rafmacalaba/data-use-mentions-extended huggingface dataset:
To test these results empirically I merge two newly available datasets: the Linked/Longitudinal Firm Trade Transaction Database (LFTTD), which links individual trade transactions to firms in the US, and the World Bank EU Electrotechnical Standards Database (EUESDB), which provides an inventory of the stock of active standards published by CENELEC and their link with standards issued by the IEC. The LFTTD spans 13 years from 1992 to 2004 and allows the researcher to use information from the Censuses of Manufactures (CM) of the Longitudinal Research Database (LRD) of the U.S. Census Bureau to pin down additional firm characteristics on a quinquennial basis. The EUESDB covers the period 1990-2007 and classifies product standards according to the International Classification of Standards (ICS). See Bernard et al. (2009) and Portugal-Perez et al. (2010) for a complete description of each database, respectively. A key contribution of my analysis is the linking of firm level U.S. manufacturing data to industry level measures of EU product standards. Since there is currently no official concordance mapping from ICS codes to any product or industry classification system, I develop a concordance method between 5-digit ICS codes and 4-digit SIC industries for the Electronic sector.

Results are largerly consistent with theoretical implications. First, I confirm that U.S. industries with relatively high harmonization exhibit relatively high export value to the EU. Furthermore, I show that product standards harmonization increases the probability that higher-productivity firms enter the EU market —the extensive margin of trade. Second, I find that this impact is more relevant for U.S. firms that were already exporters serving developing countries than for firms entering the export activity —the extensive margin of trade composition. Third, I show that this impact is negative for the intensive margin of trade: the change in the value of U.S. goods that are already exported to the EU within surviving trade relationships, e.g., the same firm exporting the same product to Europe throughout the time span.
constraints are likewise hampered productivity, as illustrated by the fact that managers in these enterprises have low educational attainment (Rothenberg et al., 2016). As a result of low productivity levels, informal enterprises typically pay low wages and are constrained to serving local markets (Rothenberg et al., 2016).

The empirical evidence has also examined factors influencing informal enterprises’ decision to formalize. One recurring theme based on qualitative interviews is obtaining access to credit. As obtaining credit presents a greater challenge in the informal sector (particularly for women), firms may find it more worthwhile to register their businesses only when they have plans to expand (Babbit et al., 2015; Rothenberg et al., 2016). In addition to accessing credit, workers’ skills may also determine whether they formalize or not. For example, the 2009 ISS revealed that the majority of workers in Banten entered the informal sector because it was the only work they knew (BPS & ADB, 2011). This could reflect a lack of information on, or access to, formal jobs. It could also reflect psychological or behavioral factors, such as preferring a known over an unknown option. These reasons may partly explain why interventions to encourage formalization have largely been unsuccessful. For example, Rothenberg and co-authors (2016) evaluated the effect of ‘one-stop shops’ aimed at streamlining applications for permits and other business licenses and found that the program did not have a significant effect on firms’ registration rates.

Despite advances in our understanding of the informal sector in Indonesia, important gaps remain. First, there is limited knowledge of the barriers or challenges faced by informal enterprises. With some exceptions, previous studies have typically relied on non-representative samples, which can limit the generalizability of findings. Second, empirical studies of Indonesia’s informal sector have adopted a strict dichotomy between formal and informal enterprises. The reality is much more complex as some enterprises start the registration process and fail to complete it, whereas others do not attempt to formalize at
(Mukhopadhyay et al. 2018). These factors can produce stresses that directly affect household welfare, including reduced timber stocks, livestock, fisheries and soil fertility; crop damage from insect infestation and water pollution; reduced access to drinking water; and forced outmigration of family members (Dasgupta, Sobhan and Wheeler 2017; Dasgupta et al. 2017). For understanding patterns of environmental perceptions across households, we conducted a survey of 600 households in three dispersed localities in the Indian Sundarbans. For each household, the survey collected extensive information on socioeconomic status variables, livelihoods, migration behavior and perceived threats from climate-related factors. We assign households to the three localities and three socioeconomic status groups, from information about their asset ownership and the education of the household head. In the survey, households performed separate ranking exercises for perceived changes in general environmental conditions: rainfall, temperature and salinity; and livelihood-related conditions: timber stocks, livestock, fisheries and soil fertility, insect infestation of crops, water pollution, access to drinking water, and forced outmigration of family members. Using the ranks for each household, we compute its numerical scores for each factor within each change group: 5 for environmental conditions, 10 for livelihood-related conditions. For each change group, we compute correlation coefficients for all unique household pairs in the sample. Then we estimate regression models to test the effects of socio-economic status, ethnicity and location of households on inter-household correlations.

