rafmacalaba/data-use-mentions-extended huggingface dataset:
, Tranchant et al. (2019) find an statistically insignificant, negative effect of food aid on the height of children aged 2–5. These findings suggest either that vouchers and food transfer

> 17 The authors find a significant increase in the access to safe drinking water by 15–30 percentage points on a base of 67 percent for the control group. Moreover, cash beneficiaries reported that their access to primary healthcare improved and their need for hospitalization decreased. Cash support presumably improved their ability to cover healthcare costs and other indirect costs such as transportation costs. Importantly, the authors find that 55 percent of those who received cash over a longer period (more than 12 months) reported better mental health (measured using the Mental Health Inventory–MHI-5), relative to 18.5 percent in the control group. However, similar effects were not detected for those who received cash over a shorter period (less than 12 months).

23
Table 7: Comparison with other studies

|**(1) Marginal impact of**<br>**expected public debt on**|basis point<br>per 1 percent|Forecast data|Countries/Group||
|---|---|---|---|---|
|**government bondyields**|||||
|This study|0.9-2.0(the share of foreign investors is 20 percent)<br>2.8-3.8(the share of foreign investors is 30 percent)<br>4.2-6.2(the share of foreign investors is 40 percent)|IMF, OECD, EC, EIU|11 AEs and 14 EMs||
|Engen and Hubbard(2005)|2.8|CBO|U.S||
|Laubach(2009)|3 to 4.4|CBO|U.S||
|Traum and Yang(2015)|3|DEGE implied data|U.S||
|Arslanalp and Poghosyan(2016)|2.7 to 2.8|IMF|22 AEs||
|Gruber and Kamin(2012)|0.4 to 1.3|OECD|19 OECD countries||
|Ichiue and Shimizu(2015)|0.7 to 0.9|OECD|10 OECD countries||
|Jaramillo and Weber (2013)|4 to 6|EIU|26 EMs||
|**(2) Marginal impact of**|basis point|Forecast data|Countries|Investor type|
|**the share of foreign investors**|per 1 percent||||
|**ongovernment bondyields**|||||
|This study|-11.8 to -8.2|IMF, OECD, EC, EIU|11 AEs and 14 EMs|Foreign Private|
|This study|-17
included in HBS data, the HBS data also encompasses a consumption (or income) variable to measure poverty, which is absent in the DHS data.

# **4.2 Integrating new urban classifications into global poverty measurement**

To improve the accuracy and consistency of poverty analysis, we apply the DOU and DB classifications instead of the previously used, but inconsistently defined, urban/rural classifications found in HBS data. This integration involves several steps.

First, we update the spatial deflators in HBS data using the DOU and DB classifications to convert nominal consumption expenditures to real ones (equation [1] and equation [2]). By incorporating spatial deflators, we are able to account for price variations across subnational areas. While constructing new spatial deflators for all countries based on the same methodology and price data would be ideal, it is not feasible due to variations in price data availability. Instead, we update the spatial deflators used for the official global poverty estimation.<sup>21</sup> The resulting changes in poverty rates are solely attributed to the reallocation of households between urban and rural areas based on the transition from official to consistent urban definitions.

Second, we modify the existing spatial deflators by recalculating them based on the new DOU/DB classification. For example, in the case of Tanzania, the official spatial deflator is a Paasche price index constructed using food unit values for 26 provinces without any urban and rural distinction. However, we utilize alternative geographic divisions, such as urban centers, urban clusters, and rural areas for six regions based on the DOU classification, and cores, suburbs, towns, and other rural areas for six regions based on the DB classification.<sup>22</sup> In most countries, we replicate the steps involved in calculating spatial deflators by adjusting their geographic divisions.<sup>23</sup>

> 21However, for countries where global poverty is estimated using nominal consumption—Bangladesh, Egypt, Ethiopia, Ghana, and
# **3. Sector-level bilateral input-output table**

