rafmacalaba/data-use-mentions-extended huggingface dataset:
||||
|**Duration of visa**||||||
|**for crew (no. of**<br>**months allowed)**|0.2084|-0.013|1|||
|**No. of documents**<br>**needed to obtain**||||||
|**a business visa**|-0.2923|-0.1922|-0.1751|1||
|**Range of visa**||||||
|**processing time**<br>**(days)**|0.0512|0.0356|0.0221|-0.0323|1|

Source: OECD STRI; own calculations

# **5. Conclusion**

The growing importance of services trade for countries across the world is well-documented in the services and trade literatures. At the same time, regulatory and administrative barriers on the movement of service suppliers to deliver services internationally have resulted in very low shares of Mode 4 trade in total services trade. Against this background, this paper attempts to conceptualize an agenda for TFiS as it would apply to MNP, providing descriptive statistical evidence from the OECD’s STRI database.

In doing so, it takes the prevailing domestic regulation on MNP across sectors as given and suggests, as components of a Mode 4 TFiS agenda, that (a) administrative and financial costs of complying with necessary policies be brought down and aligned with prevailing regulation in other countries, also to reduce the costs imposed by regulatory heterogeneity; and (b) predictability, certainty and meaningful transparency be brought to the whole process governing the design and implementation of rules and procedures regulating the temporary movement of service suppliers to deliver services across borders. The paper also suggests that some excessively restrictive and burdensome policies be sufficiently toned down if not eliminated altogether to liberalize Mode 4 trade.

More
self-employed, farm laborers, and domestic workers, which is similar to the sample restriction applied by Gerard et al. (2018) in a similar validation exercise. The analysis classifies workers as informal if they report that they have worked in the previous week, but do not have formal labor contracts.

The leave-out gender wage ratio is constructed using the PNAD data, but with a few differences. First, PNAD data are not as granular as the RAIS data. Measures are therefore calculated at the state level instead of the municipal level. Second, the industry variable reflects the broad industry definitions of PNAD, which consist of 12 categories, such as agriculture and construction. The analysis continues to use the 2011 baseline shares in each industry as the fixed proportions.

The distributions of the gender wage ratio in the formal and informal sectors overlap pretty closely if one considers the raw wage ratios (appendix A, figure A1). However, the leave-out wage ratio is higher in the informal sector than in the formal sector, suggesting that there is less gender inequality in the informal sector if one is considering a measure that reflects wage changes in other states. Using the raw measure, there are more positive changes between 2011 and 2015 in the informal sector than in the formal sector. However, the same conclusion does not hold if one looks at the leave-out wage ratio. A possible explanation is that most of the improvement in the gender wage ratio was driven by the sorting of women into industries, which is the component that is kept fixed in computing the leave-out measure.

The analysis also considered whether men or women are more well represented in the informal sector. It accomplished this by regressing on sex an indicator of activity in a formal sector job. The analysis controlled for tenure, age, age squared, race, and educational attainment. It also included year and state fixed effects. It found that women are 1 percent more likely to hold a formal sector job (appendix A, table A1). Even though the null
# **1. Introduction**

Trade credit is an important source of external financing for firms of all sizes (Demirguc-Kunt and Maksimovic, 2005). For instance, suppliers often offer working capital financing to their buyers, reported as accounts receivables, some of whom may be small or credit constrained (McMillan and Woodruff, 1999; Marotta, 2005; and Van Horen 2005). Trade credit has also been shown to act as a substitute for bank credit during periods of monetary tightening or financial crisis (see, for example, Himmelberg et al., 1995; Choi and Kim, 2005; and Love et al., 2007).

Trade credit, however, is not used for financing purposes alone. Trade credit, it has been argued, is a way for a supplier to engage in price discrimination, giving favored or more powerful clients longer terms (see, for example, Wilner, 2000; Fisman and Raturi, 2004; Van Horen, 2005; and Giannetti, Burkart, and Ellingsen, 2011). Furthermore, trade credit may simply be customary in an industry, with customs driven by economic rationales such as allowing buyers time to assess the quality of the supplied goods (Lee and Stowe, 1993).

Studies have explored the supply and demand of trade credit around the world (for instance, Petersen and Rajan, 1997; Johnson, McMillan and Woodruff, 2002; Boissay and Gropp, 2007; and Fabbri and Klapper, 2009). Yet, in part due to the lack of detailed contract-level data on trade credit terms, little is known about how the contract terms of trade credit vary across buyer and supplier characteristics. For example, what is the typical contract period of trade credit? Which buyers receive longer net days (days before payment is due)? Which firms are offered early payment discounts?

