rafmacalaba/datause-encoder-data huggingface dataset:
Zhang, Q. H. _et al._ (2016) ‘Current status of urban wastewater treatment plants in China’, _Environment International_ , 92–93, pp. 11–22. doi: 10.1016/j.envint.2016.03.024.

- Zhu, J., Baise, L. and Thompson, M. (2017) ‘An updated geospatial liquefaction model for global application.’, _Bulletin of the Seismological Society of America_ , 107(3), pp. 1365–1385. doi: https://doi.org/10.1785/0120160198.

- Zorn, C. and Koks, E. (2019) _Global liquefaction susceptibility map (Version 1) [Data set]_ . doi: http://doi.org/10.5281/zenodo.2583746.

22
MOZAMBIQUE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 4. Railway indicators for Mozambique and select other countries, 2000–05**

||CFM (Angola)|BR (Botswana)|CEAR (Malawi)|**Nacala Railroad**<br>**(Mozambique)**|**Beira Railroad**<br>**(Mozambique)**|**Ressano Garcia Line**<br>**(Mozambique)**|Transnamib (Namibia)|Spoornet (South Africa)|RSZ (Zambia)|NRZ (Zimbabwe)|
|---|---|---|---|---|---|---|---|---|---|---|
|Concessioned (1)/ state run (0)<br>|0|0|0|**1**|**1**|**0**|0|0|1|0|
|Freight density (1,000 tonne-<br>km/km)|469|827|90|**270**|**663**|**364**|475|2,427|406|902|
|Passenger density (1,000<br>passenger-km/km)|—|—|38|**103**|**44**|**44**|33|60|92|166|
|Labor productivity (1,000 traffic<br>units peremployee)|580|722|131|**710**|**281**|**—**|484|3,308|502|390|
|Locomotive productivity<br>(million traffic units per<br>locomotive)|30|41|3|**25**|**13**|**—**|25|33|25|8|
|Carriage productivity (1,000<br>passenger-km per carriage)|4,046|2,391|1,176|**3,333**|**750**|**—**|—|—|3,286|—|
|Wagon productivity (1,000 net<br>tonne-km per wagon)|950|987|82|**260**|**476**|**—**|805|913|377|195|
|Freight yield (US cents/tonne-<br>km)|—|—|6|**5**|**3**|**3**|—|—|4|—|
|Passenger yield (US|||||||||||
|cents/passenger-km)|—|—|1|**0.9**|**0.5**|**1**|—|—|1|—|
|<br>_Source:_Bullock 2009. Derived fro<br>_Note:_* With 2.5 passenger-km eq<br>— =Not available.|m AICD r<br>uivalent t|ail operato<br>o 1 traffic|r databas<br>unit, 1 ton|e(www.infra<br>ne-km equiv|structureaf<br>alent to 1 t|rica.org/aic<br>raffic unit.|d/tools/data|).|||

# **Challenges**

Even though railways in Mozambique are an important means of transport, on average the cargo and passengers transported decreased between 2005 and 2008. Total passenger-kilometers decreased by 60 percent from 305 million passenger-kmin 2005 to 113 million passenger-km in 2008 (table 5) **.** The cargo transported in the Mozambican railways declined by 10 percent, from 763 million tonne-kilometers in 2005 to 694 million in 2008.

But these aggregates mask important differences in trends among cargo operators. Whereas cargo traffic on railways under CFM management increased around 10 percent between 2005 and 2008, the lines under concession experienced important declines. A substantial decline of 60 percent of cargo traffic was registered in the Beira Railway and a 10 percent drop in the Nacala Railway (table 5).

15
On the allegation of cream skimming, there is some evidence that foreign banks report higher profits than domestic banks, but the reasons for this are most likely their higher efficiency over local banks and the imposition of restrictions that provide regulatory rents to foreign banks. The best response to cream skimming is not to protect the position of inefficient domestic banks, but to remove the restrictions that give rise to regulatory rents and also to stimulate efficiency drives among domestic banks.

Entry into Hungarian bankinb .a, now free to institutions that meet normal regulatory requirements such as minimum capital, "fit and proper" management and good computer systems. However, entry of foreign banks, especially through branches, is still highly restricted. This weakens the competitive challenge to domestic banks and is likely to delay the emergence of a sound, competitive and efficient banking system.

