rafmacalaba/datause-encoder-data huggingface dataset:
directly used to reduce monetary poverty. However, if we monetize the value of in-kind services in education and health and subtract co-payments and user fees (as done when computing the final income), those amount to the largest contributions to monetary child poverty, reducing the poverty headcount to 26% for all children. This represents a 21% decrease in the poverty headcount from market income to final income, and a 33% decrease of the poverty gap. The effect is even stronger for girls than for boys (a decrease of 25% between market income and final income for girls, compared to a 23% decrease for boys). Similarly, poverty rates decline relatively more significantly for children in rural areas (23%) than those in urban areas (14%). Those findings suggest that the overall fiscal system (including in-kind benefits) leads to convergence, i.e., reducing inequalities in poverty rates between boys and girls as well as rural and urban children. This is somewhat mirrored in the fiscal system's impact on monetary inequality; while monetary inequality for all children decreases by 17% between market income and final income, those decreases are more pronounced for girls over boys and for rural children over their urban peers. As inequality was more pronounced between urban children, this slightly increases the relative gap between inequality in rural and urban areas.

_Table 7: Monetary child inequality across income concepts, by gender and location_

|**_Income_**|**_All_**||**_Ch_**|**_ild sex_**<br>|||**_Rural/urb_**|**_an residen_**<br>|**_ce_**<br>|
|---|---|---|---|---|---|---|---|---|---|
|**_concept_**||**_Girls_**|**_Boys_**|**_Within_**<br>**_-group_**|**_Between_**<br>**_-group_**|**_Rural_**|**_Urban_**|**_Within-_**<br>**_group_**|**_Between_**<br>**_-group_**|
|Market<br>income|0.320|0.330|0.310|0.320|0.001|0.262|0.302|0.276|0.044|
|Disposable<br>income|0.310|0.317|0.301|0.309|0.001|0.257|0.299|0.272|0.038|
|Consumable<br>income|0.312|0.320|0.303|0.311|0.001|0.260|0.303|0.274|0.037|
|Final income|0.267|0.273|0.261|0.267|0.000|0.228|0.268|0.241|0.027|

**_Source:_** _Authors’ calculations; Data from ESS 2018/19._ **_Note:_** _Inequality is computed as a Theil’s index._

In summary, this analysis suggests that the overall fiscal system is not well calibrated to reduce monetary poverty, with poverty rates increasing for all groups between market income and consumable income. Only the significant in-kind transfers for education and health result in a decrease in the poverty headcount at final income. This highlights not only the essential role of those public services to deliver on fundamental child rights, but also the importance of investments in education and health in reducing poverty.

# **5. Policy simulations**

Fiscal incidence analyses – such as this particular study for Ethiopia – do not only provide crucial insights into the current impact of the fiscal system on poverty and inequality, they are also integral 20
# **Figure A.9. Predicted (Log) Consumption Based on Within-Year Imputation**

<!-- Start of picture text --> Bangladesh 2018/19 Ethiopia 2018/19<br>10.8 9.4<br>10.78<br>9.35<br>10.76<br>9.3<br>10.74<br>10.72 9.25<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Tanzania 2020/21 Malawi 2019/20<br>12.1<br>13.8<br>12.08<br>13.75 12.06<br>12.04<br>13.7<br>12.02<br>13.65 12<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Nigeria 2018/19 Vietnam 2016<br>10.22<br>11.1<br>10.2<br>11.05<br>10.18<br>11<br>10.16<br>10.95<br>10.14<br>m1 m2 m3 m4 m5 m6 m7 m8 m9 m1 m2 m3 m4 m5 m6 m7 m8 m9<br>Imputation model Imputation model<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>Estimated poverty rate (%) Estimated poverty rate (%)<br>Estimated poverty rate (%) Estimated poverty rate (%)<br><!-- End of picture text -->

**Note:** Estimates are obtained by imputing from sample 1 into sample 2. 100 simulations are implemented. The standard errors are calculated using 100 bootstrap replications and are adjusted for complex survey design. Larger hollow symbols indicate that the estimates are statistically insignificantly different from the true consumption mean. Dashed lines represent the true consumption mean. Dotted lines represent confidence intervals of the true consumption mean. Estimates are obtained using the normal linear regression models.

