rafmacalaba/datause-encoder-data huggingface dataset:
## 37% in the control group.

Second, treatment households are also less likely to resort to coping mechanisms that might harm their future consumption and investment opportunities. This includes a reduction in taking out a loan (control mean = 21%, effect size = -5.6 percentage points, p-value = 0.062) or using children’s labor (control mean = 1.1%, effect size = -1.1 percentage points, p-value=0.026) to cope with a shock. Relying less on loans to cope with shocks implies lower interest payments against future consumption, and a lower risk of default if loans are used mostly for consumption, which is the main reason for borrowing in this population. Lower reliance on loans to smooth the effects of shocks also suggests that households will have relatively more opportunity to borrow more in the future for productive purposes. Relying less on children’s work is a desirable outcome since work could compete with children’s’ schooling and long-term human capital formation.

Third, neither the likelihood of receiving remittances, nor the value of these remittances, were significantly different for treatment and control UP households. Their average value in the past month (USD 3) and regularity of receiving remittances (4.2% of UP households report receiving remittances sometimes or regularly) suggest this is not an important source of support for UP households. Consistent with the increased ability of treatment households to manage shocks, we find that treatment households are also less dependent on external gifts and support to cope with shocks (control mean = 6.1%, effect size = -3.1 percentage points, p-value=0.052).

These results suggest that the TUP program has contributed to improving the ability of UP households to recover from shocks and enhanced access to better coping mechanisms to help protect their potential consumption and investments in the future.

# **6 A Discussion on Mechanisms**

Here we explore the role of income, revenue and asset diversification as a potential tool for treatment households to weather shocks and extend development gains relative to control households. We find that treatment households have a more diversified set of livestock assets driven by the initial livestock transfer, but also benefit from a wider range of non-livestock economic activities and revenue sources than their control counterparts.

First, on livestock assets, Figure V (left) shows the total number of livestock for treatment and control groups from asset transfer to endline, estimated using a single indicator expressed in cow equivalents that allows us to aggregate all types of livestock to facilitate the analysis. The right figure presents the total value 33
|**City**|**Country**|**Region**|**PM25**|**City‐Nat GDP**<br>**per capita**<br>**growth**|**City‐Nat**<br>**Employment**<br>**growth**|**City‐Nat GVA**<br>**growth**|
|---|---|---|---|---|---|---|
|Patna|India|SAR|95.8|0.002|‐0.016|0.026|
|Taizhou, Jiangsu|China|EAP|93.8|0.018|0.017|0.000|
|Heze, Shandong|China|EAP|93.7|0.023|0.027|‐0.007|
|Yangzhou, Jiangsu|China|EAP|93.0|0.015|0.012|0.003|
|Changzhou, Jiangsu|China|EAP|92.7|0.016|0.011|0.004|
|Gorakhpur|India|SAR|91.5|0.008|0.016|‐0.010|
|Huaian, Jiangsu|China|EAP|91.0|0.029|0.027|0.002|
|Jining, Shandong|China|EAP|91.0|0.012|0.016|‐0.006|
|Shangqiu, Henan|China|EAP|91.0|0.026|0.015|0.002|
|Yancheng, Jiangsu|China|EAP|90.7|0.018|0.020|‐0.004|
|Liaocheng, Shandong|China|EAP|90.5|0.014|0.020|‐0.009|
|Fuyang, Anhui|China|EAP|90.3|0.011|0.016|‐0.010|
|Bozhou, Anhui|China|EAP|89.7|0.011|0.024|‐0.016|
|Nanjing, Jiangsu|China|EAP|89.5|0.013|0.021|‐0.008|
|Wuxi, Jiangsu|China|EAP|89.0|0.011|0.010|0.000|
|Xuzhou, Jiangsu|China|EAP|88.7|0.017|0.012|0.003|
|Zhenjiang, Jiangsu|China|EAP|88.3|0.015|0.017|‐0.003|
|Suqian, Jiangsu|China|EAP|87.5|0.025|0.023|0.001|
|Nantong, Jiangsu|China|EAP|87.0|0.017|0.025|‐0.008|
|Zhoukou, Henan|China|EAP|86.7|0.016|‐0.004|0.010|

We now look at each country income group and identify the three most polluted cities. Each group is shown in a different color and the size of the dot surrounding the city indicates the level of pollution. The first thing we notice is that all these cities show an annual mean concentration of PM 2.5 that is above what is considered safe by the WHO (10 μg/m<sup>�</sup> ), even in developed countries. However, these levels vary widely from one income group to another (see Map 1).

