rafmacalaba/datause-encoder-data huggingface dataset:
in poverty and inequality in Egypt over 2005-2008, and a broad picture of economic change are presented in section IV, showing that the growth was not benefitting the poor relatively more than the richest parts of the distribution. Fifth section discusses growth incidence curves based on panel data, and shows that growth was indeed pro-poor and that the lowest groups of the distribution benefited the most from the growth process. Section VI attempts to reconcile two opposite assessments of pro-poorness of growth in Egypt. Section VII concludes.

# **II. Pro-poor growth analysis: Review of the selected studies**

There is a growing consensus in the literature on the conclusion that sustained and rapid economic growth translates into poverty reduction<sup>3</sup> . However, there is a wide disparity in the extent of poverty reduction a growth process can achieve. The relation between growth, inequality, and redistribution is among the mostdebated topics in the economic analysis of development since the 1950s. This is because the link between growth and inequality is not unequivocal”. Changes in the distribution are not necessarily directly linked to growth and may reflect different economic factors that are specific to individual country experience<sup>4</sup> . Inequality may rise or fall temporarily for reasons which are not necessarily linked to growth. A World Bank study (2005) analyzed in some depth the relationship between changes in growth and inequality in eight countries<sup>5</sup> in the 1990s and found that growth typically was associated with growing inequality. By contrast, the experience of many high income (OECD) countries suggests that income growth is often pro-poor, both in absolute and relative terms, reducing not only poverty but also inequality (e.g. Smeeding 1990).

There are some facts in the dynamic relationship between growth, poverty and inequality that are well established. Rising inequality tends to reduce the growth elasticity of poverty reduction, i.e. weakening the impact of growth on poverty. Also the higher the initial level of inequality in a country, the higher is the rate of growth that is needed to achieve any given proportionate rate of poverty reduction (Bourguignon, 2003). This has been shown in several cross country analysis (see among others Ravallion, 2005) but also in the analysis of single country experiences. Studies on China, India, Indonesia and Brazil that analyse growth performance in the past decade and early 2000s all show that less initial inequality was associated with a greater effectiveness in reducing poverty.

> 3 See, among others, Dollar and Kraay (2002), Kraay (2006), Ravallion (2005).

> 4 Ferreira and Ravallion 2008

> 5 The countries selected were considered as relatively successful in delivering pro-poor growth. They were: Bangladesh, Brazil, Ghana, India, Indonesia, Tunisia, Uganda, and Vietnam.

3
**Figure 1: Tax administration as a constraint in Georgia in 2005, 2009 and 2013**

<!-- Start of picture text --> Share of Firms Who Rated Tax Administration as a Major Obstacle<br>30%<br>26%<br>25%<br>20%<br>20% 17% 16%<br>15% Georgia 2005<br>13%<br>15%<br>11% Georgia 2008<br>10% 8% 7%<br>4% 4% Georgia 2013<br>5%<br>0%<br>0%<br>small medium large total<br><!-- End of picture text --> Source: World Bank Enterprise Survey in Georgia in 2005, 2009 and 2013

# **3. Data and descriptive statistics**

# _3.1 General description of the data_

We use a database obtained from the Georgian Tax Authority with anonymized information on firms that are registered for tax purposes, covering tax years 2005 through 2012. Each year, this database reports the date when the firm first registered, legal status, revenue declared to the tax authority, and amount of tax paid. It also indicates whether a firm has signed up for the micro or small tax regime, along with the sign-up date. The number of employees is available for a subset of firms. The database includes both individual entrepreneurs (70% of firms in the database) and companies or other. Since only sole proprietors are eligible for the reform, we restrict our analysis to individual entrepreneurs and physical persons. Companies tend to have much higher revenues than sole proprietors and are thus likely not a valid comparison group for firms that are eligible for the reform. The average (median) revenue for companies in the database is about GEL 1 million (GEL 63,000) compared to GEL 41,500 (GEL 7,700) for sole proprietors.

