rafmacalaba/datause-encoder-data huggingface dataset:
schools whenever possible.<sup>12</sup> Students for the pupil interview were selected randomly from among those who participated in the written test. At baseline, 2,696 students were interviewed in total: 879 from WSD schools, 920 from Grant-only, and 897 from the control schools.

## **4.5. Teacher content knowledge**

In 2009, we tested teacher knowledge of content: The test was similar to the students' written test, with additional questions drawn from Gambian secondary school reading and math textbooks. A short background interview was also administered to the teachers who took the test.

## **4.6. Qualitative data**

In 2010, we added many open-ended questions to the head teacher interviews to collect some information about their views regarding school management. We addressed similar questions to parents or caregivers in a few households whose children were in the relevant schools. The research team was also heavily involved on the ground for the entire first year of this program; the associated conversations with the government, the schools, and the communities add important information that is useful for a better understanding of the findings.

# **5. Identification, empirical strategy, intermediate outcomes**

## **5.1. Identification and group comparison**

In the design of a field experiment, the goal of employing random assignment to allocate participation in the program is to achieve a situation in which each of the groups has similar characteristics – both observed and unobserved – before the implementation of the program. If the treatment and control groups are balanced at baseline, then differences in teaching activities and student learning outcomes between the groups in the follow up survey can be attributed to the WSD and Grant-only programs, rather than to some preexisting difference between the groups. Using the data from the baseline survey, we examine observed characteristics across the different groups.

We first compare the outcome variable at baseline across groups. Figure 4 shows the distribution of test scores of fifth-grade students on a written test in English, Math, and a combined score. It shows that the baseline performance level of student, across groups, comes from the same distribution. The t-test of comparison of means cannot reject the hypothesis that the underlying distribution of students’ performance at the baseline has the same mean. Similarly, the Kolmogorov-Smirnov test of comparison of distribution does not reject the hypothesis that the distributions of students’ performance are identical

> 12 Most of the students in 5th grade at baseline had finished the basic cycle by the time of the second follow up.

11
Table 1. Cross-sectional treatment effects on payroll reported by the firm

|||||**Dec**<br>|**laration pe**<br>|**riod conside**<br>|**red for end**<br>|**line**<br>||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||**Feb**|**Mar**|**Apr**|**May**|**Jun**|**Jul**|**Aug**|**Sep**|**Oct**|**Nov**|**Dec**|
|soft letter|-0.02|-0.02|-0.02|-0.02|-0.02|-0.02|-0.02|-0.02|-0.01|-0.01|-0.01|
||[0.05]|[0.05]|[0.05]|[0.05]|[0.05]|[0.05]|[0.05]|[0.05]|[0.05]|[0.05]|[0.05]|
|strong letter|-0.06|-0.06|-0.06|-0.07|-0.06|-0.07|-0.07|-0.07|-0.05|-0.05|-0.05|
||[0.05]|[0.05]|[0.05]|[0.05]|[0.05]|[0.05]|[0.05]|[0.05]|[0.05]|[0.05]|[0.05]|
|post=1|0.01|-0.11***|-0.30***|-0.15***|-0.07***|-0.04*|-0.03|-0.02|0.00|0.00|0.03*|
||[0.01]|[0.02]|[0.04]|[0.03]|[0.02]|[0.02]|[0.02]|[0.02]|[0.02]|[0.02]|[0.02]|
|soft letter #||||||||||||
|<br>post=1|-0.01|0.06**|0.10**|0.02|0.03|0.03|0.04|0.04|-0.01|0.02|0.00|
||[0.01]|[0.03]|[0.05]|[0.04]|[0.03]|[0.04]|[0.03]|[0.03]|[0.03]|[0.03]|[0.03]|
|strong letter||||||||||||
|# post=1|-0.03*|0.03|0.05|0.02|0.02|0.01|0.01|0.01|-0.00|-0.01|-0.03|
||[0.01]|[0.03]|[0.05]|[0.04]|[0.03]|[0.03]|[0.03]|[0.03]|[0.03]|[0.03]|[0.03]|
|Constant|11.25***|11.25***|11.25***|11.25***|11.25***|11.25***|11.25***|11.25***|11.24***|11.24***|11.24***|
||[0.03]|[0.03]|[0.03]|[0.03]|[0.03]|[0.03]|[0.03]|[0.03]|[0.04]|[0.04]|[0.04]|
|Observations|10342|10225|10005|10013|10019|10005|9943|9912|9835|9796|9738|

