rafmacalaba/data-use-mentions-extended huggingface dataset:
on average children under 15 comprise 36 percent of the poor. Recent estimates by Watkins and Quattri (2016) were obtained by multiplying, for each country, the fraction of the population that are children times the number of poor, with an adjustment to account for the higher fertility rate among the poor. This yielded an estimate of 409.4 million children living in extreme poverty in 2012, implying that children comprised 45.6 percent of the poor. This figure, which was subsequently published by UNICEF (2016), could underestimate the extent of child poverty, by not fully adjusting for children’s higher poverty rates.<sup>2</sup> Overall, existing estimates vary considerably in their source data and age cut-offs, with corresponding differences in results.

This paper makes two main contributions. First, it presents new estimates of child poverty as of 2013, for a sample of 104 surveys collected since 2009 in 89 developing countries. These estimates define children as those below the age of 18, in accordance with the Convention of The Rights of The Child.<sup>3</sup> These estimates of child poverty are the first estimates calculated using the Global Micro Database (GMD), which adds a set of harmonized household characteristics to the same surveys and welfare measures that are used to produce the poverty estimates published by the World Bank (Castañeda et al 2016). The 89 countries included in the database contain an estimated 84.2 percent of the population in the developing world, and 82.1 percent of the child population. Second, this analysis tests the robustness of age-group poverty rates with respect to different equivalence scales, while appropriately adjusting the poverty line to take into account the composition of families near the poverty line (Ravallion, 2015).

> 2 The fertility adjustment was made on the basis of separate data from Demographic and Health Surveys. See Watkins and Quattri (2016), p.34.

> 3 Convention on the Rights of the Child, Part
which we ignore the data collected from previous waves (pseudo-panel data) as it could entail some recall bias (we are asking for information about the first round in subsequent rounds). Though there is some small variation in terms of the magnitudes of the coefficients (between one and two percentage points in the case of change in sales), the qualitative interpretation and the statistical significance of the results do not change.

Table 6: Probability of receiving support

|Dependent variable=1 if received support|Conditional|Cont. initial<br>productivity|Conditional|Cont. initial<br>productivity|
|---|---|---|---|---|
|Informality|-0.0977**|-0.125**|||
||(0.0176)|(0.0219)|||
|Necessity|||-0.0462**|-0.0622**|
||||(0.0133)|(0.0149)|
|De Soto|||-0.0370**|-0.0450**|
||||(0.0110)|(0.0123)|
|Parasite|||-0.0314**|0.00729|
||||(0.0113)|(0.0163)|
|% change in sales during past 30 days|0.000586**|0.000697**|0.000574**|0.000675**|
||(0.0000937)|(0.000110)|(0.0000925)|(0.000109)|
|Intial productivity||-0.000408||-0.00198*|
|||(0.00104)||(0.00109)|
|Panel controls|Yes|Yes|Yes|Yes|
|Other controls|Yes
To construct our synthetic panel we use six rounds of cross-sectional data from the Household Income and Basic Amenities Survey (HIS) collected by the Department of Statistics Malaysia. The data are for the years 2004, 2007, 2009, 2012, 2014 and 2016. When Malaysia’s national poverty line was updated in 2020, the value of the line was only recalculated for the 2016 and 2019 surveys, and not for the earlier surveys. Hence, we employ our own poverty line, which corresponds to the income level of the 40th percentile in the first year of our analysis, 2004. This line is then kept constant in real terms for the subsequent years. It is close to the new official poverty line.<sup>5</sup> In addition, we study income mobility in a broader sense by drawing on the vulnerability analysis proposed by Dang and Lanjouw (2017). This allows us to define three income groups: the poor, the vulnerable (those non-poor who face a heightened risk of falling into poverty) and the economically secure. From these, upward mobility is defined as moving up one or two categories, downward mobility as moving down one or two groups. We note that this is a somewhat narrow definition of mobility that does not describe intergenerational mobility or mobility within the middle and higher reaches of the income distribution.

