rafmacalaba/datause-encoder-data huggingface dataset:
28 income growth relates to the environment.“ While recognizing the large literature on these issues, the project will examine fresh evidence for the relationship between welfare and growth. This is important both to assess whether raising income growth is a sensible policy objective, and to identify any feedback effects on growth from the social consequences of the growth process.

# a. Inequality and growth

Table 3 shows a comparison of per capita incomes of the poorest 20 percent of the population and the average per capita income for those countries that have data on income distribution. The table shows a high degree of correlation (.77) between the per capita income and the income of the poorest part of the population -- differences in inequality do not dominate differences in per capita income across countries. If we think of per capita income as the sum of past growth, this suggest that growth dominates redistribution as a factor in the income of the poor.

But the outliers give pause to the notion that cross-sectional growth differences are all that matters -- the income of the poorest fifth in Brazil is only sixty percent of that in Morocco, despite Brazilian per capita income being two and a half times larger. Similarly, the poorest in Botswana are slightly worse off than in India, even though Botswana’s per capita income is three times larger. However, even in these outliers, the relationship between growth and inequality is unclear -- would Botswana’s poor be better off if the country had not grown? A large literature has found similar patterns to that displayed in table 1, both intertemporally and cross-sectionally. Fields’ (1989) survey of the literature indicates strong evidence of reduction in absolute poverty being associated with growth, with poverty "more apt to decrease the more rapid is economic growth".“ The notion that absolute poverty tends to increase with economic growth has been decisively refuted. The 1990 WDR found that income of the poorest tenth grew more rapidly than per capita income in 9 out of 11 developing country growth episodes. But outliers still exist -- in Costa Rica, the poor suffered an absolute decline in income despite average per capita growth of 3.5 percent over 1971-86.% The famous Kuznets hypothesis that inequality first rises and then falls with income has also been examined in the literature. Cross-section studies have tended to confirm it, while intertemporal studies --which seem more appropriate -- have found little evidence for it (Fields (1989), WDR 1990).

Other studies have looked at how inequality itself may lower growth because it tends to lead to policies that harm growth. Alesina and Rodrik (1991) found that higher inequality tends to lower growth in the subsample of democracies but is insignificant in nondemocratic countries. Almost identical results were found by Persson and Tabellini (1991), although the significance of “We do not consider another issue in this section that was briefly mentioned elsewhere in this proposal: growth could be immiserizing because policy distortions may exist that cause too much investment. ‘SFields (1989), p. 174.

“WDR 1990, p. 48.
the Doha round. In contrast to mode 1, the increase in the proposed level of bindings for modes 2 and 3 is significant, rising to over 80 percent. For mode 3 market access commitments the variance across EU members would fall, but the structure of commitments remains similar to the pre-Doha status quo, i.e., the “lagging” countries are mostly the same – only Greece would converge to the EU average as a result of the offers that were on the table as of 2004. However, in all cases where national sensitivities on mode 3 appear to remain, the level of proposed commitments on mode 3 is substantially higher than for mode 1.

