rafmacalaba/datause-encoder-data huggingface dataset:
Using the SD approach, our simulated median gains in production for different crops range between 29.57% and 47.96% for barley, 71.87% and 76.35% for millet, 89.38% and 107.99% for maize, 105.64% and 108.09% for rice, 59.99% and 89.49% for sorghum, and 27.97% and 38.96% for wheat. The corresponding gains using the SRHS approach are between 33.28% and 41.87% for barley, 106.64% and 123.26% for maize, 112.56% and 114.64% for rice, 93.38% and 129.42% for sorghum, and 53.08% and 60.11% for wheat. Our results are more or less consistent despite considering a combination of conditions imposed.

# **7.2 Considering a Different Dataset for Water Supply**

Our groundwater supply measure comes from Rodella et al. (2022). We test if our results remain robust to using a different dataset for water supply, especially because groundwater volume computations tend to be probabilistic in nature. We use the Groundwater Storage Map in the digital groundwater maps of Africa<sup>10</sup> collected by MacDonald, Bonsor, ́O Dochartaigh, and Taylor (2012) to obtain a measure of groundwater storage.

In our main simulations, we rely on the volumes of groundwater associated with the local shallow groundwater aquifer classification. However, the classification system we used—including the “local shallow aquifer” classification—is not consistent with hydrogeological classifications of groundwater aquifers. Thus, we consider the subset of aquifers that closely resemble the characteristics of local shallow aquifers. These aquifers include the following:

- Unconsolidated Sedimentary (U): These aquifers consist of loose sediments, such as sand and gravel, which allow water to flow more easily.

- Consolidated Sedimentary Fracture (CSF): These aquifers are composed of consolidated sedimentary rocks that have fractures or cracks, which enhance water storage and flow.

- Consolidated Sedimentary Intergranular (CSI): These aquifers are made up of consolidated sedimentary rocks with interconnected pore spaces, allowing water to move through the rock matrix.

- Consolidated Sedimentary Intergranular / Fracture (CSIF): These aquifers are a combination of consolidated sedimentary rocks with both intergranular pore spaces and fractures, providing multiple pathways for water movement.

- Basement (B): These aquifers are located in the underlying bedrock, typically consisting of hard and fractured rocks that can store and transmit water.

These hydrogeological aquifer types are considered closest to the definition of local shallow aquifers because they share similar characteristics, such as their geological composition and hydrogeological properties, which make them suitable for groundwater storage and extraction in the context of the study.

> 10See https://www2.bgs.ac.uk/groundwater/international/africangroundwater/ mapsDownload.html.

16
18 be impossible for the individual himself to affect by choice. Given the information available in the TDHS, our vector of circumstances consists of the type of area in which the woman was born, the region where she was born, her mother’s and father’s levels of education, her mother tongue, and the number of siblings the individual reports having. The discrete categories for each variable, as well as the distribution of the population across them, are presented in Table 4.

Table 5 reports the results of regressions (10) for the wealth index and (14) for log imputed consumption, on those circumstance variables. Regressions are reported for both the main wealth index (using the full set of asset variables) and the subsidiary wealth index (which uses the set of variables common to both surveys) described in Table 1. For the regressions in Table 5 and for all of the analysis that follows, the TDHS sample is restricted to ever-married women aged 30-49. Results for the full sample of everymarried women (whose ages span 15-49 in the survey) are available from the authors on request, but are not reported here because early marriage is selective on circumstance variables.<sup>24</sup> Since this is a reduced-form regression, coefficients should not be interpreted causally. They reflect partial correlations between individual circumstance variables and the household’s wealth index (or imputed consumption), conflating both direct and indirect effects (e.g. through efforts). Nevertheless, the regression is informative. The share of explained variance, θ<sup>ˆ</sup> _rP_ (Π) , is 27% for the main wealth index, 30% for the subsidiary index, and 26% for imputed consumption, suggesting broadly similar “between-type” shares of inequality, regardless of the aggregation method.

