rafmacalaba/datause-encoder-data huggingface dataset:
# **_Introduction_**

The ratio of boys to girls rose in the South Caucasus countries in the 1990s to levels second only to China. Many parents have clearly practiced sex-selection to ensure that they have a boy. The phenomenon was not manifest in the data from preceding decades, and has thus generated a good deal of debate about what may underlie it and above all what policies might help reduce it.

This paper begins by describing the levels and trends in sex-selection in these countries, based on the country census and vital registration data, and survey data.<sup>2</sup> After sharp rises in child sex ratios, the trend seems to be turning.

Section 2 discusses whether sex-selection would be expected to be found in this region. On the one hand, traditional kinship systems in the region are similar to those of other regions with strong son preference. On the other hand, the region has been over 50 percent urbanized for decades, and has a high level of gender equity along many dimensions: characteristics that have helped reduce sex-selection elsewhere in Asia.

What then might have caused the sudden rise in sex-selection in this region? As discussed in Section 3, the timing indicates it is related to the severe economic and other shocks experienced after the dissolution of the USSR. Similar responses have been seen in China in response to severe stresses. In the 1990s, an economic collapse in this region led to heavy out-migration in search of work, and sharp fertility decline. There was much forced migration as the newlyindependent countries re-aligned themselves along ethnic lines. Territorial conflicts added to the upheavals. Systems of service delivery, pensions, and safety nets, that had been highly dependent on transfers from other parts of the USSR, were badly affected. Levels of poverty were very high in the early 2000s. Under these circumstances, one coping mechanism is to fall back on traditional family ties for cooperation and support — and in this region traditional family ties are strongest among male kin, and a dependence on sons rather than daughters to support their parents.

Much progress has been made in rebuilding basic services and safety nets, and levels of poverty have come down substantially. This may have contributed to the recent trend of slowing and even reversal of levels of sex-selection.

Section 4 discusses policy options for the region. Options being considered in this region include restricting access to sex-selective technology — a policy strongly advocated by the Council of Europe (2011, 2014) — but there is little evidence from other settings that this has much effect. Media advocacy is being tried in some countries in the region such as Armenia, and while these efforts have not yet been formally evaluated there, they have been found to be very effective elsewhere in altering a wide range of norms and behaviors. Since the region evinced sex-selection only when people were faced with the chaos following the dissolution of the USSR, the key to reducing sex-selection would seem to lie in continued re-establishment of economic security, and confidence that the security will be sustained. Section 5 concludes.

2
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SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Information and communication technologies

Ampah, Mavis, Daniel Camos, Cecilia Briceño-Garmendia, Michael Minges, Maria Shkaratan, and Mark Williams. 2009. ―Information and Communications Technology in Sub-Saharan Africa: A Sector Review.‖ AICD Background Paper 10, Africa Region, World Bank, Washington, DC.

- Infoasaid. 2011. Infoasaid Sudan media and telecoms landscape guide. Infoasaid Sudan media and telecoms landscape guide

Lange. 2010. Sudan: Telecoms, Mobile Broadband and Forecasts. A Buddecomm report. Bucketty.

Mayer, Rebecca, Ken Figueredo, Mike Jensen, Tim Kelly, Richard Green, and Alvaro Federico Barra. 2009. ―Connecting the Continent: Costing the Needs for Spending on ICT Infrastructure in Africa.‖ AICD Background Paper 3, Africa Region, World Bank, Washington, DC.

Sudatel. 2009. Annual Report 2008. http://www.sudatel.sd/extra/annrepots/2008en.pdf . Khartoum.

# Irrigation

Svendsen, Mark, Mandy Ewing, and Siwa Msangi. 2008. ―Watermarks: Indicators of Irrigation Sector Performance in Africa.‖ AICD Background Paper 4, Africa Region, World Bank, Washington, DC.

You, L., C. Ringler, G. Nelson, U. Wood-Sichra, R. Robertson, S. Wood, G. Zhe, T. Zhu, and Y. Sun. 2009. ―Torrents and Trickles: Irrigation Spending Needs in Africa.‖ AICD Background Paper 9, Africa Region, World Bank, Washington, DC.

