rafmacalaba/data-use-mentions-extended huggingface dataset:
medium- and low-tech manufacturing sectors, the labor market gains through the income channel appear to be stronger (bottom panel). Higher forward GVC participation in mining also shows a strongly positive association with labor incomes (but not employment). Both forward and backward GVC participation show a negative correlation with employment (but not labor incomes) for agriculture/fishing and business services. Increases in agricultural or business services exports in MICs possibly go hand in hand with laborsaving skill-intensive machinery and technology, thus reducing the demand for workers.<sup>16</sup> Additional analysis by region is shown in Appendix Figure 9.4.

# 2.4 How are trade-job linkages characterized in lower-income countries?

In LMICs, manufacturing exports – especially of low-tech manufactured goods – are associated with larger employment increases than exports within agriculture or mining. This section focuses on the sample of 37 LMICs in EAP, SA, LAC, MENA and SA from the ETD database, differentiating between commodity exporters and countries specialized in limited manufacturing (see Appendix 2). For commodity exporters, total goods exports show a positive association with within-sector employment, although it is very small (coefficient of 0.03 percent). The link is insignificant in agriculture which is land-intensive, but positive in both mining and manufacturing, with a slightly larger coefficient for the latter (Figure 6). For countries specialized in limited manufacturing, exports show a negative relationship with employment in agriculture, possibly driven by more intensive use of agricultural machinery in such countries complementing the increase in land use. By contrast, there is a positive and larger export-employment link in low-tech manufacturing.

However, the link between low-tech manufactured exports in lower-income countries and job creation is substantially smaller than for the full sample of HMICs, possibly driven by the stronger role of informality. A 10 percent increase in exports of low-tech manufactured goods in LMICs specialized in limited manufacturing is associated with a 1.3 percent rise in employment – both formal and informal – within sectors (Figure 6). Compared
6 percent, and a decrease in total income and labor income by 3 percent; which in turn increases moderate and extreme poverty by 1.5 percentage points. These socioeconomic indicators are further informed by 3 indexes derived from hurricane damage modeling. Studying both the macro (per capita GDP growth) and the micro (household poverty and income among the variables) levels encompasses a more comprehensive level of analysis of impacts of hurricane windstorms, resulting in a more detailed and nuanced understanding of the negative economic implications of such events.

Finally, the concluding section draws all these angles together to provide a more comprehensive analysis, provides recommendations, and throws up research questions for the future.

# 2. FROM WINDSTORM HAZARD TO DAMAGE

Recent studies have focused on improving the modeling of the natural hazard in order to more explicitly address the impact of adverse natural events on socioeconomic indicators. For example, Hsiang and Jina (2014) and Strobl (2012) evaluate the hurricane windstorm hazard data using global hurricane models to generate gridded data sets with different levels of resolution (Hsiang and Jina at the global level and Strobl for the Caribbean basin). One of the main innovations of this paper is to use a fully probabilistic hurricane windstorm model developed by Pita el al. (2015) which has been validated and calibrated for the Central America region to generate hazard information with the temporal and spatial resolution needed for this study. The windstorm hazard data are used to calculate the damage indexes which are used as input in both the macro and micro models. As a result we substantially improve the understanding of how hurricane windstorm hazards could affect socioeconomic outcomes.

## 2.1. Modeling the Windstorm Hazard

Being complex, the dynamic atmospheric environment of windstorms is often oversimplified. This study uses an accurate and comprehensive model that generates high‐resolution surface‐level sustained wind 3
Ouedraogo, Daniel Camos, and Maria Shkaratan. 2008. ―Underpowered: The State of the Power Sector in Sub-Saharan Africa.‖ AICD Background Paper 6, Africa Region, World Bank, Washington, DC.

- EIA (United States Energy Information Administration). 2009. ―Sudan Country Energy Brief 2009.‖ <u>www.eia.doe.gov/cabs/Sudan/Background.html.</u>

- Foster, Vivien, and Jevgenijs Steinbuks. 2009. ―Paying the Price for Unreliable Power Supplies: In-House Generation of Electricity by Firms in Africa.‖ Policy Research Working Paper 4913, World Bank, Washington, DC.

