rafmacalaba/datause-encoder-data huggingface dataset:
A second finding is that not all children have to<sup>be</sup> sleeping in bed-nets for the benefits of the<sup>treatment to be</sup> felt. If the community as a whole participates<sup>in the</sup> intervention, some small rates of non-compliance<sup>need not</sup> invalidate the effectiveness of the intervention.

It must be :emembered that the dipping of the nets<sup>was</sup> carried out entirely by village women under<sup>the supervision of</sup> the Village Health Worker and the Traditional<sup>Birth Attendant</sup> with the support of the women's association.<sup>No major</sup> difficulties were encountered during this process<sup>although</sup> several women did wash their nets in the middle of<sup>the</sup> intervention, thereby diluting the effects of the<sup>insecticide.</sup> The experience in the Farafenni area leads us to<sup>believe that the</sup> washing and dipping process, including making up the<sup>solution</sup> with the correct concentration, can be undertaken<sup>successfully</sup> with the minimum of supervision in other contexts.<sup>The costs of</sup> the solution are a few US cents per net dipped.

There are no signs that the intervention has produced any toxicity or other adverse reactions; the chemicals<sup>are difficult</sup> to detect two weeks after the dipping has taken place.<sup>9</sup> There are some unanswered questions about the implications<sup>of the loss</sup> of immunity whilst protected from mild infection<sup>when using a</sup> bed-net, and the effects of the bed-nets on adult<sup>mortality and</sup> morbidity have not so far been studied in detail.<sup>Mosquitos will</sup> soon develop some resistance to Permethrin but by<sup>altering the</sup> type of insecticide used every few years, it should<sup>be possible</sup> to keep ahead of these changes. Other behavioural<sup>adaptations of</sup> mosquitos denied easy access to human blood<sup>are difficult to</sup> predict. There are some indications that adults<sup>in the</sup> intervention villages received additional bites.<sup>If the</sup> 9 RW Snow, M Jawara, CF Curtis Bull **Ent Res** 1987<sup>77:279-86;</sup> RW Snow, SW Lindsay, RJ Hayes, BM Greenwood Trans<sup>R Boo Trop Med</sup> 1988.

18
_page 37_ Neary, J. P. & S. Wijnbergen (1984), 'Can an Oil Discovery Lead to a Rcccession? A Comment on Eastwood and Venables,' Economic Journal 94, p. 390-395.

Obstfeld, M. (1982), 'Aggregate Spending and the Terms of Trade: Is There a Laursen-Metzler Effect,' Quarterly Journal of Economics 97, p. 251-270.

Persson, T. & Svensson L.E.O. (1985), 'Current Account Dynamics and the Terms of Trade: Harberger-Laursen-Metzler Two Gencrations Later,' Journal of Political Economy 93, p.<sup>43</sup> -<sup>65</sup> . Salter, W.E.G. (1959), 'Internal and External Balance: the Role of Price and Expenditure Effects,' Economic Record 35, p. 226-238.

Schmidt-Hebbel, K & Serven L. (1993), 'Dynamic Responses to External Shocks in Classical and Keynesian Economies,' CEPR Conference Paper.

Sen, P. & Turnovsky S.J. (1989), 'Deterioration of the Terms of Trade and Capital Accumulation: a Re-Examination of the Laursen-Metzler Effect,' Journal of Intemational Economics 26, p. 227250.

Svensson, L.E.O. & Razin A. (1983), 'The Terms of Trade and the Current Account: the LaursenHarberger-Metzler Effect,' Journal of Political Economy 91, p. 97-125.

Warner, A. (1992), 'Did the Debt Crisis Cause the Investment Crisis?', Quarterly Journal of Economics 107, p. 1161-1186.

(forthcoming, 1994), 'Did the Debt Crisis or the Oil Price Decline Cause Mexico's Investment Collapse?'.

Wijnbergen, S. (1984), 'The "Dutch Disease": A Disease After All?', Economic Journal 94, p. 41-55. (1984b), 'Inflation, Employment, and the Dutch Disease in Oil-Exporting Countries: A Shortrun Disequilibrium Analysis,' Quarterly Journal of Economics, p. 233-250.
<!-- Start of picture text --> LKA ZWE<br>ZAF<br>UGA<br>TZA<br>SYC<br>IND<br>SEN<br>MUS<br>MDG<br>KEN<br>BGD<br>GIN<br>ETH<br>CPV<br>BWA<br>AFG BFA<br>-10 -5 0 5 -10 -5 0 5 10<br><!-- End of picture text -->