_Map 1: The three most polluted cities, by country income group_ 14
**Table A.1: Sampling and completion of SPs in the representative sample**

||**(1)**<br>**Markets**|**(2)**<br><br>**Nu**|**(3)**<br><br>**mber ofprovi**|**(4)**<br><br>**ders**|**(5)**<br><br>**Number**|**(6)**<br><br>**of MBBSpr**|**(7)**<br><br>**oviders**|
|---|---|---|---|---|---|---|---|
|||Total|Public|Private|Total|Public|Private|
|**Panel A: Sampling and completion by market**||||||||
|Total eligible|60|719|144|575|51|23|28|
|Markets selected for SP|46|649|130|519|50|23|27|
|Reasons for not sampling market||||||||
|Remote market|5|||||||
|No eligible provider|7|||||||
|Common cluster market, no provider within village|2|||||||
|Sampled for SPs||247|45|202|28|12|16|
|Not sampled for SPs|14|472|99|373|23|11|12|
|Completed SPs|46|224|36|188|23|9|14|
|**Panel B: Sampling and completion by sector**||||||||
|Public Sector|||(Number of|providers with|whom SPs wer|e completed)||
|At least 1 public provider sampled|22|151|36|115|20|9|11|
|At least 1 public provider completed|20|141|36|105|20|9|11|
|At least 1 public MBBS provider sampled|10|98|21|77|18|8|10|
|At least 1 public MBBS provider completed|9|87|19|68|18|9|9|
|Private Sector||||||||
|At least 1 private provider sampled|44|218|30|188|22|8|14|
|At least 1 private provider completed|44|218|30|188|22|8|14|
|At least 1 private MBBS provider sampled|8|68|5|63|16|2|14|
|At least 1 private MBBS provider completed|7|67|5|62|16|2|14|
|Private and Public Sector||||||||
|Markets with at least 1 public and 1 private provider sampled|20|145|30|115|19|8|11|
|Markets with at least 1public and 1privateprovider completed|18|135|30|105|19|8|11|

Notes: In 5 markets where SP work was over completed, the SP saw a provider other than a sampled provider
# **Figure 9: Share of Households that have any bills in arrears by Income Decile, 2007**

<!-- Start of picture text --> Austria Belgium Cyprus Czech Republic Denmark<br>.6 .6 .6 .6 .6<br>.4 .4 .4 .4 .4<br>.2 .2 .2 .2 .2<br>0 0 0 0 0<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Estonia Finland France Greece Hungary<br>.6 .6 .6 .6 .6<br>.4 .4 .4 .4 .4<br>.2 .2 .2 .2 .2<br>0 0 0 0 0<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Ireland Italy Latvia Lithuania Luxembourg<br>.6 .6 .6 .6 .6<br>.4 .4 .4 .4 .4<br>.2 .2 .2 .2 .2<br>0 0 0 0 0<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Netherlands Poland Portugal Slovak Republic Slovenia<br>.6 .6 .6 .6 .6<br>.4 .4 .4 .4 .4<br>.2 .2 .2 .2 .2<br>0 0 0 0 0<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Spain Sweden United Kingdom<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br><!-- End of picture text --> Source: SILC - EU Statistics on Income and Living Conditions, 2007 22
# **Table 5. WLS Estimates of Relative Wage Growth of Stayers** **<u>(weight=employment by industry in 2007)</u>**

