rafmacalaba/datause-encoder-data huggingface dataset:
Following a shock, IMMPA generates three measures for these indicators (as well as those derived from household surveys, as discussed below): a _short-term_ measure (first two periods following a shock), a _medium-term_ measure (between 3 and 5 periods), and a _long-term_ measure (between 6 and 10 periods).<sup>2 9</sup> While somewhat arbitrary in the choice of intervals, the importance of these measures (which can obviously be calculated only with a dynamic model) is clear: they allow the analyst to identify and discuss possible _dynamic trade-offs_ in the analysis of policy choices, by contrasting their short- and longer-run effects on the poor.

To assess the poverty effects of alternative shocks, we link IMMPA to a household income and expenditure survey, such as an Integrated Survey (IS) or Living Standards Measurement Survey (LSMS), which typically collect extensive information on migration, household income and expenditure (including own consumption), household assets, credits and savings, levels of education, apprenticeship and training, employment, occupational characteristics and status, as well as geographical location.<sup>3 0</sup> The various steps involved in our approach are illustrated in Figure 7. To begin with, we assume that the available survey consists of a relative large sample. This is needed to reduce non-sampling errors that may cause household income and expenditure to be underestimated-particularly in the least monetized regions of predominantly nontraded agricultural production (see Fofack (2000))-and ensure sound inference on poverty effects following changes in factor allocation and resource flows across sectors. The approach that we propose proceeds as follows:

**Step 1.** The user uses the information provided in the household survey of

29 Specifically, let xz denote the initial (base period) value of consumption (or income) for household group h and **{gt}jt1=** the (discounted) growth rate in consumption (or ijncome) generated by the model for the first 10 years following a shock. The short-run measure of consumption, **_xSR,_** is calculated as a geometric average of the period-1 and period-2 values of **_xh,_** calculated at the average growth rate for the period, _xZR(1)_ and **_xsR(2): XSR_** = **_xhR(s)XhR(_**<sup>**_2_**</sup> **_)_** = _VxO(1_ **_+g9SR) x_** _(l +_ **_gsR)_**<sup>**_2_**</sup> **_,_** where **_9SR_** = _/(1_ + 91)(1 + **92)-** 1. Thus, **_xsR_** = **_Xo(1_** + **_gsR)'_**<sup>**_5_**</sup> Similarly, it can be shown that the medium-run value of cornsumption,xmR _iS_ given by **_XhMR_** _=_ **_(_** _+_ **_9MR)_**<sup>**_2_**</sup> **_,_** where _9MR = vft.=3(1 +_ **_9t)_ -1,** whereas the long-run level of consumption, _xLR,_ is given by _xLR=_ **_x5(_** + **_9LR)_**<sup>**_3_**</sup> with **_9LR_** _= ft_06(1 + gt) -_ 1. See Chen et al. (2001) for more details on this procedure. 30 For more details on the scope and content of these surveys, see Delaine et al. (1992) for Integrated Surveys and Grosh and Glewwe (2000) for LSMS surveys. See also Deaton (1997) for a general discussion.

51
**Table 9. Difference in score distributions between Ashaninka and Non Ashaninka students (Two-Sample Kolmogorov-Smirnov Test)**

|**Statistics**||**Oral Test**|**Written test**|
|---|---|---|---|
|**N**|Non Askaninka|435|437|
||Ashaninka|40|40|
||Total|475|477|
|**Mt Et**|Absolute|.647|.537|
|**os xreme**<br>**Differences**|Positive|.000|.000|
||Negative|-.647|-.537|
|**Kolmogorov-Smi**|**rnov Z**|3.915|3.250|
|**Asymp. Sig. (2-ta**|**iled)**|.000|.000|

# **4.2. What is the relationship between fluency and comprehension?**

There are different ways to look at how fluency and comprehension measures relate. Most of the literature discusses bivariate correlation coefficients, and in some cases may present multivariate analysis to control for the effects of external factors. Here we do both, and also present a new way of looking at this relationship, using the Rasch model to transform fluency and comprehension scores into the same scale.

## **_a. Correlation between fluency and comprehension measures_**

Figure 3 shows that there is a positive relationship between fluency and comprehension as measured by both tests. In this case, the Rasch scale helps illustrate the relationship more neatly, but regardless of which scale is used, it is clear that the more fluently a child can read, the higher she or he is likely to perform in the ECE written comprehension test.

