rafmacalaba/data-use-mentions-extended huggingface dataset:
benchmark. Table 6 provides the sample size one would need under three impact scenarios: 5%, 10%, and 15% reduction in child labor.<sup>19</sup> For small impacts on child labor (5%), the top panel shows that one would need a large number of household observations to detect meaningful impacts according to the self-reported (n=4988) and proxy-reported (n=6769) statistics of a baseline measure of child labor. For expected moderate impacts (10%), the middle panel of Table 6 show the sample size requirements shrink to n=1295 and n=1684 for self and proxy reporting, while for relatively large impacts (15%) the bottom panel shows one would need sample sizes of n=583 and n=765, respectively. Thus, across these three different impact scenarios, the sample differences account for 1781, 389, and 182 additional observations if one uses the proxy reported child labor statistics as the baseline measure, relative to the self-reported child labor statistics.

Connecting these sample differences to survey costs allows one to assess the trade-offs between survey precision and costs. In rural Ethiopia where communications infrastructure is mostly absent, the costs associated with surveys fieldwork are high as any local research organization needs to move its operations from the capital city of Addis Ababa to rural areas for several days or weeks. The overall costs of collecting information in the field through multi-topic survey questionnaires that comprise a standard number of sections and questions were approximately 40US dollars per household. As indicated in columns 4 and 5 in Table 6, a large share of these costs is fixed (around 25US dollars) and thus, the variable costs associated with the additional time it would demand seeking information from each child within the household is not proportional to the number of children in the house.<sup>20</sup> Since self-reporting involves, on average, two respondents per household, as opposed to one adult respondent in proxy-reporting households, and since children are
‐12 Jul‐12 Jan‐13 Jul‐13 Jan‐14 Jul‐14 Jan‐15 Jul‐15<br><!-- End of picture text --> _Source:_ Comisión Nacional de Energía.

To encourage price competition, Resolution No. 60 issued in January 2012 requires that each retail outlet enter price information in a national online price database, which is published on the website of the National Energy Commission (Comisión Nacional de Energía, CNE). The information is available on mobile equipment, and the CNE also provides API (application programming interface) to download price information. The website displays the address of each retail outlet, fuel prices, and the date and time of the last price change. The viewer has the option of ranking prices in order of increasing or decreasing prices. Retailers are required to enter new prices no earlier than 15 minutes before price changes are implemented.
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57 log points) in the decade of signing the BLA. The migration effect lasts up to three decades after signing a BLA. The impact of BLAs is muted at 25 percent (0.24 log points) for ‘regular’ corridors - corridors with at least 10 migrants in all periods.<sup>3</sup> The estimates are of similar magnitudes with alternative sets of fixed effects, and slightly larger with an instrumental variable specification where we use ‘leave-out’ mean as the instrument. These estimates can be considered as a lower-bound of the true impact of a BLA on facilitating migration as the data on migration only captures the stock of migration measured every ten years and misses temporary migration that might have happened between the periods. We also

> 2We also test the assumption of no pre-trends using a heterogeneity-robust estimator applied to gravity settings following the recent advancements in the difference-in-difference methodologies (as developed in, Nagengast and Yotov, Forthcoming, Nagengast, Rios-Avila and Yotov, 2024) and fail to reject the null of no pre-trends.

> 3The results are robust to alternative thresholds for defining which corridors count as ‘regular’.

4
*

We undertake three sets of simulations, based on the pre-COVID data on educational attainment and estimates of pandemic-related learning losses, to quantify losses in education attainment and resulting changes in intergenerational mobility for countries.

- (i) <u>Scenario 1 (Global uniform simulations): global simulations, that take national estimates of losses</u> in learning adjusted years of schooling and apply them uniformly to all children within a given country. Thus, these global simulations account for the cross-country heterogeneity in the extent

14
many attempts to correct for this bias, often combining surveys with tax records that represent the top more accurately (Flachaire et al. 2023, Jenkins 2017, Piketty et al. 2019). Outside high-income countries, this source of data remains limited and where available provides only an incomplete picture of the top due to the absence of comprehensive personal income taxes (Van der Weide et al. 2017). In addition, it is still not obvious how best to combine survey data with administrative records, and depending on how the two are bridged, inequality estimates may change notably (Auten and Splinter forthcoming, Alvaredo et al. 2023, Flachaire et al. 2023). Therefore, it is not possible at this time to adjust all countries in PIP for the underreporting of incomes at the top. When it becomes possible to do so, the inequality estimates reported here will be revised upward and more countries will have a Gini index above 40. However, it will then also become necessary to revisit the threshold since the underlying statistic has changed.

