rafmacalaba/data-use-mentions-extended huggingface dataset:
in areas such as poverty, food security, labor market performance, demography, education, health, gender equality, housing, and agriculture.

Finally, in Bangladesh we complement data from the Household Income and Expenditure Survey (HIES, rounds 2005, 2010, and 2016) and the Quarterly Labour Force Survey (QLFS, rounds 2002, 2003, 2005, 2006, 2010, 2013, 2015, and 2016). The QLFS is a standard labor survey focused on labor market indicators such as employment, participation rates, wages, while the HIES focuses on socio-economic household characteristics.

Careful examination of these surveys and censuses questionnaires shows that labor indicators differ in subtle but important ways, including differing definitions of “employment”. The implications of these inconsistencies increase in economies where self-employment and casual work are pervasive: it is easy to recognize a standard “nine-to-five” job with a written contract and benefits attached to it, but relatively few jobs match this description in South Asia. Therefore, it is often difficult to tell whether people are working, unemployed, or inactive, and survey answers vary depending on the statistical instrument. The difficulty of measuring employment for women is exacerbated by the fact that women tend to engage more than men in activities falling in the grey areas between work, unemployment, and inactivity.

One way the definition of employment differs across statistical instruments is what constitutes the main activity of the individual. In the Bangladesh HIES, for example, each respondent lists a number of activities he or she was engaged in, and only the activity accounting for the most time determines the person’s job type, sector of activity, or institutional sector. The Indian NSS makes distinction between “usual” status and “principal” status, and only retains the “usual” status. And in the Nepal Living Standards Survey (LSS), household activities—such as fetching water, collecting firewood and dung, and making mats—are counted as work; but to remain consistent with other data sources, we did
# **1. Introduction**

There is a growing body of literature that investigates trends in the skill content of jobs in developed and developing countries. One of the main findings of this literature is that jobs are becoming less intensive in routine tasks across the world. This phenomenon is associated with a host of negative outcomes, including lower earnings and job opportunities for workers with routine skills and increasing wage inequality (Autor et al., 2006; Goos, Manning, and Salomons, 2009; Bussolo, Torre, and Winkler, 2018).

To estimate the task content of jobs, most studies rely on measures tailored for the US economy, where occupations are ranked by the tasks they typically require. These occupation-level measures are then applied to other countries under the assumption that the task content of occupations is the same as in the United States (see, for example, Apella and Zunino, 2018; Arias et al., 2014). This is a strong assumption, considering that jobs may require different skill sets across countries. For instance, manufacturing jobs in developed countries may be more intensive in routine manual tasks if the production technology is more capital intensive than in developing countries, where such jobs may be more intensive in non-routine manual tasks.

This paper is not based on this strong assumption. We use skill surveys from developing countries – i.e. the _Skills Toward Employability and Productivity_ (STEP) surveys - to create indicators of the task content of jobs comparable to those based on O*NET for the US.<sup>1</sup> We find that both sets of measures are consistent regarding the relative non-routine cognitive (i.e. non-routine analytical and non-routine interpersonal tasks) and routine manual task content of jobs across countries and over time. However, occupations relatively more intensive in routine cognitive and non-routine manual tasks are not necessarily the same according to O*NET and STEP. This implies that the estimated trends in the task content of jobs will depend on whether the O
Sindh Government’s Annual School Census (ASC) (2010/11), we ranked districts by size as measured by the number of schools and villages in each district. In consultation with the Government of Sindh, we then chose one large, one medium-sized and one small district, ensuring representativeness of the selected districts along two key education measures.<sup>8</sup> The selected districts were Matiari, Mirpurkhas and Sanghar.