**Figure 3. Relationship between oral fluency and written comprehension (N=475, adjusted)**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10 Regression line<br>95% CI<br>0<br>0 20 40 60 80 100 120 140 160<br>Fluency (number of words read per minute)<br>ECE-06 Rasch Scores<br><!-- End of picture text --> 35
- [33] Thornton R. 2008. The Demand for and Impact of Learning HIV Status. _American Economic Review_ 98: 1829–63.

- [34] World Bank. 2014. Data retrieved December 2, 2014, from World Development Indicators Online (WDI) database.

- [35] UNAIDS. 2013. "AIDS epidemic update: November 2013", Geneva, Switzerland.

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Table 1: Price elasticities of savings demand and saver surplus

|(Dufo et al., 2006)|(Karlan & Zinman, 2018)|
|---|---|
|(1)<br>|(2)|
|**Panel A: Regression estimates of demand elasticities**||
|log de<br>|posit amount<br>|
|(USD)|(PHP)|
|Intercept<br>3.37|5.64|
|(0.09)<br>[0.000]|(0.08)<br>[0.000]|
|log price (_ε_)<br>-4.22|-9.74|
|(0.26)<br>[0.000]|(8.16)<br>[0.233]|
|# of household observations<br>13,962|6,696|
|**Panel B: Estimates of compensated demand elasticities **|**and consumer surplus**|
|Compensated deposit elasticity (_φ_+_ε_)<br>-4.22<br>(Lower bound)|-9.74|
|Compensated deposit elasticity (_φ_+_ε_)<br>-3.72<br>(Upper bound)|-9.24|
|_Surplus from high interest rate treatment_<br>_per $100 control group deposit amount_||
|Marshallian surplus<br>$107.43<br>($7.29)|$1.58<br>($0.10)|
|Equivalent variation (Lower bound)<br>$94.58|$1.57|
|Equivalent variation (Upper bound)<br>$107.43|$1.58|
|_Surplus from savings_<br>_per $100 deposit amount_||
|Marshallian surplus<br>$23.70<br>($1.48)|$10.27<br>($8.61)|
|Equivalent variation (Lower bound)<br>$23.70|$10.27|
|Equivalent variation (Upper bound)<br>$26.89|$10.83|

_Notes:_ Regression coefficients and consumer surplus are presented in this table, with standard errors in parentheses and p-values in brackets. In Panel A, regression coefficients are presented; Column 1 is estimated by GMM matching average outcomes conditional on randomly assigned prices reported in Duflo et al. (2006), while Column 2 is estimated by Poisson PML with heteroskedasticity-robust standard errors. In Panel B, bounds on compensated deposit elasticities and consumer surplus are presented.

26
characteristics, such as urban or rural residence, household size, education level, gender, and race of household head.

# 3.2. Change in medical expenditure

The long-term change in spending on health care that would result from a reduction in the consumption of each product is estimated using the static model described by equation 4. Estimates of the cost of treating diseases related to the consumption of each product are distributed proportionally to the number of households in decile jj that consume product . The list of diseases that are considered related to consumption comes from the Global Burden of Diseases dataset and will be explored in section 4.2. pp jj To analyze the long-term impact on health care spending resulting from a reduction in the consumption of each product , we employ a static model as outlined in equation 4. In this model, estimates are generated by attributing the public expenditures from administrative data on treating diseases associated with the consumption of each product to the number of households in decile jj that engage in the consumption of said product . The data source for the associated medical expenditure estimates is described in section 4.3. pp jj T T <u>j</u> p j j �−1�∗ T T E Eq. 4 CCTTCCTT TToo TTE ttE TT TToo E TTEEjj jjEECCEETTCCEECC ∆MMp iippu EEeep IIppiiu = ��1 + εε ∗∆PPjj TT E EEjjEETTE EEjj

