rafmacalaba/datause-encoder-data huggingface dataset:
**<u>Table 4 Results of model uncertainty analysis</u>**

|<br>Mobility indicator|<br>Baseline<br>coefficient|<br>Positive<br>(%)|<br>Positive<br>and significant<br>(%)|Negative<br>(%)|Negative<br>and significant<br>(%)|Robustness<br>ratio|
|---|---|---|---|---|---|---|
|Intergenerational|||||||
|persistence|||||||
|HIC|4.582|100|100|0|0|2.666|
|ECA|1.077|100|5|0|0|0.587|
|LAC|1.262|93|0|7|0|0.543|
|Others|2.329|100|28|0|0|1.169|
|Intergenerational|||||||
|correlation|||||||
|HIC|3.951|100|0|0|0|1.280|
|ECA|0.652|76|0|24|0|0.525|
|LAC|5.230|100|94|0|0|1.879|
|Others|4.838|100|23|0|0|1.700|
|Upward mobility|||||||
|gap|||||||
|HIC|0.011|75|0|25|0|0.317|
|ECA|-0.194|0|0|100|76|-1.264|
|LAC|0.297|100|100|0|0|2.116|
|Others|0.348|100|95|0|0|1.784|
|Upward mobility|||||||
|in higher education|||||||
|HIC|-0.214|0|0|100|0|-0.244|
|ECA|5.639|100|100|0|0|3.643|
|LAC|5.824|100|94|0|0|2.325|
|Others|9.510|100|86|0|0|2.448|

29
publications of Customs Notices and Reports at the Australian Customs website, as well as on-site inspection of Customs Documents at the Australian Customs Office in Canberra. Information about Australia’s antidumping process is available as an online public document named “Australia’s AntiDumping and Countervailing Administration”, which can be accessed via the Customs website. For a discussion of Australia’s antidumping process see also Moulis and Gay (2005).

In addition to the standard data described in section 2, the Australian spreadsheets contain additional data, which are described in the table below.

**Table 3.2.1 : Additional Australian Data Included in AD-AUS-v1.0.xls**

|**Column**|**Variable Name**|**Description**|
|---|---|---|
|**In AD-A**|**US-Master :**||
|**U.**|**P_DUTY(MIN)**|The minimum preliminary anti-dumping duty imposed|
|**V.**|**P_DUTY(MAX)**|The maximum preliminary anti-dumping duty imposed|
|**W.**|**F_DUTY (MIN)**|The minimum final anti-dumping duty imposed|
|**X.**|**F_DUTY (MAX)**|The maximum final anti-dumping duty imposed|
|**Y.**|**INIT_GP_ACDN**|The government publication (Australian Customs Dumping Notice)<br>containing the initiation of the case|
|**Z.**|**P_GP_ACDN**|The government publication (Australian Customs Dumping Notice)<br>containing the preliminary findings of the case|
|**AA.**|**F_GP_ACDN**|The government publication (Australian Customs Dumping Notice)<br>containing the final findings of the case|
|**AB.**|**GP_ADD_ACDN**|The government publication (Australian Customs Dumping Notice)<br>mentioning the imposition of antidumping duties|
|**AC.**|**GP_ACDN_TERM**|The government publication (Australian Customs Dumping Notice)<br>mentioning the termination of the case|
|**AD.**|**GP_ACDN_REVOKE**|<sup>The government publication (Australian Customs Dumping Notice)</sup><br>mentioning the revocation of anti-dumping measures|
|**AE.**|**NOTES**|Comments|
|**In AD-AU**|**S-Foreign-Firms:**||
|**D.**|**P_DEC**|The preliminary decision for the case in which the foreign exporting firm is<br>involved|
|**E.**|**F_DEC**|The final decision for the case in which the foreign exporting firm is<br>involved|
|**F.**|**FIRM_D_MARGINS**|The dumping margin assessed for the foreign exporting firm|
|**G.**|**NOTES**|Comments|

17
29 domestic banks to be involved in the investment fund business is to develop closer relationships with existing or potential customers. This may reflect the desire of a domestic bank to have better information and exert control on companies in which they hold debt and to secure representation of at least one seat in a larger number of companies instead of a controlling 20% stake in a smaller number of companies.

