rafmacalaba/datause-encoder-data huggingface dataset:
As a second robustness check we modify the measure of word prevalence to one based on relative frequencies. Relative frequencies are computed by dividing the number of times the keywords related to poverty and inequality are mentioned in each decade by the total number of words spoken in the speeches of the same decade. By converting this ratio to a percentage, the measure indicates the proportion of the speeches devoted to poverty and inequality during each decade. Figure 4 shows the results from this analysis.

**Figure 4. Relative frequency of poverty and inequality in presidential speeches**

_(Ratio of mentions to keywords to total words spoken in the speeches, by decade)_ <!-- Start of picture text --> 0.24%<br>Poverty<br>0.16%<br>0.08%<br>Inequality<br>0.00%<br>% of total words in all speeches<br>1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010<br><!-- End of picture text --> Note: This Figure shows the ratio between the number of mentions of keywords related with poverty and inequality, and the total number of words spoken on the presidential speeches of each decade. See Appendix A for more details about the data.

Using this alternative measure, it becomes clear that the increase in the prevalence of poverty and inequality was not driven by lengthier speeches. The proportion of each speech that was devoted to these subjects increased prominently in recent decades. While in the decade of the 1900s references to poverty and inequality accounted for 0.01% and 0.02% of the total words in the speeches, by the 2010s these figures had increased by a factor of 17 and 4, respectively.

This trend is also visible—albeit at a lesser degree— when we examine other terms related to poverty and inequality that one would expect to hear when these subjects are discussed (see Esser and Williams, 2013). In Figure 5 we show the evolution of three keywords related with poverty (“indigence,” “middle class,” and “vulnerable”) and three keywords related with inequality (“equitable,” “rich,” and “equality of opportunities”). The terms related to poverty increase in usage around the same time as the “political enlightenment,” supporting the robustness of our previous findings. Although “vulnerable,” “indigence,” and “middle class” are quite unpopular terms until the second half of the 20<sup>th</sup> 8
larger share of employment, may be less subject to idiosyncratic distortions, particularly so in advanced countries. Indeed, Appendix B.2 shows that ordinary least squares (OLS) yields very similar estimates to WLS in the least developed countries, whereas it predicts substantially more correlated misallocation in advanced economies, a feature that resonates as implausible.<sup>28</sup> – Figure 2: TFPR TFPQ Elasticity, GDP, and Average Size <!-- Start of picture text --> Kenya Salvador Portugal GhanaPortugalEthioSalvadorpia Kenya<br>Ethiopia Ghana<br>ColombiaIndia<br>India BulgariaChilePeru RomaniaColombia Bulgaria Peru Romania MalaysiaBelgium Chile Hungary<br>Belgium Latvia<br>Hung Latvia M a laysiarySpainItalyFranceFinland SpainItaly FinlandFrance<br>5 6 7 8 9 10 11 12 50 100 150<br>Log GDPpc Av. Size<br>0.5 0.5<br>0.4 0.4<br>0.3<br>0.3<br>0.2<br>0.2<br>0.1<br>slope TFPR−TFPQ 0.1 Elasticity TFPR−TFPQ<br>0.0 −0.1<br><!-- End of picture text --> Note: The left panel of figure illustrates the weighted least squared (WLS) estimate of the regression defined in equation 18, with firm-level employment shares as weights, against the log of income per capita for the year 2014, as reported in the Penn World Table version 9.0. A regression coefficient of 0.15 for the US was subtracted from each country’s estimate to control for misallocation in the efficient benchmark. The measurement of TFPR preserves the parametric assumptions of Hsieh and Klenow [2009], namely an elasticity of substitution equal to 3, and factor shares taken from the US four-digit manufacturing sector. The tails of the TFPR and the TFPQ distribution were trimmed at the bottom and top 5 percent. The right panel projects the TFPR – TFPQ elasticity against average firm size constructed according to equation 17 The left panel of Figure 2 shows, as expected, a negative relationship between the productivity elasticity of distortions and GDP per capita. In particular, advanced economies such as Italy, Spain, France, and Finland feature an elasticity virtually on par with the estimate for the US, a feature that is reassuring of the WLS estimate’s accuracy. Conversely, distortions are most strongly correlated with productivity in the poorest economies, such as Kenya, Ghana, and El Salvador.

