rafmacalaba/datause-encoder-data huggingface dataset:
overall supply of these inputs in schools is largely outside of the control of school leaders,<sup>8</sup> head teachers can set and enforce policies on allocation and utilization, for example by allocating classrooms and teaching and learning materials to grades and by setting and enforcing policies ensuring the use of materials in the classroom and the right of students to take them home.

Problems of resource utilization extend to the most expensive and important education input, teachers. Malawi’s teachers demonstrate extremely low levels of motivation, attendance, and time on task. At baseline (MLSS, 2018), 13 percent of teachers at sampled schools were absent on the day of data collection, and in 50 percent of observed classrooms the teacher was out of the room when enumerators entered during scheduled lesson time. Teachers’ own estimations suggest that they spend less than 2.5 hours per day teaching. In 10 percent of observed lessons, the teacher was engaged in actual teaching activities for half of the allotted lesson time or less. Only 10 percent of teachers in observed lessons set homework for students, and only 5 percent collected or reviewed homework previously assigned to students, suggesting that thousands of teachers may be routinely giving homework that is never marked.

**Teaching practices:** Malawi’s teachers are as likely as those in neighboring countries to employ a range of positive teaching practices, including correcting students’ mistakes and providing positive reinforcement. However, Malawi’s teachers lag behind on more sophisticated pedagogical techniques: only 24 percent of observed teachers at baseline used teaching aids such as posters and models to support learning; only 22 percent used local information and materials to make lessons more relevant to students; and only 56 percent of classrooms at rural schools have instructional materials such as maps and charts displayed on the walls.

**School cultures:** Many of Malawi’s schools have not successfully developed school cultures in which all students are consistently treated with respect by teachers. Seven percent of head teachers say that teachers in their schools verbally mistreat students at least once per month, and 9 percent of students say that they have been embarrassed by a teacher in front of other students. Issues of respect extend to interactions between students: one-fifth of head teachers say that students physically harm other students at least once per month. In addition, girls achieve significantly lower learning outcomes than boys as early as Grade 4, and overage students also underperform their peers (Asim and Casley Gera, 2024), suggesting that school cultures do not equitably meet the need of all students.

> 8 Teachers and classrooms are allocated to schools by Government, although schools can hire or construct more from available financial resources. Teaching and learning materials are both supplied directly to schools in response to requests by Government and donors, and obtained through grants to schools dedicated to their purchase.

9
- ii. Additionally, _η_  ̃ _c_ could be drawn from those estimated in the first stage (semi-parametric), this is possible to do in the parametric and bootstrapped simulation.

Under Henderson’s method III (H3) there is no defined distribution for the ˆ _ση_<sup>2parameter.Consequently,</sup> bootstrapped samples of the survey data are used to obtain _σ_ ˆ _η_<sup>2and all other parameters for every single</sup> simulation. This approach is also available under ELL methods. The bootstrap is also necessary for the instances when EB is chosen. When EB is chosen, the bootstrap produces a vector of _η_ s and of _σ_ ˆ _ηc_<sup>2</sup> ̃ ˆ for each of the clusters included in the survey and thus for every simulation we draw _ηc ∼ N_ � _ηc,_ ˆ _σηc_<sup>2</sup> �. For clusters not included in the survey the drawing is: _η_ ̃ _c ∼ N_ �0 _,_ ˆ _ση_<sup>2</sup> �.

3. _e_ ̃ _ch_ can be drawn from a normal distribution with variance such as the one specified in equation 8. This is done by:

- (a) Drawing _α ∼ N_ (ˆ _α,_ Var(ˆ _α_ )) (it may also be the product of the bootstrap process) and using it on _′_

- the census data, we obtain a new _D_ = exp� _Zch_<sup>_α_�</sup> , and using the new _D_ we obtain in conjunction with the first stage _A_ = 1 _._ 05 max � _e_ ˆ<sup>2</sup> _ch_ � and Var(<sup>�</sup> _r_ ) we get:

From this it is possible to draw _e_ ̃ _ch ∼ N_ �0 _,_ ̃ _σe,ch_<sup>2</sup> �.

