rafmacalaba/datause-encoder-data huggingface dataset:
measures may fall short in the face of multiple or severe disasters occurring within short timeframes. This indicates that the capacity to adapt and mitigate climate change effects is a critical factor in determining how these events influence poverty and inequality.

In summary, understanding the mechanisms that link climate change and disasters with poverty and inequality is a multifaceted endeavor. While we have gained substantial insights from the focus on agriculture, numerous mechanisms remain relatively unexplored. These might include conflict, migration, and changes in labor productivity, which are promising areas for future research.<sup>13</sup>

# **4. New Estimates for Chronic and Transient Poverty**

# **4.1. Data and Analytical Framework**

Our empirical analysis relies on data from the Subnational Poverty and Inequality Database (SPID), a recently introduced global database mapping poverty and inequality on a subnational scale (Dang _et al._ , 2023a; Nguyen _et al._ , 2023). SPID is compiled from the Global Monitoring Database (GMD), an extensive repository of household survey data managed by the World Bank. The database derives from official national household income and consumption surveys, incorporating over 1,594 subnational units from 134 countries and spanning the period from 2003 to 2019 with most observations starting from 2010. Typically, a subnational unit aligns with a state or province (that is, first-level administrative boundaries - ADM1), but can also represent a cluster of regions, as determined by the specific household surveys’ sampling approach.

> 13 For example, climate change and related disasters could lead to an increase in conflict over increasingly scarce resources (Burke _et al._ , 2009; Koubi, 2019), which in turn could exacerbate poverty and inequality. Migration, both within and across borders, is another possible response to climate change and disasters (Gröger and Zylberberg, 2016; Missirian and Schlenker, 2017), which could either mitigate or exacerbate inequality depending on the socio-economic characteristics of the migrants and the receptiveness of the destination regions. Climate change impacts on labor productivity, especially in sectors that are highly dependent on physical labor and weather conditions could have far-reaching effects on income distribution and poverty (Somanathan _et al._ , 2021).

25
_Domingo_ are populated by, on average, wealthier families which are more likely to live far from school and use private transport for the daily commuting. If we include the last two libraries, our approximation of taking the distance between the library and the school to represent the treatment status will not be accurate. As a result, we decided to include only _El Tintal_ and _El Tunal_ libraries in this analysis.

In Colombia, schools can be classified according to four important characteristics that are closely related with the quality of education in the literature. These characteristics are: whether the school is managed by the government, the proportion of females to males attending the school, the start of the academic year and the length of the school day. In regards to the first characteristic, most of the students who would demand the services of libraries are part of the government-managed education system. Public schools are free at the primary level and have low tuition fees at the secondary level, but provide a lower quality of education than private schools (N ́u ̃nez et al., 2002).<sup>6</sup> In regards to the second characteristic, the fact that some parents may prefer specific types of education such as religious institutions or gender-specific schools could be correlated with demandside factors. With respect to the start of the academic year, schools can be calendar A or calendar B, which means they start in January or August, respectively. While calendar A is the norm, calendar B schools are typically private institutions usually designed in order to follow European or US schedules. This typically means that calendar B schools have higher test scores due to the strong selection related to the high income of students’ families. Finally, schools can serve students for a full school day (12 hours) or implement double-shifts, with some students coming in the morning and others in the afternoon. 7 Double-shifting is usually associated with lower academic results in the Latin American context as documented by Bonilla-Mej ́ıa (2011).

> 6A small number of public schools are managed by the private sector and seem to follow a different pattern (Sarmiento et al., 2005). None of them is close enough to our libraries.

> 7Other schools include night shifts or weekend shifts, but we will not consider them. Typically, these institutions are intended for young adults, who want to finish their secondary education after dropping out, thus the education incentives and the environment is totally diferent from a typical student.

