rafmacalaba/datause-encoder-data huggingface dataset:
and maintaining low inflation. A recent quantitative analysis of the performance of inflation targeting in New Zealand, Canada, and the United Kingdom conducted by Honda (2000) provides some support for this view. He estimated an unrestricted VAR model with four variables (inflation, deviations of output from trend, the short-term nominal interest rate, and the rate of change of the nominal exchange rate) over a sample period prior to the introduction of inflation targeting and simulated dynamically the model over the period following the adoption of inflation targeting. The test is that if predicted values from the model tend to overestimate actual values of inflation and the other variables in the system, then inflation targeting can be deemed as having had some favorable impact on inflation and other macroeconomic variables. However, Honda found no evidence that inflation targeting had an effect on either inflation or any other variable in the system. Of course, the power of alternative stability tests in a VAR context is an open issue, but Honda's analysis appears to be a promising way of analyzing the impact of inflation targeting on actual inflation performance.

# **6.2** Developing Countries

Few developing countries have so far adopted inflation targeting, but the number of potential candidates appears to have risen significantly in the past two or three years. Not all developing countries that have floated their currency in recent years have adopted an (explicit or implicit) inflation targeting framework. Mexico is a case in point. Carstens and Werner (2000) have argued, for instance, that the high frequency and incidence of exogenous shocks (both domestic and external, such as movements in wages and public sector prices, and nominal exchange rate fluctuations) to domestic prices makes the adoption of an explicit inflation targeting framework unfeasible in the case of Mexico. In Peru, the central bank announces an annual inflation target since 1994 but does not have a formal inflation targeting regime. Nevertheless, both countries have been viewed by some observers (see Mishkin and Savastano (2000)) as moving toward the adoption of such a framework. By contrast, Chile has a long history of announcing inflation targets (one year ahead) that goes back to 1990, although it started to make its inflation report publicly available only recently (May 2000).44 More recently, "Because Chile maintained a crawling peg exchange rate regime until September 1999, some observers have argued that the adoption of a full-fledged inflation targeting frame44
Table 6: Selected product paths, aggregated categories, 2001–2007<sup>_†_</sup>

|HS|Category|2001|2002|2003|2004|2005|2006|2007|
|---|---|---|---|---|---|---|---|---|
|code|||||||||
|||**Stronge**|**st RCA**|**lines**|||||
|14|Vegetable materials|21.78|21.81|21.23|22.01|21.55|23.08|22.57|
|01|Live animals|21.17|21.48|20.61|21.15|20.58|22.30|22.56|
|07|Edible vegetables & roots|23.87|24.66|23.86|23.44|24.46|24.39|24.97|
|54|Man-made flaments|17.86|19.16|18.60|17.43|19.38|21.64|20.89|
|09|Cofee, tea, and spices|17.07|17.62|18.47|18.39|17.63|18.54|19.47|
|||**Broa**<br>|**dest pat**<br>|**hs**<br>|||||
|19|Prepared grains|26.14|25.99|26.40|27.31|27.89|28.76|29.02|
|68|Stone and plaster art|28.32|27.93|27.03|27.25|27.46|27.98|28.66|
|34|Soap|26.02|24.62|24.91|25.36|26.90|28.09|27.92|
|||**Narro**|**west pa**|**ths**|||||
|75|Prepared grains|11.49|8.42|8.54|8.98|8.08|7.14|7.24|
|91|Stone and plaster art|10.57|10.70|10.40|10.21|9.49|8.42|7.32|
|45|Soap|8.02|6.04|5.51|5.26|6.31|7.14|8.03|

> _†_ Calculations applied at the 2-digit HS level, representing top five lines at upper bound of Table 5. Broadest (narrowest) paths represent lines with highest (lowest) path values in 2007.

not exporting? If our proximity measure is indeed a good indicator of factor substitutability between products and thus the likelihood that a country can produce one good if it produces the other the measure can be used to assess how likely it is that Syria will start exporting goods in the future that it currently does not reveal a comparative advantage in. This relationship between the proximity of new potential products to the current production structure can be represented more formally by calculating the RCA-weighted path to the total path: where _ρ_ is defined as before as an indicator variable that takes on unity if _RCAp,c >_ 1 and zero otherwise. (Hidalgo _et al._ 2007) refer to (10) as the density of a particular product _p_ for a given country _c_ which measures the closeness of a product in terms of factor substitutability to other products that Syria is already exporting with revealed comparative advantage. Put another way, it is the distance-weighted proportion of products connected with good p that Syria exports. _ρ_ is bounded by [0 _,_ 1] and higher values imply that country _c_ has relatively more export possibilities surrounding its exports of product _p_ . We report calculations of (10) for all lines at the 2-digit level in Table 7.