|Explanatory Variables:|(1)<br>Dependent Va|(2)<br>riable: Change|(3)<br>in log wages o<br>average|(4)<br>f stayers minus|(5)<br>change of the<br>|
|---|---|---|---|---|---|
|Imports from the U.S.|0.008**<br>(0.004)|0.006*<br>(0.003)|0.006*<br>(0.003)|0.008**<br>(0.004)|0.008**<br>(0.004)|
|Exports to the U.S.|0.021***<br>(0.007)|0.028***<br>(0.007)|0.024***<br>(0.007)|0.033***<br>(0.007)||
|One-period ahead: Imports from the<br>U.S.||0.000<br>(0.004)|0.000<br>(0.004)|||
|One-period ahead: Exports to the U.S.||0.016***<br>(0.004)|0.015***<br>(0.004)|0.015***<br>(0.004)|0.009<br>(0.005)|
|Related imports from the U.S.|||0.065**<br>(0.033)|0.045*<br>(0.026)|0.078*<br>(0.043)|
|Related exports to the U.S.|||0.110***<br>(0.040)|0.075*<br>(0.040)||
|One-period ahead: Related imports<br>from the U.S.|||-0.037<br>(0.041)|||
|One-period ahead: Related exports to<br>the U.S.|||0.041<br>(0.059)||0.044<br>(0.048)|
|Crisis * Exports to the U.S.||||-0.030***<br>(0.009)||
|Crisis * Imports from the U.S.||||-0.004<br>(0.011)||
|One-period ahead: Crisis * Exports to<br>the U.S.||||0.011**<br>(0.005)||
|Crisis * Related imports from the U.S.||||0.058<br>(0.104)||
|Crisis * Related exports to the U.S.||||0.118*<br>(0.071)||
|Observations|1,132|1,027|1,025|1,025||
|R-squared|0.388|0.426|0.444|0.455||

Robust standard errors in parentheses; errors are clustered by industry. *** p<0.01, ** p<0.05, * p<0.1 Time effects are included but not reported. Crisis dummy equals one during 2008q4 and 2009q1.

51
# **7. Schooling Institutions and Educational Quality**

The earlier evidence suggested that national economies were very dependent upon the quality of their economic institutions. It is hard to have a strongly growing economy without complementary institutions in the labor and product markets, without openness to trade and investments from the outside, and without effective systems of laws and property rights.

Similarly, we suggest, it is difficult to have a highly functioning education system without a supportive institutional structure. On this matter, however, there are more different opinions and perhaps a wider divergence in outcomes. Part of the reason for the divergent opinions is simply a lack of sufficient experience, analysis, and evidence.

This having been said, we see some clear general policies that are important. Foremost among these, the performance of a system is affected by the incentives that actors face. That is, if the actors in the education process are rewarded (extrinsically or intrinsically) for producing better student performance, and if they are penalized for not producing high performance, this will improve performance. The incentives to produce high-quality education, in turn, are created by the institutions of the education system – all the rules and regulations that (explicitly or implicitly) set rewards and penalties for the people involved in the education process. Therefore, one might expect that institutional features have important impacts on student learning.

We highlight three institutional features that may be part of a successful system for providing students with cognitive skills: choice and competition; decentralization and autonomy of schools; and accountability. Given the nature of this study and the limited evidence, we are only able to present main ideas here; deeper analyses, in particular of issues of design and implementation in specific contexts, have to be left for more encompassing surveys and collections on the matter (e.g., Hanushek (1994); Peterson and West (2003); Betts and Loveless (2005); and, for developing-country contexts, World Bank (2004), ch. 7; Vegas (2005)), as well as for future research.

## **_7.1 Choice and Competition in Developing Countries_**

Choice and competition in schools were proposed a half century ago by Friedman (1962). The simple idea is that parents, interested in the schooling outcomes of their children, will seek out productive schools. This demand side pressure will result in incentives for each school to produce an effective education system. These incentives will also put pressure on schools to ensure high quality staff in addition to a good curriculum.

68
9 greater than 50 percent in tuie case of the poorer developing countries. However, as in other OSECs, implicit subsidies through insurance ar.d guarantees are not regulated.

III. Trends during the 1970s and the 1980s. and the Profile of OSECs The importance of OSECs in the external financing of developing countr.es can best be captured from statistics on net resource flows. Data on outstanding debt and changes therein can provide a further check. Finally, statistics on new credit commitments are the appropriate indicators of tenuencies and turning poinits in OSEC activity. We make use of all three sets of information in our brief presentation be'cw, acknowledging the well documented statistical and methodological shortcomings underlying the available data.<sup>4</sup>

# Net Flows

The annual net flow of medi%am- and long-term OSECs from OECD to deveioping countries, which was around 2 to 3 billion dollars in the early 1970s, increased to over 13 biilion by the end of the decade (see Table **.);.** In 1977 they accounted for 27 percent of all net external capital flows to deveioping

**4** 4 See World Bank, et al (1989) and Johnson, Fisher and Harris (1990), Appendix .I.

5 Based on data given in Appendix Tables IA and IB, from the Creditor Reporting System (CRS) of the OECD (see OECD (1990b)). The flows are from the member countries of the Development Assistance Committee (DAC) of the OECD.
# Table 8: Program Impact on Cognitive Abilities: Forward and Backward Digit Spans

