rafmacalaba/datause-encoder-data huggingface dataset:
in the Southeastern region after São Paulo and the sixth lowest in the country. The state achieved considerable reductions between 2004 and 2010, although it was surpassed by São Paulo, which currently has the lowest rate in the region. It is, nonetheless, important to highlight that the introduction of performance-related pay in 2004 coincided with the change in homicide trends, but recently the trend seems to have decelerated, even recording a small increase in 2011.

Between 1999 and 2004, the homicide rate had more than doubled in the state of Minas Gerais, reaching the highest point in the state’s contemporary history. During that period, the state trend was going in the opposite direction than that of the other three states, which had started to see declining homicide levels since 2002. But comparing with the regional average is difficult because Rio de Janeiro particularly high rates, driving the average up.

In the case of Rio de Janeiro, homicide rates have been in decline since 2002, and the gap with the regional average is closing. The rate was cut in half in a short period. But despite the sizable gains, homicide levels remain high.

**_Education sector_ .** The national average IDEB score for public secondary schools has improved at an average of 4.3 percent every two years since the index was adopted in 2005. However, the improvement observed in the 2005–2013 period was more modest than that observed in primary education.  Between 2007 and 2013, Goiás, Pernambuco, and Rio de Janeiro had much larger improvements in their scores than other states that had similar scores at the beginning of the period (Figure 9).  Many observed no change or declining scores.  Still, among others, Ceará and Minas Gerais have scores above the predicted value for their GDP, while Rio de Janeiro and most southern and northeastern states still falls far below the score predicted for its income level.

**Figure 9: IDEB Public Secondary School Score Change in 2007–2013 and IDEB Score in 2007**

**Figure 10: IDEB Public Secondary School Score and GDP per Capita, 2013**

<!-- Start of picture text --> GO<br>PE<br>RJ<br>PI GO<br>PA SE RS SP<br>RO SPRS PE MG SCRJ<br>SE APNEAM NR MWCE ES RO MS PRES<br>RN PB RRTO DF MG CE AC DF<br>MA AL BA BR AC MS TO RR<br>SO PI PB AM<br>SC AP<br>MT PR MA SE BA<br>RNPA MT<br>AL<br>2 2.5 3 3.5 4 8 8.5 9 9.5 10 10.5<br>IDEB Secondary School Score 2007 LN GDP per Capita (constant 2000)<br>95% CI Fitted values 95% CI Fitted values<br>Difference IDEB Secondary School Score 2007-2013 IDEB Secondary School<br>1 4.5<br>4<br>.5<br>3.5<br>0<br>3<br>-.5 2.5<br><!-- End of picture text --> _Source_ : INEP 2013.

_Source_ : INEP 2013.

19
while the inefficiency of the aging heavy industries results in the relative decline of the Northern provinces.

# **3. THE SPATIAL CONCENTRATION OF TECHNOLOGY AND LABOR INTENSIVE INDUSTRIES: A TEST WITH PANEL DATA**

# **3.1. Statistics**

The strong overall industrial growth in China masks overwhelming differences at the sectorial level. The Chinese industrial economic statistical yearbooks classify the industries in 30 groups<sup>13</sup> . We divide these 30 industries into 2 large categories: 4 high-tech industries (electronic and telecommunications equipment manufacturing; instruments, meters and other measuring equipment manufacturing; medical and pharmaceutical products; and electric equipment and machinery manufacturing) as category A, and the other 26 industries as category B. Among the 26 industries of category B, we further divide them into two subcategories: 9 industries as category C “primary materials, energy and foodstuffs”, and the other 17 industries as category D “labor intensive industries”.<sup>14</sup> We measure the importance of a category of industry in time _t_ , noted as _ICATcate_ , _t_<sup>, by the ratio of the value-added of all the industries of the category in</sup> question (A, B, C, or D) to the value-added of the entire 30 industries:

> where _j_ stands for the provinces, _i_ the industries, _t_ the year, _cate_ the category, and _va_ the value–added of the industry.

Our results show that the high-tech industries grew more rapidly than the labor intensive industries. In 1988, the value added of the industries of category A represented only 12% of the total value-added, while it increased to 16% in 1997. To a large extent, the growth of the industries of category B is pulled up by that of the industries of category C – the value-added of the industries of category C was 24% of the entire category B in 1988, and 38% in 1997. In contrast, the valueadded of the industries of category D was more than two-thirds of that of all the industries in 1988, but only about a half in 1997.

The relative decline of the labor intensive industries and the rapid growth of the high-tech industries imply an upgrade of the production structure in China. However, this upgrade is not equally distributed across the entire territory. The

> 13 We focus our analysis using the data of 1988-1994 and 1997 for those of 1995 and 1996 are not available, and the industry classification changes after 1998. 14 See more detail in Table 2.