# g) Europe and Central Asia 1

# h) Europe and Central Asia 2

<!-- Start of picture text --> UKR ITA<br>TUR IRL<br>HUN<br>SWE HRV<br>SVN GRC<br>SVK GEO<br>SMR GBR<br>FRA<br>RUS FIN<br>ROU EST<br>PRT ESP<br>DNK<br>POL DEU<br>NOR CZE<br>NLD CYP<br>CHE<br>MKD BLR<br>MDA BGR<br>LVA BEL<br>LUX AZE<br>AUT<br>LTU ARM<br>KGZ ALB<br>-10 0 10 20 -10 0 10 20<br><!-- End of picture text --> Notes: The prediction model is log(E/LF) = -1.38 - .11*log(Population) + .10*log(GDPPC) + .11*Openness - .56*Ethnic + .47*HighIncome - .27*HighIncome*Openness _Figure 9: Union density rate and difference between public employment and predicted level_ <!-- Start of picture text --> 0 20 40 60 80 100<br>Union density<br>40<br>30<br>20<br>10<br>0<br>Public employment minus predicted<br>-10<br><!-- End of picture text --> 21
<!-- Start of picture text --> Japan: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>UK: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br><!-- End of picture text --> <!-- Start of picture text --> Spain: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br><!-- End of picture text --> 33
μij obtained from the first step with the variables xij in the smaller survey to generate the predicted consumption. However, different from proxy means testing, the predicted household consumption generated using survey-to-survey imputation methods is composed of _both_ the two terms on the right-hand side of Equation (1), that is β̂<sup>′</sup> xij and μ̂ij.<sup>13</sup> Notably, the estimation framework utilized by most existing economic studies is largely based on the seminal survey-to-census imputation method offered by Elbers, Lanjouw, and Lanjouw (2003) (which we return to more discussion in the next section).<sup>14</sup> Most recently, building on the Elbers _et al._ (2003) method, Dang, Lanjouw, and Serajuddin (2017b) attempt to bring some further improvements to the survey-to-survey poverty imputation method, which include simpler variance formulas, more guidance on the selection of control variables for model building, and formulas for standardization of variables from surveys with different sampling designs. They validate estimation results against both household consumption data and LFS data from Jordan before combining these two sources of data to provide more recent poverty estimates.

# _Examples and Remaining Challenges_

Poverty reduction in India has been subject to intense debates in the past, which was caused by comparability issues with different rounds of the National Sample Survey (NSS)—the country’s

> 13 Furthermore, μ̂ij is usually disaggregated into a cluster random effects term (υ̂cj) and an idiosyncratic error term (εĵ) as in Equation (2). This feature, as well as the addition of  μ̂ij to the error term, results in more accurate estimates of household consumption and poverty estimates than proxy means testing (see Dang _et al._ (2017a) for more detailed discussion). Also note that for consistency, the poverty line in the base survey—rather than that in the target survey— should be used together with the predicted consumption to obtain poverty estimates.

> 14 Variants on this method exist. For example, Tarozzi (2007) proposes a two-step inverse probability weighting probit estimator, with the relevant weights derived in the first step from the change in the distribution of household characteristics across the two surveys. Mathiassen (2009) also employs a probit estimator, but proposes an exact expression for the standard errors and imposes a stricter parametric functional form on the error term. On the empirical front, Christiaensen _et al._ (2012) and Mathiassen (2013) apply the Elbers _et al._ (2003) framework to provide poverty estimates based on within-survey imputation for several countries including China, Kenya, the Russian Federation, Uganda, and Vietnam. Using the same technique, other studies combine the household consumption survey with other surveys such as the DHS (Stifel and Christiaensen, 2007) or the LFS (Mathiassen (2009) and Douidich _et al._ (2016) to provide across-survey imputation.

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SIDS in table 4 was 61 percent of GDP in 2022, compared to the average of 65 percent of GDP for all EMDEs.<sup>19</sup> Nevertheless, public debt ratios vary greatly among SIDS and current high levels of public debt are identified in the DSAs as major contributors to risks of debt distress in more than a third of SIDS. However, vulnerability to macroeconomic, financial or natural disaster-induced shocks contributes to the risk of debt distress in an even larger number of SIDS, including those with low or moderate public debt ratios.

# **_4.2 The Composition of Public Debt by Creditor_**

Domestic debt comprises 38 percent of total public debt on average for the 34 SIDS in table 5, and external debt 62 percent, although domestic debt is generally larger as a share of total debt for those SIDS with the highest levels of public debt. Domestic debt is mostly held by local financial institutions, especially banks and public provident funds, although for most SIDS, there are no comprehensive data showing a breakdown of domestic debt by type of creditor.

