rafmacalaba/datause-encoder-data huggingface dataset:
# Food prices and food production

Impacts of climate change on agriculture affect poverty in two ways, first through prices and consumption, and second through farmers’ incomes. Higher food prices reduce households’ available income – especially for the poor, who spend a large share of their income on food products. In our scenarios, the impact depends on the fraction of food expenditure in total expenditure, and this fraction decreases as households get richer. Food price changes also affect farmers’ incomes. However, this channel is complex since lower yields (which are expected in many areas due to climate change) mean that higher food prices do not necessarily translate into higher farmer revenues: the net effect depends on the balance between changes in prices and quantities produced.

_Table 4 Low‐impact and high‐impact changes in the agriculture sector due to climate change (RCP8.5) in 2030 in the East Asia region. Source: GLOBIOM model (IIASA). Bold numbers highlight negative impacts, either for consumers (with higher prices) or farmers (with lower revenues)._

## (a) Low‐impact climate change in East Asia

|SSP|Socio‐|Price|difference|Corresponding|Corresponding|
|---|---|---|---|---|---|
||economic|(minimum|across all|production difference|agriculture<br>revenue|
||scenario|GCMs)|||difference|
|SSP4|Poverty|**0.37%**||‐0.74%|**‐0.38%**|
|SSP5|Prosperity|‐0.13%||‐0.75%|**‐0.88%**|

## (b) High‐impact climate change in East Asia

|SSP|Socio‐|Price|difference|Corresponding|Corresponding|
|---|---|---|---|---|---|
||economic|(minimum|across all|production difference|agriculture<br>revenue|
||scenario|GCMs)|||difference|
|SSP4|Poverty|**3.4%**||‐3.2%|0.05%|
|SSP5|Prosperity|**1.9%**||‐3.0%|**‐1.17%**|

Using the data from our analysis of food prices and production, we change the income of all workers in the agricultural sector, according to the combination of changes in prices and in the quantities that are produced in the East Asia region (see Table and Rozenberg and Hallegatte, 2015, for details). We also rescale the (real) income of all households, accounting for the change in food prices and the share of food in household budget (which decreases with income). The impact of the agriculture channel on poverty depends on the number of farmers in Vietnam, the income of these farmers, and the income of the entire population (which affects the share of food in consumption).

Our results show that in Vietnam, in the _poverty_ scenario, the number of people living below the extreme poverty line in 2030 increases by 400,000 people because of climate change impacts on agriculture (in the high‐impact scenario). And the number of people living with less than 4usd per day increases by 700,000 people ( Figure 4). Those numbers are possibly an underestimation of actual impacts because both climate scenarios (low and high impact) assume that there is CO2 fertilization, which remains uncertain.

12
# **Appendix Table 12: Balance of Controls among Boys in Cappelen et al. (2020)**

||Mean<br>among<br>Control|Preschool<br>vs Control<br>Difference|Robust<br>Standard<br>Errors|
|---|---|---|---|
|Age|7.61|0.12|0.12|
|Black|0.16|-0.03|0.07|
|Hispanic|0.79|-0.02|0.08|
|White|0.06|-0.01|0.05|
|Time of Experiment|9.73|0.37|0.45|

_Notes_ : The table investigates whether control boys and boys assigned to the preschool arm in Cappelen et al. (2020) are comparable in terms of predetermined characteristics. The first column shows means among the control boys. The second column reports the difference in means between the control and preschool boys. The third column shows robust standard errors. “Time of Experiment” is the time of day when the child took part in the experiment using a 24-hour clock. The table is comparable to Cappelen et al.’s (2020) Table 1. _N_ = 154.

47
**-40-**

# ANNEX I

# **DESCRIPTION OF MODEL**

# Overview

1. The model comprises six data input worksheets, a debt module for pipeline and new external debt, and output tables. There is also one central worksheet wh contains key behavioral assumptions and macroeconomic targets.