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A necessary step towards achieving an inclusive financial system is to evaluate its status in each country. To assist policymakers in designing effective policies and tracking progress in the area of financial inclusion at global level, this paper introduces a new set of financial access indicators for 139 countries across the globe, and describes the results of a preliminary analysis of this data set.

This data collection effort has its roots in two previous projects carried out at the World Bank. The first set of similar indicators of financial access was collected in 2003 for 99 countries (see Beck, Demirgüç-Kunt, and Martinez-Peria (2007)). In this initial effort, data were collected on number of loans, deposit accounts, bank branches, and ATMs associated with deposit money banks (as defined by the IMF). These indicators were updated for a select set of 54 countries in 2007 with the “Banking the Poor” report (World Bank (2008)) and augmented with a set of survey questions regarding various regulatory features and policy initiatives present in the country as well as a survey of the 5 largest banks in the country by total assets which collected data on banks’ fees and the procedures clients had to go through to access loans or deposit accounts.

The current set of indicators builds on these two previous works using a similar methodology to collect new data through a survey of financial regulators in 139 countries. In addition to being more recent and having broader country coverage, this new database features finer disaggregation by type of financial product and by categories of the financial institutions supplying such products – commercial banks, specialized state run savings and development banks, banks with mutual ownership structure (e.g. cooperatives), and microfinance institutions.

Next, we use this data set to conduct a scoping exercise to estimate the number and distribution of bank accounts worldwide as well as the number of banked and un-banked individuals. Our estimates suggest that there are approximately 6.2 billion deposit accounts in the world, more than one for each adult on the planet. However, these accounts are not evenly distributed. We estimate that around 160 million adults in developed countries have no bank account (or 19% of all adults) whereas somewhere near 2.7 billion adults (or 72%) of the adults in developing 3
**Table 2.9b  Sectoral composition of creative content sector in Singapore, 1986-2001 (Percentage)**

||Establishments|Employmen|t<br>Operating Receipts|Value Added|
|---|---|---|---|---|
|**1986**|||Percentage||
|IT Development|6.1|1.9|2.2|1.4|
|Published activities (excl software & mm works)<br>|46.0|93.9|73.1|76.8|
|Motion picture and video production/distribution,<br>Radio and television activities|26.6|1.4|14.7|20.6|
|Broadcasting|na|na|8.5|na|
|Dramatic arts, music and other arts activities|21.3|2.9|1.5|1.2|
|_Total creative content services_|_100.0_|_100.0_|_100.0_|_100.0_|
|**1990**|||Percentage||
|IT Development|19.2|9.2|8.3|6.2|
|Published activities (excl software & mm works)|35.8|70.4|70.0|72.4|
|Motion picture and video production/distribution,<br>Radio and television activities|31.0|18.8|13.4|20.8|
|Broadcasting|na|na|7.3|na|
|Dramatic arts, music and other arts activities|14.0|1.6|1.0|0.6|
|_Total creative content services_|_100.0_|_100.0_|_100.0_|_100.0_|
|**2001**|||Percentage||
|IT Development|43.0|53.9|57.0|71.4|
|Published activities (incl software & mm works)|29.4|16.2|12.7|11.7|
|Motion picture and video production/distribution,<br>Radio and television activities|13.0|18.8|16.9|11.4|
|Broadcasting|0.5|6.0|11.7|4.1|
|Dramatic arts, music and other arts activities|14.1|5.0|1.8|1.4|
|_Total creative content services_|_100.0_|_100.0_|_100.0_|_100.0_|

See notes for Table 2.5a

# _Sources_ :

Singapore Department of Statistics (DOS). (Various years). _Economic Surveys Series: Business Services and Real Estate_ . Singapore: DOS. Singapore Department of Statistics (DOS). (Various years) _Economic Surveys Series: Community, Social & Personal Services._ Singapore: DOS. Data obtained from DOS 20
and also from illegal diversion of DDT.<sup>10</sup> Table 1 presents summary exposure assessments from the World Health Organisation (WHO 2011c, page 215, Table 43), measured as the total of DDT and DDE concentrations in the blood serum. The numbers indicate significant differences in the exposures of IRS spray operators, IRS exposed population and general population Accordingly, our study distinguishes three population groups in terms of exposure to DDT: IRS spray operators, population directly exposed to the IRS and the remaining non-IRS exposed population.