This paper addresses these questions using a unique database that includes contract information for about 30,000 supplier transactions for 56 large buyers in the United States and Europe. The data
Z-score 2018 (0,1)|0.017|0.081|-0.042|-0.019|0.017|
||(0.038)|(0.060)|(0.040)|(0.032)|(0.032)|
|NDVI Z-score 2018 (1,2)|0.091|0.008|0.187<sup>_∗∗_</sup>|-0.097|-0.127<sup>_∗∗_</sup>|
||(0.074)|(0.107)|(0.073)|(0.063)|(0.063)|
|NDVI Z-score 2018 (2,.)|-0.065|-0.058|-0.074|0.053|0.162<sup>_∗_</sup>|
||(0.106)|(0.149)|(0.080)|(0.065)|(0.085)|
|Observations|14183|14183|14183|14183|14183|
|R-squared|0.340|0.463|0.240|0.146|0.149|
|Mean Dep var|7.751|6.648|7.229|0.414|0.455|
|Household Controls|✓|✓|✓|✓|✓>|
|Province FE|✓|✓|✓|✓|✓>|

42
because it would help such a country exit this trap (Castelló-Climent and Mukhopadhyay, 2013). Replicating our study using data for other countries, including those with high levels of poverty, denotes an interesting venue for future empirical research.

Future research will hopefully also shed some light on the channels via which IO affects future income growth. It is conceivable that IO alludes to unequal access to good schooling or discrimination in the labor market, to name two candidate channels, either of which signifying inefficiencies that will disproportionally concern the growth prospects of the disadvantaged. A potential positive relationship between IO and income growth for the rich may be harder to explain. One possibility is that the greater the share of the population who are disadvantaged by IO, the greater presumably the supply of labor at lower levels of skills, and the greater, even in a fully competitive market, the skill premium received by those who benefit from better opportunities. We should stress however that the positive effect of IO on growth for high incomes ceases to be significant when using weak instrument-robust inference.

Another empirical question worth exploring is how economic growth or development more broadly impacts on inequality of opportunity. In a preliminary investigation, Marrero and Rodríguez (2016b) find that raising the level of real GDP is associated with lowering levels of inequality of opportunity. If this is confirmed, then this may elude to the possibility of a positive cycle where economic growth lowers IO, and the reductions in IO further stimulate economic growth. However, more evidence on this will need to be obtained, including a precise analysis of the impact of pro-poor (and prorich) growth on inequality of opportunity. Finally, we can think of at least one extension of our analysis, namely one that tracks income growth by individual characteristics such as race (and other factors that are beyond the individual’s control) as an alternative to tracking growth by income percentile. See for example the recent study by Peragine et al. (2014) who develop the notion of the Opportunity Growth Incidence Curve
In addition, improving the availability of electricity could reduce the crowding-out effect of resource dependence for medium-sized manufacturing companies. Access to electricity will thus help manufacturing firms in natural resource-intensive countries to be less affected by the crowding-out effect of Dutch disease.

# **References**

Alam Muneeza Coping with blackouts: Power outages and firm choices . Department of Economics, Yale University, 2013.

- Allcott H., Collard-Wexler, A. & O’Connell, S. D How do electricity shortages affect industry? evidence from india. American Economic Review, 2016. - Vol. 106.

- <mark>Asiedu, E. (2006). Foreign direct investment in Africa: The role of natural resources, market size, government policy, institutions, and political instability. World economy, 29(1), 63-77.</mark>

- Asiedu E., Kalonda-Kanyama, I., Ndikumana, L., & Nti-Addae, A. Access to credit by firms in Sub-Saharan Africa: How relevant is gender?  American Economic Review, 2013. - Vol. 103.

- Balima Weneyam Hippolyte Do domestic bond markets participation help reduce financial dollarization in developing countries? [Journal]. Economic Modelling,, 2017. - Vol. 66.

- Bambe Bao-We-Wal and Combes, Jean-Louis and Kaba, Kabinet and Minea, Alexandru Inflation Targeting and Developing countries’ Performance: Evidence from Firm-Level Data [Journal]. - 2022.

- Bellak Christian How domestic and foreign firms differ and why does it matter? [Journal]. Journal of economic surveys, 2004. - Vol. 18.

