rafmacalaba/datause-encoder-data huggingface dataset:
KENYA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# **Challenges**

Easing Mombasa’s capacity constraints will require substantial investments. Additional berths and terminals can be accommodated at the Mombasa site, and construction is already underway. In order to make fullest use of the site, however, and to reduce bottlenecks on the landside of the port, improvements to the local road network are needed simultaneously.

**Table 4. Benchmarking port indicators: Mombasa as compared with selected other ports**

||Mombasa (Kenya)|Maputo<br>(Mozambique)|Sudan (Sudan)|Dar es Salaam<br>(Tanzania)|Cape Town (South<br>Africa)|Durban (South<br>Africa)|Apapa (Nigeria)|
|---|---|---|---|---|---|---|---|
|**CAPACITY:**||||||||
|Actual container handled (TEU/year)|436,671|44,000|328,690|198,472|690,895|1,899,065|336, 308|
|Container handling capacity (TEU/year)|600,000|100, 000|400,000|400,000|950,000|1,450,000|500,000|
|General cargo handling capacity<br>(tons/year)|1,500,000|NA|7,500,000|8,000,000|1,100,000|NA|5,000,000|
|Annual liquid bulk cargo capacity<br>(tons/year)|5,500,000|410,000|NA|2,000,000|7,500,000|NA|NA|
|Length of container berths (meters)|964|300|420|550|2,234|2,128|1,000|
|Length of general cargo berths (meters)|950|1200|2,011|1,464|2,706|200|2,307|
|**EFFICIENCY:**||||||||
|Average container dwell time in terminal<br>(days)|5|22|28|7|6|4|42|
|Average truck processing time for<br>receipt and delivery of cargo (hours)|4.5|4|24|5|4.8|5|6|
|Average container crane productivity<br>(containers loaded-unloaded per crane<br>hour)|10|11|8|20|18|15|12|
|Average general cargo crane<br>productivity (tons loaded/unloaded per<br>crane working hour)|20.82|11|8|20|15|25|9|
|**TARIFFS:**||||||||
|Average general cargo handling charge,<br>ship to gate (US$/tonne)|6.5|6.0|10|13.5|1.48|17.4|8|
|Average dry bulk handling charge, ship<br>to gate or rail (US$/tonne)|5|2.0|3|4.5|6.5|1.48|NA|
|Average liquid bulk handling charge<br>(US$/tonne)|NA|0.5|1|3.5|2.68|NA|1|

Source: Ocean Shipping Consultants 2009. Derived from AICD ports database downloadable from http://www.infrastructureafrica.org/aicd/tools/data TEU = 20-foot equivalent units.

Institutional reforms can also contribute significantly to easing capacity constraints. The more efficiently a port is operated, the more throughput can be accommodated within the physical capacity of its infrastructure. It is therefore critical to accompany investments with institutional reforms that increase the efficiency of port operations. A first key step would be to move toward the adoption of the internationally preferred landlord model of port management, whereby the public sector provides port 10
between market structures and 92 of these countries established an energy regulator within this period – usually several years prior to the market structure transformation. Regulators that led the transition helped design the tariff structure, maintain system reliability, expand access, encourage private investment, increase energy security, and manage risk. They were also empowered to implement difficult decisions. In Romania, the energy regulator’s capacity and independence improved significantly with the adoption of a new law transposing the European Union (EU) Third Energy Package—conditionalities that came with the IMF program to reform electricity and gas sectors—and eliminate several large contracts between stateowned energy companies and favored players. The regulator, Romanian Energy Regulatory Authority, enjoyed independence, stable management, and capacity. It could promote politically difficult reforms despite pressure from the large energy consumers that benefited from the regulated prices.

