rafmacalaba/datause-encoder-data huggingface dataset:
rated institutions, along the lines of the programs offered by the MDBs. In the short term at least, it may also be helpful to promote continued flexibility in the implementation of Basel II risk weighting in order to give some relief to trade finance.

# _9._ _<u>Improving transparency</u>_

It is clear that Basel II rules on the treatment of trade finance have resulted in a somewhat unfavorable treatment of this asset class as compared to other types of business lines. However it is also clear that the lack of availability of loss data for trade finance transactions as well as the ‘one size fits all’ approach by participants that all trade is low risk, is a major factor in this problem. This can only be remedied by a concerted effort on the part of all the major trade finance banks to collaborate in the collation of default and loss data so that appropriate relief can be argued with regulators and BIS. The creation of a ‘Berne Union’ of banks, which allows regular sharing of such data confidentially, could be a potential longterm solution. In inter-firm credit markets, extending ‘public credit registers’ and voluntary exchange mechanisms to developing countries, where these systems are often still being designed, and promoting the sharing of this information across trading countries could be another solution. By acquiring information about repayment history of the customer across a range of suppliers, the seller firm may increase the information on which to base its credit extension decision

# _10._ _<u>Avoiding moral hazards and wasteful subsidies</u>_

Achieving the desired aims of stimulating greater trade finance lending is a significant enough challenge. Doing so without creating substantial moral hazards or subsidizing ”winners” is an even greater one. This challenge can be partly addressed through targeted programs that restrict access to banks and firms who really need them. However, experience has shown that achieving this often results in complicated programs that end up being too cumbersome and costly to be taken up in the market. Among the practices which have been shown to be effective in limiting moral hazards and wasteful subsidies are limiting the timeframes of programs to avoid crowding out commercial banks, and sharing rather than fully underwriting risk:

- _Limiting timeframe_ : programs should stay in the market only as long as needed and avoid crowing out commercial banks and displacing private activities; identifying triggers for when the market is returning to normal (e.g., the evolution of credit spreads) and planning a ‘soft exit’ from the market is critical.

- _‘Market rate’ loans and shared risk_ : Sharing rather than fully underwriting risk is preferable to avoid moral hazard on the part of banks. Similarly offering loans at a reasonable market rate (to the degree the issue is one of supply rather than price per se) helps avoid adverse selection in loans.

20
|**Class/school-level variables(reference category)**|**_B(SE)_**|**_B(SE)_**|**_B(SE)_**|**_B(SE)_**|
|---|---|---|---|---|
|Teacher job satisfaction|1.10 (4.34)|-0.03 (4.03)|-3.32 (3.34)|-4.52 (3.17)|
|Time assigned to mathematics/science homework (15 minutes or less)<sup>b</sup>|4.63 (8.85)|9.68 (8.52)|-7.30 (11.31)|-5.47 (9.81)|
|Poor teacher timekeeping (not a problem)|||||
|minor problem|-24.39 (11.49)*|-14.89 (8.67)|2.50 (11.75)|4.86 (10.50)|
|moderate or serious problem|-2.18 (17.35)|13.44 (16.06)|-1.66 (17.19)|-0.39 (15.45)|
|Teacher absenteeism (not a problem)|||||
|<br>minor problem|8.04 (13.45)|5.03 (11.02)|1.01 (13.17)|-2.16 (10.94)|
|moderate or serious problem|-7.64 (23.90)|-8.53 (21.01)|1.87 (16.55)|5.55 (13.59)|
|Teacher major area of study (education and mathematics/science)|||||
|mathematics/science but not education|-8.20 (9.26)|-6.76 (8.18)|-2.69 (9.78)|-7.35 (8.84)|
|all other majors|-9.88 (12.49)|-18.21 (10.74)|-23.80 (18.47)|-34.13 (17.72)|
|Professional development hours on mathematics/science (more than 35 hours)|||||
|16–35 hours|-29.46 (11.69)*|-22.62 (10.50)*|3.50 (14.09)|5.25 (11.73)|
|6–15 hours|-5.35 (13.24)|4.99 (12.26)|-5.01 (12.01)|-4.52 (10.92)|
|less than 6 hours|-3.30 (14.44)|4.14 (12.28)|18.81 (15.70)|12.82 (13.31)|
|none|-1.71 (21.24)|-2.60 (16.21)|-35.32 (15.06)*|-37.77 (15.23)*|
|Professional development on mathematics/science content (no)|19.53 (10.85)|15.17 (10.60)|9.96 (12.76)|11.64 (11.14)|
|Professional development on mathematics/science pedagogy (no)|13.29 (11.26)|14.85 (9.46)|-2.53 (10.00)|-1.60 (9.00)|
|Principal years of experience|-0.13 (0.79)|-0.29 (0.71)|-0.20 (0.66)|0.15 (0.60)|
|Principal highest level of education (ISCED levels 7 & 8 — master’s or doctorate degree)<sup>c</sup>|-3.76 (15.01)|4.78 (12.47)|-11.91 (10.93)|-13.19 (9.41)|
|Principal qualification in educational leadership (no)|-4.76 (13.02)|-6.06 (10.83)|5.72 (9.43)|7.28 (8.66)|
|School library (no)|19.09(10.82)|15.10(10.29)|4.69(8.59)|7.65(7.90)|
|**Interaction terms(reference category)**|||||
|Student gender*Safe and orderly schools||-16.25 (5.13)**||-15.61 (4.93)**|
|Student gender*Student absenteeism (never or almost never) — once a week||22.88 (10.44)*|||
|Student gender*Teacher age (40 years or older) — 29 years or younger||89.46 (30.62)**|||
|Student gender*Literacy and numeracy readiness for school||||-6.29 (2.57)*|
|Studentgender*School location(urban)— suburban/medium size cityor large town||||75.76(15.57)***|
|Loglikelihood (_H0_)|-8549.92|-8533.11|-12381.76|-12361.03|
|**Fit statistics**<br>_AIC_|17193.84|17166.23|24857.51|24822.05|
|_BIC_|17442.83|17431.12|25123.27|25104.77|

