rafmacalaba/data-use-mentions-extended huggingface dataset:
# **5. Conclusion**

While the dataset presented in this paper certainly has room for improvement, the figures emerging from it seem meaningful and provide valuable insights on some of the employment issues more hotly debated in South Asia these days. The contribution of the dataset is to provide employment information that is curated in a systematic way from individual responses to over 60 primary data sources in seven countries from 2000 to 2017. The coding of the dataset is fully transparent and replicable, as we report how the different questionnaires have been used to generate information on the employment status, the sector of activity, the type of job and the institutional sector of all respondents. We show that employment outcomes are sensitive to data sources, even within the same country. But we also show that comparable data from our new dataset shed light on some of the ongoing debates in South Asia.

Thus, we show that the agricultural sector still employs the most people in all countries in the region with the exception of Sri Lanka. We also show that the employment share of the manufacturing sector is relatively low across the region, and in many countries is even matched by the employment share of the construction sector. Probably related to this pattern, only around one-tenth of the working-age population has a regular job across South Asia and even casual wage employment is relatively uncommon. Instead, self-employed and unpaid family work are the predominate forms of employment. In addition, a high share of regular wage employment is accounted for by the public sector in all South Asian countries.

The dataset confirms that the number of people at work in South Asia has not increased in line with the working-age population and that employment rates mostly declined during the last decade. This contrasts with the experience of many East Asian countries, in which the employment rate increased during the same period. The decline in overall employment rates was mostly driven by declining employment rates of women.

Last but not least, our dataset provides valuable insights on the relationship between economic growth and employment growth in
than its imports.

Since _EL_ is a function of the current account disequilibrium of the balance of payments, it should reflect the impact on the external gap (the difference between imports and exports) of deviations between domestic aggregate demand and output. Under stable real exchange rates, the external gap and the output gap (the difference between potential and actual output) should be closely related. Hence, _EL_ should be a good proxy for excess aggregate demand. Strong real exchange rate depreciations, however, could channel aggregate demand away from tradables toward non-tradables, in which case the (positive or negative) growth impact of domestic demand would be distributed in the _EL_ and _DR_ terms. But otherwise _DR_ should mainly reflect supplybased (rather than demand-based) factors, such as changes in productivity that allow a country to produce more for a given level of imports, or changes in trade policies that restrain or promote imports for a given level of output.<sup>2</sup>

> 2 Indeed, in the case of advanced economies for which good output gap historical series are available, _EL_ is generally highly correlated to the output gap, which is not the case for _DR_ .

6
Figure 2: Means, 50 and 95 Percent Confidence Intervals of the Sample Distributions of the Output Changes by Sector from Armenian-EU DCFTA—30,000 simulations.

<!-- Start of picture text --> 20<br> 15<br> 10<br> 5<br> 0<br>-5<br>efence retail trade and repair rrying<br>d pipelines ua<br>i n m nd a e vi a n o<br>d<br>Other social and personal services Electricity, gas and water supplyEducationOther land transport Manufacturing lic a b Pu Health and social work olesal h WTransport via railwaysTrnsporta Cnstructio Telecommunications ing and qMi n nkingBa Inurance s Air transport<br><!-- End of picture text --> Note: The boxes are limited vertically by the 25% and 75% quartiles. The bars in the box are the means. The vertical lines extend to the 2.5% and 97.5% percentiles.

66
Figure 9: Percentage of Workers Not Living in Poverty Across Job Quality Dimension <!-- Start of picture text --> Contract Insurance Retirement<br>100 97.3 97.8 96.7<br>81.8 80.8 77.7<br>80<br>60<br>40<br>20<br>0<br>No Yes No Yes No Yes<br>Tenure Permanent Single Job<br>100 95.9<br>86.1 86.6<br>80 81.8 79.3 76.4<br>60<br>40<br>20<br>0<br>No Yes No Yes No Yes<br>Percent<br><!-- End of picture text --> _Notes_ : Authors’ calculations using SEDLAC data.

# **5 Relationship between Job Quality and Poverty**

Which dimensions of job quality are associated with less poverty? This question is essential to understanding how poverty in Latin America has been and has the potential to be reduced. In this section, the variable of interest is whether or not the worker and his or her family is not living in poverty, _PovFam_ . This is found by adjusting the total family income using 2005 PPPs and dividing by the total number of family members in the household.

