rafmacalaba/datause-encoder-data huggingface dataset:
In row 6, we omit Spain, because its crisis was already underway when it became a donor in 1980, so that we have no pre-crisis “normal” data for that country. Also, as the donor with the earliest banking crisis, Spain is also potentially a very influential case in estimating when the dampening effects on aid eventually end or reverse. But omitting Spain does not reduce the coefficients at all.

Our counter variable applies only to one crisis per country, so in row 7 we drop Turkey, which had crises beginning in 1982 and again in 2000. Turkey's 1982 crisis, like Spain's in 1975, is also potentially very influential in estimating the longer-term impact on aid. Like Korea, Turkey is also a new donor with large fluctuations around an overall upward trend in aid. Results prove however not to be sensitive to Turkey's inclusion.

In row 8, we drop observations from the onset of a second crisis. Specifically, we drop Turkey's observations for 2000-2007, and the U.S. observation for 2007. Including those years in the sample but ignoring the second crisis can be viewed as measurement error potentially affecting results for our crisis variables. Results in row 8 differ only trivially, however, from those in Table 1. In an alternative test, in row 9, we retain all observations for Turkey and the U.S., but add a second set of dummy variables (shown in the last two table columns) defined with respect to their second crises. Results for the first-crisis variables are unaffected. Aid does not drop significantly with the onset of a second crisis, an unsurprising finding given the small number of observations in our data corresponding to second crises.

Finally in row 10 we net out aid that takes the form of contributions to multilateral aid institutions. These contributions reflect multiyear agreements among donor countries that might be more difficult than many bilateral aid programs to reduce quickly when adverse economic conditions arise. Results in row 10 are not affected much by this change, however, and the negative impact on aid is still fully realized only about 10 years after onset of the crisis.

In Table 3, we exploit the CKLN data on crisis end dates (in equations 1 and 2) and non-systemic crises (in equations 3 and 4), neither of which are coded in LV. Results in this table corroborate the major finding from Table 1 of large and long-lived effects on aid disbursements. The crisis ending dates allow us to test impacts on aid both during the crisis and afterward. For example, the impact in year t+5 of a crisis that began in year t is potentially larger if the crisis is still ongoing than if it has ended. In equation 1, we drop the 42 country-year observations with ongoing crises. Coefficients 12
**Table 4.** Attitude Indicators

||**Condom use at**|**titudes subindex**|**Sexually conse**<br>**su**|**rvative attitudes**<br>**bindex**|**Sexual abuse r**<br>**sub**|**eporting attitudes**<br>**index**|**General at**|**titudes index**|
|---|---|---|---|---|---|---|---|---|
||**One week post**<br>**intervention**|**Six months post**<br>**intervention**|**One week post**<br>**intervention**|**Six months post**<br>**intervention**|**One week post**<br>**intervention**|**Six months post**<br>**intervention**|**One week post**<br>**intervention**|**Six months post**<br>**intervention**|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Treatment students|0.170***|0.100*|0.072|0.133**|0.260***|0.112**|0.240***|0.172***|
||(0.051)|(0.051)|(0.046)|(0.058)|(0.048)|(0.054)|(0.053)|(0.056)|
|Spillover students|0.028|−0.024|0.003|0.075|0.035|0.015|0.026|0.022|
||(0.052)|(0.051)|(0.044)|(0.058)|(0.048)|(0.051)|(0.052)|(0.052)|
|Control for baseline|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|value of dep. var.|||||||||
|Observations|4,390|3,864|4,389|3,896|4,344|3,854|4,391|3,906|

_Source_ : Authors’ analysis from student surveys.

_Note_ : Dependent variable is an index of related questions. All components of the indices are standardized to mean 0 and standard deviation 1, based on the sample frame at baseline. Standard errors clustered at the school level in parentheses. *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1. _Condom use attitudes subindex_ : Respondent disagrees with statements (a) “It’s not right to carry a condom because people may think that I planned to have sex”; (b) “If a woman wants to have sex without a condom, the man must not refuse”; (c) “Only women are responsible for unwanted pregnancies”; respondent is (d) confident if requesting that a condom be used; (e) willing to delay sex if condoms are unavailable; respondent thinks (f) he/she will use a condom in his/her next sexual relationship. _Sexually conservative attitude subindex_ : Respondent thinks that (a) it is not right when people of their age have sex with several partners in the same month; (b) people of their age should wait to have sex; respondent’s answer to (c) age at which men and women should start having sex. Respondent is (d) confident he/she will have sex only when emotionally ready. _Sexual abuse reporting attitudes subindex_ : Respondent thinks that when a teenager is suffering from sexual violence (a) he/she must tell his/her family; (b) he/she must tell the authorities; (c) in case of rape, the afflicted individual must seek medical help; respondent disagrees with the idea that in case of rape the person (d) must not tell anyone. _General attitudes index_ : contains all variables used in the other columns of the table.
**TABLE 7**