_Table 3: Number of countries & territories that established an energy regulator by region in each time period_

||**East Asia &**<br>**Pacific**|**Europe &**<br>**Central Asia**|**Latin**<br>**America &**<br>**the**<br>**Caribbean**|<br>**Middle East**<br>**& North**<br>**Africa**|<br>**North**<br>**America**|**South**<br>**Asia**|**Sub-**<br>**Saharan**<br>**Africa**|<br>**TOTAL**|
|---|---|---|---|---|---|---|---|---|
|Pre-1991|0|2|2|0|2|0|0|6|
|1991-2000|0|21|16|1|0|2|10|50|
|2001-2010|15|18|6|8|0|4|11|62|
|2011-2020|2|6|6|1|1|0|16|32|
|2021-2024|2|1|0|0|0|0|0|3|
|**Total**|**19**|**48**|**30**|**10**|**3**|**6**|**37**|**152**|

Source: (Akcura 2024) _Unbundling:_ One of the key sector reforms has been separating network from generation and retail functions. Transmission unbundling in particular has been pursued as countries transitioned out of VIU into SBM and WRC structures, although the degree of unbundling varies between countries (Figure 12). Based on Global Power Market Structures Database, as of July 2024, 93 economies have instituted some type of unbundling of their power transmission. Most have unbundled transmission in phases starting with separating the financial accounts of the VIU for each segment, then to organizationally separate functions, and progressing to create separate legal entity responsible for transmission which formalizes the separation created through accounting and functional unbundling. In some countries, primarily in Western Europe, ownership unbundled transmission such that the company owning and operating a network is not allowed to be active in any competitive segment of the electricity supply chain nor have an interest with respect to those activities. This ensures that there is no conflict between those who control the network and market participants (IPPs, traders, retailers).

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Table 3: DIDM estimates of infrastructure provision (Gross Value Added)

||_Depen_|_dent variab_|_le: Gross Value Added (log)_|
|---|---|---|---|
|||N2E|N2H<br>H2B|
|1970-2000 (E)|coef.|0.035|-0.004<br>0.325<sup>_∗∗∗_</sup>|
||s.e.|(0.047)|(0.081)<br>(0.125)|
|1970-1980|coef.|0.067|0.190<br>0.469|
||s.e.|(0.071)|(0.184)<br>(0.309)|
|1980-1996|coef.|0.063|-0.018<br>0.284<sup>_∗_</sup>|
||s.e.|(0.071)|(0.112)<br>(0.167)|
|1996-2000|coef.|-0.069|-0.317<sup>_∗∗∗_</sup><br>0.367<sup>_∗∗_</sup>|
||s.e.|(0.103)|(0.103)<br>(0.156)|
|1970-2000 (D)|coef.|0.101|0.112<br>0.390<sup>_∗_</sup>|
||s.e.|(0.075)|(0.121)<br>(0.220)|
|Placebo|coef.|-0.183<sup>_∗∗∗_</sup>|0.138<br>0.046|
||s.e.|(0.070)|(0.094)<br>(0.109)|

_Notes: Bootstrapped standard errors (200 replications) in parentheses._<sup>_∗_</sup> _p<0.1,_<sup>_∗∗_</sup> _p<0.05,_<sup>_∗∗∗_</sup> _p<0.01. (E): estimated effect of the treatment at the time period of the infrastructure provision._ _(D): estimated effect one period after the infrastructure provision._ Tables 3 and 4 decompose the previous findings by showing how municipality gross value added and population responded to infrastructure investment. The first row of Table 3 shows a strong complementarity effect as the gross value added in municipalities endowed with a highway that was subsequently electrified increases by 38 percent compared to untreated municipalities. The provision of electricity alone or highways alone did not have a statistically significant impact on GVA. The first row of Table 4 does not show any statistically significant impact on the population in municipalities due to any type of infrastructure provision. Taken together, these results suggest that the increase in gross value added per capita (Table 2) is solely driven by the effects of gross value added resulting from the electrification of municipalities already endowed with a highway. We do not find any significant dynamic effects except for a positive and marginally significant effect on GVA for the electrification of municipalities already endowed with a highway (row 5). The placebo estimates of highway provision and electrification in municipalities already endowed with highways are not statistically significant, lending credibility to the parallel trend assumption.

