rafmacalaba/data-use-mentions-extended huggingface dataset:
majority of firms in 34 percent of the 278 six-digit sectors in manufacturing, in 62 percent of the 142 six-digit sectors in commerce, and in 66 percent of the 252 six-digit sectors in services. By 2013 these percentages had increased to 51 in manufacturing, 81 in commerce and 88 in services.

# **The Schooling Composition of the Demand for Labor Differs Between Formal and**

# **Informal Firms**

Transportation services in Mexico may be provided by 100 self-employed workers driving their own trucks or by a single firm with 100 salaried employees. In both cases, there will be 100 trucks and 100 drivers, but, in the latter, there will be a need for a logistics engineer doing dispatches and a sales manager. Tortillas can be produced with simple technologies in small establishments with unskilled labor or in large plants that require engineers; the same holds for apparel and food processing, among many manufacturing activities. This is also true for retail commerce, which can take place in small stores employing workers with basic literacy and numeracy or through large supermarket chains employing industrial designers. In general, the complexity of tasks and the division of labor increase with firm size and generate the need for more educated workers. But complexity is not only an issue of size: a small informal firm producing jeans to be sold in a street market will be less likely to need an accountant than a formal firm of the same size selling jeans to a large retailer.

Table 4 uses data from the employment survey to show the distribution of employment by schooling level and firm size and type.<sup>8</sup> In each cell, the upper number is the share of workers of a given schooling level in the total number of workers in firms of that size; thus, columns add to 100

> 8 If there were no mixed firms, all informal (formal) workers would be employed by informal (formal) firms. Mixed firms complicate this picture. Unfortunately, we cannot use ENOE (Encuesta Nacional de
indicators available for the specific neighborhood blocs, specifically, the share of households with “unsatisfied basic needs” ( _Necesidades Básicas Insatisfechas_ , NBI) available from the National Institute of Statistics and Census of Argentina (INDEC) at the census radius level for most of the study area. Additional details on the methodology used and the broader set of results obtained are available in Stokenberga _et al_ . (2023).

**Complementing the CDR-based analysis, the study team implemented two sets of field surveys in late 2021 and early 2022** . These included an overall interception survey with 20,306 car and motorcycle users to better understand the overall motivations for private vehicle use (including the considerations associated with the COVID-19 pandemic), followed by a phone-based stated preference survey with 600 car and motorcycle users to identify their willingness to switch to alternative modes depending on factors such as hypothetical changes in travel times and costs. This data collection effort was conducted in close collaboration with the Government or the City of Buenos Aires and was informed by its parallel data collection activities (e.g., vehicle counts at pre-determined points across the urban area). The survey randomly intercepted drivers of motorized vehicles that stopped at 25 predetermined points (service stations and parking lots), and the final sampling ensured that the survey sample meets predetermined (based on previous travel surveys) quotas of women and men that travel by car or by motorcycle and that live in CABA versus PBA. The sampling was adjusted to the approximate volume of traffic at the mentioned points and considered peak and off-peak hours based on data provided by the Secretariat of Transportation and Public Works of CABA.

**Based on the collected data, the study team analyzed overall differences in mobility behaviors and stated preferences** between male and female car and motorcycle users and implemented regression analysis to estimate the importance of various personal and trip attributes (including gender) in determining the odds of switching to private modes only since the start of the pandemic. Specifically, the analysis applies a logistic regression
countries, children from disadvantaged backgrounds were less likely to benefit from remote learning. Recent analysis by <u>Moscoviz and Evans (2022) suggests that learning losses are concentrated primarily among</u> children from poorer households, as documented in the Netherlands, Italy, United States, Mexico, Bangladesh, and Ghana.

Our results show that the Learning Poverty Gap is likely to rise. Countries experiencing an increasing Learning Poverty Gap will need to direct more resources and efforts toward children at the bottom, for example through facilitating structured pedagogy so that teachers can be more effective in closing learning gaps. The simulation results also suggest that Learning Poverty Severity is also likely to increase. Countries experiencing worsening Learning Poverty Severity will need to focus on reducing learning inequality among children at the bottom through tailored and targeted instruction, for example by adopting “teaching at the <u>right level” techniques.</u>

## Address Learning Data Gaps

Finally, to tackle the learning crisis, countries must address crucial learning data gaps by assessing student learning levels. While good learning data has been used to document learning losses in certain countries, grades, and households, many countries lack such data and are therefore flying blind. Policy makers and teachers require granular, timely, and comparable data to implement policies and actions that can help improve learning outcomes. Measuring learning losses through frequent, reliable data is an essential first step in mitigating losses. Such data is vital for the effective implementation of evidence-based strategies, such as targeted instruction, that allow teaching to be aligned with student needs.