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|||6-15 years old||6|-12 years ol|d|1|3-15 years ol|d|
||All|Boys|Girls|All|Boys|Girls|All|Boys|Girls|
|School||||||||||
|Meals|0.112|0.327|0.011|0.068|0.377|-0.094|0.369|0.149|0.457|
||[1.103]|[1.221]|[1.087]|[1.191]|[1.288]|[1.159]|[1.004]|[1.282]|[1.062]|
|Dry Rations|-0.005|n.a.|-0.104|-0.213|n.a.|-0.351|0.836|n.a.|1.011|
||[0.948]|n.a.|[0.977]|[1.026]|n.a.|[1.054]|[0.949]|n.a.|[0.965]|
|Girl|-0.142|||-0.168|||-0.178|||
||[0.236]|||[0.284]|||[0.488]|||
|Constant|8.489|7.969|8.73|8.399|7.458|8.854|12.859|13.098|12.189|
||[1.885]***|[2.819]***|[1.676]***|[1.969]***|[2.917]**|[1.721]***|[2.021]***|[3.382]***|[1.962]***|
|Observations|2679|1076|1603|2095|853|1242|584|223|361|
|R-squared|0.42|0.47|0.4|0.43|0.48|0.41|0.36|0.43|0.35|

## Robust standard errors in brackets

* Significant at 10%; ** significant at 5%, *** significant at 1%

Dependent variable is number of correct answers of to backward and forward digital spans.

Regressions also control for child age, orphanhood, parents education, ethnicity, religion and household assets (not reported). Village fixed effects are not included since the dependent variable is measured only during the follow-up.

31
# **Figure B20: Soybean meal**

<!-- Start of picture text --> Log scale<br>8<br>Log price Permanent component<br>7<br>6<br>5<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> <!-- Start of picture text --> Log scale<br>0.75<br>Medium-term cycle Transitory component<br>0.50<br>0.25<br>0.00<br>-0.25<br>-0.50<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 61 —_
# Figure 6. Macroprudential policy

# A. Capital inflow restrictions

<!-- Start of picture text --> Index Index<br>EMDEs<br>Advanced economies (RHS)<br>0.50 0.20<br>0.18<br>0.48<br>0.16<br>0.46 0.14<br>0.12<br>0.44<br>0.10<br>0.42 0.08<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br><!-- End of picture text --> <!-- Start of picture text --> B. Capital outflow restrictions<br>Index Index<br>EMDEs<br>Advanced economies (RHS)<br>0.55 0.21<br>0.53 0.17<br>0.51 0.13<br>0.49 0.09<br>0.47 0.05<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br><!-- End of picture text --> Source: Fernandez et al. (2016) A.B. Based on a database reporting the presence (or absence) of de jure capital controls for 100 countries on an annual basis differentiating between controls on inflows and outflows. This is done for controls on ten categories of assets, including money market instruments, bonds, equities, collective investment securities, financial credits, commercial credits, derivatives, guarantees, real estate, and direct investment.

Figure 7. Policies to strengthen corporate balance sheets

# A. Equity market governance reforms

<!-- Start of picture text --> A. Equity market governance reforms B. Bankruptcy rights protection<br>Number of EMDEs Distance to frontier score, best=100<br>30<br>80 Advanced economy average<br>25<br>2018 2007<br>20 60<br>15<br>40<br>10<br>5 20<br>0<br>0<br>ECA EAP LAC SAR MNA SSA<br>Source: World Bank Doing Business<br>2010 2011 2012 2013 2014 2015 2016 2017 2018<br><!-- End of picture text --> Note: EMDEs = Emerging market and developing economies, EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, and SSA = Sub-Saharan Africa.

A. Number of EMDEs with an improvement in the score for minority investor rights protection from the previous year. B. Distance to frontier score for strength of insolvency resolution. A higher index indicates reforms that improve the business climate. EAP, ECA, LAC, MNA, SAR, and SSA include 19, 20, 30, 18, 8, and 46 economies, respectively. Advanced economies include 34 economies. Based on World Bank Doing Business report.
# **Home islands of civil servants**

A common concern in Comoros is to balance the distribution of civil service jobs relative to the populations of the various islands. The perception of unequal public employment by island is sometimes used as a justification for the island governments to further recruit, claiming that either the Union Government or other Island Governments have been recruiting extensively from the population of the other islands.