The right panel of Figure 2 turns to the relationship between the productivity elasticity of distortions and average firm size in the data. As predicted

> 28For instance, under OLS, Belgium would show a higher TFPR – TFPQ elasticity than India and would be on par with Chile, whereas France would be portrayed as misallocated as India.

28
<!-- Start of picture text --> Reform in OB Indicator and Change in Average ES<br>)<br>a<br>on<br>—<br>oa<br>Ca KX, 28<br>if Ree Hita_1_3<br>a<br>ate MDA 6 6<br>doaom@ e waresGT - Njc.3_3“s"- ee CU?<br>cA I PER 24 siy9 3<br>WwW UGA 13<br>Co<br>oO DE Gplicenseo t0/DB_OpLicemse100 tt 150<br>n==20<br>DB Sample criteria for ES fonly private, domestic firms with 10-50 employees)<br><!-- End of picture text -->
# **A.3.3 Removal of warfare events**

We removed from the sample in the main analysis a number of isolated high radiance and low temperature events that are not consistent with oil production. There are two cases that cause these observations: air strikes and sabotage (by fire) by Daesh fighters. Coalition air strikes have damaged infrastructure in Iraq and Syria, and caused fires, while Daesh set fire to some wells to curb the advance of Iraqi forces and deter air strikes. In this section we provide evidence from news, daytime images, and temperature to establish the case for each observation we removed.

Table A.2:

|**Dates Removed**|**Temperature(K)**|
|---|---|
|October 21,2015|1239|
|October 31,2015|1290|
|November 1,2015|930|
|November 3,2015|NA|
|November 12,2015|1166|

**Al Omar Area:** This major field was the target of repeated Coalition and Russian air strikes. Reports confirm strikes on the 21st of October where B-1 bombers and other allied warplanes hit 26 targets in the field [24, 3, 25]. Similarly on the 3rd and 12th of November 2015, the Syrian Observatory for Human rights and local news outlets documented air strikes generated fires in the field [23, 20]. These reports confirm the existence of large scale fires in the facilities because of such interventions. We also exclude October 31st and November 1st from the sample because the temperatures below 1300 K are not consistent with oil production and the reports of air strikes before that date indicate strikes were successful at destroying the infrastructure needed to operate the field in the short run. No daytime imagery of this site was available in this period.

33
<!-- Start of picture text -->
-16a-<br>FIGURE 4<br>Sugaet Surplus (In X of GDP)<br>30<br>25<br>20<br>10~~~~~~~~~~~mr Curn SurrplusD- <<br>15<br>10 I 0<br>-5 s/jwpus<br>-10<br>-1 | urpl<br>-20<br>707172 73747576 77707980818283 64858687 0889<br><!-- End of picture text -->
sources contribute roughly half **of** the credit volume In rural areas. Feder et al. (1989) report non-institutlonaTcredit shares of between one third and two thirds In several study areas. The most common sources of informal credit In China are relatives and friends. Most **of** such loans carry no Interest charges. Possible reasons for the **absence** of a substantial<sup>profit-motivated</sup> informal credit market In China are discussed In Feder et al. (1990a). They include, unclear legal status, residual ideological resisLance and absence of collateral assets.<sup>2</sup> The present study relies on data collected in December 1987 in Gongzhuling.<sup>Gongzhuling</sup> is located in Jilin province, within the corn belt of northeastern China, where agro-climatic conditions dictate essentially one corn season a year. The original sample consists of 2U0 households selected at random from eight rdndomly selected townships. The information<sup>gathered</sup> covers inputs, outputs, financial assets, credit transactions, and household characteristics. Thirteen households are deleted after determining that tneir main activity was not<sup>agriculture or</sup> that thei: situation was unusual (e.g., a widow maintaining a home garden<sup>plot).</sup> The data show that nearly three quarters of the sample borrowed from formal<sup>sources</sup> (essentially the RCCs) during the study season. The frequency of informal credit transactions is much lower than that of formal transactions (about one fifth of the sample), and three quarters of Lhese loans were provided free of interest. Given the significant differential between the rates of interest on the two types of loans, this may be taken as<sup>evidence that</sup> .nformal credit is not a good substitute for formal credit due to limited fungibility (otherwise every borrower would exhaust his or her informal credit first before going to the RCC). The share of formal credit in the total volume of new credit is 66.5 percent.

