rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> One week after transfer<br>0.5<br>0.4<br>0.3<br>0.2<br>= | —<br>0 i |<br>-0.2<br>-0.3<br>Total Food Non-durables Durables Transfers and<br>fees<br>Two weeks after transfer<br>0.5<br>0.4<br>0.3<br>0.2<br>° |<br>-0.2<br>ao | | p |<br>-0.3<br>Total Food Non-durables Durables Transfers and<br>fees<br><!-- End of picture text -->
Table A11: Impact on aspirations

||Desired number<br>children|Occupation desired<br>High or<br>intermediate|Desired<br>remuneration|
|---|---|---|---|
|T1 (safe spaces)|0.124|-0.079|-0.250|
||(0.131)|(0.060)|(0.387)|
|T1 + Leaders|-0.139|-0.144*|-0.260|
||(0.145)|(0.087)|(0.186)|
|T2 (safe spaces + IGA)|-0.317**|0.030|-0.097|
||(0.141)|(0.047)|(0.199)|
|T2 + Leaders|-0.137|-0.090|-0.040|
||(0.126)|(0.061)|(0.165)|
|T3 (safe spaces + husbands’ clubs)|-0.220*|-0.104|0.202|
||(0.124)|(0.066)|(0.159)|
|T3 + Leaders|-0.291*|-0.066|-0.200|
||(0.166)|(0.082)|(0.298)|
|T4 (control spillover)|0.030|-0.030|0.008|
||(0.109)|(0.050)|(0.203)|
|T1 = T1 + L|.118|.497|.981|
|T2 = T2 + L|.264|.0645|.792|
|T3 = T3 + L|.697|.694|.194|
|T1 = T2|.00916|.104|.717|
|T2 = T3|.548|.0554|.151|
|T1 = T3|.0288|.748|.257|
|T1 + L = T2 + L|.991|.57|.286|
|T2 + L = T3 + L|.39|.795|.604|
|T1 + L = T3 + L|.446|.46|.85|
|N|2,946|2,965|2,993|
|Control mean|5.51|.812|11.6|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, standard errors in parentheses clustered at the locality level. The table presents estimated impacts of our 6 interventions’ combinations and the control spillover relative to the pure control group. The p- values of the difference tests between the treatments are in the second part of the table. All regressions control for the baseline value of the outcome variable, stratification fixed effects, and the following baseline variables: adolescent girl’s age, education level, an indicator for being engaged in an IGA, knowledge of modern contraception, household is rural, household head is female, and at least one household member has an agricultural activity. When the baseline value of the outcome variable is missing, it is imputed and a dummy indicating whether the variable is imputed or not is added as control.

44
increase in stunting.

Furthermore, the results from the statistical tests that are reported in tables 3 and 4, and that are used in gauging the strength and the exogeneity of our IVs lend support to the theoretical arguments presented earlier in section 4.1.<sup>25</sup> The coefficients from the first stage regressions and the Cragg-Donald Wald F statistics from the TSLS estimations discussed above indicate that the direction of the relationship between the IVs and the endogenous variable is as expected and that the IVs are sufficiently strong to avoid weak IV bias. Since an over-identification test could not be run in our preferred, just-identified model that uses Probit-based predicted probability as the IV for the dummy endogenous variable in a TSLS framework, we provide the over-identification test results from the traditional TSLS estimations in which our two IVs are directly included as excluded instruments. We fail to reject the null hypothesis that the instruments are orthogonal to the error term, and this result, which holds true regardless of the child sample of interest, is in favor of the theoretical arguments concerning the exogeneity of the IVs.

Conceptually, our findings highlight some of the conditions that may constrain children from reaching their full mental and physical capabilities, and support the view that commercialized farmers may face a distinct set of disadvantages during food price spikes and in the context of non-separable production and consumption decisions at the household level. Children that originated from tobacco-producing households that were exposed to the shock within their early development window could have suffered from undernutrition with enduring long-term effects on health and educational outcomes (Field, Robles, and Torero, 2009; Almond and Currie, 2011).

