rafmacalaba/data-use-mentions-extended huggingface dataset:
Policy Research Working Paper 8798

# **Abstract**

Using recent Demographic Health Survey data for Bangladesh and the neighboring Indian states of Bihar, Jharkhand, and West Bengal, this paper reexamines the determinants of child wasting and maternal anemia. The findings bear out the importance of commonly cited factors, such as mother’s education and age, household wealth, and child birth order. However, the findings also highlight significant and large regional differences between Indian states and Bangladeshi provinces. For example, the results for Jharkhand state in India and Barisal province in Bangladesh indicate that controlling for those commonly cited determinants, the poorest, least-educated mothers and their children in Barisal have better health outcomes than the wealthiest, best-educated counterparts in Jharkhand. Mapping analysis of the spatial variations in child wasting and maternal anemia shows clear patterns of clustering over large areas that frequently overlap state/province and national boundaries. Possible sources of these striking differences include spatially differentiated prices and availability of critical nutrients; dietary preferences related to religion and ethnicity; nutrition education; and administration of public health and nutrition policy.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at sdasgupta@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations,
destination of transactions. Lischke and Fabian (2016) leverage publicly available Bitcoin transaction data and the IP addresses of transactions to gauge geographical location. However, this approach assumes that the Bitcoin node that broadcasts the transaction to the network is also its source.

### _Crypto-assets prices and inflation_

There is a small literature that studies the link between crypto-asset prices and inflation. Using a vector-autoregressive framework, Blau et al (2021) find an empirical relationship between bitcoin prices and inflation expectations suggesting that bitcoin acts as an inflation hedge (i.e., changes in bitcoin Granger-cause changes in inflation expectations). Conlon et al (2021) confirm a link between forward inflation expectations and bitcoin and ether prices, but this relationship is only limited to the onset of COVID-19 casting doubt over the ability of these crypto-assets to hedge expected inflation going forward.

# 3 Data

## 3.1 Description of the on-chain crypto-activity data set

We use a large global monthly country-panel of on-chain crypto-asset transaction volumes of value sent in US dollars provided by Chainalysis, a global blockchain analysis company. “On-chain” transactions are directly recorded on the distributed ledger that underpins a crypto-asset. The sample spans the period April 2019 – June 2021 and covers 174 countries, 114 different cryptoassets, and five transaction size categories. We have mapped crypto-assets into four groups: 1) Bitcoin; 2) Ethereum; 3) Stablecoins<sup>11</sup> ; and 4) DeFi and Others.<sup>12</sup> We also group transaction sizes into two categories: a) less than or equal to $10,000, which is more reflective of retail use and b) greater than $10,000.

While on-chain crypto-assets transaction data are fully transparent for most crypto-assets, in light of their pseudonymous nature, the destination country of a particular transaction may not be known

> 11 These include some of the major US$-linked stablecoins: USD Coin (USDC), Tether (USDT), DAI (crypto-assets backed
conflicts in 10 of the 13 regions in Burkina Faso.<sup>5</sup> How do gender disparities manifest in terms of labor force participation and earnings? Our primary source of data to investigate this question is the “Enquête Harmonisée sur les Conditions de Vie des Ménages 2018-2019” (EHCVM). The EHCVM is a nationally representative survey carried out in each country of the West African Economic and Monetary Union (WAEMU). The Burkina Faso survey was conducted by the National Institute of Statistics and Demography (INSD), the national statistical agency of Burkina Faso. The survey used a stratified two-stage sampling, with 585 enumeration areas randomly selected in the first step, and 12 households randomly drawn from each enumeration area in the second step. EHCVM surveyed around 7,000 households. The survey asked a rich variety of questions about different individual characteristics, such as income, educational attainment, and sector of employment.

Figure 1 shows male and female employment by sector in the EHCVM data, depicted as a Venn diagram. Individuals can have more than one occupation—for example, someone may be both a farmer and a business manager, or a farmer and a paid employee. In each of the 7,000 households covered by the survey,

> 5 “Burkina Faso – Events of 2022” _Human Rights Watch_ . https://www.hrw.org/world-report/2023/countrychapters/burkinafaso#:~:text=Burkina%20Faso's%20human%20rights%20situation,result%20of%20two%20military%20coups.