The main contribution of the present study is that it provides the first evidence on poverty and income dynamics in Malaysia based on official micro-level survey data. In comparison to synthetic panel studies carried out in other countries, our addition is that we are able to use six rounds of high-quality data, rather than only two or three. Moreover, each round has a large number of observations, which facilitates within- and between-group analyses. This means that we are able to present results on a variety of dimensions, with this paper focusing on chronic poverty, upward mobility and downward mobility, also taking into account differences between population groups.

We present three headline results. The first
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so on. Further, more trade flows in the NBER-UN data are zero in 1970 than are zero today, likely due both to true changes from zeros to positive values over time and differences in reporting thresholds and/or missing data in the two data sources. In order to use the whole sample, we replace zeros in 1970 with the smallest values observed in the data.

> 75Including the instruments separately, without imposing this ratio restriction, yields similar results.

> 76Testing the exogeneity of the DVA-Ratio alone, one cannot reject exogeneity.

59
**Table A.15. Differences by household migration status in association between social norms indices**

**and women’s paid work outcomes**

||**HH Roles**|**Public Space**|**Both themes**|
|---|---|---|---|
||**(1)**|**(2)**|**(3)**|
||**Paid work**|**Paid work**|**Paid work**|
|PNB Index_HH Roles # migrant<br>HH**(**β)|-0.08<br>(0.08)||-0.07<br>(0.08)|
|PNB Index_HH Roles**(**α)|-0.07* (0.04)||-0.06<br>(0.04)|
|SNE Index_HH Roles # migrant<br>HH**(**β)|-0.04<br>(0.09)||-0.00<br>(0.09)|
|SNE Index_HH Roles**(**α)|-0.03<br>(0.06)||-0.04<br>(0.06)|
|PNB Index_Public Space #<br>migrant HH**(**β)||0.03<br>(0.05)|0.03<br>(0.05)|
|PNB Index_ Public Space**(**α)||-0.03<br>(0.03)|-0.02<br>(0.03)|
|SNE Index_ Public Space #<br>migrant HH**(*
1).

**One-year firms** : The number of firms and employees in those firms that were present in the register for only one year. These firms were those observed as (out, in, out) the register in time (t – 1, t, t +1).

**Continuing firms** : The number of firms and employees that were in the register in a given year, as well as in the previous and subsequent year. These firms were observed as (in, in, in) the register in time (t – 1, t, t +1).

The above indicators were split into 8 firm-size classes including the class of firms without employees.<sup>7</sup> The data thus allow detailed comparisons of firm-size distributions between industries and countries.

**Firm survival:** available data allow to track entering firms over time, This allows to calculate survival probabilities over the initial life of firms and to assess their changes in employment over time.

**Decomposition of productivity growth:** The database includes different types of productivity decomposition for manufacturing industries and some service industries.  Depending on the availability of output and input measures, productivity data are available in the database with reference to labor productivity, multifactor productivity using either gross output or value added as the indicator of output (see Bartelsman et al. 2004 for more details).  In this paper, the analysis is limited to labor productivity, generally defined as deflated gross output per worker. Firm level nominal values of output are deflated at the industry level

6. See www.oecd.org/data/stan.htm

> 7. For the OECD countries there are only 6 groups, with the groups between 1 and 20 combined and the groups between 100 and 500 combined.

8
51.9<br>50.0 43.8 43.6<br>39.3<br>40.0<br>30.0<br>7 day recall<br>20.0<br>9.9 12 month recall<br>10.0 6.3<br>0.4 0.7<br>0.0<br>1 2 3 4<br>Number of labor income sources<br>Source: Authors calculations based on the UNPS 2009/10<br>Proprtion of Households<br><!-- End of picture text --> The underreporting of both activity types and their number using a 7 day recall period means that indicators based on these data are plagued with measurement errors that would affect any micro level analysis. The measurement of earnings is one such example. Individual earnings will be understated in the 7 day recall period by virtue of missing out other income sources of the individual over the year. This measurement error is unlikely to be random. As Figure 1 shows, certain types of activities including temporary private agriculture jobs and casual non-agriculture jobs tend to be missed the most, and these are mainly performed by the poor. Furthermore, the shift in economic activities over the year by individuals and households will make it difficult to extrapolate 7 day recall earnings to annual earnings or to analyze variations within groups (Schaffner, 20
Figure 5 Income Shares Across the Income Distribution <!-- Start of picture text --> 2 4 6 8 10 12<br>log per capita expenditure<br>Agricultural sales Wages<br>Non-farm enterprise Other/transfers<br>Home production<br>.8<br>.6<br>.4<br>Income share<br>.2<br>0<br><!-- End of picture text --> Notes: non-parametric kernel regressions of income shares on the log of per capita household expenditure across the 54 countries included in the HIT database.