In Annex A.1, we test if using data from the World Inequality Database (WID), which adjusts for underreporting for a subset of countries where possible, would lead to different conclusions in terms of the countries classified as in high inequality. We find that 125 of the 151 countries present in both databases would be classified identically, and that most differences do not lead to large country rerankings albeit with a dozen or so notable exceptions. Using WID, the number of countries that have moved out of the high-inequality country category follows a similar but slightly more muted pattern.

There are other factors contributing to incomparable inequality estimates across countries. For example, given that rural areas tend to have lower price levels than urban areas, it matters whether countries account for such price differences when calculating the real income or real consumption expenditure of households. Currently, it differs whether countries use such spatial deflation (Mancini & Vecchi 2022). At times, there are also comparability issues
regarding informal payments tend to be less precise and more vulnerable to misinterpretation by respondents (Lewis 2000).

Other than exit surveys, data on informal payments has been extracted from national expenditure surveys, such as the Hungarian household budget survey, and the World Bank‟s Living Standard Measurement Surveys (LSMS Team 2006). A list of survey results and different study designs on informal payments can be found in a review of informal payment surveys in Lewis (2000).

# _Usefulness of population surveys_

In practical terms, facility level surveys are likely to be the main source of information in most developing countries, but they can be usefully complemented by population surveys in a number of ways. One of the most important would be to rely on population surveys to determine the relative weight of different kinds of facilities in providing health care services. Beginning from a sample frame constructed from answers to health care utilization in a population survey is the most reliable way of developing a clear picture of what kinds of facilities are most important to health care provision. Developing a sampling frame for health care facilities based on the population‟s use of those services can give a fully representative picture of governance performance across the entire health sector, across different forms of provision and across different geographic and socioeconomic categories.

Two important population survey initiatives include the World Bank‟s Living Standards Measurement Survey (LSMS) project and the Demographic and Health Surveys (DHS) initiated by USAID. The LSMS tends to have relatively few questions on health care utilization and health status compared to the DHS. By contrast, DHS tends to have relatively few questions on socioeconomic characteristics and concentrates on the health of mothers and children.

If the effort to measure governance performance were to include additional measures – such as satisfaction or perceptions of quality and corruption – then public opinion surveys can be helpful. For example, Afrobarometer is an example of a large-scale household survey that measures governance performance in many political dimensions as well as with regard to service delivery. Afrobarometer tracks trends in public attitudes
potential hires, and how such information contributes to job matching. Babajob data can contribute to an analysis of the role of information on non-cognitive skills in the job-search process.

# **6. Additional Uses of Online Job-Portal Data and Job-Search Platforms**

Beyond the five potential applications described above, there are a number of additional possibilities for using big data to improve labor market outcomes. The following section describes other prospective avenues for using online job-portal data and job-search platforms to improve skills-matching and generate more policy-relevant information.

# **_Understanding the Emerging Skills Requirements of New Technologies_**

Rapid technological change is driving the evolution of business practices and shaping the demand for workforce skills. New technologies often create new job types that do not fit into the existing scheme of employment categories and workforce skills. In this dynamic environment, analyzing trends in job titles can help to identify emerging skills requirements.

The US Department of Labor has developed an Occupational Information Network (O*NET), which comprises a regularly updated database of occupational characteristics and employee information.<sup>25</sup> It describes the knowledge, skills and abilities required for different positions, as well as the specific tasks and activities involved in each job type. O*NET collects this information by randomly sampling businesses and workers in each job category. Online job-portal data can complement this type of database by providing real-time information on new jobs, employee requirements and scopes of work. In case of India, it is useful to apply this analysis to review the skills classification and fit them against the National Skills Qualification Framework<sup>26</sup> for improving the relevance of training.

Text analysis can be especially useful for linking emerging occupations with their required skill sets. Moreover, algorithms based on closely related skill descriptions can allow for more precise classifications of jobs with ambiguous titles. For example, “computer programmer” can be refined to “application developer,” “web programmer,” “web designer,” etc. based on the job
change in expenditures for various types of households. The Mini-Susenas, with 10,000 HH sample, is a smaller version of the regular Consumption Module Susenas (the National Socio-Economic Survey), which has 65,000 HH sample. The MiniSusenas survey was first conducted in December 1998 and then repeated in August 1999 on the same sample frame, however only around 80 percent HH sample were surveyed in both rounds, providing a panel of 7,585 HH.<sup>4</sup> The 100 Village Survey was a panel survey that was first carried out in May 1997 for 12,000 HH in 100 villages (which include some urbanized areas, but no major cities) and repeated in August 1998, re-sampling only 2/3 of the households, providing a panel of 8,140 HH.<sup>5</sup> Both surveys were conducted by Statistics Indonesia (BPS), with the first round of Mini-Susenas was fmded by UNDP while the 100 Village Survey was funded by UNICEF.<sup>6</sup> The panels of households in both data sets make it possible to estimate the variability of changes in expenditures for households in category J: _O'A= e_ (Aeh -A<) _/(N 1_ ) However, there is an enormous problem with this procedure, namely measurement error. In most CCED poverty analysis, households are classified by the _measured_ expenditures. However, measured expenditures are only a very, very, rough measure 'For more details on Mini-Susenas, see BPS (2000). See Suryahadi and Sumarto (1999) for more description of the 100 Village Survey.