**Table 6 Composition of Public Debt by Creditor; averages for 21 SIDS (% of total)**

|**_Category_**<br>**_(number of_**<br>**_countries)_**|**_PPG debt to_**<br>**_GDP (%)_**|**_Share of tota_**|**_l PPG debt by ty_**|**_pe and creditor_**|**_(%)_**|
|---|---|---|---|---|---|
|||Multilateral<br>debt|Official<br>bilateral debt|Private<br>and<br>other<br>external debt|Domestic<br>debt|
|**_Full sample_**<br>**_(21)_**|65|38|20|8|34|
|**_PPG_**<br>**_debt_**<br>**_above_**<br>**_60%_**<br>**_of GDP (11)_**|99|31|12|15|43|
|**_PPG_**<br>**_debt_**<br>**_below_**<br>**_60%_**<br>**_of GDP (10)_**|28|46|28|1|25|

_All data are means._ _<u>Source: DSAs</u>_ For external debt, there are only data in the DSAs on the breakdown by type of creditor for 21 SIDS. Table 6 shows the breakdown of public debt into external and domestic and, for external debt, by type of creditor, for these SIDS. These countries are divided into two categories in the

> 19 Data from the IMF World Economic Outlook database, April 2023. The average for the SIDS excludes Palau because there are no data on this indicator for Palau in the WEO database. The PPG debt shown in table 5 (which is taken from IMF country reports) is slightly higher than general government debt because it includes government guarantees. The average PPG debt to GDP in 2022 for the 34 SIDS shown in table 5 was 67 percent. There are no data on PPG debt to GDP in the WEO database.

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# **1 Introduction**

Energy prices rose more than 140% between 2000 and 2013, according to the World Bank’s energy-price index, while the food-price index rose by more than 80%. These increases in commodity prices motivated research to assess the implications for households and workers in developing countries, as well as policy responses. An example is the World Bank’s 4 billion dollar contribution to a joint fund with J.P. Morgan to help developing countries invest in commodity-price hedging instruments launched in June 21, 2011.<sup>1</sup> Yet commodity prices have stalled since 2012, and observers are once again concerned about the social and economic consequences of declining commodity prices<sup>2</sup> . Clearly, part of the concern is about the volatility of commodity prices, as much as the medium-term trends per se.

Whether it is the trends or the volatility of commodity prices, knowledge about the effects of commodity-price fluctuations on household welfare is useful for policy as much as it is useful for understanding economic behavior. It is also important to have a sense of the magnitudes of the impacts involved.

The objective of this article is to put forth an economic framework for understanding how people in developing countries are affected by relatively permanent changes in commodity and other prices. The approach is to review the literature framed by a toy model that highlights issues relevant in the discussion of prices and welfare. The review of the literature canvasses research from various branches, including shocks to exchange rates, financial crises, and trade reforms. In all these cases, the shocks manifest themselves as price changes, and thus the findings are relevant for the discussion of commodity prices. We complement the literature review with a detailed analysis of household-survey data on commodity dependence in Latin America and Africa.

Since we are interested in the impact of commodity prices on household welfare and behavior, our framework builds on agricultural household models. This framework analyses how households choose consumption bundles and how they earn their living. In some cases, especially in rural areas, income is generated by agricultural production (profits). In other

> 1World Bank, Press Release No:2011/559/EXT, Washington, DC.

> 2See, for example, “Life after the Commodity Boom,” _The Economist_ , March 29, 2014

1
instance, all agriculture in condensed into only one code), the measure used in the regression is the total number of hours worked by all members (so that sample sizes become large enough for regression analysis).

# **Appendix 2: Robustness to Different Samples**

In most of the regressions reported in section 4.5, sample sizes change. This is because not all households in the core sample (i.e., the pre-shock aquaculture producers in 2002) report positive numbers for all the variables analyzed. An obvious example is fish income, which is not reported by pre-shock producers that exited the market in 2004. These differences in sample sizes raise concerns that our inferences from Tables 9 and 10 are based on potentially non-comparable samples. We therefore re-estimate the model for changes in income (total, per capita, and net) for the various (selected) sample sizes in Tables 9 and 10 and we compare the results with those for the core sample (from Table 5). If these results are similar across samples, then we can claim that our inferences based on the selected samples are unlikely to be driven by the differences among the samples. After performing this exercise, we find in general that the impacts on total income, per capita income and net income are indeed similar for all alternative samples. As an example, we report the results for total income, net of input purchases, in Table 11, for samples varying by sources of income, and Table 12, for samples reporting various input purchases. Clearly, the impacts on income are very similar across samples, perhaps with the exception of the sample of household reporting income from “wages and agriculture,” for which the impacts appear to be somewhat weaker.