In the absence of an updated population census, the ASC was used to set the population frame for selecting villages.<sup>9</sup> Using probability-proportional-to-size (PPS) sampling, we selected a sample of 550 schools drawn from 377 villages. The administrative data appeared to be unreliable regarding the exact location of schools and the inclusion of schools that are permanently closed. To address this, we mapped 300 of the 377 villages in a village-census exercise. The maximum of 300 villages was reached using a randomized ranking procedure.<sup>10</sup> The process through which treatment schools were selected from the village mapping exercise is outlined in **<u>Figure 2</u>** .

The school sampling strategy for the baseline covered public primary schools that were open on the day of the visit or closed for a period of less than one year prior to the day of the visit. We sampled all such schools, identified by the local community, in the main settlement of our sampled

> 8 Matiari was ranked the third smallest district, Mirpurkhas was ranked 12th, and Sanghar was ranked 18th, according to the number of schools and villages in each district. In terms of education indicators, Mirpurkhas had one of the lowest levels of education outcomes, Matiari was in the middle, while Sanghar was among the highest. They were deemed relatively safe by the government for field teams to visit.

> 9Given the heterogeneity in village size, we employed Probability Proportional to Size (PPS) sampling to create selection probabilities proportional to the number
increased capital intensity of mining, with the arrival of new labor-saving machines, technology and tools reducing the need for labor directly in the mining sector.<sup>20</sup> For business and other services exports and especially agricultural exports, the direct employment creation is much larger than the indirect job creation. Direct job creation represented 88 percent of jobs linked to agricultural exports, because linkages to supplying sectors in agriculture are restricted to few inputs such as fertilizers, other chemicals, seeds, and planting materials. However, the share of indirect jobs linked to agricultural exports has doubled since 1995 (not shown). Similarly, business and particularly other services exports create jobs mainly directly within the export sectors, with the indirect contribution representing one-third in business services and one-fifth in other services.

**Figure 15: Direct and indirect employment in exports, by broad sector and income level, 2018**

<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Agriculture & Mining Total Manufacturing Total Business Services Other Services<br>Fishing<br>direct indirect reimported<br>High Upper- middle Lower- middle High Upper- middle Lower- middle High Upper- middle Lower- middle High Upper- middle Lower- middle High Upper- middle Lower- middle<br><!-- End of picture text --> Source: Own computations. Data: OECD TiE. The country sample covers 38 OECD countries plus 13 non-OECD countries. Note: Employment in exports can be decomposed into its (1) direct contribution in the exporting sector, (2) indirect contribution in sectors supplying to the export sector, and (3) contribution of reimported inputs.

The analysis highlights different upgrading patterns along countries’ income levels for broad sectors. In business and other services, the indirect job creation loses importance for upper-middle and highincome countries (Figure 15
**non-compliance.** The study examines the extent and distribution of tax evasion among different income groups, regions, and sectors of the economy. The goal is to identify the factors contributing to tax evasion and develop measures to reduce them **.** To investigate tax evasion in Romania, we match tax administrative data with the survey data (EU-SILC) to assess Romania's tax (non)compliance. We impute tax income to EU-SILC and compare the value of imputed tax income (reported income) and survey income (true income).

**According to our results, the tax compliance at the distribution median is approximately 94%; it decreases to 83% at the 25**<sup>**th**</sup> **percentile and increases to 96% at the 10**<sup>**th**</sup> **percentile.** The average tax compliance (after censoring of top income) is 94%. The average underreporting of income is 6%. This result is close to the estimate for Hungary (9%– 13%) by Bendek & Lelkes (2011), an estimate by Kiełczewska et al. (2021) for Poland (6%), and the estimate for Estonia (12%) by Paulus (2015). Tax compliance varies across sectors of the economy. It is particularly low in transport, construction, food, and accommodation. Women are more tax-compliant than men. Tax compliance is highest among the youngest and oldest employees and lowest in prime working age. Tax compliance varies across country regions; it is lowest in the North and highest in the East and West.

