US$)<br>Domestic Credit to the Private Sector, % GDP<br><!-- End of picture text --> _Source: The World Bank (World Development Indicators). Note: CHL = Chile, COL = Colombia, BRA = Brazil, MEX = Mexico, ARG = Argentina._ 13
# ACRONYMS

|AP|Andhra Pradesh|
|---|---|
|APC|Agricultural Price Commission|
|BICP|Bureau of Industrial Costs and Prices|
|CACP|Commission for Agricultural Costs and Prices|
|EEC|European Economic Community|
|EU|European Union|
|FAO|Food and Agricultural Organization|
|FCI|Food Corporation of India|
|FSU|Forner Soviet Union|
|GATT|General Agreement on Trade and Tariffs|
|HA|Hectares|
|HFCS|High Fructose Com Syrup|
|ISA|International Sugar Agreements|
|ISEIC|Indian Sugar Export Import Corporation|
|ISMA|lndian Sugar Mills Association|
|MT|Metric Ton|
|OPS|Open Pan Sulphitation|
|SAP|State Advised Prices|
|SMP|Statutory Minimum Price|
|STC|State Trading Corporation|
|TN|Tamil Nadu|
|UP|Uttar Pradesh|
|VPS|Vacuum Pan Sulphitation|
|WPI|Wholesale Price Index|

Note: For incentive indicators see methodology 2
the capital to labor ratio (Gordon and Li 2009). We show in Section 4.4 that when controlling for capital intensity, the coefficient on size remains unchanged, while the coefficient on capital intensity is insignificant. This suggests that technological demand for labor does not impact tax inspection indirectly through its interaction with capital.

# **3.2 Empirical Specifications**

The reduced-form size gradient is estimated by directly regressing the WBES industries’ tax outcomes on the US industries size rank: Where Tax outcome _ict_ is a tax outcome of industry _i_ in country _j_ at time _t_ (e.g. likelihood of tax inspection over the past 12 months), Rank size _i_ , _US_ ,01 is the size-ranking of industry _i_ in the US, and _γct_ are country-year fixed effects. The coefficient _δ_ 1 identifies the reducedform size gradient.

The IV specification is: Where Rank size _ict_ of industry _i_ in country _c_ at time _t_ is instrumented with Rank size _i_ , _US_ ,01 of industry _i_ in the US.

In practice, we estimate three different empirical models. The first model adds a set of controls to the above equations. It allows for tax outcomes to differ non-parametrically and interactively in every country and year across a set of industry characteristics. We code all industries as belonging to above or below their country-year median age, share of exporters and share of foreign firms. We then create the matrix (Characteristics) _ict_ containing the full set of interactions across characteristics. The model allows for all factors 14
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# References and bibliography

This country report draws upon a wide range of papers, databases, models, and maps that were created as part of the Africa Infrastructure Country Diagnostic (AICD). All of these can be downloaded from the project Web site: www.infrastructureafrica.org. For papers go to the document page (www.infrastructureafrica.org/aicd/documents), for databases to the data page (www.infrastructureafrica.org/aicd/tools/data), for models go to the models page (www.infrastructureafrica.org/aicd/tools/models) and for maps to the map page (www.infrastructureafrica.org/aicd/tools/maps ). The references for the papers that were used to compile this country report are provided in the table below.

## General

- AICD (Africa Infrastructure Country Diagnostic). _Africa’s Infrastructure: A Time for Transformation_ . <u>www.infrastructureafrica.org.</u>

———. 2010. ECOWAS’s Infrastructure: A Regional Perspective. http://www.infrastructureafrica.org.

EIU (Economist Intelligence Unit). 2010. _Senegal Country Report_ . London, UK: EIU.

Foster, Vivien, and Cecilia Briceño-Garmendia, eds. 2009. _Africa’s Infrastructure: A Time for_

- _Transformation._ Paris and Washington, DC: Agence Française de Développement and World Bank.