A few results associated with the control variables in table 4 are also worthwhile to mention. Male children, on average, fared significantly worse than females in terms of stunting. Children born into higher-income households showed a general positive trend in height-for-age z-scores, although some of the insignificant differences lend weight to the idea that household bargaining might be influencing purchasing decisions.<sup>26</sup> While limited to the sample of children that were exposed to the shock, child access to bed nets, improved housing conditions (proxied by the housing index)<sup>27</sup> , and larger arable land holdings are associated with higher height-for-age z-scores as well.

> 25See Stock and Yogo (2005) for additional information on these tests.

> 26See Zere and McIntyre (2003) for further inquiry into the effects of income distribution on nutritional outcomes

> 27The housing index was constructed using principal components analysis. It takes into account: (i) whether the dwelling unit is a permanent structure, (ii) whether the dwelling has a permanent floor, (iii) whether the household has access to a protected water source, (iv) whether the dwelling has a toilet, and (v) the number of rooms per household

18
those moving in the opposite direction. Once again, Argentina stands out, with the weakening in its capital requirements having been part of the effort to ease regulation in advance of the crisis, with Korea and Japan making similar moves but in the aftermath of their crises. Argentina did not change its official supervisory power, though it should be noted that any weakening in the exercise of these powers is not measured here. There is a more noticeable balance of countries moving to strengthen official supervision, or at least provide supervisors with more explicit power, notably in Korea, Mexico, Malaysia, and to some degree in Russia. Unfortunately, as we will return to below, an increase in supervisory power was not found to be helpful in bank development, performance, and stability in our earlier work (BCL 2006), particularly in countries with a weak institutional environment, and actually was associated with increased corruption in the lending process.<sup>4</sup> Interestingly, the U.K. authorities moved in the opposite direction, and have established a working group, whose report is due shortly, to address concerns about excessive regulation and supervision.

Private Monitoring, a proxy for the third pillar of Basel II, has been found to be positively linked with a number of desirable outcomes in the banking sector, and appears generally to be on the rise in a number of countries, with Mexico once again in the lead. Only a few countries, notably including the United Kingdom and Malaysia have seen a decline in their score on this index.

As with all of these changes, we examined the changes in the individual components of the indices to identify which factors account for the variations in the indices. Thus in the U.K. case, private monitoring weakened slightly because of the change to an affirmative in the response to the question, “Does accrued, though unpaid interest/ principal enter the income statement while the loan is still non-performing? Here the rationale is that allowing accrued but unpaid interest for a non-performing loan makes it more difficult for market participants to perceive the underlying health of a bank. Readers are welcome to investigate the sources of other changes with these tables, using the data on the World Bank’s website, noted above.

We will now turn our attention to a more systematic extension of our earlier research to gauge the impact of the aforementioned changes in the regulatory environment on the development of the banking sector, its fragility, and other outcomes of interest.

> 4  This is based on a survey of bank borrowers on the extent to which they had to pay a bribe to get a bank loan. Since in this effort we controlled for economy-wide corruption, it is not the case that our results reflect countries stepping up supervision in response to greater corruption.

8
# Appendix C. Hazard Simulations

The Centre for Disaster Protection and the World Bank asked Verisk’s AIR Worldwide (AIR) to develop a series of stochastic catalogs of relevant climate variables in Malawi. These would support the ongoing effort to develop a standardized approach to ex ante estimation of the welfare impacts of moderate and severe droughts in SSA, complementing the ongoing empirical analysis. The catalogs would allow a forward-looking assessment of possible future events that goes beyond the reliance on the history of past events (i.e., would allow a more robust probabilistic view of future events).

AIR’s methodology, data sources (table C.1), data validation, and results are set out in Blanchard and Sousa (2021) and summarized below, with extracts largely taken directly from the paper.

