rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> 2000-2010 (in USD billions)<br><!-- End of picture text --> <!-- Start of picture text --> 70<br>China<br>60<br>50<br>40<br>30<br>Korea<br>20<br>10 India<br>0 Brazil<br>-10 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>-20<br>Source: UNCTAD 2011.<br><!-- End of picture text --> A large share of China‘s outward FDI is flowing to Africa<sup>24</sup> and had grown from a few hundred million dollars in 2002 to a cumulated amount of US$13 billion by the end of 2010, accounting for 4 percent of total OFDI stock (MOFCOM). About 22 percent of China‘s OFDI is presently concentrated in the manufacturing sectors, second only to 29% in mining. In 2010, Chinese firms also increased the number of jobs created overseas by 10 percent and capital investment by 2.5 percent. China ranked eighth in the world according to job creation overseas. (FDIintelligence, Special Report 2011). Two recent papers found that China‘s outward FDI has played a significant role in the growth of African countries (Weisbrod and Whalley 2011, and Mlachila and Takebe 2011).

Alternative sources note that:

- ―Manufacturing accounted for 26% of global projects, 48% of the global capital investment and 55% of global jobs creation in 2010,‖ ( _fDIintellingence_ , Financial Times unit).

- ―Indian, South Korean, Japanese and Chinese companies were among the fastest growing investors overseas in 2010‖ ( _fDIintellingence)._

> 24 Assessing the country/regional allocation of FDI is known to be difficult and alternative data sources must be used for cross-check.The official data underestimate the FDI in Africa because a large proportion of China‘s OFDI flows to Hong Kong SAR, China and several ―tax havens‖, and then being re-directed to various regions.  Another part of this outflow constitutes the so called ―round-tripping‖ investment back to China. The database by Heritage Foundation addressed this problem by tracing investments from these tax havens to its ultimate destination. For details see Derek Scissors (2010).

49
on assets and receivables. In Ras Gas' case, the contractual incompleteness primarily stems from the very specific nature of the required investment in LNG infrastructure (asset specificity), their location, and the long-term nature of the sales contract creating a bilateral monopoly.

In such circumstances, the project's investors bear the costs of unforeseen, i.e., noncontracted, contingencies and potential opportunistic behavior by the output buyers because the LNG supply contract as the major source of revenue effectively secures the debt. Consequently, we would expect capital markets to price non-contracted risks stemming from the supply contract. Using the structural default rate framework of Madan and Unal (2000), we analyze the evolution of Ras Gas credit spreads in terms of firm-specific risk variables, in particular the ultimate output buyer's credit spread (the Korea Electric Power Company, Kepco for short). Any material deterioration in the economic prospects of the output buyer, as measured by K.pco's credit spread, should increase the likelihood of breach of contract and, therefore, drive Ras Gas' spreads. Output prices as a major determinant of revenues are the second important contractrelated risk factor.

Ras Gas offers the unique opportunity of assessing the contractual dynamics arising from a bilateral monopoly on the basis of market information because both the seller (Ras Gas) and buyer (Kepco) have actively traded global bonds outstanding. Using a sample of daily data from January 1997 to March 2000, we relate Ras Gas credit spreads to their own lags, to current and lagged Kepco credit spreads, to a crude oil reference price used to settle LNG sales (13rent), Korean control variables, and the current and lagged returns on four regional emerging debt market indices (contagion and spillover effects) in a linear regression framework. We repeat the analysis in a simultaneous equation setting in order to distinguish direct effects of the risk factors 3
population size where men represent 52.7% of the total population and the within-group inequality which is higher for men.

# **5 Conclusion**

The Gini index can be formulated in many different forms. When expressed as sums across the population, several of these forms provide different values at the individual level or, in other words, have different individual functions. We asked the question of what form an individual function should take so as to represent the portion of inequality explained by each individual. Based on a set of desirable properties, we showed that only one form of function can be derived from a set of desirable properties. This is the original formulation of the Gini index as found in Kendall and Stuart (1958). We then illustrated the use of the individual contributions to the Gini for an exact decomposition of the Gini by population subgroups.

# **References**

- Bhattacharaya, N., and B. Mahalanobis (1967): “Regional Disparities in Household Consumption in India,” _Journal of the American Statistical Association_ , 22(317), 143– 161.