Being born in an urban area, having Turkish as mother tongue, and having more educated parents are all associated with higher adult levels of “wealth” and consumption. A greater number of siblings is associated with lower subsequent economic advantage. Perhaps most interestingly, once these circumstances are controlled for, there is only limited evidence of an association between birth region (at the three-region level) and

> 24 In other words, the composition of the sample for younger women is particularly sensitive to whether they were born in the East or West, and to different kinds of families, leading to potential sample selection biases. This problem arises because detailed information on family background is collected in the TDHS only for women who are currently married or have been married in the past. Nevertheless, the results for the 15-49 age range are not very different from those reported here for the preferred sample.
Table Al-I: Dependent variable: Under-5 mortality rate (In)

|Column:|1|2|3|4|5|6|7|
|---|---|---|---|---|---|---|---|
|Method|Mean (SD)|OLS|2SLS|OLS|2SLS|OLS|2SLS|
|GDP per capita<br>(In)|5,004*<br>(5,300)|-.611<br>(9.71)|_-.596-_<br>(3.67)|-.624<br>(8.16)|-.921-<br>(1.84)|-.616<br>(9.08)|-.829-<br>(2.47)|
|Public health exp.<br>(In share of GDP)|(.0198)|_-.135_<br>(1.78)|-.192-<br>(.742)|_-.165_<br>(1.73)|-.139-<br>(.359)|_-.085_<br>(.931)|.146-<br>(.374)|
|Local health Svcs<br>(In share of health<br>expend)|0.328*<br>(0.198)|||.076<br>(1.78)<br>(1.78)|-.359-<br>(.754)<br>(.754)|||
|Access to local<br>health svcs (In<br>share of pop)|0.781*<br>(0.248)|||||-.024-<br>(.227)|.336-<br>(.417)|
|Female|4.971|-.093|-.091|-.095|-.062|-.085|-.111|
|education|(2.753)|(3.54)|(2.90)|((3.04)|(.871)|(2.82)|(2.67)|
|Income|40.93|.008|.008|.004|.023|.007|.013|
|inequality|(8.706)|(1.28)|(1.17)|(.569)|(.954)|(1.17)|(1.45)|
|Percent|49.96|.001|.001|-.0001|.001|-.00004|.006|
|urban|(25.01)|(.459)|(.219)|(.030)|(.107)|(.016)|(.732)|
|Predominantly|0.120|.450|.446|.413|.305|.503|.388|
|Muslim|(0.327)|(3.18)|(2.91)|(2.83)|(1.56)|(3.49)|(1.26)|
|Ethnolinguistic|.4219|.549|.534|.487|.172|.478|.642|
|fractionalization|(.2871)|(3.43)|(2.90)|(2.52)|(.413)|(3.02)|(2.63)|
|Tropical|0.620|-.051|-.059|-.184|-.035|-.111|-.124|
|country|(0.488)|(.549)|(.619)|(1.49)|(.136)|(.849)|(.941)|
|Access to safe|67.13|-.001|-.001|-.001|-.001|-.0004|-.003|
|water|(25.73)|(.606)|(.390)|(.375)|(.172)|(.156)|(.438)|
|Additional vars.<br>I<br>I<br>_<br>|<br>___________<br>I<br>|<br>Dummy vari<br>education, inc<br>__<br>are<br>|<br>ables for area a<br>ome inequality<br>|<br>nd a constant te<br>, ethnolinguisti<br>mis<br>|<br>rm. Dummy v<br>c fractionaliza<br>sing.|<br>ariables for w<br>tion, or access to<br>|hen female<br>safe water<br>I|
|R-squared |||.9469|.9465|.9512 ||.8834|.9608|.9430|
|Num. Obs.|1<br>t00|98|98 ||73 ||73|_<br>75_||75|

Notes: White heteroskedasticity-corrected t-statistics are in parentheses. * Mean and SD of non-logged values. -Instruments are, neighbors' public health spending, neighbors' military spending, whether or not the country's main export is oil, and years since 1776 that the country has been independent), and neighbor's shares of spending on local health (column 4), and access to local health services (column 6).