7782. The data input worksheets are similar in fornat, units of account and coverage to standard tables produced by the Government. These input worksheets are: (a) Original Data: CB (b) Original Data: BOP (c) Original Data: Fiscal Data (FY) (d) Original Data: DRS (e) Original Data: National Accounts (t) Original Data: Trade (GOK) (GOK) (GOK & IM) (World Bank) (GOK) (GOK) 3. The debt module comprises two files which are separate from the central model, the first file is analogous to the Input Debt file for the LOTUS-based RMSM. Historical data are entered in millions of US dollars. Medium and long-term debt is subdivided into multilateral and bilateral donors and financial markets, including commercial banks and suppliers credits. The standard definitions of the World Bank's debtor reporting system are used. The second file is analogous to the Debt file of the LOTUS-based RMSM. Projected commitment of loans together with their termsinterest rate, maturity, grace period and disbursement profie-and grants are entered individually or by donor ty,e. The module calculates annual disbursements and debt service of ihe projected commitments and aggregates them by donor types in a manner which is consistent with the pipeline data. It also aggregates the projected debt variables with the historical pipeline data. A building block is then used to import this combined pipeline and projected debt data into the central model.

# 4. There are eleven output tables covering:

- (a) National Accounts (percentage of GDP) (b) National Accounts in Constant Prices (c) Private Sector Account (d) Balance of Payments (e) Fiscal Accounts (f) Financeable Fiscal Deficit (g) Monetary Survey (h) Monetary Authority Accounts (i) Commercial Bank Accounts (j) External Debt (k) Flow of Funds Consistency Check

Most variables in the output tables, apart from growth rates, are expressed as percentages of GDP at market prices but could be easily expressed in levels. These levels would generally be denominated
**Table 1: Selected baseline socio-demographic and pre-ART baseline CD4 count**

||**Full cohort at**<br>**baseline**<br>**(n=654)**|**Balanced**<br>**cohort**<br>**(n=421)**|**Respondents**<br>**lost to follow-**<br>**up**<br>**(n=233)**||
|---|---|---|---|---|
|Median age (years) [IQR]|37 [31-43]|37 [32-44]|36 [31-41]|**|
|Female (%)|76.8|69.2|62.4||
|Marital status:|||||
|Single|66.9|69.2|62.4||
|Not cohabiting with partner|9.6|8.9|11.0||
|Cohabiting with partner|23.5|21.9|26.6||
|_Total_|_100.0_|_100.0_|_100.0_||
|Dwelling:|||||
|Formal|74.3|74.1|74.7||
|Informal|19.7|20.1|18.9||
|Traditional|6.0|3.7|6.4||
|_Total_|_100.0_|_100.0_|_100.0_||
|Education:|||||
|None|2.5|3.4|0.9||
|Primary|27.0|26.0|29.2||
|Some secondary|47.8|47.6|48.1||
|Grade 12|19.9|20.0|19.9||
|Tertiary|2.7|3.1|1.9||
|_Total_|_100.0_|_100.0_|_100.0_||
|Median household size [IQR]|3 [2-4]|3 [2-4]|3 [3-4]||
|Mean dependency ratio (%)|33.3|33.3|33.3||
|Medianbaseline CD4[IQR]|127 [74.5-183.5]|129.5 [77-180]|185 [66-185]||

_Note_ : Results are for patients recruited into the FEATS study cohort at baseline in 2007/08. Baseline CD4 values represent the CD4 count observed closest to but prior to ARV treatment initiation. Some percentages may not add up to 100% across categories due to rounding. One, two and three asterisks denote differences that are statistically significant at the 10%, 5% and 1% levels, respectively.

_Source_ : Authors’ own calculations.

22
As an illustration, Figure 1 shows the relationship found by Ravallion and Lokshin (2010) in survey data for one of the world’s poorest countries, Malawi. The Figure gives the nonparametric regression for responses to the ―satisfaction with life‖ question against household consumption per person. The Figure also shows how the relationship varies with the respondent’s assessment of the economic welfare of their friends and neighbors. We see that SWL increases with consumption, but higher economic welfare of friends or neighbors tends to come with higher satisfaction with life at given own consumption. So this does not support the theory of relative deprivation (as this would imply lower welfare with richer friends or neighbors, at given own consumption). The parametric models presented in Ravallion and Lokshin (2010) did, however, indicate that concerns about relative deprivation emerged amongst the richer Malawians. Ravallion (2008b) calculated the implied (iso-welfare) poverty lines for Malawi, and showed that they exhibit a small weakly relativist gradient, whereby higher owneconomic welfare is needed to compensate people for having richer social comparators.

These findings are echoed in the responses to survey questions on whether poverty is perceived as absolute or relative reported by Corazzini et al. (2010), which suggest that respondents coming from richer backgrounds see poverty as more relative, though not to the point that absolute poverty is seen as unimportant.