- World Bank. 2007a. _Country Assistance Strategy for the Republic of Senegal for the period FY08–FY11_ . Washington, DC: World Bank.

- ———. 2010a. ―PPIAF-PPI Project Database.‖ www.ppi.worldbank.org/.

- ———. 2011a. _Doing Business 2011: Making a Difference for Entrepreneurs_ . Washington, DC: World Bank.

- ———. 2011b. ―Enterprise Surveys’ Database.‖ www.enterprisesurveys.org/.

## Financing

Briceño-Garmendia, Cecilia, Karlis Smits, and Vivien Foster. 2009. ―Financing Public Infrastructure in

- Sub-Saharan Africa: Patterns and Emerging Issues.‖ AICD Background Paper 15, Africa Region, World Bank, Washington, DC.

## Growth

Calderón, César. 2009. ―Infrastructure and Growth in Africa.‖ Policy Research Working Paper 4914, World Bank, Washington, DC.

- Escribano, Alvaro, J. Luis Guasch, and Jorge Pena. 2010. ―Assessing the Impact of Infrastructure Quality on Firm Productivity in Africa.‖ Policy Research Working Paper 5191, World Bank, Washington, DC.

46
# **LIST OF TABLES**

|Table 3.1|Nominal and Real GDP Growth 1986-92|11|
|---|---|---|
|Table 4.1|Distribution of Per Capita Expenditures by Population Deciles|13|
|Table 4.2|Distribution of per capita food expenditures by population deciles|14|
|Table 4.3|Pattern of Real Household Per Capita Expenditures by region|15|
|Table 4.4|Pattern of Real Household Per Capita Expenditures by<br>Geographic regions|15|
|Table 4.5|Rural/Urban Pattern of Real Household Per Capita<br>Expenditures by Geographic regions|17|
|Table 4.6|Poverty Incidence in Nigeria by Region|18|
|Table 4.7|Incidence of Extreme Poverty in Nigeria by Region<br>(Poverty line=N 197.71)|18|
|Table 4.8|Rural/Urban Incidence of Poverty by Geographical Regions<br>(Poverty line=N 395.41)|19|
|Table 4.9|Decomposition of Poverty by Geographical Regions<br>(Poverty line=N 395.41)|20|
|Table 4.10|Rural/Urban poverty incidence by Agro-climatic zones<br>(Poverty line= N395.41)|21|
|Table 4.11|Rural/Urban poverty Decomposition by Agro-climatic zones<br>Poverty line=N395.41)|21|
|Table 4.12|Poverty Incidence in Nigeria by State<br>(Poverty Line=395.41)|22|
|Table 4.13|Contribution of Poverty Incidence by State<br>(Poverty Line =395.41)|23|
|Table 4.14|Incidence of Extreme Poverty in Nigeria by State<br>(Poverty Line= 197.71)|24|
|Table 4.15|Decomposition of Extreme Poverty Incidence by State<br>(Poverty Line= 197.71)|25|
|Table 4.16|Poverty Incidence by Age Group of Household Head (1985-92)<br>(Poverty Line=395.41)|26|
|Table 4.17|Decomposition of Poverty by Age Group of Household<br>Head (1985-92)|27|
|Table 4.18|Poverty Incidence by Gender of Head of Household (1985-92)<br>(Poverty Line = 395.41)|28|
|Table 4.19|Poverty Incidence by Educational level of Head of Household (1985-|92)|
||(Poverty Line=395.41)|29|
|Table 4.20|Incidence of Extreme Poverty by Education of Head (1985-92)<br>(Poverty Line= 197.71)|29|
|Table 4.21|Poverty Incidence by Occupation of Head of Household (1985-92)<br>(Poverty Line=395.41)|32|
|Table 4.22|Incidence of Extreme Poverty by OcuLpation of Head of Household<br>(Poverty Line= 197.71)|33|