**The share of minimum wage earners in the tax data is inflated due to the underreporting of income.** According to tax data, 27.5% of employees are minimum wage earners. According to the raw EU-SILC data, this share is 13.5%. Based on the
household-specific fixed effects, and add an error term, obtaining the following equation, which is estimated on 2019 HIES data: where _k_ indexes crops, _βnhk_<sup>_A_is the share of crop</sup><sup>_k_in the household’s total food expenditures,</sup><sup>_Pnhk_</sup> is crop price, _ωk_ and _ωh_ are crop and household fixed effects (respectively), and _ε_<sup>_A_</sup> _nhk_<sup>isanerror</sup> term. To address the potential endogeneity of crop prices (e.g. due to unobserved region-specific tastes affecting both prices and expenditure shares), we follow the IV approach in Sotelo (2020), instrumenting price variable ln( _Pnhk_ ) with a dummy variable _Dnk_<sup>_GAEZ_</sup> , which takes value one if the potential yield of crop _k_ in region _n_ is nonzero, according to FAO-GAEZ data (see Appendix A.F). Thus, our identification of the cross-crop elasticity of substitution _σA_ comes from variation in crop prices due to regional shifters of agricultural supply driven by climatic and geological endowments, arguably orthogonal to unobserved factors that affecting consumer behavior. Our

> 55Here a region is one of Sri Lanka’s 25 districts since DS are not recorded in the household data.

> 56Both exogenous sources of income refer to the previous 12 months.

> 57 Boppart (2014) finds Engel elasticities around 0 _._ 20 _−_ 0 _._ 25 for the U.S., Fan et al. (2023) report an IV estimate of 0.395 for India, and Eckert and Peters (2023) calibrate an Engel elasticity of 0 _._ 93 within a spatial model. These estimates are not directly comparable due to differences in context, aggregation, and identification, but our estimates lie within the range estimated in the literature.

> 58See Appendix D.A for details on the 2SLS estimation results of ( _η_ ,
members who normally live in the household, even if they are temporarily studying or working elsewhere. An additional question asks whether any household members have gone to live in another country in the past five years. The survey asks migrants how long they have been living in their current location, the year of their last trip to the U.S., and the number of times they have been to the U.S., but does not ask the year of their first U.S. trip. We therefore classify individuals as having migrated prior to 1 January 1994 if their last trip was before this date, or if they have made at least two trips to the U.S. and their last trip was during 1994, or if they have made three (four) (five) trips to the U.S. and their last trip was during 1995 (1996) (1997). In addition to a binary classification into migrant and non-migrant households, we also construct household migration prevalence ratios, defined as the proportion of individuals aged 15 and over in the household who had been to the U.S. prior to 1 January 1994.

Table 1 presents summary statistics for the sample of households which had a child born since the start of 1994, along with t-tests which test for differences in means between migrant and nonmigrant households. Nineteen percent of households are seen to have at least one migrant, with nine percent of all adults in the sample having migrated to the United States at least once before 1994. The infant mortality rate in our sample is 23.7 per 1000 live births, which is close to the adjusted rate for all of Mexico in 1995 of 25.9 (PAHO, 1998).<sup>7</sup> According to the international standard whereby low birthweight is classified as any weight less than 2.5 Kilograms, 7.5 percent of births in our sample are estimated to be underweight. This compares with a 1997 estimate of 8 percent for
these nonlinearities are statistically significant: the quadratic and cubic terms in the first and third panels are statistically significant at the 5 percent level. At the same time, however, a visual inspection of the three graphs suggests that the shape of these nonlinearities is likely to be quite sensitive to extreme observations.

13 Throughout the paper we use data on investment rates as a proxy for saving rates. We do this mainly for reasons of data availability, as gross domestic investment rates are available for many more country-year observations than is gross national saving, reflecting gaps in data on current accounts. While many poor countries of course receive substantial recorded capital inflows, many also have substantial unrecorded capital outflows and so it is not obvious a priori the extent to which our findings are affected by this approximation.

20
7|6.2|6.9|23.7|1.0|

Note: Data is based on two-year averages of 1991-92, 2001-02, and 2007-08.

