rafmacalaba/data-use-mentions-extended huggingface dataset:
We control for bank size by including the log of assets. Bank assets averaged $3.7 million in Eastern Europe and $5.6 million in Latin America. Our indicator of bank capital is bank equity as a share of total assets. This ratio averaged close to 20 percent for banks in Latin America and 12 percent for Eastern Europe. Bank liquidity, which is captured by the ratio of liquid assets (cash and cash equivalent assets) to total assets, averaged 28 percent in both Eastern Europe and Latin America. We measure profitability by the return on assets ratio. Profitability averaged 1 percent in Eastern Europe and almost 2 percent in Latin America. We account for differences in bank funding structure using data on customer deposits as a share of total liabilities. This ratio averaged 65 percent within Eastern Europe and 51 percent in Latin America during the period 2005-2009.

In the case of foreign banks, we not only collect data on the characteristics of the local operations in Eastern Europe and Latin America of the foreign banks, but also we gather data on the parent banks.<sup>14</sup> The parents of foreign banks operating in Eastern Europe averaged $395 million in assets and those of Latin American banks averaged $733 million. The equity ratio averaged close to 10 percent among parents of Latin American banks and 7 among parents of Eastern European banks. On average, return on assets was 1.3 percent among parents of Latin American banks and 0.8 percent among those of Eastern European banks. Liquidity averaged 28 percent for parents of Latin American affiliates and almost 30 percent for those operating in Eastern Europe. On average, the ratio of deposits to liabilities was 48 percent among parents of Latin American affiliates and 43 percent among those of Eastern European affiliates.

> 14 Studies such as Kamil and Rai (2010) and de Haas and van Lelyveld (2011) show that parent bank characteristics can have an impact on foreign bank lending in developing countries.

11
on agricultural productivity in Ukraine In: Policy Research Working Paper, p. 29. The World Bank, Washington, DC Panek, E., Gozdowski, D., 2020. Analysis of relationship between cereal yield and NDVI for selected regions of Central Europe based on MODIS satellite data. Remote Sensing Applications: Society and Environment 17, 100286 Potapov, P., Turubanova, S., Hansen, M.C., Tyukavina, A., Zalles, V., Khan, A., Song, X.-P., Pickens, A., Shen, Q., Cortez, J., 2022. Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. Nature Food 3, 19-28 Schierhorn, F., Hofmann, M., Gagalyuk, T., Ostapchuk, I., Müller, D., 2021. Machine learning reveals complex effects of climatic means and weather extremes on wheat yields during different plant developmental stages. Climatic Change 169, 39 Shelestov, A., Lavreniuk, M., Kussul, N., Novikov, A., Skakun, S., 2017. Exploring Google Earth Engine Platform for Big Data Processing: Classification of Multi-Temporal Satellite Imagery for Crop Mapping. Frontiers in Earth Science 5 Shelestov, A., Lavreniuk, M., Vasiliev, V., Shumilo, L., Kolotii, A., Yailymov, B., Kussul, N., Yailymova, H., 2020. Cloud Approach to Automated Crop Classification Using Sentinel-1 Imagery. IEEE Trans. Big Data 6, 572-582 Swinnen, J., Burkitbayeva, S., Schierhorn, F., Prishchepov, A.V., Müller, D., 2017. Production potential in the “bread baskets” of Eastern Europe and Central Asia. Global Food Security 14, 38-53 Vannoppen, A., Gobin, A., 2021. Estimating Farm Wheat Yields from NDVI and Meteorological Data. Agronomy 11, 946 von Cramon-Taubadel
However, important challenges remain in the implementation of humanitarian aid operations. There are few studies on the efficiency and targeting of food aid (e.g. Owens et al., 2003). A study looking into relief aid after cyclone Gafilo hit Madagascar in March 2004 found that the likelihood of aid relief was higher in cyclone-affected areas, but there were still communes hit by cyclones that did not receive aid (errors of exclusion) and others not hit but that received aid (errors on inclusion) (Francken et al., 2009). A similar finding is documented by Dercon and Krishnan (2004) regarding food aid in Ethiopia, which was found to be reasonably responsive to local conditions, but with many affected communities not receiving aid in the early 1990s. Other studies have revealed that assistance may be ineffectively allocated due to political reasons or errors in targeting (del Ninno and Lundberg (2002) looking at 1998 floods in Bangladesh; Jayne et al. (2002) for food allocation in Ethiopia; Francken et al. (2009) in Madagascar).