Source: Authors' calculations using historical tax declaration records. Random effects model estimated. Robust standard errors in parenthesis *** p<0.01, ** p<0.05, * p<0.10 _ii. Longitudinal impacts of letters on firm payroll declarations_ The illustrative evidence in Figure 1 above is further supported by the estimation of equation (3), which accounts for both time-variant and time-invariant factors. Figure 2 below (full results in table A1 of the Annex) reports the treatment effects from the random assignment of firms to the soft or strong tone letters (relative to the control group) on the log wage bill (as such, coefficients capture the percent difference in the total wage bill). The coefficients of interest here are the interaction between the month of declaration and the random assignment, which capture the impact of each letter at a specific declaration period relative to the control group at baseline (January).<sup>11</sup> We observe large, statistically significant impacts of the soft letter across all firms in March (6.9% impact on the wage bill relative to no notification in January , p<0.01) and April (10.2% impact on the wage bill relative to no notification in January, p<0.05), but no impacts from the strong letter.

> 11 Not shown in the table are the time effects or the time invariant treatment effects, which highlight that there are no statistically significant differences in payroll across treatment assignment at baseline (January), and that there is a statistically significant drop in payroll declared (relative to January) that persists through November. Full results are provided in the annex.

12
**Table 4: Probability of widowhood or divorce and women’s characteristics: All areas**

||Prob(widow)|Prob(divorce)|
|---|---|---|
|Rural area|-0.040<sup>***</sup>|-0.088<sup>***</sup>|
||(0.014)|(0.018)|
|_Ref: Husband working in the informal sector_|||
|Husband: agricultural sector|0.015|0.042<sup>**</sup>|
||(0.016)|(0.019)|
|Husband: private formal or<br>public sector|-0.036<sup>**</sup>|-0.092<sup>***</sup>|
||(0.017)|(0.019)|
|Husband: other sector|0.109<sup>***</sup>|0.093<sup>***</sup>|
||(0.024)|(0.032)|
|Age at first marriage|-0.005<sup>***</sup>|-0.005<sup>***</sup>|
||(0.001)|(0.002)|
|Ever been to a French school|-0.027<sup>+</sup>|0.064<sup>***</sup>|
||(0.017)|(0.017)|
|Current age|0.010<sup>***</sup>|0.004<sup>***</sup>|
||(0.000)|(0.001)|
|_Ref: Wolof/Lebou_|||
|Serere|0.021|0.014|
||(0.019)|(0.023)|
|Poular|0.010|-0.007|
||(0.015)|(0.018)|
|Other ethnicity|0.058<sup>***</sup>|0.012|
||(0.016)|(0.020)|
|Mean of dep. Var|0.21|0.17|
|N|2593|2467|
|Pseudo R2|0.386|0.066|

Note: Logit model, marginal effects shown. Sample of ever-married women. “Husband’ refers to the one prior to the widowhood or divorce.<sup>***</sup> _p_ < 0.01,<sup>**</sup> _p_ < 0.05,<sup>*</sup> _p_ < 0.1,<sup>+</sup> _p_ < 0.12. Source: Authors’ estimations using PSF1.

35
consumption. In the case of 1993, the amount added was the average non-food spending of households in the third quintile (411 thousand Dong), this being the quintile whose average food intake came closest to 2100 calories per person per day. In the case of 1998, the figure of 41 1 thousand Dong was simply inflated by the value of the price index for non-food items with 1993 as the base year (1.225), giving a non-food element to the poverty line for 1998 of 1287 thousand Dong. We then took out from the non-food elements of the 1993 and 1998 poverty lines amounts to cover the costs of health care. In the case of 1993, people in the third quintile averaged 70 thousand Dong (current prices) per person per year on out-of-pocket payments for health care. We then computed a Laspeyres price index for the health sector for Vietnam for 1998, using data for 1993 _and_ 1998 on contacts per person per year and out-of-pocket payments per contact, broken down by provider type and by quintile of per capita consumption (World Bank et al. 2001). For all quintiles combined, this gave a figure for 1998 of 1.289.2 This compares to a figure for all non-food items of 1.225 and a figure for the overall CPI of around 1.430.3 Applying this index value to the health spending component of the poverty line for 1993 gives a figure for 1998 of 90 thousand Dong (=70x1.289). The non-health poverty lines for 1993 and 1998 were thus 1091 and 1700 respectively, which were then used as values for _D(x)._ As in the case of the deductions for food costs, individuals with a negative ATP were assigned a zero ATP.