**Table 19: Number of companies in which the fund is represented on the board**

||CS|IB|CP|KB|AB|PPF|YSE|CA|HCC|ZB|
|---|---|---|---|---|---|---|---|---|---|---|
|Fund category 11|DB|DB|DB|DB|DB|NB|DB|FB|<br>NB|FB|
|Board representations|<br>333|240|162|138|136|88|ei|60|37|36|

1/ DB=domestic bank sponsored; FB=foreign bank sponsored; NB=non-bank. Remark: for fund names see Table 3. Source: Mejstrik (1994) Although banks are not actually direct owners of companies, they were able to decrease information asymmetries by increasing their governance options. After the creation of a two tier banking system, commercial banks have held most of the outstanding corporate debt. The introduction of a market economy meant that a large number of firms had suddenly become unprofitable and some might not be able to survive. As court-led bankruptcy procedures were poorly developed and the overall economic and legal environment still rather uncertain, banks were likely to experience increasing difficulty in controlling their loan portfolios. After voucher privatization, banks used indirect equity control to monitor firms<sup>39</sup> . Increased corporate governance of banks helped to reduce information asymmetries between enterprises and outside suppliers of financing, reducing the risk of potential failure of both the outstanding loan portfolio and new lending activities. Although the corporate control efficiency of domestic bank sponsored funds might be lower than for funds of the other two categories, the close relationship resulting from banks being represented on boards of portfolio companies has led to significant external monitoring in many Czech firms.

**39** In the German bank based system the feature is slightly different. Banks can hold equity stakes in companies and may increase their influence by using proxy votes from small shareholders.
8 discover that they are HIV negative may decrease their demand for risky sex, especially if prevalence is high among potential sexual partners (e.g. Ahituv et al., 1996)<sup>11</sup> .

# **3 Data**

The data were collected in 2007 and 2008 in 4 provinces of Mozambique (Maputo City, Maputo Province, Sofala, Manica). The survey was designed to collect data in order to assess the impact of the scale-up of ART in Mozambique. The project delivering ART began in 2004 and had a 4-year duration. The HIV/AIDS patients of the survey were identified at the health facility where they received treatment and were interviewed at home along with the rest of the household. The questionnaire includes information on consumption, time use, labor force participation and earnings, and education as well as other health measures of the identified patient and their household members. It also included questions on adherence to treatment, health of adults and children, anthropometric measurements, and quality of life.

A group of comparison households was included in the sample, in which there were no identified HIV positive persons, to control for general trends in socio-economic circumstances. The comparison households were randomly selected in the neighborhood of each HIV household. The first wave of the survey, conducted between August and December of 2007, included 658 HIV households and 341 comparison households. In the second wave, one year later, HIV households that could not be found and interviewed were not replaced, but comparison households were. The panel consists of 896 households interviewed in both waves: 616 HIV households and 280 comparison households. At the individual level, there are 616 identified HIV positive patients, 2579 individuals living in HIV households but not

> 11Using the National Longitudinal Survey of Youth (NLSY-1979) Ahituv et al. (1996), estimate that a 1 percent increase in the prevalence of AIDS in the state of residence increases the propensity to use a condom significantly and up to 50 percent for the most prevalence-responsive groups.
POVERTY AND INFRASTRUCTURE ACCESS IN SLUMS

**Table 11. Highest education level within households**

|||Nairo<br>|bi<br>|||Daka<br>|r<br>||
|---|---|---|---|---|---|---|---|---|
||Poor||Nonpo|or|Poor||Nonpo|or|
|Education level|N|%|N|%|N|%|N|%|
|Less than primary education|164|12.8|59|12.3|379|24.5|87|25.6|
|Completed primary education|628|48.2|205|43.3|725|44.9|123|35.2|
|Some/completed secondary education|486|38.7|200|42.5|376|22.7|96|28.0|
|Postsecondary training|4|0.3|9|1.9|135|7.9|39|11.2|
|Total|1,282|100|473|100|1,615|100|345|100|

_Note_ : Difference between P-NP household is significant at 1% level for both cities.