In this paper, we estimate impacts of extreme weather events in Malawi on household livelihood outcomes. We hypothesize that shocks directly reduce agricultural production and productivity and indirectly affect livelihoods through lowered food availability. Households exposed to sequential weather shocks are hypothesized to experience larger negative impacts on consumption outcomes compared to those facing a shock only in the current period. We then investigate whether household access to humanitarian aid may have mitigated these negative impacts.

To explore these hypotheses, we leverage (i) the 2013 (pre-flood and pre-drought) and 2016 (post-flood and post-drought) rounds of the nationally-representative, multi-topic Integrated Household Panel Survey (IHPS), (ii) the 2015 (post-flood) Flood Impact Assessment Survey (FIAS) that followed a subset of households surveyed by the IHPS 2013 and that were located in districts where flooding was most pronounced; (iii) monthly data on humanitarian aid distribution by the World Food Programme and partners over the period of 2012-2017 aggregated at
Uzbekistan, Tajikistan and Turkmenistan, where the trade regimes continue to be restrictive, in good part because of the slow introduction of overall market reforms. In between, one finds the remaining countries, Azerbaijan, Kazakstan, Russia and Ukraine.

It is interesting to compare the above grouping to the most recent EBRD ranking of these countries according to its trade and foreign exchange system indicator as of 1998 (EBRD, 1998). This is done in Table 3, which also includes the EBRD ;ankings on enterprise governance and competition. The last two columns show the EBRD ranking on foreign exchange and trade regimes and a ranking prepared by the author. The two rankings are quite consistent with the exception of Kazakstan and Russia. In the latter's case, the EBRD seems to penalise Russia too much for exchange restrictions it imposed on capital account. This leads Russia to have a lower ranking on this indicator than Tajikistan, which does not seem plausible. On the other hand, the EBRD seems not to have taken into account the restrictions imposed (and recently lifted) by Kazakstan. Also, Estonia and Latvia as well as the Kyrgyz Republic, probably have more liberal trade regimes than Hungary, Poland and Romania.

Table 3

# EBRD TRANSITION INDICATORS 1998

Progress in transition in Central and Eastern Europe, the Baltic States and CIS

|_Countries_|_Governance &_<br>**_Enterprise_**<br>_restructuring_|_Competition_<br>_Policy_|_Trade_<br>_&_<br>_System_|_Foreign_|_Exchange_|
|---|---|---|---|---|---|
||_EBRD_|_EBRD_|_EBRD_|_Au_|_thor_|
|_Albania_|_2_|_2_|_4_|||
|_Armenia_|_2_|_2_|_4_|_4_||
|_Azerbaijan_|**_2_**|**_1_**|_3_|_3_||
|_Belarus_|**_1_**|_2_|*
Policy Research Working Paper 10924

# **Abstract**

Education and skills are two key determinants of earning potential, with sector specialization significantly influencing earnings. This study examines the drivers behind training choices in two high-paying sectors: information and communications technology (ICT) and energy. Drawing on data from 2,528 individuals seeking vocational training in Côte d’Ivoire, we find that a majority (72% of men and 51% of women) aspire to train in ICT or energy. For both genders, higher levels of education and larger professional networks are positively correlated with selecting training in these high-paying sectors. For women, previous training in similar fields strongly predicts their training choices, highlighting path dependency. Additionally, women benefit more from male role models, which significantly increase their likelihood of choosing a training in ICT or energy. Women with greater agency are also more likely to opt for training in these sectors. Conversely, women holding more traditional views on specific household responsibilities are less likely to choose high-paying sector training.