# Power

Briceño-Garmendia and Shkaratan. 2009. Power Tariffs: Caught between Cost Recovery and Affordability. AICD working paper, draft version. Washington DC

- Eberhard, Anton, Vivien Foster, Cecilia Briceño-Garmendia, Fatimata Ouedraogo, Daniel Camos, and Maria Shkaratan. 2008. ―Underpowered: The State of the Power Sector in Sub-Saharan Africa.‖ AICD Background Paper 6, Africa Region, World Bank, Washington, DC.

- EIA (United States Energy Information Administration). 2009. ―Sudan Country Energy Brief 2009.‖ <u>www.eia.doe.gov/cabs/Sudan/Background.html.</u>

- Foster, Vivien, and Jevgenijs Steinbuks. 2009. ―Paying the Price for Unreliable Power Supplies: In-House Generation of Electricity by Firms in Africa.‖ Policy Research Working Paper 4913, World Bank, Washington, DC.

Platts. 2009. WEPP database Rosnes, Orvika, and Haakon Vennemo. 2009. ―Powering Up: Costing Power Infrastructure Spending Needs in Sub-Saharan Africa.‖ AICD Background Paper 5, Africa Region, World Bank, Washington, DC.

> <sup>2</sup>
# integration.<sup>4</sup>

We choose to analyze the behavior of direct investment flows as opposed to portfolio equity flows, or total capital flows. This is done for several reasons. First, foreign direct investment is the fastest growing form of international capital **flows** and the most important form of private international financing for emerging market economies.<sup>5</sup> To the extent that multinationals are vehicles for improving risk sharing across countries (e.g. Errunza, Hogan and Hung<sup>**1999,**and Rowlandand Tesar2000),they may be partly</sup> responsible for the small price response of domestically listed firms after stock market liberalizations.

Second, low portfolio equity inflows may be a reflection of the weak development of the domestic capital markets as opposed to a lack of interest in pursuing greater diversification **by** foreign investors. In fact, many local companies in liberalizing countries have chosen to list elsewhere in order to reach foreign investors directly in their home market. In contrast, while in many ways foreign direct investment behaves as equity, it does not rely exclusively on the existence of developed domestic stock markets. Third, many foreign investors might choose to invest in emerging economies' equity **by** trading on country funds or ADRs listed in the New York or London markets. While the existence of these assets allows for the desired risk sharing (see Errunza, Hogan and Hung **1999),** it also makes it difficult to track portfolio capital flows across countries. This criticism is particularly relevant for the vastly used **US** Treasury International Capital dataset.

Our analysis relates the driving factors of foreign direct investment to the observed increased integration of capital markets. To motivate this connection we argue that **'A** recent study **by** Bekaert, Harvey and Lumsdaine (2002) finds that after a liberalization equity flows increase **by** an annualized rate of 1.4% of market capitalization, but drop **3** years after **by 0.55%. 5 As** a fraction of world gross domestic product, total private capital inflows to emerging markets grew from a steady annual average of **1.3** percent in the period from **1976** to **1989** to an annual average of 2.0 percent in the period from **1990** to 2000, representing a **56** percent increase. In this process foreign direct investment flows increased significantly more than portfolio equity or bond flows. The average annualized growth rate of foreign direct invetment was **17** percent in the period from 1976t o 2000 whereas the corresponding figure for all private non-direct investment flows was **7.6** percent (authors' computations using data from the World Bank World Development Indicators 2002 for low and middle income countries).