10
may have achieved due to the absence of men during conflict periods can erode in the post‐conflict period and often do not result in a comparable increase in social empowerment (Justino 2009). Calderón, Gáfaro and Ibáñez (2011) look at displacement in Colombia as an indirect impact of conflict and find that that despite a net increase in earnings, bargaining power of displaced women is not statistically different from the control group. In contrast, domestic violence is larger for displaced women, who in turn resort to violent punishment against their children. Wang and Zhou (2016) find that displacement in China during Mao’s mass Send‐Down Movement had a significantly negative effect on women and men’s nonmaterial well‐being, which they measure by marriage outcomes, social network, and happiness.

Incidence of gender‐based violence, while a prevalent global challenge in many environments even before the onset of violence, often worsens in the context of conflict and instability (Anderlini 2010; Buvinic et al 2013; Strachan and Haider 2015). Micro‐level quantitative analysis on the impact of conflict on women and girls’ vulnerability to gender‐based violence (GBV) is relatively robust. Ostby (2016) explores links between armed conflict and intimate partner violence and finds a significantly damaging effect of armed conflict on rates of domestic violence. Dijkman, Bijleveld, Verwimp (2014) explore the impact of conflict exposure on GBV across IDP camps, areas of return, and households of various income and education levels, where wealthier educated women had a higher likelihood of falling victim to GBV. Women in South Sudan have also experienced varying dimensions of gender‐ based violence (IRC and GWI, 2017; CARE, 2014; Elia, 2007; Elia, 2007). A new study highlights that rates of varying forms of violence against women and girls in South Sudan is among the highest in the world; conducted by International Rescue Committee and the Global Women’s Institute (2017), the study found that 65 percent of the women surveyed experienced some form of sexual or physical violence in their lifetime, double the global average. While intimate partner violence was most commonly reported, 33 percent of women reported experiencing sexual assault from a non‐partner, frequently linked to displacement, abduction or raids.

The international donor community has traditionally come forth with descriptive reports on the effect of armed conflict on the health and well‐being of both women and girls, which includes sections on GBV and security (UNICEF 2005; Dwyer and Cagoco‐Guiam 2011; ICRC 2001; UNFPA 2002). These studies are mostly aimed towards informing aid programming and Disarmament, Demobilization, and Reintegration (DDR) initiatives and lack the trend analysis and controlling for bias associated with quantitative analysis. Several qualitative and quantitative studies using smaller samples of respondents focus on ex‐combatant motivation and reintegration (Miranda 2003; Keairns 2003; 7
FIGURE 7. POSTNEONATAL MORTALITY TRENDS BY INCOME GROUP IN COTE D'IVOIRE, 1970-86 (LSMS 1985/1986) <!-- Start of picture text --> lHigh income<br>j'~<br>so , - All income groups<br>: | /. Low income N<br>01?O aS?, 1950 1917<br><!-- End of picture text -->

Year (1) NOTE: (1) PNMR 5 - YEAR MOVING AVERAGE
Revoredo and Fletcher (2002) recently addressed theses specific questions. They analyze both production instability (originated in producer-exporting countries) and consumption instability (originated in consumer-importing countries) and find that steady expansion of Chinese exports, which are negatively correlated with exports from US and Argentina, have been a stabilizing force in the second half of the 1990s. This occurs in spite of the fact that India, Argentina, and South Africa now transmit a higher proportion of their supply shocks to the world market (Revoredo and Fletcher 2002). It seems therefore that substitution between Chinese and US groundnuts has increased in recent years, but detailed data on substitution in world markets to substantiate this inference is as yet inconclusive.

Regarding the groundnut oil market, the influence of Senegal on world prices remains significant. In 2000/01, Senegal exported about 100,000 metric tons of groundnut oil, representing one-third of the world’s export and more than 60 percent of demand from EU, Senegal’s main export market. While 2000/01 was an exceptional production year, Granger causality tests strongly indicate that variations in Senegal’s exports Granger-caused international prices and the reverse was not true (Table A6).

**Figure 7. International prices of groundnuts, Rotterdam, CIF, $/metric ton**

<!-- Start of picture text --> 1400<br>Prices are Non Stationary Period of Stationary Prices<br>and Not Integrated (tendency to revert to their mean values)<br>1200<br>Edible groundnut<br>1000<br>800<br>Groundnut oil<br>600<br>400<br>200<br>70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 00<br>Edible Groundnut, US Shelled Runner 40/50 (CIF Rotterdam)<br>Groundnut Oil, any origin (CIF Rotterdam)<br><!-- End of picture text --> Finally as Figure A1 (Annex) shows, groundnut oil markets are broadly integrated with that of other vegetable oils (soy oil, rapeseed oil, palm oil, and sunflower oil), however, the integration between oilseeds markets other than groundnuts seems to be much stronger.