Table 1 displays the number of sole proprietors included in the database each year. The numbers show that comparatively few firms appeared in the database in 2005 and 2006, probably because the tax authority introduced electronic records only shortly before these years. From 2007 on, more than 100,000 sole proprietors are covered in the database. However, in 2007 about 42 percent of our observations have either missing or zero revenue, which indicates that the majority of these firms either did not file a tax return in that year or that the declaration was not entered into the electronic database. The percentage of firms with zero or missing revenue decreases in later years. We will thus focus the analysis on data from 2008<sup>18</sup> and later years, when data quality appears to be best. Focusing

> 18 With the caveat that the business performance was seriously affected by the Russo-Georgian War (Five-Day War) in August 2008.

7
‘star’ pattern of accessibility in the city and its low levels of average accessibility and high inequality – but also high maximum access in the center.

# **5.3 In-between Cities**

# _5.3.1 Bamako_

The transport data are based on the SOTRAMA network, which is composed of privatelyowned minibuses traveling on different routes. The maps of the Sotrama network were produced and provided by the “Cellule de Préfiguration de l’Agence Urbaine de Bamako” (CPAUB) housed in the mayor of Bamako’s office. The original data, in Autocad format, were converted into a shapefile for the purpose of the accessibility analysis. While the routes are identified in the shapefile, public transportation speeds were not determined. So, the analysis makes some assumptions on average speeds that were discussed and validated with local experts. The Sotrama speeds are assumed to be linked to the road class they circulate on as follows:

||**SOTRAMA- rush hour speeds**|
|---|---|
|**Primary**|21 km/h|
|**Secondary**|11.2 km/h|
|**Tertiary paved**|9.8 km/h|
|**Tertiary unpaved**|7 km/h|

TABLE 6: SPEED ASSUMPTIONS DURING RUSH HOUR BASED ON ROAD CATEGORY IN BAMAKO The employment data for Bamako are based on a business registry carried out in 2015, listing 8,808 firms for the Bamako urban area. The registries include all formal firms, and the wage bill is reported at the level of the firm (and not the plant). To account for this, the top 5% of firms with the biggest wage bills were dropped from the analysis, as these would run the greatest risk of reporting firm (and not plant) level data. In addition, large mining, extractive and security firms were dropped from the analysis, since it can be safely assumed that a large proportion of their operations take place outside the city.

The firms are geo-referenced at the neighborhood level. The data are somewhat limited, and only allow computation of the total number of firms and the total wage bill per neighborhood. Unlike the situations in Kigali (see above) Bamako’s central neighborhoods are small, few firms are tracked in the outlying areas, and most businesses are therefore tightly clustered in the analysis, which may also play a role in the high levels of accessibility found in the analysis. As well, the population distribution in the city is notably even, without a significant downtown peak.

21
# **1. Introduction**

Over the past two decades, the Chinese state and its state-owned banks have become the largest official lenders to the developing world, but the size and characteristics of China’s lending boom have remained exceptionally opaque. Lack of transparency owes, in large part, to the fact that the main Chinese lenders require strict confidentiality from their debtors and do not release a granular breakdown of their lending.

To address the significant gap in the data and analysis of modern international lending patterns, Horn et al. (2021) compiled a comprehensive data set of Chinese overseas loans. They document the large scale of China’s lending boom from 2000 to 2017 and find that around 50% of China’s lending was not captured by the most widely used international debt statistics. “Hidden debts” are a significant challenge to international surveillance and the pricing of risk.

As of 2021, China’s lending boom to emerging markets and developing economies (EMDEs) appears to have largely ended. Following the path of many prior lending booms, the current one has turned sour, with growing debt sustainability problems and default risks resurfacing in many debtor countries. Of the 73 low-income countries eligible for the Debt Service Suspension Initiative (DSSI) introduced at the outset of the Covid-19 crisis, more than 50% are estimated by the IMF and World Bank to be currently in debt distress or at high risk of a sovereign debt crisis. Against this backdrop, the knowledge gap on Chinese lending has expanded beyond debt (stocks, flows and terms) to include “credit events” (default and debt restructuring operations). This paper takes a step toward filling that gap.