The bar chart in Figure 9 displays the proportion of the population with access to certain job quality dimensions that are not living in poverty. For example, over 97% of workers that have signed an employment contract belong to non-poor families, compared to less than 82% of workers with no contract that belong to non-poor 31
, see Table 3.<br>Countries are classified according to the 2018 World Bank classification of countries<br>* Multiply by 100 (except values in ppts) to obtain parameter/variable values in per<br>** Number of countries for efficiencyθis based on IEI data.<br>Calculations forθK�<br>��/Y�,_MP_K�<br>�,MPeandμ, are formed by combining the data in<br>(not on a constantgroupof countries).|by income fo<br>centage shar<br>Table 5 with|r the 2017 ca<br>e or growth te<br>the efficiency|lendar year.<br>rms (%).<br> θfor each inco|me group|

One can also use the IEI to calculate the _efficient public capital‐to‐output ratio_ θK���/Y� . Because the public capital‐to‐output ratio ( KGm0 /Y0 ) is roughly constant across levels of development, but efficiency θ increases with development, the combined efficient public capital‐to‐output ratio also increases with development. Specifically, the efficient public capital‐to‐output ratio is around 0.75 in HI countries, 0.73 in UMI countries, 0.66 in LMI countries and 0.57 in LI countries. This suggests that LI countries do not have a shortage of measured public capital (as argued above), but rather a shortage of _efficient_ public capital.

> 39 Berg et al. (2015) show that for the Cobb‐Douglas production function (as used here), these two are exactly offsetting. However, even with the CES production function the two factors are mostly offsetting unless public and private capital are extremely complementary (their Figure 3).

28
up together, going to the same neighborhood school, socializing with the same cohorts of peers. Thus, we would like to exclude the siblings who grew up far apart in dierent times (or dierent places like a sibling who left home for a boarding school), and might have experienced dierent family, neighborhood, and school environment when growing up.<sup>11</sup> This includes, for example, much older children of the household head who are not coresident because they are in college or working in a dierent location after graduating from college. We restrict the age of children to 16-28 years in the survey year of a wave for our analysis.<sup>12</sup> This also ensures that possible sample truncation because of coresidency is small.<sup>13</sup> We provide estimates of sibling correlation in educational attainment for 53 developing countries and trace out the evolution of intergenerational educational mobility for three decade-wise birth cohorts (1970s to 1990s).<sup>14</sup> We use 230 waves of Demographic and Health Surveys (DHS) to build a comparable data base across countries. This ensures that the estimates are not dierent because of dierences in survey instruments or measurement of schooling (years of schooling vs. categorical). Another important advantage is that the information on parental education is not based on recall of the children, and thus are likely to contain much less measurement error. The advantages of the Demographic and Health Surveys for cross-country comparison studies have been well appreciated in the recent literature (see, for example, Bhalotra and Rawlings (2013), and Lleras-Muney et al. (2022)).

Another important contribution of this paper is that we take advantage of recent methodological advances to provide a credible answer to a long-standing policy-relevant question:

> 11This is supported by our cohort-wise estimates that show signicant changes in sibling correlation over time. Nicoletti and Rabe (2013) provide evidence
Weill (2010) use quarterly data from the financial information agency Interfax and the Central Bank of Russia to calculate the Lerner index for banks in Russia between 2001 and 2007. The paper examines whether competition depends on the ownership type of banks by comparing market power across private, government-owned, and foreign banks. The authors find that bank competition in Russia is similar to that in other European Union countries and has only slightly improved during the period studied. Furthermore, they find no differences in market power across private, government, and foreign-owned banks.

Our study adds to the work of Fungáčová, Solanko and Weill (2010) in several ways. Most importantly, rather than limiting our analysis to only one non-structural measure of competition, we pursue different approaches to ascertain the degree of banking sector competition in Russia. First, we examine various measures of concentration. Second, because concentrated banking sectors can be competitive as long as they are contestable (e.g. the Canadian banking system as shown by Allen and Engert, 2007), we analyze the regulations and supervisory practices affecting the ease of entry and exit into the banking sector to assess the degree of contestability. Third, as an alternative non-structural measure of competition, we calculate the Panzar and Rosse H–statistic. Finally, while Fungáčová et al. (2010) simply compare the Lerner Index for Russia with other authors’ estimates for European Union countries (Fernandez de Guevara and Maudos, 2007 and Cabo-Valverde et al., 2009) and contrast the estimates for banks in Russia across ownership types, we conduct rigorous tests to evaluate