Table 1 presents the distribution of loans by purpose and by type of lender. It is readily apparent that the predominant stated purpose of formal loans (all of whicn<sup>are short-term)</sup> is for the financing of current production. Most of the informal credit is reported to have been obtained for purposes other than production, with construction and social<sup>expenditures appearing</sup>
aged 5-12 engaged for at least one hour per week; (3) children aged 13-14 engaged for more than 24 hours per week or working nights (6 pm to 6 am); and (4) children aged 15-17 engaged in work for more than 44 hours a week. A stark statistic is that nearly a third of all children aged 15-17 are classed as child laborers while only a very small share (1.3 percent) are not<sup>4</sup> (Department of Labour 2016). Nevertheless, 35 percent of children are involved in household chores such as cleaning, cooking and childcare and 11 percent in unpaid household subsistence activities such as farming, collecting firewood and fetching water.

At the time of LFS 2015, about 78 percent of children were attending school and not working. Older children, however, were less likely to be enrolled in school and more likely to be working as nearly half of children aged 15 to 17 had quit school (Department of Labour 2016). Myanmar has improved its school enrollment in the last decade: the net primary enrollment rate increased from 88 percent in 2010 to 94 percent in 2017. But there is still a large dropout of students during the transition from primary to middle school with just 71 percent of school-age children enrolled in middle or high school in 2017 (CSO, UNDP and World Bank 2020). Enrollment rates drop at middle and high school, which is consistent with the ages at which children join the labor market.

For the purpose of this paper, we focus on children aged 5 to 17 doing any type of work that involves wage work, household chores, unpaid family work or household subsistence activities. Given the importance of child work in Myanmar and the concomitant persistent problems of non-enrollment and dropout after primary school, the objective of this study is to identify what factors explain child work and how child work affects schooling outcomes. Besides looking at children’s participation in market and household activities, the study seeks to understand how work intensity (the number of hours worked) affects school enrollment and attendance.

Drawing on data from LFS 2015, we use a combination of descriptive statistics and econometric analysis to address these questions. Econometric analysis, depending on the outcome of interest, relies on a probit or an OLS model. The outcome variables are enrollment (binary variable on being enrolled), attendance (binary variable on attendance during the past week) and enrollment combined with employment. In the analysis we distinguish between wage work, household chores, and any work. Wage work is defined as

> 4 Following the definition used for LFS 2015, the term “working children” in this study refers to all those in the agegroup 5-17 years engaged in economic production leading to the production of goods and services for sale (Department of Labour 2016).

3
Figure 2. Distance Puzzle in existing empirical literature Figure 2a <!-- Start of picture text --> All<br>0.8 Only Developing Countries α ˆ 3 + α ˆ m 3<br>0.7 α ˆ 2 + α ˆ 2 m<br>0.6<br>0.5<br>α ˆ 1 + α ˆ 1 m<br>0.4<br>ˆ<br>0.3 α ˆ2 α 3<br>α ˆ 1<br>0.2<br>0.1<br>0<br>1970-1979 1980-1989 1990-1999<br><!-- End of picture text --> Figure 2b <!-- Start of picture text --> 0.8<br>0.7<br>0.6 All with Zero Flows<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>1970-1979 1980-1989 1990-1999<br><!-- End of picture text --> Figure 2c <!-- Start of picture text --> 0.8<br>0.7<br>0.6 All Country Fixed Effects<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>1970-1979 1980-1989 1990-1999<br><!-- End of picture text --> _Source: authors’ calculations from the Disdier and Head (2008) database._