## **3.1 EORA: World MRIO data**

The multi-region input-output table (MRIO) that is part of the Eora database provides an in-depth window on the GVC integration of sub-Saharan countries. There exists several alternative input-output databases, but this is the only version where there is enough country detail to study African countries individually.<sup>9</sup> Unavoidably, this level of detail comes at a cost, which is the greater reliance on imputations and proportionality assumptions to complement the relatively sparse data for some countries. The underlying data that we use is freely downloadable and the construction of the various components is described in detail in Lenzen et al. (2012, 2013).<sup>10</sup> We use the global MRIO table that contains information for 190 countries over the 19902015 period for a simplified set of 26 harmonized sectors. These dimensions imply that just the matrix of bilateral input coefficients at the country-industry level is a (190*26) by (190*26) square matrix that contains more than 24 million coefficients. In addition, there are columns for 6 final demand components that add another 5.6 million pieces of information. Moreover, this information is available for 26 years.<sup>11</sup> Clearly, in order to learn something from this gigantic source of information, we will need to aggregate and zoom in on particular areas of interest. Along the three dimensions—time, industry, country—we made the following choices.

### <u>Time</u>

We observe a different IO table for 26 consecutive years, but the structure of the economy and the bilateral trading relationships only change gradually over time. Therefore, we only used the information for three equidistant years: 1995, 2005, 2015. In the analysis we will look both at the current composition of input-output relationships using the most recent IO table for 2015 and we will
of Altonji, Elder, and Taber (2005) in that it takes into account whether controls absorb residual variation. This is important for the credibility of the exercise, as one should not expect to observe coefficient instability upon adding controls that are unrelated to the outcome variable. Under the assumption that selection on observables has the same direction as selection on unobservables, the test produces lower-bound coefficients close to 0.1 (see the row headed “Oster bound” in table 3), corresponding to mission cells having a 40 percent higher likelihood of aid allocation. Importantly, the Oster bound is always significant at the 99 percent level.<sup>19</sup> The estimated correlation is higher for Catholic missions, but the difference between denominations is significant at the 90 percent level only in column 6, which uses data from two different sources. Furthermore, there is no feasible way to address differential selection of Protestant versus Catholic missionaries, so it is difficult to interpret the difference between the two coefficients. Finally, if the Democratic Republic of the Congo (DRC) is dropped from the sample, the difference between the two disappears in column 3 and halves in column 6, becoming not significant. The DRC covers 7 percent of the sample cells, making it the largest country in the sample, and it has a high concentration of Catholic missions. Hence, the differential in the coefficient size seems to be driven by this heavyweight outlier. To simplify the exposition, the rest of the article reports results from regressions on the mission data from Beach (1903) only.

# **2.5 Robustness Tests**

## **2.5.1 Subsample Analysis**

The first two robustness tests are aimed at restricting estimation of equation (1) to subsamples that are more likely to exhibit common support in the covariate distribution (see table 4). First, the sample is restricted to cells on the coast or along one of the main rivers (C/R column). As
-||0.252***<br>|-|-|
||||[0.036]|||
|Q3 2018 Indicator|-||0.227***|-|-|
||||[0.042]|||
|Interaction Between IPC Status and the Post<br>Announcement Indicator||||||
|____________________<br>Post Announcement Indicator*IPC 4 Indicator|-||-|0.095**<br>[0043]|-|
|Interactions Between the Post Announcement<br>Indicator and Individual Governorates in IPC 4<br>Regions||||.||
|____________________<br>Post Announcment Indicator*Abyan Indicator|-||-|-|0.064***<br>|
||||||[0.021]|
|Post Announcment Indicator*Al Hudaydah Indicator|-||-|-|0.008|
||||||[0.028]|
|Post Announcment Indicator*Hajjah Indicator|-||-|-|0.028<br>[0.021]|
|Post Announcment Indicator*Lahj Indicator|-||-|-|0.233***|
||||||[0.021]|
|Post Announcment Indicator*Sa'ada Indicator|-||-|-|0.279***|
||||||[0.021]|
|Post Announcment Indicator*Shabwah Indicator|-||-|-|0.016<br>[0.021]|
|Post Announcment Indicator*Taizz Indicator|-||-|-|0.130***|
||||||[0.021]|
|Observations|357|357|357|357|357|
|Controls
Visits (JD-HV). ProGres records demographic information of each individual refugee and their household at the time of registration with UNHCR including the relationship of each household member with the principal applicant. In this paper, we refer to the self-identified principal applicant as the household head – the term used in the poverty research literature. Self-identification is commonly used as a means of identifying the household head in household surveys of income, consumption and expenditure surveys which are used to estimate poverty rates (see Hanmer et al, (2020)).

