rafmacalaba/datause-encoder-data huggingface dataset:
- 65 -

Development Services (ICDS), launched in 1975, to provide a package of services such as child protection and development with stress on both prenatal services including immunization, periodical health check up, and referral services. Growth monitoring and supplementary nutrition for 300 days of the year for all children are the other components of the scheme. About 6.5 million children below 6 years of age and 1.5 million women were covered under ICDS during 1985/86.

The survey data of the National Nutrition Monitoring Bureau (NNMB), covering ten states, show steady but slow decrease in severe malnutrition during 1974-81 (Subbarao, 1989). There has been no NNMB survey published since 1981. Howeve- ijr 1984-85, baseline data on the incidence of severely malnourished childret., collated by ICDS authorities for 23 project districts, are positively and significantly correlated with child mortality rates, and thus probably assess localized variations in malnutrition rather accurately.

Severe child malnutrition looms largest in tribal Andhra Pradesh, Bihar, Rajasthan and Madhya Pradesh and also in rural Karnataka, Madhya Pradesh, Uttar Pradesh, Maharashtra and West Bengal (see Table 28). Also Rao (1985), pointed out a substantial proportion of the nation's scheduled castes and tribes who are poor live in these states. Therefore, probably povertyinduced malnutrition continues to be rampant in pockets of India, especially in regions/states with a high proportion of scheduled castes and tribes.

As with other interventions, evaluations of ICDS have pointed out many shortcomings (Subbarao 1989). Immunization levels continued to be low and there was a drop from the first to the second dose in many cases; the regularity of feeding differed from center to center; there was little
# **_4.2.3 Impacts on prices of agriculture and other commodities_**

Changes in prices are summarized in Figure 7 and detailed in Table 7. The changes in world prices (weighted average across countries) of commodities vary from -1.7% (crude oil) to 9.7% (sugar cane) under the ET scenario. These large increases for sugar cane prices are followed by higher prices for ethanol (4.1%), corn (3.6%), biodiesel (3.5%), oilseeds (2.9%), wheat (2.3%), and other coarse grains (2.2%). Processed food prices change moderately (0.5%) in the ET scenario.

The prices of gasoline and diesel products fall by 1.4%. These lower energy prices mitigate the higher cost of farm ingredients in processed food. We also compute price multipliers (percent change in price/percent change in world biofuel production), to gauge the price impact of the two scenarios. The sugar price multipliers are the largest at 22.3% (AT scenario) and 12.3% (ET scenario). The value of 22.3% could be interpreted as the percent increase in price if world biofuel doubled. The next multiplier is for the ethanol price with a multiplier of 6.9% for the AT scenario. The multipliers for the aggregate agricultural price index are 5.5% and 4%, respectively for the AT and ET scenarios. The price multipliers for other commodities are relatively moderate. For example, the processed food price multiplier is 0.5%, again clearly showing the moderate impact biofuels have on processed food prices. These small changes do not preclude some higher localized price changes, but the typical effect is clearly illustrated by Figure 10. Land prices (not shown in the figure) do change a lot in the EU (France especially, and also the UK and Spain) where land devoted to sugar crops and oilseeds sometimes more than double in value.

A number of existing general equilibrium studies reports impacts on commodity prices of the expansion of biofuels. Kretschmer et al. (2009) finds that a policy to meet EU’s 10% biofuel target by 2020 would cause world prices of corn, sugar crops, wheat and other grains by 0.2% to 1.7% higher relative to the reference scenario in 2020. Note that these impacts are much smaller as compared to our price impacts because they simulate only EU targets, whereas we simulate existing targets of all countries. Another general equilibrium study, Al-Riffai et al. (2010), finds very small effects on food prices of EU biofuel policies.

32
(Secretaria Especial de Politicas para as Mulheres & Secretaria Nacional de Seguranca Publica 2007) over 60% of DEAMs found their relations with the judicial system to be good or optimal, but relations with social services and safe houses were also lacking. These findings come from the around 250 DEAMs each year who returned survey questionnaires between 2003 and 2005. This is significant lack of response as data from the Secretaria da Mulher and municipal surveys indicate that in 2004, there were almost 400 DEAMs.

Given the shortcomings in implementation, it is particularly important to evaluate whether DEAMs have an effect on the incidence of domestic violence, and to assess the magnitude of this effect. To our knowledge, no such statistical analysis has been undertaken, and our study is a first attempt.