**COMPARING LONG‐RUN TRANSMISSION ELASTICITIES**

||_Holtham_<br>_(1988)_|_Gilbert (1989)_<br>_1965:Q1‐_|_Borensztein &_<br>_Reinhart (1994)_|_Baffes (2007)_<br>_1960‐2005_|_Baffes (2009)_<br>_1960‐2008_|
|---|---|---|---|---|---|
||_1967:S1‐_|_1986:Q2_|_1970:Q1‐_|||
||_1984:S2_||_1992:Q3_|||
|**_Non‐energy_**|—|0.12|0.11|0.16|0.28|
|**_Food_**|—|0.25|—|0.18|0.27|
|**_Raw materials_**|0.08|—|—|0.04|0.11|
|**_Metals_**|0.17|0.11|—|0.11|0.25|

**Notes:** Holtham uses semiannual data, Gilbert and Borensztein & Reinhart quarterly, and Baffes along with the present study annual. Gilbert’s elasticities denote averages based of four specifica‐ tions. Holtham’s raw materials elasticity is an average of two elasticities based on two sets of weights. ‘—‘ indicates that the estimate is not available.

**Source:** Holtham (1988), Gilbert (1989), Borensztein and Reinhart (1994), Baffes (2007, 2009).

# **TABLE 8**

**PARAMETER ESTIMATES: INDIVIDUAL COMMODITIES ON ENERGY PRICE INDEX**

||**_μ_**|**_1_**|**_2_**|**_100*_****_3_**|**_Adj‐R_**<sup>**_2_**</sup>|**_ADF_**|
|---|---|---|---|---|---|---|
|**_Wheat_**|3.27<sup>@</sup>|0.30<sup>@</sup>|0.12|‐0.49|0.84|‐4.35<sup>**</sup>|
||(6.50)|(5.02)|(1.49)|(1.07)|||
|**_Maize_**|3.15<sup>@</sup>|0.27<sup>@</sup>|0.13|‐0.74|0.80|‐3.49<sup>**</sup>|
||(6.23)|(4.66)|(0.70)|(1.58)|||
|**_Soybeans_**|3.58<sup>@</sup>|0.26<sup>@</sup>|0.25|‐0.82|0.82|‐3.85<sup>***</sup>|
||(8.11)|(4.92)|(1.51)|(1.83)|||
|**_Rice_**|3.57<sup>@</sup>|0.25<sup>@</sup>|0.32|‐1.62<sup>@</sup>|0.58|‐4.05<sup>***</sup>|
||(5.14)|(2.67)|(0.26)|(2.78)|||
|**_Palm oil_**|4.94<sup>@</sup>|0.35<sup>@</sup>|‐0.01|‐0.95|0.63|‐3.16<sup>**</sup>|
||(6.44)|(3.72)|(0.02)|(1.38)|||
|**_Soybean oil_**|5.25<sup>@</sup>|0.36<sup>@</sup>|‐0.09|‐0.42|0.70|‐2.56|
||(7.83)|(4.13)|(0.39)|(0.53)|||

**Notes** : See tables 4 and 5.

**_Page 26_**
in the slums, attacks or robberies are a constant threat (Moser and Lister 1999). Eighty percent of violent crimes in the Caribbean are committed by men, the majority of whom is under age 35. The direct costs associated with crime are the costs of lives, incarceration, and physical damage to assets, as well as a number of indirect costs such as foregone earnings, lower investment, and less tourism. Moreover, relations and human and social capital are destroyed (Moser and Bronkhorst 1999).

Domestic violence is a considerable problem in Haiti. Table 6.2 shows the percent of married youth who have experienced specific domestic violence. Violence is mainly committed by men and inflicted upon women; as may be expected women tend to report more violence than men. Table 6.2 reveals that forced sex is the most common (reported) offense in households. It will be extremely difficult to change this because many women do not even know they have the right not to be abused by their husbands (World Bank 2002).

In families with a child in primary school, 20 percent of households reported the wife had been beaten in front of the child. Witnessing violence leads to low ability to confront stressful situations and antisocial and self-destructive behavior because of developmental damage (Barker and Fontes 1996). Data from the US show that 75 percent of juveniles have been abused by family members, more than half come from singleparent families, 60 percent have parents who abuse alcohol or drugs, and a third have a brother who has been imprisoned (Ayres 1998). Therefore, it is crucial to improve conditions for youth in their respective households and neighborhoods, in order to decrease crime and violence in the future.

