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

**Figure 18. Efficient market gap for mobile telephone services in West Africa**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Existing access Efficient market gap Coverage gap<br>Percent of population<br><!-- End of picture text --> _Source_ : Mayer and others (2009) using GSM coverage figures for 2005.

_Note_ : Existing access represents the percentage of the population covered by voice infrastructure in the third quarter of 2006. Efficient market gap represents the percentage of the population for whom voice telecommunications services are commercially viable given efficient and competitive markets. The coverage gap represents the percentage of the population for whom services are not viable without a subsidy. SSA = Sub-Saharan Africa.

**Table 16. Senegal’s skyrocketing mobile teledensity**

|Subscribers/100 people|2005|2006|2007|2008|Average annual<br>growth (%)|
|---|---|---|---|---|---|
|Benin|14.10|19.20|23.7|27.7|25.50|
|Burkina Faso|5.00|7.60|11.5|14.4|42.84|
|Ghana|12.10|19.30|29.4|34.6|43.17|
|Mali|6.70|12.90|21.1|35.4|74.63|
|Niger|2.70|4.40|6.7|8.5|47.37|
|Nigeria|14.40|24.40|34.7|44.2|46.35|
|Senegal|15.3|25.8|30.5|44.1|43.68|
|Togo|8.10|10.70|13.2|15.6|24.55|

_Source_ : Republique du Mali 2010.

Internet penetration in Senegal has increased substantially. In 1999 the number of Internet users per 100 people was almost zero; this had increased to almost 9 per 100 people in 2008. This movement was accompanied by improvement in quality, expressed as bits per second per person (bps/person). In 1999 connections were very slow at 0.4 bps/person; as of 2008 the figure has reached 237 bps/person, following investments in access to the submarine cable SAT-3 (figure 19a). Senegal is ahead of other West African countries in terms of Internet users per 100 people and international Internet bandwidth (figure 19b).

32
market. They suggest that though relatively large rms are the main recipients of BNDES nancing, the impact of BNDES on smaller rms may be dierent.

In fact, Cavalcanti and Vaz (2017) use a variation in access to a targeted loan program from BNDES to assess the causal eect of better credit conditions on investment and productivity of Brazilian manufacturing rms. The estimated causal eects point to positive eects on investment and productivity for small and medium rms. The authors argue that their results support the hypothesis that nancial constraints for long-term investment matter for small and medium rm's productivity and investment decisions. In addition to signicant government intervention in credit markets, Brazil also has very large private lending spreads (the dierence between lending rates and the deposit rate). According to the International Financial Statistics, the average interest rate spread is approximately 0.7 percent in Japan, 3 percent in the United States, 10 percent in Uruguay and 40 percent in Brazil.<sup>16</sup> High spreads may arise due to several reasons, such as poor collateral, credit taxes, regulatory costs and lack of competition. The banking sector is very concentrated in many countries, but concentration in Brazil seems to be particularly high and increasing over time. Between the mid-1990s and 2016, the share of assets held by the 5-largest banks in Brazil increased from 50 percent to more than 85 percent.<sup>17</sup> Joaquim and Van Doornik (2019) use heterogeneous exposure to large bank mergers to estimate the eect of bank competition on both nancial and real variables in local Brazilian markets. Specically, they use merger and acquisitions episodes of large Brazilian banks between 2005 and 2015 as a source of exogenous variation of competition in local banking markets to identify the causal eect of bank competition. The authors nd that a reduction in bank competition results in a signicant increase in lending spreads and a decrease in loan volume. They also show that bank competition has real eects. A 1 percent increase in spreads leads to a 0.2 percent decline in employment. Moreover, they show that if Brazilian spreads fell to world levels, output would increase by approximately 5 percent.