In Table 8, we report densities for all products for which Syria did not have revealed comparative advantage in 2007. We also present each products RCA and products are ranked by the percentage change in their RCA over the period 2001-2007. The average density for all product lines is 0.324, but for goods that Syria currently does not 20
resilience and lowering vulnerability through effective social protection programs in particular and a stable and conducive macroeconomic environment in general.

Figure 1: Real GDP Growth, Rainfall, and Inflation in Sudan, 2000–2018 <!-- Start of picture text --> 350 70<br>Floods<br>60<br>300<br>High inflation 50<br>rates<br>250 40<br>Drought Erratic<br>Late rains<br>200 Rains Drought High inflation 30<br>rates 20<br>150<br>10<br>100 Conflict in 0<br>Civil<br>the South -10<br>50 insecurity<br>Secession of -20<br>South Sudan<br>0 -30<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>YEARS<br>Annual Rainfall (in mm)<br>Real GDP growth-Annual percent change (alternate axis)<br>Inflation, average consumer prices (alternate axis)<br>Source : Authors’ calculation using data from the World Bank and International Monetary Fund (IMF).<br>Figure 2: Real private consumption, 2008-2018 and Consumption Growth incidence by percentile and locality, May 2009 to<br>November 2014.<br>a) Per capita private consumption b) Consumption per capita growth<br>growth 10%<br>4%<br>6%<br>2%<br>0% 2%<br>Sudan<br>-2%<br>-2% Rural<br>-4% Urban<br>-6%<br>-6%<br>-10%<br>-8%<br>0 50 100<br>Percentile of real per capita consumption<br>-10%<br>MILLIMETERS OF RAINFALL<br>% OF REAL GDP GROWTH & INFLATION<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>consumption<br>Growth rate in %<br>Annual growth in real per capita<br><!-- End of picture text --> _Source: World Bank staff calculations. Note: growth rates for 2017 and 2018 are estimates and forecasts, respectively. Note: 95percent confidence intervals for consumption growth in panel b) indicated through dashed lines and based on 200 bootstrap replications._ 3
<u>Table 3: Poverty rates for children and adults by household size</u>

|**Household**<br>|<br>**Per capita**|<br>**approach**|<br>**FAO/WHO e**<br>**scal**|<br>**quivalence**<br>**e**|<br>**Squar**<br>**equivale**|**e-root**<br>**nce scale**|
|---|---|---|---|---|---|---|
|**size**|**_Children_**<br>**_(0-17)_**|**_Adults_**<br>**_(+18)_**|**_Children (0-_**<br>**_17)_**|**_Adults_**<br>**_(+18)_**|**_Children_**<br>**_(0-17)_**|**_Adults_**<br>**_(+18)_**|
|_1 member_|5.4|5|16.7|15.9|36.9|28.5|
|_2 members_|18.8|12.9|29.6|24.7|43|32|
|_3 members_|27.1|23.5|35.4|33.2|42.2|37.3|
|_4 members_|36.9|32.9|40.2|38|43.1|39.1|
|_5 members_|45.1|40.1|44.8|41.5|45.1|40.1|
|_6 members_|51.2|45.8|48.4|45.1|45.7|40.6|
|_7 members_|54.6|48.3|49.5|45.6|44.3|38.7|
|_8 members_|58.2|52.8|51.5|48.3|44.2|39.4|
|_9 members_|59.8|55.8|52.3|50.3|43.1|40.1|
|_10 or more_|60.6|55.3|48.1|44.6|32.4|29.4|

<u>Source: World Bank's African Poverty Database</u> The difference in poverty between children and adults may vary depending on the number of children in the household or the size of the household (see Figure 4). When considering the per capita approach, the gap between children and adults is about 6 percentage points for households with two members. It is reduced to less than 4 for households with 3 members, but gradually increases to a little more than 6 percentage points for households with 7 members. When poverty is adjusted using the FAO/WHO equivalence scale, the distribution of poverty according to the household size keeps the same pattern. However, the gap is reduced, varying only between 2 and 4 percentage points for households with 3 or more members. With the square-root equivalence scale, the children-adults gap follows a trend closer to those of the per capita approach for households with 3 members or more, probably given that both approaches do not take account of the household composition. The breakdown by the number of children shows a growing trend of children-adults differences as the number of children increases, regardless of the approach retained. If, for households with 2 or fewer children, adult poverty appears somewhat higher than that of children according to the FAO/WHO equivalence and per capita approaches, the poverty rates of the two groups become similar for households with exactly 3 children. The gap becomes unfavorable for children for households with more than 3 members.