2
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correlation between actual and observed corrupt transactions. After all, where corruption is endemic, the judiciary may also be corrupt or vulnerable to threats (Van Aaken et al. 2010).<sup>4</sup> However, judicial statistics on _cross-border corruption_ , which refers to corrupt transactions between firms headquartered in a particular country (hence, the “headquarters country”) and public officials elsewhere (the “foreign country”) permit the computation of a valid cross-national corruption index. A valid index in the lexicon of this article means one that increases along with the probability that a transaction is corrupt. The intuition behind such index is simple. To illustrate, the instances of corruption involving US firms caught bribing public officials abroad are not informative of the level of corruption _in the United States_ ; however, the _distribution_ of these cases, with respect to the nationalities of the foreign public officials involved, reveals the relative level of corruption _abroad_ . For example, if half of all the cases concerning US firms are seen to involve Chinese public officials, enough evidence would be provided that public sector corruption _in China_ is relatively high. Obviously, such a conclusion should take into account the extent of the subject countries’ bilateral transactions (the US interacts more often with China than, say, Denmark). As a suitable proxy for the number of bilateral transactions that could be vulnerable to corruption, export figures from the headquarters to the foreign country are used. In other words, the proposed measure uses information on the spatial distribution of cases enforced in a given country to evaluate levels of corruption _in all other_ countries. Moreover, it considers cases arising not only from a single jurisdiction, but from all relevant ones.<sup>5</sup> The necessary data are derived from reports of cross-border corruption ensuing from the criminalization of foreign bribery in several jurisdictions. With the passage of the Foreign Corrupt Practices Act (FCPA) of 1977, the United States became the first country to criminalize foreign

> 4 Judicial statistics have been used to measure corruption in studies at the sub-national level, where the assumption of spatially homogenous enforcement is plausible. See for instance, Glaeser and Saks (2006), Goel and Nelson (2011), Fisman and Gatti (2002), Alt and Lassen (2012), for the US; Chang et al. (2010), and Golden and Picci (2008).

> 5 This paper is arguably the first to propose an index of corruption based on cross-border incidents of corruption. McLean (2012) contemplates the possibility of computing such an index; see in particular his Figure 1 3
**Table 3: Drought Impacts on Child Anthropometrics (OLS with Cluster x Month Fixed Effects)**

|**VARIABLES**|(1)<br>**Stunted**|(2)<br>**Underweight**|(3)<br>**Wasted**|(4)<br>**Stunted &**<br>**Underwt**|(5)<br>**Any**|
|---|---|---|---|---|---|
|Drought: Birth Season|0.07***|0.03***|0.00|0.03***|0.06***|
||[5.43]|[2.95]|[0.40]|[3.96]|[4.52]|
|Drought: Season after Birth|0.04***|0.01|-0.01|0.02*|0.03**|
||[3.07]|[0.57]|[-1.28]|[1.77]|[2.43]|
|Mother ht between 152 and 160 cm|0.13***|0.06***|0.01**|0.06***|0.13***|
||[10.75]|[7.79]|[2.39]|[7.97]|[10.83]|
|Mother ht below 152 cm|0.25***|0.13***|0.01*|0.12***|0.24***|
||[17.56]|[11.00]|[1.86]|[11.53]|[16.94]|
|Mother under 19 yrs|0.05***|0.03*|-0.01|0.02*|0.05**|
||[2.72]|[1.87]|[-0.76]|[1.83]|[2.46]|
|Age:7-24 months|0.19***|0.06***|-0.01|0.07***|0.17***|
||[11.98]|[5.32]|[-1.01]|[6.44]|[10.62]|
|Age:25-59 months|0.23***|0.05***|-0.05***|0.07***|0.18***|
||[15.38]|[4.67]|[-5.86]|[6.82]|[12.27]|
|No Livestock|0.03***|0.00|0.00|0.01|0.02*|
||[2.77]|[0.33]|[0.26]|[1.19]|[1.85]|
|Does not treat water|0.03**|0.01|-0.01|0.01|0.02*|
||[2.18]|[0.88]|[-1.62]|[1.55]|[1.78]|
|Mother underweight|0.04**|0.10***|0.03***|0.08***|0.07***|
||[2.19]|[5.97]|[3.00]|[5.73]|[3.79]|
|Mother able to read part of sentence|0.01|-0.01|-0.01|-0.01|0.00|
||[0.70]|[-0.91]|[-1.30]|[-0.96]|[0.23]|
|Mother cannot read at all/Unknown|0.01|0.01|0.01**|0.00|0.02|
||[0.69]|[1.00]|[2.14]|[0.10]|[1.51]|
|Constant|-0.09**|-0.14***|0.06***|-0.16***|-0.02|
||[-2.25]|[-5.26]|[3.34]|[-6.65]|[-0.59]|
|Observations|28,017|28,152|27,987|30,057|30,057|
|R-squared|0.46|0.42|0.41|0.42|0.45|