## Prioritize Education Financing

Ensuring a safe return to school and facilitating learning recovery will require not only political commitment, but also financial support from governments, given that the vast majority of education comes from domestic government budgets (Hares and Rossiter 2021). The pandemic caused the economies of many countries to reverse a steady upward trend in public spending in education. Forty percent of low- and lower-middleincome countries reduced their education spending in 2020, after
main activity in labour force surveys. Main findings from the ILO LFS pilot studies. ILO.

Benes, E., & Walsh, K. (2018d). Measuring employment in Labour Force Surveys: main findings from the ILO LFS pilot studies. ILO.

Bick, A., Fuchs‐Schündeln, N., & Lagakos, D. (2018). How do hours worked vary with income? Cross‐country evidence and implications. _American Economic Review, 108_ (1), 170‐199.

Blair, J., Menon, G., & Bickart, B. (2004). Measurement effects in self vs. proxy response to survey questions: an information‐processing perspective. _Measurement errors in surveys_ , 145‐ 166.

Bonke, J. (2005). Paid work and unpaid work: diary information versus questionnaire information. _Social Indicators Research, 70_ (3), 349‐368.

Budlender, D. (2008 _). The statistical evidence on care and non‐care work across six countries._ United Nations Research Institute for Social Development Geneva.

Comblon, V., & Robilliard, A.‐S. (2015). Are female employment statistics more sensitive than male ones to questionnaire design? Evidence from Cameroon, Mali and Senegal.

Dillon, A., Bardasi, E., Beegle, K., & Serneels, P. (2012). Explaining variation in child labor statistics. _Journal of Development Economics, 98_ (1), 136‐147.

Ellis, F. (1998). Household strategies and rural livelihood diversification. _The Journal of Development Studies, 35_ (1), 1‐38.

Fox, L., & Pimhidzai, O. (2013). Different Dreams, Same Bed: Collecting, Using, and Interpreting Employment Statistics in Sub‐Saharan Africa‐The Case of Uganda. _Working Paper, World Bank_ .

Freedman, D., Mueller, E., Barnes, R.,
several times over time: Between October 1986 and March 1992 the coverage was 100% of TL 3 millions and 60% of the next TL 3 millions; between March 1992 and April 1994 the coverage was 100% of TL 25 millions and 60% of the next TL 25 millions; between April 1994 and May 1994 the coverage was TL 150 millions without co-insurance. In the wake of the crises in 1994, all deposits have been brought under coverage between May 1994 and June 2000. Between June 2000 and December 2000 the coverage was TL 100 billions which then reduced to TL 50 billions in January 2001 just to be replaced by another blanket guarantee between July 2003 and July 2004. Since then coverage limit remained at TL 50 billions. _Sources_ : Central Bank of Turkey (1983), Own survey of deposit insurers, IADI Survey: Turkey (2003).

**Turkmenistan.** In 2000 Turkmenistan introduced a full guarantee on deposits including those denominated in foreign-currency. It is officially administered and has a compulsory membership policy. _Source_ : Barth, Caprio, and Levine (2004).

**Uganda.** ( _Deposit Insurance Fund, Financial Institutions Act, 1993_ ) The fund in Uganda was established in 1994. It is officially administered by the Bank of Uganda and jointly funded. Membership is mandatory for all banks and credit institutions and they are required to pay a 0.2% flat rate assessed annually on weighted deposit liabilities. The coverage is U Sh 3 millions per depositor per institution. Foreign currency and inter-bank deposits are not covered. _Sources_ : Bank of Uganda (2004), IADI Survey: Uganda (2002).

**Ukraine.** ( _Fund for the Guarantee of Deposits of Natural Persons, Decree 996/_ 98) The deposit guarantee scheme of Ukraine was established in September 1998. It is officially administered and jointly funded. The initial capital of UAH 20 million was provided by the National Bank of Ukraine and will lend when necessary. Deposits of insiders and their families
climate change scenarios (Bangalore et al., 2016).

The remainder of this paper is structured as follows. Section 2 describes the data used in the analyses. Section 3 shows the district‐level overlay of environmental risks and poverty in 2010. Section 4 compares environmental risks at the commune level across household groups and urban and rural areas. Section 5 deepens these analyses by presenting results from various regression models explaining consumption differences between households and changes over time by environmental risks at the commune level. Section 6 concludes.

# **2. Data**

This study combines household data from the Living Standard Measurement Surveys, VHLSS 2010, 2012, and 2014, and recent geo‐spatial data measuring environmental risks at the district and commune levels.

## **2.1 Socioeconomic data**

The study mainly relies on household data from the VHLSS 2010, 2012, and 2014. These surveys are conducted by the General Statistics Office (GSO) with technical support from the World Bank in Vietnam. They are nationally and regionally representative and contain detailed information on individuals, households and communes. In total 9,400 households nationwide are included in each round. Half of these households were also interviewed in the previous round so that the data set includes a short‐term panel.