This paper’s principal objective is to determine the relationships linking co-location and socioeconomic status to perceived environmental changes, and the potential implications for local and regional environmental governance.

3
.41|16.74|16.67|55.43|0.05|2.59|17.00|2,057.40|
|age 15-19|5.09|35.76|16.68|53.25|0.66|12.33|142.98|4,384.87|
|age 20+|9.14|53.66|20.10|57.79|3.79|71.00|48.07|933.99|

Source: Author’s elaboration from Enterprise Survey (2014)<sup>b</sup> For Pakistan only 23 firms with information available

# 5. Innovation Patterns

We start the analysis of innovation patterns by distinguishing innovations into two types: (i) technological innovations, which involve product and process inventions, and (ii) non-technological innovations, which involve organizational and marketing inventions.

## 5.1 Overall Pattern

Countries in the South Asia region can be divided into two groups: leaders, i.e., Bangladesh and India, and laggards, i.e., Nepal and Pakistan, according to technological innovations (Table 4). On the one hand, we find Bangladesh and India, as strong innovators, with innovation rates around roughly 80 percent, respectively, showing a performance that surpasses the average of the ECA and Africa regions. On the other hand, we find Pakistan and Nepal, with innovation rates of 14.8 percent and 21.2 percent, respectively, displaying a performance below the average of the ECA and Africa regions.

10
Data from the National Demography and Health Survey ( _Encuesta Nacional Demografia y Salud 2010,_ ENDS<sup>28</sup> ) shows that knowledge about HIV/AIDS in Colombia is almost universal. Nevertheless: (i) the majority of the population identifies and visualizes the disease as “others’ problem”; (ii) there is little knowledge about safe practices to avoid transmission (in particular, in departments such as Putumayo, Vaupés, Amazonas, Vichada, Guainía and Caquetá); and (iii) rejection and stigma towards people with HIV/AIDS still persists.

In sum, the prevalence of HIV/AIDS for the entire population of Colombia is low, the epidemic is concentrated, and the number of HIV cases reported has been gradually growing vis a vis AIDS cases.

# **3. HIV/AIDS National Response**

The national response to HIV/AIDS in Colombia is administered by the Ministry of Social Protection (MPS), and is articulated by a two-fold mechanism:

- An insurance component that provides health care through a network of insurance companies (the Empresas Promotoras de Salud, EPS) and the health care centers (the Instituciones Prestadoras de Servicios, IPS).

- A public health component that includes collective interventions for health promotion and prevention—such as awareness and information campaigns— financed by the territorial health entities and provided by local hospitals.

> 28 Ministerio de la Protección Social (February 2011) Encuesta Nacional de Demografía y Salud 2010, Bogotá: MPS.

24
Table 13: share of material goods

||Manufacturing|ICT|Retail|Other industries|
|---|---|---|---|---|
|Manufacturing|0.73|0.18|0.09|0.31|
|ICT|0.07|0.04|0.02|0.09|
|Retail|0.01|0.13|0.56|0.07|
|Other Industries|0.18|0.66|0.34|0.53|

_Sources:_ UNCTAD-EORA

## _•_ What is the total number of employees?

These questions have been asked for the years 2018, and 2017.<sup>30</sup> We use the number of square meters owned and rented by a firm as a measure for _Hi_<sup>_j_(</sup><sup>_z_</sup> _i_<sup>_j_).</sup> Table 12 reports the results from this analysis. The third column tests the hypothesis that the input share for the ICT sector is the same as for the other industry groupings, and the last column reports the resulting coefficient. The ICT sector uses significantly less immobile factors than the other industries in the economy, with the exception of the last industry grouping, which is 0.14, compared with an average economy of 0.19. Unsurprisingly, the point estimate for the wholesale and retail trade sector is the largest, given its higher need for storage.

