rafmacalaba/datause-encoder-data huggingface dataset:
ECOWAS‘S INFRASTRUCTURE: A REGIONAL PERSPECTIVE this state of affairs may simply reflect small size (as for The Gambia and Guinea-Bissau), but more generally it reflects low traffic volumes and limited purchasing power in the domestic market. Both of these facts make it difficult for air transport to compete with surface transport alternatives, such as longdistance bus services. Nigeria has the second-largest domestic air market in Sub Saharan Africa, after South Africa, accounting for 11 percent of the overall scheduled domestic traffic.

By and large, each country has at least one daily connection to one of the main regional hubs. What appears to be low connectivity may not be a problem as long as each country has frequent connections to one or more of the main regional hubs. Whether this is so can be assessed by looking at the origindestination matrix for the ECOWAS countries (table 2.10), which shows that all of the countries in the region indeed have at least seven flights per week to one of the main regional hubs. However, only two countries (Mali and Togo) have as many as two daily flights to their respective hubs. Abidjan is the main hub for Benin, Burkina Faso, Guinea, and Togo. Dakar is the main hub for Cape Verde, Gambia, Guinea-Bissau, and Mali. Accra is the main hub for Liberia and Sierra Leone. While strongly connected to Accra, Lagos is not so strongly

**Table 2.9  International and domestic connectivity for 2007 (number of city pairs served)**

|Country|Domestic<br>city pairs|International<br>city pairs|Intercontinental<br>city pairs|
|---|---|---|---|
|Benin|0|19|1|
|Burkina Faso|1|11|1|
|Cape Verde|10|5|23|
|Central African Republic|0|2|1|
|Côte d'Ivoire|0|23|5|
|Gambia|0|7|7|
|Ghana|5|19|9|
|Guinea|0|8|2|
|Guinea-Bissau|0|2|1|
|Liberia|0|8|1|
|Mali|0|16|1|
|Niger|0|8|1|
|Nigeria|22|31|19|
|Senegal|4|24|18|
|Sierra Leone|0|8|2|
|Togo|0|11|1|
|Source: Derived from Bofinge|r 2009.|||

connected to the francophone hubs or to any of the other surrounding countries in the region.

Beyond basic connectivity, it is important to evaluate the convenience and velocity of air travel. For some countries, most of the flights that originate from within the country are direct. To a large extent, flights that orginate from Ghana and Nigeria are direct, because Accra and Lagos are hubs for air travel in West Africa. Senegal is striking in that of the 80 flights that depart, only 59 are direct. On average, flights from Burkina Faso, Guinea Bissau, and Togo cover fewer kilometers per hour than other parts of the subregion (table 2.11).

37
**xm l T -urm**

_Rprn I Projected Popudatlon (billions) in Less Developed Countries, 1985-2025, Compared with U.N. and Previous World Bank Projections_ <!-- Start of picture text --> 7, 0<br>645 .S<br>4. ,<br>3.5<br>1965 1990 1995 2000 2005 2010 2015 2020 2025<br><!-- End of picture text --> _igure 2 Projected Population (billions) in More Developed Countries 1985-2025, Compared with U.N. and Previous World Bank Projections_ <!-- Start of picture text --> 1 39<br>I, 37-<br>1 36 -<br>1 35 -<br>1 34<br>1 33 -CUARE U N<br>1 32 -PREVIOUS<br>1 31<br>1 30<br>1 29-<br>1 29-<br>1 27-<br>1 26-<br>1 25-<br>1 24-<br>1 23-<br>1 22-<br>1 21-<br>1 20-<br>18 19~<br>1995 1990 1995 2000 2005 2010 2015 2020 2025<br><!-- End of picture text -->
# 1. Introduction

The World Bank prides itself on being a knowledge-based institution, to a much greater extent than most of its peers. And rightly so. The World Bank was one of the first international organizations to appoint a Chief Economist, in 1972; since then, two of the job holders went on to win the Nobel Prize in economics. With the _World Development Report_ , a series introduced in 1978, it pioneered the production of regular flagship studies shaping the narrative on development economics. In 1990, it championed an approach to the measurement of poverty – back then, “one dollar a day” – that has become widely used. Its _Doing Business_ reports, launched in 2002, prompted lively policy debates on regulation, and triggered uncountable economic reforms around the world. In 2011, it embraced an open access approach, putting a vast majority of its reports and data under a Creative Commons license; today its _World Development Indicators_ are among the most widely used cross-country databases worldwide.

