rafmacalaba/data-use-mentions-extended huggingface dataset:
threshold significantly divergent from one's initial conditions, aspirations cease to exhibit a meaningful correlation with enhanced outcomes. This phenomenon may be attributed to the onset of frustration or other complex factors at play (Janzen, et al., 2017; Ross, 2019; Bloem, 2021).

# **3. Methods**

# **3.1. Data and Survey Instruments**

Recent surveys targeting youth have incorporated questions aimed at understanding their career aspirations and life goals amid the backdrop of the COVID-19 crisis. The ‘Youth Aspirations and Employment Module’ was designed based on household surveys<sup>2</sup> and implemented in World Bank's Living Standards Measurement Study (LSMS) High Frequency Phone Surveys (HFPS) in Ethiopia, Malawi and Nigeria<sup>3</sup> to collect information about (i) education history, including the level of education already achieved (note that in multi-topic household surveys, some of this information is partially captured within the Education Module), (ii) work history, such as the age at which respondents began working, their previous work experiences, and sources of income, (iii) future plans, which encompass intended activities following the completion of education or training programs, and (iv) career aspirations, covering desired occupations, role models, adherence to social norms, and interest in migration.

Understanding youth aspirations can be complicated due to measurement issues. Firstly, all empirical measures of aspirations rely on self-reported survey questions. Secondly, the measures of aspirations may vary in the underlying concepts they capture. While most authors agree on aspirations being multidimensional, the specific survey questions used to capture them differ in formulation, potentially yielding diverse results and policy implications. A key distinction among survey questions is whether respondents are asked about their aspirations without any constraints or limitations (Roy, et al., 2018; Ross, 2019; Favara, M., 2017).

This analysis examines three key dimensions (i) educational aspirations,
both the actual share of small firms with bank loans and the predicted share from regression (2).<sup>13</sup> The correlation between the predicted share of household and the actual share of households is 58%. Given the limited sample of firms surveyed by the WBES in each country and the lack of census data on firm financing patterns, the predictive power of aggregate loan use indicators is more limited than in the case of deposit services.

While these are preliminary results that have to be interpreted with caution due to the small number of observations, they show the potential usefulness of our aggregate outreach indicators. In the absence of consistent household- and firm-survey based measures of access to and use of financial services, these outreach indicators can be very useful since they can be used to calculate approximate values.

# **5.  Explaining Outreach**

What explains the large variations in outreach indicators across countries? Do institutional quality, regulatory policies, physical infrastructure, and the market structure of the banking system play a role? This section explores the empirical relation between our outreach indicators and an array of potential explanatory variables; Appendix Table A.2 presents descriptive statistics of the different country variables. Table V provides correlations between all of our outreach indicators and the explanatory variables, while Tables VI–IX report regression results of the different outreach indicators on (i) population density, (ii) economic size of the country, and (iii) one country characteristic at a time. In Tables VI-IX, we separate country characteristics by type, distinguishing between those measuring institutional quality (Table VI), credit information sharing and banking freedom (Table VII), banking system structure (Table VIII) and physical infrastructure (Table IX).

> 13 As in the case of regression (1), we use a tobit regression to predict the share of small firms with bank loans.

15
# 2.2. Employment dynamics

Trade has had a significant impact on employment-intensive industries, most notably in textiles and apparel. However, Lesotho’s unemployment rate remains high, at over 25%, and has not declined in recent years according to modeled ILO estimates. Youth unemployment is almost 10% higher. The country has among the lowest labor productivity rates in the world and production remains very labor-intensive: compared to other Southern African countries. An overarching concern remains the relatively small size of the private sector. This makes up just 14.7% of GDP (Molapo 2016). Recently international observers have urged, and the Government of Lesotho itself has recognized, a need to shift towards a more private sector-led growth model (see e.g. World Bank 2016). However, the private sector remains small. Of this, MSMEs make up 85% and employ approximately 200,000 people. While there are several larger firms, especially in the textile and apparel and mining sectors, there remains a missing middle with only 6% of firms considered medium-sized. A large number of manufacturing firms are also single enterprises according to the most recent Manufacturing Census, and over half (50.8%) are foreign-owned, though in about 20% of these nationals and foreigners have a joint venture (Bureau of Statistics 2016).