# **6.4 Input-Output Linkages**

We use data from UNCTAD-EORA on input-output tables for the Jordanian economy in 2015 to estimate the relevant parameters for our model. This database measures trade in value added following the procedure described by Koopman et al. (2010). From the same database, we also use data on the compensation of employees for each industry. Using these data we compute the share
Wave 1**|**Wave 2**||
|---|---|---|---|
|**By monthly nominal net**<br>**earnings interval LCU**|CFUWBES1<br>Modeled|CFUWBES2<br>Modeled|CFUWBES2<br>SurveyMeasured|
|**400 or less**|21.1|11.2|10.2|
|**401 – 600**|19.1|7.5|8.6|
|**601 – 800**|18.4|4.0|6.1|
|**801 or more**|16.4|-3.0|4.3|
|**Total**|19.0|5.9|7.8|

Sources: CFUWBES1 and LFS 2019.

# **6. Conclusions**

This paper analyzes the channels of impact from COVID-19 on the loss of permanent formal private sector (PFPS) jobs in two upper middle-income countries — Jordan and Georgia — and provides a methodology to estimate such job losses in other countries where there is no timely or nationally representative labor market survey to measure actual losses. We take into account labor supply conditions (essentialness of industries, 42
J</mark> ournal of African Economies_ , _3_ _<mark>2</mark>_ <mark>(Supplement_2), ii273-ii295.</mark> Day, E., Fankhauser, S., Kingsmill, N., Costa, H., and Mavrogianni, A. (2019). Upholding labour productivity under climate change: an assessment of adaptation options. _Climate policy_ , 19(3):367–385.

- De Chaisemartin, C., d’Haultfoeuille, X. (2020). Two-way fixed effects estimators with heterogeneous treatment effects. _American Economic Review_ , 110(9), 2964-2996.

Diffenbaugh N. S., Burke M. (2019). Global warming has increased global economic inequality. _Proceedings of the National Academy of Sciences_ , 116(20), 9808-9813.

Fischer, E., Sippel, S., and Knutti, R. (2021). Increasing probability of record-shattering climate extremes. Nature Climate Change, 11(8):689–695.

Fruttero, A., Halim, D., Broccolini, C., Coelho, B., Gninafon, H., & Muller, N. (2023). Gendered Impacts of Climate Change.

ILO. (2018). _Women’s and Youth Empowerment in Rural Tunisia – An assessment using the Women’s Empowerment in Agriculture Index (WEAI)_ / International Labour Office, Taqeem Impact Report Series, Issue 11. Geneva: 2018 15
However, there is not just one definition of multi-dimensional poverty or one set of multi-dimensional poverty indicators. Gordon et al. (2003a) distinguishes between eight severe deprivations related to food, health, education, water, sanitation, shelter, information and access to basic services. Others have only analyzed poverty among a subset of these deprivations, imposed stricter deprivation criteria, following the MDG and SDG indicators, requiring for instance a child to have access to improved sanitation, rather than just any sanitation facility in order to count it as non-deprived of sanitation or added other dimensions, such as energy deprivation (Poppola and Adetola, 2016; Ajakaiye et al., 2016). Over time, there has been a convergence to a core set of child poverty dimensions; namely, Health, Nutrition, Education, Information, Water, Sanitation, and Housing as discussed in Abdu and Delamonica (2018). These are also the dimensions, which were adopted in a 2006 resolution by the UN general assembly defining child poverty as multi-dimensional (UN, 2007).<sup>2</sup> While some studied one or several poverty dimensions in isolation, others have mapped the poverty incidence probability combining various dimensions using a first order dominance approach (Arndt et al., 2012), whereas others have constructed multi-dimensional poverty indices of several dimensions whereby the latter have sometimes attempted to attach weights to the different dimensions, such as Alkire and Foster (2011). Considering however that all dimensions are also one of the child’s rights as defined in the Convention of the Child, a child being deprived of any right is thus poor. Therefore, it is argued that the dimensions should not be weighted when constructing indexes (see Abdu and Delamonica (2018)).