Notes: *** p<0.01, ** p<0.05, * p<0.1. Robust standard errors are clustered at the DHS cluster level. All regresssions employ cluster x month fixed effects. The weights used are person weights adjusted for the national populations. All regressions use all available data on rural households from 5 Southern African countries (Mozambique, Malawi, Tanzania, Zambia and Zimbabwe). Additional controls are for roof, floor and wall quality, cooking fuel, employment in agriculture, source of water, type of toilet, ownership of radio, tv, bicyles, refridgerators, cars, trucks, birth order, birth interval, gender of child and the gender of the household head. All metrics are based on children being measured below -2 standard deviation thresholds. 41.0 percent of the children are stunted, 15.1 percent are underweight, 5.1 percent are wasted, 12.6 percent are stunted and underweight, and 45.2 percent are malnourished based on any of the three metrics.

25
Table 1: Summary Statistics

||Mean|SD|25th percentile|75th percentile|
|---|---|---|---|---|
|hourly wage (rupees)|10.43|9.11|5.71|12.00|
|log of hourly wage (rupees)|2.12|0.62|1.74|2.48|
|infant mortality rate in birth year|113.0|41.9|81.3|137.6|
|sanitation coverage in birth year|16.8|25.7|0.0|34.0|
|birth year|1979.4|4.9|1975|1983|
|age in survey|25.6|4.9|22|30|
|urban|0.3|0.5|0|1|
|_n_ (adult men)|12,783||||

Table 2: Regression of Wages on IMR

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Panel A: OLS|||||
|IMR in birth year|-0.00174*|-0.00195*|-0.00166*|-0.00170*|
||(0.000706)|(0.000796)|(0.000742)|(0.000699)|
|district fxed efects|✓|✓|✓|✓|
|year of birth fxed efects|✓|✓|✓|✓|
|state _×_ urban fxed efects||✓|✓|✓|
|social group _×_ urban indicators||✓|✓|✓|
|state-specifc linear time trends||✓|✓|✓|
|job category fxed efects|||✓|✓|
|education indicators||||✓|
|_n_ (adult men)|12,783|12,783|12,783|12,783|
|Panel B: IV (linearly interpolated|IMR instru|mented with|log interpolat|ed IMR)|
|IMR in birth year|-0.00166**|-0.00167*|-0.00139*|-0.00156*|
||(0.000597)|(0.000718)|(0.000696)|(0.000673)|
|district fxed efects|✓|✓|✓|✓|
|year of birth fxed efects|✓|✓|✓|✓|
|state _×_ urban fxed efects||✓|✓|✓|
|social group _×_ urban indicators||✓|✓|✓|
|state-specifc linear time trends||✓|✓|✓|
|job category fxed efects|||✓|✓|
|education indicators||||✓|
|_n_ (adult men)|12,783|12,783|12,783|12,783|

Note - _p_ values: _†_ = 0 _._ 1 _, ∗_ = 0 _._ 05 _, ∗∗_ = 0 _._ 01 _, ∗∗∗_ = 0 _._ 001. Standard errors clustered at the district level.

