rafmacalaba/data-use-mentions-extended huggingface dataset:
, and forecasting. By leveraging predictive algorithms, AI systems are now performing complex data operations to spurn new datasets from sparse data points (Nguyen, et al., 2023; Savage, 2023; Lu, et al., 2021; Gavin & Mandal, 2002; Aldoseri, et al., 2023). While AI applications for complex analysis are not new, the embedding of modern analytical models and real-time coupling with multi-channel data or information pipelines has unlocked advanced capabilities of AI algorithms. One of such revolutionary capability is the monthly imputation of price data, which was developed for 25 countries (Andrée, 2021a; Andrée & Pape, 2023) and is now deployed by the World Bank to cover over 2,100 markets across 36 countries and monitor food prices, energy prices, and unofficial parallel market exchange rates.<sup>1</sup> The RealTime-Prices (RTP) dataset is created through a resource-intensive Markov Chain Monte Carlo framework involving hundreds of thousands of models to address missing data points in underlying surveys gathered from country systems, the World Food Programme and the Food and Agriculture Organization. To ensure the rapid processing required for maintaining up-to-date outputs as new data arrives, the algorithm employs a swift tree-based approach known as Cubist. This method integrates local models that capture relationships within specific regions of the feature space, similar to the concept of local receptive fields in convolutional neural networks (CNNs).

> 1 Andrée, B. P. J. (2021). Monthly food price estimates by product and market (Version 2024-01-25). WLD_2021_RTFP_v02_M. Washington, DC: World Bank Microdata Library. <u>https://microdata.worldbank.org/index.php/catalog/4483</u> Andrée, B. P. J. (2024). Monthly energy price estimates by product and market (Version 2024-01-25). WLD_2021_RTFP_v02_M. Washington, DC: World Bank Microdata Library. <u>https://microdata.worldbank.org/index.php/
the raising of outside sources of capital is a good alternative, given the nature of the business and the capital requirements. It will normally have decided to pursue either a debt or equity issue, but will rely on the investment bank to help in its decision of the security characteristics, the process, and the timing.

The lead investment bank charges a fee as a percentage of the issue size, which comprises structuring, placement, and underwriting. The range of fees is wide, and depends on a host of firm and issue characteristics. The level of complication of the deal is the primary driver of the fee; for debt, a plain vanilla bond is the cheapest, while structured deals invoke the highest costs. The credit quality and market reputation of the firm also plays a significant role in fee determination, as the more unknown and risky firm’s securities will be more difficult to place, and more risky to underwrite. In the case of fixed income, typically the longer maturity bonds command higher fees as well, again due to the extra risk of the security. Finally, market conditions can supercede all normal pricing methodologies. In a highly competitive environment, some investment banks will charge fees below costs, in order to gain reputation and elevate their status on league tables.

> 1 Data on the costs of international equity issuance is pending and will be added soon.

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with a lower environmental footprint. Alternatively, firms may become environmentally aware and therefore engage in green investments or green management practices.

The Enterprise Surveys allows for the possibility to test some of these channels. The main channels are largely infrastructure service disruptions such as water and power outages. There is some evidence of effects through labor productivity (sales per worker) and also limited access to finance – negative precipitation shocks decrease the likelihood than firms use banks to financing working capital and have access to overdraft facilities. A new channel uncovered in this study is governance. The intensity of negative precipitation shocks increases exposure to corruption. While there is no effect uncovered regarding weather shocks and investment in machinery and equipment, this could be due to the countervailing effects of limited access to finance and the need to replace damaged capital. However, extreme dry days do increase the probability of investing in land and buildings. Firms that are innovative in terms of process, have website ownership, and use technology licensed from foreign firms experience more muted effects of extreme dry days on firm performance. There is also some evidence that digital technologies and innovation can buffer against climate shocks (Zhao and Parhizgari, 2024; Liu et al., 2023). Finally, for a cross-section of 2019 surveys largely conducted in the Middle East and North Africa and Europe and Central Asia, a green module included in the survey instrument captures green investments and green management practices. This study leverages this new data for a subset of firms where it is available but finds no correlation between precipitation shocks and green management practices or green investments. One explanation could be that firms might adopt such practices only after repeated dry spells over time. However, these survey questions are limited. Adoption of green management practices or investments are captured through a binary variable – where a value of 1 means adoption and 0 implies no adoption - that pertains only to three years prior to the survey. Hence a firm that adopted any green management practices or made green investments 4 years prior may be coded as
# **References:**

Baltagi, B. H. (1985), "Pooling Cross Sections with Unequal Time Series Lengths" _Economics Letters,_ 18 p. 133-136.