- (b) As an alternative it is also possible to draw from the estimated _e_ ˆ _ch_ from the first phase (semiparametric). In the case of the ELL variance estimation with a parametric drawing of _β_<sup>_′_</sup> _s_ then this will be done from one _N ×_ 1 vector, when performing bootstrap the drawing is done from the _N ×_ 1 vector corresponding to each simulation.

- (c) Under the assumption of homoskedasticity, the error terms are drawn from _e_  ̃ _ch ∼ N_ �0 _,_  ̃ _σe_<sup>2</sup> �.

4. Once all simulated parameters have been obtained, it is possible to obtain the simulated vector of target values:

- (a) This is repeated multiple times (usually 100) in order to obtain a full set of simulated household welfare vectors.

If the interest is small area estimates of income and poverty, once all simulations are finalized a set of incomes for all observations in the target data may be used to obtain _R_ simulated poverty, or inequality rates for each domain of interest. Making use of the _R_ indicators produced, it is possible to obtain the mean indicator for each domain of interest (i.e. municipality in the context of a poverty map), as well as the associated standard errors for the indicator.

# **5 Computational Considerations**

The first stage of the analysis is standard procedure among the typical Stata commands, since the analysis is done on survey data. There are no considerable computational requirements, and the analysis may likely 12
Table 8 Summary statistics of non-monetary indicators used in consumption model, by GHS wave

||2010/11 GHS|2012/13 GHS|2015/16 GHS|2018/19 GHS|
|---|---|---|---|---|
|North-Central|13.78%|13.27%|13.93%|14.07%|
|North-East|9.80%|11.12%|10.18%|8.28%|
|North-West|21.03%|20.21%|20.16%|19.51%|
|South-East|14.53%|15.60%|14.41%|13.13%|
|South-South|15.95%|16.11%|16.11%|17.90%|
|Gender|84.34%|82.21%|79.14%|80.17%|
|Dependency Ratio|43.9%|44.75%|44.05%|43.09%|
|Employed: waged|20.70%|17.42%|16.63%|17.21%|
|Employed: non-farm|41.97%|42.65%|40.99%|39.74%|
|Main floor material: cement|69.96%|70.39%|69.09%|71.39%|
|Main cooking fuel: wood|66.14%|67.33%|64.71%|60.57%|
|No toilet|19.85%|23.28%|24.31%|29.03%|
|Air Conditioner|2.29%|1.66%|2.62%|1.68%|
|Washing Machine|0.57%|0.44%|1.52%|1.96%|
|Cars and other vehicles|10.43%|9.48%|11.90%|9.99%|
|Generator|22.82%|25.74%|31.76%|25.49%|
|Microwave|2.23%|2.33%|2.94%|1.74%|
|TV Set|42.86%|44.89%|50.15%|47.73%|
|Computer|4.61%|4.48%|4.78%|4.17%|
|Imported rice|49.11%|52.12%|53.87%|47.13%|
|Beef|47.27%|45.36%|44.55%|43.91%|
|Fish-fresh|15.32%|12.65%|12.98%|15.71%|
|Recharge cards|56.90%|64.54%|79.68%|85.07%|

Note: the table shows the average value of non-monetary indicators used in the consumption model for survey-to-survey imputation purposes for each wave of the GHS. For the 2018/19 GHS we use a zone-weight adjustment using population weights from the official 2018/19 NLSS to ensure comparability.

Table 9 Imputed poverty headcount rates at the US$1.90 and US$3.20 poverty line, by GHS wave

||Poverty rate US$1.90|95% Confide|nce Interval|
|---|---|---|---|
|2010/11 GHS|43.54%|40.97%|46.11%|
|2012/13 GHS|42.49%|39.77%|45.20%|
|2015/16 GHS|40.75%|37.30%|44.20%|
|2018/19 GHS|41.88%|38.31%|45.44%|
||Poverty rate US$3.20|95% Confide|nce Interval|
|2010/11 GHS|72.88%|70.45%|75.32%|
|2012/13 GHS|72.12%|69.58%|74.67%|
|2015/16 GHS|70.44%|67.00%|73.89%|
|2018/19 GHS|72.28%|69.22%|75.34%|

Note: the table shows imputed poverty estimates for each wave of the GHS. We develop a consumption model using data on 23 non-monetary indicators and household consumption available in the 2018/19 NLSS. Using the estimated parameters, we then impute in each round of the GHS using the same non-monetary indicators and 100 imputations. The imputed consumption vector is then converted to 2011PPP and poverty estimates are reported at the US$1.90 and US$3.20 poverty lines. Poverty estimates are reported with the respective 95 percent CI.