Another aspect that weakens competition in the market is the burden of nonperforming loans. As discussed below, Hungarian banks operate with high nominal spreads. These may be necessary in order to cover future losses from nonperforming loans. However, when spreads are unduly high, good borrowers tend to subsidize bad ones. Under these conditions, good borrowers would seek finance outside the banking sector, either by issuing commercial paper and corporate bonds or by raising funds in the international markets.

These potential developments could create several problems. First, recourse to international markets would raise issues of international indebtedness and would require appropriate policies of risk management and hedging by the corporate sector that might not be readily developed and implemented.

Second, access to the capital markets (domestic or foreign) would be feasible only for the largest and strongest of enterprises with the best credit ratings. Their defection to the capital markets would weaken further the quality of the loan portfolio of Hungarian banks.

Third, the continuing high cost of bank finance would hinder the financing of new firms, especially in the industrial sector where financial requirements would be greater than those in the trade and other service sectors. Smaller firms would be penalized by their inability to access marketbased sources of finance.

All these factors suggest that dealing with the problem of nonperforming loans should be a high priority in the process of creating a sound and competitive banking market.

# Credits

Available NBH data report bank credits in forints to the enterprise sector<sup>6</sup> by size of bank. The following trends are highlighted. First, total fhrint credits to the enterprise sector fell in real terms between 1987 and 1990. Second, banks pla; A a growing emphasis on conservative lending policies, increasing their holdings of liquid assets and concentrating on short-term self-liquidating loans. Third, after suffering a big fall in 1988, short-term credits expanded in real terms in 1989 and 6 The NBH data refer to credits to entrepreneurs. Given their size this is taken to represent the whole enterprise sector, including state owned enterprises.

_5_
# **I. Introduction**

The financing of small and medium-size enterprises (SMEs) has been a subject of great interest both to policy-makers and researchers because of the significance of SMEs in private sectors around the world. Data collected by Ayyagari, Beck, and Demirgüç-Kunt (2007) for 76 developed and developing countries indicate that, on average, SMEs account for close to 60% of manufacturing employment. More importantly, a number of studies using firm-level survey data have shown that SMEs not only perceive access to finance and the cost of credit to be greater obstacles than large firms, but these factors constrain SMEs (i.e., affect their performance) more than large firms (Schiffer and Weder, 2001; IADB, 2004; Beck, Demirgüç-Kunt, and Maksimovic, 2005; and Beck, Demirgüç-Kunt, Laeven, and Maksimovic, 2006). While these studies have advanced our understanding of the demand side of SME lending, little research exists on the supply side of bank financing to SMEs across countries. Furthermore, there is often the perception among policy-makers that banks, especially large ones, are not interested in financing SMEs.<sup>1</sup> This paper tries to fill the void in the literature and to inform policy-makers about whether, why, and how banks are financing SMEs around the world.

Using newly gathered data for 91 banks from 45 countries, this paper documents the state of bank financing to SMEs. First, we examine how banks perceive the SME segment, discussing the factors driving and impeding SME financing. On the driving side, we analyze the role of the segment’s profitability vis-à-vis other factors, such as competition and thinning margins in other segments. In terms of obstacles, we examine the importance of competition in the segment as well as of macroeconomic, regulatory, contractual, SME-specific, and/or bank specific factors. We also examine the role of government programs and of prudential regulations in driving and deterring SME finance. Second, we describe the business models banks have adopted to serve SMEs. Here we examine whether banks have set up separate departments and/or decentralized their operations to serve SMEs. Also, we look into banks’ use of scoring models to approve loans and we investigate the main criteria banks consider when evaluating a loan, including the availability and types of collateral. Finally, we quantify the extent and type of bank lending to SMEs. In particular, we examine the ratio of total loans going to SMEs, the percentage of

> 1 The majority of studies on bank financing to SMEs have focused on the US or other developed countries such as Germany, Italy and Japan. (See Berger, Rosen and Udell, 2007 for a recent review).
improvement in scores for height for age of children by 1.3 percent and 1.2 percent respectively. Results for weight for height are not conclusive.