In a reputable ranking of top academic institutions in the field of development, the World Bank comes first, followed by its own research department, ahead of the Massachusetts Institute of Technology, the National Bureau of Economic Research, and other prestigious economics departments (RePEc 2022). And operational counterparts in developing countries often report that they value the knowledge they get from the World Bank more than the financial resources it provides (Knack et al. 2020).

Few other international organizations – if any – may match this record. And yet, even seasoned insiders would be hard pressed to explain how the World Bank accomplishes all this. How is rigorous analytical work produced, what ensures that it is relevant to development practitioners, and how it reaches its intended audience seem to be more art than science.

This is not for lack of trying. The phrase “the knowledge Bank” was coined in 1996, and it is certainly catchy. But beyond obvious themes, such as tapping the World Bank’s global expertise, blending development knowledge with policy engagements, and leveraging digital technologies, it is thin on the specifics (Forbes 2020). Over the half-century since the first World Bank Chief Economist was appointed, multiple reforms revamped the way analytical work was funded, how it was conducted and reviewed, and how it was connected to lending (Dethier 2009). Even major reorganizational reforms were justified on the need “to improve knowledge flows and the delivery of multisector solutions to clients” (World Bank 2014). However, outsiders would find it difficult to pinpoint any turning point, from an organizational 2
micro or small firms and medium or large firms. The human capital effects are shown in Table 4; the complete results are shown in Tables A.2 and A.3.

**TABLE 4. MARGINAL PRODUCTIVITY DIFFERENTIALS AND RELATIVE WAGES BY FIRM SIZE**

|||Micro<br>|and Smal<br>|l Fir<br>|ms<br>||Mediu<br>|m and Lar<br>|ge Fi<br>|rms<br>|
|---|---|---|---|---|---|---|---|---|---|---|
|_Variable_|_Producti_<br>_Different_|_vity_<br>_ial_|_Relati_<br>_Wag _|_ve_<br>_e_|_Reject_<br>_i_<br>_i_<br>_o_<br>_H_<br>λ<br>φ =<br>:|_Producti_<br>_Differen_|_vity_<br>_tial_|_Relati_<br>_Wag _|_ve_<br>_e_|_Reject_<br>_i_<br>_i_<br>_o_<br>_H_<br>λ<br>φ =<br>:|
|_Ho: coefficient=1_|||||||||||
|Men|1.68<br>(0.16)|*|1.33<br>(0.05)|*|*|2.22<br>(0.24)|*|1.57<br>(0.07)|*|*|
|Upper<br>|1.61|*|1.36|*||2.22|*|1.46|*|*|
|secondary|(0.18)||(0.07)|||(0.24)||(0.06)|||
|University or|3.06|*|1.99|*|*|5.09|*|2.72|*|*|
|more|(0.35)||(0.11)|||(0.57)||(0.11)|||
|(3 - 10] years|1.03||1.19|*|*|1.25|*|1.15|*||
|in the firm|(0.07)||(0.04)|||(0.10)||(0.04)|||
|(10 - +) years|0.5|*|0.81|*|*|1.56|*|1.39|*||
|in the firm|(0.05)||(0.03)|||(0.13)||(0.05)|||
|In-house|1.27|*|1.19|*||1.25|*|1.13|*|*|
|training|(0.11)||(0.05)|||(0.07)||(0.03)|||
|External formal|1.55|*|1.29|*|*|1.44|*|1.19|*|*|
|training|(0.15)||(0.07)|||(0.09)||(0.03)|||
|Adjusted R<sup>2</sup>|0.84||0.93|||0.69||0.82|||
|Observations|3,301|||||3,565|||||

_Notes:_ Calculations based on ENESTYC 2001. Standard errors are shown in parenthesis. * denotes significance at 5 percent level. The Wald statistic was used to test the equality of the estimated wage and productivity differential. Reference group: women, lower secondary or less, until three years of experience in the firm, and without training.