# 2.2 Poverty in Nigeria

There exists limited evidence on aggregate poverty and child poverty in Nigeria. As in the literature in general, there is a lack of evidence on child poverty that connects the findings of studies focusing on specific poverty indicators and or
or use internet. The analysis of use of infrastructure complements the analysis of infrastructure investments. Not all variables are available for the whole period so we restrict our analysis to the period 2008-2018.

**District characteristics** We use additional data to control for district heterogeneity. We use population data from the Global Human Settlement Layer (GHSL),<sup>10</sup> land categories from the European Space Agency land cover (see Defourny (2017)), distance from the coast from the Global Self-consistent, Hierarchical,<sup>11</sup> High-resolution Geography Database (GSHHG), distance to the border,<sup>12</sup> access to a city larger than 50,000 inhabitants from the Malaria Atlas Project,<sup>13</sup> temperature from Land Processes Distributed Active Archive Center,<sup>14</sup> and elevation

> 9DHS coordinates of rural (urban) EAs are randomly displaced within a 0-10 kilometer (0-5 kilometer) radius

> 10GHSL: Population count from the Global Human Settlement Layer. Based on population data from Gridded Population of the World v4.10 polygons, distributed across cells using the Global Human Settlement Layer global layer. Source data provided in 9 arc-second (250m) grid cells.

> 11Distance to the coast (on land only) is measured in meters. It is derived using World Vector Shorelines (Wessel and Smith, 1996).

> 12Distance to country borders is measured in meters. It is derived using the database of Global Administrative Areas (GADM) 2.8 ADM0 (Country) boundaries.

> 13Data incorporate data from Open Street Map (OSM) and the Google roads database. See Weiss et al. (2018) 14Yearly daytime land surface temperature are from Wan and Hook (2015).

8
0.91|0.98|0.98|0.65|0.98|
|Piped drinkingwater|0.03|0.64||0.93|0.46|0.11|0.48|0.98|
|Improved sanitation|0.58|0.22||0.70|0.96|0.76|0.59|0.90|

<u>Source: Authors’ calculations from the Global Monitoring Database (GMD)</u> <u>Notes: These statistics are computed on a subsample of household heads only. The first two covariates of poverty,</u> mean years of schooling and average asset index, are continuous variables. The remaining covariates are binary indicators. For example, 4% of household heads in Mali (2009) own at least a computer or landline. The asset index is not comparable across countries but is added to the table for completeness. Mali, Pakistan, and Colombia have data on only three assets. In these cases, _asset ownership_ variable is instead created as a binary variable of owning at least a computer or landline phone. Empty cells indicate missing data, or cases that are not applicable (i.e., asset index and asset ownership which are mutually exclusive). The survey period spans two consecutive calendar years in Nigeria (2018.75), Mali (2009.89), India (2011.5), and Pakistan (2018.5). By convention, the decimal indicates the share of the survey conducted in the second year. For example, 75% of the Nigeria survey was conducted in 2019. The floor of the survey period is indicated in the table.

31
51.28%|
|21-Apr-04|Seamless steel pipes|Russia|111.37%|79.65%|
|||Romania|42.00%|42.00%|
|13-May-04|Tubulargoods|Venezuela|54.69%***|49.94|
|3-Aug-04|Sodium<br>hexametaphosphate|China|124.96%|102.22%|
|4-Aug-04|Steel connections|China|111.41%|81.04%|

Source: Based on data from relevant determinations published in the DOF.