# **[Insert Table 1]**

Table 2 presents the estimates of elevated risks of some of the diseases due to the DDT exposure by exposed population groups. These estimates are drawn from the Risk Ratio (RR) of the disease with and without DDT exposure listed in the literature. The RR of an IRS spray operator is based on the results of the highest exposure levels that is found among the existing studies, and presented in column 2. Assuming that the risk of a disease is directly proportional<sup>11</sup> to the DDT exposure levels, it is estimated that people in the IRS households will face 25% of the RR faced by the IRS spray operators and the people in the non-IRS households will face 10% of the RR faced by the IRS spray operators. Based on the exposure levels of the spray operators in Table 2, the risk among the IRS households and the non-IRS households in the area are then computed using differential exposure of the respective groups. The results are presented in columns 3 and 4.

# **[Insert Table 2]**

10 Even when DDT is not being currently used, exposure can still occur in the general population from the residue left over from its earlier use as DDT is a very long lasting chemical with a long half life. Exposure can also occur from illegal use of DDT in agriculture often referred to as “leakage from vector control operations into agricultural use”.

> 11 The relationship between the toxin levels and the risk of disease is generally non-linear over the total range of toxin accumulation. But for the shorter ranges of toxins, a linear relationship may be a reasonable approximation.  Furthermore, it is difficult to model any non-linear relationships as it can take many functional forms and can differ with each toxin. So, the assumption of a linear relationship may be the only feasible option in this case.

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**_Population:_** Population data are from the Gridded Population of the World v4. GPWv4 depicts the distribution of human population across the globe. Source data are provided in 30 arc‐second (~1 km) grid cells (CIESIN 2016).

**_Land area:_** Land areas are extracted from European Space Agency data, with land types reported in 300m square pixels. These are aggregated into our land areas. Rural land is defined as irrigated and rain‐fed land, and mosaic cropland. Urban land includes all built areas (Defourny 2017).

**_National Accounts_** _:_ National Accounts data on Gross Value Added by sector are from United Nations accounts data for 2016 http://data.un.org . Total populations and GDP are given below in Table 1. There is large variation between the countries in the region, with per capita GDP nearly 10 times as large in Kazakhstan as it is in Tajikistan. The share of each sector in GVA is shown in Table 2 below. China‐3 and Kazakhstan have the highest share of GVA in manufacturing, while primary sectors are relatively important in Turkmenistan and Tajikistan.

Table 1: GDP and Population of sample countries.

||GDP (Billion USD)|GDP per capita (USD)|Population (Millions)|
|---|---|---|---|
|China‐3|280.5|5066|55.4|
|Kazakhstan|135.0|7585|17.8|
|Kyrgyzstan|6.6|1101|6.0|
|Pakistan|282.5|1580|178.8|
|Tajikistan|7.0|796|8.7|
|Turkmenistan|36.2|6712|5.4|
|Uzbekistan|67.8|2128|31.9|

Table 2: Sectoral shares of GDP (percent)

||Primary|Manufacturing|Non‐tradables|
|---|---|---|---|
|China‐3|14.7|40.7|44.7|
|Kazakhstan|20.5|34.7|44.8|
|Kyrgyzstan|17.3|19.0|63.7|
|Pakistan|29.5|24.9|45.6|
|Tajikistan|29.4|23.3|47.3|
|Turkmenistan|37.0|28.0|35.0|
|Uzbekistan|21.8|33.2|45.0|

**_Trade:_** Trade values are reported on a country to country basis, using value data from the BACI database of CEPII, 2016, www.cepii.fr (Gaulier and Zignago 2010). These data are split by sector into agricultural and mining trade (primary production) and manufacturing and services trade.  For the countries we consider, the trade data report the value of total trade in business services to be just 1.4% of the value of total trade in manufacturing, and we combine this trade and business services

10
the extent that employers’ and employees’ social contributions are often proportionate to wages, this will not affect estimates of relative wage structures in countries, but it will affect, and often underestimate, inter-country differences in labour costs or living standards.

