rafmacalaba/datause-encoder-data huggingface dataset:
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41
Sintle Commodity Models. Results of a cocoa buffer stock stabilization effort were reported by Kim, Goreux and Kendrick (1975). This modeling attempt went beyond the then accepted teuhnique of buffer stock simulation which attempted only to demonstrate what levels of buffer stock operation might be necessary to achieve various degrees of price stabilization. Instead it employed control theory such that levels of buffer stock operation could be selected which would be optimal from the point of view of stabilizing price as well as maximizing export. revenue stabilization. A cocoa model also was constructed in that era by Yeung et al. (1979) based on adjustments in the market for cocoa beans and a similar study of the coffee market also was undertaken by de Vries (1975). Some idea of the beginnings of rubber modeling in the 1970's can be obtained from Grilli et al. (1980). That model demonstrated the importance of analyzing rubber consumption by examining market shares of natural relative to synthetic rubber. Technological change proved to be an important factor in this analysis. Measurements of the extent of the substitution between natural and synthetic rubbers took place in relation to movements in relative prices. Although rubber production estimates were introduced exogenously, the model still provided an overall view of rubber market adjustments.

The first attempt to benchmark the progress of this early work took place in the form of the Aarhus conference held by the Bank in 1979 where some nine different models were evaluated, i.e., see World Bank (1981). Already a more advanced econometric rubber market model had been constructed by Grilli, Heterline and Pollak (1981). This time the major purpose was to simulate the impact of exchange rate and taxation policies on the supply and prices of natural rubber. The model dealt with 23
Policy ReseaRch WoRking PaPeR 4666

# **Abstract**

This paper uses a sample of 73 developing countries to estimate the change in the cost of alleviating urban poverty brought about by the recent increase in food prices. This cost is approximated by the change in the poverty deficit, that is, the variation in financial resources required to eliminate poverty under perfect targeting. The results show that, for most countries, the cost represents less than 0.1 percent of gross domestic product. However, in the most severely affected, it may exceed 3 percent. In all countries, the change in the poverty deficit is mostly due to the negative real income effect of those households that were poor before the price shock, while the cost attributable to new households falling into poverty is negligible. Thus, in countries where transfer mechanisms with effective targeting already exist, the most costeffective strategy would be to scale up such programs rather than designing tools to identify the new poor.

This paper—a product of the Development Economics Vice Presidency (DEC)—is part of a larger effort in the department to analyze policy-relevant topics rigorously with the best available information to support decision making. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at sdessus@ worldbank.org, sherrera@worldbank.org, rdehoyos@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Figure 10. Debt Composition of BRI-recipient LIDCs (In percent of total debt) <!-- Start of picture text --> a. By type of financing<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>2007 2012 2016<br>Bilateral concessional Multilateral concessional<br>IMF Bilateral non concessional<br>Multilateral non concessional Bond<br>Other private debt<br><!-- End of picture text --> Source: International Debt Statistics.

<!-- Start of picture text --> b. By risk of debt distress (2016)<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Low Moderate High<br>Bilateral concessional Multilateral concessional<br>IMF Bilateral non concessional<br>Multilateral non concessional Bond<br>Other private debt<br><!-- End of picture text --> Source: International Debt Statistics.

<!-- Start of picture text --> c. By Type of creditor<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>2009 2012 2014 2016<br>Paris Club bilateral Non-Paris Club bilateral<br>Multilateral Bond<br>Other private debt<br>Source: World Bank.<br><!-- End of picture text --> <!-- Start of picture text --> d. By type of creditor and risk of debt distress<br>(2016)<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Low Moderate High<br>Paris Club bilateral Non-Paris Club bilateral<br>Multilateral Bond<br>Other private debt<br>Source: World Bank.<br><!-- End of picture text --> Public debt levels in terms of GDP have increased in EMs with BRI investments, but without affecting their credit ratings (Figure 11). The composition of external debt confirms an increase in the share of bond issuance, as observed in most EMs (World Bank, 2018b) (panel a); but mainly in those countries where debt vulnerability is considered lower (panel b). A growing share of BRI EMs has reached investment grade (panel c);<sup>23</sup> even though in few countries credit quality has deteriorated (panel d). The increased reliance on bonds could increase refinancing risks in a few countries in the medium term, but unlike other EMs there is no increasing trend in the repayment profile.

> 23 Defined as a Moody’s ratings of Baa3 or above.

12
<!-- Start of picture text --> c<br>Z_<br>8<br>3<br>o<br>=<br>30<br>ea<br>3<br>8<br>5<br>Ze<br>2<br>3<br>a<br>a<br>Ss<br>=<br>=<br>oN<br>3“<br>a 1950 1960 1970 1980 1990 2000 2010 2015<br>Expected year of lower secondary school completion<br>———— Northern : Eastern -—-—-—- Provinces excl. N/EAV<br><!-- End of picture text -->
By averaging the values of across all 15 countries, it is possible to estimate the impact of the PPP update on the lower and upper thresholds: = 1.13 . On the other hand, the update in the lower threshold from δδ $ 13 in 2011 PPP to $ 14 in 2017 PPP implies a 7.7% increase, and the change in the upper threshold results δδ̅ in a 15.7% increase. In conclusion, the combined effect of all other factors is -5.3% in the lower threshold and an additional 2.7% in the upper threshold.