In addition to a high average level, there is a large dispersion in credit spreads to Brazilian rms. Cavalcanti et al. (2021) develop a quantitative dynamic general equilibrium model and calibrate it to the Brazilian data for the period 2005-2016. They use data from the Brazilian credit registry, a condential loan level data set covering all the credit operations in Brazil and containing information on loan characteristics and interest rates. They merge these data with Brazil's linked employer-employee administrative dataset to examine how interest

> 16See Cavalcanti et al. (2021).

> 17See Joaquim and Van Doornik (2019). They report that during this same time span the share of assets held by the 5-largest banks in the U.S. increased from 30 percent to more than 45 percent. Averaging across countries, the share of assets held by the 5 largest banks is 78 percent.

16
health expenditures is related to the higher incidence of disease in cash transfer villages compared to the control group, a variable in which there was imbalance at baseline, as noted above. Since no significant impact on food consumption is observed, the differences in non-food consumption and health expenditures induce a decrease in the share of food consumption in total consumption among cash transfer beneficiary households.<sup>37</sup> We do not find evidence that the interventions affected food prices in a way that would explain these results. There is no significant impact on the share of households reporting increases in food prices over the 12 months before the follow-up survey (Table 5, last column).<sup>38</sup> Panels A and C show that the BCC intervention offsets some of the cash transfer impacts on non-food consumption. In both Panel A and Panel B, the BCC component induces a consistent decrease in non-food consumption, which essentially counterbalances the increase observed among cash transfer beneficiaries. ITT estimates in Panel C also suggest that the BCC component reduces cash transfer impacts on total consumption and offsets the reduction in the food share of total consumption among cash transfer beneficiaries. These effects are not robust in Panel A, however.

Table 6 provides results for assets and savings. Consistent with the behavioral intervention offsetting cash transfer impacts on non-food consumption, Panel A shows that the BCC component induces lower accumulation of household durables relative to households receiving cash transfers only. Few differential effects are found on savings or investment in productive assets (including livestock), though cash transfers beneficiaries are marginally more likely to save.<sup>39</sup> These results are consistent with lower investments in household durable goods among cash transfer beneficiary household assigned to the BCC component. They are also consistent with intra-household reallocation of resources between adults and children, as discussed in Section 6.1.

> 37 Panel B of Table 5 shows no significant changes in consumption, among cash transfer non-beneficiary households in villages assigned to the BCC component. 38 In addition, Table A.7 tests for impacts on village-level prices for the products most frequently purchased in the follow-up survey (Panel A). No significant difference in prices is found. Note that the likelihood that food products are purchased by any household in the village is lower for some items in cash transfer villages (Panel B). Filmer et al. (2018) find that cash transfers have adverse effects on child nutrition by increasing prices of perishable goods in villages with a high share of beneficiaries. In our sample, cash transfer beneficiaries represent 40% of households in the village on average, and the saturation levels never reach the levels found in Filmer et al. (2018). We do not find price effects in villages with higher saturation levels either.

> 39 Panel B of Table 6 shows no significant changes in assets or savings among cash transfer non-beneficiary households in villages assigned to the BCC component.

26
qualitative results are very similar.

# **IV.** **<u>Conclusions</u>**

Using a representative data set summarizing household finance in China in 2012, we focus on how household structure, wealth, political and social networks, financial literacy levels, and the density of the local bank branch network are associated with access to, and the costs of, credit. We pay particular attention to usage of formal versus informal financial sources, and how access to sources of finance differs for rural and urban residents.

We find that the access to loans is reasonably good in China, but this is achieved mainly through the informal channel, especially in rural areas. Surprisingly, many of these informal loans carry maturities longer than a year. However, interest-bearing informal loans are significantly more expensive than bank loans. The most important uses for formal and informal loans are for housing purchases, though informal loans are also important for emergency uses in both rural and urban areas.