Platts. 2009. WEPP database Rosnes, Orvika, and Haakon Vennemo. 2009. ―Powering Up: Costing Power Infrastructure Spending Needs in Sub-Saharan Africa.‖ AICD Background Paper 5, Africa Region, World Bank, Washington, DC.

> <sup>2</sup>
that require more skills, or compete without the right skills and techniques and therefore are more exposed to financial losses.<sup>9</sup> The last row of Table 5A confirms that state banks have a much higher average ratio of loan loss provisions to gross loans. The Bankscope database does not provide detailed information on the composition of loan portfolios, but this result probably reflects losses resulting from lending to state enterprises and favored sectors. In this regard, state banks might be fulfilling their development mandates, whereby they are directed to finance projects and sectors that may generate low returns or entail excessive risks, but that are regarded as strategic or capable of generating positive externalities. These losses are probably aggravated by the own internal operational deficiencies of state banks in fulfilling these mandates.

By contrast, private banks pay higher average wages in order to attract higher-skilled workers and develop more sophisticated and profitable business lines while being able to manage the associated risks relatively well. At the same time, they are also able to maintain a lower ratio of employees to assets. Thus taken together, these results show that private banks are ultimately able to generate higher profits compared to state banks despite their smaller average size, by exploring more profitable business lines, containing their operating costs, and managing their risks more effectively.

# _Domestic private versus foreign private banks_

Next we analyze the differences between foreign and domestic private banks. In 2008, there were 90 private banks in non-GCC MENA countries of which 45 were domestic and 45 were foreign. As shown in table 5B, we find that foreign banks are significantly smaller, reflecting at least partly their more recent entry into banking systems in the region (see the previous section).

Foreign banks have been more profitable than private domestic banks, as indicated by higher ROAs and ROEs. Although these differences in profitability are not statistically significant, this still suggests important efficiency differences, given that foreign banks are smaller. Foreign banks also generate higher NIMs but the differences vis-à-vis private domestic banks are not

>
year FE|Yes|Yes|Yes|Yes|Yes|
|log employment|No|Yes|Yes|Yes|Yes|
|industry-specifc trends|No|No|Yes|Yes|Yes|
|frm-specifc trends|No|No|No|Yes|Yes|
|imported inputs|No|No|No|No|Yes|

NOTES: IV-FE regressions of log employment and employment shares on export intensity (exports/sales). The instruments are the weighted average of the real exchange rate of a firm export partners, _z_<sup>0</sup> , and its interaction with initial sales (in 2001). Column (1): firm fixed-effects and year fixed-effects; column (2): adds log total employment (firm size); column (3): adds controls for industry-specific trends (i.e., interactions between year dummies and industry dummies); column (4): adds initial conditions to control for firm-specific trends; column (5): adds the share of imported inputs. Data are from the Encuesta Nacional Industrial Anual (National Annual Industrial Survey), Chile 2001-2005.

19
reduce recall burden, but with limited depth in questionnaire content. Taken together, these findings point to promising potential for the combination of phone surveys and in-person surveys for agricultural data collection going forward.

Phone surveys are cheaper on a per-interview and per-question basis than in-person surveys, but what are the cost implications of combining the two in a mixed-mode survey? The baseline scenario is complementing in-person surveys with phone interviews to improve data quality, while leaving the inperson survey component unchanged. In this case, the phone survey rounds add costs to the overall survey operation. Based on data from their survey experiment in Tanzania, Arthi et al. (2018) estimate that adding one phone interview round to a two-visit in-person survey increases the total cost by 6 percent, adding ten phone interview rounds increases total costs by 54 percent, while adding 20 phone interview rounds more than doubles the total cost (+108 percent). Similarly, Dillon (2012) estimates the added costs of one phone survey round to be 7 percent. For Malawi, Kilic et al. (2021) estimate that an inperson survey with subsequent semi-weekly phone calls over the course of 12 months was 77 percent more costly than the baseline of two in-person visits over the same 12-month period. The exact cost is likely to differ significantly depending on the implementation details of the phone surveys. For instance, the cost calculations in Arthi et al. (2018) and Kilic et al. (2021) include mobile phones distributed to respondents (as well as harvest diaries in the case of Kilic et al., 2021). Overall, the additional cost of phone surveys ranges from marginal for a small number of rounds of interviews to substantial if many phone survey rounds are added to an existing in-person component that is left unchanged.