5
percentile results to adjust for savings patterns and thereby better proxy for income.

The modelling results can be compared to the small number of countries for which full microsimulation models will be constructed as part of other projects over the period of the project. For example, a full microsimulation model will be constructed using newly available income survey microdata for Ethiopia in late 2022 as part of a project examining potential PIT reform in Ethiopia.

To enable up-to-date monitoring of progressivity in IDA countries, it is proposed that two sets of calculations be made for each country:

1. Modelling the tax rules for the year of the most recent income or consumption survey data, on that survey data.

2. Modelling the tax rules for each year from 2023-2025 on the most recent income or consumption survey data (inflation adjusted to the year of measurement 2023-25), to enable monitoring.

The second approach effectively would assume that the income distribution has remained unchanged between the survey year and the year of measurement. As income distributions can be expected to change relatively slowly, the older distribution should provide a reasonable approximation of the current distribution for most countries, with the small degree of inaccuracy a reasonable tradeoff against the benefit of providing a timely indication of effective progressivity.<sup>39</sup>

> 38 From the cumulative income/consumption distributions it is possible to ‘back-out’ the average income/consumption of each percentile (with the average income and population data that is also provided with the percentile results), thereby enabling this data to be used for PIT modelling.

> 39 This approach differs from a progressive capacity approach where the ‘base’ distribution would not necessarily have any link to the actual distribution. That is, we are using the older distribution as a proxy for the current distribution, not as a replacement for it.

31
5That is, a team of a man and a woman is more productive than a team of two men or two women (other things equal). Standard economic models, including ours, imply that the labor of men and women are perfectly substitutable. More specifically, Ostry et al. (2018) argue “the economically relevant range turns out to be one of partial substitutability, or, equivalently, partial complementarity...[elasticity of substitution] estimates are clustered below 1 in the macro data, between 1 and 2 in the sectoral data, and between 2 and 3 in the firm-level data.” (p6)

> 6These results are the average of gains with elasticity of substitution of 1.5 and 2 report in the top right corner of their Table 4.

4
Figure 2: Returns per hectare of major crops <!-- Start of picture text --> Tobaco<br>Sugarcane<br>Urad Onion<br>Cotton<br>Ragi Arhar Paddy Gram<br>Groundnut Jowar SunflowerMoong<br>Maize<br>Bajra<br>−5000 0 5000 10000<br>Mean<br>20000<br>15000<br>10000<br>Std. Deviation<br>5000<br>0<br><!-- End of picture text --> Source: Author’s estimation based on the Cost of Cultivation Statistics for Andhra Pradesh, Ministry of Agriculture, GoI.

34
format. In order to overlay these with flood maps, the pond datasets are converted into high-resolution raster data. This reduces the accuracy of the data, as ponds are not square and thus do not exactly match the raster grid. To increase robustness of the analysis, two different rasterization strategies are employed that yield an under and overestimation of the actual pond area.<sup>6</sup> The averaged result of the two strategies is used in further calculations. As the resolution of the flood maps is coarser than the ponds map, the impact of the information lost in the pond rasterization on the result is small. The remaining methodology for aquaculture exposure estimates is equivalent to that for agriculture. It involves downscaling of the flood raster, computing overlays per province, averaging the results from the two-pond rasterization strategies, and applying the flood extent offsets to account for flood protection infrastructure. This is repeated for all return periods in each of the two sets of flood maps – coastal and riverine – and each coastal province in the two deltas. The results for a 20-, 25-, 100- and 500-year return period are shown in figure 4.3.