Some of the literature has also thrown light on the role played by expectations. Ravallion and Lokshin (2002) find strong lagged income effects on subjective welfare in Russia, consistent with the view that that those on a positive (negative) income trajectory are likely to rate their welfare higher (lower) than others at given current income (and other covariates).<sup>23</sup> Knight and Gunatilaka (2012) argue that ―aspirations‖ for higher income also matter negatively to subjective welfare, and that this mitigates appreciably the direct income effect. Using data for rural China they proxy ―aspiration income‖ by answers to a version of the MIQ and find that the latter has a significant negative coefficient in predicting SWL and ―satisfaction with income.‖ Aspiration income is in turn found to be an increasing function of own income (consistently with past research on the SSPL). They interpret their results as evidence of an ―hedonic treadmill‖ whereby higher actual income need not raise happiness given offsetting

> 23 Ravallion and Lokshin (2000) also find that Russians on a positive (negative) income trajectory are also less (more) likely to support redistribution from the rich to the poor.

14
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<!-- Start of picture text --> 23 HUNUPITIVA<br>24 SUDUWELLA<br>26 PANCHIKAWATTE<br>26 MARADANA<br>2 MALIGAKANDE i<br>28 MALIGAWATTE EAST | oo<br>23 DEMATAGODA / ’ f<br>30 WANATHAMULLA ; ) . i<br>31 KUPPIYAWATTE EAST =<br><!-- End of picture text -->

<!-- Start of picture text --> 33 BORELLA NORTH i a .<br>uM NARAHENPITA :<br>35 BORELLA SOUTH j<br>6 CINNAMON GARDEN &)<br>a7 KOLLUPITIVA<br>38 BAMBALAPITIYA<br>3 MILAGIRIYA<br>40 THIMBRIGAS YAYA —<br><!-- End of picture text --> <!-- Start of picture text --> 42 HAVELOCK TOWN a<br>43 WELLAWATTE NORTH<br>44 KIRILLAPONE q<br>45 PAMANKADE EAST \<br>46 PAMANKADE WEST 45)<br>47 WELLAWATTE SOUTH<br><!-- End of picture text --> <!-- Start of picture text --> 0rs750 a. 500 eyetersfs .<br><!-- End of picture text -->
included; the firm was asked to directly rate the intensity of competition it faced in its main market. The answer was again requested on a four-point scale, ranging from severe competition to no competition.<sup>17</sup> The related regression results are presented in Table 6. The estimations yield different results, depending on which competition measure we use. Two out of three of the estimated odds ratios for the dummy variables measuring different levels of perceived competition are significantly lower than one in the case of nominal wage rigidity. This implies that firms who face severe competition (the excluded category) are more likely to be subject to rigidity in nominal terms than firms facing lower competition levels. Thus, there seems to be a positive (although not monotonous) relationship between product market competition and nominal wage rigidity. However, this empirical finding depends on the way competition is measured – a similar significant relationship is not present if we use the price-reduction-based competition measure instead of perceived competition.<sup>18</sup> If more competition is associated with higher wage rigidity due to the absence of a wage cushion to lower wages during a downturn, we should find stronger effects for competition in countries where it is more likely that severe competition is associated with lower wage levels, and where competition forces a larger proportion of workers to earn wages that are close to the statutory minimum level. This is more likely to be the case in the non-euro area countries included in our sample, since these countries tend to specialise in labour-intensive technologies and have a higher tendency to be involved in industries where competition is price-driven as opposed to quality-driven. We test this possibility by running separate regressions for the euro area and non-euro area countries. The regression results are presented in Table 7. The estimated odds ratios indicate that competition indeed has a much stronger (and monotonous) positive relationship with nominal wage rigidity in non-euro area countries, although similarly to the pooled regression results this significant relationship is present only for the measure that is based on perceived competition.

> 17 Note that the use of the second measure (perceived competition) results in a significant reduction of the sample size, since the related question was not included in the national surveys of Austria, Belgium, Spain and Italy.

> 18 Note that the significance of the estimated effects can also depend on the sample coverage, since the measure of perceived competition is available for only 10 countries out of 14. We tested for this possibility by estimating the regression including the price-reduction-based competition measure for the same set of 10 countries (i.e. excluding Austria, Belgium, Spain and Italy). The estimated effect was still insignificant, which implies that the results depend on the way competition is measured and not on different sample coverage.