**<u>Table 2: EU Member GATS Offers by Mode of Supply in the Doha Round, 2003-2004</u>**

|||Market|Acces|s|Na|tional|Treatm|ent||MA|+ NT|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Mode:|1|2|3|4|1|2|3|4|1|2|3|4|All|
|EU*|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Belgium|57.1|89.4|84.5|0.4|56.6|90.3|86.7|2.2|56.9|89.9|85.6|1.3|58.4|
|Germany|56.6|87.6|81.9|1.8|55.3|88.5|85.0|2.2|56.0|88.1|83.5|2.0|57.4|
|Denmark|60.2|89.4|85.4|2.7|59.3|90.3|84.5|4.4|59.8|89.9|85.0|3.6|59.5|
|Spain|57.5|89.8|78.3|1.3|57.5|90.3|86.7|1.3|57.5|90.1|82.5|1.3|57.8|
|France|51.8|88.5|79.2|9.7|54.0|90.3|84.5|2.7|52.9|89.4|81.9|6.2|57.6|
|Greece|55.8|89.4|83.6|4.9|54.9|90.3|85.8|0.9|55.4|89.9|84.7|2.9|58.2|
|Italy|50.4|88.9|77.0|5.3|49.1|90.3|85.4|9.3|49.8|89.6|81.2|7.3|57.0|
|Ireland|56.6|89.9|85.8|0.0|55.8|90.3|86.7|0.9|56.2|90.1|86.3|0.5|58.3|
|Luxembourg|61.5|89.8|86.7|0.4|60.2|90.3|86.7|0.9|60.9|90.1|86.7|0.7|59.6|
|Netherlands|59.7|89.8|86.7|0.0|58.4|90.3|86.7|0.9|59.1|90.1|86.7|0.5|59.1|
|Portugal|54.0|89.9|74.8|2.7|53.1|90.3|84.1|6.6|53.6|90.1|79.5|4.7|56.9|
|UK|60.6|89.9|86.7|3.5|59.3|90.3|86.7|0.9|60.0|90.1|86.7|2.2|59.7|
|Austria|65.5|87.2|82.3|6.2|62.4|88.1|85.0|1.8|64.0|87.7|83.7|4.0|59.8|
|Sweden|57.5|80.5|74.3|1.3|56.6|81.4|74.8|3.1|57.1|81.0|74.6|2.2|53.7|
|Finland|58.8|77.0|74.3|0.9|58.8|78.3|75.2|57.1|58.8|77.7|74.8|29.0|60.1|
|Standard deviation|3.8|3.8|4.7|2.8|3.3|3.7|3.9|14.2|3.5|3.7|4.0|7.1|1.6|
|Mean|57.6|87.8|81.4|2.7|56.8|88.6|84.3|6.3|57.2|88.2|82.9|4.5|58.2|

* “EU” is an artificial benchmark schedule that does not have country-specific factors. The calculated numbers represent the percentage of committed sectors in the total, weighted by the type of binding (full, partial, unbound).

_Source_ : Langhammer (2005).

# **2. The Pattern of GATS Commitments of Transition Economies**

The transition economies on which this paper focuses are a very heterogeneous group of countries. They range from central European EU accession countries to landlocked, small Central Asian countries that were members of the Soviet Union. Some were already GATT-1947 contracting parties (Poland, Hungary, the Czech and Slovak Republics, Romania, Yugoslavia), but most were not. Some are proximate to the EU-15, sharing a common border. In what follows we divide the 16 countries into four groups: (a) the major Central European EU accession 7
affected by the shift in the past two decades from predominantly pearls and precious stones to mineral products. Exports of mineral products (ores and metals) represented 83 percent of total exports in 2013, followed by fuel (8 percent). The importance of pearls and precious stones exports in DRC’s export basket has diminished constantly throughout the last decade from 62 percent in 2000 to just about 7 percent of total exports. Both agriculture and manufacturing remain less important (see Figure 2).

**Figure 3. DRC: Exports by Destination (percent of total exports, 2000-2013)**

<!-- Start of picture text --> 85<br>80<br>75<br>70<br>65<br>60<br>55<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br>China Korea EU27 US SSA<br><!-- End of picture text --> Source **:** calculated using data from UNCTAD database.

5. **China has become the main export market for DRC in the last decade, hence replacing the European Union (EU27).** The EU has traditionally been the main market destination for DRC exports, but has lost its importance to China for most of the last decade. China alone accounted for 41 percent of DRC’s exports in 2013. DRC’s exports to China grew at an average annual rate of over 102.9 percent between 2000 and 2013 and multiplied more than 509 times their share in DRC’s exports. Meanwhile, the share of total exports going to the EU27 declined by an average annual rate of 4.6 percent, from 77.4 percent in 2000 to 23.6 percent in 2013. Despite the significant decline in share of total exports, the EU27 as a block remains as one of the main destinations for DRC’s exports. The Republic of Korea also increased its importance as a main export destination in the past few years reaching about 3.3 percent of total exports in 2013 (up from just 0.04% in 2000). In recent years, the United States appears to have lost its significance as one of DRC’s main trade partners, dropping from 18.5 percent of total exports in 2000 to just 1.2 percent in 2013 (see Figure 3).