# **References**

Atkinson, A. B. and N. Stern (1974). “Pigou, Taxation, and Public Goods,” _Review of Economic Studies_ , 41 (125), pp. 119-124.

Banerjee, A., S. Cole, E. Duflo, and L. Linden (2007) “Remedying Education: Evidence from Two Randomized Experiments in India,” _Quarterly Journal of Economics_ , August, pp.

33
earnings inequality among high income countries are heavily influenced by the rewards for educational attainment (Lemieux 2008, 2006b, 2006c).

Thus, for policy purposes, understanding how heterogeneous the returns estimates are in a particular country is important. For example, if the returns to education are higher for those at the top of the income distribution, then further investments in education – doing nothing else different – will lead to an increase in inequality. That is, if marginal schooling returns in a particular country are higher for the less able (assuming that ―ability‖ is captured by the residuals of the earnings function), educational opportunities should be expanded for this section of society, as education and ability are substitutes. Furthermore, in such a case the interaction of education and ability has an equalizing effect on earnings. But if education leads to more inequality, then compensatory interventions may be necessary in order to equalize the chances of the less able. If, on the other hand, education tends to equalize earnings, then further investment is warranted, without changes in the way it is provided.

# **_2. Returns to different education levels – The changing trends_**

Conventional wisdom, based on the theoretical expectation of diminishing returns, suggests that the economic reward of an extra year of education will fall as years of schooling rise. Diminishing returns lead to a concave earnings function whereby earnings increase with education but at a decreasing rate. The slope of the education-earnings relationship (which is the measure of the rate of return to education) is steep at low levels of education but becomes progressively flatter (the marginal return to education falls) at higher levels. The theoretical prediction of concavity is confirmed by empirical evidence from the 1960s to the 1990s. For instance, meta-studies (see, for example, Psacharopoulos and Patrinos 2004) find that the general pattern of returns to education across a large number of studies is that the return to education is 5
|**Table 1: Pre‐determined Charact**|**eristics**<br>Ballot Winners|Ballot Losers|P‐value|
|---|---|---|---|
|Age|40.11|41.80|0.045|
|Male|0.66|0.67|0.814|
|Born in Outer Islands|0.23|0.24|0.973|
|Sample Size|203|182||
|Joint orthogonalitytestp‐value|||0.306|

**Table 2: Impact on Income Gain**

||ITT|ITT|ITT|LATE|LATE|LATE|
|---|---|---|---|---|---|---|
|**Panel A: Impact on**<br>|**Income Gai**<br>|**n in New Ze**<br>|**aland Dollars**<br>||||
|Ballot winner|246.9***<br>(32.8)|240.0***<br>(35.8)|220.9***<br>(36.7)||||
|Migrate||||340.0***|334.1***|328.1***|
|||||(24.5)|(31.0)|(33.9)|
|Weighting used:|w1|w2|w3|w1|w2|w3|
|Sample Size|385|385|385|385|385|385|
|Control Mean|125.6|125.6|125.6|125.6|125.6|125.6|
|**Panel B: Impact on**<br>|**Income Gai**<br>|**n in PPP‐adj**<br>|**usted New Ze**<br>|**aland Doll**|**ars**||
|Ballot winner|260.5***<br>(34.1)|252.8***<br>(37.0)|233.3***<br>(37.8)||||
|Migrate||||358.7***|351.9***|346.5***|
|||||(24.8)|(31.2)|(34.0)|
|Weighting used:|w1|w2|w3|w1|w2|w3|
|Sample Size|385|385|385|385|385|385|
|Control Mean|126.1|126.1|126.1|126.1|126.1|126.1|

Notes: Robust standard errors in parentheses, *, **, *** indicate significance at the 10, 5, and 1 percent levels respectively.

All regressions include controls for age, gender, island of birth, ballot years entered, and survey form (long or short).

Weights: w1 weights for population proportions of compliers versus non‐compliers, w2 also weights migrants for observed movements out of New Zealand, w3 additionally allows ballot losers who migrate to have twice the weight of those who remain in Tonga.