> Mkrtchyan (2005), which focuses on Armenia; Prasad and Ghosh (2005), which investigates the case of India; Mamatzakis et al. (2005), Drakos and Konstantinou (2005), and Yildirim and Philippatos (2007) that study competition in Central and Eastern European countries; Levy Yeyati and Micco (2007), which analyzes banking competition in Latin America; and Anzoategui, Martínez Pería, and Rocha (2010
**Figure 4.** Employment rate by type of job.

<!-- Start of picture text --> Percent<br>80<br>60<br>40<br>20<br>0<br>Nepal Afghanistan Bhutan Bangladesh India Sri Lanka Pakistan<br>Regular employee Casual employee Self-employed worker Unpaid family worker<br><!-- End of picture text -->

# **Source:** South Asia Employment Dataset.

**Note:** Period of reference specified in footnote 4. The survey design of Nepal did not allow us to separate unpaid family workers from self-employed workers.

The public sector accounts for less than one-tenth of total employment across South Asia with the exception of Sri Lanka, where the share attains 15 percent (Figure 5). Bangladesh is at the other extreme, with only 3 percent of the employment being in the public sector. However, a high share of regular wage employment is accounted for by the public sector in all South Asian countries. More than half of the regular wage jobs in Sri Lanka are in the public sector and the share is also high in India and Pakistan. At around 15 percent, Bangladesh has the lowest share of public sector employment among regular wage employment in South Asia.

**Figure 5.** Employment by institutional sector.

<!-- Start of picture text --> Percent<br>60<br>40<br>20<br>0<br>Share of total employment Share of regular jobs<br>Sri Lanka Pakistan India Bangladesh<br><!-- End of picture text -->

**Source:** South Asia Employment Dataset. **Note:** Period of reference specified in footnote 4.

11
**Annex 3. Replications of Malaysia-focused analyses about the similarity of skills required in very-low, low, medium, and highly digital occupations for the occupational skills profile, Cambodia, Thailand, and Vietnam**

**Table A.3.1. Share of skills categories across digital occupation levels in the occupational skills profile, Cambodia, Malaysia, Thailand, and Vietnam (replication of data behind figure 6)**

||**Oc**|**cupati**<br>**pro**|**onal ski**<br>**file**|**lls**||**Cam**|**bodia**|||**Mal**|**aysia**|||**Tha**|**iland**|||**Vie**|**tnam**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Di**|**gital o**<br>**lev**|**ccupati**<br>**els**|**on**|**D**|**igital o**<br>**lev**|**ccupati**<br>**els**|**on**|**D**|**igital o**<br>**lev**|**ccupat**<br>**els**|
.0997|0.0462|0.0339|0.2910|0.1215|0.0726|
|**_Education_**||||||||||||
|**_p.**only, for all|281.427|0.587|0.614|0.680|1.451|0.0648|0.0293|0.0209|_ 0.1985|0.0800|0.0469|
|_ , p[. for all|292.292|0.579|0.588|0.662|1.385|0.0498|0.0246|0.0188|0.1718|_0.0659|0.0386|
|p.t, .d,r,cx,**1P,**<br>for all|254.675|0.580|0.590|0.666|1.410|0.0774|0.0434|0.0348|0.2151|0.0937|0.0606|
|Source: Based**on**"Pesquisa Nacional**por**Amos|tra de Domicilio|s" (PNAD) of|1985 and 1996|.||**=**||||||
The structure of the remainder of the paper is as follows. Section 2 discusses how official definitions of urban areas in LAC countries compare to those in non-LAC countries and introduces both the AI and cluster methods for the consistent measurement of urbanization. Section 3 describes the data used to implement both the AI and cluster methods, focusing, in particular, on the input gridded population data. Section 4 presents the results from re-assessing LAC’s position relative to the basic stylized facts of urbanization, development and structural transformation when urbanization is consistently measured. Finally, Section 5 concludes.