6. **The decline and/or stabilization of shares of the EU27 and the United States did not correspond to similar patterns in terms of value, due to the overall increase in DRC’s exports** . Indeed, while the share of exports to the EU27 has declined significantly between 2000 and 2013, 4
place during periods of low growth – meaning that not all regions within a country end up better off than before changes in trade patterns took place – in cases when trade widens an already wide gap between rich and poor regions, and in cases when new territorial inequalities resulting from trade reinforce pre-existing social, political, cultural, or ethnic divides, the rise in inequalities may not just be a temporary stage, but one that becomes entrenched. Under these circumstances, increasing regional inequality may lead to a fragmentation of internal markets and to social, political, and/or ethnic tensions which may threaten the very growth and prosperity that greater trade is supposed to bring about.

This paper delves into the assumptions about the link between trade and regional inequality present in the WDR 2009 and for which existing literature offers no conclusive indications. More specifically, the paper focuses on the first two assumptions highlighted earlier: a) whether changes in trade matter for the evolution of spatial inequalities and whether openness to trade affects developed and developing countries differently and b) whether there is a dynamic element to this association. The analysis covers the evolution of regional inequality across 28 countries – including 15 high income and 13 low and medium income countries – over the period 1975-2005<sup>3</sup> .

In order to achieve this, the paper combines the analysis of internal factors – in the tradition of Williamson – with that of change in real trade as a potential external factor which may affect the evolution of within-country regional inequality. Internal factors considered include both Williamson’s (1965) level of real economic growth and development, as well as a series of other factors, used as structural conditioning variables following the new economic geography theory (NEG), which aims to account for apparent differences in the relationship between trade openness and spatial inequality. The analysis is conducted by running unbalanced static panels with country and time fixed effects, in order to address whether changes in trade patterns are connected with changes in spatial inequalities, followed by dynamic panel

> 3 The analysis of the evolution of regional disparities requires good subnational data series, which imply a degree of sophistication by national statistical offices. Thus, using the most recent World Bank classification, no country included in the sample can be considered as low income, sensu strictu, while only China, India, Indonesia, and Thailand are classified as lower middle income countries.

4
_2 PRIVA_ **_TE_** _EXPENDITURES INEDUCA_ **_TION_** On the demand side, household enrollment patterns are highly dependent on the cost of schooling. The total monetary cost for the household, without considering the opportunity cost, comprises school fees, tuition and unforeseen expenses, transportation cost, textbooks, stationery, and uniforms. Table 4 in appendix I shows how much an average household spend on education by poverty status and educational level. Table below illustrates the fact that most students who are in private schools spend in education more than twice than those students who are in public schools. As it can observed in this table, the share of the expenditures in services and materials are similar for both private and public school, while the fees and unforeseen expenses constitute the differences in total school expenditures between private and public schools. In private schools, fees and unforeseen expenses account for 70% of the school expenditures compared to 38% in public schools. Moreover, the educational expenditures in the urban areas are twice as high as in rural areas (see table 5 in appendix 1).

**Table 1. Household Expenditure in Education by Poverty Status, 1996**

|**Poverty Status**<br>|**Expenditure**<br>**Fees/Unforeseen**<br>**expenses**|**s per stud**<br>**Services**|**ent****_(%)_**<br>**Materials "**|**Total**|**Educational**<br>**Expenditures**<br>_(%)2/_|**Services, materials**<br>**Expenditures**<br>2/|**Number of**<br>**Households**|
|---|---|---|---|---|---|---|---|
|**Private schools**||||||||
|Extreme<br>|70.3<br>|1.0|28.7|100.0|14.6|4.3|12|
|Moderate<br>|75.1<br>|4.3|20.7|100.0|11.1|2.8|50|
|Non poor<br>|70.8<br>|5.5<br>|23.7|100.0|16.7|4.9|499|
|Total|70.9|5.4|23.7|100.0|16.6|4.8|561|
|**Public schools**<br>||||||||
|Extreme<br>|32.2<br>|1.2|66.6|100.0|6.3|4.3|2825|
|Moderate<br>|35.2|2.4|62.4|100.0|7.0|**4.5**|2511|
|Non poor<br>|41.8<br>|_5.4_<br>|52.7|100.0|6.7|3.9|2544|
|Total|38.3|3.8|57.8|100.0|6.7|4.2|7880|

_Source:_ ENIGH 96

**1/** Textbooks, stationery, etc.

2/ As percentage of household's expenditures Table 2 below compares the expenditure of poor and non-poor students by education level, showing significant disparities. At primary level, non-poor students in public school spend four times the amount than extreme poor students spend in education. While at the university level non-poor individuals spend 1.4 times as much as poor students. These differences might be partly explained by scholarships or discounts on tuition fees among the poor.