There is no difference in the rate of access to bank finance for urban and rural residents (both are low), but the usage of informal finance is 6.6 percentage points higher for rural residents. In general, proxies for wealth tend to be associated with access to more and/or better finance. Households with better education and stronger financial and economic literacy tend to rely more on bank financing relative to informal financing, especially those with college education. Rural households with connections to the Communist Party obtain more bank loans; such connections do not appear to help in obtaining informal finance, however. In contrast, the extent of private social networks (as reflected in the number of siblings) is positively associated with access to informal finance for both urban and rural households. Access to long-term finance tends to be determined by the same factors that drive access to finance in general. Moreover, our conclusions on access to finance are robust whether we measure it by the 26
The contribution of this paper is fourfold. First, it analyses the impact of the investment climate on a new dimension, namely firm survival. It shows the findings on raising transactions costs and weakening market mechanisms are robust to many alternative measures available. Second, it controls for multiple dimensions of the investment climate simultaneously. Thus omitted variable bias is less likely to be a concern than if only a single dimension is included. Third, it uses comparable firm-level data across 27 countries, allowing for within-country and cross-country variation to be analyzed. Fourth, the disaggregated data make it possible to test for non-linear impacts across important firm characteristics, namely size, age and ownership. Overall, the paper provides evidence that the more productive firms stand to gain the most from improvements in the investment climate, whether that is lowering transaction costs or improving market mechanisms.

The next section provides an overview of the literature. Section 3 describes the data and the model of exit that is used. Section 4 describes the results. Section 5 concludes.

# **Literature review**

Dunne et al.’s (1988) finding that 8-10% of manufacturing firms in the US exited each year, with rates higher for small and younger firms, sparked a large interest in understanding firm exit patterns (Davis et al. (1996), Caves (1998)). Many were surprised at the magnitude of firm closings. In part this had been masked by their additional finding that entry and exit rates were correlated so that net turnover is generally reasonably low. Similar rates have been found in developing countries. Haddad et al. (1996) find the rate in Morocco between 1984-89 is 6 percent; Tybout (1996) report a rate of 10 percent in Chile between 1979-86; Roberts (1996) finds a rate of 11 percent in Colombia over 1977-85. (Haltiwanger et al. (2006)). The data used here, covering 27 countries in Eastern Europe and Central Asia, 4
<!-- Start of picture text --> 120<br>100<br>80<br>60<br>40<br>20<br>0 -<br>2017 2018 2019 2020 2021<br>Funding Requirements mTM Funding Met<br><!-- End of picture text --> <!-- Start of picture text --> 300 &<br>250 o<br>6<br>200 P<br>3<br>2150 4x<br>2<br>= im a<br>z<br>30<br>o .<br>2018 zo1g 2020<br>Meme OperationWelcome ——8of Rorairna's expenditure<br><!-- End of picture text -->
<!-- Start of picture text --> Commodity 1<br>Non-energy<br>Intermediate ...<br>demand Bundle<br>Commodity n<br>Unskilled Female<br>Output<br>Unskilled Labor<br>Unskilled Male<br>Land Capital<br>Value Added K + SK Bundle Skilled Male<br>Bundle<br>Skilled Labor<br>Skilled Female<br>Oil<br>K + SK + E Bundle<br>Gas<br>Energy Bundle Coal<br>Electricity<br>Refined<br>Petroleum<br><!-- End of picture text -->

**Figure 3: Schematic structure of the CGE MANAGE model (K: Capital; SK: Skilled Labor; E: Energy)**

This production structure allows for an endogenous energy intensity of production, which fluctuates with carbon pricing policies. For example, an increase in carbon pricing will endogenously encourage energy substitution of capital as firms invest in energy efficient technologies to reduce production costs. The MANAGE model also has a vintage capital structure where old and new capital are treated differently in terms of substitutability with energy. The new capital is substitutable for energy while the old capital is a near complement: it means that existing productive assets need a certain amount of energy to produce, while there is flexibility to invest in new assets with higher energy efficiency in response to an increase in energy prices. In other words, the vintage capital structure captures semi-putty/putty relationships between inputs with more elastic behavior in the long run compared to the short run.

Power production in this version of the MANAGE model distinguishes seven types of power generation activities: nuclear,<sup>11</sup> coal, gas, oil, hydro, solar, and wind. The electricity generation mix follows the results of an Electricity Planning Model, see (Chattopadhyay, de Sisternes, & Oguah, 2018).

> 11 Although Türkiye does not have an active nuclear power plant as of 2018, a new plant is currently being built. To be able to incorporate nuclear into the model baseline and scenarios, we introduced a very small nuclear power activity in the base year based on an average cost structure from the GTAP power database and followed the expected growth of share of nuclear in power mix in the baseline and scenarios.

8
# **Using Mobile Data to Understand Urban Mobility Patterns in Freetown, Sierra Leone**

Dunstan Matekenya<sup>1</sup> , Xavier Espinet Alegre<sup>2</sup> , Fatima Arroyo Arroyo<sup>3</sup> and Marta Gonzalez<sup>4</sup>

**JEL:** C88, I18, O18, Q54, R42

**Key Words** _:_ Urban Transport, Resilient Transport, Mobile Data, CDRs, Big Data

> 1 _Data Scientist, DEC, Data Group, World Bank Group_ Washington, USA, dmatekenya@worldbankgroup.org

> 2 _Senior Transport Consultant, Transport GP,World Bank Group_ Washington, USA, xespinetalegre@worldbank.org

> 3 _Senior Urban Transport Specialist, Transport GP, World Bank Group_ Washington, USA, farroyoarroyo@worldbank.org

> 4 _Department of City and Regional Planning, University of California-Berkeley,_ Berkeley, USA martag@berkeley.edu
# Capital and Labor:

# The Factor Income Composition of Top Incomes in the United States, 1962-2006

Anthony B. Atkinson and Christoph Lakner<sup>_∗_</sup> JEL classication: D31, D33, E25. Keywords: Inequality, factor incomes, top incomes, United States.

> _∗_ Corresponding author: clakner@worldbank.org. A. Atkinson: Nueld College, Oxford, London School of Economics and Institute for New Economic Thinking at the Oxford Martin School. C. Lakner: World Bank and Centre for the Study of African Economies, University of Oxford. A. Atkinson sadly passed away in January 2017, before the new version of the paper was nalized. This paper would not exist without his contributions. Subsequent revisions were carried out by C. Lakner who is responsible for any errors. This paper has been developed from one of the chapters of C. Lakner's DPhil thesis. Emmanuel Saez participated in the rst stages of this project, and we should like to thank him for his assistance with the US data. We are most grateful to seminar participants at the Oxford INET, the World Bank and the ECINEQ conference, and to Rolf Aaberge, Francisco Ferreira, La-Bhus Fah Jirasavetakul, Stefan Klasen, Sebastian Königs, Branko Milanovic, Bob Rijkers, Francis Teal and Adrian Wood for discussions and comments on earlier versions. Lakner gratefully acknowledges nancial support from the Economic and Social Research Council [grant number ES/G011974/1], the Studienstiftung des deutschen Volkes, the Institute for New Economic Thinking (INET) at the Oxford Martin School and the Center for Equitable Growth at UC Berkeley. The ndings, 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 aliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
# **1 Introduction**

One of the main achievements of contemporary international economic cooperation has been the liberalization of international trade. Starting with the General Agreement on Tariffs and Trade, the world trading system has evolved into one which has seen the complete elimination of trade barriers on some products and widespread constraints on the use of trade policy in relation to most others. Given the general commitment to lower trade barriers, deviations from this norm assume great importance and are only possible if certain criteria are met.<sup>1</sup> These exceptions are designed to be quite narrow - imports of very specific products can face high tariff barriers while very similar products remain unprotected. The most important exception, anti-dumping duties (AD), are applied discriminately, affecting some countries’ exports of the specified product and not other countries’ exports. The narrowness of the targeted products and the policy’s discriminate use provide a setting in which relative comparisons between affected and unaffected imports can be used to gain insight into the nature of the pressures that animate trade policy.

In this paper we use monthly U.S. import data at the detailed product level to characterize the evolution of imports in a two-year window leading up to U.S. industries’ formal requests for protection via AD. A decomposition technique developed by Hummels and Klenow (2005) allows a comparison of imports of the specified products from countries that will, and will not, face formal complaints of unfair trade. This within-product decomposition can also be applied to closely related products in which no such complaints arise. The comparison of the time profiles of imports from ‘subject’ and ‘non-subject’ countries among ‘subject’ and closely-related ‘non-subject’ products generates novel insights about the developments in import markets that typically precede formal requests for protection.<sup>2</sup> The evidence points to AD petitions being filed in the aftermath of a specific combination of economic shocks. Our analysis suggests that the typical case appears to be preceded by a broad-based, and persistent, relative supply shock favoring exports from the countries named in the petition. Despite sizable increases in imports from subject countries, aspects of the status quo remain intact; the overall level of imports from all sources and the average import price remain relatively constant in the face of the relative supply shock. However, just prior to the filing of a petition, it appears

> 1There is a large literature on the design of trade agreements and their exceptions. See for example Bagwell and Staiger (1999), Bagwell and Staiger (2002), Bagwell and Staiger (2012), Saggi and Sara (2008) and Saggi (2006).

> 2We appropriate the terms ‘subject’ and ‘non-subject’ from the language used in the case law on AD. The petition identifies the countries and products that are subject to the petition.

1
& Hao, 2022).<sup>10</sup> The project was supported in part by a World Bank loan, including $80 million focused on pre-primary quality (World Bank, 2022b).

A new curriculum, textbooks, and teacher guides were developed, along with complementary online content (Moustafa, Elghamrawy, King, & Hao, 2022). The new materials for KGs were quite detailed, laying out a daily schedule,<sup>11</sup> providing teaching strategies, and detailed, scripted instructions in the teacher’s guide (Ministry of Education and Technical Education, 2019).<sup>12</sup> Children had corresponding workbooks. Activities often required corresponding materials, e.g. manipulatives for counting. Continuous professional development was planned, with initial training on the new education system and materials offered and ongoing training planned. Training was primarily offered in a cascade model (Moustafa, Elghamrawy, King, & Hao, 2022). The standard amount of professional training was three days per term.

# **3 Hypotheses, data, and methods**

# **3.1** **_Hypotheses_**

We hypothesize that there are disparities in children’s access to quality environments (in-home and out-of-home) as well as in child development. We have three specific hypotheses:

- 1) Egyptian pre-primary students have unequal ECD that depends on their SES, with higher SES children demonstrating more developed skills and competencies.

- 2) Egyptian pre-primary students have experienced unequal home environments that depend on their SES, with higher SES children experiencing more stimulating and supportive home learning environments.

- 3) Egyptian pre-primary students have unequal pre-primary environments that depend on their SES, with higher SES children experiencing higher quality pre-primary learning environments.

We test these hypotheses for outcomes based on factor analyses for ECD, pre-primary quality and its dimensions, and home stimulation. We use data from a sample of KGs designed to be nationally representative and assess the magnitude and statistical significance of relationships between outcomes and SES using descriptive approaches and regression models, as detailed below.

# **_3.2 Data collection tools, training, and fieldwork_**

> 10 There were four learning dimensions (learning, employability, personal empowerment, and active citizenship) which encompassed 14 life skills (creativity, critical thinking, problem-solving, cooperation, productivity, negotiation, decision-making, self-management, resilience, accountability, communication, respect for diversity, empathy, and participation) (Ministry of Education and Technical Education, 2019).

> 11 The schedule for KG, provided in correspondence with MoETE, had three primary windows (sessions, multiple per day): Arabic, multi-disciplinary and math (multiple windows per week). Physical education, religion, and English language had one or two windows per week.

> 12 Take-up of the new curriculum was high in our sample; 84% of classes were using the new curriculum and 80% of teachers were following the corresponding teacher’s guide.

7