**Table 12. Employment status across individual adults (>15 years of age)**

|||Nairo<br>|bi<br>|||Daka<br>|r<br>||
|---|---|---|---|---|---|---|---|---|
||Po<br>|or<br>|Nonp<br>|oor<br>|Po<br>|or<br>|Nonp<br>|oor<br>|
||N|Percent|N|Percent|N|Percent|N|Percent|
|Unemployed|873|29.4|135|15.7|592|5.7|98|6.1|
|Self-employed (non-farm)|544|18.3|182|21.1|2,418|23.1|364|22.7|
|Regular employee|646|21.8|308|35.8|798|7.6|146|9.1|
|Casual employee|716|24.1|197|22.9|878|8.4|140|8.7|
|Nonpaid family worker<sup>1</sup>|13|0.4|1|0.1|2,611|25.0|432|27.0|
|Student/apprentice|144|4.9|28|3.3|1,807|17.3|245|15.3|
|Others<sup>2</sup>/Don't know|30|1.0|10|1.2|1,354|13.0|178|11.1|
|Total|2,966|100|861|100|10,458|100|1,603|100|

1 Includes housewife/homemaker 2 Includes pensioner/retired persons and disabled persons

**Table 13. Employment status within households**

||Poor|Nairo|bi<br>Nonp|oor|Po|Daka<br>or|r<br>Non|poor|
|---|---|---|---|---|---|---|---|---|
||N<br>|Percent|N|Percent|N|Percent|N|Percent|
|Self employed a|1,282|18.6|473|22.0|1,442|18.5|321|20.8|
|Casual employed|1,282|27.8|473|27.9|1,442|6.2|321|8.1|
|Regular employed a, d|1,282|23.4|473|36.8|1,442|5.8|321|8.8|
|Unemployed a|1,282|25.4|473|10.5|1,442|4.7|321|4.2|

_Note:_ Statistical significance for difference in the corresponding indicator value between “poor” and “nonpoor” households: (1) In Nairobi: “a”— 1% level; “b”—5% level; “c”—10% level; (2) In Dakar: “d”—1% level; “e”—5% level; “f”—10% level.

35
31

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<u>https://ec.europa.eu/newsroom/just/document.cfm?doc_id=48782</u>

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.

<mark>OECD (2014), Measuring the Digital Economy: A New Perspective, OECD Publishing</mark>
tic investment of developing countries (see, for instance, Agenor and<sup>Montiel</sup> (1999, Chapter 1)) and have been shown to have a significant impact<sup>on per</sup> capita income growth in cross-section and panel data studies (see<sup>Lee(1995)</sup> and Mazumdar (2001)).

These possible modifications and extensions to adapt our framework<sup>to</sup> particular country circumstances also lead us to emphasize the importance of adequate econometric research to estimate key behavioral<sup>relationships</sup> and provide reliable parameters for calibration purposes. For instance,<sup>as-</sup> sessing whether private investment is responsive to the debt-to-taxes<sup>ratio or</sup> not matters a great deal in assessing the incentive effects of debt<sup>reduction;</sup> and determining whether the stock of public capital in infrastructure<sup>has</sup> a large complementarity effect on private investment is crucial to<sup>calculate</sup> the growth implications of alternative investment allocation strategies<sup>by the</sup> public sector. Likewise, estimating the parameters of the rural-urban<sup>migra-</sup> tion function (and whether factors that income differentials matter), as<sup>well</sup> as the skills acquisition function, is essential to assess the medium- and<sup>long-</sup> term effects of policy shocks on poverty. Finally, assessing the sensitivity of the finance premium to the ratio of borrowers' net worth to the<sup>amount</sup> lent is also critical to assess the degree of interaction between the<sup>realand</sup> financial sectors.