# 3.3. Change in years of productive life

The impact of a reduction in the consumption of processed and ultra-processed foods on long-term income is derived from the reduction in years of life lost (YLL) and the associated loss of income in those years. The use of both processed and ultra-processed foods is due to a data limitation that will be explored in section 4.2. Equation 5 provides an estimation of the average number of working years lost within decile and is derived from the years of life lost due to diseases associated with product . This estimation takes into account the proportional distribution based on the number of households in decile that engage in the pp consumption of product . The calculation is carried out as follows: jj pp jj Y =<sup>Y</sup> j u u Eq. 5<sup>YYj∗SShuupppee hh pIIppuuIIiiIIaa ppuuppuuj</sup> YY jj In order to be able to compare the impact of years of life lost with the other two changes, we monetize PPp iippII In order to be able to compare the impact of years of life lost with the other two changes, we monetize the value of these years. To do so, we estimated the income loss due to the YLL in each decile using the average income of the decile. This estimation involves determining the average income level that would potentially have been attained by individuals within the decile if they had lived their full life expectancy. The exercise does not intend to estimate a complete income stream of individuals that died prematurely, it does not take into account the potential skill gains, economic growth or any potential changes. Instead, it aims to provide a simple and comparable impact of this hypothetical income to the actual income earned before premature death, allowing us to estimate the income loss caused by YLL.

Eq. 6 8
<!-- Start of picture text --> @ Capital m Labor<br>5 a TEP #Potential Growth<br>4<br>eg<br>a<br>g2g,<br>5<br>oe 1<br>0<br>{ 2000-09 2010-17<br><!-- End of picture text --> <!-- Start of picture text --> ws:<br>10 —Labor Force—Fertility Rate(LHS) 5<br>0.5 1.5<br>a ¢<br>80.0 1 8<br>a.° a2<br>-0.5 0.5<br>-1.0 0<br>- oO = oO - ~<br>ao oO oO oO - -<br>[o>] fo>) Oo oO fo) oO<br><!-- End of picture text -->
**Table 6:** Differences in employment outcomes between IDPs and non-IDPs.

||IDP|Non-IDP|∆|_p_(∆)|
|---|---|---|---|---|
|**Employment status**|||||
|Employed|0.55|0.45|0.09***|0.00|
|Paid labor for someone else|0.37|0.31|0.06***|0.00|
|Unpaid labor in family non-farm business|0.30|0.21|0.09***|0.00|
|Non-agricultural self-employment|0.24|0.22|0.02**|0.02|
|Agricultural self-employment (own or family)|0.08|0.11|-0.03***|0.00|
|Unpaid training or workshop|0.05|0.06|-0.02***|0.00|
|**Among workers**|||||
|Number of activities|1.87|1.98|-0.11***|0.00|
|Hours worked|34.76|37.31|-2.54***|0.00|
|Months worked|7.91|9.21|-1.30***|0.00|
|Sector of main activity is agriculture|0.35|0.14|0.21***|0.00|

Note: Employed equals 1 if an individual engaged in at least one of the following five forms of work in the past 7 days: paid labor for someone else, unpaid labor in an off-farm family business, non-agricultural self-employment, agricultural self-employment; and participation in a training or workshop.

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number of illegally killed carcasses is to account for patrol efforts. (Burn et al., 2011) discuss the validity of PIKE as an unbiased measure of poaching. (Burn et al., 2011) and (CITES et al., 2013) provide a detailed description of the data and analyze the local, national, and global covariates of PIKE. The continental series indicates that PIKE has crossed the 0.5 mark in 2010 (CITES et al., 2013) and has remained above ever since (CITES, 2016). CITES considers poaching levels above that 0.5 cutoff “are cause of concern” (CITES, 2016). Given birth and natural mortality rates, Wittemyer et al. (2014) actually estimate that populations are likely to start declining when the PIKE value is above 0.54.

Since the main purpose of the analysis is to estimate a supply elasticity, our preferred specification is to use the logarithm of PIKE on the left-hand side (and the logarithm of prices on the right-hand side). We also carry estimations controlling for (the log of) the total number of carcasses (the denominator of the PIKE ratio). The rationale for this specification is to control for patrol effort more flexibly. While the variable might be potentially endogenous, it has the added benefit that the coefficient of the price variable can then be interpreted as a supply elasticity of poaching with respect to ivory prices, controlling for patrol effort among other things. Robustness checks include a host of other specifications that confirm that our finding of a positive correlation between price and poaching is robust to the functional form adopted to estimate it.