Information at the individual level on schooling expenditures is available only for school fees, tuition and unforeseen expenses, but assuming that the amount spend on materials and services is fixed for all levels of education, the individual total educational expenditures are much lower than the government subsidy. In fact, the public subsidy compared to the average student expenditure is 2.8 times for primary level, 2.3 for lower secondary, 2.2 for upper secondary and 5.2 for university.

3
The Development Impact of Financial Regulation: Evidence from Ethiopia and Antebellum USA<sup>_∗_</sup> Nicola Limodio<sup>_†_</sup> Francesco Strobbe<sup>_‡_</sup>

# JEL code: G21, G38, O16, O55, O51

> _∗_ We thank Ato Yohannes Ayalew Birru, Ato Getahun Nana, and Ato Muluneh Ayalew from the National Bank of Ethiopia for their support, and the Vice-Presidents and Credit Directors of all Ethiopian banks for their insights and time. We are indebted to Tim Besley for exceptional feedback and overarching comments at different stages of this work. We also are grateful for useful suggestions from and discussions with Oriana Bandiera, Juliane Begenau, Robin Burgess, Guang Zhe Chen, Stefan Dercon, Leonardo Felli, Greg Fischer, Sebastian Galiani, Luis Garicano, Alessandro Gavazza, Maitreesh Ghatak, Doug Gollin, Charles Goodhart, Boyan Jovanovic, Joseph Kaboski, Alex Karaivanov, Michael Koetter, Shiva Makki, Lars Christian Moller, Francesco Nava, Gianmarco Ottaviano, Gerard Padr ́o i Miquel, Daniel Paravisini, Michele Piccione, Ronny Razin, Ricardo Reis, John Sutton, Eric Verhoogen, Shengxing Zhang, and seminar participants at the LSE Development, Economic History, Macro and Public Student Seminars, the York-MMFBank of England Macro PhD Workshop, ThReD Conference, SED Conference, Belgrade Young Economist Conference and the OxDev Workshop. This project benefited from support from the World Bank Research Support Budget. We thank Raeye Daniel, Michael Demissie, Sara Demsis, Cristiana Gentea, Wollanssa Taddesse, and Boris Turkin for superb research assistance. We also acknowledge editorial assistance by Rachel Lumpkin. We are responsible for all errors and this work does not reflect the view of the World Bank, its Executive Directors and is not an official World Bank document.

> _†_ N.Limodio1@lse.ac.uk, Corresponding Author, London School of Economics, Department of Economics, 32 Lincoln’s Inn Fields, WC2A 3PH, London.

> _‡_ Fstrobbe@worldbank.org, World Bank, 1818 H Street NW, 20433, Washington DC.

1
Water users in the Guadalquivir basin have relied primarily upon surface water resources to supply their needs. There are 52 identified groundwater areas in the river basin (Giansante 2003), and groundwater overdrafting is an isolated rather than widespread problem in the basin. Precipitation is greatest in the mountains along the edges of the basin and lowest in the valley floor, where most of the population and irrigation are concentrated.

Precipitation and stream flows are highly variable, exposing residents to risks of flooding as well as drought. Mean annual precipitation is 596 mm, but this has ranged from as little as 300 mm during drought to as much as 1100 mm. Years of high or low precipitation have often clustered together, compounded their effects. Seasonal variability is also great, with most precipitation concentrated in the winter months and peak rainfall occurring from November through March. Long, dry summers follow, during which precipitation is virtually nil and evapo-transpiration soars.