- Blackorby, C., D. Donaldson, and M. Auersperg (1981): “A New Procedure for the Measurement of Inequality Within and Among Population Subgroups,” _Canadian Journal of Economics_ , 14, 665–685.

- Bourguignon, F. (1979): “Decomposable Income Inequality Measures,” _Econometrica_ , 47, 901–902.

- Ceriani, L., and P. Verme (2012): “The origins of the Gini index: extracts from Variabilit`a e Mutabilit`a (1912) by Corrado Gini,” _The Journal of Economic Inequality_ , 10(3), 421–443.

10
# **The Impact of Remittances on Rural Poverty and Inequality in China**<sup>+</sup>

Nong Zhu (INRS-UCS, University of Quebec)<sup>*</sup> Xubei Luo (The World Bank, East Asia and Pacific Region)<sup>**</sup>

## **JEL Classification:** D63, O15, Q12

**Key words:** Migration, poverty, inequality, China

> + The authors would like to thank Deepak Bhattasali, Jean-Jacques Dethier, Chorching Goh, Sudarshan Gooptu, Yukon Huang, Louis Kuijs, Russell Pittman, Maurice Schiff, and Qi Ye for useful comments.

> * E-mail: nong.zhu@ucs.inrs.ca

> ** E-mail: xluo@worldbank.org
# **4b. Business objective By type**

<!-- Start of picture text --> Commercial Development<br>Maximize profits Social Objective<br>Other Multiple<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br><!-- End of picture text -->

**4c. Sectoral objective By type**

<!-- Start of picture text --> Commercial Development<br>SMEs financing Agriculture<br>Infrastructure Exim<br>Other Multiple mandates<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br><!-- End of picture text -->

# **By granular region**

<!-- Start of picture text --> Benchmark Europe WEST ECA EAST ECA<br>Maximize profits Social Objective<br>Other Multiple<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br><!-- End of picture text -->

# **By granular regions**

<!-- Start of picture text --> Benchmark Europe WEST ECA EAST ECA<br>SMEs financing Agriculture<br>Infrastructure Exim<br>Other Multiple mandates<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br><!-- End of picture text --> Looking at regions (Figure 4c), SME financing is an objective for more than half of the SOFIs in Benchmark Europe and West ECA under the more granular regional splits, while objectives are more balanced in East ECA (Figure 4c). The Exim function is particularly pronounced in West ECA, possibly aiming to reap more benefits from the geographic proximity to the EU. The mandates of SOFIs in Benchmark Europe appear to be less specific, with 4 out of 5 having a less specific – i.e. “Other” – mandate.

The ranking of target markets shows a diverse orientation of SOFIs across the three regions, particularly exemplified by Benchmark Europe (Figure 5c). Financing SMEs is the most important sector in West ECA. Financing in East ECA (which is driven by Commercial SOFIs) is dominated by large private corporations – probably driven by extensive privatization in the 1990s – and stateowned enterprises – possibly driven by common shareholding and state ownership in key strategic 17
the Arab Spring (2007-2010) and the period after the Arab Spring (2010-2012). For the regional analysis, we construct the synthetic panel using data from 16 countries, including the Arab Spring countries (Egypt, Libya, Syria, Tunisia, and Yemen) and other countries (Algeria, Bahrain, Iran, Iraq, Jordan, Kuwait, Lebanon, Morocco, Qatar, Saudi Arabia, and United Arab Emirates), but for the country-level analysis we focus on only nine countries with larger sample sizes (i.e., Algeria, Egypt, Iraq, Jordan, Lebanon, Morocco, Syria, Tunisia, and Yemen).<sup>13</sup> Life satisfaction in the Gallup Poll was measured using a question known as the “Cantril Ladder” or “Self-Anchoring Striving Scale” (Cantril, 1965). The question is stated as follows: _“Please imagine a ladder, with steps numbered from 0 at the bottom to 10 at the top. The top of the ladder represents the best possible life for you and the bottom of the ladder represents the worst possible life for you. On which step of the ladder would you say you personally feel you stand at this time?”_ The higher the given score is, the closer the respondent’s life is to his or her “ideal” life.<sup>14</sup>

# **III.2. Setting Welfare Thresholds**

Table 1 displays the country-specific thresholds for the bottom 40 percent and the middle 40 percent for both objective measures and subjective measures, based on our first definition of the welfare categories. As discussed above, these thresholds are obtained from the data in the first survey period and are then kept fixed in the second period for the welfare dynamics analysis. This table illustrates our earlier discussion that the bottom 40 percent represent the poor and the vulnerable in a country but to different extents across countries. For example, the bottom 40 percent in Egypt includes all the poor and some of the vulnerable to poverty as the cutoff line for

> 13 We do not make comparisons of monetary and subjective well-being indicators at the regional level due to the different time intervals and country compositions in the two databases.