In the case of gender, the main conclusion still holds independent of firm size. The higher wages of men is explained by their higher productivity relative to women. Nevertheless, the productivity differential for men with respect to women is larger than the wage differential. However, for micro and small firms, the differentials are much smaller: 68 percent for productivity, and 33 for wages. The medium and large firms have slightly higher differentials than before: 122 percent for productivity and 57 percent for wages.

The results for education demonstrate a robust relationship between higher education and higher productivity. The major difference is that in micro and small firms we cannot reject the equality of the productivity and wage differentials for workers with 15
**Using Paradata to Assess Respondent Burden and Interviewer Effects in Household Surveys: Evidence from Low- and Middle-Income Countries**

Ardina Hasanbasri<sup>†</sup> , Talip Kilic<sup>⁑</sup> , Gayatri Koolwal<sup>⁑</sup> and Heather Moylan<sup>⁑1</sup>

**JEL Codes** : C81, C83

**Keywords** : Survey Methodology, Household Surveys, Paradata, Respondent Burden, Interviewer Effects, Cambodia, Ethiopia, Tanzania.

> 1 The authors are listed alphabetically. Corresponding authors: <u>ardina.hasanbasri@yale.edu</u> or <u>tkilic@worldbank.org.</u> † Jackson School of Global Affairs, Yale University, ⁑ Living Standards Measurement Study, Development Data Group, World Bank.
||(0.133)|(0.203)|(0.164)|(0.162)|
|---|---|---|---|---|
|Observations|9,370|2,567|4,881|1,902|
|Adjusted R square|0.233|0.329|0.233|0.238|
|Other vehicles|0.179|0.279|0.002|0.051|
||(0.137)|(0.309)|(0.205)|(0.117)|
|Observations|9,448|2,578|4,917|1,933|
|Adjusted R square|0.393|0.406|0.405|0.388|
|Durable goods|0.348***|-0.113|0.075|0.057|
||(0.130)|(0.428)|(0.149)|(0.134)|
|Observations|9,476|2,588|4,930|1,937|
|Adjusted R square|0.429|0.486|0.444|0.403|
|Medical|0.146*|0.584**|-0.049|0.169|
||(0.082)|(0.290)|(0.113)|(0.145)|
|Observations|9,395|2,559|4,887|1,928|
|Adjusted R square|0.362|0.381|0.363|0.367|
|Health and fitness|0.222**|0.151|-0.047|-0.012|
||(0.107)|(0.144)|(0.107)|(0.114)|
|Observations|9,478|2,587|4,934|1,936|
|Adjusted R square|0.299|0.419|0.334|0.284|
|Education|-0.142|-0.414*|-0.158|0.086|
||(0.116)|(0.241)|(0.119)|(0.203)|
|Observations|9,417|2,563|4,899|1,934|
|Adjusted R square|0.217|0.226|0.215|0.271|
|Home repair|-0.065|-0.064|-0.010|-0.447***|
||(0.082)|(0.383)|(0.130)|(0.131)|
|Observations|9,470|2,586|4,931|1,932|
|Adjusted R square|0.452|0.434|0.463|0.460|
|Gifts|-0.013|0.562|-0.009|-0.306**|
||(0.101)|(0.387)|(0.119)|(0.154)|
|Observations|7,802|1,983|4,172|1,631|
|Adjusted R square|0.111|0.142|0.117|0.162|

Note: Extended model with additional variables of control, including the lagged log household per capita income growth, lagged log value of household expenditure per capita of the consumption category in question, household average years of schooling, household gender ratio, average age squared/100, dependency ratio, and urban-rural dummies. Robust standard errors clustered at the county level are presented in parentheses. *** p<0.01, ** p<0.05, * p<0.1.

·· 38
# **Figure 4.2 Comparison of public and private investment growth with long‐term average**

_In the majority of EMDEs, both public and private investment growth since 2010 have been below their long‐run averages._

**A. Countries with public investment growth below 1990‐2008 average**

**B. Countries with private investment growth below 1990‐2008 average**

<!-- Start of picture text --> Percent Percent<br>2006-07 2010-13 2014-15 2006-07 2010-13 2014-15<br>100 80<br>70<br>75 60<br>50<br>50 40<br>30<br>25 20<br>10<br>0 0<br>AEs EMDEs AEs EMDEs<br><!-- End of picture text --> Sources: Eurostat, General Statistics Office of Vietnam, Haver Analytics, International Monetary Fund, Ministry of National Economy of the Republic of Kazakhstan, OECD, Reserve Bank of India, Sri Lanka Ministry of Finance, World Bank. Note: Public and private investment growth rates are weighted average of gross fixed capital formation growth rates in the public and private sectors, respectively, in constant 2005 U.S. dollars. The sample includes 20 advanced economies and 99 EMDEs for 1990 to 2015. Figures show the share of EMDE and AEs (in percent) in which public and private investment growth was below the 1990‐2008 average during the periods specified. Line indicates half of the sample.