# **3. Data**

It is notoriously difficult to obtain the data on domestic violence. Self-reported measures gravely underestimate the problem (Ellsberg et al. 2001). Moreover, measurement issues related to selfreported data are a challenge because of selection issues regarding the revealing of the incidents. Although overall violence in the household may be falling, women may report more domestic violence as their outside options improve and fear of retaliation subsides or as it is perceived as more socially acceptable to renounce abusers. On the other hand, women who are abused but live in a world where violence against them is a cultural norm may not perceive the injustice.

Data from Brazil’s national household survey illustrates this challenge. In 2009, PNAD (Pesquisa Nacional por Amostra de Domicilios) included a section on victimization and justice. The questions on crime include asking if the subject had suffered aggression within the last year and the identity of the perpetrator. There is certainly underreporting taking place, as only 1.65% of women aged 15-49 admit having ever been a victim of aggression, and only .5% report that the last incidence of aggression was by a domestic partner. This is much lower than the numbers presented by the 2005 WHO study, where 29-37% of Brazilian women interviewed reported having been victims of domestic violence. The discrepancy may arise due to the fact that the focus of the WHO study was domestic violence, so the enumerators were women trained to elicit trust from respondents. As a single question on a survey focused more on economic measures, the need for establishing a rapport of this caliber would be less important for the PNAD survey team, and the enumerators were not necessarily women. The WHO study also finds that 20% of women had 7
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and restraint policies and noncore countries not having the means to counteract and offset core countries‘ ̳beggar thy neighbor‘ policies, a growing competitiveness gap emerged leading to widening euro imbalances.

28. And this impacted countries‘ external position: the current account balance for noncore countries deteriorated sharply during the finalization of the European integration process, and even more so following the adoption of the euro. Much like in the United States<sup>11</sup> , this deterioration in the current account was driven by the sharp increase in housing prices.<sup>12</sup> While many Northern European countries‘ current account positions improved, including those of Austria, Finland and the Netherlands, it was the massive increase in Germany‘s current account surplus by over 10 percentage points of GDP since 2000, partly because of increasing exports to periphery countries, that was at the core of the emergence of intra-European imbalances (Figure 7). Figure 8 shows the evolution of export market shares for Greece, Spain and Portugal. Growth in these countries was more and more dominated by internal consumption.

**Figure 7: German trade surplus in Europe**

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> 11 See also Figure 40 based on Laibson and Mollerstrom (2010), in Lin and Treichel (2012).

> 12 Bibow (2012) p.12.

14
**Table 7** Contribution rates required to generate various pension replacement rates

|**_7a:_**_Contribut_|_ion rates n_|_ededt _|_o_**_gener_**|**_ate_**_a_**_r_**|**_lacer_**|**_nentrate_**|_of_**_15%_**|_of ave_|_rae_**_m_**|**_A_**_adul_**_t_**|_earnin_|_2zs_||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||||_dY-_|<br>_0%_||dY|_1%_||dY_=_|_1.5%_||dY|_=2%_|
|Contribution|age|18|21|25|18|21|25|18||21|25|18|21|25|
|||Death<br>Iage__||||||||||||_|
|Real<br>i = 2%||||||||||||||
|Retire at 55|80|7|8|8|8|9|10|9|9|10|10|10|11|
||81|_7_|8|9|9|9|10|9|10|<br>11|10|10|11|
||83|8|8|9|9|9|10|10|10|11|10|11|12|
||85|8|9|10|9|10|11|10|11|12|11|12|13|
|Retire at 60|80|5|5|6|6|6|7|7|7|7|7|8|8|
||81|5|6|6|6|7|7|7|7|8|7|8|8|
||83|6|6|7|7|7|8|7|8|8|8|8|9|
||85|6|6|7|7|7|8|8|8|9|8|9|9|
|Retire at 65|80|4|4|4|4|5|5|5|5|5|5|6|6|
||81|4|4|4|5|5|5|5|5|6|6|6|6|
||83|4|4|5|5|5|6|6|6|6|6|6|7|
||85|4|5|5|5|6|6|6|6|7|7|7|7|
|Retire at 70|80|2|3|3|3|3|3|3|3|4|4|4|4|
||81|3|3|3|3|3|4|4|4|4|4|4|4|
||83|3|3|3|4|4|4|4|4|4|4|5|5|
||85|3|3|4|4|4|4|4|5|5|5|5|5|
|Real_i_=3%|____|||||||||||||
|Retire<br>at55|80|5|6|7|6|7|8|7|7|8|8|8|9|
||81|6|6|7|7|7|8|7|8|8|8|8|9|
||83|6|6|7|7|7|8|7|8|9|8|8|9|
|_________|85<br>|16|7|7|7|8|9|_8_|8|9|8|9|10|
|Retire at 60|80|4|4|5|5|5|5|5|5|6|6|6 I|<br>_6_|
|___________81||4|4|5|5|5|6|5|6|6|6|6|7|
||--83|4|5|5|5|5|6|6|6|6|6|6|7|
||85|5|5I|<br>5|5|6|6|6|6|7|7|7|8|
|Retire at65|80|3|3|3|3|4|4|4 I|<br>4|4|4|1<br>4|5|
||81|3|3|3|4|4|4|4|4|4|4|5|5|
|_____________|83|3|3|4|4|4|4|4|4|5|_5_|5|5|
||85|3|3|4|4|4|5|4|5|5|5|5|6|
|Retireat70|80<br>|1 2|2|2|2|2|3|3|3|3|3|3|3|
||81|2|2|2|2|3|3|3|3|3|3|3|3|
||83|2|2|3||3|31|<br>3|3|3|3|4|4|
||85<br>|1 2|2|3|**3**||3<br>3|<br>34|||41|||