Home visits began at the same time as the UNHCR cash assistance program was launched. The program aimed to assist vulnerable Syrian households to meet their basic needs. During the home visits, UNHCR determined whether a household met the criteria to qualify for assistance, considering economic poverty and the presence of members in the household requiring protection. Since 2013, these visits have been made to every registered Syrian refugee household in Jordan outside the camps (UNHCR, 2014).

At registration, each household is assigned a unique registration number that serves to crossreference the initial registration data with Home Visits data. Home Visits data are collected for about one-third of the registered households. In both waves, the selected households to be interviewed are those UNHCR considers to be most vulnerable in terms of education, specific needs and the use of livelihood coping strategies,<sup>7</sup> thus, they are a nonrandom sample of all registered Syrian households in Jordan. During Home Visits, data on household expenditures, aid, income, schooling, and other demographics are collected. Each wave of data is a snapshot of a different nonrandom sample of registered refugees because the pool of registered households changed over time. However, because of data confidentiality, we could not link records across years. For this reason, we treat each wave as an independent sample of cross-sectional data.

We compare 204,941 individuals in 54,900 cases (households) in the 2013 wave, with 195,930 individuals in 43,
<u>Annex1</u> Page 1 Annex 1. Computation of Distribution of Household Income in AMC The principle source of household data for computing distribution of income by household is the National Sample Survey (NSS) data. The NSS rounds provide survey data on expenditure by household. The data is compiled by district, then divided into rural and urban samples. Therefore the closest approximation of the area of our study within the NSS framework is the Ahmedabad urban district, of which the AMC population is over 80% of total population. The NSS rounds of measuring household expenditure are available for 1999/2000, 2004/05 and most recently 2009/2010, which was released as this study was being prepared. It is generally recognized that the 1999/2000 round is not comparable with the more recent rounds so we did not use it for estimating household expenditure distribution for the start year of our study of 2001. Instead, we took the expenditure data from 2004/05 and rebased it to 2001 using the growth rate of nominal per capita income in Gujarat State between 2000/01 and 2004/05 as published by the Central Statistical Organization. Unfortunately, income growth for the AMC or Ahmedabad Urban Agglomeration is not published in Gujarat, unlike in some other states. Income growth in Ahmedabad could well have been higher over this period, since urban areas tend to grow more rapidly than rural areas. On the other hand, Gujarat has experienced exceptionally rapid agricultural growth over the last ten years, so the difference in Gujarat may not be as pronounced as it might be in other states. The correction factor applied for this purpose is 0.71. To estimate income distribution in 2011, we used data from the sample survey of the NSS 2009/10 round. This was then adjusted to 2011 by multiplying by the increase in net per capita income for the state of Gujarat between 2009/10 and 2010/11, which reflected growth of 14% in nominal terms. Since we measure affordability in relation
Migration in Malawi is common. Data from the most recent nationally representative survey in the country (the 2004/05 Integrated Household Survey, IHS2) show that slightly over half of adults (15 and older) have not always resided in their current location. About one in five of adults moved into their current village or neighborhood in the last five years. Among workingage adults, migration rates are even higher (see Table 1). Among those who moved into their current community in the last five years, the vast majority (more than 90 percent) moved from within the same district. So while moving is common, these are not long distance moves.

The focus in this paper is on young adults, but of note is that migration is also observed among children. Child fostering is not uncommon in Sub-Saharan Africa and it implicitly results from migration of either parents or children. Malawi is no exception. Ansell and van Blerk (2004) suggest that internal child migration in Malawi has historically been a coping strategy for families dealing with stress and that its importance has increased due to the HIV/AIDS epidemic. While orphanhood rates have increased, even among non-orphans in Malawi there has been an increase in propensity of non-orphans to reside without either parent (Beegle et al. 2010).