12
agencies, usually within one to two notches of one another. The differences (if any) are typically due to the different timings of ratings.

**Figure 3: Sovereign ratings by different agencies are highly correlated – the correlation coefficient ranges between  0.97-0.99**

<!-- Start of picture text --> Moody's S&P Fitch<br><!-- End of picture text --> _Source:_ Standard and Poor’s, Moody’s and Fitch Rating. The figure shows ratings as of December 6, 2006 for developing countries rated by all three rating agencies.

An examination of rating changes over time reveals stickiness (figure 4) reflecting the fact that re-rating does not occur with any regularity, but only when a country requests (and pays for) it, or when some significant, unforeseen event prompts the rating agencies to revisit a rating. Changes to ratings announced by different agencies also tend to be similar in direction and magnitude. A rating upgrade (or downgrade) by one agency is typically followed by a similar change by the others, usually with a lag. Rating agencies came under criticism for failing to predict the Asian crisis, and then for downgrading the countries after the crisis which further deepened the crisis (Ferri, Liu and Stiglitz 1999, Reinhart 2002b). Indeed, ratings were not downgraded by Moody’s ahead of, or after, the financial crisis in Mexico in 1994/5, and in Turkey in 2000 (figure 4).

**Figure 4: Evolution of sovereign ratings in selected countries**

<!-- Start of picture text --> Argentina Mexico<br>CC 20 Ratings BB- 13 Ratings<br>BB 12<br>CCC 18<br>Moody's BB+ 11<br>B- 16 S&P Fitch<br>BBB- 10<br>B+ 14 Moody's<br>BBB 9<br>BB 12 S&P BBB+ 8<br>Fitch<br>BBB- 10 A- 7<br>198611198801198903199005199107199209199311199501199603199705199807199909200011200201200303200405200507200609 199102199202199302199402199502199602199702199802199902200002200102200202200302200402200502200602<br><!-- End of picture text --> 6
- 9 -

Table **1.4: Impact of Eliminating Tranport-Related Distortions** (thousand of dollars)

||_Morocco_|_Algeria_|_Tunisia_|
|---|---|---|---|
|**Variation in Trade Balance**||||
|Variation in "Shipments" Credit (+)|12,600|27,100|43,600|
|Variation in "Shipments" Debit(-)|(3,033)|(1,548)|(25,592)|
|**Variation in Current Account**||||
|1990 Current Account|(200,000)|1,420,000|(500,000)|
|Current Account Variation|43,239|122,089|114,748|
|Expected Current Account|(156,761)|1,542,089|(385,252)|
|Current Account Improvement|21.6%|8.6%|23.0%|

_Source: Local surveys, 1993_
Table A.16: Under-5 mortality: Heterogeneity by caste x socioeconomic status