40
Table 2: Distribution of output per worker across industrial clusters in Sri Lankan Industry, 2003

|||Outp|utper worker in 2005|Int'l$at PPP|||Value Adde|dper worker in 2005|Int'l$at PPP||
|---|---|---|---|---|---|---|---|---|---|---|
|**Industry**|10%|25%|50%|75%|90%|10%|25%|50%|75%|90%|
|31 Food, Beverages & Tobacco|$1,426|$3,758|$15,164|$35,138|$54,070|$722|$1,757|$5,968|$14,061|$18,111|
|32 Textile, Wearing Apparel & Leather|$2,194|$3,879|$5,665|$13,059|$25,012|$1,285|$2,226|$3,016|$6,143|$12,982|
|33 Wood, Wood Products & Furniture|$4,063|$5,676|$8,115|$15,050|$17,938|$1,857|$2,550|$4,190|$7,701|$9,648|
|34 Paper Products, Printing & Publishing|$4,864|$6,432|$9,364|$21,185|$31,749|$1,904|$2,611|$3,980|$8,061|$11,139|
|35 Chemicals, Petroleum, Rubber & Plastic|$5,273|$6,373|$15,849|$29,235|$47,621|$2,407|$3,036|$6,499|$12,532|$18,637|
|36 Non-matalic Mineral Products|$3,131|$4,782|$5,815|$13,885|$46,649|$1,733|$2,265|$2,815|$6,863|$25,205|
|37 Basic Metal Industries|$6,678|$8,663|$36,422|$81,713|$82,368|$2,215|$3,975|$7,791|$23,144|$24,026|
|38 Metal Products, Machinery & Equipment|$5,104|$6,707|$9,382|$15,047|$33,188|$2,209|$3,324|$4,682|$7,788|$15,245|

|||Outp|utper worker in 2005|Int'l$at PPP|||Value Adde|dper worker in 2005 I|nt'l$at PPP||
|---|---|---|---|---|---|---|---|---|---|---|
|**Province**|10%|25%|50%|75%|90%|10%|25%|50%|75%|90%|
|Central Province|$1,426|$4,794|$8,000|$14,202|$30,690|$794|$2,057|$3,626|$6,256|$15,032|
|North Central Province|$1,925|$3,284|$6,106|$21,114|$45,153|$850|$1,606|$3,263|$8,060|$17,710|
|North Western Province|$3,319|$4,849|$7,098|$15,364|$27,951|$1,733|$2,496|$3,407|$5,416|$10,836|
|Sabaragamuwa Province|$2,855|$4,041|$5,728|$14,635|$29,495|$1,375|$1,968|$2,901|$5,968|$12,532|
|Southern Province|$3,540|$5,045|$8,152|$17,197|$27,351|$1,720|$2,593|$3,841|$8,311|$13,300|
|Uva Province|$1,422|$5,128|$7,348|$11,277|$20,745|$680|$2,594|$3,447|$5,608|$10,161|
|Western Province|$6,448|$10,068|$16,279|$33,188|$77,519|$2,974|$4,663|$7,556|$15,513|$25,799|

Source: Sri Lanka Census of Industry, 2004. Notes: See Table 1.

35
and the current account surplus fell to 5 percent of GDP in 2009 from more than 9 percent in 2008 (table 1).<sup>1</sup>

# **Box 1** China’s Fiscal Stimulus in 2008

China's fiscal stimulus amounted to RMB 4 trillion, or about 12.5% of GDP, in 2008. The projects and programs that were underpinned by the stimulus were implemented over a period of 27 months. The stimulus plan included investments of about RMB 1.2 trillion by the central government and nearly RMB 3 trillion of supporting investment projects and programs by the local governments and non-governmental sectors, which to a great extent were financed by supporting loans provided by policy banks and commercial banks. These investments covered livelihood improvements, R&D, environmental protection and other sectors. More than one-third of the resources were allocated to the infrastructure sector:

|Housing<br>guarantees|Rural<br>construction|Energy<br>conservation<br>and emissions<br>reduction<br>The composit|Infrastructure<br>development<br>ion of the 4 trill|Social<br>services<br>ion RMB sti|Industrial<br>restructuring<br>muluspackage|Post-disaster<br>reconstruction<br>of Wenchuan|<br>Total(RMB)|
|---|---|---|---|---|---|---|---|
|0.4 trillion|37million|21 million|1.5 trillion|15 million|37 million|1 trillion|4 trillion|

Source: World Bank (2010), Development Research Center for the State Council (2010), and “NDRC reveals details of stimulus package”, China Daily, November 27, 2008.