A fourth problem is that even with the ILO’s detailed specification of skills, the work performed in a given occupation can vary from one country to another. Even in one country, skills differ within the narrow ILO categories. The range of skills displayed by cooks employed in restaurants and hotels (one of the ILO’s specified occupations) in the United Kingdom varies considerably, depending on the size of an establishment, the type of cuisine offered, and the number of stars in the guidebook. Such differences are likely to be even greater between countries. To the extent that differences in skills within occupations are associated with education, the workers in advanced industrialized countries are likely to be more skilled than those in less advanced, developing countries. Finally, there is the problem of the quality of the data provided to the ILO. As already mentioned, countries send the ILO data obtained from a range of different sources: government agencies; collective agreements; legally determined scales, such as minimum wage rates; and surveys of varying quality. Approximately half the data are based on surveys, mostly enterprise surveys. There are potential quality problems with each of these sources, depending on the data-gathering process. At our request, the ILO’s Bureau of Statistics has classified the various data sources into four quality groups, ranging from “not acceptable”, “poor quality”, “acceptable/good” to “excellent”.<sup>5</sup> The vast bulk of the data were rated as being in the “acceptable/good” category (52.6 per cent) or the “excellent” category (32.4 per cent), and 15% was rated as “not acceptable” or “poor”. The data of acceptable/good or excellent quality was further cleaned by the author and still another 16% of the observations were dropped.<sup>6</sup>

> 5 The quality assessment of the October Inquiry data was based on information available within the ILO on the data sources, consistency of the data (trend, regularity, consistency between wage rates and earnings, etc.), comparisons with other wage data received for publication in the Yearbook of Labour Statistics, and questions raised with countries and the types of replies received.

> 6 The time-series for each country/occupation pair was inspected, and observations that were clearly deviating from the time pattern were omitted. Entire occupation/year pairs were omitted if there was no pattern whatsoever. No cut-off point with respect to very low or very high reported wage gaps was imposed. Differences in gender gap across reported locations and gender differences in hours worked were taken into account. Sometimes whole countries were dropped because too many occupations had to be dropped, suggesting serious data problems.

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Lifelong learning activities can be subsidized in various ways. Providing (implicit or explicit) scholarships or grants to those enrolled is heavily used in many (higher) education systems around the world. Such subsidies confront students with prices that do not reflect the true marginal costs of their activities. From an efficiency perspective this is only justified if the social marginal returns of their activities exceed the private marginal returns. The mixed evidence on the relevance of externalities casts doubt on the validity of this argument. A vast number of studies using data from the United States indicate that student enrollment in higher education is responsive to changes in prices. A $1,000 change in tuition fees changes enrollments rates by 4 to 6 percentage points. Results for other countries suggest that students in these countries are less responsive to price changes. This is probably due to the fact that tuition levels in these countries are below United States levels.

To stimulate lifelong learning activities of adult workers, it is often believed that simply making such activities available below marginal costs provides insufficient incentives. Instruments such as vouchers, entitlements and individual learning accounts give potential learners a very explicit confirmation of their increased purchasing power. This should strengthen people’s awareness of the availability and importance of learning activities. The available evidence regarding the effects of these subsidy forms is rather limited. Evidence from United States veterans indicates that the voucher type education subsidies that they received through the GI Bill, increased their educational attainment, but also widens the education gap between various groups. This evidence obviously pertains to a rather particular group, and the estimation results should be interpreted as the joint effects of military service and the GI Bill.

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are exposed to different levels of female labor-force participation than in their country of origin. This situation has strong predictive power for female labor-force participation in the United States and for men’s acceptance of this participation.

Cross-country analyses are challenging because comparable and meaningful measures of customs and institutions are difficult to find. Following the recent practice in cross-country studies (e.g., Branisa et al. 2009), we now describe customs and institutions that relate to gender inequality using the OECD gender-linked institution and development database.