However, phone surveys can also substitute parts of in-person surveys. For instance, if agricultural labor data are collected via phone, these data may no longer need to be collected in-person, reducing the time and burden of in-person interviews. Especially if
_Ingo Borchert and Mattia Di Ubaldo_ World Input-Output Database,<sup>6</sup> and find that deeper agreement result in both more goods and services trade, with the latter generally being more affected by deep PTAs. A crucial difference among these studies, however, is that Mulabdic et al. (2017) and Dhingra et al. (2021) measure depth with a count of legally enforceable provisions in each PTA, whereas Dhingra et al. (2018) go beyond aggregate effects and identify services, investment and competition provisions to be those most effective at lowering trade barriers<sup>7</sup> , especially for sectors such as transport and storage, that facilitate supply chain activity.

This paper’s contribution is to identify with a great deal more precision which aspect of the structure and content of services PTAs affects cross-border services trade. Compared to Dhingra et al. (2018), who assess the effect of the presence of a services chapter, in this paper we examine services PTAs only and distinguish between particular configurations of services provisions. This analysis is made possible by the availability of detailed data on the design and content of services PTA from the World Bank Deep Trade Agreement Database 2.0 ( _vertical depth_ ). Another important contribution of this paper is to exploit the most comprehensive data to date on services trade, the ITPD-E data described below, covering a great deal more countries and sectors than the WIOD data.

We also contribute to the literature linking the presence of trade agreements to valuechain activity. Production along global value-chains (GVC) also stands to benefit from services liberalization, as services have various roles in GVCs. Transport, communication and logistics link manufacturing activities across countries, while R&D, design, and

> 6Despite the similarity of approach, these three papers differ in their focus, with Mulabdic et al. (2017) analysing the differential impact of PTA depth for UK trade, Dhingra et al. (2018) narrowing the focus on the effect of provisions related
schooling (51 percent had completed high school) and prior labor experience (60 percent). The p-values for the coefficients of OLS models that regressed treatment status on baseline covariates (left panel) are above 0.10 in all cases, indicating that individuals in the treatment and control groups came from the same population. These baseline characteristics are broadly similar to those of poor individuals aged 15-30 in Ulaanbaatar as reported in the 2014 Mongolian socioeconomic survey, which shows similar share of idle youth, age mean composition and average years schooling. The MVTP sample, however, shows a higher proportion of women (65 vs. 55 percent), a higher share of individuals living in gers (82 vs. 56 percent), smaller household size (4 vs. 6), and higher marriage rates (45 percent vs. 35 percent). Put differently, these differences suggest that the MVTP served, on average, a poorer set of Mongolian youth.

For the newsletter-treatment group, on the other hand, the right panel in Table 2 shows that the p-values for most variables do not reject the equality of means between experimentally determined treatment groups. Equality is rejected regarding a few variables, with the exception of those related to chosen vocations. This is expected because the random allocation of MVTP participants to the newsletter-treatment group occurred at the course rather than at the individual level.

Compared to other vocational-training programs for youth in developing countries (see Table 1), administrative data reveal that lack of full compliance with the treatment is relatively high for the MVTP. Out of the 766 applicants randomly assigned to receive vocational-skills training, 439 (57 percent) did show up for training, while other studies have reported take-up rates from 77 to 99 percent. Appendix Table S1.4 gives detailed information on enrollment numbers.

Self-reported survey information indicated that, among those who did not take-up the treatment, 35 percent cited family and personal commitments (e.g., household chores or 16
There are limitations on the viability of including housing costs in the budget standard. Not only do housing costs vary greatly by location and between household types, even those at the same or a similar standard of living, they also constitute a relatively large share of a household’s total budget. It would be conceptually flawed to “standardize” this cost. Unlike housing costs, given the relatively low pricevariance and high supply elasticity of non-housing costs, it is possible to derive a workable non-housing standard within a particular geographical region. This is used to calculate housing affordability using the RIM approach (Stone, Burke, and Ralston 2011).