32
age of women). Around half the potential beneficiaries are married; 24 percent are widowed; and 14 percent and 11 percent, respectively, are divorced or single. The average number of household members in each group yields an average household size of around 3.7 members, of which 1.9 are females. About two-thirds of the potential beneficiaries have at least finished primary education, but the number who have secondary education or beyond is fairly low (about 9 percent and 2 percent, respectively); 13 percent are incapacitated and cannot work. By construction, the average of the IVs is almost equal to the global average of program participation.

**Table 11: Descriptive Statistics of Control Variables**

|_Variable_|_Observations, number_|_Mean_|_Standard deviation_|_Minimum_|_Maximum_|
|---|---|---|---|---|---|
|_Control variables_||||||
|Other urban|22,546|0.59|0.49|0|1|
|Rural|22,546|0.32|0.47|0|1|
|Age|22,546|68.42|9.73|52|108|
|Sisben|22,546|28.48|8.68|1.54|43.63|
|Female|22,546|0.58|0.49|0|1|
|Widowed|22,546|0.24|0.43|0|1|
|Divorced|22,546|0.14|0.34|0|1|
|Single|22,546|0.11|0.31|0|1|
|Potential beneficiaries, number|22,546|1.65|0.68|1|5|
|Other adults, number|22,546|1.00|1.15|0|10|
|Minors, number|22,546|1.01|1.38|0|12|
|Females, number|22,546|1.90|1.37|0|11|
|Primary|22,484|0.57|0.50|0|1|
|Secondary|22,484|0.09|0.28|0|1|
|Postsecondary|22,484|0.02|0.15|0|1|
|Incapacitated|22,546|0.13|0.33|0|1|
|_Instrumental variables_||||||
|Municipality-year rollout|22,546|0.26|0.17|0|1|
|Submunicipal average rollout|22,546|0.26|0.17|0|1|

For our more detailed analysis, we also look at a number of more granular outcomes. The ENCV has detailed questions on (a) the size of the workplace, (b) the location of work, (c) the type of (self-)employment, and (d) the sector of occupation. We are particularly interested in whether the monetary benefit plays a role in easing liquidity constraints and thus provides seed money for smallscale self-employment. For this reason, we are interested in whether, among the first group of outcomes, there is an increase in people working alone. In the second group, we have created a category consisting of work that takes places at home or work as a street vendor (either from a fixed stall or selling door to door); a second category of interest captures whether a person works in a different household; and a third category covers whether work takes place in the countryside, on a river, or on the sea. Among the types of employment, we show results on whether individuals are employed in the private sector or public sector, work independently, work their own land (including 25
**Table 16**

Developed and Developing Economies: Anti-Dumping Investigations by WTO Reporting Members 1989-1997 (in number of cases and %)

||**Developed**<br>**Members**|**Developinp**<br>**Members**|**Developed As**<br>**% of Total**|
|---|---|---|---|
|1989|_66|30|_<br>69|
|1990|147|**18**|.<br>89|
|1991|184|44|81|
|1992|._261|65|80|
|1993|141|158|47|
|1994|115|113|50|
|1995|73|83|47|
|1996|73|148|33|
|1997|118|115|51|

Explanation:

* Includes Poland (25 investigations) and Israel **_(I5_** investigations).