||**Illit**|**erate**|**Lit**|**erate**|**Botton **|**40% wealth**|**Top 20**|**% wealth**|
|---|---|---|---|---|---|---|---|---|
||SC<br>(1)|Non-SC<br>(2)|SC<br>(3)|Non-SC<br>(4)|SC<br>(5)|Non-SC<br>(6)|SC<br>(7)|Non-SC<br>(8)|
|_Firstborn girl_ * _Female_|2.277|3.762***|-0.688|1.797**|2.477|3.551***|5.626**|0.970|
||(1.852)|(0.761)|(3.227)|(0.724)|(2.483)|(1.008)|(2.700)|(0.796)|
|_Firstborn girl_ * _Female_ * _Post1_|-1.285|-2.325*|0.550|-0.711|-1.756|-1.845|-3.897|0.651|
||(2.390)|(1.137)|(3.614)|(0.948)|(2.922)|(1.431)|(3.067)|(1.244)|
|_Firstborn girl_ * _Female_ * _Post2_|-0.201|-2.776**|-0.232|-1.091|-3.172|-2.069|-6.795|1.023|
||(3.080)|(1.251)|(3.726)|(1.031)|(3.591)|(1.873)|(6.203)|(1.576)|
||**Unem**|**ployed**|**Emp**|**loyed**|**R**|**ural**|**Ur**|**ban**|
||SC|Non-SC|SC|Non-SC|SC|Non-SC|SC|Non-SC|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|_Firstborn girl_ * _Female_|2.045|3.056***|2.025|2.394***|2.297|3.757***|-1.757|0.493|
||(2.033)|(0.724)|(2.073)|(0.702)|(2.139)|(0.693)|(2.285)|(1.015)|
|_Firstborn girl_ * _Female_ * _Post1_|-0.506|-1.924*|-1.952|-0.804|-1.388|-2.524**|2.899|0.969|
||(1.890)|(0.957)|(3.093)|(1.036)|(2.675)|(0.997)|(3.093)|(1.210)|
|_Firstborn girl_ * _Female_ * _Post2_|-0.864|-2.716**|-1.894|-1.198|-0.889|-3.411***|1.681|0.907|
||(2.638)|(1.050)|(3.267)|(0.973)|(2.879)|(1.161)|(2.993)|(1.388)|

NOTES: This tables reports estimates from the specification in column 3 of Table 3 for various sub-samples. Each column within a panel is a separate OLS regression. SC denotes scheduled castes. The wealth categories are based on the national household wealth distribution. Standard errors in parentheses are clustered by state. Baseline mean refers to under-5 mortality for children born in the pre-ultrasound period, by mothers with the specific socioeconomic status. The third column in every panel shows a test of the significance of the difference between the coefficients for the sub-groups shown in the first two columns. Data: NFHS. *** 1%, ** 5%, * 10%.
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39
# Table III-3

# World Bank supported population, health, and nutrition projects by type, region and year, as of November 30, 1986

The number of projects appears first, followed by the loan commitment (in U.S.$ Thousands).

# Population and Population/Health

|<72|72-75|76-79|80-83|>83|
|---|---|---|---|---|
|S. Asia|2(36.2)|1(32.0)|2(64.0)|3(199.0)|
|E. Africa|1(12.0)||1(23.0)|2(6.35)|
|E. Asia/P|3(43.2)|4(111.5)|2(65.0)|1(46.0)|
|W. Africa||||1(5.3)|
|LAC<br>2(5.0)||2(11.8)|||
|EMENA<br>1(4.8)<br>.__________--------------------------------------------------__---------|1(12.0)|1(25.0)|1(12.5)||
|Total<br>3(9.8)|7(103.4)|8(180.3)|6(164.5)|7(256.65)|
|Health and Nutrition<br><72<br>-------------------------------------------------------------------|72-75|76-79|80-83|>83<br>--|
|e<br>Asia||1(32.0)|1(32.0)|__|
|E. Africa|||1(6.8)|3(31.8)|
|E. Asia/P|||1(27.0)|3(204.0)|
|W. Africa<br>.|||1(15.0)|6(142.3)|
|LAC||2(44.0)|2(36.5)|4(153.5)|
|EMENA<br>-------------------------------------------------------------|||2(17.6)|2(41.9)<br>--------|
|Total<br>Total||3(76.0)|8(134.9)|__<br>18(573.5)|
|<72|72-75|76-79|80-83|>83|
|S.Asia|2(36.2)|<br>2(64.0)|3(96.0)|<br>3(199.0)|
|E.Africa|1(12.0)||2(29.8)|<br>5(38.15)|
|E.Asia|3(33.2)|<br>4(111.5)|<br>3(92.0)|<br>4(250.0)|
|W.Africa|||1(15.0)|<br>7(147.6)|
|LAC<br>2(5.0)||4(55.8)|2(36.5)|<br>4(153.5)|
|EMENA<br>1(4.8)|<br>1(12.0)|<br>1(25.0)|3(30.1)|<br>2(41.9)|
|-------------------------------------------------------------|||||
|Total<br>3(9.8)|<br>7(103.4)|11(256.3)|14(299.4)|__---------<br>25(830.15)|

26
Realizing this shortcoming, Nandini Gupta, John Ham and Svejnar (2008) analyze the problem that arises in the studies that ignore the fact that better or worse firms may be privatized first. They note that there may be several reasons why a government may choose to sequence the privatization of SOEs. First, the government may incur excessively high transaction and congestion costs if it tries to privatize all firms simultaneously. Second, by sequencing it may reveal information about the firms to investors (later buyers may observe the quality of the firms sold earlier) if there is uncertainty about the quality of the firms being privatized, or avoid political opposition to reforms (Dewatripont and Roland, 1995). Finally it may want to sequence privatization so as to avoid unemployment (Aghion and Blanchard, 1994; and Barbara Katz and Joel Owen, 1993).