Budget standards are not widely implemented in the developing country context for several reasons. First, creating a budget standard is a complex, time-consuming, and specialized task that can take several years to implement, depending on how many household types are being considered, and at how many different points in the income distribution. Second, households of different compositions and incomes differ enormously in terms of their levels and patterns of spending, the degree to which they can engage in cost substitution, and how they allocate their time (Saunders and Bedford 2017; Heylen 2018).

One way to get around this challenge is to arrive at a ‘non-housing expense threshold’ from household income or expenditure survey (HIES) data that are more easily available. For example, Kutty (2005) operationalized a residual income standard for the United States with the non-housing standard set at two-thirds of the federal poverty threshold and applied it to compute what she calls “housing-induced poverty.” Another challenge in the application of the RIM in many developing countries is the difficulty in obtaining accurate income data. A way around this is to explore the substitution of income data for expenditure data, in particular for the lowest income quintiles who presumably have low or no savings accumulation. An adaptation of the RIM methodology is used by micro finance institutions (MFIs) and affordable housing finance companies (AHFCs) in India as they work with the
evidence on bribery and bank debt. _World Development_ , _68_ , 308–322.

https://doi.org/10.1016/j.worlddev.2014.12.009 Islam, A., Palacios Lopez, A., & Amin, M. (2019). Decomposing the Labour Productivity Gap between Migrant-Owned and Native-Owned Firms in SubSaharan Africa. _Journal of Development Studies_ , _55_ (9), 2065–2082. https://doi.org/10.1080/00220388.2018.1520215 King, R. G., & Levine, R. (1993). Finance, entrepreneurship, and growth Theory and evidence. _Journal of Monetary Economics_ , _32_ , 513–542.

Klapper, L., Laeven, L., & Rajan, R. (2006). Entry regulation as a barrier to entrepreneurship. _Journal of Financial Economics_ , _82_ (3), 591–629. https://doi.org/10.1016/j.jfineco.2005.09.006

- Kuntchev, V., & Ramalho, R. (2013). What have we learned from the enterprise surveys regarding access to credit by SMEs?, (May), 1–35. Retrieved from

https://openknowledge.worldbank.com/handle/10986/16885 La Porta, R., Lopez-De-Silanes, F., Shleifer, A., & Vishny, R. W. (1997). Legal determinants of external finance. _Journal of Finance_ , _52_ (3), 1131–1150. https://doi.org/10.1111/j.1540-6261.1997.tb02727.x Lui, F. T. (1985). An Equilibrium Queuing Model of Bribery. _Journal of Political Economy_ , _93_ (4), 760–781.

Mauro, P. (1995). Corruption and Growth. _The Quarterly Journal of Economics_ , _110_ (3), 681–712.

Mckenzie,
and academics in the relationship between concentration in the banking sector and financial stability, because market concentration may have played a role in the run-up to the crisis, and, more importantly, has tended to increase ever since, raising stability concerns (Committee on the Global Financial System, 2018).

Our empirical analysis rests on a data set comprising available cross-country, annual observations for 68 countries over the period 1997-2015. Financial stability is proxied by the probability of entering both a systemic and a non-systemic banking crisis. Data are drawn from an updated version of the Reinhart and Rogoff (2009) dataset, which gives 42 crisis episodes during the sample period. Crisis data are given by a binary dummy that equals 1 if country _i_ at time _t_ has entered a crisis, and zero otherwise. Concentration is measured by the share of banking assets held by the three largest banks. However, we also study the impact of alternative concentration measures, such as the share of banking system assets concentrated in the five largest banks. We proxy for the mediators through which concentration affects financial stability by using bank return on assets (ROA) for _profitability_ ; real lending rate for the _cost of credit_ ; bank foreign assets plus bank foreign liabilities to banking system assets for _diversification_ ; and the (log) number of banks for the _ease of monitoring._ Following Beck et al. (2006), we estimate a binomial logit model, and control for the following variables: real GDP growth, inflation, nominal exchange rate depreciation, current account balance, real domestic credit growth, and (log) real GDP per capita. In order to disentangle the role of each mediator through which concentration is expected to impact financial stability, we estimate a model where proxies for the mediators are allowed to interact with the level of concentration. This allows us to estimate the marginal effects of the mediators conditional upon selected percentiles of the concentration distribution, and calculate their net effect on crisis probability.