77
**Figure 1: Risk Assessments by Neighborhood Regime Type & GDP7**

<!-- Start of picture text --> Civil War Risk Across Regime Neighborhoods<br>35<br>30<br>25<br>20 Democratic Region<br>Autocratic Region<br>15 Anocratic Region<br>10<br>5<br>0<br>GDP<br><$100$100-150$150-175$175-200$200-$400$400-650$650-900$900-1500$1500-2300$2300-3400$3400-5100$5100-9000$9000-12000$12000-17000$17000-24000$24000-37000$37000-51,000$51,000-66,700<br>% Risk<br><!-- End of picture text --> Model 4 demonstrates that the political character of states interacts differently with surrounding regions. Anocratic states in a democratic region have a significantly higher risk of civil war than democratic states in democratic regions. However, civil war risk in all states is heightened by belonging to an autocratic or anocratic region. Again, these interactive regional relationships alter the effect of GDP. Across three regime types (democratic, autocratic, and anocractic), the following patterns are clear: 1) civil war risk within democratic neighborhoods’ hovers between 2-4% across all regime types (see Figures 2-4). This does not vary significantly across income, indicating that democratic neighborhoods have a peaceful effect on member states, regardless of domestic politics; 2) the slopes of war risk at increasing levels of income is similar across autocratic and anocratic neighborhoods, but the proportional decrease is significantly higher in anocratic neighborhoods, 3) anocratic states and regions are the most conflict prone, and anocratic states in anocratic regions

> 7 These estimates for risk assessments by regime neighborhood were created by Clarify (Thoms, Wittenberg & King, 2005) (see <u>http://gking.harvard.edu/clarify/docs/clarify.html). Clarify does not create estimates with</u> GLLAMM models, so these risks are based on logit models. Smaller differences remain, but the general trends are quite similar.

22
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Merhotra, S. 2002. "Basic Social Services for All? Ensuring Accountability through Deep Democratic Decentralization." Background Paper for the 2002 Human Development Report. Geneva and New York: UNDP.

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Reinikka, R. and J. Svensson. 2002. "Coping with Poor Public Capital." _Journal of Development Economics 69_ (1): 51-69.

Republic of Uganda. 2002a. "Leadership Code Act, 2002." Acts Supplement No. 8, Uganda Printing and Publishing Company, Entebbe.

Republic of Uganda. 2002b. "Second National Integrity Survey: Final Report." Kampala: Inspectorate of Government.

Rivera-Batiz, F. L. 2002. "Democracy, Governance, and Economic Growth: Theory and Evidence." _Review of Development Economics 6_ (2): 225-47.

Smarzynska, B. and S. J. Wei. 2001. "Corruption and Foreign Direct Investment: Firm-Level Evidence." CEPR Discussion Paper: 2967.

Stromberg, D. 2001. "Mass Media and Public Policy." _European Economic Review 45_ (4-6): 652-63.

Svensson, J. 2003. "Who Must Pay Bribes and How Much? Evidence from a Cross Section of Firms." _Quarterly Journal of Economics 118_ (1): 207-30.

Tanzi, V. 1998. "Corruption around the World: Causes, Consequences, Scope and Cures." _IMF Staff Papers 45_ (4): 559-94.

Tanzi, V. and H. Davoodi 2000. "Corruption, Public Investment and Growth." Cheltenham UK, Edward Elgar Van Rijckeghem, C. and B. Weder. 2001. "Bureaucratic Corruption and the Rate of Temptation: Do Wages in the Civil Service Affect Corruption, and by How Much?" _Journal of Development Economics 65_ (2): 307-31.