10
sup>6</sup> We apply state-of-the-art techniques from computer science to our input-output matrix to derive the full connectivity of each firm. Compared to our baseline case ( _ε_ = 0 _._ 55), we find that the quarterly fall in GDP induced by a negative 25% shock to firms in _Red_ zones would be 2 _._ 68 _pp_ less in a model where firms in the same HS-4 product category are substitutes ( _ε_ = 2) and 0 _._ 99 _pp_ more when they are almost Leontief ( _ε_ = 0 _._ 001).<sup>7</sup> The additional losses due to firm-level complementarities translate into a GDP loss of 870 million USD, which is about 25 USD per capita per quarter. Additionally, under our estimated elasticity, the shock to _Red_ zone firms spreads widely through the network to other parts of the economy.

In our policy counterfactuals, we quantify the importance of firm connectivity separately from firm size and the effects of allowing the most connected firms (measured by all direct and indirect connections) to operate during lockdowns as opposed to allowing the most directly connected firms to operate. In policy and academic circles, much importance has been paid to large firms, as Hulten (1978) emphasized the importance of firm sizes in the propagation of shocks through production networks. We show that controlling for size, the fall in GDP is much larger if

> 6As a reference, the typical sector-level input-output matrix from the BEA 2012 is 405 by 405.

> 7 We find that a 25% productivity shock to firms in _Red_ zones reduces GDP by 10 _._ 95%. As an empirical benchmark, the state’s annual GDP fell by 11 _._ 3% in 2020/21.

4
Moreover, in contrast to the aggregate data, the most detailed figures on investment continued to be gathered under the principles set out in the early 1950s rather than using the internationally agreed System of National Accounts (SNA). In addition, the gathering of economic data during the 1960s was sparse and sometimes was interrupted completely. Despite these problems, the Chinese authorities have made considerable efforts to produce standardized economic accounts back to 1950. However, for investment, the official macroeconomic data lack the granularity that is found in the accounts of many countries, forcing analysts to use a variety of supplementary data.

The absence of granularity for investment data is of particular concern given the high and rising level of investment. It is well-known that this rise has been driven by infrastructure and housing. Some also maintain that it has been driven by investment by state-owned enterprises, giving rise to excess capacity. This note attempts to improve the granularity of data for gross fixed capital formation in China by focusing on four sectors: housing, infrastructure, government and the business sector. By providing corresponding estimates of capital stock, a judgement can be made on the extent of overinvestment and the movement in the productivity of capital in the business sector of the economy Until recently, capital stock estimates for China have oscillated between providing estimates for the whole economy and for the non-residential sector. While earlier studies used the non-residential sector data (Chow, 1993), with the publication of aggregate official data for gross fixed capital formation, attention switched to estimates for the whole economy. Recently attention has switched back to disaggregated data. Bailliu et al. (2016) considered the non-residential capital stock. Others 2
.8_|1.2|2.6|2.7|_2.3_|
|_Aggregate_|_2.9_|_2.0_|_1.5_|_2.0_|_2.4_|_3.8_|_2.4_|_2.8_|
|_Aggregate excluding_<br>_mining_|_4.8_|_2.4_|_2.1_|_2.8_|_4.5_|_4.7_|_2.8_|_3.8_|

Source: Authors’ calculations based on data from DOSM Note: The excluded years are 1997-1999, 2001-2003, and 2008-2010.

**Figure 10: Output per worker by sector, 19872018**

<!-- Start of picture text --> 200,000.00<br> 150,000.00<br> 100,000.00<br> 50,000.00<br> -<br>1987 1997 2007 2018<br>Construction Other Agriculture<br>Trade Manufacturing Transport<br>Business<br>Output per worker (2015 RM)<br><!-- End of picture text --> Source: Authors’ calculations based on data from DOSM

**Figure 11: Ratio of sectoral productivity to aggregate productivity, 2018**

<!-- Start of picture text --> 2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Source: Authors’ calculations based on data from DOSM<br>Note: A value higher than 1 indicates that a sector is more<br>productive than the economy as a whole, vice versa.<br>productivity<br>Other Trade Business<br>Ratio of sector productivity to aggregate Construction Agriculture Manufacturing Transport<br><!-- End of picture text --> Between 1987 and 1997 and between 1997 and 2007, the correlation
Bank PovcalNet’s interpolation methodology.<sup>27</sup> This alternative interpolated trend closely resembles the backcasted series described in Section 5 and further reinforces the findings of this analysis. In turn, this casts further doubt over the 17-percentage point drop implied by simply comparing the HNLSS 2009/10 and the 2018/19 NLSS: progress towards poverty reduction appears to have been much slower.