The results of this exercise are shown in column [c] of Table 2. Evidently, deductions are less regressive on pre-payment when defined in terms of an allowance for all goods and services (except medical care) than when defined in terms of simply an allowance for food ( _irK_ is less negative). However, since 6is much larger when the more generous deduction is used, the progressivity-enhancing effect of deductions is larger. Out-of-pocket payments emerge as more regressive on ATP when deductions cover nonfood as well as food items, and more regressive than when deductions are set equal to actual food spending. However, the pattern across the two years is the same whichever of the three deductions is used-out-of-pocket payments became more regressive on ATP despite becoming less regressive (in fact becoming progressive having been regressive) on pre-payment income.

2 One rnight argue that the index value for the 3rd quintile ought to be used rather than that for the sample as a whole. There was, however, no discernible trend in the Laspeyres price index across quintiles. The values for the bottom through top quintiles were respectively: 1.085, 1.288, 1.147, 1.009 and 1.304.

3 The lower rate of price inflation in the health sector reflects real reductions in the out-of-pocket payments per contact for all provider types except public hospitals, but this reflected in turn the large reduction in the real price of drugs and medicines-20-30% between 1993 and 1998-more than offsetting the steep rise in fees among all providers, especially public providers _14_
23 from developing countries also helps<sup>explain why the overall trend in processed</sup> food prices was less favorable.<sup>1 6</sup>

# V. Summary and Conlclusion

Two object.ves of irteinaticnal commodity policy have been to Leduce instability in exporter's earnings and<sup>importer's prices through international</sup> (buffer stock) agreements and to encourage<sup>further processing</sup> of domestically produced commodities by develop~ing countries. However, it appears that little attention has been given to potential interrelations<sup>between these objectives.</sup> Specifically, if processed commodities are traded in markets<sup>that are ger.ally</sup> more stable, and if these items experience<sup>more favorable</sup> longer-term price trends, this would be a further previously unrecognized attraction of natural resource-based industrialization strategies. Also, if a significant number of developing countries are shifting their<sup>composition of exports toward processei</sup> commodities, and if the markets for<sup>these items are in fact less unstable, this</sup> could alter the prLcrity attached to negotiating commodity price stabilization agreements.

Using the Wor'd Bank's commodity processing classification scheme this study shows that a major structural shift<sup>occurred from the mid-1960s to late</sup> 1For all nonpetroleum products reported in Table 5 the average annual primary stage price change was 18 percent,<sup>or5points higher than the processed</sup> products. Due to the relatively high weight of foods,<sup>the secular price change</sup> for primary stage products (198 percent) was 3 points higher than<sup>that for</sup> processed goods.

> 17 Alternatively, if a shift to exports of processed commodities leaves a diminishing and relatively small number of developing countries reliant on exports of primary (unprocessed) commodities it may be more attractive to work for export earnings stabilization measures<sup>(like the European Community's STPSEX</sup> program) rather than standard commodity agreements or Common Financial Fund operations.
<!-- Start of picture text --> HB 0.00-0.000.00-0.04 0.04-0.27 ;<br>Wi 0.27-0.97 4<br>r<br><!-- End of picture text --> <!-- Start of picture text --> 0.000-0.001 ;<br>©) 0.002-0.0030.001-0.002 r<br>I 0.003-0.010 ;<br>BH 0.010-0.241 be,<br><!-- End of picture text --> <!-- Start of picture text --> . 6x Pa a<br>4 ty Fae Ci =<br>\wy i<br>0.000-0.002 2 f “a<br>HiMH 0.0 1002 -0. 1001 0 , yy<br>HH 0.100-0.241 }<br>4 ‘7<br><!-- End of picture text --> <!-- Start of picture text -->
- ~~ — r<br>‘<br>0.000-0.002<br>I é<br>HE 0.0 0210 -0. 0110 0 e w<br>HB 0.100-0.241 4<br>v4<br><!-- End of picture text -->
Moors, E.J., A. Groot, H. Biemans, C.T. van Scheltinga, C. Siderius, M. Stoffel, C. Huggel, A. Wiltshire, and C. Mathison, 2011. Adaptation to changing water resources in the Ganges basin, northern India. Environmental Science & Policy 14: 758-769.