Details on the crisis response by Chinese lenders are hard to come by, for several reasons. Chinese official loans and related credit events are not on the radar screen of credit rating agencies such as Moody’s or Standard & Poor’s. Data on Chinese defaults and restructuring is also not collected systematically by international organizations such as the OECD, the IMF or the Paris Club. Chinese lenders themselves do not share information on their overseas loans in arrears or default and on whether debt restructurings took place and at what terms. In effect, as Gelpern et al. (2021) document, China’s loan contracts often explicitly rule out being part of a transparent, multilateral, coordinated debt relief initiative with comparable treatment. Most Chinese debt restructuring deals are arranged bilaterally and outside the public domain.

As a result, defaults or restructurings of Chinese overseas debts remain largely “hidden”. As we discuss, “hidden defaults” are neither new nor unique to Chinese creditors. OECD 2
reform programs. Despite the liberalization, the interest rate spread remained almost constant, fluctuating by only about 5 percent (see figure 7).

# **Table 1 Financial depth, efficiency, and competitiveness indicators in six middle‐income countries, 2015/16**

|_Country_|_Private bank_<br>_credit/GDP_|_M2/GDP_|_Credit/deposit_|_Liquid_<br>_liabilities/_<br>_GDP_|_Lending_<br>_rate less_<br>_deposit_<br>_rate_|
|---|---|---|---|---|---|
|**Egypt, Arab**|34.1|90|40.59|72.8|5.7|
|**Rep.**||||||
|Malaysia|65.3|90|111.0|42.2|1.5|
|Morocco|123.9|130|130.3|42.8|n.a.|
|South Africa|66.9|60|73.8|108.6|3.11|
|Thailand|114.8|n.a.|96.5|132.5|3.1|
|Turkey|66.1|50|98.6|109.2|n.a.|

_Source_ : Data from IMF (2017), IFS (2017), and WDI (2017). _Note_ : n.a. Not available.

# _Ratio of private bank credit to GDP_

The private bank credit to GDP ratio followed different trends across the six countries (figure 3). Malaysia had the highest ratios, followed by Thailand and South Africa. Financial development remained constant in Morocco over the years, improving slightly in the last decade.

Egypt had the lowest ratios of the six countries in the last decade. These ratios were lower in 2009–16 than they were before 2009, reaching a trough between 2012 and 2015. The decline can be attributed partly to the political crisis that hit the country in 2011 and extended past 2014.

11
explicitly pertain to any specific year. We can think of them implicitly as reflecting an equilibrium situation after fully deploying the policy instruments being studied. Note that the results measure changes in key variables (GDP, sectoral outputs, international trade) due to ETS and ETS cum RES as compared to the situation in the absence of ETS and RES. They do not represent any particular year although we used SAM of 2007.

# **4.1 Marginal abatement costs by sectors and regions**

The starting point for any emission trading study is to understand the marginal costs of CO2 abatement of various sectors in various regions. This is the basis of trade between the sectors and also among the regions. A sector with marginal abatement cost higher than market clearing CO2 permit price buys CO2 permits whereas a sector with marginal abatement cost lower than market clearing CO2 permit price sells CO2 permits. Figure 3 shows the marginal abatement cost (MAC) curves of energy and energy-intensive sectors in the base case (i.e., before the emission trading). For a given sector, the marginal abatement cost are significantly different across the regions representing how expensive it would be to reduce CO2 emissions from that sector in a region. In most regions, the electricity sector has lower marginal abatement cost as compared to other sectors due to more flexibility to produce electricity from different sources. For example, due to its utilization of an abundant endowment of fossil energy resources, Inner Mongolia offers the highest CO2 emission mitigation potential from electricity generation, and coal and oil mining, for a given abatement cost. More generally, western resource abundant regions, such as Shanxi, Inner Mongolia, and 17
# **Figure 15**