Figure 6 Comparison of imputed, backcasted and interpolated poverty rates for the period 2009-2019 <!-- Start of picture text --> 60.00%<br>56.40%<br>55.00%<br>50.00%<br>45.00%<br>46.3% 43.54%<br>41.53%<br>40.00% 42.8% 40.49% 41.88%<br>41.8%<br>38.9% 39.1%<br>35.00% 38.0%<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br>HNLSS 2009/10<br>Backcast, pass-through=1<br>Interpolated series without 2009/10<br>Imputed poverty rate at US$1.90, GHS Original model<br><!-- End of picture text --> Note: the figure compares the different results of this analysis over the decade 2009/2019 and compares them to the HNLSS 2009/10-based poverty headcount rate estimate. The backcasted series uses sectoral GDP growth rates to backcast household consumption from the 2018/19 NLSS using household’s head sector of employment to map macro and micro-data. The interpolated trend applies PovcalNet’s methodology and interpolates between the 2018/19 NLSS and 2003/04 NLSS using growth rates in GDP per capita, excluding the HNLSS 2009/10 estimate. Imputed series use survey-to-survey imputations, data from 2018/19 NLSS household consumption and GHS
Moreover, while textiles take 6.5 days to clear on average, they take only five days in South Africa and 10 days in Uganda. These findings confirm that the type of commodity is an important determinant of cargo dwell time.

# **4. Conclusion**

Finally, Table 4 summarizes the main assumptions of the theoretical model and the findings of the statistical analysis.

|**Table 4: Theoretical Assumptions and Findings from the Statistical Analysis**|
|---|

|_Theoretical assumptions_|_Survey findings_|
|---|---|
|Warehousing-derived terminalization: terminals<br>are cheap storage units for shippers.|Verified. Small and medium retailers are likely to<br>use the port as a cheap storage facility.|
|Product characteristics and market structure are<br>the main determinants of dwell time.|Verified for market structure. Monopolies are<br>time-efficient in Kenya, Nigeria, Uganda, and<br>Zambia, but competition is time-efficient in South<br>Africa. Hence low dwell time is not necessarily<br>reflected in lower prices in the case of<br>monopolies-oligopolies (which might seek to<br>maximize profits); however, it might keep prices<br>low in competitive situations (South Africa).<br>Verified<br>for<br>product<br>category.<br>Important<br>differences are found in cargo dwell time among<br>product categories.|
|Dwell time is also about perception and<br>information. In uncertain contexts, shippers build<br>delay into their production schedule to plan for<br>the worst. Dwell time statistics are often unknown<br>or incorrect.|Verified. When the dwell time perceived as<br>―normal‖ is higher than the actual dwell time<br>(Kenya, Nigeria), shippers are likely to exert<br>pressure to shorten it
# **3 Data descriptions**

In this paper, we use the data from China’s LSMS, which was conducted in July 1995 and July 1997 in two provinces: Hebei and Liaoning.<sup>11</sup> The survey includes information from 787 rural households, which were selected from a total of 31 sample villages of 15 towns or townships in 6 counties. Although the sample size is limited, the LSMS employs standardized sampling techniques on household selection and offers a satisfactory sample for in-depth studies.<sup>12</sup> The sample can be considered a rough approximation of the rural population in Hebei and Liaoning.<sup>13</sup> Compared with other survey data, which can merely serve for descriptive analysis, the LSMS data have significant advantages. To our knowledge, the LSMS is the most detailed and professional survey of rural households in China in recent years.

Rural household income consists of two major parts: farm income and non-farm income. The farm sector includes agriculture, livestock, forestry, fishing, and hunting. The non farm “sector” includes all economic activities in rural areas except the above farm activities.<sup>14</sup> Formal or informal wage-paying income and self-employment income are the two major sources of nonfarm income.<sup>15</sup> Among the 787 households in the sample, 205 received only farm income, 537 both farm income and non-farm income, 38 only non-farm income, and 7 neither farm income nor non-farm income.<sup>16</sup> To focus the study on farm income and non-farm income, we exclude the households that do not have farm income (which represents about 5% of the total sample), and take “the income from other sources” out of total income of the household in the econometric analysis by considering this income as exogenous.<sup>17</sup> Table 1 describes the sample characteristics of the remaining 742 households.

> 11