# **III. Young Malawians on the Move**

In this section we focus on the descriptive evidence on migration among young adults in Malawi, a population facing complex life-cycle events. We do not present a comprehensive framework or model on the transition to adulthood in Malawi. Rather, we hope this analysis serves as a starting point for such a framework, noting that the main features of the transition to adulthood center on marriage, work, and school, all of which are linked to local mobility. In the case of secondary boarding school, this mobility may even be quite distant.

6
Masson et al., 1998; and Miles, 1999).

The international evidence on whether saving follow the LCH is mixed if low and middle-income countries are also considered — and highly dependent on how income is estimated. Households do not seem to run

> 1 In a separate VAR model where public saving and the old-age dependency ratio are included in the equation system, the result still holds. These supplementary results are available upon request.

> 2 There is one important caveat of this analysis, however. The data on public and private saving in Brazil is not as reliable as one would hope. The period 1984 to 1994 are somewhat unreliable, because the public and private saving were not available from IPEA, and this is the period where public and private saving is then seen to display a spike. The IMF is as the source for public saving within this period. On the other hand, Morandi (1998) finds a similar spike in private saving, though smaller. Oliveira et al. (1998) find a similarly smaller spike within this period.

4
.025<sup>******</sup><br>0.021<sup>******</sup>|0.018<sup>******</sup>|
||(0.009)<br>(0.008)|(0.009)|
|M|53%<sup>******</sup><br>54%<sup>******</sup>|61%<sup>******</sup>|
||(2.02%)<br>(3.09%)|(5.01%)|
|D|65%<sup>******</sup><br>76%<sup>******</sup>|76%<sup>******</sup>|
||(14.8%)<br>(12.2%)|(9.9%)|

_Source_ : Baseline Survey 1997 and ENIGH 1996 _Notes_ :<sup>**</sup> Significant at 5% level;<sup>*</sup> Significant at 10% level. Standard Deviations (in parentheses) computed by bootstrapping.

The BFL model also makes the estimation of program effects on poverty and inequality possible, which is useful to examine the importance of _PROGRESA_ as an anti-poverty program in the short 30 Leite (2007) estimates the set of parameters of interest using a sub-sample of children around the means test threshold. This is done by defining two homogenous groups – potential beneficiaries who are below the means test threshold (treatment) and non-beneficiaries who are above the threshold (control) – who have made their choices subject to similar income constraints. He finds that 65 percent of the
# **3.2 Extensive margin**

Table 8 summarizes the rates of entry and exit of firms into the census of exporters. There is significant churning of exporters in the data – anywhere between 16 to 37 percent of firms exit, and another 19 to 37 percent enter each year. While there seems to be significant destruction and creation of exporters in the data, the net effect of the number of firms seems to be marginal. Moreover, entry and exit rates are lower than in countries at a comparable level of development (Figure 2 1 ).

||||**Tabl**<br>**Export Entr**<br>|**e 8**<br> **y and Exit**<br>|||
|---|---|---|---|---|---|---|
|**Year**|**Exits**|**Entries**|**Net Entry**|**% Exits**|**% Entries**|**% Net Entries**|
|2003|5,481|5,435|-46|37.3|37.0|-0.3|
|2004|4,368|4,397|29|29.7|29.9|0.2|
|2005|3,798|3,633|-165|26.1|24.9|-1.1|
|2006|3,497|3,628|131|23.8|24.7|0.9|
|2007|2,870|3,250|380|19.0|21.6|2.5|
|2008|2,775|3,057|282|18.1|19.9|1.8|
|
, and assign it to observation surveyed in the second round ( ) ss1

- ss yy

- 10. Estimate movements in-out of poverty and other income dynamics mm1 ss1

- mm yy� = yy

- 11. Repeat 100 times all steps 3 to 10

This process allows for estimating standard errors for the statistics in interest. For every bootstrap sample of the data, a new LASSO is fit and the whole welfare vector is constructed via the improved LASSO PMM process described above. As a result, both point and interval estimates are calculated for every poverty dynamics indicator.