Gupta, Ham and Svejnar (2008) consider five competing government objectives for privatization: i) maximizing Pareto efficiency through resource allocation; ii) maximizing public goodwill from the free transfers of shares to the public; iii) minimizing political costs stemming from unemployment;<sup>10</sup> iv) maximizing efficiency through information gains and v) maximizing privatization revenues. They use firm-level data from the Czech Republic to test the competing theoretical predictions about the sequencing of privatization and find strong evidence that the firms the government privatized first were more profitable, were firms in downstream industries, and in industries subject to greater demand uncertainty. Privatizing more profitable firms first is hence inconsistent with maximizing Pareto efficiency but it is consistent with the model of maximizing privatization revenues, maximizing public goodwill and minimizing the political cost of unemployment. However, the implication of the political cost model that employment growth in the firm’s industry should affect sequencing is not supported by the results. Gupta, Ham and Svejnar’s (2008) finding that firms in downstream industries and in industries with greater demand uncertainty were more likely to be privatized early suggests that the government placed emphasis on efficiency in the Glaeser and

> 10 Political configurations can influence the pace and timing of privatization, as was found by Bortolotti and Paolo Pinotti (2003) in their study of 21 OECD countries over the period 1977-2002. In particular, the authors found that political fragmentation gave several groups the opportunity to veto or otherwise block large-scale privatization, and hence delay or even halt the process.

13
**Table 2. Percent change in the average annual real wages after a US$100 increase in imports per worker (Egyptian pounds of 2010)**

|Type of w|orker|2009-<br>2010|2009-<br>2012|2009-<br>2015|2009-<br>2017|
|---|---|---|---|---|---|
|||(1)|(2)|(3)|(4)|
|All|Coefficient|0.008|-0.001*|0.003|-0.001|
||t-statistic|(0.24)|(-1.95)|(0.20)|(-1.04)|
||N|249|250|248|246|
|Manufacturing|Coefficient|-0.000|-0.002|0.001|-0.002|
||t-statistic|(-0.24)|(-1.46)|(0.85)|(-0.52)|
||N|121|119|114|110|
|Services|Coefficient|0.014|-0.000*|0.017|-0.002|
||t-statistic|(0.10)|(-1.69)|(0.05)|(-1.00)|
||N|248|250|248|246|
|Males|Coefficient|0.004|-0.001**|0.001|-0.001|
||t-statistic|(0.45)|(-2.09)|(0.45)|(-0.70)|
||N|249|250|248|246|
|Females|Coefficient|-0.002|-0.000|-0.001|-0.001**|
||t-statistic|(-0.61)|(-0.89)|(-0.97)|(-2.57)|
||N|202|201|204|205|
|Low Skill|Coefficient|0.003|-0.000|0.000|-0.001|
||t-statistic|(0.41)|(-0.39)|(0.04)|(-0.79)|
||N|245|247|245|242|
|High Skill|Coefficient|-0.001|-0.000|-0.001|-0.000|
||t-statistic|(-0.36)|(-1.01)|(-0.54)|(-1.07)|
||N|233|236|232|231|
|Young|Coefficient|0.026|-0.001|0.046|0.000|
||t-statistic|(0.05)|(-1.64)|(0.02)|(0.37)|
||N|247|246|245|244|
|Old|Coefficient|0.005|-0.001|0.002|-0.002|
||t-statistic|(0.35)|(-1.60)|(0.32)|(-0.75)|
||N|248|250|248|246|
|Rural|Coefficient|-0.021|0.006|-0.095|-0.003|
||t-statistic|(-0.79)|(0.09)|(-0.15)|(-0.38)|
||N|148|149|147|146|
|Urban|Coefficient|0.004|-0.001**|0.003|-0.001|
||t-statistic|(0.27)|(-2.15)|(0.20)|(-1.15)|
||N|156|156|160|149|

22