Our results differ from much of the previous literature that finds that rising food prices pose risks to children’s health (see Woldemichael, Kidane, & Shimeles (2017); Arndt _et al.,_ (2016); Vellakkal _et al.,_ (2015); Miller and Urdinola (2011); Torlesse _et al.,_ (2003)). There are a range of linkages from rising crop prices to child nutrition that go beyond the direct impacts on household incomes to include access to health.

There are two important differences in our approach compared to the previous literature on food prices and child undernutrition. First, with regard to the price data used, the approach implemented here uses cereal price data collected from market stalls when the survey of child nutrition and household outcomes was conducted. Thus, our cereal prices may reflect more closely the prices households actually face compared to the price data collected by statistical agencies used in previous studies. The latter may be a less direct measure of the prices faced and typically suffer from the challenges of missing values and averaging. Second, our approach utilizes a panel data fixed effects model compared to the frequently used pooled regression analysis. Our results may be different since we track the same child between waves of the Living Standards Measurement Survey to examine the how changes in cereal prices are associated with changes in child undernutrition. Lastly, note that the majority of households in the LSMS survey come from rural areas and their welfare is likely to be closely related to agricultural development in terms of both farm and non-farm incomes.<sup>23</sup>

# **7. Conclusions**

The study investigated the association between food prices and child undernutrition in Ethiopia. The increase in crop prices between wave 2 (2013/14) and wave 3 (2015/16) in the LSMS survey created a unique opportunity to identify how food price changes affect child undernutrition. We use height for age (stunting) and weight for height (wasting) as indicators of child undernutrition and zone-level market prices of three cereals - teff, maize and wheat. Using a panel data fixed effects model, we show that increases in crop prices are associated with improved child stunting rates for children between 6 months and five years, while the

> 23 We could not run separate regressions to differentiate net buyers and net sellers since our survey does not provide sufficient information to make that distinction.

14
Cameroon became eligible for debt relief under the Enhanced HIPC<sup>5</sup> Initiative. In this context, the government adopted a Poverty Reduction Strategy (PRS) in 2003. The strategy is designed to cut the number of poor by half by 2015 through strong and sustainable economic growth. Cameroon reached the Completion Point in May 2006, after three full years of implementation of the 2003 PRS. This achievement signals the satisfaction of Cameroon’s development partners with the implementation of this strategy.

How much poverty reduction has this improved policy environment brought about? Preliminary analysis by the National Statistical Office based on the most recent household survey (2007) indicates that the overall incidence of poverty is still around 40 percent, about the same level as in 2001. The Gini index of inequality seems to have dropped a couple of percentage points from 41 percent in 2001 to 39 percent in 2007. These observations raise some interesting evaluative questions in terms of the social impact of economic growth in Cameroon. To what extent has the growth process been inclusive in Cameroon? What are the sources of observed variations (over time and across socioeconomic groups) in the distribution of economic welfare?

The purpose of this paper is to use available household level data, particularly the 2001 and 2007 surveys, to try to answer these questions using counterfactual decomposition of changes in the distribution of economic welfare. To put things into perspective, we present in section 2 a profile of growth, inequality and poverty for the period 1996-2007. In that section we use the Shapley decomposition to explain

> 5 HIPC stands for Heavily Indebted Poor Countries.  This initiative was launched in 1996 by the International Development Association (IDA, the World Bank’s fund designed to provide concessional credits and grants to the poorest countries) and the IMF.  The initiative was enhanced in 1999 to tighten its link with poverty reduction and to widen its scope and make it more efficient (in terms of speed of relief delivery).  Eligibility is based on three criteria: (1) qualify only for concessional assistance from IDA, (2) debt situation remains unsustainable after full application of traditional relief mechanisms, and (3) a track record of reforms combined with the development of a Poverty Reduction Strategy (presented in a document known as Poverty Reduction Strategy Paper or PRSP).  The whole process entails reaching a _Decision Point_ and a _Completion Point_ .  Two conditions must be met by a country to reach the Decision Point: (1) satisfactory preparation of an interim PRSP, and (2) satisfactory performance under the IMF’s Poverty Reduction and Growth Facility (PRGF).  At this point, the country gets conditional (on continued good performance) interim relief.  At the Completion Point debt relief becomes irrevocable.  Reaching this point requires the following: (1) maintain macroeconomic stability under a PGRF; (2) satisfactory implementation of a full PRSP for one year; (3) implementation of structural and social reforms agreed upon at the Decision Point.