> 14 The Gallup Poll also collects data on self-reported income. But since this variable is much less meticulously constructed compared to household consumption data in the household surveys, we do not construct synthetic panel data for income. We come back to this issue in the next section.

12
Méon, P. and Sekkat, K. (2012) “FDI waves, waves of neglect of political risk” _World Development_ , 40, 2194-2205.

Mina, W.M. (2012) “The institutional reforms debate and FDI flows to the MENA region: the “best” ensemble” _World Development_ , 40, 1798-1809.

Noorbakhsh, F., Paloni, A. and Youssef, A. (2001) “Human capital and FDI inflows to developing countries: new empirical evidence” _World Development_ , 29, 1593-1610.

Pennings, E. and Sleuwaegen, L. (2004) “The choice and timing of foreign direct investment under uncertainty” _Economic Modelling_ , 21, 1101-1115.

Van der Ploeg, F. and D. Rohner, (2012) "War and natural resource exploitation" _European Economic Review,_ 56(8), 1714-1729.

Rodrik, D. (2013) “Unconditional convergence in manufacturing” _Quarterly Journal of Economics_ , 128, 165-204.

Schneider, F. and Frey, B. (1985) “Economic and political determinants of foreign direct investment” _World Development_ 13, 161-175.

Urdal, H. (2006) “A Clash of Generations? Youth Bulges and Political Violence,” International Studies Quarterly, 50(3), 607-629.

Wang, X., Xu, L., and Zhu, T. (2012) “Foreign direct investment under a weak rule of law: theory and evidence from China” _Economics of Transition_ , 20, 401-424.

Wheeler, D. and Mody, A. (1992) “International investment location decisions: the case of U.S. firms” _Journal of International Economics_ , 33, 57-76.

World Bank (2012) _World Development Indicators_ Washington DC: World Bank.

24
Second, it provides evaluation of the effectiveness of an important component of Brazil’s policy aimed at curbing domestic violence, focusing on understanding heterogeneity in its effects and providing practical policy recommendations.

The paper is structured as follows: first, we describe Delegacias Especializadas do Atendimiento as Mulheres in Brazil and provide some summary statistics on the recent trends in violence in the country. Then we describe the data and estimation and results, with a particular focus on heterogeneity in the impacts. The fourth section concludes.

# **2. Women’s police stations in Brazil**

Delegacias Especializadas de Atendimento à Mulher (DEAMs), literally translated as specialized police service for women, constitute a part of the Civil Police and are focused on crimes such as rape and domestic violence that target women. Although not a legal requirement, the units are primarily staffed with women. Among other responsibilities, similar to those of a regular police station, DEAMs are charged with requesting urgent protective measures from the judge in case of domestic violence.

The first DEAM was established in Sao Paulo in 1985 (Jubb 2010). Other municipalities copied this model, and the network of DEAMs has been expanding ever since. Figure 2 illustrates expansion since 2000, when the first available municipal survey included questions on women’s police station existence. As the women’s movement grew and domestic violence became a national priority, in August of 2006 the law _Maria da Penha_ turned the establishment of DEAMs into a federal matter as financing and establishment was facilitated by the central government. By 2009, DEAMs had been established in just under 500 municipalities (out of 5,564), where 60% percent of population age 15 to 49 resides.