- Bernard Andrew B and Jensen, J Bradford Entry, expansion, and intensity in the US export boom, 1987--1992 [Journal]. Review of International Economics, 2004. - Vol. 12.

- Bernard Andrew B and Jensen, J Bradford Exceptional exporter performance: cause, effect, or both? Journal of international economics, 1999. - Vol. 47.

- Biggs Tyler and Srivastava, Pradeep Structural
using Standard Occupational Classifications (SOCs). For the creation of GGS-OCC estimates, the same sampling frame was used, and OES staffing patterns were matched to the firm’s green percentage calculated with the GGS. The results of the GGS-OCC survey showed that 30.6 percent of 2011 employment was in establishments producing a green output and that occupations whose duties are expected to be directly linked to green activities are also employed in non-green establishments (e.g., environmental engineers, environmental scientists and specialists).

# The process approach

The **process approach** estimates the number of workers involved in technologies used to reduce the environmental footprint of the firms in which they work, regardless of the outputs the firm produces. This approach is useful when analysts’ main objective is to understand firms’ labor needs to implement new technologies, but without a tight link to occupational and skills data this approach falls short of generating enough information to adequately design skills policy solutions.

The Bureau of Labor Statistics (BLS) of the U.S. developed this approach and designed a survey to apply it. The Green Technologies and Practices (GTP) survey is a firm-level survey that captures whether firms use certain environmentally friendly technologies and practices (or GTPs) that can reduce environmental impact.<sup>24</sup> The survey also collects information on whether workers are involved in these GTPs by researching, developing, maintaining, using, or installing GTPs, or training other employees on them. In cases where workers spend more than half of their time involved in the GTPs, the survey also collects the occupational job description, which is then used to code jobs into the 4-digit Standard Occupational Classification (SOC), along with wages for such jobs. The following are the GTPs surveyed:

1. Generate electricity, heat, or fuel from renewable sources primarily for use within the establishment

2. Improve energy efficiency within the establishment 3. Reduce greenhouse gas emissions (other than by 1 and 2) 4. Either reduce
# **References**

- Abadie, A., and Cattaneo, M. D. (2018), “Econometric Methods for Program Evaluation,” Annual Review of Economics, 10, 465–503

- <mark>Aga</mark> _<mark>,</mark>_ <mark>Gemechu; Maemir, Hibret. 2021. COVID-19 and Africa firms: Impact and Coping Strategies. Policy Research Working Paper;No. 9642. World Bank, Washington, DC. © World Bank.</mark>

- <mark>Amin, Mohammad; Viganola,Domenico.2021. “Does Better Access to Finance Help Firms Deal with the COVID-19 Pandemic? Evidence from Firm-Level Survey Data . Policy Research working paper;no. WPS 9697;COVID-19 (Coronavirus) Washington, D.C. : World Bank Group.</mark>

- Bennedsen, M, B Larsen, I Schmutte and D Scur (2020), "Preserving job matches during the COVID-19 pandemic: Firm-level evidence on the role of government aid", Covid Economics: Vetted and Real-Time Papers 27, 9 May.

- <mark>Bruhn, Miriam (2020) Can Wage Subsidies Boost Employment in the Wake of an Economic Crisis? Evidence from Mexico, The Journal of Development Studies, 56:8, 1558-1577, DOI:</mark>

- Chetty R., J. N. Friedman, N. Hendren, M. Stepner, and T. O. I. Team. How did covid-19 and stabilization policies affect spending and employment? a new real-time economic tracker based on private sector data. Working Paper 27431, National Bureau of Economic Research, June 2020.

- <mark>Cirera Xavier, Marcio Cruz, Elwyn Davies, Arti Grover, Leonardo Iacovone, Jose Ernesto Lopez Cordova, Denis Medvedev, Franklin Okechukwu Maduko, Gaurav Nayyar, Santiago Reyes Ortega, Jesica Torres, Policies to Support Businesses through the COVID-19 Shock: A Firm Level Perspective, The World Bank Research Observer
gains associated to innovation. The objective of this paper is to shed some light on these questions using a large national employer-employee census (RAIS) in Brazil, and employing a novel measure of innovation based on the share of technical and scientific occupations of the firm (PoTec).