Baltagi, B.H. (1996) _Econometric Analysis of Panel Data,_ John Wiley and Sons (New York).

Baltagi, B. H. and B. Raj (1992), "A Survey of Recent Theoretical Developments in the Econometrics of Panel Data," in Bjom, Erik,(1981) Estimating Economic Relation from Incomplete Cross Sectional/Time Series Data, _Journal of Econometrics_ 16:221-236.

Bjom, E. and E.S. Jansen (1983) "Individual Effects in a System of Demand Functions, _Scandinavian Journal of Economics_ 85:4 1983.

Berninghaus, B. and H G. Seifert-Vogt (1993), The Role of the Target Saving Motive in Guest Worker Migration, _Journal of Economic Dynamics and Control_ 17, p181-205.

Eckstein, Z. and K I. Wolpin (1989) "The Specification and Estimation of Dynamic Stochastic Discrete Choice Models, a Survey, _The Journal of Human Resources, 24:4_ Evans, D.S. and B. Jovanovic (1989), "An Estimated Model of Entrepreneurial Choice under Liquidity Constraints, _Journal of Political Economy_ 97:4 pp. 808-826 Evans, D.S. and L. Leighton(1989), "Some Empirical Aspects of Entrepreneurship," _American Economic Review_ 79.

Harris, J.R. and M. P. Todaro (1970), "Migration, Unemployment, and Development: A Two Sector Analysis," _American Economic Review,_ 60:1, 126-142.

Hsiao, C.(1986), _Analysis of Panel Data,_ Econometric Society Monograph no. 11, (Cambridge University Press, Cambridge).

Jovanovic, B. (1979), "Job Matching and the Theory of Turnover." _Journal of Political Economy_ 87:5 972-90.

Nijman, R.,
To obtain household information, we administered a light survey during April-May 2010, about 2.5 years after the start of LTR in the pilot cells. The sample was designed to yield numbers of households in each pilot cell equivalent to their share in the total, with a size of 3,554 households with some 6,330 land parcels, intended to be split equally across pilot and their neighbor cells. To sample inside pilot cells, we used parcel index maps created by the program. As no such maps were available for the adjacent (control) cells, we used high resolution satellite imagery to identify dwellings that could then serve as a sample frame. Figure 2 illustrates the procedure graphically. To ensure that the share of households in each pilot cell corresponded to the cell‟s share in the target population, the widths of the bands from which households were selected were adjusted.<sup>8</sup> Note also that, as we lack enough statistical power to make strong conclusion for the urban sample, i.e., the city of Kigali, the empirical analysis given below is solely based on data from rural areas.

Table 1 shows the distribution of sampled households in the pilot and their adjacent cells. Information was collected both at household and parcel levels. Household level information includes demographics, housing, assets, participation in the credit market, participation in the registration program and knowledge of the law and the GPS coordinates of the main residential plot. Parcel level questions included land characteristics, investment, inheritance dynamics as well as participation in land sales and rental markets.

# **4.2 Econometric approach**

To examine the effects of LTR, we estimate the following equation: where **_Yph_** is the outcome of interest for parcel **_p_** in household h, **_T_** is an indicator for LTR participation, **_X_** is a set of parcel characteristics, **_Z_** household characteristics, and  an
# **Table A4: Impacts of mental health on labor outcomes – Non-standardized mental health score**