58
||Healthcare|0.0202**<br>(0.00954)|0.0253***<br>(0.00919)|
|---|---|---|---|
||Consumer Discretionary|0.0237**<br>(0.0119)|0.0204***<br>(0.00726)|
||Industrials|0.0237<br>(0.0164)|0.0181**<br>(0.00785)|
|Investment-Grade|Energy|-0.00972|-0.00445|
|Bonds||(0.0177)|(0.0155)|
||Materials|-0.00716|-0.00753|
|||(0.0159)|(0.0154)|
||Financials<br>|-0.0261**<br> (0.0110)|-0.0263***<br>(0.00970)|
||_Aggregate Index_|-0.0308**<br>(0.0137)|-0.0308**<br>(0.0122)|
||Communications|-0.0343*<br>(0.0195)|-0.0322*<br>(0.0180)|
||Healthcare|-0.0433***<br>(0.0145)|-0.0440***<br>(0.0130)|
||Industrials|-0.0458***<br>(0.0159)|-0.0467***<br>(0.0141)|
||Utilities|-0.0547***<br>(0.0172)|-0.0555***<br>(0.0156)|

We see similar trends among the high-yield bond indices. Focusing on column 4 (regressions with lags), we find that the Bloomberg Energy high-yield bond index stands out as the most affected high-yield sector. A 10% decline in WTI oil price causes the Energy high-yield bond index to drop by 0.90%. Interestingly, high-yield bonds of most other sectors also suffer, ranging from Materials (0.51%) to Industrials (0.18%).

Cheap oil improves investment-grade corporate bonds, except those in Energy and Materials sectors. The signs for almost all sectors are negative, implying a negative relationship between oil prices and the investment-grade corporate bonds’ indices: when oil prices are lower, the corporate bond indices are higher. However, we do not find evidence for a negative relationship between cheap oil and prices of investment grade bonds in the Energy or Basic Materials sectors. This suggests that 22
# **Figure 2 Employment Rates by Age Cohort in China, Indonesia, Korea, the UK and the US**

<!-- Start of picture text --> China (CHARLS) Indonesia US<br>1 1 1<br>.8 .8 .8<br>.6 .6<br>.6<br>.4 .4<br>.2 .2 .4<br>0 0 .2<br>45 50 55 60 65 70 75 80 85 90 45 50 55 60 65 70 75 80 85 90 0<br>age age 45 50 55 60 65 70 75 80 85 90<br>Urban Male Urban Female Urban Male Urban Female age<br>Rural Male Rural Female Rural Male Rural Female Male Female<br>China (CHNS) Korea UK<br>1 1 1<br>.8 .8 .8<br>.6 .6<br>.6<br>.4 .4<br>.4<br>.2 .2<br>0 0 .2<br>45 50 55 60 65 70 75 80 85 90 45 50 55 60 65 70 75 80 85 90 0<br>age age 45 50 55 60 65 70 75 80 85 90<br>Urban Male Urban Female Urban Male Urban Female age<br>Rural Male Rural Female Rural Male Rural Female Male Female<br>Lowess Plot bw=0.3<br> Working / Total<br> Working / Total<br>Working / total<br> Working / Total<br><!-- End of picture text --> Notes: Employment rates by age cohort are calculated using non-parametric locally weighted regression (LOWESS) with a bandwidth of 0.3. The following data sources are used: China: 2009 CHNS and the 2008 CHARLS pilot; Indonesia: 2007 Indonesia Family Life Survey (IFLS); Korea: 2006 Korean Longitudinal Study of Aging (KLoSA); United States: 2008 Health and Retirement Survey (HRS); United Kingdom: 2008/9 English Longitudinal Study of Ageing (ELSA).