23
|**Table 2: Means of explanatory**|**and out**|**come varia**|**bles at ho**|**usehold le**|**vel overall**|**and for s**<br>**Co**|**ub-popul**<br>**nv.**|**ations**||
|---|---|---|---|---|---|---|---|---|---|
||**Total**|**RPRP**|**DPIP**|**New par**|**ticipants**|**partici**|**pants**|**Non-par**|**ticipants**|
|||||<br>**RPRP**|<br>**DPIP**|<br>**RPRP**|<br>**DPIP**|<br>**RPRP**|<br>**DPIP**|
|**Explanatory Variables**||||||||||
|**_Household Demographics_**||||||||||
|Household in hamlet|0.21|0.19|0.24|0.20|0.26|0.14|0.26|0.23|0.23|
|Scheduled tribe|0.17|0.16|0.20|0.19|0.30|0.16|0.30|0.18|0.11|
|Scheduled caste|0.21|0.24|0.16|0.31|0.18|0.28|0.14|0.19|0.16|
|Other backward caste|0.45|0.41|0.51|0.33|0.45|0.40|0.47|0.39|0.56|
|Other caste|0.17|0.20|0.13|0.17|0.07|0.15|0.09|0.24|0.17|
|Household size|4.39|4.14|4.76|4.27|5.06|4.51|4.98|3.93|4.54|
|Household female headed|0.12|0.12|0.12|0.12|0.10|0.11|0.11|0.13|0.13|
|Some member can write|0.76|0.73|0.80|0.75|0.82|0.82|0.84|0.67|0.78|
|**_Household Initial Conditions_**||||||||||
|Female social capital|0.00|0.75|-1.14|0.44|-1.19|0.68|-1.12|0.62|-1.12|
|Female econ. empowerment|-0.02|0.09|-0.19|0.14|0.08|0.11|-0.08|0.06|-0.36|
|Female political participation||||||||||
|Mud floor|0.54|0.55|0.52|0.55|0.59|0.54|0.62|0.56|0.45|
|Unprotected water only|0.24|0.18|0.33|0.18|0.38|0.19|0.38|0.16|0.29|
|Owned any land|0.49|0.40|0.62|0.37|0.66|0.45|0.70|0.39|0.58|
|Durables p.c. (1,000Rs)|0.54|0.53|0.56|0.43|0.45|0.50|0.45|0.55|0.65|
|**Outcome Variables**||||||||||
|**_Female Empowerment (change_**|**_)_**|||||||||
|Social capital|0.05|-0.65|1.10|-0.60|1.28|-0.67|1.52|-0.66|0.86|
|Economic empowerment|0.04|-0.39|0.68|-0.39|0.85|-0.34|0.88|-0.43|0.53|
|Political participation|0.05|0.03|0.08|0.03|0.09|0.04|0.13|0.03|0.05|
|<br>**_Household Consumption, Inco_**|**_me (level)_**|**_, and Asset_**|**_s (change)_**|||||||
|Spending p.c. (Rs/year)|6951|6871|7072|6325|6741|6848|7241|6851|7169|
|<br>Income p.c. (Rs/year)|5858|6795|5462|5843|5163|6104|5434|6168|5673|
|Asset (Rs)|2532|2278|4406|2806|2502|2806|2502|3253|1685|
|<br>**_Nutritional Intake per capita (L_**|**_evel)_**|||||||||
|Energy (cals/day)|2052|2031|2104|1956|2075|2031|2097|2046|2095|
|Protein intake (g/day)|47|46|49|44|49|45|50|46|48|
|Numberofobservations|6340|3824|2516|583|656|1149|502|1446|1358|

_Source:_ Own computation from CESS impact evaluation survey.

18
Figure 14. Financial upturns: duration, amplitude and slope <!-- Start of picture text --> Duration<br>(in quarters)<br>21<br>18<br>Upturns Booms<br>15<br>12<br>***<br>9 *<br>6<br>3<br>0<br>Credit House Price Equity Price<br>Amplitude<br>(in percent)<br>70<br>***<br>60<br>Upturns Booms<br>50<br>40<br>30<br>20 ***<br>***<br>10<br>0<br>Credit House Price Equity Price<br>Slope<br>(in percent)<br>12 ***<br>10<br>Upturns Booms<br>8<br>6<br>4 ***<br>***<br>2<br>0<br>Credit House Price Equity Price<br><!-- End of picture text --> Source: Claessens et al (2012a).

Note: The amplitude and slope correspond to sample medians. For duration, the means are shown. Duration is the time it takes to attain the level of the previous peak. Amplitude is the change in one year after the trough of each variable. Slope is the amplitude from the trough to the period where the financial variable reaches its last peak, divided by duration. Booms are the top 25% of upturns calculated by the amplitude. ***, ** and * imply significance at the 1%, 5%, and 10% levels, respectively. Significance refers to the difference between financial booms and other financial upturns.