The VHLSS provide detailed information to estimate consumption expenditure, which can be used to estimate poverty rates. This study uses district‐level poverty maps based on estimates from the VHLSS 2010 combined 3
strong for the publishing sector and IT and information services. In contrast, trading partners sharing the conditional model show greater levels of digital services trade compared to country-pairs with dissimilar data models. Regarding the limited model, country-pairs sharing this model seems to be again associated with lower trade in digital services than countries sharing different models.

# **5.1 Adding Control Variables**

A number of robustness checks are also conducted by adding several control variables, providing a panel dimension, and finally using the ITPD-E as an alternative source of trade data. For each subsequent table, results are reported when entering the two categories of CB and DR together.

Starting with the first robustness check, Table 5 shows the results when adding the data restrictiveness index as developed in Ferracane and van der Marel (2018) based on the ECIPE’s DTRI. This index measures level of data policy restrictiveness with respect to the cross-border transfers of data and the domestic processing of data of the partner country, namely the importer.<sup>24</sup> The table shows that results remain largely unchanged. In all instances, variables stay significant, indicating that sharing the open model on cross-border data transfers tends to be positively associated with digital services trade, while sharing the open model for domestic processing of data tends to be negatively associated. This pattern reverses for country-pairs sharing the conditional model. The

> 24 Note that the interpretation for both components CB and DR of the Data Restrictiveness Index in Ferracane and van der Marel (2018) is different than the one developed in this paper. The main differences are that the index more generally aims to measures the extent to which data transfers and data processing create restrictions on trade, while this paper simply categorizes the countries under different data models to estimate whether sharing such models has any impact on digital services trade.

29
The analysis starts with econometric estimation based on municipality-level panel data using both OLS and IV methods. The IV employs the instrument constructed by Lipscomb et al. (2013) and its estimates suggest that a 1 percentage point increase in electricity access increases the share of services by 1.06-1.14 percentage points; whereas the shares of agriculture and manufacturing decline, respectively, by 0.65-0.7 and 0.42-0.43 percentage points. The theoretical model simulations further support the important role of advances in electricity on the structural transformation process, explaining 17 percent of this process for the Brazilian case during the period 1970-2006. The model, however, predicts that the growing public capital mainly favors the manufacturing sector, which is what the OLS findings imply. Interestingly, the theory is able to rationalize these two different econometric results and supply a clearer view of the effects.

The reason is that the role of electric power infrastructure arises through two channels. The first one, which is what the IV method mainly captures, runs through consumption and investment as a consequence of supply-side forces linked to the output production technologies. These supply-side effects accelerate the reallocation of production inputs towards services. More specifically, in the model, when sectors are complementary, their consumption and investment shares increase with the relative product price. Because manufacturing is the most energy intensive sector, and also the one that experiences the fastest decrease in fixed costs as electricity availability improves, the relative prices of primary and tertiary products grows with advances in electricity access, and hence the reallocation of resources towards those sectors (especially services) speeds up.

The second one, on the other hand, represents a demand-side channel that favors the growth of manufacturing and, to a lesser extent, the one of services, because the creation of infrastructure demands additional investment that comes mainly from the secondary sector. A further increase in the investment effort is allowed by the net reduction in fixed costs that the economy enjoys with electrification. Due to the large impact of investment on manufacturing
decomposed to facilitate the analysis of policy consequences of the economic measure introduced. However, unlike some other studies, this analysis does not adopt a formal general equilibrium model; instead it tries to understand each issue in a partial equilibrium setting. Although this might have disadvantages in terms of deriving economy-wide relevant policy lessons, it has the advantage of enabling the data to be fully utilized for each policy consideration. It is hoped that this will provide policy makers with relevant information for assessing the impact of past policies and designing new policies towards reducing poverty.