30
Table 13: Worker-level BOS wage premium, 2016-2020.

|||De|pendent vari|able: _ln_(_wifjt_|)||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|BOS|0.338***<br>(0.087)|0.156***<br>(0.041)|0.185***<br>(0.043)|0.011<br>(0.008)||0.0184**<br>(0.00829)|
|Public/Mixed|||||0.045***<br>(0.014)||
|Age|0.298***<br>(0.005)|0.289***<br>(0.003)|0.255***<br>(0.003)|0.771***<br>(0.018)|0.770***<br>(0.018)|0.911***<br>(0.0261)|
|Gender|-0.223***<br>(0.003)|-0.157***<br>(0.001)|-0.140***<br>(0.001)||||
|Primary school|0.087***<br>(0.004)|0.093***<br>(0.003)|0.039***<br>(0.002)||||
|Middle school|0.168***<br>(0.005)|0.159***<br>(0.004)|0.083***<br>(0.003)||||
|High school|0.301***<br>(0.007)|0.254***<br>(0.004)|0.160***<br>(0.003)||||
|College or higher|1.092***<br>(0.012)|0.903***<br>(0.007)|0.602***<br>(0.006)||||
|Tenure|0.086***<br>(0.001)|0.073***<br>(0.0007)|0.065***<br>(0.0007)|0.026***<br>(0.0005)|0.026***<br>(0.0005)|0.0278***<br>(0.000656)|
|Temporary|0.022*|0.284***|0.166***|0.050***|0.050***|0.0510***|
||(0.012)|(0.024)|(0.018)|(0.008)|(0.008)|(0.00784)|
|Firm size|0.043***|0.043***|0.044***|0.024***|0.024***|0.0190***|
||(0.002)|(0.001)|(0.001)|(0.0006)|(0.0006)|(0.000740)|
|Year||Yes|Yes|Yes|Yes|Yes|
|Sector||Yes|Yes|Yes|Yes|Yes|
|Occupation|||Yes||||
|Region|||Yes||||
|Worker||||Yes|Yes|Yes|
|Observations|147943807|147943807|147943807|137063393|137063385|109720650|
|R-squared|0.410|0.489|0.562|0.910|0.910|0.916|

_Note:_ This table reports worker-level regressions where the dependent variable is the logarithm of the hourly wage. Firm size is measured as employment. The inclusion of fixed effects for year, sector (4-digit CNAE code), occupation (2-digit CBO code), region, and worker is indicated at the bottom of the Table. Data from RAIS, 2016-2020. Robust standard errors clustered at the firm level in parentheses. Significance levels: * 10%, ** 5%, *** 1%.

24
THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 3. Road indicators, benchmarked against Sub-Saharan African fragile and low-income countries**

|Indicator|Unit|Fragile<br>states|CAR|Low-<br>income<br>countries|
|---|---|---|---|---|
|Road network density [1]|km/1000 km<sup>2</sup>of land area|145|41|132|
|GIS rural accessibility|% of rural pop within 2 km from all-season road|30|58|23|
|Classified paved road network condition [2]|% in good or fair condition|80|62|86|
|Classified unpaved road network condition|% in good or fair condition|72|2|56|
|Classified paved road traffic|AADT|843|200|1,288|
|Classified unpaved road traffic|AADT|31|14|39|
|Primary network overengineering|% of primary network asphalted with 300 AADT or less|47|61|30|

_Source:_ AICD Road Sector Database. [1] Total network includes the primary, secondary, and tertiary networks. [2] Classified roads are those that have been included in the roads legislation as public roads. GIS = geographic information system; AADT = average annual daily traffic; the CAR = Central African Republic.