# **3. The Regionalization of Trade for Low-income countries**

The gravity model with separability in trade costs has two important implications for the evolution of trade. First, it predicts that a relative fall in border-related costs should lead countries to increase the volume of international trade (relative to internal trade). This prediction is largely borne out by the data: since 1980, world production has increased by 75% while international trade has increased by 300% (Berthelon and Freund, 2008). Second, a reduction in all costs related to distance (including better 13
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In several sections of the analyses information drawn from U.S. Federal Maritime Commission sources is used to supplement transport cost rates derived from the United States trade statistics. The FMC records all liner conference tariffs on United States inbound and outbound trade. Since this information also contains details on anv special rebates (which **mn._t** by law be reported to the Federal Maritime Commission) offered to individual shippers, as well as on any additional fees for bunker or currency surcharges, it is useful for comparing actual freight costs for specific commodities shipped on different liner routes. 7/ Two different points should be noted concerning the statistical sources employed in this analysis and the uses to which the data are put.. First, data drawn from U.S. import statistics include both the transport and insurance cost component of shipments while information from the Maritime Commission relates only to freight charges. Comparisons between the two sources indicate that insurance costs roughly average about 10 per cent of the total transport and insurance cost bill irrespective of the exports' origin. Second, while data on actual freight charges are reflected in both FMC records and the United States import data these statistics have been convertei into nominal equivalents in this study to facilitate cross-country and crossport problems in Caribbean countries often are of key importance for exporters.

- 7/ There are several major differences between the data drawn from the U.S. trade statistics and Maritime Commission statistics, as well as in the uses to which these data can be put. First, the Commission provides information only on liner conference freight rates while the U.S. trade statistics incorporate information on rates for liners, tramp vessels and tanker shipments, as well as products shipped by air freight. Second, the U.S. trade data are easily converted to ad valorem equivalents while the Maritime Commission data are expressed in terms of monetary charges. Third, the Commission's data contains freight rate information for both United States imports and exports, while the U.S. trade statistics apply solely to imports.
of pregnant women that each team member brought to the clinic in a month. This allocation further incentivized health workers to test new practices.

The in‐depth interviews uncovered several innovative strategies that treatment clinics developed to identify pregnancies early. For instance, health workers started to follow up women who used birth control pills.<sup>23</sup> Specifically community health workers prioritized home visits to women who had not picked up their pills. Second, health workers started targeting women at high risk of not coming in for an early checkup. According to the interviewed doctors, mothers who already have children are less likely to initiate their prenatal visits early in a new pregnancy. However, many of these women are also eligible for weekly free milk distribution for their older children. Health workers met these mothers at the time of the milk distribution, enquired about their last menstruation date, and offered an instant‐read pregnancy test to those women whose menstruation was overdue. Third, health workers identified difficulties in providing early prenatal care to adolescents, as they might be unwilling to reveal a pregnancy, especially to their parents. Community health workers therefore decided to change the timing of home visits, so as to increase the chance of finding adolescents by themselves. In one of the interviewed clinics, the work flow was modified so as to ensure predictable availability of a gynecologist on certain days of the week. This in turn provided an easy way for community health workers and administrative staff to schedule patient appointments. Other clinics introduced new ways of keeping track of “at risk” patients, such as a notebook that kept track of any visits to the homes of women that were at risk, or a map that identified catchment areas of community health workers with corresponding (potential) pregnancies.

We are able to substantiate the claims of increased outreach using clinic administrative records on the number of community outreach activities that resulted in actual maternal‐child service at the clinic.<sup>24</sup> Figure 6 displays the average and median number of outreach activities that resulted in actual maternal‐child services for the pre‐intervention, intervention, and post‐ intervention I periods.<sup>25</sup> The results show that there is little difference in outreach activities between treatment and control clinics in the pre‐intervention period. In the intervention period the treatment

> 23 Birth control pills are dispensed free of charge by each health facility’s pharmacy unit, though women cannot collect more than a monthly supply at any one time. The pharmacy unit keeps records of all birth control pill collections.

> 24 Plan Nacer finances clinic outreach activities on a fee‐for‐service basis and employs an external independent auditor to audit clinic activity reports. Treatment and comparison clinics were paid the same fee for these activities before, during and after the experiment.

> 25 The medians are better measures of central tendency as the densities of both activities are asymmetric heavily skewed to the right.

16
The expression in brackets [ _Wo,G,k/Mo,G,k?_ ] is the ratio of female migrants to male migrants from origin _o_ to all destination countries in the destination subregion _G,_ across all decades _k_ , for which data exist. Of course, complete data are not available for the current decade _t_ since, were that the case, equation (4) would be preferred. Again, once calculated, this share is multiplied by the total number of migrants to determine the number of female migrants.