**_Source:_** _YflO, Antidumping database._

Most of developed country -antidumping investigations and definitive measures against developing countries have been directed to higher and middle income developing countries - frequently the same countries which have been making an increasing use of antidumping measures themselves - and non WTO Members (see below). Bangladesh is the only least-developed country to have been 3ubjected to antidumping investigations and definitive measures on three occasions, in 1992 (see Miranda et al., 1998).

The major product groups which are the object of anti-dumping investigations are muchf the same for developed as for developing countries: Table 17 shows that Basic Metals, Chemicals, Plastics and Machinery and Electrical Equipment are the four sectors which account for the bulk of ant-dumping investigations in both developed and developing countries - although the emphasis naturally tends to differ from country to country. The four sectors together accounted for more than two-thirds of all the antidumping investigations world-wide over the decade 1987-1997.

56
PoLcY RiESEARCI-I WORKING PAI'ERI 2890

# Abstract

Estacihe an1d Kouassi analyze thc deteriilnanits of the efficiency levels reached bv 21 African vwater utilities. They assess efficiency throLighi the estimationl of a production frontier for the sector in Africa. T1-he efficiency estimates confirm mucIh of the Commllonl perceptions from partial productivity indicators. They point to a great heterogeneity in the African water utilities' performanices, the preclomi nancc of constant returins to scale, and the great rate of technological progress. And the authors show that the institutionial capacity of the countrv, as well as its governanice quality, are significant driving factors in the performance of each firm.

Tshis paper-a product of the Governanice, Regulation, and Finance Division,<sup>WorldBanikInstitute-is part of alargereffort</sup> in the institute to promote the uliderstanidinig of regulatory issues. Copies of<sup>thepaperareavailahlefreefromiithe World</sup> B3ank, 1818 H Street NW, Washingtoni, DC 20433. Please contact<sup>GahrielaCheniet-Smilithl,room13-147,telephiolic202-</sup> 473-6370, fax 202-676-9874, email address gcheneit(PNorldbanik.org.<sup>P'olicyResearchWorkinlgPapers arealso postedon</sup> the Web at http://econ.worldhank.org. Antonio Estache **may** he contacted<sup>at aestache(0worldhank.org.September2002.</sup> (2 1 pages)

> _/77? Poli, Reseaicb Wsrking Paipf r SPries dissedminates t findings Re_ ac _oe rk is progress_ af _to courage the excbafe ofideas about dev/elopmen(wt issiies._ **_A,,_** _objiectivol.thet l7 sericis is t,, g.et thec_ **_fiiidllzgs_** _,,,t_<sup>_qulickiv,-_</sup><sup>**_L'vJn_**</sup><sup>_iftlepl eS0It7tIC)IS a7r( lessth7an_</sup><sup>**_/u1l!v_**</sup><sup>_polisb7ed.7be_</sup> _papers_ **_cr.7ry_** _tl7e_ **_0am7)s_** _of tlbe aiitbo0rs anld sb170Zdd be cited accordintglY._<sup>_T17,_</sup><sup>**_findinZgs,_**</sup><sup>_inlterl)relathonzs_</sup><sup>**_an}d_**</sup><sup>_conZclusion2sexYpressedi71ibis_</sup> _pelper ere_ **_eiitiy-ely_** _tl70se of tl7e .1tbor1s. /17Cy do no0t nZecessarilyl represenZt thi, I'iCew ofthe7 Wofrld_<sup>_l3anskitsExecustive D)irectws,optl7e_</sup> _coiii itries tb7c) represenlt._