14
vary their education, age, wealth, or cropping patterns. Thus, the results that follow cannot be interpreted causally, but as descriptive evidence. In addition, CFs are, on average, of higher status than other farmers, which reduces the number of variations we have access to in establishing a counterfactual. Finally, to yield interpretable results, we need this exercise to focus on a single technique rather than on an aggregate measure of adoption. We focus on the adoption of pit planting among other farmers as the outcome, since it is the only single practice which was statistically signicantly adopted by CFs, when adjusting our inference for multiple hypothesis testing.

Table 11 displays the results from interacted regression models accounting for farmer heterogeneity in the treatment eects, with the additive eect of the treatment and its interaction with the CF characteristic reported in the last row. Overall, we nd CFs with above median total landholdings were 4.4 percentage points more likely to convince other farmers to adopt pit planting, a 64.7% increase relative to the control (signicant at the 10% level). Credibility in the source of information appears to inuence all farmers, CFs with larger farms perhaps commanding more trust and respect within the community. More interestingly, similarities in crop portfolios between CFs and other farmers appear to inuence adoption rates. Other farmers' adoption grew an additional 5.2 percentage points when they had access to a directly trained CF who grew similar crops to theirs. This is consistent with the idea that homogeneous farming conditions are conducive to social learning (Munshi, 2004). Delays in adoption may stem from dierences in production technologies and an inability to extrapolate demonstrated activities to their own plot.

# 4.4 Cost-Benet Analysis

To provide perspective on the cost-eectiveness of the program, we compare the average annual costs of directly training each CF to the average annual benets realized by the CF. We consider three scenarios where the private returns to a direct CF training are in the form of maize revenue, labor earnings, and both.<sup>36</sup> To compute the value of labor savings, we multiply the estimate of the intervention's impact on farm labor savings by the shadow value of labor.<sup>37</sup> Benets to other farmers

> 36This approach ignores the social benets produced by the technologies which cannot be quantied over a shortterm horizon, such as soil and water quality.

> 37We price the shadow value of labor at the minimum agricultural wage oered in Mozambique. Minimum wage rates are provided by the U.S. State Department: http://www.state.gov/e/eb/rls/othr/ics/2013/204700.htm. Agriculture is the lowest wage rate at 74 USD per month.

21
# **3 The Data**

## **3.1 The ELMPS Survey**

For the empirical analysis, we use data from the Egypt Labor Market Panel Survey (ELMPS).<sup>5</sup> The ELMPS is a rich wide-ranging nationally representative survey carried out by the Economic Research Forum (ERF) in cooperation with Egypt’s Central Agency for Public Mobilization and Statistics (CAPMAS) since 1998. In this paper, we focus particularly on the 2012 round (ELMPS 2012). As in a typical labor force survey, the ELMPS covers topics such as employment, unemployment, job dynamics, and earnings but also provides very rich information on education, migration, and socio-economic characteristics. In 2012, the ELMPS interviewed 12,060 households, which yields to a total sample size of 49,186 individuals. The ELMPS 2012 includes a refresher sample of 2,000 households that was selected from an additional 200 Primary Sampling Units (PSUs) randomly selected from a new master sample prepared by CAPMAS. By design, it over-sampled areas with high migration rates, and is nationally representative once weights are applied (Assaad & Krafft, 2013).<sup>6</sup> We rely on the return migration module that surveys all individuals aged between 15 and 59 years old. In 2012, about 10% of the households had a returnee. In the ELMPS, a returnee is defined as an individual who has worked abroad for more than six months, but was back in Egypt at the time of the survey. This module features return migrants’ characteristics, incidences of migration, reasons for migration, financial situation before migration, year and country of first migration episode, year of final return, earnings and savings abroad, remittances, as well as documents obtained to travel and work abroad. In addition, the ELMPS 2012 collects rich retrospective information on previous employment characteristics and their location. At the same time, households are asked about current members of the household who were overseas at the time of the survey, and collects information on their overseas destination, overseas employment, migration duration, and frequency of contact/visits to the household back home.

> 5 See OAMDI (2016) for data documentation.