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# **Section OA2. Scripts for live calls and SMS in Mexico**

# **A. Script for live calls (Savings)**

## **SAVINGS**

**Good morning/afternoon, my name is Guadalupe Gomez and we are calling you as a part of START a pilot study on financial literacy in the name of Condusef. The goal of this call is to provide call you some advice on how to choose an account to save your money. This call will take up to 5 minutes. Have you ever signed Condusef is a public institution that protects and defends up for an account to the rights of the users of financial services. As part of this save your money at mission, we are calling you today to provide some advice on any financial how to choose an account to save your money. institution? YES NO Before opening an account to save your money, you should not only be aware of the offered interest rate (i.e. yield), but also be aware of the fees incurred. The Was the total yield minus the monthly fees determine if the information I just account will provide gains or losses to your savings. gave you clear? NO For example, if your total yield is 5 pesos but you pay 10 pesos per month in fees, your savings will decrease by 5 pesos every month. YES Additionally, we always recommend comparing at least 3 institutions when opening an account and choosing the one that offers the highest yield and Thank you for your time. We hope the information provided was useful. charges the lowest fees. If you have any questions or need help regarding financial products and services, please access** **<u>www.condusef.gob.mx.</u>**

<!-- Start of picture text --> END<br>call<br><!-- End of picture text --> 5
reserves the right to adopt measures related to the list of products and export and import licensing. These limitations do not appear in their respective AFAS schedules.

Vietnam has pre-committed to liberalize retail distribution. It will phase out in five years after the date of entry into force of the agreement the Economic Needs Test (ENT) to which it currently subjects the establishment of outlets for retail services (beyond the first one) (Annex I of Vietnam, page 6).<sup>16</sup>

**Figure 4: STRI for Retail Distribution - Doha Offers, ‘Best PTA’, TPP Commitments and Applied Policy of 2015**

<!-- Start of picture text --> Retailing: Doha, PTA, TPP and applied policy<br>BRN MYS CHL SGP MEX CAN VNM USA PER JPN AUS NZL<br>Doha PTA<br>TPP Policy '15<br>Note: Absense of a bar means there are no restrictions (STRI score is zero)<br>100<br>75<br>50<br>25<br>Services trade restrictiveness index<br>0<br><!-- End of picture text --> Source: The World Bank TPP Database, 2016

# **Transportation**

Transport services are among the most protected services in many countries (Borchert et al., 2014). One of the ironies of services trade negotiations is that these services which are most in need of coordinated liberalization, have, de jure (air transport) or de facto (maritime transport), often been carved out of the scope of the GATS and other earlier trade agreements. The TPP too has not fully grasped this nettle. Although the TPP expanded the coverage of auxiliary air transport services to include ground handling, airport operations and specialty air services, the cross-border air traffic services are excluded. Thus we read in Article 10.2.5 that: “This Chapter [on cross-border trade in services] shall not apply to air services, including domestic and international air transportation services, whether scheduled or nonscheduled, or to related services in support of air services...”

> 16 The ENT is based on pre-established publicly available procedures, and approval is based on objective criteria, including the number of existing service suppliers in a particular geographic area, the stability of market and geographic scale.

16
|**Country**|**Intervention**|**Effect**<br>**Sizes**|**Country**<br>**GNI Per**<br>**Capita,**<br>**2015 PPP**|**Labor**<br>**Share of**<br>**Income,**<br>**latest**<br>**available**<br>**year**|**Average**<br>**income,**<br>**2015**<br>**PPP**|**Return to**<br>**literacy**|**Percentage**<br>**change in**<br>**earnings**|**NPV$**|
|---|---|---|---|---|---|---|---|---|
|Philippines|Reading materials, teacher<br>training to incorporate<br>reading into curriculum|0.06|8,900|0.28|2,481|0.25|0.02|643|
|India|PicTalk: Year 2, Machines and<br>activities|0.06|6,020|0.29|1,769|0.51|0.03|959|
|India|PicTalk : Year 2, Activities<br>only|0.06|6,020|0.29|1,769|0.51|0.03|959|
|India|The Pratham PicTalk<br>programme: Year 2,<br>Machines only|0.06|6,020|0.29|1,769|0.51|0.03|959|
|Uganda|Northern Uganda Literacy<br>Project (NULP): Gov<br>treatment arm|0.08|1,780|0.25|445|0.59|0.05|339|
|India|PicTalk: Year 1|0.08|6,020|0.29|1,769|0.51|0.04|1,279|
|Philippines|Reading materials, teacher<br>training to incorporate<br>reading into curriculum|0.09|8,900|0.28|2,481|0.25|0.02|1,023|
|Colombia|Teacher incentives|0.1|13,520|0.33|4,431|0.72|0.07|6,397|

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