- Motoyama, H., O. Takeshi, and T. Yamada, 1987. Winter runoff in the glacierized drainage basin in Langtang Valley, Nepal Himalayas. Data Centre for Glacier Research, Japanese Society of Snow and Ice, Bulletin of Glaciological Research 5: 29-33.

- Nakićenović, N., J. Alcamo, G. Davis, B. De Vries, J. Fenhann, S. Gaffin, K. Gregory, A. Grübler, T.Y. Jung, and T. Kram, 2000. IPCC Special Report on Emissions Scenarios (SRES). Cambridge: Cambridge University Press. ISBN 92-9169-113-5.

Nayava, J.L. and D.B. Gurung, 2010. Impact of Climate Change on Production and Productivity: A Case Study of Maize Research and Development In Nepal. Journal of Agriculture and Environment 11: 59-69.

- National Biodiversity Center (NBC), 2011. National Paper on Biodiversity Persistence and Climate Change. NBC, Ministry of Agriculture and Forests. Royal Government of Bhutan. Available at http://www.nbc.gov.bt/wp-content/uploads/2010/06/National-Paper-on- <u>Biodiversity-and-Climate-Change-_Bhutan1.pdf. Accessed 12 Jan 2012.</u>

- Pandey, B.D., S.K. Rai, K. Morita, and I. Kurane, 2004. First Case of Dengue Virus Infection in Nepal. Nepal Medical College Journal: NMCJ 6:157.

Pandey, A.C., S. Ghosh, and M.S. Nathawat, 2011. Evaluating patterns of temporal glacier changes in Greater Himalayan Range, Jammu & Kashmir, India. Geocarto International 26: 321-338.

Panigrahy, S., D. Anitha, M.M. Kimothi, and S.P. Singh, 2010. Timberline Change Detection Using Topographic Map and Satellite Imagery. Tropical Ecology 51:87–91.

- Patz, J.A., D. Campbell-Lendrum, T. Holloway, and J.A. Foley, 2005. Impact of Regional Climate Change on Human Health. Nature 438: 310-317.

- Pemola Devi, N. and R.K. Jauhari, 2006. Climatic Variables and Malaria Incidence in Dehradun, Uttaranchal, India. Journal of Vector Borne Diseases 43:21.

- Qin, J., K. Yang, S. Liang, and X. Guo, 2009. The Altitudinal Dependence of Recent Rapid Warming over the Tibetan Plateau. Climatic Change 97: 321-327.

Qin, N., X. Chen, G. Fu, J. Zhai, and X. Xue, 2010. Precipitation and Temperature Trends for the Southwest China: 1960–2007. Hydrological Processes 24: 3733-3744.

40
We construct alternative social sustainability indices with three indicators instead of those used in the original indices to assess whether changing the number and composition of the indices has any effect on the results.<sup>2</sup> As can be seen in Figure 7 below, the correlations between P1, P2, P3, P4 and per capita GDP continue to be positive and stronger than for poverty and inequality (as was the case with the original indices with more indicators). Correlations with poverty are no longer negative for all four sustainability dimensions: more resilient societies are now linked with higher poverty levels. This is also the case with inequality: more resilient societies are also found to be associated with higher, rather than lower, inequality. Greater cohesion is now also associated with higher inequality. For the other dimensions of social sustainability, inclusion and legitimacy, their associations with poverty and inequality remain negative. The choice—and number—of indicators has an effect on the strength and robustness of correlations.