59
before year _t_ and zero otherwise. Standard errors are clustered at the plant level to capture the autocorrelation within a given plant location across nearby pixels across time. Coefficient _β_ 2 captures the impact of plant operation on upwind pollution and income. Coefficient _β_ 2 + _β_ 3 measures the impact of operations on downwind pollution and income. Note that _β_ 1 is not absorbed by _Pixeli_ as pixels could be in the upwind direction of some plants but in the downwind direction of others.

Figure 11 offers a visual summary of the diff-in-diff analysis. The horizontal axis denotes the year relative to the operation start year. Red lines represent downwind pixels, while blue lines depict upwind pixels. The first stage analysis (Figure 11 left) highlights a stark increase in the pollution levels of downwind locations. Upwind areas observe a slight increase due to upwind pollution in the immediate vicinity of plants. The same pattern is not apparent in the reduced form (Figure 11 right); GDP per capita at the pixel level does not display significant differences between up- and downwind locations following the activation of coal plants. For related charts on GDP and HDI, refer to Figure S4.2.

Figure 11: Event study figures <!-- Start of picture text -->
-24 -20 -16 -12 -8year relative to operation start year-4 0 4 8 12 16 20 24 -24 -20 -16 -12 -8year relative to operation start year-4 0 4 8 12 16 20 24<br>90° upwind 90° downwind 90° upwind 90° downwind<br>.8<br>10<br>.6<br>9.5<br>SO2 .4 9<br>8.5<br>.2 ln(GDP per capita)<br>8<br>0 7.5<br><!-- End of picture text -->

_Notes_ : Y-axis in the left panel is in the logged unit. Average SO2 among all pixels that are upwind before operations is 0.214 in the figure logged unit, 0.815 in _μ_ g/m<sup>3</sup> ; That for downwind pre-period is 0.291 in the figure logged unit, 1.20 in _μ_ g/m<sup>3</sup> ; upwind after: 0.365 and 1.53 _μ_ g/m<sup>3</sup> ; downwind after: 0.593 and 2.46 _μ_ g/m<sup>3</sup> .

Full first stage and reduced form results are reported in Table 2 and Table S3.1, respectively. After plant operations, downwind regions have 17.7 percent higher SO2 compared with the same pixel before operations. In contrast, upwind regions only have a 3 percent increase in SO2. This indicates a strong first stage result - plant operations significantly worsen air quality in downwind areas; upwind areas serve as counterfactuals. However, reduced form results report small and insignificant estimates, suggesting that upwind and downwind areas behave similarly in their income levels. In short, these results can neither support nor refute the notion that coal-plant driven air pollution causes income reductions in affected areas.

19
# **3.3 Water and Sanitation**

We consider the seven most important private firms that provided water and sanitation around the world in 2003. These companies are present in at least 61 countries. The set is made by: Ondeo, Veolia, RWE, Saur, United Utilities (UUT), Anglian Waters (AWG) and Severn Trent (SET).<sup>13</sup> The information is provided by each firm in its annual report (2003) available from the web site of these companies or from the national stock market commission for listed companies. We also relied on information collected from the web site of the Public Services International Research Unit and by a 2003 report on the sector by Lehman Brothers.<sup>14</sup> The total information available is, per country and firm, the population coverage by water service and sewerage. In addition, there is a set of 19 more private firms present in these 62 countries. Typically, these are spin-offs of local construction companies. The water business is increasingly popular indeed in the construction sector in developing countries. Most of the Brazilian market for instance is catered to by Brazilian construction firms. The global information is however limited and we can only determine how many of these firms there are per country as well as the share of the total population they cover. We call this set as “Other Private Providers” (OPP).<sup>15</sup> They represent about one third of the global market and their share in regional markets varies from less than 15% in Eastern Europe to almost 60% in Latin America where all the local construction companies have become such big players in many countries that they control the largest market of the region (i.e. Brazil). Hall et al. (2004) for instance document the role of many Asian companies in Asia, alone or in partnerships with OECD companies (Veolia, Ondeo/Suez and Thames). From Hong Kong, Citic Pacific, NWS Holding, China Everbright and China Water are all active in the Chinese market. In Malaysia, Pucak Niaga, Ranhill Utilities and Taliworks Corp are three of the larger water and sanitation companies in the country. Other Malaysian companies such as Pilecon, PPB/Keery Utilities, PBA Holdings Berhad and Salcom are mostly active in China.