|Dependent variable:|Employ<br>|ment status<br>|Weekly labor<br>A<br>|income ($1,000<br>UD)<br>|
|---|---|---|---|---|
||Logit-FE|IV-Logit-FE|FE|IV-FE|
||(1)|(2)|(3)|(4)|
|Mental health scores|-0.008***|-0.011***|-0.0008***|-0.032***|
||(0.002)|(0.001)|(0.0002)|(0.009)|
|_First stage of 2SLS (depende_|_nt variable is non_|_-standardized mental_|_health scores)_||
|Trauma experience*Year<br>after arrival||-0.229***<br>(0.053)||-0.347***<br>(0.096)|
|Kleibergen-Paap F stat.||17.692||19.635|
|AR 95-CIs||[-0.023, -0.007]||[-0.067, -0.021]|
|Dep. Mean|0.455|0.455|0.716|0.716|
|Other controls|Yes|Yes|Yes|Yes|
|Individual FE|Yes|Yes|Yes|Yes|
|Survey wave FE|Yes|Yes|Yes|Yes|
|Observations|3,687|3,687|3,687|3,687|
|Number of individuals|1,609|1,609|1,609|1,609|
86|0.63|2.20|2.35|2.03|
|El Salvador|38.20|42.29|36.42|0.50|0.75|0.52|1.86|2.12|1.75|
|Uruguay|38.64|41.38|33.64|0.49|0.55|0.37|1.80|1.90|1.52|

_Source_ : Authors’ calculations based on data from the Socioeconomic Database for Latin America and the Caribbean, World Bank and Center for Distributive, Labor and Social Studies of the Universidad Nacional de La Plata (CEDLAS) (http://sedlac.econo.unlp.edu.ar/eng). _Note_ : Wages are defined as real hourly income (using 2005 purchasing power parity conversion rates) in the main occupation. The sample was restricted to individuals ages 18 to 65 years who were employees or self-employed. The 1st and 99th percentiles of the country-year wage distributions were trimmed.

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With respect to discriminatory restraints on foreign investors, Kenya requires that foreigners conduct business only in areas designated as general business areas. Local partners are encouraged, but not required. Expatriates employees are limited and the company must demonstrate that the skills are not available locally.

## **Professional Services**

There are rather severe restrictions on the rights of foreigners to operate with a license in many of the professional services sectors, including legal, accounting, auditing and engineering services. The East African Customs Union is making its first foray into commitments in the services areas by encouraging mutual recognition agreements among the members. In appendix D, we provide details on the situation in engineering services sectors.

# **III. Estimation of the Tariff Equivalence of the Regulatory Barriers**

Estimates of the ad valorem equivalents of the regulatory barriers in services are key to the results. In order to make these estimates, we first need to assess the regulatory environment in the services sectors in our model. We commissioned a 54 page survey of the regulatory regimes in key Kenyan business services sectors, namely, insurance, banking, fixed line and mobile telecommunications services and maritime transportation services.<sup>15</sup> We supplemented this information based on a good set of studies on the services sectors that were presented at the conference on ―Trade in Services‖ in Nairobi, Kenya on March 26, 27, 2007. In particular, we examined the papers by: Kiptui (2007) on financial services; Ndaro (2007) or communication services; Helu (2007) on maritime services; Ochieng (2007) on transport services; and Oresi (2007) on railway services. The study by the World Bank (2007) provided additional detail on the key issues in the sectors and the Telecommunications Management Group (2007) provided extensive details on telecoms. Tarr (2007a, 2007b) summarized this information in telecommunications 15 We thank Ms. Sonal Sejpal of the law firm of Anjarwalla & Khanna Advocates for leading this research effort
educated segment of Mexico’s labor force. Table 5 presents growth rates by schooling levels over the 1996-2015 period.

# **Table 5. Growth Rates of the Labor Force by Schooling Levels, 1996–2015**

||**_A_**<br>|**_nnual average growth_**<br>|**_rate_**<br>|
|---|---|---|---|
||**_WAP_**|**_EAP_**|**_Sample_**|
|**Incomplete Primary**|−1.22|−1.68|−3.39|
|**Complete Primary**|0.85|0.81|−1.12|
|**Incomplete Junior High**|0.44|0.45|−1.99|
|**Complete Junior High**|5.03|5.03|3.45|
|**Incomplete Senior High**|1.19|0.75|0.00|
|**Complete Senior High**|6.18|6.16|5.77|
|**University**|4.67|4.39|4.79|
|**All**|2.20|2.31|2.32|
|**Years of schooling**|—|—|—|

_Source:_ Authors’ computations based on Economic Census and data from Encuesta Nacional de Ocupación y Empleo and Encuesta Nacional de Empleo.