4
# **Table A3**

Sectoral distribution of non-agricultural manual laborers

||Share of non-agricultural|
|---|---|
||manual laborers in sector|
|Construction|0.60|
|Transport|0.07|
|Retail|0.05|
|Domestic work|0.05|
|Building materials|0.04|
|Other|0.17|

Notes: This table shows the share of nonagricultural manual laborers in the five largest industries. The sample is the full rural population in the 68th round of the National Sample Survey (2011-12).

# **Table A4**

Impact of new road on distribution of landholdings

||Landless|0-2 Acres|2-4 Acres|4+ Acres|
|---|---|---|---|---|
|New road|-0.009|-0.018|-0.007|0.034*|
||(0.029)|(0.027)|(0.013)|(0.019)|
|Control group mean|0.433|0.287|0.120|0.160|
|N|11440|11440|11440|11440|
|R2|0.39|0.41|0.22|0.47|

_<u>∗ ∗∗ ∗∗∗</u> p <_ 0 _._ 10 _, p <_ 0 _._ 05 _, p <_ 0 _._ 01 Notes: This table presents regression discontinuity estimates from the main estimating equation of the effect of new road construction on the share of village households with landholdings in a given range. The first column reports the estimate effect on the share of households reporting no agricultural land, followed by three columns for households owning agricultural land. For each regression, the outcome mean for the control group (villages with population below the threshold) is also shown. The specification includes baseline village-level controls for amenities and economic indicators, as well as district-cutoff fixed effects (see Section V for details). Heteroskedasticity robust standard errors are reported below point estimates.

48
|**Impact assessment**|Where data processing is likely to result in high risk to individual rights, the controller<br>must conduct an impact assessment prior to processing, including the foreseen<br>measures to address the risks|
|---|---|
|**Designation of data**<br>**protection officer**|Controllers and processors must designate a data protection officer under certain<br>circumstances, including if the bulk of the processing activities require regular and<br>systematic monitoring of data subjects on a large scale, or if it consists of processing of<br>sensitive data on a large scale|

A key aspect of data protection is who monitors and enforces the implementation of the regulation. The establishment of a capable and effective implementing agency is central to ensuring adequate implementation of the regulation and to providing individuals with a policing entity to which resort in case of violations. When it comes to the enforcement of cybersecurity measures, a single central regulator tends to be the most adequate solution, providing oversight and complaints management (UNCTAD 2016). They can inform <mark>data subjects about their rights, supervise and assist data controllers with regard to compliance, and ensure the enforcement of data regulations. Data protection requirements also include registration with the authority and notification of data processing activities (UNCTAD 2016). Depending on the ease, cost, and recurrence of these requirements, they may impose a burden for businesses trying to enter a foreign market (UNCTAD 2016). The OECD revised its privacy guidelines in 2013, highlighting the importance of establishing privacy enforcement authorities (OECD 2013).</mark> Most countries that have adopted a comprehensive framework on data protection have buttressed it with a data protection agency (DPA). Certain countries, such as France, have had a DPA since the 1970s, when large state-owned datasets were the main concern of the public. While most DPAs are independent agencies, some countries have embodied them with ministries or policymaking bodies (Makulilo 2016).

|**Table 14: Cybe**|**rsecurity meas**<br>**Encryption**|**ures around t**<br>**Internal**<br>**policy**|**he word**<br>**Data manager**|**Risk**<br>**assessment**|**Breach**<br>**notifications**|**Supervisory**<br>**authority**|
|---|---|---|---|---|---|---|
|**Albania**||**●**||||**●**|
|**Armenia**|**●**|**●**|||**●**|**●**|
|**Bangladesh**|||||||
|**Burkina Faso**||**●**||||**●**|
|**Canada**||**●**|**●**||**●**|**●**|
|**Colombia**||**●**||**●**|**●**|**●**|
|**France**|**●**|**●**|**●**|**●**|**●**|**●**|
|**Honduras**|||||||
|**Indonesia**|**●**|**●**|||**●**||
|**Kazakhstan**||**●**||||**●**|
|**Kenya**||**●**|||||
|**Korea, Rep.**|**●**||**●**|**●**|**●**|**●**|
|**Kyrgyzstan**||**●**|**●**||**●**|**●**|
|**Malaysia**||**●**||||**●**|
|**Mexico**||**●**||**●**|**●**|**●**|
|**Moldova**||**●**||||**●**|
|**Pakistan**|||||||
|**Senegal**||**●**||||**●**|
|**Tanzania**|||||||
|**Vietnam**||**●**||**●**|**●**|**●**|