Figure 3 illustrates considerable variation in “discriminatory practices” across the sample countries. Some of the components of this index, such as restrictions on freedom of movement, have clear implications for women’s participation in economic and political life. The correlation between the incidence of discriminatory practices and GDP is negative and precisely estimated, with the usual caveat that causality is impossible to establish. A recent paper by Field et al. (2010) provides some microlevel evidence for the effect of social norms on returns to human capital. The authors design and implement a field experiment in which a random selection of poor, self-employed women is provided with lessons on financial literacy and investments. In their sample, Muslim women face the most social restrictions on mobility, followed by Hindu upper classes and Hindu scheduled castes, although the difference between the first two is four times as large as the difference between the last two. They find evidence suggesting that social restrictions on mobility reduce returns to financial training because treated upper-caste Hindu women are most likely to take out loans (13 percentage points) and are significantly more likely to funnel loans into their own microenterprises and to discuss business plans with their families. Although this situation suggests that social norms slow women’s economic participation, the three groups differ along other characteristics that might correlate with business decisions. Additional studies on this subject are needed to consolidate the findings.

Next, we move from social norms to institutions. Figure 4 describes the variation in a composite index of various women’s property and inheritance rights. Interestingly, the cross-country distribution is bimodal, suggesting that rights are correlated or bundled within countries. These rights are directly linked to women’s economic opportunities, and institutions that discriminate against women are likely to reduce returns to female 15
interest rates can—and should—still diverge to reflect domestic economic conditions, but this divergence will be around a larger, shared global component.

Finally, this increased global component in monetary policy does not necessarily imply that central banks will be responding more to global supply shocks (including oil and other commodity shocks). Instead, global shocks will likely continue to be predominantly demand shocks—as occurred during the 2020-24 rate cycle. Correctly identifying the components of global shocks that are demand versus supply is important as this decomposition could imply different monetary policy responses—with supply shocks generally requiring a more muted response (albeit subject to the characteristics of the shock and state of the economy, as discussed in Bandera et al., 2023). It is possible that overestimating the supply component of the post-2021 global shocks resulted in a slower-than-optimal response to the post-pandemic inflation. Granted, identifying global supply and demand shocks in real time is challenging, especially during periods of heightened macroeconomic volatility. Improving our ability to accurately identify these global demand and supply shocks will be increasingly crucial, however, as international factors are likely to continue to play an outsized role in the determinants of interest rates and inflation.

# References

Afrouzi, H., M. Halac, K. Rogoff, and P. Yarad. 2023. “Monetary Policy Without Commitment.” NBER working paper 31207.

Adrian, T., and Estrella, A., (2008), “Monetary Tightening Cycles and the Predictability of Economic Activity”, Economics Letters, Vol. 99, No. 2, pp. 260-64.

Antolín-Díaz, J., and Rubio-Ramírez, J. F., (2018), "Narrative Sign Restrictions for SVARs", American Economic Review, Vol. 108, No. 10, pp. 2802-29.

Aruoba, S. B., and Drechsel, T., (2024), “The Long and Variable Lags of Monetary Policy: Evidence from Disaggregated Price Indices”. Unpublished mimeo, University of Maryland.

Auer, R. A., Levchenko, A. A., and Sauré, P., (2019), “International Inflation Spillovers through Input Linkages”, Review of Economics and Statistics, Vol. 101, No. 3, pp. 50721.

Avdjiev, S., Gambacorta, L., Goldberg, L., and Schiaffi, S., (2020), “The shifting drivers of global liquidity”, Journal of International Economics, Vol. 125, 103324.

Baffes, J., Kose, M. A., Ohnsorge, F., and Stocker, M., (2015), “The Great Plunge in Oil Prices—Causes, Consequences, and Policy Responses”, Policy Research Note 1, World Bank, Washington, DC.

Ball, L., (2011), "The Performance of Alternative Monetary Regimes." Handbook of Monetary Economics, Elsevier Press, Volume 3B, pp. 1303-1343.

Bandera, N., Barnes, L., Chavaz, M., Tenreyro, S. and von dem Berge, L., (2023), “Monetary policy in the face of supply shocks: the role of inflation expectations”, Sintra ECB Forum working papers.

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