rafmacalaba/data-use-mentions-extended huggingface dataset:
The addition of time-use data can make the analysis even richer. Time-use data demonstrated that in both Armenia and Jordan, some children and elders can aid with household tasks and care for family members. This participation in social reproduction from some dependents can reduce gender gaps in labor earnings. In these countries, therefore, a formal extension of affordable and convenient public childcare or elderly care services should positively influence women’s labor supply decisions as well as provide access to fiscal policies that support social reproduction.

In sum, the results discussed in this paper from the cases of Jordan and Armenia bode well for the application of the proposed Engendered CEQ methodology in other countries, in different fiscal and socioeconomic settings, and in settings with different cultural expectations regarding the economic and social roles of men and women. In both Armenia and Jordan, richer results were discovered in a contextualized household, in the application of the CEQ assessment methodology across different household types, and in the incorporation of time-use data as a link between household organization and the fiscal policies under examination. The flexibility of the approach should allow analysts and researchers to carry out an engendered analysis, which in turn can help policy makers understand how fiscal policies treat individuals in their different roles and improve reform efforts that benefit men, women, and children.

Going forward, a number of methodological extensions can be explored. We already noted the incorporation of time-use data. Further work can be done to incorporate savings into the notion of income. More granularity can be added to taxes. For instance, it would be interesting to examine in any particular country context the structure of rates and deductions in personal income taxes; for instance, higher marginal rates on secondary income affects women more than men as they are the ones considered to be secondary earners. How can the CEQ be applied to countries with schedular, e.g., different rates for personal income, capital income, and capital gains, as opposed to global rate structures? Other extensions include adding presumptive taxes for small entrepreneurs,
Policy Research Working Paper 8779

# **Abstract**

This paper presents a new dataset of comparable employment indicators for South Asian countries, constructed from more than 60 primary data sources from 2001 to 2017. The main contribution of the paper is to curate the information provided by individual respondents to censuses and surveys, in a way that is consistent across countries and over time. The usefulness of the dataset is illustrated by conducting a rigorous assessment of employment characteristics, of changes in employment over time, and of the short- and long-run relationship between economic growth and employment growth in South Asia. The exercise shows that agriculture still employs the majority of the working-age population across the region and, except in Sri Lanka, more than half of the employment is self-employment or unpaid family work. The paper also shows that employment rates are generally decreasing in South Asia, and that in some countries female employment rates are falling rapidly. Seasonal growth patterns are shown to affect the composition of employment, while non-seasonal changes in short-run growth affect the overall level of employment. The paper estimates that, in the long run, one percentage point growth of gross domestic product has led on average to a 0.34 percent increase in employment.

This paper is a product of the Office of the Chief Economist, South Asia Region. 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 rcmbeyer@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
for each permit issued. I restrict my analysis to top 6 countries (Malaysia, Qatar, Saudi Arabia, United Arab Emirates, Kuwait and Bahrain) that cover more than 98 percent of all the permits issued by DoFE.

The sample I use consists of 1.34 million permits issued from January 2009 to December 2013. These migrants are predominantly male (98 percent) and quite young. Almost all migrants are aged 18-45 with an average age of 27 years. During this period, Malaysia led other countries as the most popular destination choice with over 9,000 permits being issued every month. As Figure 2 shows, outflow of migrants is increasing in recent years for most destinations. By 2013, over 1,100 migrants were leaving the country per day with 40 percent going to Malaysia, 25 percent to Qatar and 20 percent to Saudi Arabia.

I aggregate this data up to the level of district-destination-month cells by taking the counts of migrants (for total outflow), and means for wages, fees and occupation choices. As Table 2 shows, the average migrant outflow per district per month per destination is 50 (top panel, column 1). The average reported monthly wage in the data is $230 with average fees paid to intermediaries of $580. The average monthly wages rose from $200 in 2009 to $270 in 2013, which, combined with the rise in US dollar exchange rate, led to an increase of 60 percent over the period (middle panel, columns 3 and 5). Similarly, fees charged fell (in Nepali rupees) by 18 percent over the duration (middle panel, columns 4 and 5). A large share (53 percent) of the migrants reported their occupation as ‘labor’ which probably refers to manual work possibly in construction sectors. Since it is not possible to 5
57.8|
||0<br>10<br>20<br>30|40<br>50<br>60<br>70<br>80<br>90<br>100|
||Agriculture|Industry<br>Services|

_Notes_ : GVA data series is limited to 2007-2011.