24
perfectly correlated with C might be limited in practical applications, a highly correlated matching variable might be sufficient to ensure that the relationship between C and I, given X, is close to its true underlying relationship (Donatiello et al., 2016b).

**The concept of statistical matching draws from the concept of imputations often applied to missing observations in survey data (Bacher and Prander, 2018).** There are three overarching approaches to imputing missing values of the added variable from the recipient data (C) in the donor data: parametric approaches, nonparametric approaches, and mixed methods (European Commission, 2014; Lewaa et al., 2021). Parametric approaches are regression imputations. They make use of the functional form between X and C in the donor dataset to estimate the missing values of C in the recipient dataset (Bacher and Prander, 2018). While they are more parsimonious than nonparametric methods, they rely on pure predictions and model specifications, so the accuracy of the underlying model. Nonparametric approaches rely on distance functions and identify similar observations to impute actual observed values from the donor to the recipient dataset (European Commission, 2014). They often involve the segmentation of observations by socio-economic characteristics or geography as well as choosing a suitable distance function (Bacher and Prander, 2018). Examples of nonparametric approaches are hot deck approaches. Mixed methods combine parametric with nonparametric approaches, such as the estimation of a stochastic regression imputation followed by a nearestneighbor hot deck.

**Table 1 describes the basic concept behind the application in this paper: imputing energy expenditure shares to the EU-SILC in Bulgaria** . The goal of this paper is to create a synthetic dataset that contains information on income-based indicators, households' access to social protection programs, other indicators of households' welfare and living conditions, and energy expenditure shares, as well as energy poverty. To this end, we follow the approach in Table 1, which is highlighted in blue. More concretely speaking, we first create our variable of interest, the energy expenditure

9
21 How does the foreign worker population fare relative to the domestic population with respect to income? In Singapore, there is a higher concentration of foreigners at both the bottom and at the top of the income scale (Chew and Chew, 1992). Whereas non-citizens accounted for 10 percent of total employment in 1989, nearly 25 percent of the lowest income group were non-Singaporean (see Figure 5); the same pattern emerges among top income earners. This suggests that low-wage unskilled jobs are more likely to be held by foreign workers compared to their proportional representation in total employment, supporting the assertion that immigration policy targets foreign workers for unskilled positions and for 90 highly skilled professional jobs. In the middle income categories, the opposite pattern is observed; although Singaporean workers represent 90 percent of total employment, more than 90 percent of middle income earners are Singapore nationals.

<!-- Start of picture text --> Figure 5<br>Income by Citizenship in Singapore<br>Gross monthly income (S$)<br>10°/0 30%/<br>0<br>Cu<br>8anl<br>0<br>V~<br>-Singaporean N r'n-Singaporean<br>--- _<br><!-- End of picture text --> Using the Malaysian survey data, the sample can be broken into deciles according to household per capita income to evaluate the distribution of foreigners and nationals across income groups. A pattern similar to that observed in Singapore emerges. Whereas the sample mean for the share of non-citizens is 4.5 percent, the poorest three deciles **Figure 6** <!-- Start of picture text --> Figure 6<br>Citizenship Distribution by Declie<br>100% _____ -10%<br>98% 8%<br>M 96 6<br>c<br>* 0<br>90% 0%<br>1 2 3 4 5 6 7 8 9 10<br>% Malaysian + % Non-Malaysian<br><!-- End of picture text --> have a moderately higher concentration of non-citizens relative to the sample mean, whereas deciles 4 through 6 have a lower concentration. Noncitizens become more prominent again in the 7th decile and above (see Figure 6). Although these deviations from the sample mean are small in magnitude, the evidence nevertheless suggests a