# **2. Measuring Urbanization**

# 2.1. _Official definitions of urban areas in LAC versus non-LAC countries_<sup>13</sup>

Most published research on urbanization and development relies on data which utilizes national definitions to measure cross-country differences in levels of urbanization, and it is this same data that underpins the stylized facts depicted in Figure 1. These data are usually taken from the United Nations’ World Urbanization Prospects (WUP) database or, equivalently, the World Bank’s World Development Indicators (WDI) database (see also footnote 2). As part of its database, the UN provides supporting documentation on how each of the 232 included countries defines its urban areas. Table 1 shows that 133 of these countries use one or more of four basic types of criteria to define their urban areas.<sup>14</sup> By far the most commonly used criterion is a minimum population size threshold (103 countries). The remaining three criteria are less frequently used. Hence, 30 countries include references to the availability of infrastructure and services (e.g. piped water or schools) or other “urban” characteristics in their definitions; 16 countries to the structure of the local economy, mostly referring to the presence or predominance of non-agricultural activity, and 11 countries to a minimum population density threshold. The remaining 99 countries do not make use of any explicitly stated criteria. Rather, they either
Table 2: Alternative measures of import market concentration

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dependent variable|Share of|Share of|Log number|Log number|Log|Share of|
||the largest|the 4 largest|of|of source|unit|commodity|
||importer|importers|importers|countries|value|exporters|
|Panel A)|||||||
|Commodity export share ∈[0,1]|0.04*|0.02|-0.25*|-0.23***|0.33*|0.00|
||(0.02)|(0.01)|(0.13)|(0.08)|(0.18)|(0.02)|
|R-squared|0.73|0.85|0.94|0.90|0.82|0.56|
|Number of observations|2,176,363|2,176,363|2,176,363|2,176,363|1,818,137|1,969,637|
|Dependent variable mean|0.530|0.807|2.616|1.762|1.739|0.189|
|Panel B)|||||||
|Export commodity price index (1=2012)|0.09*|0.04|-0.40|-0.58***|0.95***|0.01|
||(0.05)|(0.04)|(0.35)|(0.
# ⮚ **_Modeling approach_**

The nutritional status has been modeled from DHS data using a multinomial logit regression for each type of individual (children and adults). In order to capture the heterogeneity that may be present between zones - standard of living, local agricultural cultures, accessibility of markets, etc. - a model for each of the six zones of Nigeria was compiled. The following variables were identified as factors influencing the nutritional status of individuals:

- The asset index, which is supposed to represent the household's standard of living and includes characteristics of the dwelling, the movable and immovable assets owned by the household

- Socio-demographic characteristics of the household’s head such as age, gender, education level, marital status

- Socio-demographic characteristics of the individual when applicable

- Location (urban/rural) and household size

# ⮚ **_Inference approach_**

Nutritional status has been attributed in NLSS data using, on the one hand inferred probabilities from the model built in DHS data and on the other hand, estimated thresholds such that NLSS data could reproduce DHS individuals’ nutritional status per zone.

Especially, thresholds have been chosen such that shares of adult women categories (undernourished, normal, obese) and shares of children (normal, moderately stunted, extremely stunted) are similar to shares in DHS data per zone. However, due to lack of information in DHS data on other demographic categories of population (children between 6 and 14, adult men between 15 and 49 and elders over 50), some assumptions have been made on shares of undernourished/obese among those people, in a conservative approach, on the basis of available third source information:

- Half as many obese men as obese women in the 15-49 age group, in each of the zones of Nigeria, a conservative assumption in light of Chukwuonye et al (2013).

- No undernourished men in the 15-49 age group (conservative approach)

- All individuals in the 6-14 and over-50 age groups are in a normal nutritional condition
Table 15: Medium-term impacts of _Mais Educação_ on educational outcomes for schools that chose culture and arts field

|Dependent variable|Dropout rates|Test score<br>mathematics|Test score<br>Portuguese|
|---|---|---|---|
||(1)|(2)|(3)|
|Panel A: Imp|acts on fifth-grade va|riables||
|Dummy_Mais Educação_* year 2011|0.432|–4.559**|–2.563|
||(0.991)|(1.865)|(1.651)|
|_R_ <sup>2</sup>|0.703|0.809|0.819|
|Observations|1,688|1,688|1,688|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|
|Panel B: Imp|acts on ninth-grade v|ariables||
|Dummy_Mais Educação_* year 2011|–0.00425|–3.045|–0.194|
||(1.030)|(2.174)|(2.025)|
|_R_ <sup>2</sup>|0.745|0.779|0.784|
|Observations|1,340|1,340|1,340|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined culture and arts field in 2008 and remained in the activity until 2011. This table reports coefficients from the ordinary least squares estimation of equation (5) in the paper, where the dependent variable is in column (1) is dropout rates, in
is likely that the distributional changes that occurred in these countries during the mid-1990s were more closely related to terms of trade changes that were rather independent of the banking conditions.