# **_4.2 Water Resource Issues and Problems_**

Water management challenges include flood control and drought protection to sustain the basin’s substantial population and significant agricultural production. For the past century, management efforts have focused primarily on regulating river and tributary flows for both flood control and water supply purposes—to minimize the damage of wet periods and maximize water conservation and storage for dry periods.

Irrigation is the dominant consumptive water use in the basin, with 80-85% of water consumption. Municipal and industrial uses account for 12-15%, with the remaining 5- 8% apportioned among environmental and other water needs. Irrigation’s place as the foremost user of water is even more apparent when assessed on a per capita basis (Table 1).

**Table 1. Water Use by Sector in the Guadalquivir Basin, 1998**

|Sector|Water<br>demand<br>(millions of cubic<br>meters peryear)|Percent<br>of<br>Total<br>Water Demands|Per Capita Consumptive<br>Water Demand (cubic<br>meters per resident per<br>year)|
|---|---|---|---|
|Irrigation|3,140|83.5|752|
|Urban|532|14.1|127|
|Industrial|88|2.4|22|
|Total|3760|100.0|901|

Sources: Ministry of the Environment 2000.

Each category of water use is increasing, however, and projected to continue to do so, with little effect on the percentage distribution among uses. Agricultural use is projected to account for 78.4% in 2012 compared with 79.7% in 1992, municipal and industrial use for 11.7% in 2012 compared with 11.8% in 1992, and environmental and other uses for 9.9% in 2012 compared with 8.5% in 1992 (CH Guadalquivir 1995). Thus, 8
|(9)<br>PCA<br>index<br>cols. 1-8|-0.348**<br>(0.148)<br>0.183**<br>(0.081)<br>0.19<br>-0.165<br>0.25<br>0.118<br>-0.448|2256<br>0.05|
|---|---|---|
|(8)<br>coping<br><br>strategy<br>index|-0.712<br>(0.586)<br>-0.112<br>(0.288)<br>0.21<br>-0.823<br>0.17<br>0.336<br>-1.982|2274<br>4.57<br>les.|
|(7)<br>food<br>d<br>insecurity<br>score|-0.041<br>(0.097)<br>-0.118*<br>(0.062)<br>0.23<br>-0.158<br>0.09<br>0.025<br>-0.342|2274<br>2.34<br> 90th percenti|
|(6)<br>any day<br>w/reduce<br>meals|0.016<br>(0.030)<br>-0.012<br>(0.017)<br>0.19<br>0.005<br>0.88<br>0.064<br>-0.055|2274<br>0.22<br> 10th and|
|(5)<br># food<br>groups<br>consumed|-0.380***<br>(0.098)<br>0.170**<br>(0.062)<br>0.13<br>-0.210<br>0.02<br>-0.031<br>-0.389|2270<br>5.08<br>_<_0_._001<br>sorized at the|
|(4)<br>per adult<br>equivalent<br>calories|-1762.095<br>(1841.631)<br>405.184<br>(1382.837)<br>0.17<br>-1356.911<br>0.43<br>2022.784<br>-4736.605|2274<br>64728.56<br>_<_0_._05_, ∗∗∗p _<br>lories are Win|
|(3)<br>food<br>cons.<br>score|-3.864**<br>(1.279)<br>1.345*<br>(0.780)<br>0.12<br>-2.519<br>0.04<br>-0.169<br>-4.870|2270<br>45.33<br>0_._10_, ∗∗p _<br>. Total ca|
|(2)<br>ln (p.c.<br>food cons.)<br>last week|-0.128**<br>(0.051)<br>0.047<br>(0.034)<br>0.15<br>-0.082<br>0.08<br>0.009<br>-0.173|2274<br>6.16<br>A level. _∗p <_ <br>ns 6, 7, and 8<br>ed efects.|
|(1)<br>ln (p.c.<br>food exp.)<br>last week|-0.193**<br>(0.089)<br>0.066<br>(0.053)<br>0.09<br>-0.127<br> top-ups<br>0.13<br>0.037<br>-0.291|2260<br>5.27<br>rs clustered at the E<br>ood security of colum<br> week-of-interview fx|
|pendent variable:|P<br>P * Top-up<br>squared<br>tal efect on top-ups<br>value for non-zero efect on <br>per bound (total efect)<br>wer bound (total efect)|servations<br>an of dep. var.<br>control group<br>S estimates. Standard erro<br>gher values indicate worse f<br>timates include district and|
|De|PW<br>PW<br>R-<br>To<br>P-<br>Up<br>Lo|Ob<br>Me<br>in <br>OL<br>Hi<br>Es|