10
# **Table 2: HIV status, the 2008 food crisis and household welfare indicators in Mozambique. OLS**

**Regressions with and without household fixed effects.**

|**VARIABLES**|**Consumption**|**Consumption**|**Income**|**Income**|**Food**<br>**consumption**|**Food**<br>**consumption**|
|---|---|---|---|---|---|---|
|**HIV household**|-0.338***||-0.392***||-0.327***||
||(0.0596)||(0.113)||(0.0650)||
|**Year 2008**|-0.159**|-0.197***|-0.459***|-0.380***|0.00741|-0.0529|
||(0.0695)|(0.0585)|(0.128)|(0.123)|(0.0759)|(0.0697)|
|**HIV household*year 2008**|0.204**|0.215***|0.0507|-0.0329|0.189**|0.227***|
||(0.0836)|(0.0704)|(0.155)|(0.151)|(0.0913)|(0.0838)|
|**Constant**|7.665***|6.677***|6.830***|6.465***|7.059***|5.993***|
||(0.160)|(0.0229)|(0.322)|(0.0500)|(0.175)|(0.0273)|
|**Household fixed effects**|**no**|**yes**|**no**|**yes**|**no**|**yes**|
|**Observations**|1733|1733|1480|1480|1733|1733|
|**R-squared**|0.266|0.014|0.204|0.048|0.172|0.017|

Notes: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0. Controls not shown but included in the regressions without fixed effects are characteristics of the household head (age, age squared, education, sex), the number of children aged 0 to 14 in the household and province dummies.

**Table 3: HIV status, the 2008 food crisis and child nutritional status indicators. OLS regressions with household/child fixed effects**

||**H**|**ousehold fixed effe**|**cts**||**Child fixed effect**|**s**|
|---|---|---|---|---|---|---|
|**VARIABLES**|**Weight-for-age**|**Height-for-age**|**BMI-for-age**|**Weight-for-age**|**Height-for-age**|**BMI-for-age**|
|**Year 2008**|-0.243*|-0.0667|-0.253**|-0.280*|-0.0784|-0.283**|
||(0.134)|(0.109)|(0.101)|(0.149)|(0.122)|(0.123)|
|**HIV household*year 2008**|-0.150|0.145|-0.118|-0.0876|0.233|-0.113|
||(0.164)|(0.131)|(0.121)|(0.183)|(0.146)|(0.147)|
|**Constant**|-0.142|-0.685***|1.039***|-0.154***|-1.320***|0.353***|
||(0.118)|(0.117)|(0.110)|(0.0581)|(0.0451)|(0.0449)|
|**Observations**|1897|3015|2967|1897|3017|2969|
|**R-squared**|0.017|0.015|0.038|0.024|0.004|0.029|

Notes: Standard errors in parentheses.*** p<0.01, ** p<0.05, * p<0.1. Controls not shown include age and sex of child. The weight-forage z-score is calculated for children aged 1-10 years. The weight-for-age z-score is calculated for children aged 1-10 years. The heightfor-age and body mass index-for-age are calculated for children aged 5-19 years.

24
TABLE 3.5

# Fixed and Random Parameters (Standard 3rrors) of FLnal **Models** for ZJC English and Mathematics

|Model|11|11|
|---|---|---|
|Response Variables|MathematLcs|Englih|
|CONS|-1.909<br>(.3709)|-2.052<br>(.2844)|
|GR7M/3|.3841<br>(.0128)|.6355<br>(.0160)|
|M/ATEXT|2.113<br>(.5728)|1.73<br>(.4264)|
|-TCTHis<br>Ran^d|.2134<br>(.087)|.3067<br>(.0636)|
|School (%)|.5696 (25%)<br>(.171)|.2364 (12%)<br>(.0909)|
|Class (%)|.3001 (14%)<br>(.0482)|.351 (18%)<br>(.0551)|
|Pupil (%)|1.377 (61%)<br>(.0306)|1.393 (70%)<br>(.0311)|
|TOTAL|2.2467|1.9804|
|No. Pupils|4190|4147|
|No. Classes<br>|138<br>|138<br>|
|No. Schools|33|33|

'Not sLgnlflcant 37
31 Table 1: Summary of Nominal Rates of Assistance for import-competing, exportable, nontradable, and all covered agricultural products, Relative Rate of Assistance and Trade Bias Index, 19 African focus countries, 1961–64 to 2000–04 (percent)

||Covered<br>exportables|NRA,<br>Covered<br>importables|agricultural p<br>All covered<br>tradables<sup>b</sup>|roducts<sup>a</sup><br>Covered<br>nontradables|All covered<br>products|Standard<br>deviation<br>of NRAs<sup>b</sup>|RRA<sup>c</sup>|Tradables<br>share (%) of<br>value of all<br>covered agric.<br>production|
|---|---|---|---|---|---|---|---|---|
|1961-64|-30|123|3|0|-1|34|5|49|
|1965-69|-39|62|-15|0|-11|33|-12|55|
|1970-74|-47|30|-27|0|-17|31|-19|55|
|1975-79|-52|22|-30|-1|-23|37|-27|54|
|1980-84|-47|4|-28|-1|-18|35|-17|46|
|1985-89|-50|49|-26|-2|-15|33|-22|46|
|1990-94|-49|5|-27|-2|-16|31|-19|41|
|1995-99|-32|3|-15|-3|-10|25|-11|39|
|2000-04|-32|7|-16|-3|-10|26|-18|43|