A large part of Lesotho’s economy is made up of informal businesses, with few efforts to measure their significance. One notable exception is Matsoso (2015) who used household surveys and census data to establish that the informal sector makes up a substantial but declining share of GDP, estimated at 55.4% of GDP in 2011 and 44.2% by 2014. The industries with the most substantial informal sector are agriculture, hunting and forestry, wholesale and retail trade, hotels and restaurants and “other community, social and personal activities” (see Table 1).

**Table 1: Informal economy output at current prices by industry and absolute values of
**MONGOLIA'S INFORMAL SECTOR** -

**Size and Trends**

**9**

contributions of 0.5% of wages, to the social insurance fund,' Having made contributions for at least two years, a person who becomes unemployed can then collect benefits equal to 45-70% of the level of wages during their last three months of employment, depending on total years of service; unemployment benefits last until a new job is found, up to a maximnum of 2 '/2 months.'<sup>2</sup> 15. The ERO also acts as an employment broker, trying to match the skills of registered unemployed people with the requests of enterprises. The ERO staff interviewed for this study reported that they frequently have difficulty matching the skills of the unemployed with the demands of employers. Employers typically request skilled labor, while only about half of registered unemployed have skills. Although unemployment benefits expire after 2 1/2 months, there is no limit to how long an unemployed person may remain registered for the purposes of finding a new job, as long as they visit the ERO - regularly people who don't visit for more than three months are automatically dropped from the registry.

16. There are two sources of raw employment data that are used to compile employment statistics in Mongolia. The number of unemployed people is compiled monthly from surveys of the EROs, and therefore includes only the officially registered unemployed. The number of employed people is drawn from an annual census, called the Annual Labor Force Balance, conducted by each _duureg/soum._ This annual census enumerates the population, the number of able-bodied people of working age, and for those who are able-bodied and of working age but report that they are not employed, the annual census enumerates the reasons., placed into several categories. These categories do not permit a clear distinction between people who are unemployed and looking for work and those who are not in the labor force. For this reason
)|37,325<br>45,244<br>4,797,372|
|Mozambique (2017)|37,325<br>67,218<br>6,119,847|
|Burkina Faso (2019)|13,142<br>334<br>875,872|
|Madagascar (2018)|31,182<br>14,328<br>4,518,322|

administrative area level, and then each pixel of the images is labeled based on the administrative area to which they belong. Finally, we average the pixel-wise asset wealth index map to obtain a scalar value as the ground truth for each image. Since a different PCA is constructed for each country, a value of 0 in one country does not correspond to the same level of wealth as a 0 in another country.

**Satellite imagery.** For country-level wealth and its change prediction, we collected daylight Landsat (30m/pixel) satellite imagery for Malawi, Mozambique, Madagascar, and Burkina Faso, where Malawi and Mozambique have bitemporal image pairs. Our Landsat imagery dataset was constructed using a 3-year median of cloud-free pixels, centered around the census year for each country. For countries with two census periods, imagery from Landsat 5 and Landsat 7 was used for the earlier census, while Landsat 8 was utilized for the more recent census. Each Landsat image has a fixed size of 150 × 150 pixels, resulting in each image covering 20.25km<sup>2</sup> (4.5 × 4.5km<sup>2</sup> ). These images have six bands that are red, green, blue, near-infrared, short-wave infrared 1, and short-wave infrared 2. For city-level wealth prediction, we collect PlanetScope (3m/pixel) and SkySat (0.5m/pixel) multispectral satellite imagery to cover each administrative area in Lilongwe and Blantyre. Based on the average size of administrative areas,
|

The table presents 2SLS estimates of the relationship between industry exposure to robots, aggregate productivity and its decomposition according to the formula Π _it_ = Π<sup> ̄</sup> _it_ +<sup>�</sup> _f ∈i_<sup>(</sup><sup>_sft−s_ ̄</sup><sup>_it_)(</sup><sup>_ωft−ω_ ̄</sup><sup>_it_)</sup><sup>_≡_ ̄Π</sup><sup>_it_+</sup><sup>_COV_(</sup><sup>_sft, ωft_).Exposuretorobotsisinstrumentedwiththe</sup> average exposure in the OECD region. Standard errors are clustered at the industry-level. Weights are constructed using 2006 industry employment. The coefficients with<sup>_⋆⋆⋆_</sup> are significant at the 1% level, with<sup>_⋆⋆_</sup> are significant at the 5% level, and with<sup>_⋆_</sup> are significant at the 10% level.