Theoretical literature on intra-household bargaining would suggest that DEAMs are likely to trigger a reduction in domestic violence, through dissolution of abusive relationships as well as through lowering conflict in continuing relationships. Considering that a Nash equilibrium marriage is determined by each individual’s options outside marriage, reservation utility is key to the partners’ marital bliss (Manser and Brown 1980). Through issuing restraining orders and assisting with the court procedures, DEAMs may be contributing to creating safer outside options for battered women: if they decide to leave the marriage, they are less likely to become a victim of the abuser’s retaliation. DEAMS do not just provide police protection, but also assist women 5
OIC countries. We also included the developing countries in our analysis as another benchmark to see whether the OIC countries are lagging behind the average developing economies on a global level in terms of financial inclusion enhancement.

Below, we discuss main gaps in current implementation of _Shariah_ -compliant microfinance, SME, and micro-insurance which are hindering their effectiveness to enhance the financial inclusion.

# **IV.1. Gaps in Financial Inclusion – Microfinance (MF)**

# **Strong demand for Islamic microfinance services in OIC countries is not met by the**

**supply.** An IFC-commissioned market studies in the MENA region suggest that between 20 and 60 percent of interviewed microenterprises and low income individuals indicated a preference for _Shariah_ -compliant products. For some, the lack of _Shariah_ -compliant products is an absolute constraint to financial access, while for others, this is a preference and they continue to use conventional financial services in the absence of competitive Islamic ones.<sup>27</sup> This gap is supported by statistics in

**Figure 3** , which suggests that despite the fact that OIC countries have more MF deposits and accounts per thousand adults, values of MFI deposits and loans as percentage of GDP are still much lower in OIC countries (0.61% and 0.79%) compared with developing countries (0.78% and 0.97%) and low income countries (0.92% and 1.19%).

# **Figure 3 Limited scale of Microfinance deposits and loans**

<!-- Start of picture text --> 68 1.19%<br>62<br>0.97%<br>0.92%<br>48<br>44 OIC countries 0.78% 0.79% OIC countries<br>35 0.61%<br>30<br>Developing Developing<br>countries countries<br>Low‐income Low‐income<br>countries countries<br>Deposits: Loans: Accounts Deposits: Value Loans: Value as<br>Accounts per per thousand as percentage percentage of<br>thousand adults adults of GDP GDP<br><!-- End of picture text -->

> 27 (Pearce 2010)

26
households.<sup>5</sup> Wealth data, such as the number of livestock, and information about the mother’s health and demographic status were also collected.

Height for age is generally accepted as a good indicator of the long-run nutritional status of children (Thomas, Lavy, Strauss, 1996). Children with low height for their age are considered stunted, an indicator of chronic malnutrition. According to Alderman (1993), _“Though a stunted child may have some catch up growth, for the most part, a child whose growth has faltered in the first two years of life will be on a different growth trajectory during the rest of his/her life.”_ We compute z-scores for each child’s height for age, where the z-score is defined as the difference between the value for an individual child and the median value of the reference population (in this case, the United States), divided by the standard deviation of that reference population (Cogill, 2001). On average, across households in all regions of Rwanda, children were two standard deviations below the average height of a child from the United States.

The UNICEF survey was linked to a prior agricultural survey that ran from October 1988 to September 1991 and was organized by the Rwandan Department of Agricultural Statistics (DSA) and Michigan State University.<sup>6</sup> The DSA survey was also nationally representative and collected agricultural and economic data (including household production, land holdings, and labor inputs) from half of the 2,496 rural households (see Clay, 1996 for additional details about the DSA survey).<sup>7</sup> For part of our analysis, we use the agricultural DSA survey to isolate the

> 5 We exclude children with height-for-age z-scores less than -6 or greater than 6 due to probable errors in either height or age data.

> 6 To ensure survey quality, DSA and UNICEF had one supervisor in the field per prefecture.  This supervisor monitored the performance of each of the interviewers in that prefecture.  The supervisor collected completed questionnaires, screened for inconsistencies, and went back to the farm households with the interviewers if needed. The office staff made frequent field visits to see how the questionnaires were understood and completed.  Data were checked again in the office, both visually for each questionnaire and with the help of data cleaning programs.