34
Finally one may consider contagion effects resulting from the presence of other countries defaulting. A country's default may make it more likely that other developing countries will default through several channels. Firstly, it may reduce the political costs and reputation effects of defaulting, as other countries are also defaulting. Secondly, once one country defaults, this is likely to have a detrimental effect on the availability of external funds to other developing countries and hence on their debt-servicing ability. We attempt to account for such effects by means of a variable indicating the number of other countries defaulting in the current period.<sup>8</sup> We use defaults both to the IBRD, and to the Paris Club for _ffiis_ purpose.

<!-- Start of picture text --> Figure 4: G7 CA Balance/GDP and<br>OECD Business Cycle<br>3.000<br><, 1.000 E r<br>- G7 CA/GDP<br>0 .1.000 -a Business Cycle<br>~ 2.000<br>-3.000<br>year<br><!-- End of picture text --> Source: World Development Indicators 1999 CDROM, Washington DC, The World Bank.

# 3.4. **Arrears history**

Default or nonaccrual on an IBRD loan is said to occur when a country is more than 180 days in arrears on its payments. Before this occurs, loans with arrears exceeding shorter periods also have consequences for countries - for example after 60 days, disbursements are suspended on outstanding loans.<sup>9</sup> One might suspect that the frequency with which a country goes into arrears of shorter periods could be a harbinger of future defaults, and hence our intention was to include some form of repayments history as an explanatory

> B This eliminates the endogeneity problem which would occur if we used total number of defaulting countries in the given year.

> **9** Prior to July **I t,** 1991, a suspension warning was issued after 60 days, with disbursements suspended after 75 days, the new cut-off dates are 45 days and 60 days respectively.

12
18 Table 1- Percentage distribution of types of household in the Ethiopian 2005-06 HICES

|Extended households|34.7% (7401)|
|---|---|
|Nuclear/biological children of head-spouse|34.3% (7298)|
|Nu<br>p<br>clear/ non-biological children of head-s|17.6% (3751)|
|Nuclear/o<br>on, etc.)<br>ther types (one pers|13.4% (2849)|
|All Nuclear Types|65.3% (13898)|
|All Household Types|100% (21299)|

|ase/non-purchase of selected adult goods<br>Table 2- Survey incidence of purch<br> <br>|
|---|
|Adult Personal effects and services*<br>24.2% (7606)|
|Adult Clothing<br>35.2% (5656)|
|Coffee<br>26.2% (5222)|
|Tobacco<br>36.5% (7872)|

*mostly shaving-related items and services, perfume, hand bag, wallets, sticks, wig, etc.
# **1. Introduction**

Climate change, which is expected to increase global mean temperatures by 0.9 degrees to 5.4 degrees Celsius by the end of this century (Hsiang and Kopp 2018; IPCC 2014), poses a challenge to economic growth and poverty reduction in emerging markets and developing countries (EMDEs). Warming is associated with large declines in GDP (Bilal and Kanzig 2024; Burke, Hsiang, and Miguel 2015; Dell, Jones, and Olken 2014). Extreme heat increases mortality and morbidity (Ebi 2021), worsens performance among students (Garg, Jagnani, and Taraz 2020; Graff Zivin, Hsiang, and Neidell 2018), necessitates costly migration (Hoffmann et al. 2020; Mueller, Clark and Kosec 2014), reduces agricultural yields and lowers labor productivity (Aragon 2021; Somanathan; Sudarshan, and Tewari 2021; Zhang, Malikov, and Miao 2024).