Table 6.2: Domestic Violence Inflicted or Experienced, 2001 (percent)

||Metropolitan|Urban|Rural|Haiti|
|---|---|---|---|---|
|Pushed/grabbed|7.7|5.2|5.6|5.9|
|Slapped|6.2|3.0|4.6|4.6|
|Thrown things at|2.1|1.4|1.6|1.6|
|Kicked|5.4|1.9|2.6|3.0|
|Hit with objects|1.7|1.2|1.2|1.3|
|Choked|3.4|1.0|0.9|1.4|
|Violent behavior|2.9|3.2|2.6|2.8|
|Forced sex|13.8|10.0|11.4|11.6|

_Source:_ Own calculations based on ECVH 2001.

An estimation of the impact of individual characteristics on the probability of being out of school was performed in Section 4. More in-depth estimations have been conducted on a smaller sample, including only one randomly selected individual aged 15 or older from each household, and therefore fewer youth are included than in the previous estimations shown. This smaller sample was used because it contains information on a range of potentially significant variables describing health, crime and security issues, drug use, and domestic violence. In order to use as large a sample as possible, youth as household heads are included.<sup>36</sup> 36 To avoid collinearity, as a result of youth being the household head in around half of the observations, the household head’s education has not been included in the models presented. Consequently, 29
# <u>Village experience</u>

Following Carletto et al. (1999), we also introduce a variable ( _Village_t_ ) reflecting the cumulative effect of village-level adoption. This time-variant variable is computed as the share of villagers who grew snowpeas the year prior to the farmer’s decision to adopt. The effect of the diffusion of snowpeas among fellow villagers on the household decision to adopt is ambiguous: on the one hand, there are possible economies of scale in transport and economies of scope in the diffusion of information that may increase the propensity to adopt. On the other hand, factors like growing competition, crowding out of new adopters and increasing land prices as a result of the adoption of more profitable crops may have an increasingly negative impact on adoption. Over time, this negative impact is expected to become the more influential force, pushing producers to withdraw through competition for markets and resources, as well as reducing productivity as intense cultivation with fertilizers and pesticides leads to toxic buildup in the soil.

# _Village fixed effects_

Finally, we include controls for village fixed effects. While the diffusion of NTX cultivation at the village level is already included, the fixed effects are expected to capture other differences by location, including distance, local infrastructures, as well as the fact that snowpeas were not uniformly introduced to all communities at the same time.

# **III. Data and descriptive statistics**

This study is based on data collected in the spring of 2005 in six communities in the Central Highlands of Guatemala served by the _Cuatro Pinos_ cooperative.<sup>17</sup> The survey design entailed re-interviewing, after a 20-year span, the original households of a survey carried out in 1985 (von Braun et al., 1989).<sup>18</sup> Prior to the 2005 survey, extensive fieldwork was carried out in the fall of 2004 to locate the original sample of households

> 17 The six communities are Santiago Sacatepéquez, Pachalí, San José Pacúl, Santa Maria Cauqué, San Mateo Milpas Altas and El Rejón. The cooperative is located in Santiago Sacatepéquez.

> 18 For details on the original sample design, see von Braun et al. (1989).

16
The next step is to analyze inequality between individuals by assessing how the prevalence of jobs that can be performed from home varies with income. Figure 5 shows how the DN2020 and our internet-adjusted measure vary with income across different groups of countries, based on the ILO database of average wages across occupations. The home-based work-individual income gradient is steepest in high-income countries which have the highest prevalence of telecommutable jobs. At the top income decile in high-income countries, almost 80 percent of the jobs can be performed from home. Accounting for internet access hardly impacts the gradient in rich countries, where internet access is much less likely to be a binding constraint. By contrast, the gradient is the least steep in low income countries, which have few telecommutable jobs to start with. Accounting for internet access flattens the gradient the most in poor countries with limited internet access. The impact is strongest for the higher income people in the least developed countries since they are more likely to have jobs that can be done from home but face internet access constraints. Poorer households, in contrast, do not have jobs amenable to home-based work and are therefore not affected by internet restrictions. In short, COVID-19’s impact is quite different in rich and poor countries. Absent interventions, inequality is likely to rise the most in rich countries, but poor countries have higher shares of jobs at risk.

Figure 6 illustrates these patterns using representative data at the individual level from India, Brazil, Mexico, and Turkey. The chance of having a job that can be performed from home increases with individual income in all countries but less so in India, where labor market vulnerability is most widespread and internet access most limited. A worker in the top earnings decile in India has a 19% chance of having a job that can be done from home, whereas a worker in the bottom percentile has less than 1% chance of having such a job. By contrast, a worker in the top earnings decile in Turkey (which has the highest PPP adjusted GDP per capita in this group) has a 55% probability of having a job that can be performed from home, whereas a worker in the bottom decile only has a 7% chance of having such a job. COVID-19 is thus likely to exacerbate inequality within almost every country, but more so in higher-income countries.