# **4.3 Local Labor Market Analysis**

In order to examine the local labor market impact of automation we use Indonesian regencies as our key unit of observation. That represents the second level of sub-national administrative divisions (the first being the province). A number of features make it a reasonable proxy for local labor markets in Indonesia. First, the mobility of labor is limited across regencies. In 2010 for example only 5 percent of the workforce worked in a different regency than their residence.<sup>30</sup> Second, regencies hold significant administrative

> 30Indonesia’s island geography and often underdeveloped transportation infrastructures make the hypothesis of limited mobility likely to hold.

29
Table 3: PM2 _._ 5 and children’s health

||Cough and<br>short breath<br>(1)|Fever<br>(2)|Diarrhea<br>(3)|Anemia<br>(4)|
|---|---|---|---|---|
|Panel A|||||
|PM 2.5 above|0.093**|0.039|0.025|-0.001|
|35 mg/m3|(0.047)|(0.054)|(0.034)|(0.074)|
|Observations|712|712|712|492|
|R-squared|0.053|0.060|0.067|0.238|
|Panel B|||||
|ln(PM 2.5)|0.073*|0.008|0.007|-0.054|
||(0.042)|(0.043)|(0.035)|(0.056)|
|Observations|712|712|712|492|
|R-squared|0.053|0.060|0.067|0.240|

Notes: Robust standard errors in parentheses. Standard errors are clustered at the survey block level. * denotes significant at 10%, ** significant at 5% and *** significant at 1%. All regressions are estimated using OLS and include month and year fixed effects, individual controls (age and gender of child, mother’s age, indicators of household wealth, indicator for a smoking mother), and logs of monthly temperature and humidity. PM 2.5 refers to average PM2 _._ 5 in weeks _t −_ 1 and _t −_ 2, where _t_ is the period of 7 days prior to interview. Note
of 381.85 mt CO2e including LULUCF.|0.0|Yes|IDA|
|**Bhutan**|Remain carbon neutral.|Unquantifiable NDC|n.a.|IDA|
|**India**|45 percent reduction in emissions intensity of GDP.|0.0|Yes|MIC|
|**Maldives**|Limit of 2.43 mt CO2e by 2030 excluding LULUCF.|8.5|No|IDA|
|**Nepal**|Reduce fossil fuel dependency by 50 percent via various<br>renewables/sectoral targets.|Unquantifiable NDC|n.a.|IDA|
|**Pakistan**|Limit of 1362.55 mt CO2e including LULUCF.|0.0|Yes|IDA|
|**Sri Lanka**|4 percent reduction in emissions (14.5 percent conditional on<br>external financing)against the baseline.|23.7|No|IDA|

Source: UNFCCC (https://di.unfccc.int/time_series), and authors' estimates using CPAT.

Note: Nationally Determined Contributions (NDCs) are assumed to be unconditional on external financing, except for Afghanistan. ‘2030 NDC-consistent carbon tax’ assumes a gradual introduction of the carbon tax rate (in equal increments) between 2024 and 2030, as well as absence of consumer/producer fossil fuel subsidy or price control phaseouts. If NDCs are assumed conditional on external financing the ‘2030 NDC-consistent carbon tax’ would be equal to 36.9 US$/ton CO2e for Bangladesh and 40.2 US$/ton CO2e for Sri Lanka, respectively. International Development Association (IDA) countries are more likely to be eligible for grants to finance mitigation and/or adaptation. Bhutan and Maldives borrow on small economy terms. Bangladesh borrows on blend credit terms. As of 2022, Sri Lanka has been reclassified as an IDA country, to allow for concessional financing. MIC=
***|0.174**|0.600***|0.193**|0.124|0.003|0.000|0.140**|0.357***|0.134|
||(0.034)|(0.075)|(0.225)|(0.077)|(0.090)|(0.002)|(0.000)|(0.056)|(0.074)|(0.089)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|240,805|32,934|8,937|35,928|22,455|52,332|13,473|26,946|64,269|16,465|
|R‐squared|0.029|0.041|0.218|0.073|0.081|0.027|0.028|0.047|0.055|0.069|

