rafmacalaba/datause-encoder-data huggingface dataset:
While the table and figures above demonstrates unsustainable and unpredicted levels of military expenditure by Juba and Khartoum, Table 3 below shows how this military expenditure has substantial opportunity costs in terms of development. Progress on most of the Millennium Development Goals is not likely to be “on track” even for the peaceful and relatively better off states in the North, much less in the South, where the ten Southern states are demonstrably lagging in progress in all indicators behind their counterparts in the North and the mean for Sub-Saharan Africa.

# **Table** **<u>3:</u> Development Indicators,** **<u>Sudan vis-à-vis Sub-Saharan Africa</u>**

||**Sudan's**<br>**Millennium**<br>**Development**<br>**Goals**|**Average of**<br>**Northern**<br>**States**|**Average of**<br>**Southern**<br>**States**|**Sudan National**<br>**Average**<br>1|**Sub-**<br>**Saharan**<br>**Africa**|
|---|---|---|---|---|---|
|**Net primary school**<br>**attendance rate (%)**|100 (by 2015)|67|17|47|69|
|**Ratio of girls to boys**<br>**attending**<br>**primary education**|1 (by 2005)|0.91|0.76|0.85|0.89|
|**Measles immunization**<br>**coverage (%)**|2|73|43|61|64|
|**Proportion of births attended**||||||
|**by**<br>**skilled personnel (%)**|3|70|33|55|44|
|**Contraceptive prevalence (%)**|2|9|4|7|23|
|**Proportion of population**<br>**using effective malaria**<br>**prevention measures (%)**|2|32|22|28|N.A.|
|**Use of improved drinking**<br>**water (%)**|77.5 (by 2015)|55|62|58|56|
|**Use of improved sanitation**<br>**facilities(%)**||38|7|26|37|

_Source_ : Sudan Household Health Survey (2006), World Development Indicators (2007).

1 Simple averages are used for all indicators. Averages reported in the Sudan Household Health Survey are weighted by state, however, reliable estimates of weighted state populations are not available for use in calculation here. 2. The goal is to halt by 2015 and begin to reverse the spread of HIV/AIDS and the prevalence of malaria and other major diseases.

3. The goal is to reduce 1990 maternal mortality by two thirds.

39
<mark>(see, e.g., Wooldridge, 2010), which jointly estimates Equations (2) and (3) with maximum likelihood methods. We also report results from 2SLS and control function models to check for robustness. For interpretation, we use the results from the bivariate probit model.</mark>

# **<mark>5. Empirical results</mark>**

# _5.1. Testing the instrumental variable_

The RDD method relies on the assumption that the conditioning variable cut-off is exogenous or random. Thus, <mark>the key identification strategy in this paper is the exogeneity of being born in December versus January for children age 1-5. This instrumental variable is assumed to affect childcare attendance and not to be correlated with the error terms of the outcome equation. This means that the variable “born in December” can affect maternal employment only through the channel of childcare attendance (conditional on control variables). To test the exogeneity of being born in December, we first compare the proportion of children born in different months. F</mark> igure 5 shows that the proportion of children born in December is slightly lower than the proportion of children born in January. However, the difference is not statistically significant.<sup>13</sup> Thus there is no evidence that the threshold or cut-off is not random.

The month of birth is stated on birth certificates and is difficult to manipulate. Moreover, if a number of people manipulate their children’s birth month on their birth certificate in order to send their children to childcare earlier, the proportion of children with a reported birth month in December will be higher than the proportion of children with a reported birth month in January. Thus, there is no evidence of manipulation of birth months in our data.

The number of children born in October is larger than the number born in other months. Without in-depth studies on this issue, it is difficult to provide an accurate explanation for the higher rate of births in October. In Vietnam, traditional New Year festivals often take place in late January and early February. People have a long holiday

> 13 If the conditioning variable is continuous, we can use the manipulation test developed by McCrary (2008) to test the exogeneity of the conditioning variable. In our study, the conditioning variable is binary (children born in December versus January). Thus, we simply compare the proportion of children born in these two months.