# Source: Anderson and Valenzuela (2008)

a. Nominal rates of assistance for the 19 African focus countries are weighted by the gross value of production at undistorted prices for the relevant sub-sector.

b. The simple average of the 19 focus countries’ standard deviation of NRA around its weighted mean.

c. The RRA is defined as 100*[(100+NRAag<sup>t</sup> )/(100+NRAnonag<sup>t</sup> )-1], where NRAag<sup>t</sup> and NRAnonag<sup>t</sup> are the percentage NRAs for the tradables parts of the agricultural and non-agricultural sectors, respectively. The regional RRA is a weighted average of national RRAs, with weights being the gross value of production at undistorted prices for all agriculture.
<!-- Start of picture text --> Lowess smoother<br>Central Eastern<br>Northern Western<br>0 2 4 6 0 2 4 6<br>Distance to markets index<br>bandwidth = .8<br>1<br>.5<br>0<br>1<br>.5<br>Use of hybrid, improved seed<br>0<br><!-- End of picture text --> Source: Staff calculations using UNPS 2005/06‐2013/14 Other factors that are likely to affect the decision to adopt agricultural inputs are weather conditions and agricultural prices.<sup>18</sup> Given that the decision to adopt agricultural inputs is taken early on in the cropping season, we analyze whether lagged values of weather index and agricultural output prices affect a farmer’s decision to use inputs. Past weather conditions may affect the present decision to use inputs through short‐term bias. Individuals tend to weigh recent information more heavily when making a decision, and thus, if the previous season was a good one (bad one) as a result of good weather conditions (bad weather conditions), it is likely that the farmer will decide to (not) use inputs. We measure weather conditions through the Water Requirement Satisfaction Index (WRSI), which is estimated based on a maize crop model using satellite rainfall data at the sub‐county level.<sup>19</sup> The index ranges from 0 to 100, where 100 means there was no deficit in the water needed, and each household was assigned the average between the main and short seasons for the previous cropping season. As shown in Figure 4.2, weather conditions vary considerably across regions and over time.

Similarly, lagged output prices may affect input use as they are usually a good predictor of current prices, as evidenced by the fact that commodity prices (particularly maize and banana) are often characterized by autocorrelation models of order one (Deaton and Laroque 2003, Cafiero at al. 2005). Thus, if prices were high in the previous cropping season, the farmer may expect similar prices for the current season and decide in favor of adopting inputs. For the analysis, we use the same set of prices used to estimate the financial returns in Section 3: wholesale prices collected in eight different markets by UBOS for the construction of the CPI.

The availability of information, technical advice as well as proper functioning credit markets have been also identified as key factors for input use. Thus, we analyze whether adoption rates are higher among 18 In a focus group with three groups of farmers in the Northern region (Gulu), farmers reported that poor weather conditions and low market price for their crops are two major sources of uncertainty and vulnerability for their livelihood. Uncertain outcomes and returns likely constrain the decision to adopt improved inputs, as the expected value is too ambiguous.

> 19 The WRSI is calculated for each pixel using a maize crop model calibrated to the growing seasons across Uganda. Specifically, the geoWRSI v 3.0 was used with the global PET and RFE v2 (2001‐2017) time series.

24
deviation. This measure is set to missing for individuals who are missing data for the working at all variable, and otherwise is the average over all employment variables with non-missing data.

_Ever formally employed between course end and January 2012:_ this is an indicator variable coded as one if the individual is reported in the social security administrative data as being in formal employment at any time between the end of their course date and January 2012, the midpoint of the follow-up survey.

_Formally employed in August 2013:_ An indicator variable which takes the value one if social security administrative data reveal the individual to be formally employed in the month of August 2013.

_Formal income earned in August 2013:_ Income for the month of August 2013, as reported in the social security administrative data.

_Ever formally employed between course end and November 2013: :_ this is an indicator variable coded as one if the individual is reported in the social security administrative data as being in formal employment at any time between the end of their course date and November 2013.