Produiced bv the Researci Adv isorv Staff
# **2.2 Econometric Issues**

There are a number of issues that needs to be addressed. The first involves the endogeneity of tariff-inclusive relative prices. Following Equation (4), we regress the share of each good _n_ on their tariff-inclusive relative prices and the endowments of the EU, where the share of each good _n_ is constructed based on the trade value of each HS 6-digit good from each trading partner, relative to the GDP of the EU. In this specification, the tariff-inclusive relative prices of HS 6-digit products, ln _pp−_<sup>_<u>tt</u>_</sup> _<u>ndn</u>_<sup>_,_couldbeendogenoustotheshareofeach</sup> good, _s_<sup>_t_</sup> _nd_<sup>,whichcouldleadtoinconsistentestimates.Moreover,ifthesmallopeneconomy</sup> assumption is violated, an increase in tariffs may also cause foreign prices to change and affect ln _<u>p</u>_<sup>_t_</sup> _<u>nd</u>_<sup>Toaddresstheseendogeneityissues,weusethetariff-inclusiverelativepriceofthe</sup> _p−_<sup>_<u>t</u>_</sup> _n_<sup>.</sup> United States (US), ln _pp−_<sup>_<u>tt</u>_</sup> _ndn,US,US_<sup>_,_astheinstrumentinaFixedEffectsTwoStageLeastSquare</sup> (FE-2SLS) regression. The identification assumption is that relative prices are affected by common shocks: while the price of a product in the EU and the US could be correlated due to the world market conditions, the share of each good in the GDP of the EU should not be correlated with the trade policy and prices of the US a priori. One of the reasons the US prices and the EU prices are correlated is because they faced the common exogeneous shocks, such as productivity/technology, and climate/weather/natural disaster/pandemic, as well as China shocks and the wars in other countries. These exogeneous events affect product prices in the world markets hence caused the US and EU prices to move together. At the same time the US prices should not be affecting or influenced by the share of UK products in the EU’s imports; hence, they are consistent with the exclusion restrictions and acceptable as 8
of 11 percent to just 5 percent in Rwanda and to 10 percent and 8 percent in Malawi and Uganda respectively (World Bank 2016, Tables 4.2 and 3.12).

Indicators of growth potential (e.g. the share of investment, savings, and exports in GDP, the gains in total factor productivity) convey mixed signals. Gross investment rates in 2016 were moderate in both Uganda (24 percent) and Rwanda (26 percent) but low in Malawi (15 percent). In the first two they have risen sharply since 2000 thanks in large part to the generosity of aid donors. In Malawi, investment has declined from an already suboptimal 17 percent in 2005. Gross domestic savings in 2016 were a meager 12 percent of GDP in Malawi and 10 percent in Rwanda; they were somewhat higher (16 percent) in Uganda; in comparison, the rate for lower middle-income countries was 27 percent i.e. savings in the three countries need to rise a lot if they are to transition to and stay on a high growth path (World Bank 2017, Table 4.8).

Both Rwanda and Uganda have low export ratios – a little over 18 percent in 2017. Malawi’s is higher, but with exports of goods and services amounting to just 29 percent of GDP it falls far short of other low-income countries such as Cambodia with a ratio of 61 percent and Lao PDR with 34 percent<sup>10</sup> . Net exports are substantially negative in all three countries, which diminishes their contribution to growth. For example, in 2012, the share of net exports in Rwanda’s GDP was -20.4 percent and that of Uganda was -15.6 percent (Diao and McMillan, 2014, Table 2.5).

Malawi, Rwanda, and Uganda’s exports and revealed comparative advantage (RCA) are indicative of countries at the foot of the development ladder. Ores and concentrates, coffee and tea were Rwanda’s top exports in 2014 and its overall RCA was in these commodities. Its fastest growing exports albeit from low bases, were metallic compounds and garments. Malawi’s six principal exports were tobacco, tea, uranium oxide, sugar, beans and cotton and expectedly its RCA is predominantly in the production of tobacco, tea and groundnuts. During 2010-2014, exports of fresh and frozen vegetables rose most rapidly as did exports of construction materials and some electronic parts and components. Uganda’s export performance in 2014, largely depended upon the export of coffee, sesame seeds, cement, tobacco, fish fillet, and cocoa beans. Its RCA was strongest in wood products, oilseeds, fish, and coffee. Between 2007 and 2014, Uganda benefitted from the rapid increase in exports of sugar, tobacco products, wheat flour and items such as paint and pigments.

The increase in total factor productivity, which is the primary determinant of growth over the longer term, has been negative in most African countries since 2009. In Uganda’s case, it was positive from 2000 through 2006, the average being 1.5 percent; it has been negative since, declining by 2.0 percent per annum between 2009 and 2014 and by 1.6 percent per annum in 2015-2016. In Malawi, the increase in TFP was close to zero between 1999 and 2006; it rose to 2.2 percent per annum

> 10 https://data.worldbank.org/indicator/NE.EXP.GNFS.ZS

6