# _REFERENCES_

- Beine M., Docquier, F., and H. Rapoport. 2007. ―Measuring International Skilled Migration: A New Database Controlling for Age of Entry.‖ _The World Bank Economic Review_ 21 (2): 249–54.

- Bhargava, A., and F. Docquier. 2008. ―HIV Pandemic, Medical Brain Drain, and Economic Development in Sub-Saharan Africa.‖ _The World Bank Economic Review_ 22 (2): 345–66.

- Bilsborrow, R.E., G. Hugo, A.S. Oberai, and H. Zlotnik. 1997. _International Migration Statistics, Guidelines for Improving Data Collection Systems._ Geneva: International Labour Office.

- Docquier, F., and A. Marfouk. 2006. ―International Migration by Educational Attainment (1990-2000): Release 1.1.‖ In _International Migration, Remittances and Development,_ ed. C. Ozden and M. Schiff, [151-201]. New York: Palgrave Macmillan.

- Docquier, F., B.L. Lowell, and A. Marfouk. 2009. ―A Gendered Assessment of Highly Skilled Emigration.‖ _Population and Development Review_ 35 (2): 297–321.

- Harrison, A., T. Britton, and A. Swanson. (2003. _Working Abroad: the Benefits from Nationals Working in Other Economies._ Paris: Organisation for Economic Cooperation and Development.

- Integrated Public Use Microdata Series. [2008]. International: Version 6.0 [Machinereadable database]. Minnesota Population Center, University of Minnesota, Minneapolis, MN.

- Mayda A. M. 2007. ―International Migration: A Panel Data Analysis Of The Determinants Of Bilateral Flows.‖ CReAM Discussion Paper Series 0707, Centre for Research and Analysis of Migration, Department of Economics, University College London.

- OECD (Organisation for Economic Co-operation and Development). 2002. Trends in International Migration database. SOPEMI 2002 Edition. Paris: OECD.

- ———. 2008. _A Profile of Immigrant Populations in the 21st Century: Data from OECD Countries_ . Paris: OECD.

- Parsons, C.R., R. Skeldon, T.L. Walmsley, and L.A. Winters. 2007. ―Quantifying the International Bilateral Movements of Migrants.‖ In _International Migration, Economic Development and Policy,_ ed. C. Ozden and M. Schiff. New York: Palgrave Macmillan.

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In most of these other studies, as well the authors generally find that the inclusion of second order effects has only a marginal impact on the results that are found from considering only the firstorder terms.

Other studies have sought to identify the impact of food price spike on consumption measures. For example, in a study focused on Ethiopia (Alem & Söderbom, 2012), the authors focus on estimating the effect of food prices on household consumption of food, and self-reported measures of the effect of the shock on the quantity of food consumed and overall effect as perceived by the household head using three rounds of panel data. Their results indicate that those households with low levels of assets are likely to be more vulnerable to food price shocks.

In other related work Verpoorten, Arora, Stoop, & Swinnen (2013) examine the impact of the price rise using self-reported food insecurity measures from the Afrobarometer data set for two periods – 2005 and 2008. Their data set covers numerous countries including Mozambique. The authors argue that these self-reported measures provide a better picture of the impact of food price changes on food security rather than consumption data from household surveys, which might be prone to measurement error. Other studies have pointed towards this issue, among other potential problems that arise when using survey reported measures of consumption to measure welfare impact (Headey & Martin, 2016). Using the self-reported measures for Africa Verpoorten et al. find that there was only a small increase in the incidence of food insecurity and decreases in the depth of food insecurity, contrary to the conclusions widely drawn in other studies.

# **2.3 Food price impacts in Mozambique**

There is a previous study focusing on the effect of the 2007-08 food and fuel price spike on household welfare in Mozambique (Arndt et al., 2008). The authors use data from the 2002/03 National Household Consumption Survey (Inquérito aos Orçamentos Familiare (IOF) 2002-03) which provides information on income from the sale of agricultural output and on own consumption levels but has no information on production of specific agricultural commodities. Therefore, in their analysis, they calculate NBR using share of the value of agricultural sales and own production (‘auto-consumption’) in total household income as proxy for production share and the ratio of total expenditure on all food items, including the value of own consumption, relative 10