6
<br><!-- End of picture text -->

# F. Non-resident holdings of

# local-currency debt

<!-- Start of picture text --> ARA metric Percent of total debt<br>3.5 Median Mean 30 Interquartile range Mean Median<br>3.0<br>25<br>2.5<br>20<br>2.0<br>15<br>1.5<br>10<br>1.0<br>0.5 5<br>0.0 0<br>2007 2019 2007Q4 2018Q2<br><!-- End of picture text --> Source: Ha, Kose and Ohnsorge (2019); International Monetary Fund; World Bank.

A. Unweighted average of total external debt-to-GDP ratios for 31 EMDE commodity exporters and 30 EMDE commodity importers.

B. Based on data for 61 EMDEs.

- C. Unweighted average of current account balance-to-GDP ratios for 88 EMDE commodity exporters and 55 EMDE commodity importers.

- D. Based on data for 143 EMDEs.

E. Based on data for 48 EMDEs. Dark blue bars show minimum and maximum values. Assessing Reserve Adequacy (ARA) metric is based on IMF (2011) which determines the appropriate reserve cover on a risk-weighted basis covering short-term debt, medium and long-term debt, and equity liabilities. Broad model and export earnings. Risk weights are based on observed outflows during periods of exchange rate pressure. Values above 1 suggest that countries are fully able to meet balance of payments needs using reserves.

F. Based on data for 23 EMDEs.
Figure 7: Digital services trade, geographic distance, and data models (2015) <!-- Start of picture text --> Distance and data model Distance and data model Distance and data model<br>US exports of digital services EU exports of digital services China's exports of digital services<br>32 32 32<br>16 16 16<br>CHE<br>SGP<br>NOR<br>8 CAN 8 ISLMAR 8<br>MEX<br>4 4 4<br>ISR ZAFMYS<br>HKG<br>2 BRA SAUTWNPHLTHAAUS 2 KORCRIPERCHL 2 VNM<br>IND<br>NZL ARG<br>1 1 COLJPN 1<br>KHM<br>.5 TUR .5 .5<br>KAZIDNRUS TUN<br>.25 .25 .25 BRN<br>.125 .125 .125<br>.06 .06 .06<br>1500 4500 7500 10500 13500 1650 1500 4500 7500 10500 13500 16500 1500 4500 7500 10500 13500 16500<br>Distance in km Distance in km Distance in km<br>Open FTA Conditional FTA Limited FTA<br>Digital services exports/Partner's GDP (% log) Digital services exports/Partner's GDP (% log) Digital services exports/Partner's GDP (% log)<br><!-- End of picture text --> Source: Authors’ calculations using TiVA trade data, World Bank WDI and ITPD-E. Countries are categorized on the basis of the data model regarding the crossborder data transfers.

19
|4.80|5.68|
|Chose sector as it had low capital requirements|0.17|0.40|0.32*|n.a.|
|Chose sector for profit potential|0.18|0.11|0.18**|n.a.|
|Willingness to Take Risks|5.64|4.28|4.40|6.08|
|Save regularly|0.71|0.62|0.73**|0.67|
|Household Asset index|0.29|‐0.40|0.14***|n.a.|
|Household has a Cellphone|0.94|0.88|0.91|0.22|
|**Sample Size**<br>Notes:<br>Means shown unless indicated otherwise. Trimme<br>*, **, and *** indicate high profit women statistica<br>at the 10%, 5% and 1% levels respectively.<br>a. Figures for Sri Lanka are reported as of March 20<br>an approximate exchange rate of 100 Sri Lankan ru<br>n.a. indicates not available in Sri Lankan data.|**290**<br>d subsampl<br>lly differen<br>05 Sri Lank<br>pees to 1 c|**296**<br>e used.<br>t from the low p<br>an baseline, con<br>edi.|**179**<br>rofit women<br>verted at|**190**|