Natural disasters, which are expected to become more frequent and intense due to climate change, also wreak economic damage. Floods, for example, increase mortality and morbidity (Ahern et al. 2005), cause school closures (Dahlin and Barón 2023), reduce agricultural wages (Banerjee 2010; Mueller and Quisumbing 2011) and industrial output (Balboni, Boehm, and Waseem 2023), dampening long-term growth (Krichene et al. 2021).

Poor households are especially vulnerable to these impacts of climate change. They have fewer resources to invest in adaptation, use lower-quality housing and infrastructure, and have less access to post-disaster relief mechanisms than better-off households (Carter 2007; Anttila-Hughes and Hsiang 2013; Hallegatte, Fay, and Barbier 2018). They also tend to be disproportionately dependent on agriculture and informal microenterprises, which are worse placed to adapt to climate change than large firms (Rexer and Sharma 2024).

The poor may also be more _exposed_ to climate shocks because they are less able to relocate to safer places and invest in environmental protection. We explore this question in the context of South Asia by empirically examining the relationship between household wealth and two climate shocks which are very salient to the region: extreme heat and floods. The region’s average maximum temperature is 30 degrees Celsius, which is the heat threshold for a threat to occupational safety and health in the United States (Occupational Safety and Health Administration 2017). South Asia is predicted to experience more extreme heat as a result of climate change (Watts et al. 2017). The region’s average share of land area that is flooded is above the EMDE average. The region is also expected to experience an increase in extreme rainfall 2
1972 interacted with age-cohort dummies. Following Duflo (2000;2001), I also add interactions between age-cohort dummies and the allocation of water and sanitation programm, the second largest INPRES program administered at the time.

The fact that younger women were more exposed to modernization and associated preference shifters compared to the oldest women in the sample is another potential source of bias. Moreover, younger women were likely to have pregnancies later on and thus benefit from a range of maternal health-related policy interventions during the 1990s such as the midwife program, which were aimed at encouraging of use of formal health services. Younger women’s preference for using formal health services may, therefore, be a consequence of not only their higher level of schooling but also their exposure to modern ideas and improved access to health services. I control for this by including cohort of birth fixed effects and pregnancy-year fixed effects.

The second assumption implies that the instrument is highly correlated with years of schooling and would, therefore, be a good predictor of schooling in the maternal health care model specified in (15) above. I test this assumption in the first stage of my empirical analysis, where I estimate the effect of the school construction program on education.

# **Selection issues**

As explained in the previous section, this paper is concerned with the effects of education on a cohort of women born between 1950 and 1973. Although the survey contains 28
Both the premium, which reflects the likelihood and expected level of loss, and the insurance guarantee, which is generally set to reflect the expected yield, are parameters that depend upon the underlying yield distribution. Thus, accuracy in the determination of insurance contract parameters is intimately linked to an accurate representation of the distribution of yields. Most crop insurance contracts establish the yield guarantee to reflect a proportion of the expected yield. In the U.S. crop insurance program, growers are able to select to insure from 50-85% of their expected yields. Expected yields are generally measured by taking an average of previous realized yields. In the case of the U.S. program, expected yields are the simple average of the preceding 4-10 years of yields.

The insurance premium is usually expressed as a “premium rate” which represents the dollars paid in premium for each dollar of liability (i.e., as a proportion or percentage of total liability). Abstracting from administrative and program costs, an actuarially fair premium will equal the expected insured loss (expected indemnities). Thus, the insurance premium rate will be expressed as the ratio of expected loss to total liability. Note that the price at which indemnities (yield losses) will be paid is irrelevant to the risk of the policy (assuming that risk is not endogenous to price) and thus to the premium rate. Total premiums and total liability will move proportionally as price is changed and thus their ratio, the premium rate, will be unchanged by price changes.

Consider an insurance contract that will pay indemnities anytime yields fall beneath a certain proportion λ of the expected insured yield μ. Expected insured loss will be given by the product of the probability that a loss will be realized times the expected loss, given that a loss occurs. In other words, Expected insured loss (y) = Emax[λμ-y,0] = Prob[y < λμ][λμ – E(y|y< λμ)].