13
# **Classification Results: Sentiment by Category**

|Date|Category|Negative|Neutral|Positive|
|---|---|---|---|---|
|January 2011|Distrust institutions|94.7|5.3|0.0|
|April 2011|Distrust institutions|98.4|1.6|0.0|
|May 2011|Distrust institutions|97.8|2.2|0.0|
|August 2011|Distrust institutions|92.9|7.1|0.0|
|May 2012|Distrust institutions|100.0|0.0|0.0|
|August 2012|Distrust institutions|100.0|0.0|0.0|
|September 2013|Distrust institutions|100.0|0.0|0.0|
|January 2011|Irrelevant|38.5|61.5|0.0|
|April 2011|Irrelevant|66.2|33.8|0.0|
|May 2011|Irrelevant|66.7|33.3|0.0|
|August 2011|Irrelevant|62.5|37.5|0.0|
|May 2012|Irrelevant|41.3|58.7|0.0|
|August 2012|Irrelevant|45.7|54.3|0.0|
|September 2013|Irrelevant|11.8|88.2|0.0|
|January 2011|Lack of information|96.7|3.3|0.0|
|April 2011|Lack of information|96.2|3.8|0.0|
|May 2011|Lack of information|100.0|0.0|0.0|
|August 2011|Lack of information|100.0|0.0|0.0|
|May 2012|Lack of information|89.5|10.5|0.0|
|August 2012|Lack of information|94.1|5.9|0.0|
|September 2013|Lack of information|80.6|19.4|0.0|
|January 2011|Other|18.2|81.8|0.0|
|April 2011|Other|31.3|68.7|0.0|
|May 2011|Other|33.6|66.4|0.0|
|August 2011|Other|34.1|65.9|0.0|
|May 2012|Other|16.3|83.7|0.0|
|August 2012|Other|15.5|84.5|0.0|
|September 2013|Other|6.3|93.7|0.0|
|January 2011|Partisanship|49.0|51.0|0.0|
|April 2011|Partisanship|42.3|57.7|0.0|
|May 2011|Partisanship|79.2|20.8|0.0|
|August 2011|Partisanship|100.0|0.0|0.0|
|May 2012|Partisanship|30.2|69.8|0.0|
|August 2012|Partisanship|42.9|57.1|0.0|
|September 2013|Partisanship|76.0|24.0|0.0|
|January 2011|Personal economic impact|100.0|0.0|0.0|
|April 2011|Personal economic impact|90.5|9.5|0.0|
|May 2011|Personal economic impact|91.1|8.9|0.0|
|August 2011|Personal economic impact|83.3|16.7|0.0|
|May 2012|Personal economic impact|100.0|0.0|0.0|
|August 2012|Personal economic impact|100.0|0.0|0.0|
|September 2013|Personal economic impact|85.7|14.3|0.0|

Table 12: The classification results for the sentiment of all the tweets by category. Source: Author’s calculations.

26
conflict, depending on the type of resource being exploited: resources that increase local wages (such as agricultural products) may decrease conflict, while resources that create appropriable rents (such as oil, diamonds, and minerals) may encourage it.

For instance, Angrist and Kugler (2008) find that an increase in coca prices in Colombia led to modest local economic gains, but increased violence in rural areas. They interpret these findings as evidence that the financial opportunities provided by coca fueled internal conflict. Gawande, Kapur, and Satyanath (2012) examine the Naxalite conflict in India. They find that negative agricultural shocks are associated with an intensification of conflict. This is consistent with the opportunity cost effect.

Dube and Vargas (2013) provide convincing evidence that both the opportunity and rapacity effects are important. They use data at t h e municipality level from Colombia and exploit variation in commodity prices. The main idea is that some commodities, such as oil, are more likely to generate rents than changes in local wages, and thus affect conflict mostly through a rapacity effect. In contrast, agricultural commodities, like coffee, may have relatively more influence on wages than rents and thus mostly affect the opportunity cost of conflict. This study finds that increases in oil, coal, and gold prices are associated with an intensification of conflict. In contrast, the increase in international prices of agricultural products, such as banana, coffee, palm, sugar, and tobacco, decrease it.

# **3.2.4 Impact of mining-related pollution**

There is a vast literature highlighting the potential for extractive industries, such as mining, to pollute the environment. Similarly, there are several studies documenting the negative effect of pollution on human health and, through that channel, on labor supply and labor productivity (see references above). Despite these findings, there is little empirical work directly examining the socioeconomic impacts of mining-related pollution (table AB.4).

Rau, Reyes, and Urzúa (2013) examine the impact of mining-related pollution on educational achievement. They focus on a case of environmental negligence in northern Chile in which hundreds of houses were built in the proximity of a deposit of mineral waste, which had hazardous levels of lead and other heavy metals. This study finds that children living in the proximity of the mineral waste had higher concentrations of lead in their blood, and worse academic performance. They estimate that this translates into a significant loss of 30
e25-

# **THE NErHERLANDS**

Since 1895 the Netherlands development policy has emphasized well-integrated multisectoral development. The primary categories of assistance are rural and industrial development. Components such as health care, family planning or support for women in development are accepted as contributing elements but not as sectoral objectives. Health projects are identifiable for statistical purposes, but health is not programed as a separate sector. The budget categories include:

# Category 1: Sector Programs

- Rural Development -- food production, nutrition, food aid, infrastructure (including water, health care, family planning), energy and ecology, technical training, education, research (including health).