_Source:_ Turkish Statistical Institute data compiled form Household Labor Force Statistics (publication), Household Labor Force Data Base (internet) TR-6 Akdeniz and TR-3 Ege are the two most touristic regions in Turkey. They also share the fertile land along the Mediterranean-Aegean coast which has led to industrial agricultural production in both regions. TR-3 Ege includes Izmir, the third largest city in Turkey, which houses various professional service and manufacturing jobs. TR7-Orta Anadolu has the second largest agricultural share of GVA among all regions at 18.3 percent. The region also includes some of the “Anatolian Tiger” provinces which have experienced large growth levels over the last decade through small and medium sized companies that specialize in manufacturing and other industrial sectors. TR8-Bati Karadeniz and TR-9 Dogu Karadeniz share the heavy rainfall of the Black Sea coast which leads to dense forests suitable for forestry and specific types of agricultural products. Tea and hazelnut are the two most recognizable of these products which are grown in TR9-Dogu Karadeniz. TR8-Bati Karadeniz also has a substantial mining industry with large coal and steel mines that has played a key role in the industrialization of the region.

TRA-Kuzey Dogu Anadolu, TRB-Orta Dogu Anadolu, and TRC-Guney Dogu Anadolu are the three poorest regions in Turkey. Each region has a significantly higher agricultural share of GVA compared to the national average. TRA-Kuzey Dogu Anadolu, and TRB-Orta Dogu Anadolu are the most mountainous regions in Turkey making the regions less suitable for large-scale agricultural production. Husbandry and forestry, along with light manufacturing sectors account for most of the production in the regions. TRCGuney Dogu Anadolu, despite having high levels of poverty similar to TRA and TRB, has a more developed industrial agricultural system and higher levels
16 The heterogeneity in food price inflation, both spatial and across commodities, reinforces the importance of conducting the welfare analysis with data that is disaggregated along both dimensions. Like Friedman and Levinsohn (2002) - but unlike most other recent studies - we exploit substantially disaggregated local information on both prices and expenditure shares in the estimations presented below.

# _The Expenditure Effect_

As noted above, we begin by estimating a first-order approximation to the expenditure effect of the price increases on household welfare. This effect is given by the first term on the RHS of equation (6): ( ). It is estimated for each household _h_ using expenditure data on budget shares from the POF, and price increases from the INPC, in the region where _h_ is located.<sup>8</sup> The expectation, of course, is that the distribution of this effect across households will be regressive. Food is a necessity, with a “textbook” Engel curve: the share of expenditure allocated to food declines with total expenditures throughout the entire domain (Figure 4): around 32 percent of expenditure goes into food among the poorest households, compared to about 10 percent at the top of the distribution (captured by POF).<sup>9</sup> This expectation is confirmed by the expenditure effect PIC, which is given by equation (7) and depicts the percentage reduction in welfare at each percentile of the distribution ranked by income per person. The expenditure effect PIC is given separately for Brazil‟s large urban areas in Figure 5, for rural areas in Figure 6, and for the country as a whole in Figure 7.<sup>10</sup> The bands around the PIC are bootstrapped 95% confidence intervals. Horizontal lines denote the average effect (and the confidence interval around it). Average expenditure effects are fairly substantial, ranging from 5 percent of baseline income in large urban areas, to 12.5% in rural areas. For Brazil as a whole, the
1|9.0|6.9|16.4|3.5|6.6|11.1|10.0|9.3|6.3|
|Uganda|3.1|4.9|4.4|8.6|2.3|8.6|8.8|8.7|5.3|4.0|
|Zambia|2.1|5.2|4.1|8.6|1.4|4.1|6.2|5.9|4.9|3.8|
|Median|1.7|2.5|2.2|4.9|1.1|3.3|3.6|3.5|2.7|2.0|

_Source_ : World Bank staff calculations based on utility information and household survey data. a These numbers are for Johannesburg. Depending on eligibility, poor households are provided with 50, 100, or 150 kWh of free electricity a month in Johannesburg.

Table 6 presents results that are more easily compared to those of Briceño‐Garmendia and Shkaratan (2011), except that, thanks to significant progress in energy efficiency improvement, power consumption at the subsistence level has declined from 50 kWh a month to 30 kWh in the intervening years. The table shows the hypothetical expenditure shares for those with and without actual expenditures on electricity as well as for those with and without access to the grid (definition 1 on page 6). It also calculates the percentage of households for which the expenditure share lies below 5 percent. The expenditure shares for those who reported non‐zero expenditures on electricity lie close to the expenditure shares of those with access to the grid due to a considerable overlap between these groups. As expected, with the exception of Angola and Nigeria, a greater percentage of those not connected to the grid found electricity unaffordable than grid‐ connected households
may have increased more in urban areas than in rural areas.

The simulations project an increase in income (or consumption) inequality for most countries, albeit by a small amount. The Gini index for income in 2020 with COVID-19 is higher than what it would have been without COVID-19 for 29 out of 34 economies (Figure 13). The average expected increase in the Gini due

> 18 The 34 countries are from EAP (3), ECA (6), LAC (13), MNA (2) and SSA (10). The simulations are based on country-specific sectoral growth estimates from MPOs (April 2021) and harmonized HFPS data (as of July 2021). Results may change as growth estimates updated with the next round of MPOs (October 2021) and HFPS data are updated with surveys for more countries.