# **III. DOMESTIC GROUNDNUT POLICIES OF MAJOR COUNTRIES IN WORLD MARKETS**

This section reviews the groundnut policies of the US, China, India, Argentina and SSA. Since domestic producer support/taxation and trade policies essentially determine excess supply and trade flows, it is necessary to examine them in some detail to anticipate the potential implications of policy changes on the distribution of gains/losses across countries.

# **Groundnut Policies in the United States**

Groundnut products are a minor sector nationally, but they are a key component of agriculture and rural development in the Southern part of the United States. Based on USDA 14
# OvERvIEW

_The Global Financial Inclusion (Global Findex) database provides in-depth data showing how people save, borrow, make payments, and manage risk. It is the world’s most comprehensive set of data providing consistent measures of people’s use of financial services across economies and over time. The 2014 Global Findex database provides more than 100 indicators, including by gender, age group, and household income. The data collection was carried out in partnership with the Gallup World Poll and with funding by the Bill & Melinda Gates Foundation. The indicators are based on interviews with about 150,000 nationally representative and randomly selected adults age 15 and above in more than 140 economies._ _The Global Findex database reveals that between 2011 and 2014, 700 million adults worldwide became account holders. The number of adults without an account—the unbanked—dropped by 20 percent to 2 billion. Globally, 62 percent of adults have an account, up from 51 percent in 2011._

## Financial inclusion and why it matters

Financial inclusion has been broadly recognized as critical in reducing poverty and achieving inclusive economic growth. Financial inclusion is not an end in itself, but a means to an end—there is growing evidence that it has substantial benefits for individuals. Studies show that when people participate in the financial system, they are better able to start and expand businesses, invest in education, manage risk, and absorb financial shocks.<sup>1</sup> Access to accounts and to savings and payment mechanisms increases savings, empowers women, and boosts productive investment and consumption. Access to credit also has positive effects on consumption—as well as on employment status and income and on some aspects of mental health and outlook.<sup>2</sup> The benefits go beyond individuals. Greater access to financial services for both individuals and firms may help reduce income inequality and accelerate economic growth.<sup>3</sup> Informed by a fast-growing body of knowledge and experience, policy makers and regulators are beginning to make expanding financial inclusion a priority in financial sector development. An increasing number of national governments are introducing comprehensive measures to improve access to and use of financial services. Among bank regulators in 143 jurisdictions, a recent survey found, 67 percent have a mandate to promote financial inclusion.<sup>4</sup> International organizations, including the G-20 and the World Bank, are also beginning to formulate strategies to promote financial inclusion. In recent years more than 50 countries have set formal targets and ambitious goals for financial inclusion.<sup>5</sup> Financial inclusion, at its most basic level, starts with having a bank account. But it doesn’t stop there—only with regular use do people fully benefit from having an account. Both these outcomes can be difficult to achieve. Digitizing payments can play an important part. Shifting payments such as wages or government transfers from cash into accounts can increase the number of adults with an account. And digitizing payments such as those

> THE GLOBAL FINDEX DATABASE 2
# FIGURE 10. RURAL AND URBAN POSTNEONATAL MORTALITY TRENDS IN COTE D'IVOIRE, 1970-86 (LSMS 1985/1986)

'S <!-- Start of picture text --> Rural<br>so0 _ All<br>%0 ._--- Urban<br>o :#" S oi<br>n~~~~~~~~~~~~~~~~~~~~~~~~~<br>0<br>0 Z a9<br><!-- End of picture text --> Year (1) NOTi: (1) PNM 5 -YEAR MOVING AVERAGE
# **2. Sample and Study Design**

Our study sample consists of over 1,300 urban poor households in Ahmedabad, a metropolitan city in the state of Gujarat, India. To manage the large sample size, we conducted the study in four waves. The sample size in each wave is reported in Table 1, Panel A. Respondents came from different _chalis_ (neighborhoods) which were mutually exclusive across waves. Furthermore, all respondents were associated with Saath, our non-government partner organization. About half were clients of Saath’s microfinance services, while the other half were participants of Saath’s other urban development programs such as livelihood training.

The study design consists of two main components. First, two-thirds of the study sample was randomly selected to take part in a comprehensive classroom-based financial education program. The remaining one-thirds of the sample was invited to attend a similar classroom-based health education program. Second, the design includes three additional treatments: pay for performance, financial counseling, and goal setting, which are described in detail below.

The pay for performance treatment was randomly assigned within the classroom treatment arms, wherein half the financial education group and half the health education group were offered financial incentives for correct answers on a follow-up knowledge test. The goal setting and financial counseling treatments were randomly assigned among the financial education treatment group.