> 6 In every wave of the survey, a refresher sample of 2,000-3,000 observations is included to allow for a more in-depth examination of phenomena of interest. While the 2012 round oversampled highmigration areas, the 2018 round included a refresher sample of 2,000 households to oversample rural communities that were among the 1,000 poorest villages of Egypt (Krafft, Assaad, & Rahman, 2019). The 2012 round is therefore advantageous for our analysis as it oversampled areas with high migration rates, which allows us to have a larger sample on migrants and to better disentangle the effect of illegality. In the robustness checks section, we also rely on the 2018 round and show that our results are fully robust to using more recent data.

8
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# **5.3.- Performance indicators of airport infrastructures**

The performance indicators presented in this work are commonly used in the airport industry. However, in some cases it might be necessary to make a selection or an _ad hoc_ design according to the special airport features and services to be assessed.<sup>Although the</sup> proposed list is not exhaustive, it intends to cover those aspects or areas that might<sup>be</sup> problematic for regulators or managers. There are particular areas where<sup>the infringement</sup> of public interests is more likely. For example, at airports subject to price regulation, there are problems regarding incentives for investing in new capacity and with the quality of service. This is the result of strong operator tendencies toward reducing costs at the<sup>expense</sup> of service quality. Therefore, it is important to have a set of financial<sup>and economic</sup> indicators available that would help in analyzing airport performance. These could include costs coverage, profitability, asset investments and the use of available resources.

In Table 5. 1, a set of financial indicators is presented. Two groups are distinguished. The first are the _strategic_ indicators that are needed to evaluate policies with medium and long term effects, such as return on capital investments. Secondly, _other financial indicators,_ where measures such as cash-flow provide an accurate evaluation of the day to day financial situation of the airport.

**Table 5.1. Financial performance indicators**

||*Rcturn on capital investment<br>*Payback period|
|---|---|
|Strategic indicators|*Current assets/Liabilities<br>*Self financing ratio<br>*Debtors and creditors ratio|
||**C -hiows**|

Source: Lemaitre (1997) Efficiency economic indicators are shown at Table _5.2._ These are classified into six distinct categories: overall costs performance, labor productivity, productivity of capital employed, revenue-generating performance, performance of commercial activities and overall profitability. In order to asses the economic efficiency of an airport through time, or to check whether regulated standards are being met, specific indicators are required. For example, we might need to explore labor and capital productivity if we wish to establish that resources are being used in the most efficient way. Alternatively, to determine the _49_
international migrations), what are the information needs? And how to prioritize? What are the opportunities offered by the household survey data collection? What would it be possible to achieve with slight modifications to the current data collection instruments utilized by popular, long-established household survey programs, such as the World Bank’s Living Standards Measurement Study (LSMS)?

This critical review aims to shed light on the issues above. Given the vastness of the topic and the huge body of work in the field, a few preliminary clarifications on the scope of this review are in order: first, this review has an explicit orientation toward the _empirical_ and _data_ perspective on the climate changemigration links. Hence, we look at retrospective studies using historical data, we assume as given the state of the art of the theoretical underpinnings of the current empirical investigation, and we do not consider studies on projections or predictions of future migratory flows; second, since it is now widely recognized that developing countries, which are both hotter and poorer, will be disproportionately affected by climate change (Auffhammer, 2018; Dell, Jones and Olken, 2014; Nordhaus and Moffat, 2017; Tol, 2018), the focus of this work will be on migration originating within and/or from these countries; third, we will focus on the more _recent_ empirical advances. This means we limit our exploration to studies and works published in the last decade (specifically, from 2010 on);<sup>2</sup> fourth, in our assessment of data gaps in household survey data, we will look specifically at the Living Standards Measurement Study (LSMS) collection by the World Bank. The LSMS has been the World Bank’s flagship household survey program since 1980, and it is currently a global leader in the methodological development of rich and extensive multi-topic questionnaires designed to study various aspects of household welfare. <mark>Within the broader LSMS program, we restrict our attention to the LSMS-ISA household survey program, the explicit a</mark> im of which is to improve the availability, quality, and relevance of agricultural data in multi-topic, multi-purpose household surveys (Carletto and Gourlay, 2019) <mark>. The reason is that the LSMS-ISA has a distinct advantage with respect to standard LSMS surveys when it comes to studying issues specifically related to climate change, as its strong focus on agriculture allows one to study the impacts of weather- and climate-related events on several household welfare outcomes, that might either trigger or prevent migratory flows, as well as exploring farmers’ adaptation responses, especially</mark> _<mark>in situ</mark>_ <mark>ones, that complement migration as coping strategies in the face of climatic shocks.</mark> Nonetheless, most of the reflections and recommendations we propose throughout this paper are also relevant to LSMS surveys more broadly and to multi-topic and multi-purpose household surveys in general.