## **Developing the Stochastic Catalogs for Malawi**

Under the scope of this project, AIR developed stochastic catalogs of three drought-related variables for Malawi— precipitation, soil moisture, and vegetation index.

The 10,000-year stochastic catalog produced for each variable includes 10,000 samples of annual time histories for the variable of interest, which all reflect conditions that are likely under the current (and near-current) climate.

**Table C.1. Data sets used to generate the stochastic catalogs**

|**Variable**|**Description**|**Data set**|**Resolution,**<br>**frequency,**<br>**period covered**|**Source**|
|---|---|---|---|---|
|Atmospheric<br>moisture|Atmospheric<br>reanalysis of the<br>global climate|ECMWF Reanalysis<br>v5 (ERA5)|0.25°, three-<br>hourly, 1979–<br>2018|ECMWF|
|Precipitation|Atmospheric<br>precipitation<br>estimates from<br>rain gauge and<br>satellite<br>observations|CHIRPS 2.0|0.05°, dekadal<br>(10-day), 2000–<br>2020|CHIRPS|
|Soil moisture|Soil Water Index<br>(SWI) at a depth of<br>40 cm|SWI10-40|0.1°, dekadal,<br>2007–present|Copernicus Global<br>Land Service|
|Vegetation index|Normalized<br>Difference<br>Vegetation Index|MOD13C2 Version 6|0.05°, monthly,<br>2000–2020|MODIS|

_Source:_ Blanchard and Sousa 2021.

_Note:_ CHIRPS = Climate Hazards Group InfraRed Precipitation with Station; ECMWF = <mark>European Centre for Medium-Range Weather Forecasts;</mark> MODIS = Terra Moderate Resolution Imaging Spectroradiometer.

The approach is based on random sampling of stochastic perturbations of historical data to generate stochastic realizations of precipitation values over Malawi. The stochastic catalogs of NVDI and SWI are then derived from the stochastic precipitation catalog.

More specifically, the 10,000-year stochastic precipitation catalog has a spatial resolution of 0.05° and dekadal temporal resolution. It was developed using random sampling of stochastic perturbations of atmospheric moisture data (ERA5 [( <mark>European Centre for Medium-Range Weather Forecasts)</mark> Reanalysis v5]) between 1979 and 2018, at a native resolution of 0.25° and three-hour intervals (as an input to the process). While CHIRPS data could not be used in place of ERA5 at the modeling stage, they were used to augment a climatological adjustment to reflect “real” rainfall readings and climatology compared to ERA5.

41
# Table 5

## Present and Initial Size of Sample Non-Eletrical Machinery Firms, by Penlod of Founding

|Perlodof Founding<br> <br>|<br>P<br>|resen<br>___|t Size|(Numb|er<br>of W|orkers|)|Aver<br>Sl|age<br>ze|
|---|---|---|---|---|---|---|---|---|---|
|and Initial<br>Size|All|<20|20-49|50-74|75-99|100-200|>200|**Start**|**Now**|
|Pre -1980||||||||||
|Initial<br>size||||||||||
|45|7|2|2|1|2|||2.9|4.7|
|5-9|7|0|1|0|0|3|3|6.9|197|
|10-20|8|0|2|1|0|3|2|15.1|255|
|>20|1|0|||||1|32|230|
|na|3||0|<br>1|||2|na|187|
|All|26|<br>2|5|<br>3|<br>2|6|8|9.3|174|
|**1980**<br>and on||||||||||
|Initial<br>size||||||||||
|.<5|3|1|1||1|||3.0|41|
|5-9|6|1|5|||||5.5|27|
|10-20|3|1|2|||||13.3|35|
|>20|3|||1|<br>1|1||50|79|
|na|3|||||2|1|na|135|
|All|18|<br>3|8|<br>1|<br>2|3|1|15.5|57|
|||||||||9.0|42'|
|Whole<br>Sample||||||||||
|Initial<br>size||||||||||
|c5|10|<br>3|3|<br>1|<br>3|0|3|2.9|45|
|5-9|13|<br>1|6|<br>0|<br>0|<br>3|3|6.2|119|
|10-20|11|1|4|1|<br>0|<br>3|2|14.6|178|
|>20|4|||1|1|1|1|45.5|203|
|na|6|1|1||1|<br>1|2|na|161|
|All|44|<br>5|14|<br>3|<br>5|<br>8|8|11.9|130<br>125a|