Table 1, Panels B and C indicate the percentage of our sample that received these treatments. We also note that treatments were stratified in each wave based on the respondent’s gender, whether the respondent was currently a client of Saath microfinance, and their _chali_ (neighborhood).

Data collection included a comprehensive baseline survey followed by program implementation, a post-intervention knowledge survey administered in the respondents’ households three weeks after the final training session, and a final endline survey implemented ten months later.

7
approaches: macro studies, analyses of survey data and case studies. The dominant view from the recent empirical literature on the causes of civil war onset can be summarized as follows: A high risk of civil war is robustly associated with low and stagnating income, high dependence on natural resources and other insurgency promoting environmental and demographic factors - such as high and sparsely dispersed population or rough geographic terrain. On the other hand, factors associated with “grievance motives” – such as democracy, ethnic and religious fractionalization, ethnic dominance, ethnic and religions polarization - have been marginalized as causes of civil strife.

Bodea and Elbadawi (2007) reevaluate the effects of grievance factors on the occurrence of political violence. It also embeds the study of civil war in a more general analysis of varieties of violent contestation of political power within the borders of the state. Empirically, other possible manifestations of irregular and violent contestation of political power are coups and riots. Bodea and Elbadawi (2007) use a multinomial logit specification, in which the manifestations of violence range from lower intensity armed violence to coups and civil wars. If civil war is just one of the alternative expressions of violent contestation of political power, a multinomial model is more appropriate than the use of logit or probit models. Also, a multinomial framework is more appropriate than a bivariate model of domestic conflict (civil war, coups and armed violence lumped together) because it recognizes that different forms of conflict may have different determinants (Reagan and Norton, 2005; O’Brien, 2002). To investigate the determinants of conflict, Bodea and Elbadawi (2007) estimate a family of encompassing multinomial regressions using a global database from 1950-1999, accounting for three types of domestic violence (civil wars, coups and other violent outcomes) as well as a host of “grievance” and “opportunity” variables commonly analyzed in the recent empirical literature.<sup>9</sup> Social characteristics and political institutions play a large role both in explaining political violence (Bodea and Elbadawi, 2007) and as well as when we estimate the determinants of economic growth later on in this paper. Therefore, we go on and describe them in greater detail. We use the typology of democracy put forward by Goldstone et al. (2005) in their study of political instability. Specifically, we follow Goldstone et al. and rely on two underlying component variables of the Polity IV score: The measure of executive recruitment (exrec) and the competitiveness of political participation (parcomp) (Appendix

> 9 We control for the following variables:  Lagged log of GDP/capita ( _lgdpenl_ ); Log of lagged population ( _lpopl1_ ); Political instability ( _instab_ ); Countries that are oil exporters ( _oil_ ) and are geographically mountainous ( _lnmtn_ ).

11
Countries with higher logistics performance tend to have a clear definition of freight forwarder as a principal; or the countries have properly adjusted definitions in laws to the change in the freight forwarders’ role. This is why some countries have both definitions (□A [traditional definition] and □B [extended definition]) in their laws. It is fine to have two definitions since both types of operator exist on the ground. But it is necessary that regulations define who the freight forwarders are with which responsibility. Clear definition helps to establish their legal status and set the scope for liability. A form of affreightment for forwarding business is always indirect between shippers and carriers. Therefore, if operators want to pursue reliable door-to-door logistics services, the clear definition of freight forwarder as principal is crucial to avoid fragmentation of liability.

Figure 2: Difference in liability between freight forwarder as “agent” and freight forwarder as “principal” <!-- Start of picture text --> Carrier’s Liability<br>A<br>Forwarding contract Contract of carriage<br>Shipper / Freight<br>Carrier<br>Consignee Forwarder<br>By “Contracting Contract of carriage 1 Contract of carriage 2<br>Carriers”<br>B Freight Forwarder’s Liability<br>By “Performing<br>Contract of carriage<br>Carriers”<br><!-- End of picture text --> (Source: Schramm 2012 and author) In statutory laws, countries like Sudan and Pakistan do not have laws to define the difference between “agent” and “principal”. But in the Standard Trading Condition (STC) for contracting in Pakistan, the STC apparently distinguishes these two with a description of liability. Operators know the importance of clarifying their obligations to clients, but due to the lack of legal support, operators could be in trouble.

# **2) Who regulates freight forwarders and the industry?**

While an official definition of freight forwarders matters in laws to determine its legal status in their operations, the definition implies which ministry to supervise operators and what to be regulated. Regulators for the freight forwarding industry vary in countries; in the selected countries, regulators include ministries of transport, ministries of infrastructure, ministries of trade / commerce, self-regulators / 14