# **4.3 Stationarity and co-integration Tests**

As discussed earlier, we test for the stationarity of the price data in China using the DickeyFuller test, allowing for time trends in both the null and the alternative hypotheses. The upper panel of Appendix Table A3 reports the results. Due to the trend break around 19921993, we run the test separately for the periods before and after 1993.<sup>7</sup> The null hypothesis of random walk cannot be rejected in either case. Robustness checks using Phillips-Perron tests, which account for potential serial correlation and heteroskedasticity in the residuals, yield similar results. Appendix Table A3 also shows that we fail to reject the null of a unit root in gold prices, consistent with Smith (2002). The model predicts that poaching data have a unit root. We test whether this is actually the case empirically. As we did for prices, we test for unit root for the global poaching and PIKE data. The results are presented in Appendix Table A3. Here also, we cannot reject that the logarithm of global PIKE has unit root.

> 7We drop 1992 and 1993 data points for the test due to extreme fluctuations around the trend break.

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increase in household income. The increase is most prominent for good quality protein. Although inelastic, the expenditure elasticity at the initial level of income is higher for total protein than total calorie intake. Household economies of scale appears to be significant for calorie and protein (in OLS estimates) and for vitamin A (in HFE estimates) intakes. In terms of agricultural seasonality, individual intakes of calorie, protein and good quality protein appear to be lowest in round 3.

**Vitamin C, D and Iron** : Table 5 presents the results of intrahousehold bargaining and individual allocation of vitamin C, D, iron, and haem iron. The adequacy ratio for all age-sex groups and pregnant and lactating women are significantly lower than that for adult males for each of these nutrients. Within each age-group, the adequacy ratio for males are significantly higher than that for females for each of these nutrients, while pregnant and lactating women have significantly lower adequacy ratio than adult males. For instance, based on fixed effect estimates adult females’ iron adequacy ratio are about 93% lower than adult males’, while the former’s haem iron share is 108% lower than that of adult males. A pregnant woman’s iron adequacy ration is 171% lower than that of an adult male, while her haem iron allocation (as a share of her total iron requirement) is about 178% lower than that for an adult male. Similarly, a female adolescent’s iron adequacy ration is about 18% lower than that of a male adolescent, while her haem iron share is about 25% lower than that of a male adolescent. The degree of sex-disparity in iron allocation is comparatively lower among children as a boy’s iron adequacy ration and haem iron share are about 9% higher than those of a girl. Among children, the magnitude of sex-disparity appears to be higher for vitamin C and D than iron.

Significant intrahousehold bargaining is observed for haem iron share for boys, male and female adolescents, and for lactating adolescents (see bottom panel of Table 5). A head’s assets negatively affect the haem iron allocation for boys and adolescents, while a wife’s assets has the opposite effect. For instance, doubling a wife’s assets would increase the haem iron share by 10% for female adolescents while the corresponding increase in head’s assets would decrease it by 1.5%.

The effect of _κ_ implies that households tend to compensate for lower endowments. Doubling an individual’s endowment would reduce his/her adequacy ratio of vitamin C by 1%, vitamin D by 2%, iron by 3%, and haem iron share by 2%, approximately. Among these nutrients, the expenditure elasticity seems to be highest for vitamin D at the initial level of income, while household economies of scale appears to be significant for vitamin C and iron intakes. Vitamin C intake appears to be significantly lower in all three rounds compared to round 4, while vitamin D consumption is about 16% lower in round 1 and 39% lower in round 3 than that in round 4.

24
30 or over increased from 64 percent to 69 percent (an 8 percent increase over the period), and those aged 45 or over increased from 22 percent to 29 percent (a 32 percent increase). Yet, the increase in years of schooling documented in Figure 1 proved sufficient to offset the age effect on experience: as shown in Table 1, average potential experience in the labor force experienced a slight decline from 23.1 years in 1995 to 22.3 years in 2012.

[Insert Table 1 around here] The second group of factors concerns changes in Brazil’s labor market institutions. Under this heading, we focus on two variables in particular: changes in the level and coverage of the national minimum wage; and changes in the extent of formal and informal employment, as well as self-employment. Panel A of Figure 3 plots the trajectory of minimum wages in the 1995-2012 period, alongside those of mean and median earnings. While real mean and median earnings increased by 14 percent and 43 percent, respectively, the real minimum wage increased by 103 percent over the full period. Interestingly, the bulk of that increase took place in the second sub-period: between 2003 and 2012, the real minimum wage index in Figure 3 (Panel A) rose from 1.26 to 2.03.