**_<u>Chart 5.7   Island of Origin in the Governments of Anjouan, Ngazidja and the Union</u>_**

||_Anjouanais_|_Mohelians_|_Ngazidjans_|**_Total_**|
|---|---|---|---|---|
|Anjouan Government|4,504|12|5|**4,521**|
|Ngazidja Government|284|74|2,827|**3,185**|
|Union Government|841|124|2,058|**3,023**|
|**Total**|**5,629**|**210**|**4,890**|**10,729**|

Source: Author database, 2011.

Chart 5.7 points to two interesting facts. First, and surprisingly, the number of Anjouanais who are civil servants, 5,629 to be precise, exceeds Ngazidjans, who number 4,890, a difference of 15%. Second, the presence of Anjouanais in the Ngazidja government, while small, is notable, with 284 out of the 3,023 in total.

**_Chart 5.8   Island of Origin of Civil Servants, in absolute numbers and as a proportion of population_**

||Anjouanais|Ngazidjans|
|---|---|---|
|Civil Servants|5,629|4,890|
|2003 Island Population|243,732|296,177|
|**Civil Servants as % of 2003 Population**|**2.31%**|**1.65%**|

Source: Source: Author database, 2011; Census of the Union of Comoros, 2003.

The difference in the number of civil servants by island of origin is even more striking in proportion to the total population. According to the 2003 Census, Ngazidjans outnumber Anjouanais by 22%. As such, as a proportion of the 2003 population, Anjouanais represent 2.31% while Ngazidjans represent 1.65%. Such a difference is not driven by a large portion of Anjouanais in the Union government, as shown above, but rather the relatively large Government of Anjouan.

In sum, the data present a mixed picture. On the one hand, Anjouanais are indeed underrepresented in the Union Government. However, because of the size of the Island Government of Anjouan, and particularly the high level of recruitment since 2008, the number of Anjouanais in the civil service exceeds that of Ngazidjans.

# **Respect for recruitment standards: the level of education among teachers**

A common observation, particularly under systems driven by political clientelism, is that the government recruits unqualified candidates. While a comprehensive evaluation of the merits of various civil servants is difficult, the survey focused on the Education Ministry and Commissions, perusing their qualifications in terms of the level of education. The data do not allow an analysis targeted at teachers specifically, as it includes the staff of the Commissions of Education in the Island Governments of Anjouan and Ngazidja. However, only a small percentage of this staff does not represent teachers in the primary, middle or high schools.

14
# **Modelling of Current Air Quality**

The starting points for modelling air quality are the air quality measurements at specific locations in the ZMVM. Figure 2.1 shows the RAMA air quality measurement network.<sup>5</sup> The empirical data from the measurement stations is used to derive an air quality data field for the entire ZMVM. Since the use of an emission database and an atmospheric transport model are beyond the scope of this study<sup>6</sup> , we apply a simple approach to generate the air quality fields. We use measurements at the stations between 1995 and 1999, spatial interpolation in a geographical information system (GIS), and take the average over the institutional units (16 delegations in FD) and 28 municipalities in the State of Mexico.

Because the measurement stations tend to be located in areas with high levels of pollution, information from relatively good air quality areas is patchy. Therefore, interpolating only on the basis of measured data could give unrepresentative results. We avoid such interpolation results by assuming pseudo air quality data at locations where low pollution is expected.<sup>7</sup> Another difficulty was selecting a reference air quality year for comparison with future air quality. Given the variability of meteorological conditions the reference air pollution year-represented as an air quality frequency distribution-was derived from the distributions for 1995-99. This reference distribution preserves the following three baseline statistics: the average over the five-year interval; the standard deviation over the five-year interval; and the maximum concentration in the five-year interval (see Cesar and others 2000 for more details).

We developed the following distribution metrics for air quality:

- for ozone, a daily 1-hour maximum, a daily maximum 8-hour running average, and a daily average;

- for PMIo, a daily average.

Figure 3.2 presents estimates based on the regionally differentiated air quality models for annual average ozone (daily 1-hour maximum) and PM1o (daily average) over the ZMVM during 1995-99.

> 5 The network has 19 ozone stations and 10 PM 1o stations.

> 6The Germnan Fraunhofer Institute developed such a model for CAM during the period of the present<sup>study.</sup> Unfortunately, results of this development were not (yet) usable for the present project.

> 7We consulted experts to make the best estimate of air quality across the ZMVM.

_8_