Sources: Based on UN COMTRADE Statistics (trade data) and UNIDO database (production data).

Acceleration of import growth from other developing countries to about 25 percent p.a. during the second period is especially dramatic. In all countries excluding China, share of imports from developing countries increased substantially along with higher import growth rates. In China also, import growth has increased to 25 percent per annum but the demand increase has been much higher than the import growth rates leading to declining shares of imports from developing countries in domestic demand. During the first period, excluding China and Malaysia, the contribution of market share changes are greater than demand increases. Even in the second period when demand increases dominate, absolute contributions of market share changes reach almost double digit levels in six out of eight countries. Thus China and to a lesser extent Malaysia are exceptions to the general trend of increasing share of imports from developing countries. While it has reduced its share of imports from other developing countries it 17
The panel component of the AWMS R3 further reinforces that the trends in the assets and consumption variables between the IE-LFS and AWMS R3 reflect actual changes in living conditions. An advantage of the AWMS R3 compared to other phone surveys is that about 82 percent of the sample comprises the same households that participated in the original household surveys.<sup>10</sup> Using the pure panel component, it is possible to validate the observed changes in the household’s living conditions and isolate any changes as stemming from the reweighting process. Table 6 shows the living conditions for the same households in the IE-LFS and AWMS R3 with and without weights. Changes in the variables between the IE-LFS and the AWMS R3 hold even in the absence of weights, further supporting that the imputed poverty rates are the results of actual changes in living conditions and are not driven by sampling bias toward households with better-off initial conditions.

**Table 6: Unweighted trends on key household model variables between IELFS and AWMS R3**

|||Panel household<br>AWMS R3|s|
|---|---|---|---|
||IELFS (1)|<br>_panel_ (2)|Ratio (2)/(1)|
|**Household Characteristics**||||
|Household Size|7.8|8.4|1.1|
|**Asset Ownership**||||
|Refrigerator|18.3|18.2|1.0|
|TV|48.3|42.3|0.9|
|Bicycle|11.2|23.1|2.1|
|Motorcycle|19.1|20.9|1.1|
|Car|12.2|7.1|0.6|
|Washing Machine|15.1|16.8|1.1|
|Vacuum Cleaner|7.7|7.
Other work has measured similar impacts of electrification. Kularni et al. (2007) have found that higher electrification rates lead to better educational outcomes in Nicaragua and Peru. Barkat et al. (2002) similarly found that electrification leads to higher literacy rates and increased school enrollment in Bangladesh. These results are confirmed more recently by Asaduzzaman et al. (2010), who also found that school children’s study times increase substantially for families who gain access to electricity. Asaduzzaman and Latif (2005) found that electricity demand by families with electricity access rises substantially with the number of school children. This suggests a strong effect on household electricity demand from school children’s need for evening light access. The two latter studies however indicate that schooling and electricity access may be endogenous in a more comprehensive model, thus calling for caution in interpreting results. Kanagawa and Nakata (2008) found that literacy rates in the state of Assam, Pakistan, among children above six years of age would increase substantially, from 63 to 74%, given complete electrification of the state (which was claimed to be reachable by 2012). Grogan and Sadanand (2009) modeled how changes in home production technology might affect fertility and women’s time use in Guatemala. Using difference-in-difference type estimators, household electrification substantially reduces cooking times and increases working outside the home among Guatemalan women (the probability of outside employment increases by nine percentage points). Dinkelman (2009) assesses the impact of the national electrification program in South Africa using two waves of community-data from rural areas in KwaZulu-Natal. Results indicate that female employment rises by a significant 9.5 percentage points in communities that receive an electricity project. This author also shows that electrification has larger effects on female employment in middlepoor communities and for women in their thirties and forties, who are less likely to live with young children requiring full-time care.