# II.2 Defining the middle-class thresholds

The major challenge regarding the estimation of the LAC region’s vulnerable and middle-class groups is the identification of the lower and upper thresholds. These were defined initially by Lopez-Calva & OrtizJuarez (2014) and Ferreira et al. (2013). These authors focus mainly on defining and computing the lower bound. An individual is defined as vulnerable if the probability of falling back into poverty over a five-year interval is greater than 10 percent. Both references use panel data from three different sets of countries. While Lopez-Calva & Ortiz-Juarez (2014) use Chile, Mexico, and Peru, Ferreira et al. (2013) use Argentina, 7
while exceeding 85 percent of GDP in Austria, China, and United Kingdom. The annual average value of private credit across countries was 39 percent with a standard deviation of 36 percent.

An alternative to private credit is total banking assets to GDP, a variable that is also included in the Global Financial Development Database. Compared to private credit, this variable includes also credit to government and bank assets other than credit. It is arguably a more comprehensive measure of size, but it is available for a smaller number of countries and has been used less extensively in the literature on financial development. In any case, the two variables are rather closely correlated, with a correlation coefficient of about 0.9 (Figure 5), so private credit can provide a reasonably close approximation for total banking assets.

Despite the literature‘s focus on banks, the recent crisis has highlighted issues in non-bank financial institutions (NBFIs). The coverage of NBFIs by data is much less comprehensive than that of banks. Nonetheless, to acknowledge this point, the Global Financial Development
_Table A2.4 Weighted Intention‐to‐treat effects of the original intervention on main psychological and behavioral outcomes._

||(1)<br>ITT<br>(no controls)|(2)<br>ITT<br>(controls)|
|---|---|---|
|_Main outcomes – Ps_|_ychological and beha_<br>|_vioral_<br>|
||‐0.036|‐0.022|
|Psychological wellbeing index|(0.476)|(0.646)|
|Risk index|‐0.054|‐0.062|
||(0.394)|(0.292)|
|Trust index|‐0.013|‐0.033|
||(0.811)|(0.548)|
|Crime and violence index|‐0.110**|‐0.119**|
||(0.029)|(0.023)|
|Migration index|‐0.045|‐0.036|
||(0.363)|(0.482)|
|List experiment cattle index|0.215**|0.210**|
||(0.034)|(0.037)|
|List experiment argument index|‐0.125|‐0.120|
||(0.179)|(0.201)|
|Observations||1495|

|1523|
|---|

_Note: Observations are weighted by their inverse likelihood to be in the final sample, based on who was easy to reach during the phone survey. All regression control for gender‐state fixed effects. Control variables of column (2) include all baseline controls that were significant determinants of attrition and of selection between receiving “training and grant” vs “training, but no grant”. In particular
urban and rural areas.<sup>12</sup> Referring to Nicita (2004) for a detailed explanation of the model, the estimating equation is given by: where _X gt_ is the primary control variable (the international price of good _g_ expressed in domestic currency), while _Z gtr_ is a vector of control variables that includes local supply and regional income, _R_ and _U_ denote rural and urban dummy, and ε _gtr_ is an i.i.d. error term. The coefficients of interest are: γ that represents the tariff pass-through elasticity and<sup>γ</sup> 1<sup>and</sup> γ 2 that are its adjustment for distance from the US border. The pass-through is “full” or “complete” if γ = 1 and the pass-through is “incomplete” if γ < 1. Similarly, the effect of the pass-through will be identical in all urban areas if γ 1 = 0 . On the other hand, if local prices vary as a consequence of movement in the tariff, then γ 1 ≠ 0 . Similar reasoning is applied in the case of rural areas, where the coefficient of interest is γ 2 .

The econometric estimation of equation (1) combines a time series of crosssectional data set into a pseudo panel.<sup>13</sup> The data consist of domestic prices for 63 regions and six time periods. Average prices for each region are arranged into a panel data set and the estimation is performed separately for agriculture and for manufacturing. Table 3

> 12 Higher transport costs and local supply suggests a lower price pass-through in rural areas, especially on agricultural products.

> 13 From a time series of six cross section surveys (from 1989 to 2000), synthetic cohorts are defined as groups with fixed membership, whose individuals (or households) can be identified as they show up in the surveys. For this reason, groups are defined according to some time invariant variables. Means within each cohort are