Note: _WAP_ (Working-age Population) includes all persons 18 years of age or older. _EAP_ (Economically Active Population) is a subset of _WAP_ who participate in the labor market. _Sample_ population consists of private sector employees between 18 and 65 years of age, living in localities of 100,000 inhabitants or more, and working between 30 and
| POLICY RESEARCH WORKING PAPER 2467

# Summary findings

Laeven uses panel data on 394 firms in 13 developing countries for the years 1988-98 to learn whether financial liberalization relaxes financing constraints on firms. He finds that liberalization affects small and large firms differently.

Small firms are financially constrained before liberalization begins but become less so after liberalization. The financing constraints on large firms, however, are low both before and after liberalization. The initial difference between small and large firms disappears over time.

Laeven hypothesizes that financial liberalization has little effect on the financing constraints of large firms because they have better access to preferential directed credit in the period before liberalization.

Financial liberalization also reduces imperfections in financial markets, especially the asymmetric information costs of firms' financial leverage.

Countries that liberalize their financial sectors tend to see dramatic improvements in political climate as well. Successful financial liberalization seems to require both the political will and the ability to stop the preferential treatment of well-connected, usually large, firms.

This paper-a product of the Financial Sector Strategy and Policy Department-is part of a larger effort in the department to study the benefits and risks of financial liberalization. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Rose Vo, room MC9-624, telephone 202-473-3722, fax 202-5222031, email address hvol@worldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. The author may be contacted at llaeven@worldbank.org. October 2000. (49 pages)

**_The Policy Researcb Working Paper Seoes disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the autbors and sbould be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of
- <mark>The use of credit cards is much more widespread in the region than mortgage debt. However, credit card use has had a negligible effect on household income and investment during the crisis. It is unlikely therefore that credit card debt may jeopardize future economic activity in</mark> the region.

- <mark>FX mortgages hardly pose a threat to future household consumption and investment. Only a minor fraction of households in the region are exposed to foreign currency mortgages. Moreover those countries in which FX mortgages are most prevalent did not experience major exchange rate shocks during the crisis.</mark>

# **_4.3 Debt Overhang in the Banking Sector_**

In this section we turn to the supply side of credit and examine which countries‟ banks are more likely to face debt overhang problems and which customer group might be more affected from these. First, we relate the quality of banks‟ loan portfolios to their profitability and their lending activities. Second, we review the literature on foreign banks‟ lending behavior during the crisis to assess whether they have contributed to the transmission of a shock that evolved unrelated to the local economic environment.

# <u>Non-Performing Loans</u>

Throughout the transition region non-performing loan (NPL) ratios increased substantially in 2009. Figure 4.3 illustrates the increase in NPL ratios between 2007 and 2009 by country and relates to bank performance and bank lending structure. NPL ratios in Ukraine increased by nearly 50 percentage points in just two years, while banks in Georgia, Lithuania, Latvia and Moldova also experienced NPL increases of more than 10 percentage points.

The very high level of the NPL ratio in Ukraine compared to the Baltic countries, which also experienced substantial economic contraction during the crisis, has been attributed to the large exchange rate depreciation in Ukraine and a subsequent rise in defaults on FX loans (see De Haas and Knobloch (2010)). The household-level and firm-level survey data presented in sections 4.1 and 4.2 cast
sup>||||1.045***<br>(0.059)|0.519***<br>(0.081)|0.797***<br>(0.041)|
|Waze Delay (hr)<sup>3</sup>||||-0.051***<br>(0.005)|-0.015***<br>(0.005)|-0.034***<br>(0.003)|
|Constant|13.698|-25.808|7.475|54.842***|19.236|46.879***|
||(13.278)|(38.932)|(13.085)|(8.078)|(20.416)|(7.799)|
|Observations|2981|744|3725|2981|744|3725|
|R<sup>2</sup>|0.771|0.755|0.776|0.907|0.898|0.905|

_Notes_ . Robust standard errors are reported in parentheses. *** (**) (*) denotes significance at 1% (5%) (10%) level. Regressions include fixed effects for week and day of the week, and controls for weather (temperature, precipitation and two measures of wind). Google speed data is missing for some hours.