- * Industrial Development

# Category HI: Country and Regional Programs

- e Health, population, women in development (WID).

# Category III: Multilateral Programs

- Health, population, WID.

# Category IV: Joint Financing and Volunteer Program

- Health population financing through major NGO groups.

# Category V: Netherlands Antilles and Aruba.

# Category VI: Other Expenditures.

- Refugee assistance.

Budget categories follow development policy. Financial statistics in health are not routinely identified or collected except for bilateral medical and health care data reported under the special criteria of the DAC1OECD Creditor Reporting System. It is central to Dutch development policy that improvement of health in the context of development is a multisectoral consequence and cannot be defined in narrow terms. Dutch policy emphasizes the importance of responding to articulated demand for development assistance. While primary health care, for example, is strongly supported, so are programs which influence health status such as literacy, education, employment, water supply and rural developmvr;. Under this policy it is unlikely that the Dutch would support SM as a separate vertical program.

In view of longstanding Government support for primary health care and family planning, it is consistent that the Dutch have supported the Nairobi Safe Motherhood Conference and the SM workshops implemented by Family Care International. These are considered important activities for
# FIGURE 6 Global inflation factors: various inflation measures

A. Global factors: Headline CPI, PPI, and import price B. Global factors: Headline CPI, core CPI, and GDP deflator <!-- Start of picture text --> Percent<br>5 IMP PPI CPI<br>4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>1971 1976 1981 1986 1991 1996 2001 2006 2011 2016<br><!-- End of picture text --> <!-- Start of picture text --> Percent<br>6 DEF CORE CPI<br>5<br>4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>1971 1976 1981 1986 1991 1996 2001 2006 2011 2016<br><!-- End of picture text --> C. Contribution of global and group-specific factors to inflation variation: Various measures D. Correlations of headline and core CPI inflation factors with other global factors <!-- Start of picture text --> Percent<br>70<br>Global factor<br>60 Group factor<br>50<br>40<br>30<br>20<br>10<br>0<br>IMP PPI CPI DEF CORE<br><!-- End of picture text --> <!-- Start of picture text --> Correlation 1970-85 2001-16<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Import prices PPI Import prices PPI<br>Core CPI Headline CPI<br><!-- End of picture text --> Notes: The global and group inflation factors are estimated with a baseline dynamic factor model (two-factor model) using detrended inflation rates in 38 countries (25 advanced economies and 13 EMDEs) for 1970-2016. CORE = core CPI; CPI = headline consumer price index; DEF = GDP deflator; GDP = gross domestic product; IMP = import prices; PPI = producer price index.

22
(table 13.13); extraction of natural gas (in millions of cubic meters for the year 2000, table 13.14); thousands of tons of mined coal for the year 2000 (table 13.15). This allows us to calculate the value of industry output by sector and region. For each industrial sector, we then proportionally scaled the value of region level output so that the sum of industrial output across all regions was equal to the value of national output of the sector from the national input-output table.

We infer regional demand (and supply) of services, assuming that intermediate and final demand for services share a common intensity of demand in all regions as in the national model. For example, if telecommunications costs are x percent of the costs of nonferrous metals production in the national model, we assume that telecommunications costs are x percent of nonferrous metals costs in each of the regions. Demand for telecommunications from non-ferrous metals will differ across regions, however, since the share of total output attributable to non-ferrous metals differs across regions.

We have total external exports and imports by region, as well as the commodity structure of external exports and imports by region for the year 2001 (tables 23.1 and 23.2). We also have unpublished data supplied to us by Rosstat on inter-region exports and imports by sector. That is, for each of over 250 key commodities, we have an 88 by 88 matrix of bilateral trade flows among the regions.

Supply and demand balance by region and by commodity requires adjustment of trade intensities. These adjustments assure that region exports and imports in aggregate are consistent with national import and export values, we have to adjust the region import and export intensities. We did this using a methodology that minimized the sum of squares of the difference between the original data on exports and imports and the adjusted exports and imports data, subject to the constraints of supply-demand balance and consistency with the national model data. Since we had greater confidence in the validity of the region output data than the inter-region trade flow matrix, in this optimization process, we fixed the output levels of the regions at the levels we had calculated above. We do not need to make any other adjustments, as the production technologies are assumed consistent across the regions. See appendix C of Rutherford and Tarr (2006) for the GAMS code that created the regional inputoutput tables.