The methodological approach presented in this paper differs from previous work in three ways: i) the use of synthetic panels instead of actual panels, ii) the definition of the thresholds, and iii) the inclusion of more countries (15 rather than 3 in the original study). To assess the impact of these differences on the real value (aside from the effect of the PPP update) of the new lines, some additional calculations were done. When applying the proposed methodology to the three original countries (Chile, Mexico, and Peru) using 2011 PPPs, the lower and upper bounds would be 3.8 and 11.4% higher, compared to the $ 13 and $ 70 thresholds, respectively (Table 3). This is the combined effect of working with synthetic panels and introducing a new definition of thresholds. On the other hand, when this same analysis is conducted across all 15 available countries, the lower and upper bounds would be 3.8 lower and 3.4% higher, respectively. As a result, it could be concluded that the overall effect of expanding the universe of analysis from 3 to 15 countries is a decrease of 7.4 and 7.2% in the lower and upper thresholds, respectively.

Table 3. Decomposition of changes to middle-class thresholds

|**Threshold**|**s in 2011**<br>**PPP**|**Original**|**Proposed**<br>**approach with**<br>**3 countries as**<br>**in Lopez-Calva**<br>**& Ortiz-Juarez**<br>**(2014)**|**Proposed**<br>**approach**<br>**with all 15**<br>**countries**<br>**available**|
|---|---|---|---|---|
|**Lower**||13|13.5|12.5|
|**Upper**||70|78|72.4|

Source: Own estimations based on SEDLAC (CEDLAS and the World Bank).

# **V. Impacts of 2017 PPPs on poverty, vulnerable, and the middle class**

In this section, regional, sub-regional and country-level poverty, vulnerable, and middle-class estimates are presented over time with the 2017 PPPs, in comparison with the 2011 PPPs. This analysis helps understand how incidences and the geographic distribution of the poor, vulnerable, and middle-class populations would change when using 2017 PPPs and higher thresholds. Profiles for these populations are presented over time with the 2017 PPPs, relative to the 2011 PPPs. It is important to assess whether this update of PPPs and thresholds significantly impacts the characteristics of these populations. Marked changes in what the poor and the vulnerable look like affect policy design.

16
Table 6: Interaction results from baseline specification using foreign share

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐TFP Ack|erberg_et al_. u|sing country|specific IO‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐T|FP Ackerberg|_et al_. using E|U‐wide IO‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|Overallt‐2|‐0.338***||||||‐0.206||||||
||(0.000)||||||(0.152)||||||
|Overallt‐2* For|‐0.357**||||||‐0.547***||||||
||(0.020)||||||(0.001)||||||
|Entryt‐2||0.006|||‐0.097|‐0.093||‐0.037|||‐0.090|‐0.070|
|||(0.928)|||(0.215)|(0.234)||(0.809)|||(0.526)|(0.615)|
|Entryt‐2* For||0.094|||0.125|0.148||0.110|||0.214|0.230|
|||(0.332)|||(0.340)|(0.261)||(0.358)|||(0.253)|(0.221)|
|Conductt‐2|||‐0.186***||‐0.207***|‐0.212***|||‐0.122*||‐0.131*|‐0.153**|
||||(0.000)||(0.000)|(0.000)|||(0.100)||(0.084)|(0.045)|
|Conductt‐2* For|||‐0.245***||‐0.212**|‐0.190**|||‐0.421***||‐0.412***|‐0.394***|
||||(0.003)||(0.018)|(0.034)|||(0.000)||(0.000)|(0.000)|
|FDI Overallt‐2||||0.075||‐0.069||||‐0.093||‐0.171***|
|||||(0.120)||(0.133)||||(0.114)||(0.000)|
|FDI Overallt‐2* For||||0.057*||‐0.092*||||0.059||‐0.066|
|||||(0.067)||(0.099)||||(0.257)||(0.394)|
|FE firm|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐YES‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|FE c‐y|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐YES‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|FE s‐y|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐YES‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|Observations|4983642|5431331|5064175|5848487|4983642|4983292|4966253|5413942|5046786|5818544|4966253|4965903|
|R2A|0.982|0.981|0.982|0.985|0.982|0.982|0.982|0.981|0.982|0.985|0.982|0.982|
|R2W|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|RMSE|0.130|0.135|0.129|0.135|0.130|0.130|0.130|0.135|0.129|0.135|0.130|0.130|

Note: * p<0.10; ** p<0.05; *** p<0.01. The dependent variable is in log. Robust standard errors clustered at the country‐industry‐year level.