> 7 There are two caveats on how representative our findings are using the UNICEF data.  First, because of the DSA survey’s focus on agriculture, households who did not own or cultivate land (mostly young wage laborers no longer living with their parents) were excluded from the sample.  Given that these households generally are poorer than average, this may lead to an underestimate of the true effect of poverty on child health.  Second, since the UNICEF survey was linked to the prior DSA survey, which was panel data, we are concerned with possible attrition in the

8
5 savings--the General Director of Foreign Trade for Madagascar has stated that inspections **save** the count.y a minimum of FMG 500 million (about US$740,000) annually although no indication was given as to . w this estima-e was derived. 8 Finally, Madagascar's imports largely originate **in** a relatively few industrial countries (with France by far the most important, see Table 1) which, with the **exception of the United States,** compile **the detailed** value and quantity trade **statistics required for** this analysis.

**Table** 1: The Origin of Madagascar's **Imports** Major Product Groups, 1979-86

|Product Category (SITC)|Year|Att iTmports<br> _CS_miIlions)|<br> <br>Total|Share of<br>Europe<br>France|Madagas<br>an Commun<br>Germany|car's i<br>ity (10)<br>Italy|mports<br> <br> U.K.|origina<br>Japan|ting in<br>EFTA|(X)<br> <br>USA|
|---|---|---|---|---|---|---|---|---|---|---|
|**Ali Goods (0 to 9)**|**1986**<br>1985<br>1983<br>1981<br>1979|<br>**373.6**<br> <br>465.1<br> <br>411.5<br>473.0<br>698.4|**47.8**<br>46.6<br>51.6<br>61.9<br>48.5|31.3<br> <br>29.4<br>35.5<br>37.6<br>30.0|6.7<br>**5.6**<br>4.3<br>10.0<br>8.6|2.8<br>_2.0_<br>3.9<br>5.1<br>3.8|3.2<br>**4.6**<br>2.6<br>2.7<br>1.9|**6.5**<br>**2.7**<br>4.1<br>3.1<br>5.5|2.6<br>**1.7**<br>1.8<br>6.4<br>2.8|<br>10.7<br> <br>**16.2**<br> <br>8.6<br>4.4<br>10.0|
|Manufactures (6 to 8 Less 68)|1986<br>1985<br>|176.5<br>252.4<br>|70.1<br>58.S<br>|44.9<br>36.3<br>|10.2<br>7.4<br>|4.3<br>3.1<br>|6.5<br>7.0<br>|9.1<br>4.6<br>|2.7<br>1.5<br>|9.9<br>26.4|
||1983<br>1981<br>1979|193.1<br>293.7<br>377.3|72.0<br>66.4<br>56.9|49.0<br>40.7<br>:S.!|6.7<br>12.0<br>11.0|5.9<br>5.7<br>.. 1|5.3<br>3.2<br>3.1|3.2<br>4.5<br>9.4|1.9<br>9.2<br>3.5|11.1<br>3.7<br>16.5|
|ChemicaLs (5)|1986<br>1985<br>|47,0<br>49.0<br>|73.8<br>83.0<br>|52.1<br>57.6<br>|.S<br>.0<br>|3.8<br>2.0<br>|0.6<br>6.3<br>|1.5<br>1.6<br>|7.4<br>5.9<br>|4.9<br>3.2<br>|
||1983<br>|41.6<br>|82.5<br>|62.7<br>|7.2<br>|3.1<br>|1.0<br>|3.1|7.5<br>|1.4<br>|
||1Q81<br>|45.5<br>|84.2<br>|54.5<br>|14.3<br>|4.6|3.5|C.9<br>|5.9<br>|2.2<br>|
||1979|76.8|77.0|44.4|20.2|**4.3**|0.9|0.8|5.6|1.7|
|Foods (0+1+22+4)|1986<br>1985<br>|56.9<br>58.5<br>|19.5<br>25.0|12.5<br>9.9|1.0<br>0.9|0.7<br>0.2|0.2<br>1.2|13.5|0.5<br>0.3|33.4<br>9.6|
||1983<br>|82.3<br>|25.0<br>|12.0<br>|0.4<br>|3.0<br>|0.1<br>|11.4|0.2<br>|14.5<br>|
||1981<br>1979|66.6<br>105.5|52.3<br>34.6|33.5<br>27.1|5.0<br>0.6|0.6<br>0.2|1.7<br>0.9|-|0.9<br>0.3|6.4<br>4.2|

**Souree: Madagascar's reported** imports as recorded in United Nations Series 0 Trade Tapes.

> **8 Published statements** from Midi Madaaasikara, February 16, 1987.