By mid-2009, the macroeconomic stimulus and other interventions had succeeded in boosting demand for migrant labor. By early 2010, scarcity reemerged in China’s labor market, and wages started to climb once again (Giles and others 2012). In 2010, China’s exports recovered to precrisis levels, and real GDP grew 10.5 percent, which slowed down to 9.2 percent in 2011 and is now projected to slow down further to around 8 percent 2012 due to the slowdown in the global economy.

The success of China’s fiscal stimulus spurred domestic demand and supported the global economic recovery. Strong economic growth in the world’s second-largest economy stimulated

> 1 The latest IMF data shows  a sharp increase in general government gross debt as percent of GDP to 33.5 percent in 2010 before starting to decline to about 26 percent in 2011, though remaining well above its average level of about 17.5 percent in 2008-9.

6
# **Table 1: Key Variables at Baseline**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Variable|Meals|THR<br>C|ontrol|Difference w|ith control|
|||||Meals|THR|
||Child lev|el variables||||
|Enrolled|0.281|0.241|0.243|0.039|-0.001|
||[0.033]|[0.032]|[0.025]|[0.042]|[0.041]|
|Math answers|1.823|1.923|1.818|0.005|0.106|
||[0.113]|[0.143]|[0.132]|[0.173]|[0.194]|
|Math time-adjusted|180.528|171.661|183.433|-2.905|-11.772|
||[6.070]|[6.328]|[6.913]|[9.199]|[9.372]|
|Days missed in May|0.513|0.713|1.276|-0.763*|-0.563|
||[0.165]|[0.189]|[0.382]|[0.416]|[0.426]|
|Child labor (any)|0.848|0.87|0.852|-0.003|0.018|
||[0.017]|[0.019]|[0.018]|[0.025]|[0.026]|
|Child productive labor|0.65|0.637|0.603|0.047|0.034|
||[0.033]|[0.031]|[0.034]|[0.047]|[0.046]|
|Farm labor|0.585|0.595|0.574|0.011|0.021|
||[0.042]|[0.032]|[0.039]|[0.057]|[0.050]|
|Non farm labor|0.292|0.236|0.163|0.129**|0.073|
||[0.039]|[0.055]|[0.032]|[0.050]|[0.063]|
|Household chores|0.643|0.656|0.686|-0.043|-0.030|
||[0.022]|[0.033]|[0.029]|[0.037]|[0.044]|
|Cooking|0.334|0.315|0.344|-0.009|-0.028|
||[0.020]|[0.023]|[0.024]|[0.031]|[0.033]|
|Fetch water|0.467|0.493|0.527|-0.059|-0.034|
||[0.027]|[0.041]|[0.039]|[0.048]|[0.057]|
|Fetch wood|0.359|0.396|0.36|-0.001|0.035|
||[0.022]|[0.032]|[0.036]|[0.043]|[0.048]|
|Tend||||||
|youngsters|0.237|0.198|0.186|0.052|0.012|
|Other hh|[0.028]|[0.024]|[0.031]|[0.042]|[0.039]|
|chores|0.391|0.388|0.413|-0.022|-0.025|
||[0.015]|[0.026]|[0.025]|[0.029]|[0.036]|
|weight (kg)|23.135|23.397|22.747|0.388|0.650|
||[0.682]|[0.706]|[0.631]|[0.929]|[0.947]|
|height (cm)|125.627|125.542|124.941|0.686|0.601|
||[1.020]|[1.315]|[1.362]|[1.702]|[1.893]|
|Body mass index|14.378|14.569|14.331|0.047|0.238|
||[0.269]|[0.192]|[0.201]|[0.336]|[0.278]|
|Weight-for-age (6-60 months)|-2.202|-2.521|-2.394|0.192|-0.126|
||[0.172]|[0.159]|[0.178]|[0.248]|[0.238]|
|Height-for-age (6-60 months)|-2.351|-2.086|-2.317|-0.034|0.231|
||[0.152]|[0.111]|[0.146]|[0.211]|[0.184]|
|Weight-for-height (6-60 months)|-0.786|-1.125|-0.903|0.117|-0.222|

24