34
Schwartzstein, Joshua. 2014. “Selective Attention and Learning.” _Journal of the European Economic Association_ 12 (6): 1423–52. https://doi.org/10.1111/jeea.12104.

Sheahan, Megan, and Christopher B. Barrett. 2017. “Ten Striking Facts about Agricultural Input Use in Sub-Saharan Africa.” _Food Policy_ 67: 12–25. https://doi.org/10.1016/j.foodpol.2016.09.010.

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43
# 1 INTRODUCTION

Policy makers and practitioners in developing and developed countries often invest heavily in brief in-service teacher training to enhance education outcomes. Spurred by the targets of the Millennium Development Goals (MDGs), developing countries have also rapidly expanded school infrastructure in the past decade and ramped up in-service teacher training. These investments have aimed to satisfy the growing demand for teachers and help improve educational quality (GOM, 2007; Bunyi et al., 2013; Kidwai et al., 2013). However, conclusive evidence on the impact of in-service teacher training on student achievementas measured by a comprehensive set of test scoresscarcely exists, particularly in developing countries. Moreover, the dierential impact of such training on achievement when students and teachers have access to appropriate books to eectively implement the lessons learned during trainingversus when they do not has not been investigated. Previous studies have focused on the individual provision of either teacher training or books and have not examined a potential complementarity between these inputs.

Properly documenting the impact of such investments on student outcomes can address this gap. The few rigorous evaluations of teacher training programs conducted to date suggest a moderate potential to improve student outcomes, but the evidence is mixed. A recent systematic review by Glewwe et al. (2013), which examined impact evaluation studies from 1990-2010, concluded that there is only modest evidence that teacher training improves student test scores. Specically, 11 of the 29 estimates included in their analysis demonstrate positive, signicant impacts (one is signicant and negative). But, only three of these studies were well identied, experimental or based on natural experiments. Other works on the impacts of teacher training also do not provide conclusive positive evidence: improvements in test scores were documented by some (see Jacob and Lefgren (2004); Zhang et al., 2013; Raudenbush et al., 1993), while others nd no evidence (see Angrist and Lavy, 2001; Harris and Sass, 2011; and Lai et al., 2011). Evans and Popova (2014) noted that the type of teacher training also matters; a one-time in-service training might be as eective as long-term peer mentoring/coaching.

With regards to the impacts of books, the same review by Glewwe et al. (2013) revealed that, in general, there is strong, but non-unanimous, evidence for the positive 1
counteract the former effect of more non‐food consumption being captured by the HBS 2011/12.<sup>3</sup> On the other hand, the HBS 2011/12 recall module appears to capture non‐food consumption more comprehensively than the HBS 2007 recall module.

**_Diary‐recall reconciliation:_** Both the HBS 2007 and 2011/12 collected non‐food expenditures through the recall module as well as through the consumption diary.<sup>4</sup> In 2007, the diary and recall module used the same item codes which allowed for comparisons in the reported expenditure for the same item across the recall and diary (though over a different time period). This comparison suggested that neither of the two sources alone captured non‐food expenditures comprehensively in 2007. In the HBS 2011/12, item codes in the diary did not correspond to the recall module and the latter grouped some of the items together that were recorded separately in the diary, which made a comparison of expenditures across the two sources more difficult.

**_Recall periods:_** The HBS 2007 used a uniform 12‐month recall period for all non‐food related expenditures with the exception of rent, while the HBS 2011/12 used recall periods of 1, 3 and 12 months depending on the item. As stated above, the changes in the recall period can have effects on measured consumption and poverty.

**_Survey supervision:_** There is evidence of improved supervision and survey implementation in the HBS 2011/12 compared to the HBS 2007. In particular, the HBS 2007 diary showed a strong pattern of respondent fatigue over time, as the number of transactions and measured consumption declined over the course of the diary. The HBS 2011/12 does not show such a trend, except for a drop from the first to the second day (see Figure 7), which suggests that efforts to improve the quality of data collection paid off.<sup>5</sup>

> 3 For instance, the HBS 2011/12 did not ask for expenditures on personal care items, though enumerators could record such expenditures under ‘other personal articles’ or ‘other expenditures’ in an aggregated manner. Conversely, the HBS 2007 included ‘personal care items’ as a separate category and enumerators recorded all expenditures item‐by‐item.

> 4 This is despite efforts by the NBS to minimize overlap, see the discussion above and the Poverty Assessment for Mainland Tanzania for further details.

> 5 While the HBS 2007 started each diary at the beginning of the month, the HBS 2011/12 staggered the beginning of the diary across the month. This helped disentangle to what effect the pattern in HBS diary consumption was influenced by patterns over the course of the calendar month (e.g. related to pay days) or to enumerator fatigue.  There is no strong pattern of declining consumption over the course of the calendar month, which suggests that this was not the reason for the decline in transactions in the HBS 2007.

18