Note: Non‐compliance=1. Firm size, firm age, baseline non‐compliance, and length of participation in program are all identified as a dummy variable equal to 1 if the firm is above the median. Standard errors in parentheses. * = 10% significance, ** = 5% significance, *** = 1% significance. Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

25
# _Transition matrices from panel data_

Previous figures describe net job flows of employment and unemployment. However, these net flows only give a partial description of the workings of the labor market. Net job flows can be decomposed into four components, as follows: that is, the change in total employment between final ( ) and initial period ( ) equals the growth of employment due to transitions from inactivity (and from out of the labor force) ( ) and from unemployment ( ), minus those who lost employment and became inactive ( ) or unemployed ( ). Similar decompositions can be done for net flows in unemployment: and in inactivity These components can be observed in the cells of a transition matrix:

||Inactive**f**|Unemployed**f**|Employed**f**|
|---|---|---|---|
|Inactive **i**|-|=|=|
|Unemployed **i**|=|-|=|
|Employed **i**|=|=|-|

This transition matrix provides more nuanced information about job flows and reveals how changes in employment status are the consequence of gross flows to and from unemployment and inactivity. These flows also indicate the likelihood of moving from one status to another. The Mexican Occupation and Employment Survey ( _Encuesta Nacional de Ocupación y Empleo_ —ENOE) has a rotating panel that interviews the same household for five consecutive quarters. This implies that we can observe the same household and their individuals, and therefore its employment dynamics, for 20
Y|Y|Y|Y|
|N1(base survey)|4,644|4,644|4,644|4,644|4,644|4,644|
|N2 (target survey)|701|701|701|701|701|701|
|**True poverty rate in TA 1**<br> <br>Note: Estimates shown in boldface or with a “<sup>**a**</sup>” respective<br>net linear models and Random Forest models are trained in<br>(validation). For multiple imputation, we use five nearest n<br>is the estimate directly obtained from the survey data.|ly fall within the 95<br>the first round when<br>eighbors with the pre|% confidence int<br>the data is split<br>dictive mean mat|21<br>(2.<br>erval or one stan<br>into two subsets<br>ching method. 5|.0<br>2)<br>dard error of th<br>: 50% used for<br>0 simulations a|e true poverty ra<br>training and 50%<br>re implemented.|te. Lasso, Elastic<br>used for testing<br>True poverty rate|

Note: Estimates shown in boldface or with a “<sup>**a**</sup> ” respectively fall within the 95% confidence interval or one standard error of the true poverty rate. Lasso, Elastic net linear models and Random Forest models are trained in the first round when the data is split into two subsets: 50% used for training and 50% used for testing (validation). For multiple imputation, we use five nearest neighbors with the predictive mean matching method. 50 simulations are implemented. True poverty rate is the estimate directly obtained from the
_CHAPTER 6. UNIT LEVEL MODELS: GENERAL INDICATORS_ 49 as a fixed census and rawing 500 samples from it using a realistic sampling method. In these simulation experiments, the CEB estimates were practically identical to EB ones, and both performed generally better, in terms of MSE, than the traditional ELL.

As the EBLUP, neither the original ELL estimators, nor the EB or the Census EB estimators based on the nested error model, account for the sampling design, and might be biased under informative sampling. Similar to the Pseudo EBLUP of You and Rao (2002a), Guadarrama, Molina and Rao (2014) defined Pseudo EB predictors that incorporate the survey weights in the EB procedure. They noticed that the conditional distribution (6.9) defining the EB predictor under the homoscedastic nested error model (5.1) with _kdi_ = 1 for all _i_ and _d_ , depends on the sample data **y** _ds_ only through the (unweighted) area sample mean _y_ ̄ _d_ , that is, _δ_ ˆ _d_<sup>_EB_</sup> = _E_ **y** _dr_ ( _δd|y_ ̄ _d_ ). Then they defined the Pseudo EB predictor of _δd_ as _δ_ ˆ _d_<sup>_PEB_</sup> = _E_ **y** _dr_ ( _δd|y_ ̄ _dw_ ), where the expectation is now taken with respect to the distribution of out-of-sample units, given the weighted area means ̄ _ydw_ . The resulting conditional distribution, for each out-of-sample unit _ydi_ , _i ∈ rd_ , is now

where the conditional mean and variance are given by for _γdw_ defined in (5.9). Guadarrama, Molina and Rao (2014) adapted the Monte Carlo simulation procedure for EB estimation to the Pseudo EB and proposed a parametric bootstrap procedure to estimate the MSE of the Pseudo EB predictor. A Census version of the Pseudo EB predictor, similar to the Census EB, can be easily defined.