> 19 Argentina is not included for rural-urban breakdowns, since only urban data is available in the official household survey.

18
sources – the latest household survey for each country, the World Bank’s HFPS, and national accounts data – to get around the lack of income or consumption data from surveys conducted during the pandemic. The exercise as of now covers 34 countries, which are a mix of all developing regions of the World Bank other than South Asia, and countries at different levels of development.<sup>18</sup> Briefly, the simulation methodology is as follows (see Annex B for more details): (a) using the last household survey available for each country (mostly pre-2019) to estimate the income distribution for 2019, assuming all households’ welfare have grown in accordance with growth in national accounts till 2019; (b) using a variable in HFPS indicating whether a household lost income in 2020 to predict the probability of households experiencing an income loss based on their characteristics (education, demographic, urban/rural); (c) merging these probabilities with the household survey data for 2019 to predict for each household a probability of experiencing an income loss in 2020, based on which an income loss is stochastically assigned; and (d) using sectoral growth figures from national accounts and assumptions mapping sectoral income growth to rural/urban to determine the size of income loss for each household. The exercise produces a distribution of household income in 2019 (pre-COVID-19) and 2020 (with COVID-19) for all 34 countries in the sample, using which poverty can be projected for these years.

To assess the impact of the pandemic on inequality and poverty, comparisons are made with a counterfactual 2020 income distribution, which is produced by using the last pre-pandemic sectoral GDP forecast for 2020. To isolate the impact of changes in inequality on poverty projections, another counterfactual scenario is created assuming that COVID-19 hit all households equally, by adjusting all households’ income in 2019 with the growth rate of 2020.

The estimated probability of changes in income for rural and urban households by country are shown in Annex A (Figure A-5
Table 4: Baseline estimation results for specification (5).

|Variables|**(Model 1)**|**(Model 2)**|Dependent<br>**(Model 3)**|variable is the<br>**(Model 4)**|cdfat50km<br>**(Model 5)**|<br>**(Model 6)**|**(Model 7)**|
|---|---|---|---|---|---|---|---|
|Ad valorem trucking costs|-0.265<sup>_a_</sup>|-0.337<sup>_b_</sup>|-0.263<sup>_b_</sup>|-0.250<sup>_b_</sup>|-0.183<sup>_b_</sup>|-0.208<sup>_b_</sup>||
||(0.037)|(0.155)|(0.122)|(0.098)|(0.078)|(0.088)||
|Ad valorem trucking costs (residual)|||||||-0.261<sup>_a_</sup><br>(0.078)|
|Asian share of imports|||-1.639<sup>_a_</sup>||-1.309<sup>_a_</sup>|-1.132<sup>_a_</sup>|-1.118<sup>_a_</sup>|
||||(0.413)||(0.379)|(0.380)|(0.383)|
|oecdshare of imports||
Table 5 examines the contribution of different governorates in such decline. It is clear from the results that the largest contributors were respectively Zarqa, Amman, Irbid and Mafraq. Indeed, around 75 percent of the poverty decline in the past decade came as a result of the reduction in poverty in these 4 governorates. This primarily reflects the economic development observed in these governorates, particularly the Northern ones, and the weight that these governorates constitute in the population, especially the capital Amman.

|**Poverty Line = JD813.7**<br>**Table 5: Region**|**Absolute change**<br>**al Poverty Deco**|**Percentage change**<br>**mposition**|
|---|---|---|
|Change in poverty (P0)|-17.1|100.0|
|Total Intra-sectoral effect|-17.0|99.8|
|Population-shift effect|-0.1|0.7|
|Interaction effect|0.1|-0.4|
|**Intra-sectoral effects:**|||
|Amman|-3.7|21.7|
|Balqa|-1.3|7.6|
|Zarqa|-4.6|27.1|
|Madaba|-0.2|1.5|
|Irbid|-3.2|18.7|
|Mafraq|-1.3|7.5|
|Jarash|-1.1|6.5|
|Ajlun|-0.1|0.7|
|Karak|-0.6|3.2|
|Tafiela|-0.2|1.1|
|Ma'an|-0.4|2.3|
|Aqaba<br>Source: World Bank Staff Calculat<br>Expenditure Surveys.|-0.3<
.9 percent across all values of alpha and theta of 0.2 or more. This lower bound of 0.2 is far lower than any commonly used equivalence scale and lower than any of the scales used by Batana et al (2013). Poverty rates using the “square root scale” of theta and alpha equal to 0.5 and 1, respectively, are 11.6 percent for adults and 18.6 percent for children. We therefore conclude that higher extreme child poverty rates are robust to any sensible two-parameter equivalence scale.