The CAR relies heavily on its regional corridors for the efficient movement of goods and people, but its neighbors are not prioritizing the maintenance of their portions of the corridors. The Douala-Bangui and Point Noire–Brazzaville–Bangui corridors are still not completely paved. Some sections of the Douala-Bangui corridor (about 250 km in Cameroon and 210 km in the CAR) are being upgraded as part of the Economic and Monetary Community of Central Africa (CEMAC) Transport Transit program. Meanwhile, all of Cameroon’s segment of the Point Noire–Brazzaville–Bangui corridor (308 km) is unpaved, as is 1,000 km on the Congolese side. This means that the CAR does not have a single allweather road corridor to its coastal port gateways (table 4).

Furthermore, only 48 and 21 percent of the Douala-Bangui and Point Noire–Brazzaville–Bangui corridors, respectively, are in good condition. In both cases the problem seems to lie in the coastal gateway country’s neglect of road quality. Only 30 percent of the Cameroonian section in the DoualaBangui corridor is in good condition, versus 100 percent of the Central African section. Similarly, only 56 percent of Cameroon’s segment of the Point Noire–Brazzaville–Bangui corridor—and none on the Republic of Congo side—is in good condition, whereas 100 percent of the the CAR segment is in good condition (table 4). It is worth noting, however, that a good portion of the the CAR segments consist of surface treatment pavements that do not have the same properties and durability as asphalt. Around 80 percent and 35 percent of the CAR’s share of the Point Noire–Brazzaville–Bangui and Douala-Bangui corridors, respectively, are surface treated. Clearly, the incentives for a coastal country to maintain hinterland road corridors do not seem to be very strong, since coastal countries’ economies are typically concentrated along the coast, making the up-country segments regional public goods **.** 12
lower scores mean better performance (observed scores go from 8 to 72), and divisions are broader categories of performance. In 2013, almost 300,000 students sat for the UCE exams.

_District League Table (DLT)_ : The District League Tables (DLT) are published in the Annual Health Sector Performance Report by the Ministry of Health and comprise both quality of care and management indicators. Data from local facilities is compiled using the Health Management Information System (HMIS) and aggregated at the District level. We use the 14 indicators for performance and the total score for the FY 2014/2015 (See below for details).

|||**Weight**|<br>**Explanation**|
|---|---|---|---|
||||Dihthi-tt-ti (DTP3)|
||DPT3 Coverage (%)|15|peraeanusperusss<br>immunization coverage|
||||Share of infants delivered in|
||Deliveries in gov’t and PNFP facilities (%)|15|government or Private Non for Profit|
|**re**|||facilities|
|**ality ca**|OPD Per Capita|10|Number of outpatients department<br>(OPD) visits per capita|
|**qu**|HIV testing in children born to HIV+ women (%)|10|-|
|**and**|Latrine coverage in households (%)|10|-|
|**ge**|||Share of pregnant women who|
|**era**|IPT2 (%)|5|completed Intermittent preventive|
|**Cov**|||therapy (IPT) against Malaria<br>Proportion of pregnant women|
||ANC 4 (%)|5|receiving 4 or more antenatal care<br>visits|
||TB TSR (%)|5|Tuberculosis Treatment Success Rate|
||Approved posts filled (%)|10|-|
|**ent**|% Monthly reports sent on time|3|-|
|**em**|% Completeness monthly reports|2|-|
|**nag**|% Completeness facility reporting|3|-|
|**Ma**|Completeness of the annual report (%)|2|-|
||Medicine orders submitted timely (%)|5|-|

Figure 12: DLT Score - Components’ weights and description : _2011 local and presidential elections_ we use data from the 2011 elections, compiled by the Uganda Electoral Commission. For Directly Elected Councilors, we compute all the results at the electoral area level and then obtain the district-level average across all areas. The Herfindahl Index is most commonly used as measure of firm competition in a given 68
funding sources are typically short-term and highly sensitive to market conditions, in contrast with retail deposits which are usually more stable. FOREIGN FUNDING is a proxy for the banking system’s reliance of foreign sources of funding relative to total funding, calculated as the ratio of FOREIGN LIABILITIES to the sum of FOREIGN LIABILITIES AND DEPOSITS. FOREIGN FUNDING complements LDR and provides information on the extent to which the deposit funding gap captured by the LDR measures is filled with foreign sources of funding. Lastly, FOREIGN OWNERSHIP measures the fraction of banking system assets that are majority-owned by non-residents. These data are taken from the World Bank’s Bank Regulation and Supervision Survey.