22
(Arbia, et al., 2023). To enable comparisons, all prices were converted to unit price per kilogram (i.e. ₦/kg). Note that this is a simple rescaling that does not control for possible discounts that are factored into larger quantity prices such as those tracked by the AIestimated data. The AI-estimated data contain monthly Open, High, Low and Close (OHLC) price estimates, while the crowd-sourced and enumerator data is available at an intra-month interval. For easier comprehension, we first present the overall summary of the correlative relationships between paired price datasets (by commodity, and their sub-types), based on monthly averages.

We hone-in on the results for enumerator versus crowdsourced commodity prices to position the crowdsourced data relative to enumerator-submitted ground truth data. We then position the AI-estimated data relative to the crowdsourced data, leveraging the longer time interval for which these data are available. The focus is initially on yellow maize prices, which shows similar trend with white maize prices, to elucidate important ramifications of the observed correlative relationships between ground truth and crowd sourced prices at different time intervals. We then contrast monthly maize prices (yellow maize and white maize) in the crowdsourced data with same commodity prices in the AI-estimated dataset (Figure 2). Additional insights for rice (as a whole and by sub-types) is presented in the appendix sections.

**Figure 2:** Comparison of data volume, frequency, and temporal trend of maize grain prices from AI-estimated and ground truth sources in the northern region of Nigeria over three years (2019–2021) – **(a)** ; and during validation period (Mar. 2021 – Oct. 2021) – **(b)** and **(c)** .

**_Ground Truth Enumerator versus Crowdsourced Commodity Prices:_** During the 8-month data validation period, the trend and progression of daily averaged submitted prices were comparable between enumerator-submitted and crowdsourced ground truth data, notwithstanding indicative differences in
**Table 3. Addressing possible endogeneity concerns about access to credit**

|**Explanatory Variables**|**Dependent Variable: The fir**<br>|**m introduced a newpr**<br>|**oduct or service**<br>|
|---|---|---|---|
|Has a bank account?|0.002|0.002|0.002|
||(0.032)|(0.033)|(0.032)|
|Positive credit shock|0.047|0.044|0.045|
||(0.039)|(0.037)|(0.039)|
|Positive credit shock X State bank|0.172***|0.053|0.173***|
||(0.040)|(0.041)|(0.041)|
|Positive credit shock X State bank||0.206*||
|X bank health||(0.088)||
|Positive credit shock X Foreign|0.024|0.064|0.025|
|bank|(0.051)|(0.059)|(0.050)|
|Positive credit shock X Foreign||-0.057||
|bank X Bank health index||(0.048)||
|Positive credit shock X State|||0.897***|
|foreign bank|||(0.126)|
|Ln(age)|-0.002|-0.001|-0.002|
||(0.009)|(0.009)|(0.009)|
|20-99 emloees|0.031|0.033|0.031|
|py|(
<!-- Start of picture text --> Population Census in past 10 years<br>Agriculture Census in past 10 years<br>Business/Establishment Census in past 10 years<br>Household surveys: 2 or more in 10 years<br>Agriculture Surveys: 2 or more in 10 years —_—<br>Labor Force Surveys: 2 or more in 10 years ad<br>Health Surveys: 2 or more in 10 years<br>Business/Establishments: 2 or more in 10 years ——_—_-<br>Complete Civil Registration and Vital Statistics System ‘2<br>Availability of Data at ist Admin Level (ODIN) Score — 7 > - ——<br>0 1 2<br>Coefficient estimates and 95% confidence intervals<br><!-- End of picture text -->
particular (random) “diary day”. We then assume that this noisy “diary day” measure is representative of this individual’s average daily hours worked. We also do not have days worked per year, so we impute that all individuals work 250 days a year unless they report being part time and work _>_ 8 hours on their diary day, in which case we impute their days worked as 125. Our sample consists of all individuals reporting a positive income, thereby abstracting from the possibility of joint familial labor supply decisions. We show that our findings all hold with the smaller sample of single individuals, shown in Appendix C.3. We drop individuals who say they are involuntarily under-employed, hopefully mitigating the effect of labor supply frictions on our inferences. Our final sample from the ATUS then consists of data on (inflation adjusted) incomes and diary hours for 34,470 unique individuals from the years 2003-2015. See Appendix B for more detail on our sample construction.