For the analysis, detailed price indices were developed to be used in conjunction with the household expenditure data used in this study. The methodology used in this exercise is similar to one utilized in other countries (World Bank, 1990; Ravallion, 1993). Since the main redistributive feature of the adjustment process was a shift in the rural-urban terms of trade towards rural areas, the effects can be highlighted by disaggregating results along rural/urban and (geographical) regional dimensions. However, for policy relevant analysis, it is important to identify even further, where poverty and welfare effects were concentrated, by looking at who were the losers and gainers. As this study is concerned with understanding how the overall incidence, depth and severity of poverty has evolved, and what changes may have occurred in the 1/ See Appendix 4, for previous research on poverty in Nigeria.
8 The panel data for Vietnam are from the two waves of the Vietnam Living Standards Survey (VLSS) conducted in 1992-93 and 1997-98. The panel consists of 4302 households (20493 individuals) from 59 of Vietnam’s 60 provinces. The results below are for household consumption rather than income, defined and computed in the same way in both years.<sup>10</sup> The 1998 data have been deflated using the national CPI. The panel data imply a real annual rate of growth of per capita household consumption of 6.6%, and a rise in the Gini from 0.3223 in 1993 to 0.3346 in 1998, giving a value of _∆ G_ equal to 0.0123 (Table 1). This change in the Gini is somewhat smaller than the VLSS-based figure reported by Glewwe (2003), namely 0.023. This seems likely to be due to the fact that the figure reported here is based on the households interviewed in both years, while Glewwe’s is based on the full 1998 sample which includes 1,200 households interviewed in 1998 but not in 1993.

The data from the CHNS and VLSS panels are thus very consistent with a story of rapid income and consumption growth, accompanied by very modest increases in inequality. The next section uses the decomposition of section II to see how far these very slight increases in the Gini coefficient mask horizontal and vertical redistribution in the move from period 0 to period 1.

# IV. **DECOMPOSITION RESULTS**

Tables 2 and 3 show the results for China (1989-97) and Vietnam (1993-98) for different bandwidths.<sup>11</sup> Consistent with the results of van de Ven et al. (2001), _V_ initially increases in absolute size as _w_ is increased, but subsequently declines as _w_ is increased further. This reflects two opposing tendencies: an ‘averaging effect’, which tends to increase _V_ as _w_ is raised (the

> 10  The VLSS measure of
not as coarse as continents, but which at the same time are still assumed to provide robust projections (Solomon and Qin 2007).

At the regional level, the seasonality of changes is also important, since changes in annual averages do not uniquely determine the way in which the frequency or intensity of extreme weather events might change in the future. For instance, in Europe, where the annual mean temperature is likely to increase, the largest warming is projected to be likely in winter in Northern Europe and in summer in the Mediterranean area (Christensen and Hewitson 2007). Levels of confidence in projections of changes in frequency and intensity of extreme events, in particular regional-level statements concerning heat waves, heavy precipitation and droughts, can be estimated using different sources of information including observational data and model simulations. For extreme rainfall events, it is expected that these will be unrelated to changes in average rainfall. Average rainfall amount depends on the vertical temperature gradient of the atmosphere which in turn depends on how quickly the top of the atmosphere can radiate energy into space. This is expected to change only slightly with changes in carbon dioxide concentrations. On the other hand, extreme precipitation depends on how much water the air can hold, which increases exponentially with temperature. Thus it is reasonable to expect that in a warmer climate, short extreme rainfall events could become more intense and frequent, even in areas that become drier on average. Some studies have found that in regions that are relatively wet already, extreme precipitation will increase, while areas that are already dry are projected to become drier due to longer dry spells (Fussel 2009). The spatial extent of areas with severe soil moisture deficits and frequency of short-terms drought is expected to double until the late 21<sup>st</sup> Century, and long term droughts are projected to become three times more common, in particular in the Mediterranean, West African, Central Asian and Central American regions (Fussel 2009).

Uncertainties in climate projections are not only due to
employment of new migrants, the relative size of the migrant population in the local labor-market decreases it. They interpret the latter result as a congestion effect among migrants competing for jobs.

A criticism of this kind of work is that the potential endogeneity of the social network is seldom controlled for. One exception is Munshi (2003) who studies the role of social networks in the migration of Mexicans to the United States, using a panel dataset of individual location decisions and labor outcomes from multiple communities. In this work, the number of other migrants from the same origin-community is instrumented with rainfall in the origin-community since rainfall is a push factor that naturally affects rain-fed agriculture employment.<sup>28</sup> With this endogeneity control, it is found that an individual is more likely to be employed and to hold a higher paying job when his network is larger.

**Policy implications** -- These works suggest that education or social networks can help migrants in their gradual process of labor-market assimilation in the city even though _the specific channels through which networks and education operate are not identified_ . These channels probably include different kinds of support or opportunities that migrants may lack. In this respect, policies that could help migrants overcome assimilation barriers are diverse. Measures can aim at improving the bargaining power of migrants on the labor-market, for instance through sharing information and collective negotiation (Mosse _et al._ , 2002). Governments can see to it that migrants, and especially poor migrants, are not credit rationed so that entrepreneurship is not discouraged, that migrants are not discriminated against in the housing market, and that they find a satisfactory access to public goods, including child care, health and education.<sup>29</sup> Supporting the political involvement of migrants in local politics can help migrants ensure that their needs are taken into account by local policy makers. Several other measures can ensure the protection of rural migrants in cities, providing them with information on labor legislation and rights, favoring their unionization