These important cases highlight the possibility that banking crises can be accompanied by falling inequality. The next section examines the full set of documented banking crises to see what have been the associated movements of inequality.

# **4. Broad cross-country evidence**

In this section, we look at cross-country data on Gini coefficients of income or expenditure and on the distribution-related changes in poverty. A caveat is in order. It is not implied that Gini, or indeed relative inequality in general, is the most important aspect of the welfare impact of financial crises. Indeed, as mentioned above the Gini is very far from being a useful measure of welfare in this context, not least because an uniform percentage reduction in cash income may have a much greater impact on the welfare of the poor than that of the rich.<sup>15</sup> Instead, the purpose here is the analytical one of isolating distributional effects from the (larger) overall welfare effects which result especially from the across-the-board decline in mean income at all level of society which is typical of many crises.<sup>16</sup>

> 15 Despite the logical difficulties involved in making interpersonal comparisons, Atkinson’s index based on the perspective of an inequality-averse _social_ welfare function, or poverty measures such as the FGT class can be used to address the wider welfare questions.

> 16 The common presumption that poverty headcount invariably increases around crises is also contradicted by Povcal data (cf. Figure 3, where the dates and cases correspond to those used in Table 3b).   In most

11
are to be expected regardless of how efficient a utility is, any difference between this and a utility’s actual, total losses can be referred to as “inefficient losses.” A higher level of losses implies higher costs. In terms of O&M, higher losses are linked to the use of more energy and chemical products. On the capital side, higher losses imply greater investment to cover the additional production needed to serve customers. We estimate the incremental cost associated with the difference between the 15 percent assumed in the model firm estimate and the level reported in IBNET with the following equation: I I + ∆C I (11) II II II Where I is the total cost differential associated with higher losses for utility ∆CCllCCI = ∆OO&MMllCCI CCIICCllCCI _s_ ; I is the O&M cost differential associated with higher losses for utility II _s_ ; and llCCI ∆C ∆CC III is the CAPEX differential associated with higher losses for utility _s_ . llCCI ∆OO&MM II CCIICCllCCI I + C (12) II II II II <u>(</u> llCCI <u>)</u> ∆C I∆OO&MM= = (IICC ) ∗(TTCCTT∗C I −TTQQTT)I � (13) ) II TTCCTT<sup>ss</sup> −TTIITT<sup>rr</sup> llCCI Where CCIICC are the reported electricity costs for utility (1−TTCCTT<sup>ss</sup> ∗�KK<sup>II,PPrrCCdd,II</sup> CCIIQQrrII I _s_ ; C are the reported chemical costs for TT<sup>II,WWppI</sup> utility _s_ ; are the reported total losses for utility _s_ ; are the efficient total losses from the II Chilean sector; IICC<sup>II</sup> is the estimated asset base for water production of size _m_ for utility _s_ in the IBNET database (calculated using weighted average of the unit
including only one hazard per country, the number is in line with other simulations in macro frameworks.

**Figure 6.** Vulnerability rates comparison between counterfactual scenario and actual scenario.

<!-- Start of picture text --> Low harm Actual harm<br>45% 41% 41% 41%<br>39%<br>40% 37%<br>33%<br>35%<br>30%<br>25%<br>20%<br>17%<br>20% 14% 16% 16%<br>15% 12%14% 13% 11%14% 12% 15%<br>10% 6%7%<br>5%<br>0%<br>Floods Landslides Earthquakes Cyclones Droughts<br>Ecuador Mexico Dom. Rep. Ecuador Brazil Honduras Dom. Rep. Ecuador Brazil Honduras Mexico Honduras Brazil El Salvador Honduras<br><!-- End of picture text --> Note: The results correspond to the specifications, including one type of natural hazard controlled by exposure, coping mechanisms, and region. Source: authors' calculations based on ThinkHazard!, EMDAT, and SEDLAC database.

**Vulnerability profiles**

18