> 13 Bechtel is probably the only major actor missing from this list but we did not have enough information to include it.

> 14 Missing from the report are the British Biwater and the Dutch Ne-on who often team up as consortia during the 1990s in particular in East Asia.

> 15 The information available on OPPs includes urban and country population covered by these OPPs in each country together with the number of these firms per country. Therefore, it is not possible to compute the market share of each OPP. However, to compute the CRm, this information is not required since the aggregate market share and the number of the OPPs per country is enough.

16
Policy Research Working Paper 7843

# **Abstract**

This paper employs nationally representative household survey data on parents of adult individuals to analyze the intergenerational transmission of education in nine SubSaharan African countries. The paper provides the levels, trends, and patterns of intergenerational persistence of educational attainment over 50 years, with a special focus on gender differences. The study finds a declining cohort trend in the intergenerational educational persistence in all the countries, particularly after the 1960s. The increase in educational mobility coincides with drastic changes in educational systems and a huge investment in human capital accumulation in the region following independence.

Nevertheless, the education of parents’ remains a strong determinant of educational outcomes among the children in all the countries. Ghana, Guinea, Nigeria, and Uganda experienced the highest intergenerational mobility, and the Comoros and Madagascar the lowest. In all the sample countries, more mobility is observed in the lower tail of the distribution of education. Intergenerational educational persistence is strong from mothers to children, and the effect is more pronounced among daughters than sons. The results highlight the need for targeted redistributive policies that improve intergenerational mobility in the region.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. 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 e.yitbarek@maastrichtuniversity.nl.

_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
# **Gravity Model Simulation**

For each city pair, we use equation (4) to predict trade before and after road network upgrading. We compute ex-ante values for city-to-city trade, aggregate to country-to-country totals, and standardize to intercountry value totals from the IMFDOT data. To compute ex-post values, we upgrade minimum road link quality to 45 (intermediate between Zimbabwe and The Gambia) for all city pairs, re-compute intercity and intercountry index values, and standardize to the projected intercountry value totals from our econometric analysis of the IMFDOT data.

Our comparative results for the current and upgraded networks provide estimates of trade expansion for 77 cities, 36 countries, and 5 regions (West, Central, East, Southern and South Africa<sup>28</sup> ). Table 7 displays regional values for current trade, estimated values after network upgrading, and changes.<sup>29</sup> Total within-region trade grows by $11.1 billion, with large gains in West Africa ($4.5 billion), Central Africa ($1.2 billion), and East Africa ($5.4 billion). Total cross-region trade grows by $8.6 billion, with $1.6 billion attributable to expanded trade among West, Central and East Africa, $5.3 billion to trade expansion between South Africa and West, Central and East Africa; $224 million to expanded trade between Southern Africa and West, Central and East Africa; and $1.4 billion to trade growth between Southern Africa and South Africa.

28 Data problems prevent inclusion of cities in Botswana, Lesotho, Namibia, Swaziland, Liberia and Somalia. The other countries are grouped into regions as follows: West: Benin, Burkina Faso, Chad, Cote d'Ivoire, The Gambia, Ghana, Guinea, Guinea-Bissau, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, Togo Central: Angola, Burundi, Cameroon, Central African Republic, D.R. Congo, Gabon, Rep. Congo, Rwanda, Zambia East: Djibouti, Eritrea, Ethiopia, Kenya, Malawi, Sudan, Tanzania, Uganda Southern: Mozambique, Zimbabwe South: South Africa 29 Trade volume within South Africa remains the same, because current road quality (100) is already greater than the upgrading target for other regions.

20