21
16 between different output varieties within an industry, σ n , which is not sufficiently identified by the share equations alone. The output shares of the hypothetical country, appearing as s<sup>*</sup> nt , are measured using sample averages in (18), but will be fully absorbed by year fixed effects, δ nt in the share equations (16). Then the equality of σ n can be imposed between (16) and (18) to identify these elasticities.

Our data set covers 34 countries from 1982 to 1997, a total of 342 observations. GDP is measured in constant 1995 U.S. dollars to make cross country and time series comparisons appropriate. We will use (9) to measure export variety from country c to the U.S. in every years 1972 – 1988 (using the TSUSA data), and 1989 – 1997 (using the HS data). This gives a consistent comparison of export variety in each country relative to the hypothetical country producing all varieties.

There are three kind of primary factor endowments: labor, capital and agriculture land. Labor is defined as the number of persons in the labor force of each country. Capital is constructed using perpetual inventory method using real investment of the countries. Real investment is obtained by deflating the gross domestic capital formation of the countries with the respective GDP deflator on domestic capital formation. In addition, we construct the base year capital stock using an infinite sum series of investment prior to the first year, assuming that the growth rate of investment of the first five years are good proxy for investment prior to the first year. All these data, together with price deflators of GDP, exports and imports are available in World Development Indicators (World Bank, 2003).

We aggregate up all export goods into N = 7 sectors: agriculture, textiles & garments, woods & papers, petroleum & plastics, mining & basic metals, machinery & transport equipment, and the electronics. The value added of
# Abbreviations and Acronyms

|AFOLU|Agriculture, Forestry, and Other Land Use|
|---|---|
|AQUASTAT|global water information system of the FAO; developed by the FAO Land and Water Division|
|BRICS|association of five emerging countries, Brazil, Russia, India, China and South Africa|
|CGIAR|Global partnership of 15 organizations engaged in research dedicated to reducing rural<br>poverty, increasing food security, improving human health and nutrition, and ensuring<br>sustainable management of natural resources|
|CMIP5|Coupled Model Intercomparison Project Phase 5|
|CO2|Carbon Dioxide|
|CWATM|Community Water Model; open source hydrological model developed at IIASA|
|EFR|Environmental Flow Requirement|
|EPIC|Environmental Policy Integrated Climate Model; globally gridded crop model|
|FAO|Food and Agriculture Organization of the United Nations|
|GCM|General Circulation Model|
|GDP|Gross Domestic Product|
|GFDL/GFDL‐ESM2M|General Circulation Model of NOAA Geophysical Fluid Dynamics Laboratory|
|GGCM|Globally gridded crop model|
|GHG|Greenhouse gas|
|GLOBIOM|GLobal BIOsphere Management Model; developed at IIASA|
|HadGEM/Had GEM2‐ES|General Circulation Model of Met Office Hadley Centre|
|IIASA|International Institute for Applied Systems Analysis|
|IPSL/IPSL‐CM5A‐LR|General Circulation Model of Institut Pierre‐Simon Laplace|
|ISI‐MIP|Inter‐Sectoral Impact Model Intercomparison Project|
|LPJml|Lund‐Potsdam‐Jena managed land model; dynamic vegetation model developed at the<br>Potsdam Institute for Climate Impact Research|
|LUC|Land use change|
|LULUCF|land use, land use change, and forestry|
|MER|Market Exchange Rate|
|MESSAGE|Model for Energy Supply Strategy Alternatives and their General Environmental Impact;<br>energy model developed at IIASA|
|MIROC/MIROC‐ESM‐CH|General Circulation Model for Japan Agency for Marine‐Earth
the interview.