_Ever left a formal job between course end and November 2013:_ an indicator variable which is coded as one if the individual is reported in the social security administrative data as having left their first formal job worked in during this period.

# **_Well-being measures_**

_Expected probability of working in two years:_ The expected chance of having a job in two years time, coded as missing if an answer outside of the 0 to 100 range is given.

_Mental health index MHI-5:_ this is a five item index of Veit and Ware (1983) which has a maximum score of 25 and minimum score of 5. Higher scores are desirable in that they indicate the experience of psychological well-being and the absence of psychological distress. Individuals are asked how often in the past four weeks they have done each of the following, each answered on a 5 point scale, where 1 denotes none of the time and 5 all of the time, and the MHI-5 is the sum of these responses:

- Been a nervous person (reverse-coded)

- Felt so down in the dumps that nothing could cheer them up (reverse-coded)

- Felt calm and peaceful

- Felt downhearted and blue (reverse-coded)

- Been a happy person

_Current subjective well-being:_ individuals are asked which step on a 10-step Cantril ladder, where on the first step stand the poorest people, and on the tenth step standard the richest, they think their household stands today.

27
<!-- Start of picture text --> 2<br>cr.)<br>2<br>Bo<br>c<br>7)<br>fe<br>fe)<br>a<br>E<br>°<br>oo<br>Tv<br>=<br>co)<br>4<br>a<br>et,<br>te)<br>£<br>°<br>a<br>£<br>on<br>=<br>°<br>no PTA Low depth Medium depth High depth<br><!-- End of picture text -->
|||Average in|
|---|---|---|
|Service|Disaggregate Service|2008-2013|
||Computer and related|9%|
|Business|OtherProfessionalservices|27%|
||<br>Real Estate|10%|
||Rental and leasing|54%|
||Asset financing and management|39%|
|Financial|Financial, including leasing|39%|
||Securitiesservices|23%|

|Industry|STRI|Meantrade cost|
|---|---|---|
|Manufacturing|n.d.|0.769|
|wholesale trade|n.d.|3.041|
|Retail trade|75|6.02|
|transportation|62.30|3.224|
|Information|50|TATA|
|Finance and Insurance|48.06|3.669|
|Real estate and leasing|n.d.|22.05|
|Professional services|87.5|4.887|
|Admin services|n.d.|4,657|
|overall|65.72|3.166|
19

- _Short- and medium-term gas_ is sold or resold to suppliers, with the period of delivery ranging from one month to one year. In one such transaction 100,000 therms a day of short-term gas were traded on July 25, 1996, for delivery in October 1996 (see line 6 of table 3). Similarly, two blocks of 25 therms a day of medium-term gas were traded on July 24, one for delivery in the fourth quarter of 1996 and the other for delivery in the 12 months starting October 1996 (see lines 3 and 4).

- _Time spread gas_ represents an exchange of gas contracts with different times of delivery. Shippers settle the price difference on the basis of their projection of the price of the gas. On July 26, 1996, shippers exchanged two gas contracts for delivery of 25,000 therms a day of gas in August and October (see line 8 of table 3). They agreed that the price of the contracts was identical, based on the prevailing spot market prices.

**Table 3 On-System Trading, July 22-28, 1996**

||Day of trade|Period of delivery|Volume<br>(thousands of therms a day)|Price<br>(pence per therm)|
|---|---|---|---|---|
|I|July 22|Sept. 1-30|25|12.95|
|2|July23|Aug. 1-31|25|13.15|
|3|July 24|Fourth quarter, 1996|25|13.525|
|4|July 24|Oct. 1996-Sept. 1997|25|12.95|
|5|July 25|July balance|50|13.00|
|6|July25|Oct. 1-31|100|13.20|
|7|July 26|Aug. 1-31|25|13.30|
|8|July 26|Time spread|25|no price|
|||Aug. vs. Oct.||difference|
|9|September<br>24|Day ahead|25|13.1|

Source: PH Energy Analysis, British Spot Gas Markets, Petroleum Argus, European Natural Gas Daily.