**Figure 7. Log per capita GDP in PPP (2017), poverty headcount ratio at national poverty lines and income inequality, with alternative three-indicator social sustainability indices**

<!-- Start of picture text --> Log per capita GDP<br> Social inclusion Social cohesion Resilience Process legitimacy<br>6 8 Log GDP pc 10 12 6 8 Log GDP pc 10 12 6 8 Log GDP pc 10 12 6 8 Log GDP pc 10 12<br>Monetary poverty<br> Social inclusion Social cohesion Resilience Process legitimacy<br>0 .2 Monetary Poverty.4 .6 .8 0 .2 Monetary Poverty.4 .6 0 .2 Monetary Poverty.4 .6 .8 0 .2 Monetary Poverty.4 .6 .8<br>Inequality<br> Social inclusion Social cohesion Resilience Process legitimacy<br>.2 .3 Gini.4 .5 .6 .2 .3 Gini.4 .5 .6 .2 .3 Gini.4 .5 .6 .2 .3 Gini.4 .5 .6<br>Note: Fitted values and 95% confidence interval shown<br>Source: Social Sustainability Global Database 2022.<br>1 1 1 1<br>.8 .8 .8 .8<br>social inclusion .6.4 resilience .6.4 social cohesion .6.4 process legitimacy .6.4<br>.2 .2 .2 .2<br>0 0 0 0<br>1 1 1 1<br>.8 .8 .8 .8<br>social inclusion .6.4 resilience .6.4 social cohesion .6.4 process legitimacy .6.4<br>.2 .2 .2 .2<br>0 0 0 0<br>1 1 1 1<br>.8 .8 .8 .8<br>social inclusion .6.4 resilience .6.4 social cohesion .6.4 process legitimacy .6.4<br>.2 .2 .2 .2<br>0 0 0 0<br><!-- End of picture text --> 2 In the specification reported in the main text, the social inclusion component includes access to labor force participation, one’s own bank account, and improved sanitation. The resilience component includes the average share of assets in a household, share of population that saves money, and share of population with several income sources. The social cohesion component includes trust in people, share of people who are active members of organizations, and share of people who voted. We tried other combinations of three variables per dimension and the results hold. It is the correlation of social cohesion and resilience that become flatter, sometimes holding, at other points reversing the correlation. Results available upon request from the authors.

20
<!-- Start of picture text --> 60<br>" °<br>50<br>ss 40<br>}<br>2<br>=<br>= 30 8<br>“ao5 2010 we e y= 0.0008x+ 13.821<br>.° R2 = 0.8404<br>°<br><!-- End of picture text --> <!-- Start of picture text --> io,<br>0<br>0 10,000 20,000 30,000 40,000 50,000 60,000<br>GDP per capita (USS at 2010 prices)<br><!-- End of picture text -->
> One of the most obvious additional explanation for the differences in water coverage is the institutional arrangements adopted for the sector. Table 1 gives a snapshot of the

> organization of the sector in Africa during the period covered by this analysis. The data is collected from a survey of twenty one Africa water utilities. From a statistical viewpoint, it

> yields an unbalanced panel of twenty one utilities observed over the period 1995-1997 is

> representative of the total of one hundred and fifty water utilities in the region. The salient

> feature are that: (i) most utilities are in the public domain (85,71%); (ii) most of them are also

> water suppliers (77%); (iii) private sector participation in these utilities was very limited

> during the period analyzed (9.52%).