Table 2 summarizes comparisons of neonatal, postneonatal, and infant mortality rates by basic characteristics. As noted, postneonatal mortality is greater in high malaria risk zone and is significantly lower in households that own at least one ITN. This difference is significant both overall and within high malaria risk zones. Postneonatal mortality is also lower for infants from

> 6 We categorize survey clusters from the DHS into ―low malaria risk zones,‖ ―intermediate malaria risk zones,‖ and ―high malaria risk zones‖ using data assembled by the Malaria Atlas Project.  The Annex provides a description of this classification. The risk zones are defined in terms of prevalance of Plasmodium falciparum, which is the most deadly strain of malaria and is estimates to be responsible for 98 percent of malaria cases in Sub-Saharan Africa (World Health Organization 2008).
shows the value of GVC exports for SACU and other regions like ASEAN, Eastern Europe, Mercosur and the East African Community. Even accounting for size, SACU lags: SACU countries export less than 3 percent of the value of GVC products that ASEAN exports, with a population that is roughly 10 percent of ASEAN and GDP that is 18 percent of ASEAN. Within SACU, South Africa accounts for about 92 percent of the region’s GVC exports.

**Table 7: Value of GVC Exports, 2012, by Region**

|**Region**|**Value(in US$000s)**|
|---|---|
|ASEAN|492,000,000|
|Eastern Europe|319,000,000|
|Mercosur|31,200,000|
|South Africa|11,200,000|
|SACU(excl. South Africa)|985,222|
|‐Lesotho|530,794|
|‐Swaziland|229,695|
|‐Botswana|137,418|
|‐Namibia|87,315|
|East African Community<sup>11</sup>|589,240|

Source: Authors’ calculations based on data from UN Comtrade SACU countries have accumulated different levels of GVC‐related capabilities (Table 8). In terms of human capital, as measured by the average years of schooling, Botswana and South Africa score high (at over eight years of schooling), while Namibia lags behind the other SACU countries (at six years). South Africa’s total natural capital valuation far outpaces other countries, with Botswana in a distant second. Botswana and South Africa have a substantial advantage on physical capital per capita. Having successfully managed revenues from resource extraction, Botswana’s institutions are rated ahead of the other countries, including South Africa, with Lesotho and Swaziland lagging behind the others in this area. All countries are in a similar geographic situation, relative to other markets
North Africa e ‘Western Europe and North and North North America @ South Asia<br>© Sub-Saharan Africa Fitted values<br><!-- End of picture text --> <!-- Start of picture text --> v8<br>@srs ‘sim<br>322 === eK Pre p) =33 en @ -sresre<br> 5 e . e@Ksytt ern 5 53 533 an OY MOK =<br>eo jini ore Fo° o2R RRS eS om aw en<br>=e oCU mr ® mr ® ® ota or 25 ” wane” oo eecs @rou_© ota<br>a @ gt —“@ ceo CHy BBY @ITA e 25 OMe een wis OSNews OoNus<br>mit)== @AF150 oth a8 "eis ore RRR? 2308 ems<br>a ot ERR WO OMIM ica @KHN eres? SQ SQ eo<br>e@zaR eM ec @SHA @cTM @RWA ex ect ec<br>om °<br>6 8 10 12 6 8 10 12<br>Log of GDP of GDP GDP per capita capita Log of GDP GDP per capita capita<br>@@@ East Asia and Pacific e Eastern Europe and Central Asia @ Latin Latin America and Caribbean © Eest Asia and Pacific Pacific e Eastem Europeand Europeandand Central Asia @ Latin Latin America and Caribbean and Caribbean<br>Middle East and North Africa e ‘Western Europe and North North America @ South Asia @ Middle East East and North Africa e ‘Western Europe and North and North North America @ South Asia<br>© =Sub-Saharan =Sub-Saharan Africa Fitted values values © Sub-Saharan Africa Fitted values<br>Proportion of females females in each sector<br>co}<br>er<br>ek<br>@Kaz<br>i} osal T ew<br>Be eK GRE: oem oem<br>os OTs @UGA
corresponds to about 60 percent of the capitalization required for domestic banks.<sup>12</sup> Ghanaian credit unions are not subject to MCR. For Tanzania, the minimum capitalization requirements for regional banks and rural community banks appear to be at fairly accessible levels for investors in and promoters of institutional microfinance. The response, however, has been limited: only one regional bank and two rural community banks have been organized. The recently enacted Law on Microfinance Companies appears unlikely to foster significant new entry, as the currently unregulated or unlicensed MFIs are unlikely to achieve the stipulated (but still being discussed) MCR of T Sh 1 billion. Tanzanian credit unions are not subject to MCR.