74
|**Table5 **-**A**||_||||||
|---|---|---|---|---|---|---|---|
|**_li .u-Iat**<br>**_R_**<br>|<br>_<br>**_r**<br>_t_|__<br>_ _ _ _ _|_ _ _ _<br>_ _ _ _|_ _<br>_ _ _ _<br>_ _|_ _ _ _ _<br>_||__<br>_ _ _<br>_ _ _|
|**_awn_**<br>**_data_**_(pa_<br>|_r2):__<br> <br>|<br>||||||
|Share in Ag<br>|Share<br>in In<br>|Share<br>in Sv<br>|Laborin Wage<br>|Labor in Wage<br>|LaborinWage|Share in WF||
|that is wage<br>|that is wage<br>|that is wage<br> <br>|agriculture<br>|industry<br>|services<br>|that is wage||
|**(00o)**<br>'|**(%)**<br>|**(**)<br>__° o)<br>|(thousands)<br>|(thousands)|(thousands)|(%)|_|
|7.9|58.76|64.66|85760|281960|517137|37.59|World|
|38.18<br>|89.13<br>|85.61<br>|6453|93094|195067|84.37|**Highinconeecononmes**|
|25.63<br>|76.71<br>|68.23<br>|48270|125913|177393|57.40|Middle Income economnles|
|3.57|29.80|46.38|31037|62954|144677|17.14|Lowincome econonles|
|4.49<br>|36.21<br>|51.93<br>|24357<br>|60932|117194|21.62|**East AslaandPaelfic**|
|20.96<br>|82.56<br>|80.46<br>|11097<br>|64603|71792|66.91|**Europe and Central Asla**|
|41.42<br>|77.27<br>|68.84<br>|14894|30834|56012|64.71|**LatinAnierica**and Caribbean|
|20.96<br>|73.44<br>|72.32<br>|4803|11812|21610|55.49|**Middle East and North Africa**|
|37.93<br>|89.17<br>|85.63<br>|6328<br>|90898|190559|84.36|O.E.C.D.|
|3.71<br>|30.40|42.48|9868|19051|41836|16.56|South Asia|
|9.29|38.14|37.99|12067|7067|20039|19.47|**Sub-Saharan Africa**|

37
access to schooling. Despite the stark difference in the approach chosen in this paper, our estimates are similar to the findings of studies using compulsory schooling. For example, compelling youths to attend school until age 16 reduces the probability of a teenage birth by 4.7% in the US and 3.5% in Norway (Black, Devereux and Salvanes 2008). We find that one more school per hundred females reduces the rate of teenage births for a cohort by 1.4%.<sup>5</sup> The second contribution comes from our focus on a middle-income, as opposed to highincome, country. Teenage childbearing in Brazil is significantly higher than in many high-income countries, but lower than in many of the poorest countries.<sup>6</sup> In addition, the focus on Brazil enables us to study a recent and rapid expansion of secondary schools over a relatively short time period, which is not possible in the context of high income countries, where such expansions have taken place decades earlier and often in a more gradual fashion.<sup>7</sup> We are therefore able to provide the first quantification of the role of expanding education access in the substantial reduction of teenage fertility rates observed across many middle-income countries over the last two decades. The expansion of secondary schooling in Brazil provides a blueprint for understanding the effect of the expansion of the educational system on fertility. The estimates provided here are therefore of relevance for a large group of low- and middle-income countries currently experiencing or on the brink of similar expansions of their educational systems.

The remainder of this paper is organized as follows: In Section 2 we provide the background on the provision of secondary education in Brazil. In Section 3, we discuss the data to be used in the main analysis. In Section 4, we introduce the empirical strategy and show that the introduction

> 5 Based on our preferred estimate of -0.565, and the corresponding mean cohort birth rate: (−0.565 × 100)/41.0. 6 The World Bank reports teenage birth rates (in births per 1000) of 82.2 for Brazil, 12.7, 28.1 and 46.2 for Germany the United Kingdom and the United States. The highest birth rate was for Niger, at 217.2 births per 1000 teens (World Bank 2020).

> 7 This limits in many instances the data that is available on the rollout of school expansions in these settings. Brazil provides a great setting for our study by being able to investigate the largescale school expansion making use of highquality administrative data for the entire country.

6