> 2 For reference to the literature before 2010 see Piguet et al. (2011).

3
# **Technology Trap and Poverty Trap in Sub-Saharan Africa**

## Hippolyte Fofack<sup>1</sup> The World Bank

JEL Classification Numbers: E60, I23, J24, O33, O41, O55

**Keywords** : global technology frontier, low-technology trap, poverty trap and R&D

> 1 This paper has benefited greatly from suggestions and comments by Melvin Ayogu, William Easterly, Mohamed Hassan, Roumeen Islam, Paulus Gerdes, Aart Kraay, Adolf Mkenda, Raj Nallari, Leonce Ndikumana, Benno Ndulu, Yaw Nyarko, Anwar Shah, Xiao Ye and Shahid Yusuf. I would like to thank them all and Nihal Bayraktar for creative research assistance.
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# **Appendix 1: Putting together the firm sample for Experiment 1**

From September 2011 through January 2012 we put together a database of firms in Amman that could serve as potential clients for the program. We identified 41,497 legally registered firms through the Chamber of Commerce, and 10,025 legally registered firms through the Chamber of Industry. We excluded firms in the construction, oil, and automobile industries, and only sampled meat, tobacco, and alcohol oriented firms at half of the rate of other firms due to their low hiring rates for women. After making these exceptions, we drew a representative sample of 6,611 commercial enterprises, and a sample of 1,744 industrial firms (1,267 firms with more than 10 employees, and 477 with four to nine employees). We listed over 5000 firms from these two samples and screened on whether they were looking to hire a worker in the next six months, and would consider hiring recent graduates and females for these positions. This was used to arrive at the sample of 1,600 commercial firms and 400 industrial firms used as the experimental sample for this study. In March, we added an additional set of 277 firms stratified and randomized as above to the experimental sample to make up for businesses whose contact information was no longer valid. Appendix Table 1 provides summary statistics on this sample.

|Appendix Table 1: SummaryStatistics of Firms||||||
|---|---|---|---|---|---|
||Control Firms (N|=1142)|Treatment Fir|ms (N=1137)|T Test|
||Mean|SD|Mean|SD|P Value|
|# of Male Full Time Employees|15.6|58.3|14.7|44.9|0.67|
|# of Female Full Time Employees|2.8|10.3|2.8|8.9|1.00|
|# of New Employees in 2011|1.6|4.6|1.6|4.4|0.83|
|# of New Employees in 2010|1.3|7.3|1.4|5.4|0.70|
|# of Terminated Employees in 2011|1.2|4.5|1.2|4.3|0.90|
|# of Terminated Employees in 2010|0.9|7.1|0.9|4.7|0.98|
|Proportion that SayIt is Difficult to Distinguish between Good and Bad C|0.6|0.5|0.6|0.5|0.31|

In May 2012 we opened the matching eligibility to any firm in Amman, and lifted restrictions on matching for firms previously assigned to the control group. Through a series of television, radio, newspaper, job fair, and Facebook advertisements, we contacted nearly 1,000 additional firms, of which 175 were looking to hire. These 175 firms were larger and more prestigious, and typically had human resource departments.

# **Appendix 2: Experiment 2 low-prestige occupations and firms**

Appendix Table 2 shows the occupations we considered as potentially low prestige, and the percentage of individuals who said on their baseline survey that they were willing to work in these occupations.

38