# **4.3 Timing of the Curfew**

When the curfew is revised on June 6, 2020 to start two hours later, we can look at how the timing of the curfew can affect the externality on road safety.<sup>8</sup> There is a significant but smaller drop in the number of crashes during the revised curfew period (Table 5
<!-- Start of picture text --> Transport feed data Road network data Flood maps Household &<br>(GTFS) (Open Street Map) (Fathom) Commuter surveys<br>Public transport mapping Flood depths and Travel patterns and<br>under dry and wet Walk and drive networks extents for multiple travel modes +<br>conditions return periods Income data<br>Multimodal networks under dry<br>and wet conditions<br>Flood impacts Accessibility Critical road segments Economic costs due to<br>on transit patterns under dry and wet conditions identification travel delays<br><!-- End of picture text -->
|||**ELMPS**|**ELMPS**|**Economic**|**Establishm**|
|---|---|---|---|---|---|
||**Pooled**|**2012 in est.**|**2018 in est.**|**Census**|**ent Census**|
|Lower Egypt|1.331***|0.948|0.937|1.956***|1.438***|
||(0.081)|(0.187)|(0.233)|(0.123)|(0.009)|
|Upper Egypt|1.431***|0.932|1.022|2.738***|1.414***|
||(0.096)|(0.202)|(0.263)|(0.282)|(0.011)|
|**N**|766840|1200|1155|52287|712198|
|**Pseudo R-squared**|.113|.0821|.0953|.119|.0989|

Source: Authors’ calculations based on ELMPS 2012, ELMPS 2018, EcC 2012/13, EsC 2017 Notes: *p<0.05; **p<0.01; ***p<0.001.

We now move to the results from Model 2a estimated in the EcC data and shown in Table 3. The results on size are in the same direction as before, but are somewhat attenuated because of the inclusion of other regressors associated with firm size. The results on firm age, industry, and region are little changed when compared to the results of Model 1 on the EcC data. With regard to
# **6. Final remarks**

Our analysis shows how using a gender lens that differentiates between types of households and considers economies of scale can enrich the understanding of poverty in situations of forced displacement. In the case of Syrian refugees, specific household structures, particularly those that result from disruptions caused by displacement, are more prone to experiencing poverty.

However, while we have described how patterns of poverty have changed over time, lack of data on critical variables, most importantly the amount of social assistance received by households and labor force participation, employment and earnings data limits our analysis. While some of the changes in poverty over time experienced by male and female PA households are consistent with some of the changes in the circumstances of Syrian refugees, specifically their improved access to labor markets and better access to services, it is not possible to understand the extent to which these and potentially other factors have made a difference to their risk of poverty.

Overall, our results show that the share of female PA households has increased substantially from from 26 to 38 percent of all Syrian refugee households between 2013 and 2018. There are differences between the characteristics of male and female PA households in terms of education, marital status, and family types. While male PAs are likely to be married, the vast majority of female PAs are either single, divorced or widowed or married but separated from their spouse. However, by 2018 the share of married female PAs living with their spouse has increased from 10 percent in 2013 to 18 percent, suggesting some men who had stayed behind in Syria left to rejoin their families in Jordan in this time period.

We use a relative poverty line and examine changes in the composition of the types of households that lie in the bottom 40 percent of the distribution between 2013-14 and 2017-18. We show that assessment of the evolution of poverty over time is highly sensitive to assumptions made about economies of scale. Assuming a moderate amount of economies of scale, we find that female PA households have fared worse
Africa was not randomly assigned across the continent, as illustrated by the case studies in Johnson (1967) and confirmed empirically in Jedwab, Meier zu Selhausen, and Moradi (2018). If the factors that determine the selection of mission station locations correlate with present-day aid allocation, the coefficient _β_ in equation (1) will be biased.

The first factor to consider is accessibility. Missionaries came by sea, and inland penetration was difficult, so they followed the tracks of early European explorers, which partly correspond to the courses of the main rivers. There is evidence that areas along the coast and along large rivers have an advantage in development and are more densely populated (Gallup, Sachs, & Mellinger, 1999), so the regressions control for the (log) distance (in kilometers) from the closest point on the coast and from the closest main river.<sup>16</sup>

> 14Results obtained using the Roome (1924) data are available from the authors upon request.

> 15The correlation increases as the cell size increases.

> 16Logarithms of distances are used because the marginal effect of a unit of proximity

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