For an area mean _Y_<sup> ̄<
mark>_ <mark>(6), 216995.</mark> Klenow, P.J., and Andrés Rodríguez-Clare (1997). <mark>"The Neoclassical Revival in Growth Economics: Has It Gone Too Far?"</mark> _<mark>NBER Macroeconomics Annual</mark>_ <mark>12 (1997): 73-103.</mark> <mark>Melitz, M., & Polanec, S. (2015). Dynamic Olley-Pakes productivity decomposition with entry and exit.</mark> _<mark>The RAND Journal of Economics, 46</mark>_ <mark>(2), 362-375.</mark> <mark>Midrigan, V., & Xu, D. Y. (2014). Finance and misallocation: Evidence from plant-level data.</mark> _<mark>American economic review</mark>_ <mark>,</mark> _<mark>104</mark>_ <mark>(2), 422-58.</mark> <mark>Olley, S., & Pakes, A. The Dynamics of Productivity in the Telecommunications Industry.</mark> _<mark>Econometrica</mark>_ <mark>, Vol. 6</mark> 4 ( <mark>1996), pp. 1263–1298</mark> .

Petrin, A. and <mark>Jagadeesh Sivadasan (2</mark> 013). Estimating Lost Output from Allocative Inefficiency, with an Application to Chile and Firing Costs. _Review of Economics and Statistics_ , 95 (1), 286301.

<mark>Restuccia, D., & Rogerson, R. (2017). The causes and costs of misallocation.</mark> _<mark>Journal of Economic Perspectives</mark>_ <mark>,</mark> _<mark>31</mark>_
antidumping (AD) import protection. The estimated IRR on the impact of an appreciating domestic currency is no longer statistically different from 1. Furthermore, while the qualitative nature of the IRRs is similar to column (1), there is again a slight reduction to the size differential (away from 1) of the estimated IRRs for the domestic unemployment rate change and the foreign trading partner’s real GDP growth. This result stands in contrast to research covering earlier periods of data whereby an exclusive focus on TTBs through AD policy alone may have been sufficient to capture the relationship between macroeconomic shocks and import protection (e.g., Knetter and Prusa, 2003). One important implication of our results is that a sole focus on antidumping underestimates the relationship between macroeconomic shocks and time-varying import protection for these economies.

In specification (6), we introduce a new covariate to the baseline model – the _interaction_ of the domestic unemployment rate change _jt-1_ with real GDP growth of trading partner _it-1._ We

> 16 This is consistent with earlier results for the United States alone based on annual data (e.g., Irwin, 2005).

13
last years a firm is observed in the data. Since RAIS does not directly contain the entry year for each firm, we compute age considering that a firm’s entry year is the earliest observed admission year reported for workers in that firm.<sup>3</sup> We correct these age measures whenever they are inconsistent with the entry observed in our sampled period.

To define state-owned firms, we primarily use firms’ legal nature information available in RAIS. The problem of using only legal nature is that it omits a large footprint of the state via investing in private firms. Therefore, we complement our analysis by identifying private firms with government participation using ownership information from the Orbis database available for 2019. Specifically, we define three types of BOS based on the magnitude of state participation within a firm: public, mixed, and participated firms. Using the information on the legal nature of firms provided in RAIS, we identify fully public and mixed-capital firms (those where the government owns the majority of voting shares). Participated firms are defined as private firms with minority state participation.<sup>4</sup> These are firms for which state ownership is identified through the Orbis database but that are classified as fully private in RAIS.<sup>5</sup> Whenever we generally speak of BOS, we are referring to the definition that encompasses all these three groups of firms, unless stated otherwise.

Table 1 details the merge of firms with state participation in Orbis with the RAIS dataset. Of 1,302 firms with state ownership containing a valid Brazilian tax identifier in 2019, we can find 1,067 of these firms in RAIS within the same year (82%). Even though RAIS is supposed to be a census of all Brazilian firms in the formal sector, we cannot match 235 firms. Additionally, 323 of the matched firms did not report worker-level information in 2019, either because they had no active working contracts or halted operations during the reference year. Of