A thematically related paper, focusing as we do on children’s school attendance in Ghana, is Lavy (1996), which is based
In this section, we pursue our two objectives with three cases that compare our GBIF species maps with expert maps from recently-published research. Our comparisons identify thousands of species with GBIF The first case retains the traditional focus on vertebrates in a comparison with mammal range maps estimated by Marsh et al. (2022). The second extends the comparison to invertebrates, whose huge numbers are greatly underrepresented by existing expert maps (Kass et al. 2022). As Table 1 shows, our exercise provides more ample representation by estimating maps for 213,268 Arthropods. In this section, vascular plants, for which we have estimated 232,693 maps. Our comparison focuses on the more limited 3.3 The Global Distribution of Mammals Marsh et al. (2022 — henceforth Marsh) map the native ranges of global mammals using the authoritative In our GBIF occurrence maps database, we identify 3,530 mammals that are also mapped by Marsh. We rasterize both sets of maps using a global grid with 0.05 degree (5 km) resolution. densities by cellwise addition across 3,530 rasters for each set. Figure 2 provides comparisons of cell Where the two patterns diverge, the technical explanation seems clear. The Marsh maps estimate Marsh ranks have many species with reported occurrences beyond their estimated native ranges; regions with lower GBIF ranks have occurrence reports clustered in subareas within native ranges. This difference could reflect underreporting for GBIF species in lower-ranked areas, although many higher-ranked areas seem similarly-disadvantaged for species observation. The alternative, and in our view more plausible, explanation is that lower-ranked regions are populated by many species whose ranges have contracted over time. Continuing accumulation of GBIF occurrence reports should help resolve this question.

For each species map, rasterization assigns a value of 1 to grid cells that overlap with the map and 0 to the other
How Equitable Is Access to Finance in Turkey? Evidence from the Latest Global FINDEX Joao Pedro Azevedo Osman Kaan Inan Judy S. Yang The World Bank<sup>1</sup> JEL Codes: D14, D63, E2, G21, I3 Keywords: Access to Finance, Equity, Human Opportunity Index, Tukey, Benchmarking, Poverty This paper has benefitted from comments by participants from presentations made in World Bank Washington, DC and Ankara offices, more specifically from the Poverty and Equity Global Practice’s Europe and Central Asia Team and the Turkey Country Office as well as inputs by our colleagues in the Finance and Markets Global Practice. The team thanks Martin Raiser for his guidance and support. We are also thankful for comments and assistance received from Alper Ahmet Oguz, Ilias Skamnelos, Jose Montes, and Minh C. Nguyen. The team is grateful to Leora Klapper and the Global FINDEX team for their assistance with the FINDEX dataset. The usual disclaimer applies. This paper is a product of the FY2015 Turkey Poverty Team in the World Bank’s Poverty and Equity Global Practice.

> 1Contacts: Joao Pedro Azevedo (Lead Economist, jazevedo@worldbank.org); Judy S. Yang (ET Consultant, jyang4@worldbank.org); Osman Kaan Inan (Junior Professional Associate, oinan@worldbank.org) This paper has benefitted from comments by participants from presentations made in World Bank Washington, DC and Ankara offices, more specifically from the Poverty and Equity Global Practice’s Europe and Central Asia Team and the Turkey Country Office as well as inputs by our colleagues in the Finance and Markets Global Practice. The team thanks Martin Raiser for his guidance and support. We are also thankful for comments and assistance received from Alper Ahmet Oguz, Ilias Skamnelos, Jose Montes, and Minh C. Nguyen. The team is grateful to Leora Klapper and the Global FINDEX team for their assistance with the FINDEX dataset. The usual disclaimer applies.  This paper is a product of the FY2015 Turkey Poverty Team in
112)|(0.110)|(0.110)|(0.123)|(0.141)|(0.180)|(0.200)|(0.177)|
|Observations|103,839|103,010|101,221|97,326|103,839|51,483|23,345|23,345|103,839|
|Importer-Industry-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Author's analysis based on data described in text. Notes: See Table 3.