The paper contributes to the literature in three dimensions. First, it contributes to narrow the knowledge gap in relation to estimating the innovation wage premium for a middle-income country like Brazil. Second, it empirically tests the hypothesis that firms pay higher wages before becoming innovative. Third, it measures the extent to which workers appropriate rents created by innovative activities. In the first part of the paper, we estimate the innovation wage premium in Brazil by sector, type of firm and type of skill during the period 2014-2017. To advance some of the main findings, we find a positive innovation wage premium that is robust across different definitions of skills and also across different firm sizes. The premium is higher for manufacturing compared to agriculture and services, and although larger in large firms, it is also positive in all firm size types. In the second part of the paper, using the empirical tools of the “self-selection” and “learning-by-exporting” literature (Bernard 3
Askew, I., & Bellows, B. (2014). Assessing the populationlevel impact of vouchers on access to health facility delivery for women in Kenya. Social Science & Medicine, 102, 183–189. Retrieved from

- http://www.sciencedirect.com/science/article/pii/S0277953613006825

- PS Consulting. 2009. _Introducing Voucher Systems for Healthcare Provision: Health Provider Baseline Survey._ <u>Kampala.</u>

- Ross, J., D. W. Han, S. Keeny, G. P. Cernada, T. C. Hsu, and T. H. Sun. 1970. “Korea/Taiwan 1969: Report on National Family Planning Programs.” _Studies in Family Planning_ 1 (54): 1– 16.

- Tura, G., M. Fantahun, and A. Worku. 2013. “The Effect of Health Facility Delivery on Neonatal Mortality: Systematic Review and Meta-Analysis. _BMC Pregnancy and Childbirth_ 13 (1): 18, doi:10.1186/1471-2393-13-18.

- Uganda DHS. 2011. _Uganda Demographic and Health Survey: Preliminary Report_ .

37
more accurate predictions in sample than out of sample. EBP shows the largest drop in performance out of sample, as shown in subsection 3.1. The more flexible nature of the machine learning models appears to be better suited for predicting into out-of-sample areas than EBP. In addition, out-of-sample EBP predictions suffer from the unavailability of sample data to condition on when estimating the random area effect.

There are also noticeable differences in precision across in-sample and out-of-sample areas. Since the machine learning models do not include a random effect conditioned on the sample, the bootstrap procedure estimates confidence intervals that are roughly the same size for in-sample and out-of-sample areas. Out-of-sample accuracy is lower than in-sample accuracy, meaning that out-of-sample coverage rates tend to be lower as well; for example, average coverage rates for wealth estimates using XGBoost fall from 91.8 percent to 83.3 percent. This does not result from overfitting the model to the sample, given that LASSO is used for model selection in the EBP model and that regularization methods are built into the machine learning algorithms. Instead, because villages are sampled proportional to their population size, out-of-sample areas tend to be less populated and may therefore systematically different from in-sample areas, even after taking into account sampling probabilities. Although sample weights are included, out-of-sample predictions suffer from the relative paucity of training data from rural, less populated areas.

To better understand which geospatial predictors are important in the models, we examine the prevalence of different types of variables selected by LASSO for the EBP models. In addition, we calculate Shapely decompositions of the impact of predictors on the predictors generated by extreme gradient boosting models (Lundberg et al., 2019). Overall, land cover classification variables, including vegetation indices, consistently emerge as important predictors. Population estimates, pollution variables, and night time lights also make important contributions in different contexts. This is consistent with geospatial predictors acting as proxies for population density and urbanity, which are in turn systematically correlated with wealth and poverty. (Newhouse, 2024)
service combined have 20 times more branches in rural areas than banks do. (CGAP and World Bank Financial Access 2009; M Khaled, pers. comm.)

**Post office branch networks are in many cases among the most extensive delivery channels**

**for financial services in MENA, as noted earlier.** The Moroccan post office, for example, provided basic financial services (savings passbooks and checking accounts) to a large customer base (more than 2.5 million people). In recent years, it has successfully leveraged on the large postal network (1,762 branches in 2008) to attract a significant number of unbanked and rural customers (14 percent of the Moroccan population have postal accounts in 2008, up from 10 percent in 2005).

# _Financial Inclusion and the Global Financial Crisis_

**Loans to GDP declined in MENA** in 2009 as a result of the crisis (with Tunisia a notable exception) for both loans and deposits, due to falls in commercial bank lending. Deposit volumes also fell, with some exceptions, while the number of deposit accounts continued to rise. This

> 8 Microbanking Bulletin data. It should be noted that MFIs reporting to the Microbanking Bulletin, as to the Microfinance Information Exchange, do so on a voluntary basis. Stronger and larger MFIs are therefore more likely to provide data than small and poor performing MFIs.