Our measure of hours worked is noisy due to measurement and aggregation errors. Importantly, this noisy measure of hours worked is fine for our purposes so long as the noise is unbiased in the sense that the sample joint distribution of incomes and hours worked is representative of the true population joint distribution. Even if the sample

> 27We also assume that all individuals face a constant linear tax rate even though our method is easily adaptable to tax schedules with kinks, see Appendix A.4. This is for simplicity and consistency with the main body of the text and is likely inconsequential given the lack of bunching in the empirical income density.

> 28We discuss in Appendix B.2 why we do not use the hours worked measure from the CPS.

23
|23|90|3|0.3|0.0|118|
|Togo|70|-9|79||0|100|0.3|1.0|54|
|Myanmar|59|0|11||47|19|0.2|0.1|29|
|Colombia|55|21||78|111|0|0.2|0.0|109|
|Azerbaijan|55|5|41||8|82|0.2|0.5|71|
|<br>Kyrgyzstan|38|0|39|3|3|103|0.2|0.5|121|
|<br>Uganda|37|-5|38||4|90|0.1|0.5|52|
|<br>Mozambique|25|-3|26||2|93|0.1|0.3|16|
|Ethiopia|22|0|2||20|9|0.1|0.0|38|
|South Africa|21|0||39|60|0|0.1|0.0|73|

Source: ICAC (2005).

a Cotton, refers to ginned lint or raw cotton. It does not include seed cotton, linters, cotton mill waste, or cotton fibers subjected to any processing other than separation of lint from seed by the gin. Annual data are for the cotton year beginning 1 August.<sup>b</sup> Supply is output plus change in stocks.
# **ENDNOTE**

1 Some rounds exclude Azad Jammu, Kashmir, North Areas, Federally Administered Tribal Area (FATA), military restricted areas, and protected areas of NWFP. The population of the excluded areas constitutes about 3 percent of the population.

2 Sample PSUs is drawn with probability proportional to size method.

3 Enumeration blocks are defined as 200-250 households on average with well-defined boundaries and maps.

4 PSLM2004/2005 uses a slightly different employment module than PIHS2001/2002 and PIHS2005/2006. This makes the trend analysis difficult to interpret.

5 Casual wage workers include casual paid employees, workers paid a piece rate or according to the work performed, and paid non-family apprentices. Own-account workers include self-employed workers, owner cultivators, share croppers, and contract cultivators.

6 Inactiveness means that youth do not participate in the labor force or enrolled in school. However, inactive youth might engage in many domestic activities. This is especially the case for female youth.

7 Note that the percentage of unemployed youth is different from the unemployment rate for youth. The former uses all youth as the denominator while the latter uses all youth in the labor force as the denominator.

8 Tables are available from the author upon request.

9 The author would like to control for the consumption or income variable, but LFS does not contain the consumption module and only reports the earnings for paid employees. Thus, the income variable is not usable since it only captures part of the household income. I controlled for household head education, which is positively correlated with income and expenditures both in magnitude and significance.

10 The Investment Climate Assessment Survey also shows that firms hire through “family or friends” more frequently.

11 The author used the Pakistan Integrated Household Survey (PIHS) data, and the sample was restricted to wage and salaried employee.

12 The authors also use the PIHS data and broaden the analysis to wage, self-employed, and agricultural workers 30
<!-- Start of picture text --> 12 (percent) @ Average of annual increase rate of housing prices, 2001-06<br>10 Ol Average of annual increase rate of housing prices, 2007-08<br>3<br>6<br>4<br>2<br>Oo<br>-2 3S & co & a Oo PF ww<br>-4 SO SP FS 1S a SP<br>“MP wr we ST SG Vs < FS<br>-6 =Ss we oFaad =ee Ssos<br>we<br>-3<br><!-- End of picture text -->
to sovereign bonds spreads. To address the first challenge, we include country and time effects in the regression. For the second, we control for biases due to the presence of simultaneous or reverse causality in the regression analysis. We control for the endogeneity of data transparency by using Freedom of Information (FOI) and the lagged values of explanatory variables as instruments.<sup>3</sup> Our IV estimations robustly show that data transparency instrumented with FOI can predict bond spreads and that FOI cannot predict future shocks to bond spreads.