Table 2B shows that LDR, FOREIGN FUNDING and FOREIGN OWNERSHIP are significantly higher in ECA countries, relative to non-ECA countries, although the standard deviations also suggest that there is substantial variation across countries within each of the two regions. This is an important result that will also be further explored in Section V.

# _C. Correlations_

Panel A of Table 3 presents the correlations between our PVAR variables for the full sample, as well as for ECA and the rest of the sample (“non-ECA”). As expected, the correlation between DEPOSITS and PRIVATE CREDIT is high in all samples. We also observe that FOREIGN LIABILITIES are correlated with PRIVATE CREDIT (0.40). However, this correlation is almost twice as high in ECA compared to non-ECA (0.50 vs. 0.23). This suggests that credit in the ECA region is more vulnerable to foreign funding shocks than in the rest of the world, a hypothesis we formally test with PVARs in Section V.

Panel B of Table 3 displays the correlations for the sample split variables and clearly shows the different funding model in ECA, reflected in the high correlation between FOREIGN FUNDING and LDR in that region and the absence of a correlation between these two variables outside the region. Indeed many banks in ECA relied on foreign funding to supplement domestic deposits and sustain high credit growth, resulting in high loan-to-deposit ratios (see also Figure 1). Some countries in the non-ECA region have high LDR ratios but these reflect primarily domestic 11
and children’s school readiness." _Journal of Research on Educational Effectiveness_ 12 (1): 1037.

- Wolf, S., J.L. Aber, J.R. Behrman, and M. Peele. 2019b. "Longitudinal causal impacts of preschool teacher training on Ghanaian children’s school readiness: Evidence for persistence and fade‐out." _Developmental Science_ 22 (5): e12878.

- Wong, V.C., T.D. Cook, W.S. Barnett, and K. Jung. 2008. "An effectiveness‐based evaluation of ‐

- five state pre kindergarten programs." _The Journal of the Association for Public Policy Analysis and Management_ 27 (1): 122-154.

- Yoshikawa, H., D. Leyva, C.E. Snow, E. Treviño, M. Barata, C. Weiland, C.J. Gomez, et al. 2015. " Experimental impacts of a teacher professional development program in Chile on preschool classroom quality and and child outcomes." _Developmental Psychology_ 51 (3): 309.
Policy Research Working Paper 5565

# **Abstract**

This paper investigates the main factors explaining long container dwell times in African Ports. Using original and extensive data on container imports in the Port of Douala, it seeks to provide a basic understanding of why containers stay on average more than two weeks in gateway ports in Africa while long dwell times are widely recognized as a critical hindrance to economic development. It also demonstrates the interrelationships that exist between logistics performance of consignees, operational performance of port operators and efficiency of customs clearance operations. Shipment level analysis is used to identify the main determinants of long cargo dwell times and the impact of shipment characteristics such as fiscal regime, density of value, bulking and packaging type, last port of call, and region of origin or commodity group on cargo dwell time in ports is tested. External factors, such as performance of clearing and forwarding agents, shippers and shipping line strategies, also play an important role in the determination of long dwell times. Cargo dwell time distribution has many specificities, including broad-tail, high variance or rightcensoring, which requires in-depth statistical analysis prior to any design of policy recommendations.

This paper is a product of the Transport Department, Africa Region. 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. Authors may be contacted through Gael Raballand (graballand@worldbank.org).

_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