This paper is a product of the Gender Innovation Lab, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mzenaki@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank. org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>
Estimates of Equations (1) and (2) have the advantage of shedding light on the long-term effect of the conflict on primary education. However, by focusing only on individuals who are generally old enough to have finished primary schooling (i.e., are aged 14 or older at the time of the 2006 DHS), we introduce the risk that estimates may be biased due to migration. An alternative strategy is to compare the educational attainment of children too young to have migrated away from the district for work or marriage, between 2001 and 2006 using Equation (3). By focusing on children under 14 years old, these estimates may not capture the long-term effect of conflict, but they are unlikely to be much affected by migration patterns. In addition, since we also observe household characteristics at the time of survey, it is possible to gain precision by including time-invariant household covariates.<sup>5</sup>

# Table 6 goes about here

Findings for children aged between 5 and 14 years old at the time of the survey are presented in Table 6. Column (1) of Table 6 indicates that an increase in violence by one standard deviation increases completed years of education by about one quarter of a year, for girls aged 5 to 14 in 2006 compared to girls from the same district who were the same age when surveyed in 2001 before the conflict escalated. However, there is no discernible effect on enrollment, and no effect on either outcome for boys. The combination of improved accumulation of years of education for females without evidence of increased enrollment suggests that the channel through which girls obtained more education is through an increase in the speed at which they accumulate years of education.<sup>6</sup> Results in Table 7 are obtained in the same way as those in Table 6, but for the subgroup of individuals aged 15 to 18 at the time of the survey. This sample is more likely to be selected due to migration patterns, but it is interesting
# **Using ORBIS to Build a Global Database of**

# **Firms with State Participation**

Andrea Dall’Olio, Tanja Goodwin, Martha Martinez Licetti, Jan Orlowski, Fausto Patiño Peña, Francis Ratsimbazafy, Dennis Sanchez-Navarro

**JEL Classification:** L32, C80, L25, L10 **Key Words:** Public Enterprises, Firms, State Participation
# **5.1.1 Gorontalo**

Our estimation using data on Gorontalo suggests that RPOS-Grab distribution imposes a positive and significant impact on restaurant tax payment (Table 6). Restaurants that obtained RPOS and opted for integration with Grab application on average paid taxes by 68 percent higher compared to those in the control group, which showed visually in Figure 9 Panel A. Meanwhile, restaurants which obtained RPOS units but opted out from direct connection with Grab interface made only indistinguishable tax payments on average compared to their control counterparts.

That applies also in the case of TMD, which generated statistically negligible effect on restaurants’ tax compliance. Tax receipts had already widely differed between treatment and control group long before the POS distribution program being enacted (see Figure A.18).

Table 7 presents the main estimation results for Gorontalo’s hotel observations. According to the table, neither access to PAC Hotel nor TMD units imposed statistically significant effect on the hotels’ tax compliance (as measured by their tax receipts). Hotels that have been given access to PAC Hotels are relatively small, hence there was a chance that (1) in practice, they did not utilize PAC Hotel or (2) they used it, but they just ended up paying the equally low amount of tax to the authority due to their naturally low revenue from the start. Another possible explanation for this could be the low statistical power given the minuscule size of treatment group in both PAC Hotel and TMD estimation. These all are visualized into rather narrow payment gaps between the treatment and control groups in 9 Panel B.

18
the yearly stock of refugees. More formally, we run the following regression for country _i_ in year _t_ : _refugeesit_ = _incidenceit_ + _phaseit_ + _δi_ + _δt_ + _εit_ Our outcome of interest is _refugeesit_ , which is the stock of refugees. The variable _incidenceit_ is conflict incidence. In one specification of the model we use the definition from the PRIO dataset of at least 25 battle related deaths caused by an internal conflict. In the other specifications we use a more stringent definition of internal conflict which requires at least 1000 battle related fatalities.<sup>24</sup> Variable _phaseit_ is a vector of dummies we use to show the dynamics in the stock of refugees in different phases of the conflict. In one specification we control for the _year before the conflict starts_ and _first year of conflict_ . Then, we include in succession dummies for the _last year of conflict_ and _recovery years_ up to the tenth year of recovery.