Our baseline regression equation of capital flows presents the following specification:

> 3 We also conducted regressions instrumented with the lagged values of policy variables. These results are available from the authors upon request. The political indicators used as instruments are the index of political constraints expressed as 3-veto points and 5-veto points in the policy-making structure (polcon3 and polcon5, respectively) from Henisz (2000 2002), the polity score and the executive constraints from the Polity IV codebook, measures of checks and balances and government stability from the WBG database of political institutions (Beck et al. 2001).

6
# **C Mother Tongue Instruction**

## **C.1 Background**

Ethiopia has more than 80 languages (IPUMS, 2017), and in 1991, for the first time in more than a generation, the transitional government introduced classroom instruction in languages other than Amarigna. Amarigna was the language preferred by the government throughout the previous decades; however, by the end of the Transitional Government’s second year in power, the mother tongue language for 80 percent of the country’s residents had been introduced as a language of instruction (Boothe and Walker, 1997; Zenebe Gebre, 2014). The introduction of the mother tongue instruction program was complex.<sup>21</sup> In 1991, the Transitional Government’s Council of Representatives selected the first four languages to be introduced in the mother tongue instruction program. These languages were selected from a list of 14 that were originally used in a 1979 adult literacy campaign (Boothe and Walker, 1997). The most common language selected was Oromigna, it was spoken by 31 percent of the population at the time of the 1994 population census, roughly the same portion of the population that spoke Amarigna. The other three languages selected for the initial wave of the program were Tigrigna (6%), Sidamigna (3.5%), and Wolayitigna (2.3%). These languages were respectively the fourth, fifth and sixth most common mother tongues in the country at the time. The selection of what was almost precisely the largest languages reduces concern of favoritism from the central government, and because the new regional boundaries were drawn along traditional ethnolinguistic borders, there was little variability of where the languages could be introduced. The following round of languages were introduced in 1993, this group comprised of Somaligna (6%) and three languages within the diverse Southern Nations, Nationalities, and Peoples’ Region (SNNPR) totaling an additional 4 percent of the population (Boothe and Walker, 1997; Zenebe Gebre
# Empirical Model

The literature discussed above implies that any schooling decision would be influenced by a host of factors, ranging from the person's learning ability, to wages in the labor market, to school availability and school inputs. Without data on cognitive ability or on aggregate wages for types of labor or school availability at the time of schooling, which is sometimes decades ago, we can only specify a partial model of educational attainment. Given these constraints, we estimate the following relationship: _- -_ **a'X +** _c_ (1) where E is the number of years of schooling at the time of the survey i/; X is a vector of explanatory variables; and, el is a random disturbance term. We estimate this relationship separately for males and females to allow the a coefficients to vary between sexes. The specific measures for these sets of factors are discussed later in section V.

Equation (1) is estimated using the full sample, that is, all individuals who attended school as well as those who never attended. For the former group, we are able to estimate a second schooling attainment regression in order to examine the effects of school characteristics. Data on school 91 Number of years of schooling does not include the number of grades repeated. A student who completed five years of schooling but repeated one grade would have spent six years in school. For this person, the dependent variable E would equal 5, not **6.** 16
raw phone transaction records it extracts signals plausibly related to repayment, and uses a machine learning approach to combine these signals into a prediction of repayment. Using only minor tweaks to off-the-shelf machine learning methods,

> 1 Typically, if approved for a household solar energy loan, consumers receive the device and pay installments via mobile money. If consumers miss payments, their device is remotely switched off via the mobile phone network.

> 2 Nonpayment is particularly problematic in developing societies, as creditors have little recourse if a borrower were to default: borrowers have little in the way of collateral, and systems for legal enforcement are limited.

2