# **_Capital Adequacy_**

59. In Ghana, the capital adequacy standard (CAR) prescribed for banks, including rural banks, is 6 percent of risk assets. A higher standard of 10 percent of risk assets is prescribed for S&Ls and deposit-taking NBFIs. The Ghana Credit Union Association follows the World Council of Credit Unions standard of 10 percent of total assets. In Tanzania, regional banks and rural community banks are subject to a uniform CAR of 6 percent of risk assets. No prudential standards for capital adequacy are imposed on credit unions in Benin, Ghana, and Tanzania other than voluntary norms recommended by international CFI networks. As credit unions in Benin, Ghana, and Tanzania are not subject to minimum capitalization requirements, consistent with international practices that allow easy entry for member-based CFIs, this puts the burden of prudential regulation on the maintenance of CAR, for which measurement and standards should be similar for CFIs and for banking institutions.

60. The CAR applicable to licensed MFIs in Ghana and Tanzania appear to be lower than is prudent, especially because shareholders of MFIs generally have difficulty in quickly responding to calls for additional capital infusion. For a number of reasons (see Christen and Rosenberg 2000), self-sufficient MFIs (other than rural banks) in the Microfinance Information Exchange database maintain a higher ratio of capital
hourly wage growth for workers with “at most a primary education” whereas the relationships are not statistically significant for the other education groups. His findings are consistent with a rise in labor demand for unskilled workers, but the study does not show or model a mechanism through which the tariff cut translated into the rise in wages for unskilled workers. Also, since the education groups of “no formal education” and “primary” are aggregated in McCaig (2011), one cannot know whether the U.S. tariff cut is associated with the rise in wages for the _least_ educated workers (who are the most likely to be the poor). In terms of the data, whereas McCaig (2011) used cross section data aggregated at the province level, I used panel individual data. Finally, McCaig (2011) and most of previous studies used hourly wages in evaluating wage change, I used monthly wages which reflect both the compensation per unit of time and hours worked.

25
_MACHINE LEARNING AND HIGH-FREQUENCY SURVEYS IN PAPUA NEW GUINEA 15_ possible temporal overlap between a higher number of food items may again be exploited to estimate (“stitch together”) a complete basket price over a long time period, albeit with a higher share of missing data.

When compared to the average FCS country considered by Andr ́ee (2021), the PNG data stands out in the sense that it has more food items but fewer markets. The data availability is below the average of FCS countries considered by the application presented by Andr ́ee (2021).

# _A. Validation results_

Table 3 summarizes the cross-validation results along with basic aggregate statistics calculated from the imputed Food Price Index. Compared to results obtained in other FCS countries, the annualized inflation rates, maximum drawndown, and annualized volatility are similar to what has been observed for staple food prices in comparable countries. The annualized volatility is slightly higher, but not by a standard deviation. higher. The _R_<sup> ̃2</sup> here is more than a standard deviation below the FCS median. Nevertheless, it is within the range of FCS results (the results presented for Chad reached 0.69, and 0.75 for Central African Republic). The results therefore show that the new approach reaches comparable results even with lower data coverage and a much higher number of food items.

Table 3—Summary of raw food price data in Papua New Guinea.

||Annualized|Annualized|Max.|Avg. monthly|Avg. annual|CV-|
|---|---|---|---|---|---|---|
||Infation|Volatility|Drawdown|market corr.|market corr.|score|
|2009-2022|6.21%|15.33%|-21.28%|0.18|0.33|0.71|
|2014-2022|4.9%|11.