36
Figure 3.1: INDIA, Kernel density estimates of PF variables (without M.V in PF variables and with M.V in any PF variable) <!-- Start of picture text --> A. Sales B. Materials<br>0 5 10 15 20 25 5 10 15 20 25<br>Log-sales Log-materials<br>Reported sales and the rest of PF vars Reported materials and the rest of PF vars<br>Reported sales with m.v in at least one PF var Reported materials with m.v in at least one PF var<br>C. Labor D. Capital<br>6 8 10 12 14 16 0 5 10 15 20<br>Log-employment (hours per year) Log-capital stock<br>Reported employment and the rest of PF vars Reported capital and the rest of PF vars<br>Reported empl. with m.v in at least one PF var Reported capital with m.v in at least one PF var<br>.3<br>.25<br>.2<br>.2<br>.15<br>Density Density<br>.1<br>.1<br>.05<br>0 0<br>.4 .4<br>.3 .3<br>.2 .2<br>Density Density<br>.1 .1<br>0 0<br><!-- End of picture text --> Notes: Reported X and the rest of PF variables is the distribution of those establishments reporting all PF variables Reported X with m.v in at least one of the rest of P.F is the distribution of those establishments reporting the corresponding PF variable and also reporting at least one missing value in the remaining PF variables Epanechnikov kernel. Each point estimated within a range of 300 values. Source: Authors’ estimations with ICSs data.

41
coefficients calculated before and after the adjustment for the disutility of work. For the first implementation (using the whole NSS), the Gini coefficient rises from 31% to 43%, which is a substantial increase. Comparing the Bihar sample of the NSS with the World Bank’s Bihar survey sample as a whole, the rise in Gini coefficient is clearly higher for NSS Bihar (+0.14 for NSS Bihar versus +0.5 for World Bank survey sample as a whole). We find less change in measured inequality among the un-rationed households, with a Gini coefficient increasing from 27% to 30%, but this may be explained by the fact that the participation rate in casual work is lower in this sub-sample (around 18% of the un-rationed households reported a valid Li and Wi , versus 40% in the sample as a whole).<sup>17</sup> Turning next to assessments of targeting performance, we estimate f<sup>∗</sup> and f for the NSS data (rural households only) using α�=0.52 as suggested by the above results on Ci⁄Wi. The result of this estimation is showed in Figure 5 (top left hand-side). For both f<sup>∗</sup> and f the participation rate decreases as consumption rises. Using the original consumption (without our adjustment) we see that participation rates are lower than average for roughly the richest 50%, but we see no clear sign of better targeting within the poorer half of the distribution ( f(x) is quite flat for x∈[1,50] ). However, after adjusting for the disutility of work, we see much better targeting performance among the poor, particularly for FCi∗(x) ∈[10,40].

ˆ We also test for robustness with respect to the choice of α . For this purpose, we repeat ˆ the analysis using several values for α (Figure 6). Since the first derivative of the adjustment term e<sup>−α�(Li−LR)</sup> with respect to α� is negative for “high” supplier (−(Li −L<sup>R</sup> )e<sup>−α�(Li−LR)</sup> < 0, ∀Li > L<sup>R</sup> ), we expect to observe a greater impact on targeting performance on the lower part of the distribution when using higher α� (the lower e<sup>−α��Li−LR�</sup> the higher the impact). This is confirmed by Figure 6. The second derivative with respect to α� is positive ((Li −L<sup>R</sup> )<sup>2</sup> e<sup>−α�(Li−LR)</sup> ≥0) and we observe consequently a diminishing increase of the impact when increasing α�.