Finally, _δi_ and _δt_ are respectively country and year fixed effects: the former control for time invariant country-specific impact on the change of the number of refugees, while year fixed effects capture the average state-invariant influence of each year _t_ on _refugeesit_ .

Table 6 reports the results. In column (1) conflict incidence coincides with the PRIO definition of at least 25 battle related deaths, i.e variable _25+ battle deaths_ . The estimated coefficient indicates that during conflicts more than 144,000 people leave their home country on average. This average rises markedly when we use the more stringent definition of at least 1000 battle related fatalities as conflict incidence variable. In fact, in column (2) the estimated effect of conflict incidence on _refugeesit_ more than doubles. Note that in this specification we include the dummy _25-1000 battle deaths_<sup>25</sup> in order to check whether people have an incentive to leave their country when some violence occurred, but the situation has not escalated into a conflict with more than 1000 deaths in a given year. We notice that during these years
except for El Salvador, whose exports are Cost, Insurance and Freight (CIF) figures. Finally, we consider the universe of export transactions in agricultural, mining, and manufacturing sectors but we exclude exports in HS Chapter 27 (hydrocarbons such as oil, petroleum, natural gas, coal etc.). Cebeci, Fernandes, Freund, and Pierola (2012) argue that the raw exporter-level datasets are reliable based on comparisons of export totals (excluding HS 27) with export flows in the WITS/COMTRADE database (excluding HS 27) for each country and year.

Three remarks on the data are worth mentioning. First, to filter out potential noise and focus on true entrepreneurial ventures in export markets (rather than for example the shipping of samples or other small shipments) we drop from the sample in each country exporting firms whose total export values are lower than 500 USD in all their years in the sample. Second, in the case of Costa Rica we exclude the largest exporting firm (INTEL) from the sample in each year, following Lederman, Rodriguez-Clare, and Xu (2011).<sup>11</sup> Third, in the case of Nicaragua our dataset includes firms located in Export Processing Zones even though exports reported to WITS/COMTRADE by that country exclude the exports from those firms.<sup>12</sup> The analyses cover two time periods: (i) the period 2005-2007 which has the greatest coverage across LAC countries and represents a growth period or ‘normal times’ and (ii) the period 2008-2009 which covers the global financial crisis that started in 2008 and allows us to present for the first time evidence on the effects of a major crisis on export entrepreneurship in emerging economies.<sup>13</sup>

# **_3. Methodology_**

The first objective is to describe recent patterns of export entrepreneurship in LAC countries using the list of indicators defined in Table 1. Those indicators defined at the countryyear level are analyzed in Section 3
. These high rates of turnover are not consistent with formal jobs being so valuable and desired that workers never want to leave once they attain such positions.

The implicit assumption behind search and matching interventions in particular is often that search frictions make it costly and difficult for firms to find workers. Simple queries of firms often find firms _saying_ that they find it hard to find the right workers. But one also sees firms being reluctant to raise wages or spend more money in getting better matches. Groh et al. (2015) conducted a survey in Jordan where they tracked firms as they opened up job vacancies, and found that only 6 percent of positions required more than 4 weeks to find a new employee, and most firms could, in fact, fill jobs quite quickly. De Mel et al. (2016) similarly find firms in Sri Lanka say it would take 16
Vuolteenaho, 2004). In Serbia, the MMIR indicator is statistically significant and negative in the short run, which remains in alignment with the long-run relationship coefficient. Moreover, in the case of Serbia, the results imply statistical significance by the GDPPC indicator for the first time, although the estimating association coefficient is minor. This result draws comparisons to research claiming that in poorer markets with lower stock exchange capitalization and liquidity, the GDPPC indicator exhibits a greater correlation of the macroeconomic indicators to a systemic movement in stock prices (Morck, Yeung, & Yu, 2000). However, the respective statistical indicators for Serbia show that of the research countries in Southeast Europe it has the second weakest capitalization and liquidity, but a median GDPPC level.