26
<!-- Start of picture text --> 0.000%ee<br>-0.050%<br>o<br>38wn<br>c<br>2 -0.100%<br>£<br>a<br>fo)<br>re)<br>-0.150%<br>-0.200%<br>LSR OBR ABR IBOR IBER<br><!-- End of picture text --> <!-- Start of picture text --> 0.0% a<br>2 5.0%<br>25<br>wy -10.0%<br>3<br>c=<br>-15.0%<br>£<br>S)<br>-20.0%<br>LSR OBR ABR IBOR IBER<br><!-- End of picture text --> <!-- Start of picture text --> & 15<br>n<br>c<br>2<br>o<br>Sa<br>xo}<br>2<br>Cc<br>2 10<br>wn<br>2<br>E<br>oO<br>rasfo}<br>oO<br>Cc<br>Cc<br>2<br>~oO 5<br>a<br>7)<br>°<br>[o}<br>oO<br>Lo)<br>©<br>g<br>a<br>LSR OBR ABR IBOR IBER<br><!-- End of picture text -->
|Country|Year|Source|Stunting<br>headcount|Stunting<br>gap|Stunting<br>gap squared|<br>t-stat<br>for gap|
|---|---|---|---|---|---|---|
|Peru|2008|DHS|27%|10%|6%||
|Syrian Arab Republic|2006|MICS|27%|17%|18%|14.6|
|**Years: 2011‐2013**|||||||
|Benin|2011|DHS|45%|37%|45%||
|Ethiopia|2011|DHS|44%|25%|23%|17.9|
|Lao PDR|2011|MICS|44%|23%|20%|21.6|
|Congo, Democratic Republic|2013|DHS|42%|26%|25%||
|Bangladesh|2011|DHS|41%|19%|16%|11.3|

_Source: Authors’ calculations using DHS and MICS._

# _4.3: Regional Analysis_

Until now we have focused on headcount and gap measures only at the country level. Extending this analysis to the regional level may provide further insight on malnutrition across the world. Therefore, we examine the regional averages of malnutrition. As survey data are not available every year for most countries, only a few countries in a particular region have a survey in a given year, with some regions having no survey conducted in certain years.

There are two common practices for calculating regional averages in such cases: (1) modeling methods and (2) aggregating over a range of years. An example of modeling methods closely related to this study is the UNICEF-WHO-World Bank joint child malnutrition estimates (JME) (UNICEF, WHO, and the World Bank 2018). The JME adopts linear mixed-effect models allowing for random effects at the country level and for heterogeneous covariance structures. One model is fitted for each region or country group for calculating its aggregated number. Such modeling methods are beyond the scope of this study. For simplicity, we chose to calculate regional averages for a range of years following the exercise in Nguyen and Wodon (2015). Thus, we create regional averages for five-year periods: 1993-1997, 1998-2002, 2003-2007, and 20082012. We use these five-year periods so that the middle years of these ranges,1995, 2000, 2005, 17
**Figure 2: Kenyan retail and international wholesale sugar prices At nominal levels (left panel), inclusive of import costs and other standard costs (right panel)**

_Source: No.11 Raw Sugar futures contract from the Coffee, Sugar and Cocoa Exchange, obtained from the International Monetary Fund (IMF). Kenyan retail prices from Kenyan CPI. Note: All prices in Kenyan Shillings per kg. Adjustments in the right panel include VAT, SDL, international shipping, port clearing, inland transport, and retails margins. See appendix for details._

**With the Kenyan market at least somewhat decoupled from the international market by tariff and non-tariff barriers, we look to the domestic supply to understand the origins of the 2011 crisis.** Sugar cane deliveries, sugar production, and sugar imports are all potential contributors to be assessed.  We show below that the price spike in 2011 can be very substantially accounted for by a combination of a very large local production drop and lower than expected imports. High world (and COMESA) sugar prices likely played only a marginal role in the crisis.

**The supply of sugar cane to factories dropped precipitously in mid-2011, with the production of sugar mirroring the change closely (see the left hand panel of Figure 3).** A drop in the supply of cane to local sugar producers just after the middle of the year is a normal seasonal pattern but the drop in 2011 was very large even for the low season. Unfortunately data on cane deliveries are not available over a longer period. However, we do have data on sugar production reaching back to 2000 (see the right hand panel of Figure 3), and we can reasonably infer that production is substantially driven by the seasonality in cane deliveries as illustrated in the left hand panel. At its lowest point in 2011, sugar production fell to the lowest levels in a decade.

**Figure 3: Deliveries of sugar cane and sugar production (left panel) , Sugar production only (right panel)**

_Source: KNBS, author calculations. All series have been smoothed using a four-month moving average (current month and three previous)._ 9