<!-- Start of picture text --> Median forecasted share of RE in t+30 by energy project and according to year of publication<br><!-- End of picture text --> <!-- Start of picture text --> Note: US scenarios are depicted as large squares<br><!-- End of picture text --> <!-- Start of picture text --> 20<br>19<br>18<br>17<br>16<br>15<br>14<br>13<br>12<br>11<br>10<br>9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Department of the Interior EUOGEP Energy Research and Development Agency European Union / DGTREN<br>Ford Foundation Greenpeace Harvard Business School IEA<br>IIASA IPCC OECD PEW<br>Shell International BV Union of Concerned Scientists WEC WEC and IIASA<br>RE share (%x100)<br><!-- End of picture text --> 24
24 Table 4. Effect of Remittances on Poverty for Non-Remittance and Remittance-Receiving Households, Guatemala, 2000

||**Receive no**<br>**remittances**<br>**(1)**|**Receive**<br>**internal**<br>**remittances**<br>**(from**<br>**Guatemala)**<br>**(2)**|**Receive**<br>**international**<br>**remittances**<br>**(from USA)**<br>**(3)**|**Receive**<br>**internal and**<br>**international**<br>**remittances**<br>**(4)**|**Percent**<br>**change (No**<br>**remittances**<br>**vs. internal**<br>**remittances)**<br>**(5)**|**Percent**<br>**change (No**<br>**remittances**<br>**vs.**<br>**international**<br>**remittances)**<br>**(6)**|**Percent**<br>**change (No**<br>**remittances**<br>**vs. internal**<br>**and**<br>**international**<br>**remittances**<br>**(7)**|
|---|---|---|---|---|---|---|---|
|Poverty headcount<br>(percent)|54.55|54.21|55.33|53.47|(-0.63)|+1.06|(-1.98)|
|Poverty gap<br>(percent)|25.15|23.34|23.66|24.40|(-7.20)|(-5.93)|(-2.99)|
|Squared poverty gap<br>(percent)|17.93|14.15|14.38|16.84|(-21.09)|(-19.80)|(-6.18)|
|Gini coefficient|0.500|0.489|0.490|0.501|(-2.20)|(-2.00)|+0.20|
|Predicted mean per<br>capita household<br>expenditure<br>(including<br>remittances)<br>(quetzals)<br>N|7,733.09<br>7,276|7,990.76<br>7,276|7,854.01<br>7,276|8,091.79<br>7,276|+3.33|+1.56|+4.64|

Notes: Column (1) uses predicted income equations to measure the situation excluding remittances for all 7,276 households. Column (2) measures the situation for all households when only internal remittances (from Guatemala) are included in predicted household expenditure. Column (3) measures the situation for all households when only international remittances (from USA) are included in predicted household expenditure. Column (4) measures the situation for all households when both internal and international remittances are included in predicted household expenditure. Poverty calculations made using 2000 World Bank poverty line of 4,319 quetzals/ person/ year. For predicted income equation, see equation (2) and text. All values are weighted.

In 2000, 1 Guatemalan quetzal = US $0.128.

Source: Calculated from Guatemala 2000 ENCOVI Survey
drug-resistant disease, more patients will be given drug-sensitivity tests, and approximately 800,000 MDR-TB patients will be treated with regimens including second-line drugs. HIV testing and counseling will be provided to 27 million TB patients, and antiretroviral therapy and co-trimoxazole preventive therapy will be offered to 3.2 million. Approximately 200 million people infected with HIV will be screened for TB, and 24 million will be offered isoniazid preventive therapy.

The scenarios do not account for the implementation of new technology (drugs, diagnostics, and vaccines) that may emerge due to research investments specified in the Global Plan.

# **3. Methodology**

In this paper we evaluate the economic benefits of extending the World Health Organization’s DOTS strategy (a multi-component approach that includes directly observed treatment, short course chemotherapy and several other components) using a model that combines epidemiological projections of averted mortality and economic benefits measured using a value of statistical life (VSL) approach. Other methodological approaches to this evaluation and the rationale for selecting the VSL-based approach are described in another paper that preceded these analyses (Laxminarayan 2006). Briefly these include cost-of-illness approaches using the human capital method or stated preference; sectoral approaches estimating the effect of disease on a particular sector of the economy (such as of malaria on tourism); and macroeconomic estimates based on models or cross-country growth regressions. These other approaches may lead to different estimates of economic benefits.