# **5. Conclusions**

This research paper empirically demonstrates that there is a statistically significant relationship between macroeconomic indicators and stock prices. The results reaffirm that the capital markets in Southeast Europe are weak form information inefficient. The results of the aggregate group data panel PMG for the selected markets demonstrate the nonexistence of an estimated impact of the statistically significant short-run individual macroeconomic indicators on the relationship with SMI. The result is in line with older and recent research utilizing panel PMG in three Asian countries (Megaravalli & Sampagnaro, 2018).

The results of the empirical research test on the existence of statistically significant longrun cointegrating relationships show an associating negative impact from an increase in the MMIR and HICP macroeconomic indicators on the value of the SMI. Such results also are in line with previous research (Azar, 2010; Balduzzi, 1994; Campbell & Vuolteenaho, 2004). The existence of a significant long-run relationship association versus the nonexistence of a short-run significant association contradicts the expected performance of a decreasing long-run coefficient impact because of the implementation of control factors such as policy adjustments and a shift in investor sentiment.

Moreover, the nonexistence of significant short-run relationships raises questions about the prevalence of portfolio investors as the ones who are more likely to be sensitive to short-run deviations because of their strategy of
. This was compounded by the liberalization of coffee transportation in 1990, removing the government mandate that the industry use rail transportation (World Bank 2005).

**Table 5. Performance of Ugandan railways, 2005**

||Labor<br>productivity|Carriage<br>productivity|Locomotive<br>productivity|Wagon<br>productivity|Freight yield|Passenger<br>yield|
|---|---|---|---|---|---|---|
|Djibouti, CDE|||||12.5|3.1|
|Ethiopia/Djibouti, CDE|71|3,037|11|156|||
|Kenya, KRC (RVRC)|203|1,159|24|218|3.8|0.6|
|Sudan, Sudan|77|329|9|225|||
|Tanzania, TRC|259|3,264|25|583|4|1.6|
|Tanzania, TAZARA|352|2,770|13|551|3|1.1|
|Uganda, URC (RVRC)|156||9|144|||
|Rail concessions|350|2,945|23|491|5|2|

_Source:_ AICD railways database.

_Note:_ Labor productivity = ‘000s traffic units per employee; locomotive productivity = millions of traffic units per locomotive; carriage productivity = ‘000s passenger-km per carriage; wagon productivity = ‘000s net tonne-km per wagon.

# Air transport

## **Achievements**

_Uganda’s efforts to liberalize the air transport market have increased international air traffic and boosted sector performance_ . In 2001, Uganda shut down its national flag carrier and opened up the market for liberalized traffic. After the initial drop in capacity, in 2007 a new carrier—Air Uganda—was 21
Policy Research Working Paper 9414

# **Abstract**

This study highlights how COVID-19 has affected small and medium enterprises, drawing on newly released World Bank Enterprise Surveys in 13 countries. The study shows that firms of all sizes are severely affected in multiple dimensions; however, firm size matters for the intensity of the different channels of transmission and firms’ responses. Small and medium enterprise sales shrink by more and their cash drains faster than large firms in the same sector and country. Among them, faster growing firms experience the demand shock somewhat less severely, but they are more exposed to international trade disruption, supply, and finance shocks. Yet, a range of firm responses to the downturn seem to be out of reach. Fewer small and medium-size enterprises, for example, start remote work, leaving their workers exposed to health risks. To make it through the pandemic, the majority of smaller firms do not turn to banks for loans; they need grants. Although development finance is not enough to fill the financing gap, development - finance institutions are relevant—in investment mobiliza tion, demonstration, and know-how—as economies move toward recovery and rebuilding. Delivering these requires rapid efforts to build partnerships and gather information in places where development finance has been limited in the past.

This paper is a product of the Economics and Private Sector Development Vice Presidency, International Finance Corporation. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at iadian@ifc.org, ddoumbia@ifc.org, ngregory@ifc.org, aragoussis@ifc.org, areddy1@ifc.org, and jtimmis@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective