rafmacalaba/datause-encoder-data huggingface dataset:
# **References**

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32
migrants from rural to urban areas are expected to come from the homelands; but even the steady outmigration from these areas will not exceed population growth (Urban Foundation, 1990).

It is difficult to get a grip on efficiency issues in the homelands; the existing data base is very weak and probably underestimates the importance of agriculture, even though it is extremely constrained by the overcrowded and poor resource base. For example, official estimates of homeland yields are consistently lower than case study data (e.g. Cairns, 1990).

However, the available case-study material does suggest that relatively few farmers are engaged in agriculture full-time in the homelands. According to Nicholson and Bembridge (1991), the majority of households in South Africa's homelands does not have enough land to provide for subsistence needs. Thus most rural households engage in farming only part-time, and most of their output is kept for consumption at home.

Several studies have also explored the relationship between farm size and efficiency in the homelands, where farms are in general much smaller than those in the commercial sector. Nieuwoudt (1991) notes that small farmers may use land much more intensively than do large farmers. Latt and Nieuwoudt (1988) used data on 140 households in Umbumbulu district of KwaZulu to conduct a discriminant analysis of input use. They found that farms of less than 1 hectare applied inputs much more intensively than farms larger than 1 hectare; thus they suggested that smaller farms may maximize returns to land (their scarce resource), while larger farms maximize returns to labor or capital.

Moreover, even in comparison with the commercial sector, homeland agriculture is sometimes found to be more efficient. Case study material shows that in dryland cotton smallholders in KwaZulu are more viable than large farmers (Wheeler and Ortmann, 1990:251). These trends are also confirmed by the two case studies mentioned earlier where small farmers, given the same support structures in tea and sugar farming, have done as well or outperformed larger farms (Van Zyl and Vink, 1992).

In a study of 60 farmers in Gazankulu, half of whom were identified by extension staff as commercially-oriented, Nicholson and Bembridge (1991) found that "commercial farmers" cultivate more land than "typical subsistence farmers" (12.2 hectares versus 2.3 hectares), own more cattle and equipment, are better educated, and more likely than their neighbors to keep records. Similar findings are reported for Transkei (Bembridge, 1991a), KwaZulu (Bembridge, 1991b; Wheeler and Ortmann, 1990; Nieuwoudt and Vink, 1989) and Lebowa (Van Zyl and Coetzee, 1990). The authors of these latter studies also note the importance of income from non-agricultural sources, such as wage employment, in providing working capital for the purchase of seeds, fertilizers, and other production inputs.

14
# _Enrollment_

33. Most Brazilian children, on average 94 percent, are enrolled in school, with little difference between girls and boys (94 percent and 93 percent respectively). For working children, average enrollment is only slightly lower at 83 percent. The numbers do not, however, reflect the low levels of educational attainment of Brazilians owing to late entry into school, slow progression, and high drop out rates. The numbers of children who lag in grade as well as the levels of educational attainment of working and non-working boy and girls are more pertinent; and a correlation may exist between these indicators although not necessarily causality.

# _Attainment byvAge_

34. According to the PNAD household survey, the educational attainment of children in Brazil is only about 55 percent of what it should be for their age. Boys tend to lag behind more than girls, especially when they are working (60 percent compared to 51 percent for girls)."<sup>2</sup> The greater number of boys involved in child labor may explain part of this gender disparity in education lags."<sup>3</sup> However, as mentioned previously, these numbers may be distorted by measurement error because many girls work in domestic labor, which often goes uncounted.

35. Contemporaneous evidence of educational attainment between girls and boys who only work, only study, do both or neither in rural and urban areas suggests gender differences. In rural and urban areas, the difference between boys and girls who only study is relatively insignificant. However, for children in rural areas who only work, the evidence clearly demonstrates a substantially lower level of attainment for boys (1.7 years) than for girls (2.5 years). In urban areas, girls who only work or also both study and work, also exhibit higher attainment on average than boys.

36. Although it does not necessarily indicate a causal relationship, it is perhaps more telling to examine the relationship between the number of hours worked and educational attainment (see Table 9). In rural areas, girls who work 10 to 20 hours per day have a 35 percent higher attainment rate than their male counterparts who work the same number of hours, and a 20 percent higher rate than boys if both are working 5 to 10 hours. In urban areas, attainment for girls working 5 to 10

> 12 Note that this considers children who are currendy enrolled in school and currently working and does not describe overall attainment.

> 1 Sedlacek, Gustafsson-Wright and Ilahi (2000)

14
**Figure 3: Historical spread between 10Y US Treasury and Bunds (%)**

<!-- Start of picture text --> 2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>Mar‐12 Jun‐12 Sep‐12 Dec‐12 Mar‐13 Jun‐13 Sep‐13 Dec‐13 Mar‐14 Jun‐14 Sep‐14 Dec‐14 Mar‐15 Jun‐15 Sep‐15 Dec‐15 Mar‐16 Jun‐16 Sep‐16 Dec‐16<br><!-- End of picture text --> Source: Bloomberg

**Figure 4: Forward 10Y interest rate and spread**

<!-- Start of picture text --> 4<br>3.5<br>3<br>2.5<br>2<br>1.5<br>1<br>0.5<br>0<br>UST Germany Spread<br>Yield (%)<br>3/24/2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037<br><!-- End of picture text --> Source: Bloomberg 6
# **Infrastructure and Economic Growth in Egypt**<sup>*****</sup>

Norman V. Loayza World Bank Rei Odawara George Washington U. and World Bank

* This study is part of the Public Expenditure Review process, sponsored by the World Bank in conjunction with the Egyptian Ministry of Finance and funded partly with the Dutch Government's Trust Fund established for this purpose.  We also acknowledge the financial support of the Japanese Consultant Trust Fund, which funded Rei Odawara’s participation in the project. For insightful advice and support throughout the preparation of the study, we are especially grateful to Santiago Herrera (Lead Country Economist for Egypt).  We also thank the useful comments and suggestions provided by Farrukh Iqbal, Xavier Devictor, Michel Bellier, Sherine H. El-Shawarby, Luis Servén, César Calderón, Auguste Kouame, Alex Kremer, Ziad Nakat, Paul Noumba Um, Mustapha Rouis, Andrew Stone, Marijn Verhoeven, and other colleagues and seminar participants at the World Bank.  We gratefully recognize the Egyptian Center for Economic Studies (ECES) and its director Dr. Hanaa Kheir-El-Din for hosting the dissemination of the study in Cairo; and Dr. M. Fathy Sakr (Principal Advisor to the Minster of Economic Development), Eng. Fawzeya Abou Neema (First Under-Secretary, Ministry of Electricity), and other participants of the ECES seminar for their valuable feedback and suggestions.  Tomoko Wada provided excellent research assistance.  The views expressed in this paper are those of the authors and do not necessarily reflect those of the World Bank, its Board of Directors, or the countries they represent.
property rights at the country level are often subjective and not precisely defined and measured. The ICRG measure of corruption, for instance, is ordinal going from 1 to 6, and the increases of one point at different initial points do not have the same meaning. And, how these numbers are constructed remains a black box. Yet micro measures of corruption—such as the ratio of bribes to sales—are directly comparable across countries. Indeed, whether tax or corruption are more damaging, an issue of great concern for the literature of corruption, is more naturally studied in a micro set up, in which corruption can be measured as bribes over sales, with the same unit as tax burdens (i.e., total taxes paid over sales). Their coefficients are then directly comparable (Fisman and Svensson 2007; Cai et al. 2011).

In this section, we summarize how recent firm-level studies have pushed forward our understanding for the following unsettled issues about property rights and development: Do property rights matter? What are the specific mechanisms through which property rights affect the economy? Are all components of property rights created equal? Do the effects of various components of property rights differ by the country context?

# **The Importance of Property Rights**

Recent firm-level studies of property rights in transition suggest that the importance of property rights likely depend on the stage of transition. In early stages of transition, property rights are found to be overwhelmingly important by Johnson et al. (2002), who use a 1997 sample of firms from Poland, Romania, Russia, Slovakia, and Ukraine to study the relative importance of property rights and finance. The sample consists of relatively small yet profitable firms (with 7 to 270 employees). After-tax profits over sales, ranging from 5.7 percent in Slovakia to 21 percent in Russia, are high because of unfulfilled market niche, entry barriers, and immature transitions. A nice feature of this paper is their objective measures of property rights at the firm level, which rely on whether firms pay extralegal payments for licenses, for protection, and for services, and whether courts cannot enforce contracts. The access to finance is measured as whether firms have collateral and had loans in the previous year. At the country level, there is a strong association between property rights and reinvestment rate (i.e., the share of firms’ profits that are used for investment): Russia and Ukraine fare the worst while Poland and Romania fare the best in measures of property rights. Indeed, the reinvestment rate in the latter group is on average one-third higher. Further regression analyses of firm reinvestment rate on firm-level perception of property rights robustly show that reinvestment rates are significantly related to the perception of secure property rights, but not with firm access to finance. Since the perception of property rights and the access to finance are all measured at the firm 10
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DOTs were banned implies that the refiners earn lower profit after the reform, because per unit profit of refiners does not depend on the quantity sold (see equation (17) above).<sup>32</sup> The evidence in Table 4 that the effects of the reform on the intercept and slope of the wholesale margin equation became small or statistically insignificant after first 9 months of the implementation of the reform supports the conclusion that the market was reverting back to the old DOT system after the first 9 months of the reform. Interviews with the refiners by the authors in 2013 and 2016 confirm that they were unhappy with the reform, and surreptitiously went back to the DOTs to offload their accumulated inventory, as the dealers were reluctant to take oil delivery. A retrospective survey of 50 wholesale traders in Dhaka by the authors conducted in February of 2016 also corroborates this conclusion. According to the survey, the wholesale traders gradually went back to the DOTs starting from after 3 months of the implementation of the reform, and two of the smaller refiners Nurjahan Group and Bangladesh Edible oils limited were unable to survive the market disruption caused by the reform and had to exit. Taken together, different pieces of evidence strongly suggest that the refiners’ profit was adversely affected by the reform, consistent with the prediction of the credit rationing model.

An important piece of evidence comes from a survey the authors conducted in 2013 in the edible oils market in Bangladesh (Emran et al. (2015)). Using data on 6176 transactions between DOTs and wholesalers in Dhaka and Chittagong markets, an estimated 30 percent of the transactions between DOTs and wholesalers is on credit. In terms of quantity, the supplier credit from the DOTs accounted for 32 percent of the volume. Thus the market seems to have reverted to the relational credit arrangements by 2013, and credit provided by the DOTs to the wholesale traders played a vital role. Although 32 percent of the quantity at the wholesale level are financed by the DOTs, we would expect the reduction in quantity because of the reform in 2011 to be lower than 32 percent, as the wholesalers seek out alternative credit sources presumably at higher interest rates.

To the best of our knowledge, there is no reliable estimate of price elasticity of Palm oils

> 32One might wonder if the refiners could extract most of the surplus from the dealers using a two part pricing scheme. The reform allowed the refiners to charge a “security money” of 200,000 taka which could be seen as a two part pricing scheme. However, as noted before, the refiners found it difficult to establish a network of dealers, and the city group waived the security money for the 3000 dealers they appointed. The inability to pay the security money by prospective dealers is not surprising if the dealers were credit constrained.

33
38 Table 5. Average effect of the Venezuelan migration shock on educa– tional outcomes Private schools (marginal effects)

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Dependent Var.|Ln(Enrollment)|Promotion Rate|Dropout Rate|Math Std.<br>Score|Language Std.<br>Score|
||**P**|**anel A: Without **|**Controls**|||
|Foreign Males|0.0027**|-0.0111|0.0045|0.0031**|0.0034***|
||(0.0011)|(0.0121)|(0.0156)|(0.0014)|(0.0009)|
|Native Males|0.0000|-0.0060|0.0008|0.0003|-0.0000|
||(0.0018)|(0.0151)|(0.0187)|(0.0013)|(0.0010)|
|Foreign Females|0.0022**|0.0022|-0.0019|-0.0009|-0.0010|
||(0.0010)|(0.0149)|(0.0173)|(0.0025)|(0.0012)|
|Native Females|-0.0006|-0.0067|0.0032|0.0003|-0.0003|
||(0.0019)|(0.0151)|(0.0184)|(0.0013)|(0.0008)|
|R-Squared|0.8363|0.2059|0.2087|0.7915|0.7415|
|||**Panel B: With C**|**ontrols**|||
|Foreign Males|0.0025**|-0.0110|0.0046|0.0032**|0.0035***|
||(0.0011)|(0.0119)|(0.0153)|(0.0014)|(0.0008)|
|Native Males|-0.0002|-0.0059|0.0009|0.0005|0.0001|
||(0.0017)|(0.0149)|(0.0184)|(0.0013)|(0.0010)|
|Foreign Females|0.0020**|0.0023|-0.0018|-0.0007|-0.0009|
||(0.0010)|(0.0148)|(0.0169)|(0.0025)|(0.0012)|
|Native Females|-0.0008|-0.0066|0.0032|0.0005|-0.0002|
||(0.0018)|(0.0149)|(0.0181)|(0.0014)|(0.0009)|
|R-Squared|0.8364|0.2063|0.2091|0.7916|0.7417|
|Native Males Mean|4.009|96.50|2.373|0.906|0.756|
|Native Males SD|1.373|8.971|8.263|1.174|1.046|
|Native Females Mean|3.905|97.06|2.225|0.510|0.813|
|Native Females SD|1.405|8.565|8.119|1.074|1.040|
|Foreign Males Mean|0.871|96.01|2.841|1.788|1.359|
|Foreign Males SD|0.999|16.30|14.10|1.833|1.621|
|Foreign Females Mean|0.853|96.75|2.519|1.125|1.251|
|Foreign Females SD|0.988|14.76|13.27|1.656|1.632|
|Observations|130,834|130,834|130,834|39,396|39,396|

Clustered standard errors by municipality in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. All columns include School FE, Year FE and Department _×_ Year FE. Selected controls include the following variables interacted with year dummies: N. of Terrorist Attacks _×_ 2012, 2014 and 2016, Informal Labor _×_ 2018, UBN _×_ 2013.
**Pollcy Research Working Paper Series**

||**Title**|Author|Date|Contact<br>for paper|
|---|---|---|---|---|
|WPS920|Economic Incentives and Point<br>Source Emissions: Choice of<br>Modeling Platform|Raymond J. Kopp|June 1992|C. Jones<br>37754|
|WPS921|Road Infrastructure and Economic<br>Development: Some Diagnostic<br>Indicators|Cesar Queiroz<br>Surhid Gautam|June 1992|M. Laygo<br>31261|
|WPS922|Central America at a Crossroads|Sylvia Saborio<br>Constantine Michalopoulo|June 1992<br>s|M. Colinet<br>37044|
|WPS923|Listening to Firms: How to Use Firm-<br>Level Surveys to Assess Constraints<br>on Private Sector Development|Andrew H. W. Stone<br>|June 1992|P. Infante<br>37642|
|WPS924|How Reduced Demand for Children<br>and Access to Family Planning<br>Accelerated the Fertility Decline in<br>Colombia|Rafael Rofman|June 1992|0. Nadora<br>31091|
|WPS925|A General-Equilibrium-Based<br>Social<br>Policy Model for Cote d'lvoire|Ngee-Choon Chia<br>Sadek Wahba<br>John Whalley|June 1992|A. Cox<br>34778|
|WPS926|Options for Reshaping the Railway|Neil E. Moyer<br>Louis S. Thompson|June 1992|B. Gregory<br>33744|
|WPS927|General Equilibrium Effects of<br>Investme;.t Incentives in Mexico|Andrew Feftenstein<br>AnwarShah|June 1991|C. Jones<br>37699|
|WPS928|Pesticide Externalities, Comparative<br>Advantage, and Commodity Trade:<br>Cotton in Andhra Pradesh, India|Nalin M. Kishor|July 1992|D. Ballantyne<br>38004|
|WPS929|Managing Pollution Control in Brazil:<br>The Potential Use of Taxes and<br>Fines by Federal and State Governm|Antonio Estache<br>Kangbin Zheng<br>ents|July 1992|A. Estache<br>81442|
|WPS930|Participatory Development: Myths<br>and Dilemmas|Robert Picciotto|July 1992|.A. Muhtasib<br>84573|
|WPS931|How Much to Commi**t**o an Exchange<br>Rate Rule: Balancing Credibility and<br>Flexibility|Alex Cuklerman<br>Miguel A. Kiguel<br>Nissan Liviatan|July 1992|R. Luz<br>34303|
|WPS932|Interest Rates, Official Lending,<br>and the Debt Crisis: A Reassessmen|Asli DemirgOg-Kunt<br>t Enrica Detragiache|July 1992|K. Waelti<br>37664|
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25

**TABLE** 42

|**Senega Numbe**|**r**of**Mod1mS**|**eo**<br>**Iobim**|**d FPm**||
|---|---|---|---|---|
||1963|1977|1980|1984|
|Food|30|120|106|102|
|Groundnut Processing|7|6|6|2|
|TextdLeandFootwear|19|18|23|26|
|Woodware and Packaging|7|21|14|16|
|Chemicals|7|28|36|38|
|**Metal Engineering**|22|30|**35**|**44**|
|Construction Materials|6|7|3|6|
|TOTAL|98|230|223|234|

Source: Pfeffermann (1968, p. 4) for 1963. Direction de la Staistique for 977, 1980 and 1984.' _4.3.1 Employment of Pemanent Workers_ Table 43 provides the official data on employment of permanent workers,by ifiiustry since 1977. Total manufacturing employmnent remained roughly constant between 1977 and 1982, (nearly 27,000), but it increased by about 2,000 jobs during the slight economic recovery of 1984. Generally, the modern industrial sector has not been a major creator of permanent jobs, although the overall stagnation disguises any variation across individual industries. In particular, textles, clothing, and leather, wood, paper, and construction are among the industries whose level of permanent employment has decreased, whereas printing, chemicals, and mechanical industry substantialy increased their employment of permanent workers since 1977. This finding is curious given that production in the former group (excluding construction) grew more rapidly than the average during this period while production in chemicals and mechanical industry declined. (See Table 2.3.) Before attempting to explain this striking phenomenon, we present similar data for a sample of 80 industrial enterprises. The sample, which we designed jointly with officials in Senegal's