**Table 1: African Water Utility Firms 1995-1997**

|Country<br>|Water<br>**Utility**<br>|Typeof<br>Utility<br>|Areaof<br>Jurisdiction<br>|ScoreofActivity<br>|PrivateSector<br>Participation<br>|
|---|---|---|---|---|---|
|Benin<br>|SBEE<br>|Public<br>|Country<br>|WaterSupply<br>|No<br>|
|Burkina<br>|ONEA|Public|Country|WaterSupply and Sewerage|No|
|Faso<br>||||||
|CBte<br>'|SODECI|Private|Country|WaterDistribution|Leasing|
|dIvoire<br>||||||
|Ethiopia<br>|AA WSA<br>|Public<br>|Municipal<br>|Water Supply<br>|No<br>|
|Ghana|GWSC<br>|Public<br>|Country<br>|WaterSupply|No|
||CWA|Public|Country|Water Supply|No|
|Mauritius<br>||||||
|Morocco<br>|ONEP<br>|Public<br>|Country<br>|WaterSupply<br>|No<br>|
|Morocco<br>|RED<br>|Public<br>|Country<br>|WaterSupply<br>|No<br>|
|Namibia<br>|WM<br>|Public<br>|Province<br>|Water SupplyandSewerage<br>|No<br>|
|Niger<br>|SNE<br>|Public<br>|Country<br>|Water Supply<br>|No<br>|
|Nigeria<br>|KdSWB<br>|Public<br>|Region<br>|Water Supply<br>|No<br>|
|Nigeria<br>|KtSWB<br>|Public<br>|Region<br>|Water Supply<br>|No<br>|
|Nigeria<br>|BoSWB<br>|Public<br>|Region<br>|Water Supply<br>|No<br>|
|Nigeria<br>|EdSWB<br>|Public<br>|Region<br>|Water Supply<br>|No<br>|
|Senegal<br>|SDE<br>|Private<br>|Country<br>|Water Supply<br>|Affermage<br>|
|South<br>|UMGENI|BothPublic<br>|Region|WaterSupplyandSewerage|No|
|Africa<br>||andPrivate<br>||||
|South<br>Africa<br>|RAND-W<br>|Public<br>|Region<br>|WaterProduction<br>|No<br>|
|Togo<br>|RNET<br>|Public<br>|Country<br>|Water Supply<br>|No|
|Tunisia<br>|SONEDE<br>|EPIC2<br>|Country<br>|Water Supply<br>|No<br>|
|Uganda<br>|NWSC<br>|Public<br>|Country<br>|Water Supply<br>|No<br>|
|Zambia<br>|LMSC<br>|Public<br>|Province<br>|Water Supply<br>|No|
|Notes: aPu|blicenterpri|sebutwithap|rivatizedmanag|ement.||

What the table does not tell (partially because there is little specific data on this topic), is that the organization of the sector is shifting towards a community-driven development 4
to all adjacent squares,<sup>25</sup> if that threshold is reached or exceeded. Enumerators are instructed to approach all informal businesses in the area (regardless of structure, including households and hawkers). Enumeration, in turn, develops, networks,<sup>26</sup> which can be given survey weights and considered as geographically representative (Appendix A.1). This method requires many informal businesses to be enumerated, so many that a lengthy (termed here as “long-form”) survey would be prohibitively costly and timely. As a result, a sub-set of businesses were selected randomly for a “longform” interview. In total, 3,687 businesses were enumerated in 129 networks<sup>27</sup> and a total of 515 long-form surveys were completed (see Aga _et al_ , 2017 for a full explanation of implementation).

VI. Econometric Specifications & Descriptive Statistics

# _<u>Main Econometric Specification</u>_

The basic econometric specification uses a probit estimate of bribery, which follows from Eq. 3: Y<sup>*</sup> is a latent variable for the binary outcome of interest, Y. The main outcome measure is BRIBE, which records if an informal business reports having to pay a bribe to remain informal.<sup>28</sup> This measure uses a ‘vague present’ time reference and is not linked to the “last completed month” as are several other variables. Such a direct measure of bribery may be unreliable if respondents are reticent to admit having to pay a bribe (e.g. Kraay and Murrell, 2016), as a result a “yes” value to BRIBE is likely a lower bound.

The log of the last month’s sales (R�) is the main measure of having resources to pay. The data include a monthly measure of revenues, as without formal accounting, businesses’ recall of the last month’s sales is considered reliable and readily available in respondents’ minds. D� is a measure of the opportunity cost of staying informal, though clearly difficult to measure. The survey instrument

> 25 Note that ACS with or without stratification can implement various types of expansion rules, including to squares on the cardinal (N,S,E,W) axes or diagonals.

> 26 In the literature on ACS, “network” is defined to mean all contiguous squares meeting or exceeding the threshold. Networks may be bordered by adjacent “edge units” where informal businesses may be encountered. While such accommodations were used in data collection, for the sake of not introducing further confusion, “network” is used here to include all squares enumerated from the selection of the same initial PSU.

> 27 The actual number of networks enumerated was higher as there were some non-unit networks.

> 28 The exact wording of questions is presented in Appendix A.II.

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