This conclusion is supported by Figure 7, which presents estimates of the impact of COVID-19 on earnings inequality —measured by the Gini coefficient— under alternative assumptions about its impact on incomes. We assume COVID-19 leaves the incomes of those working in jobs that can be performed from home unaffected while all other workers lose, respectively, 30% and 50% of 14
-14-

# (ii) Private Investment

The investment equation is based on an eclectic "accelerator" model. Private fixed capital formation (ie investment net of stock changes) depends, first of all, anticipated future sales, proxied here by lagged output (Y(-l); clearly data on current output are not available when investment decisions are taken). In addition, the real after-tax lending rate (RLEND), converted into a real rate using the GNP deflator, is used to capture the cost of funds.<sup>4</sup> However prevalence of credit rationing and the use of credit subsidies suggest that quantities, in addition to prices, are likely to be important.

This effect was captured, in an admittedly crude way, by including the ratio of credit to the private sector over output (CRD/Y) as an explanatory variable. In addition, capacity utilization in manufacturing (CPUTL) was included as a proxy of the ratio between expected sales and output capacity. The final explanatory variable is less conventional. The (lagged) share of infrastructure investment in total public investment, SHINF, is included in an attempt to assess the impact of allocation of public sector investment on private investment. The econometric results are remarkably good:

- - (5) log(INFKP) 15.68 + 1.24 log(CRD/Y)

(6.49) (4.17) R<sup>2</sup> - 0.79, DW - 1.66, Sample Period 1970-1986, TSLS

- g/ See Chhibber and van Wijnbergen (1988) for documentation of the interest rates and various tax wedges that have been incorporated in the derivation of the lending rate figures, as well as for the theoretical basis and data to estimate the private investment equation.

ChhibborDisk/chhibber-Turkey Paper/ac:07-28-88:1c
- 26 -

changes in parameter estimates; a small change in the coefficients could yield a very different picture of the future. Notably, however, the difference between the dynamic forecasts and one-step-ahead projections is relatively tolerable in any time series.

Figure 8. Monthly Hydrological Forecasets, 2025 (Million m<sup>3</sup> per month) (Vishnugad Pipalkoti, India) (Upper Kotmale, Sri Lanka) <!-- Start of picture text --> 1200 70<br>90% dependable year 90% dependable year<br>Dynamic forecasts Dynamic forecasts<br>1000 One-step-ahead projections (based on IPCC) 60 One-step-ahead projections (based on IPCC)<br>50<br>800<br>40<br>600<br>30<br>400<br>20<br>200<br>10<br>0 0<br>Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep<br><!-- End of picture text --> (Thac Mo, Vietnam) <!-- Start of picture text --> 800<br>90% dependable year<br>700 Dynamic forecasts<br>One-step-ahead projections (based on IPCC)<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec<br><!-- End of picture text -->

# **_Impacts on power generation_**

To focus on the effect of climate changes while controlling for the existing difference in project design, objective and operation, suppose that all plants provide base load energy, meaning that they always operate as long as water resources are available. The individual
the slope in India is more than twice as steep as in the U.S. Approximately, a one standard deviation increase in height is associated with about a 5 percentage point increase in the linear probability of being able to read words among Indian eight year olds.

The results of this paper can be combined with Spears’s (2012a) estimate of the effect of the TSC on children’s height-for-age to verify the mutual consistency of these numbers. Analogously to using the TSC as an instrument, the ratio of the coefficients from this paper and Spears (2012a) form an estimate from different samples of the gradient between height and cognitive ability: The numerator is this paper’s estimate from Table 2. The denominator is Spears’s (2012a) estimate of the comparably-scaled effect of the TSC on height for age. The ratio, an approximately 9 percentage point increase in reading association with a one standard deviation increase in height, is roughly similar to the 5 percentage point slope found by Spears (2012b). The consistency of these results may be reassuring about all of the estimates involved.

# **6 Evidence for pre-program parallel trends**

The estimates of the effect of the TSC in section 5 are only valid under the assumption of parallel trends: on average, districts exposed to more TSC intensity would have evolved similarly to districts exposed to less TSC intensity, if the program had not happened. Although this counterfactual assumption can never be verified directly, here we provide evidence of parallel trends before the program from three sources. First, we show that district-level trends in literacy between the 1991 and 2001 censuses are uncorrelated with TSC intensity in the period studied. Second, we use the India Human Development Survey to show that there was no difference in cohort-to-cohort literacy trends among children slightly too old to have been exposed to the program or among women who have children of the age studied in our sample. Third, we use data from the IHDS to show that there is no association between 21
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