11
relative size of its import market and its geographic proximity). The results are provided for two years, 1990 and 1998, to illustrate whether the South Asian countries demonstrate an increasing or decreasing tendency to trade with one another.

**Table 3: Trade Intensity Ratios of South Asia**

|**PARTNER**||||**EXP**<br>|**ORTING**<br>|**COUNT**<br>|**RY**<br>||||
|---|---|---|---|---|---|---|---|---|---|---|
|<br>**COUNTRY**|**Ind**<br>|**ia**<br>|**Pakis**<br>|**tan**<br>|**Sri La**<br>|**nka**<br>|**Bangla**<br>|**desh**<br>|**Nep**<br>|**al**<br>|
||**1990**|**1998**|**1990**|**1998**|**1990**|**1998**|**1990**|**1998**|**1990**|**1998**|
|India|-|-|1.28|3.14|1.55|1.29|1.89|1.88|10.19|42.81|
|Pakistan|1.16|2.28|-|-|8.11|4.09|6.57|4.58|1
The empirical work of Mitra, Thomakos and Ulubasoglu, and McCalman are notable for taking the theory to data outside the U.S.<sup>19</sup> While they affirm the model’s prediction, they too find an implausibly high implied value of the government preference parameter _a_ . In both Turkey (Mitra et al.) and Australia (McCalman), governments attach more than 50 times as much weight on aggregate welfare as on contributions. This is grossly inconsistent with large deadweight losses that protection imposes on consumers as a number of U.S. case studies of protection demonstrate.<sup>20</sup> Do agricultural protection data yield similar results?

Agricultural data allow us to add new features to the empirical testing of the Grossman-Helpman model, and thus significantly differentiate this study from the studies cited above and other forthcoming works. We are able to incorporate the use of subsidies into the model because they are pervasive in agriculture but rare in U.S. manufacturing. The pooling of both equations in (2) into one model is a unique feature of the paper. We also consider the two equations separately. We estimate three cross-sectional econometric models based on stochastic versions of equation (2). The first model is given by the two-equation set: In (3) ε _i_ is an identically independently distributed normal homoskedastic error term, which specifies the data generating process for the dependent variables. This model is estimated for the full sample of imported and exported goods. The model may be written with both equations stacked into a single equation with the terms redefined as:

> 19 Mitra, Thomakos and Ulubasoglu (2002) and McCalman (2002).

> 20 See e.g. Hufbauer, et al. (1986) and de Melo and Tarr (1990). Gawande and Krishna’s (2004) survey draws attention to this puzzle.

10
sudan. _World Bank Policy Research Working Paper_ , (6321).

- Dillon, B. (2012). Using mobile phones to collect panel data in developing countries. _Journal of international development_ , 24(4):518–527.

- Ding, P., Feller, A., and Miratrix, L. (2016). Randomization inference for treatment effect variation. _Journal of the Royal Statistical Society: Series B: Statistical Methodology_ , pages 655–671.

- Etang, A. and Himelein, K. (2020). Monitoring the ebola crisis using mobile phone surveys. In _Data collection in fragile states_ , pages 15–31. Palgrave Macmillan, Cham.

24
br>0.1 0.3 0.7 0.6 0.9 4.5 3.4 4.1 5.1 4.1<br>1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>Year Year<br>Total Men Total Men<br>Women Women<br>20.0 40.0<br>15.0 30.0<br>10.0 20.0<br>5.0 10.0<br>Ninis starting own household (% relevant Ninis population) Ninis starting own household (% relevant Ninis population)<br>0.0 0.0<br>with children & not searching for work with children & not searching for work<br>20.0 50.0<br>40.0<br>15.0<br>30.0<br>10.0<br>20.0<br>not searching for work) 5.0 not searching for work) 10.0<br>Ninis starting own household (% Ninis in relevant population Ninis starting own household (% Ninis in relevant population<br>0.0 0.0<br>with children & not searching for work with children & not searching for work<br><!-- End of picture text --> Source: authors’ own computation with data from CEDLAS and The World Bank (2015)

# **6. Conclusion**

This note has offered a descriptive diagnostic of the nini phenomenon in Latin America and the Caribbean—the patterns at a regional level and within individual countries, as well as the evolution of the phenomenon between 1992 and 2010. The analysis draws on data from 238 household surveys collected together and harmonized in the SEDLAC database supplemented by surveys assembled in Cárdenas, de Hoyos and Székely (2015). While this analysis is just a first cut at the data, it reveals 23