# _The flexibility mechanism_

The flexibility mechanism provides a framework for trading gas imbalances in BGT's pipeline network. Its primary purpose is to eliminate system imbalances caused by the deviation of shippers' gas injections and withdrawals from the approved schedule. BGT uses an auction mechanism to buy gas from or sell it to shippers in order to restore system balance. The flexibility mechanism thus effectively establishes a spot market - the - flexibility market that is much like the on-system market, except that trading takes place between BGT and shippers rather than among the shippers. Shippers bid volumes and prices, and BGT selects the bids that minimize the cost of restoring system balance. Ideally, competition among shippers should lead to bids that reveal the true system short-
to high quality groundnut sold in the EU market (Figure 9).

<!-- Start of picture text --> Figure 9. Prices of African Shelled Groundnut Exports Compared to other<br>Developing Countries (US$/ ton)<br>2500<br>2000<br>1500<br>1000<br>500<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000<br>SSA Argentina China India<br><!-- End of picture text --> Source: Oil World Shifting out of groundnut oil and upgrading the quality of edible groundnut may be an option for African producers. Unlike demand for oil and meal, demand for confectionary groundnut (the higher quality edible groundnut) has been rising and is expected to continue to increase in the medium term. At the same time, confectionary groundnuts receive a price premium that can be as high as 100 percent compared to grades used for oil and meal. In Senegal, one ton of first grade confectionery groundnut sells for $800 to $900, which is equivalent to the price of groundnut oil. It takes, however, three tons of unshelled groundnuts to produce one ton of oil. Similarly, prices of Gambian groundnuts are about $300, $450 and $600 FOB for (crushing), bird food and edible respectively. Were Gambia able to upgrade 50 percent of its 10,000 tons of exports from crushing to edible, the increased revenue would be $1.5 million.

# **Groundnut Trade Policies of High-income Importers**

In spite of a general pattern of tariff escalation, barriers to groundnut are not a major obstacle in high-income major importers: the two largest groundnut importers in this category, the EU and Canada, have a zero tariff for unprocessed groundnut and low processed groundnut tariffs for GSP and LDC countries (Table 15). Assessment of market access in these countries should however take into account the strict quality standards. In contrast to the EU and Canada, Japan and Korea have a high tariff regime for groundnuts. Japan applies a high tariff on processed groundnuts and it offers a very limited preference margin of 4 percent for groundnut exports from LDC. Korea has very high tariffs on both raw and processed groundnuts, with tariffs on the former standing at more than 200 percent. This high tariff on raw groundnut may reflect the government desire to stimulate production, which has plummeted over the past 15 years. In contrast to edible groundnuts, groundnut oils and meal enter into all these high-income countries duty free.

21
shocks in the first and second seasons of life have an impact on child health is not enough to say that they have a long-lasting effect, because they might affect the younger children but not the oldest.

## Table 7

To analyze this issue we group children into cohorts according to the number of completed rainy season that they have experienced since birth, and then interact each cohort with rainfall deviations in different time periods as explained in Equation 3. Examining exogenous shocks in several periods at early life for each cohort, allow us to identify the most critical periods, and ultimately to understand the persistency of their impacts. Results presented in columns 1 and 2 of Table 7 suggest that weather shocks (both rainfall and temperature) are transitory, since it is only the last season shock that matters for HAZ scores ( _ΔR1_ and _ΔT1_ for the first cohort and _ΔR2 and ΔT2_ for the second cohort). In the last two columns we split the sample between boys and girls to see if there is any difference across gender groups. Again, results are indicative of similar (in magnitude) transitory impacts of weather shocks for both groups.

The short-lived nature of past weather shocks seem to indicate that children who have experienced a shock can rapidly catch-up with their cohort. Our findings are in line with some results reported in the epidemiologic literature, but seem to contradict Maccini and Young (2009) who report that shocks during the first 12 months since birth have an effect on socioeconomic outcomes of women as adults.

# **6. Conclusions**

In this study, we investigate the extent to which rainfall shocks affect the welfare of children in rural Nigeria. Understanding the socio-economic consequences of rainfall variability is a prerequisite for the integration of adaptation activities into poverty reduction efforts in Nigeria. This is especially relevant in the context of global climate change and the projected increase of weather variability.

We used data from the 2003 and 2008 rounds of the Demographic and Health Survey that contain measures on two key dimensions of child welfare such as child weight-for-height and height-forage, commonly used by nutritionists as indicators of short-run and long-run nutritional status.

Among the key findings of this study is that rainfall shocks have a significant impact on child health irrespective of the dimension of child welfare examined in rural Nigeria. In addition, it appears 26