The diverse ways in which LDR has been applied result from the different criteria held by officials engaged in administering antidumping instruments. This situation also reveals the broad discretionality prevailing in the administration of the antidumping system, despite all past attempts in the opposite direction.

34
<br>-4 -2<br>-6<br>-8<br>-4<br>-10 Recessions Banking crises Epidemics<br>Recessions Banking crises Epidemics<br>Source: World Bank.<br><!-- End of picture text --> Note: Blue bars are coefficient estimates from local projections model. Orange whiskers indicate 90 percent confidence interval. Recessions are defined as the period from the peak preceding a business cycle trough to the trough, with the troughs defined as years in which output growth is both negative and one standard deviation below the long-term average. Banking crises are identified as in Laeven and Valencia (2012, 2018, 2020). Epidemics include SARS (2003), swine flu (2009), MERS (2012), Ebola (2014), and Zika (2016). Sample includes unbalanced panel of 32 advanced economies 97 EMDEs for 1981-2020.

31
electricity output (in addition to the capacity of the baseline). The pipeline database reports the additional electricity generation capacity and expected hours of operation for individual projects. Using this data, the expected electricity output measured in megawatt hours (MWh) are calculated for each year of the project, as well as discounted-weighted lifetime totals.

Individual CDM projects widely vary across types in terms of expected annual average CERs and electricity output. The smallest project in the CDM pipeline is expected to generate only 400 CERs per

> 10 The CDM pipeline database reports the annual increment or decrement in a variable namely ‘slope.’ The expected CER issuance over the life of a project is approximated with a straight line that goes through the annual average value in the mid-year. For a positive (negative) value of ‘slope,’ expected CERs increases (decreases) each year by the ‘slope’ amount. There is no change in expected CERs over time when ‘slope’ is zero.

11
interventions. If an intervention increases learning and the effects can be sustained in human capital accumulation, we can calculate the lifelong wage gains from that increased learning. We use the returns to cognitive skills among adults to project the effects of improved learning on lifetime earnings.<sup>4</sup> The net present value (NPV) of lifetime earnings is – on the one hand – a reductive way to think about the value of improved learning, but it has the distinct advantage of allowing concrete discussion of the potential returns to education interventions, which may facilitate discussions of education investments relative to other investments with non‐education experts, such as Ministers of Finance.

The second objective is to characterize the size of the impacts of these interventions relative to learning in a business‐as‐usual setting. The motivation for the first objective is that a common critique of the learning interventions characterized above is that the average effect is inadequate to make a substantive difference (Alkire et al. 2018). For example, the median effect of pedagogical interventions from a recent review – including experimental and quasi‐experimental studies – was 0.13 standard deviation in learning (Snilstveit et al. 2015). The median effect across randomized controlled trials in a recent systematic review (McEwan 2015) was 0.07 standard deviation. But how much do students learn in their education systems normally? Are these effects small or big next to a realistic counterfactual of education in low‐ and middle‐ income countries? This paper characterizes the size of a range of learning interventions relative to what students normally learn in school in low‐ and middle‐income countries.

Of course, knowing the returns is only one side of the equation: adding cost data would allow policy makers not only to understand the metric by which the benefits are measured, but also to make fully informed decisions. Unfortunately, cost data are reported far less systematically and less often than impact estimates. However, recent work has provided estimates across a range of studies
.576)|(0.574)|
|Observations|514|514|514|581|581|581|
|Mother controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Child controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|Yes|Yes|Yes|

<mark>Table V presents estimated effects of maternal mental health on child vocabulary at age 5, separated by whether</mark> the HH is in the upper or lower wealth half of the distribution. We estimate the model selected from LASSO procedure. Selected control regressor set includes mother's age, wealth index, living in urban area, child's age, consumption of durable goods index, household size, number of children younger than 5 years in the household, and Height for age Z-score. Sample is restricted to children with available information on maternal mental health in 2002 and PPVT scores in 2006 and 2009. Robust standard errors in parentheses.

30