Here we describe the full-income approach to estimating the economic burden of a baseline scenario of No DOTS and then evaluate the economic benefit of moving to the Sustained DOTS and the Global Plan (enhanced DOTS) scenarios described earlier. An important methodological challenge is posed by the large number of TB-infected people worldwide, especially in Sub-Saharan Africa, who also are co-infected with HIV. Since there is no easy way to disentangle the effects of the two diseases, two sets of estimates are presented for TB-related deaths, one excluding HIV co-infection and another 8
costs. However, empirical studies and surveys of transport costs in Africa have shown that costs are inversely proportional to load size: the cost of transporting a unit of maize is lower the bigger the load (World Bank, 2007; Teravaninthorn and Raballand, 2009). Since large loads are easier to coordinate during the harvest season, the cost of transport per ton maize is minimized. So, standard transport costs are unlikely to be the explanation, indicating a role for search costs in explaining these findings through the channels discussed in the analytical framework.

_Other explanations and the strength of the proxy variable_ One of the challenges of using proxy indicators is the identification problem. Using distance to market as a proxy for search costs opens the possibility that our identifying variable measures factors other than search costs. If this is the case, then it would be difficult to attribute the results we describe in this paper to the effects of search and information costs associated with farm-gate buying. The question is whether there are other reasons besides buyer search costs and seller information costs that would cause the prices received by households that are distant from the market to be lower than those nearer the market. We identify two such factors.

The first is competition. On average, rural trading businesses face competition from only five other businesses, providing each one with a relatively high market share (World Bank, 2007). In this context, maize growing households that are distant from the market may be reached by fewer buyers, creating an opportunity for monopsony rents that push producer prices down as the distance from market increases. In addition, since remote households tend to be more vulnerable, their bargaining power will be further diminished. This will be the case if buyers tend to concentrate around the village and market centers and avoid covering long distances in search of maize.

Nonetheless, this effect is unlikely to be present. In this scenario, we would expect monopsonistic market power at the village level to be most concentrated during the lean season when buyers are already relatively scarce. So we would expect remote households to experience a significant downward pressure on maize prices at that time. However, our results show the opposite effect, namely that maize prices decrease more with distance during the harvest, not the lean season. This leads us to conclude that weak competition and monopsony rents do not comprise an underlying and undetected factor influencing our results. It also corroborates the 18
narrow transitional strip of semi-arid climate. The rainy season extends from around midOctober to early May, with the rainfall peaking from December through February. Rainfall varies considerably from the north to the south. The highest rainfall is observed in the North and center parts of the country and the lowest in the southern part. The average annual precipitation range is between 151.94 mm to 772.6 mm and the average annual temperature ranges between 15.92<sup>0</sup> C to 23.91<sup>0</sup> C. A more detailed classification of the climatic zones in Israel can be delineated by 12 geo-climatic zones (Goldreich 2003). This classification is based on comprehensive climatic data and adjustment for physiographic conditions. This classification expresses the synthesis between regional similarity by climatic parameters and the special physiographic characteristics of the various regions. We base our data sampling on the geo-climatic zones since they reflect better the climate zones relevant for agriculture.

Israel's agricultural sector is characterized by an intensive system of production stemming from the need to overcome a scarcity in natural resources, particularly water and arable land. The country's varied climate and seasonal temperatures have stimulated the development of unique agro-technological solutions. The climate conditions enable especially the warmer regions to produce vegetables, fruits and flowers during the winter off-season, particularly for export markets in Europe (Sheskin and Regev, 2001). This ability to be the first to the market affords them high prices in the European markets as well as in the local market. In this case warm temperatures are an advantage.

About half of the 282 thousands hectares of crop area are allocated for growing field crops. On about a quarter of this area farmers grow vegetables, potatoes and melons. About 16 percent of the crop land is used for fruit orchards, 7 percent for citrus orchards and 2 percent for flowers and other garden plants (Israeli Central Bureau of Statistics, 2005). Almost all the crops excluding field crops are irrigated. Field crops are grown on large plots of marginal lands and depend partially on rainfall. The agricultural sector is the main water user in Israel. About 60% of the water supply (from wells, reservoirs, effluent water, etc.) is used for irrigation.

Underground and surface water are state property by the Israel water law. Each year the Israel water commissioner allocates for each village an annual water quota for irrigation. Historical initial quotas were determined according to factors such as: total land suitable for irrigation, soil type, population size, location, water usage prior to 1959 and political affiliation of the village. Water quotas are adjusted periodically in order to take into consideration new water sources and new villages. The price of water is determined 4