Since in every step of the process, we calculated region shares of the national inputoutput table, the process yields a set of 88 regional input-output tables which portray a regional disaggregation of the national input-output table. That is, summing over all the 88 input-output tables will yield the national input-output table, including wholesale and retail distribution margins, investment demand and government expenditure. Crucially, we may aggregate the 88 regions into a set of non-overlapping subsets and any such aggregation will 20
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- Dillon, Brian, and Christopher B Barrett. 2014. “Agricultural Factor Markets in Sub-Saharan Africa An Updated View with Formal Tests for Market Failure.” _World Bank Policy Research Working Paper_ No. 7117.

- Foster, AD, and MR Rosenzweig. 2003. “Agricultural Development, Industrialization and Rural Inequality.” Mimeo. Harvard University.

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- ———. 2014b. “The Agricultural Productivity Gap.” _Quarterly Journal of Economics_ 129 (2) (May 1): 939–993.

- Haggblade, Steven, P. B. Hazell, and T. A. Reardon. 2007. “Structural Transformation of the Rural Nonfarm Economy.” In _Transforming the Rural Nonfarm Economy: Opportunities and Threats in the Developing World_ , edited by S. Haggblade, P. B. Hazell, and T. A. Reardon, Chapter 4. Baltimore, Md.: Johns Hopkins University Press.

- Haggblade, Steven, Peter Hazell, and Thomas Reardon. 2010. “The Rural Non-Farm Economy: Prospects for Growth and Poverty Reduction.” _World Development_ 38 (10): 1429–1441.

- Headey, DD, and TS Jayne. 2014. “Adaptation to Land Constraints: Is Africa Different?” _Food Policy_ 48: 18–33.

Hsiang, Solomon M. 2012. “Visually-Weighted Regression.” Working Paper and Stata Code.

- Lele, Uma, Manmohan Agarwal, and Sambuddha Goswami. 2013. “Lessons of the Global Structural Transformation Experience for the East African Community.” East African Community International Symposium and Exhibition on Agriculture. Kampala, Uganda.

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reliance on proxy respondents leaves significant room for improvement. In the case of the DHS and the WEAI, the respondents are women and men of reproductive age, and the principal man and woman, respectively. The disadvantage, in turn, is that we do not have information on ownership of and rights to physical and financial assets among nonrespondents.<sup>20</sup> There are a handful of studies that allow us to explore the implications of interviewing multiple interview targets in the same household while collecting information on individual asset ownership and control. Jacobs and Kes (2014) report that the majority of couples in the study sites in Uganda and South Africa disagree on whether land or housing is owned jointly by couples. Women are more likely than men to report joint ownership of these assets. Twyman et al. (2015) find higher levels of agreement on who jointly owns land in Ecuador, with couples agreeing that 79 percent of parcels are owned jointly. They note the existing disagreement stems from women owners reporting joint ownership on parcels to which men claim sole ownership.<sup>21</sup> Unfortunately, there is no “gold standard” against which to measure these different approaches. For a few large assets, such as land, housing, and potentially vehicles, there may be legal records identifying the owner. However, it has not yet been possible to match administrative records with the survey results and there are a multitude of reasons why a gold standard measurement approach anchored in administrative records could prove to be elusive (Kilic and Moylan, 2016).

Qualitative fieldwork conducted in the Gender Asset Gap Project found that an individual was more likely to know about the physical assets owned by other household members than about the financial assets held by others. Thus, the survey instruments treated physical and financial assets differently. While information on physical assets were collected in both the household and individual survey instruments, data on financial assets were only collected in the individual survey instrument; each of the two respondents was asked only about his or her own financial assets, and whether these were held individually or jointly with someone else.

Kilic and Moylan (2016) report the results from MEXA, which sought to explore, in the context of a randomized household survey experiment the impacts of different approaches to respondent selection and questionnaire design on the analysis of ownership of and rights to physical and financial asset assets. The authors find that with respect to the standard

> 20 While it would be possible to ask a proxy respondent to provide information on each household member, this has not yet been done in the context of the DHS and the WEAI.

> 21 An open empirical question is whether women are more likely to report joint ownership if the legal system supports and enforces it, and are less likely to do so where the enforcement is weak and customary law prevails, as in Africa.

22