Redoing the same estimation but on the NSS Bihar sample and the Bihar Survey, we find that f(x) is flatter than f<sup>∗</sup> (x) for the poorer half of the distribution (Figure 5). In the Bihar

> 17 Recall the category un-rationed includes also households reporting that they do not want to participate in NREGS works.

12
**<u>Table 7: Determinants of Learning, PISA 2006 - Extended Model</u>**

||Coef.|S.E.|
|---|---|---|
|School determines pedagogy|25.3|(7.99)*|
|School competes for students<sup>*</sup>|35.8|(6.72)*|
|School can fire teachers|14.1|(6.74)**|
|Achievement data used to evaluated teachers|-4.9|(4.91)|
|Public|40.5|(5.64)*|
|Student-teacher ratio|-0.3|(0.13)*|
|Adequate supply of teachers|11.3|(3.75)*|
|Math hours|12.5|(0.37)*|
|Teachers with certificate|-4.5|(4.11)|
|% of students repeating grade|-1.7|(0.39)*|
|Rural (<3,000)|-10.8|(3.45)**|
|9<sup>th</sup>Grade|15.6|(2.51)*|
|10<sup>th</sup>Grade|65.7|(4.03*|
|11th Grade|65.9|(6.25)*|
|Age|-8.2|(1.81)*|
|Female|-19.3|(2.30)*|
|Mother’s education - Primary|5.6|(2.37)*|
|Mother’s education - Lower secondary|9.1|(2.55)*|
|Mother’s education - Upper secondary|13.8|(3.48)*|
|Mother’s education - University|9.7|(3.94)*|
|11–100 books (regressor: none-10 books)|1.0|(1.30)|
|101-500 books (regressor: none-10 books)|1.5|(1.54)|
|One or more computer at home|35.3|(4.26)*|
|Language speak at home (language of test)|-4.5|(2.79)|
|Constant|353.9|(32.13)*|
|Observation|7,746||
|R<sup>2</sup>|0.43||
|Total Sample|10,761|0.72|

_Source_ : Program for International Student Assessment ( PISA) 2006 _Notes_ : *** 90%; ** 95%; *99 Despite the impressive gains that were made by Indonesian students in math in the 2006 PISA, Indonesia still has a long way to go to improve its academic standing. In 2006, almost threequarters of 15-year-olds scored at level 1 and below. Too few students scored at levels 2 and 3, and an insignificant number scored at levels 4 or above. Understanding the reasons why the scores increased in 2006 should help the Government of Indonesia to build on its strengths and make further improvements in the future.

# **Conclusions**

In the 2006 PISA, Indonesia’s score in math increased by 30 points, or 0.3 of a standard deviation, in just three years. We explored the reasons behind this increase by Indonesia’s 15year-old students in relation to various family, student, school, and institutional inputs. We decomposed the change over time into its constituent components using the traditional OaxacaBlinder method, based on the estimation of a cognitive achievement production function. Our decomposition results suggest that almost all of the test score increase was unexplained, or, in 17
on food insecurity.<sup>9</sup> Alternative household behaviors (e.g., receiving aid from family or friends, selling assets or using savings, etc.) also played an important role in buffering the impact of the disaster on household food consumption.

In order to assess the level of food insecurity after the earthquake and to learn more about the efficiency of emergency assistance programs and coping strategies, a second Rapid Emergency Food Security Assessment (EFSA II) survey was conducted in June 2010 by the CNSA and its partners. The survey methodology is similar to the previous one (EFSA I). Several features of this second survey are worth noting. First, although five months had passed since the earthquake, the distributions of pre-earthquake declared wealth—as obtained from both EFSA surveys (February and June surveys)—appear to be very similar. This suggests small recall bias. Second, as households were interviewed long enough after the disaster, the role of assistance programs and the dynamics of poverty can be better assessed. Indeed, we can assess, on the one hand, whether households are better off in June than in February and, on the other hand, whether assistance programs and other strategies have played a role in this recovery. Third, information on households in non-directly affected areas enables us to assess how the hosting of displaced people has impacted households.