[Insert Figure 3 around here] Panel B of Figure 3 plots the density functions for real earnings for the years 199596, 2002-03 and 2011-12.<sup>13</sup> Vertical lines indicate the values of the minimum wage in each year, and the corresponding spikes are clearly visible. One can also see how the earnings distribution shifted to the right and became more compressed over time. Nevertheless, although such a large increase in minimum wages is clearly associated with a move of density mass to the right, there is a non-negligible mass of workers that remains below the minimum wage threshold. Over the full period, the proportion of employed workers earning strictly less than the minimum wage actually _increased_ by about four percentage points, from 12 to 16 percent. As documented in Table 1, this tendency was dominant in the first sub-period: Between 1995 and 2003 the share of workers below the minimum wage increased by 6 percentage points, and then declined during the 2003-2012 boom. This turns out to have important consequences for our results.

The relative extents of formal and informal employment also changed during this period, with a marked increase in the proportion of employees with formal labor contracts (“ _carteira de trabalho assinada”_ ). This trend may reflect two underlying forces. First, the economic boom of the 2000s, which reduced unemployment rates to record levels and thus

> 13 These pairs of surveys were pooled to increase precision in the non-parametric estimation of the kernel densities.

10
**Table 1. Projected average income changes between 2019 and 2020 initial household head educational level and occupation. In percentages**

||Sri Lanka|Turkey|Philippines|South Africa|Brazil|
|---|---|---|---|---|---|
|Panel A: By household head educational level||||||
|Up to complete primary education|0.4%|-0.7%|-8.3%|-8.6%|-11.6%|
|Complete or incomplete secondary education|-1.6%|0.0%|-6.0%|-6.8%|-10.1%|
|Complete or incomplete tertiary education|-1.0%|0.0%|-4.5%|-6.3%|-8.4%|
|Panel B: By household head occupation||||||
|Employer|-0.3%|-1.8%|-2.6%|-5.7%|-13.7%|
|Wage employee|-2.4%|0.0%|-7.8%|-7.3%|-10.7%|
|Self-employed|0.3%|-0.1%|-4.2%|-19.1%|-13.5%|
|Unpaid worker|-0.1%|0.8%|-10.6%|-5.9%|-10.0%|
|Unemployed|0.2%|-0.2%|-5.4%|-7.5%|-9.1%|
|Notes: Projections following the methodology det|ailed in Sectio|n 3.1. Estim|ates based o|n reported em|ployment data.|
|Estimates do not include changes in non-labor inc|ome.|||||

# _Projected welfare changes_

<mark>Figure 7 presents the 2019-2020 change in our projections for the poverty rates (using the international 2.25, 3.65 and 6.85 USD per day poverty lines in 2017 PPP), size of the vulnerable class (defined as a household per capita income between 6.85 and 13 USD a day in 2017 PPP), and size of the middle-class (defined as a household per capita income between 13 and 70 USD a day in 2017 PPP).</mark><sup>17</sup> <mark>In general, we observe poverty rates increasing, as expected given the income declines across the entire income distribution. The share of the population that are vulnerable and middle class declined, indicating that a larger number of people exited these groups by becoming</mark> vulnerable or poor than entered from a more advantageous position.

<mark>In line with the projected average income changes discussed before, some of the largest poverty increases appear in Brazil, the Philippines, and South Africa. In Brazil, the 2.25, 3.65 and 6.85 USD a day poverty rates are expected to increase 2.7, 4.0 and 5.2 percentage points respectively, in a scenario without emergency transfers. These are substantial changes implying increases of between 20% and 47% in the poverty rates –for instance, the 2.25 USD-a-day poverty rate would have increased from 5.7% to 8.4%. Interestingly, the share of the vulnerable population is expected to decline in only 0.3 percentage points while the share of middle-class is projected to suffer a reduction of 4.3 percentage points (again in a scenario without the generous emergency transfer</mark> program).

<mark>In the Philippines, our projections show increases of 1.3 and 3.1 percentage points in the 2.25 and 3.65 USD-a-day poverty rates respectively, and a rise of 4.4 percentage points in the 6.85 USD-aday poverty rate. These increases imply that the poverty rates will be between 10% and 31% higher in 2020 than in 2019 –for instance, the 2.25 USD-a-day poverty rate is expected to grow from 4.3% to 5.6%. The share of the vulnerable and the middle-class population is predicted to fall by</mark> 2.0 and 2.3 percentage points, respectively.

> 17 Table A4 in the Appendix presents the estimated poverty, vulnerability, and middle-class rates.

19