36
# **Table 6A: Bank Ownership, Profitability, Interest Margin and Securities (Unweighted)**

Regressions are estimated via OLS at bank level for the year 2001 to 2008. Robust t statistics in brackets and bank level clustered t statistics in parenthesis. All regressions control for time and country dummies. ***, ** and * represent significance at 1, 5 and 10% level respectively.

||1|2|3|4|5|6|7|
|---|---|---|---|---|---|---|---|
||**ROA **|**ROE **|**Net Interest**<br>**Margin (Total**<br>**Assets)**|**Interest**<br>**Income**<br>**to Assets**|**Interest**<br>**Expenses**<br>**to Assets**|**Total Securities**<br>**to Assets (All**<br>**Countries)**|**Total Securities to**<br>**Assets (Excluding**<br>**Lebanon)**|
|**Dummy Public Ownership**|**-0.513**|**-6.731**|**-0.061**|**0.35**|**0.067**|**5.304**|**6.579**|
||[2.86]***|[3.02]***|[0.48]|[1.38]|[0.41]|[2.55]**|[2.98]***|
||(1.76)*|(2.02)**|(0.26)|(0.67)|(0.22)|(1.18)|(1.32)|
|**Dummy Foreign Ownership**|**0.04**|**1.333**|**0.107**|**-0.105**|**-0.381**|**-2.337**|**-3.71**|
||[0.28]|[0.83]|[1.20]|[0.67]|[3.39]***|[2.28]**|[3.48]***|
||(0.18)|(0.53)|(0.65)|(0.37)|(1.70)*|(1.18)|(1.73)*|
|**Dummy Listed**|**0.269**|**3.654**|**0.241**|**0.038**|**-0.221**|**-3.317**|**-1.915**|
||[1.82]*|[2.70]***|[3.01]***|[0.27]|[1.96]*|[2.62]***|[1.47]|
||(1.39)|(1.99)**|(1.79)*|(0.16)|(1.19)|(1.57)|(0.91)|
|**Lag Total Assets (Log)**|**0.047**|**1.457**|**-0.025**|**-0.026**|**0.046**|**1.521**|**0.79**|
||[1.08]|[2.60]***|[0.77]|[0.44]|[1.20]|[3.50]***|[1.41]|
||(0.70)|(1.91)*|(0.45)|(0.28)|(0.79)|(1.92)*|(0.74)|
|**Non-Interest Income to Assets**|**0.296**|**2.197**|**-0.219**|**-0.291**|**-0.158**|**-1.248**|**-0.706**|
||[4.11]***|[3.24]***|[4.13]***|[2.76]***|[2.51]**|[2.01]**|[1.10]|
||(3.16)***|(2.63)***|(2.74)***|(2.10)**|(1.79)*|(1.38)|(0.76)|
|**Deposits to Assets**|**-0.014**|**0.151**|**-0.027**|**-0.01**|**0.025**|**0.02**|**0.133**|
||[2.21]**|[1.64]|[6.00]***|[1.44]|[4.07]***|[0.36]|[2.33]**|
||(1.78)*|(1.44)|(3.68)***|(0.91)|(2.54)**|(0.20)|(1.25)|
|**Loans to Assets**|**-0.008**|**-0.119**|**0.002**|**0.016**|**0.022**|||
||[2.34]**|[2.73]***|[0.67]|[2.21]**|[5.73]***|||
||(1.53)|(1.94)*|(0.43)|(1.31)|(3.59)***|||
|**Observations**|518|516|557|573|563|573|420|
|**R-squared**|0.18|0.15|0.41|0.43|0.73|0.49|0.42|
|**Number of Countries**|9|9|9|9|9|9|8|
|**Number of Banks**|118|119|119|120|118|117|83|

40