According to these surveys, in the directly affected areas, food insecurity has dropped from 31% in February to 27% in June. In this paper, we will use the June survey in order to focus on how adequately assistance is allocated.

Prior to the 2010 earthquake, Haiti was already plagued by chronic poverty: over than three quarters of the population lived in poverty-ridden conditions and one half earned less than one dollar per day. Nearly half of the population had no access to healthcare services, and 58 percent did not have access to drinking water. Furthermore, risks are widespread in Haiti, as people regularly face various forms of covariate shocks—such as increases in food prices, hurricanes, floods, droughts and irregular rainfall—as well as a number of idiosyncratic shocks such as human or animal diseases<sup>10</sup> , casualties or death of a household member.

In the wake of the 2010 earthquake, Hurricane Tomas, the cholera epidemic, the rising prices of basic foodstuffs, and the socio-political uncertainty following the election results of

> 9 One explanation is that, at the time of the February survey, households who got aid shared with those who did not. So, this may muddy the observed relationship between the food consumption score and access or not to food assistance.

> 10 Haiti has had several covariate shocks on animal and plant diseases in recent history. However, declaration of households concerned here their own animals.

3
and their market economic systems seemed to work in<sup>the absence of pluralistic political systems. In</sup> the past few years, however, all three have made significant<sup>transitions toward more open, competitive,</sup> and participatory political systems, suggesting that _sustaining_<sup>as opposed to establishing market based</sup> growth may require the development of political representation.>2<sup>With the renewed interest in open</sup> political systems as a corrollary to open economic systems,<sup>we can expect a new generation of research</sup> on these variables.

This review provides other important lessons for those<sup>interested in the technical aspects of better</sup> governance. Improving governance is a complex and long-term<sup>endeavor, but as Zafar Ahmed (1990)</sup> argues, "One cannot make a tree grow faster by pulling it from<sup>outside; it has to grow from its roots"</sup> (p. 1). It takes generations and perhaps centuries to build effective<sup>bureaucracies; the issue is not simply</sup> skills but _volition --_ and much of that volition comes from<sup>effective social pressure on the state. This</sup> needs to be understood by donors who may wish to make<sup>"governance" the temporary trend of the</sup> 1990s. As Callaghy (1989) argues, "In much of the current<sup>policy work on the development of state</sup> capacity in Third World countries . . . there is a strong<sup>voluntarist or architectonic streak that argues</sup> that state capacity can be 'built' as part of a policy imperative.<sup>The historical record belies this</sup> assumption" (p. 117). The question for donors should be: how can the process<sup>by which</sup> accountability and the rule of law become the norm in a society<sup>best be nurtured?</sup> Institutional economists examine the very long-term conditions<sup>under which the institutions that</sup> underpin both market and state actians are formed. They also<sup>can help explain why different policies,</sup> such as those suggested in structural reform packages, lead to<sup>such different and seemingly unpredictable</sup> results: although outwardly the new rules may seem the same<sup>in two countries, the institutions that</sup> underpin economic responses - local enforcement, behavioral<sup>norms, organizational forms and interests,</sup> "See, for example, TLAI-jen Cheng, "Democratizing the Quasi-Leninist Regime in Taiwan,"<sup>MVorId</sup> Politi 41, no. 4, July 1989; and Guillermo O'Donnell and<sup>Philippe C. Schmitter,</sup><sup>**Transitions ftom**</sup> Authoritarian Rule: Tenative Conclusions about Uncertain Democracies<sup>(Baltimore, MD: Johns Hopkins</sup> University Press, 1988).

40