# **5. Conclusion**

This paper presents new evidence on child poverty and its sensitivity to alternative adult-equivalence scales. The analysis is based on a data set of 89 countries taken from the Global Micro Database, a new database that adds harmonized household characteristics to the same surveys and welfare aggregates that are used to calculate official World Bank poverty estimates. The results confirm that children are disproportionately poor. The poverty rate among children in the sample aged 0 to 17 is estimated to be 19.5 percent, which is over twice the 9.2 percent rate for adults 18 and above. The estimated global poverty rate for 0 to 4 and 5 to 9 year olds is 21 and 21.5 percent, respectively, and declines for each successively for older age group. Furthermore, an estimated 44.5 percent of children live on less than $3.10 per day, as opposed to 26.6 percent of adults.

> the household (the consumption of one member prevents the consumption of another of the same good), while θ=0 would imply that all goods consumed by the household are public goods.

> 13See: <u>http://www.oecd.org/els/soc/CO_2_2_Child_Poverty.pdf</u>

17
increases in the demand for non-routine manual tasks and routine cognitive tasks. Based on employer-employee data from Brazil, Almeida et al. (2017a) find that the rollout of the internet between 1996 and 2006 was associated with a reduction in employment and a fall in demand by technology-intensive industries for routine tasks, thus shifting the composition of employment towards non-routine tasks. Finally, Almeida et al. (2019) find that the use of advanced software reduces</mark> the relative demand for analytical skills by Chilean firms. This indicates that the impact of technology on the demand for different types of skills depends on the technology considered. Their finding is relevant for our study, where the share of households with a computer is used as a proxy for the level of technological development accessible by firms. This proxy may be signaling the use of more advanced technologies by firms, which could have a different impact on the demand for skills than simply the adoption of computers.

Second, our paper is related to studies analyzing the impact of trade on employment and the demand for different types of skills. There is substantial evidence showing that increased exposure to international markets affects the level and occupational composition of employment. Autor et al. (2015) find that competition from Chinese goods significantly reduced employment in the United States, especially in manufacturing industries and among low-educated workers. Cortes et al. (2016) find that workers who lose their routine jobs in the United States due to import competition tend to move to non-routine manual jobs or non-employment. The offshoring of some production tasks, mainly routine tasks, can reduce employment. However, offshoring also enables firms to be more productive, increasing their output level and employment (Grossman and RossiHansberg, 2008). As a result, the final effect on employment is ambiguous. Ebenstein et al. (2014) show that offshoring from the United States to China did not have a negative impact on employment, but reduced labor force participation. For developing countries, Poole et al. (2017) provide
the potential of geospatial data to improve socioeconomic measurement. This, in turn, sparked interest in combining survey and satellite indicators for the purposes of small area estimation. Using appropriate methods for this type of “data fusion” is important because small area poverty estimates have implications for the targeting and evaluation of public interventions and can shed light on economic geography more generally. At the same time, in part because of recent advances in machine learning algorithms, different disciplines and authors have taken very different methodological approaches to combining geospatial data and survey data for the purposes of small area estimation.

This paper provides a non-technical review of selected evidence from this relatively new literature. It builds on two recent reviews (Burke, 2021, McBride et al, 2022) but focuses exclusively on the small area estimation of wealth and poverty, devoting particular attention to differences in statistical methodology across studies. In particular, it ignores some of the excellent recent work on agricultural crops and yields (Lobell et al, 2020, Erciulescu et al, 2019), labor (Merfeld et al, 2022), and other indicators. There is now a robust literature documenting that estimates of wealth and poverty derived from survey and geospatial data are correlated with benchmarks derived from surveys or censuses. The strength of these correlations varies widely and depends on a myriad number of factors, including the country context, the method used for prediction, the target area for prediction, the exact indicator being predicted, the choice of geospatial variables, and the nature of the training and evaluation data.

Because the literature is relatively new, no consensus has yet emerged around the optimal prediction method in different contexts. Furthermore, comparisons of alternative prediction methods in the same geographic context remains rare, and some of the few examples of these comparisons have not yet 1
directives applicable to industry r between exporter i and importer j in year t. HAR_Mijrt counts the number of directives applicable to industry r between importer j and any country other than exporter i in year t. For instance, HAR_Mijrt = 1 for imports of United Kingdom from China in the sector of machinery tools since 1995, because the United Kingdom has implemented a single directive applicable to machinery tools along with other EU members at the end of 1994. HAR_Eijrt measures the number of directives implemented in industry r between exporter i and any country other than importer j in year t.

> 17 Czech Republic and Slovak Republic are excluded because of the lack of sectoral trade data in 1993 while Belgium and Luxembourg are considered as one unit throughout the period.

> 18 The lists of EU directives and MRAs are provided Table A.1 and Table A.2 in Appendix A.

15