Assuming one has an adequate representation of the yield density f(y), this is given by: This discussion demonstrates the importance of adequate measurement of the probability distribution function for yields. All key parameters of the insurance contract are related to such a measurement of yield risk. Though straightforward in theory, a number of complicating issues may arise when this is put into practice with actual yield data.

In order to illustrate these concepts, a set of 21 years of regional yield data for groundnuts in an important producing country was assembled. No time trend was found. The average yield is 751.3 kg/ha, the standard deviation is 304.2 kg/ha and the skewness is slightly negative (-0.14). Maximum likelihood procedures were used to estimate parameters of a beta density (the minimum possible yield was fixed at zero while the maximum possible yield was chosen to maximize the likelihood function). In addition, a normal distribution was used to model insurance contract parameters (using the mean and variance calculated from the 21 yield observations). Finally, nonparametric kernel density 17
# 3. **The Data**

Home to the Everest, Nepal is located nearly entirely at the foot of the Himalaya mountains. It is composed of essentially four regions: the Himalayas themselves, which run along the entire Northern boundary of the country and are very sparsely populated; the central valley around Kathmandu; mountains (locally called 'hills' to contrast them with the Himalayas) that run East-West parallel to the Himalayas; and the plain of Terai that borders India to the South and has the best agricultural land (Government of Nepal 1995).

Nepal is largely rural, with 86% of its 21 million inhabitants living in villages or towns of less than 10,000 people. Kathmandu, the capital city and largest urban center, has a population of around half a million people.<sup>3</sup> . There are only 34 cities and towns of 10,000 inhabitants or more, most of which can be found either in the central valley or in Terai. Given the mountainous terrain, communications are generally more difficult within Northern Nepal itself than within Terai. In fact, people living in the remote Northern part of Nepal must trek many hours by foot or bullock cart before reaching the nearest road. Nepal thus offers a perfect testing ground to examine the effect of isolation on spatial specialization and market participation.

The data we use comes from the Living Standard Measurement Survey (LSMS) of 1995/96. The survey covers some 3373 urban and rural households spread among 274 villages or 'wards' distributed over all regions of the country (Figure 1 - Map of Nepal with location of surveyed villages). 28 of these wards are located in Kathmandu alone. As with other LSMS surveys, data coverage is quite comprehensive. For our purpose, information is available on agricultural production, cropping patterns, self-employment in non-farm activities, wage employment by sector, sales and purchases of crops, and migrations. Unlike other LSMS surveys, the Nepal survey also contains detailed information about distance and travel time to markets and towns.

**3421,000** inhabitants in 1991.
38 d, rather than 8I was used (as suggested by the specification tests of appendix B). Because we are working with cross sectional data, the White heteroskedasticity-robust standard errors were calculated in this estimation.

The results of the estimation of equation (6') for LICs and SSA are reported in appendlx tables (B.4.1.) and (8.4.2.), respectively. Table (4.3) presents the estimated marginal effects of the program for both groups. In addition to the above variables described in Section 2 above as potential candidates for the estimation of equation (6'), we introduce an internal shock variable -reflecting the impact of natural conditions on domestic food production-as a regressor (see appendix A for details). Moreover, because of our small sample size, substantially statistically insignificant effects in the panel model for the SSA countries, have been eliminated from the final regression equation.

After controlling for external and internal shocks, the prevailing political atmosphere, and the initial conditions and policy stance followed in the pre-program period by each country; we find that adjustment programs do not seem to have had a statistically significant marginal effect on the growth of EIAL countries of SSA compared to the rest of SSA, while there is some evidence (a 9% significance level) that it increased real growth by 1% in program LICs relative to other nonprogram LICs.

The estimates of table (4.3) regarding saving, show a marginally significant and negative marginal program effect for the SSA comparison, where adjustment programs were estimated to have reduced saving by 8.3 percentage points of GDP; no significant marginal program effect is found for the LICs' case, however. In the case of investment, the adjustment programs appear to have