_Note:_ Null mathematics model: _Intercept_ ( _SE_ ): 402.02 (4.44), _ICC_ = 0.31, _H0_ = -32210.89, _AIC_ = 64427.78, _BIC_ = 64447.59. Null science model: _Intercept_ ( _SE_ ): 407.42 (4.88), _ICC_ = 0.30, _H0_ = - 32704.43, _AIC_ = 65414.86, _BIC_ = 65434.67. * _p_ < .05; ** _p_ < .01; *** _p_ < .001.<sup>a</sup> Higher scores indicate less frequent bullying;<sup>b</sup> Other category: 16 minutes or more;<sup>c</sup> Other category: ISCED Level 6 — bachelor’s or equivalent level.

23
(1990–2000)’, in Ç. Özden and M. Schiff (eds), _International Migration,_ _Remittances and the Brain Drain_ , Basingstoke, UK: Palgrave-Macmillan, ch. 5. Docquier, F., B.L. Lowell and A. Marfouk (2009), ‘A gendered assessment of highly skilled emigration’, _Population and Development Review_ , **35** (2), 297– 321.

Docquier, F., Ç. Özden and G. Peri (forthcoming), ‘The wage effects of immigration and emigration’, _The Economic Journal._ Dumont, J.C. and G. Lemaître (2004), ‘Counting immigrants and expatriates in OECD countries: a new perspective’, mimeo, Paris: OECD.

Dumont, J.C., J.P. Martin and G. Spielvogel (2007), ‘Women on the move: the neglected gender dimension of the brain drain’, Discussion Paper Series No. 2920, Institute for the Study of Labor.

Dunlevy, J. A. (2006), ‘The influence of corruption and language on the protrade effect of immigrants: evidence from American states’, _Review of Economics and Statistics_ , **88** (1), 182–6.

Dunlevy, J. A. and W. K. Hutchinson (2001), ‘The pro-trade effects of immigration on American exports during the period 1870 to 1910’, Vanderbilt University Working Paper 01-W25.

Felbermayr, G. J. and B. Jung (2009), ‘The pro-trade effect of the brain drain: sorting out confounding factors’, <u>Economics Letters,</u> **104** (2), 72–5. Felbermayr, G. and F. Toubal (2008), ‘Revisiting the trade–migration nexus: evidence from new OECD data’, mimeo.

Feng, S.F, A.B. Krueger and M. Oppenheimer (2010), ‘Linkages among climate 51
those surveyed. Through a longitudinal household survey, Gray and Mueller (2012a) studied the consequences of drought on population mobility in Ethiopia’s rural highlands, providing evidence that drought increases long-distance and work-related relocation of men, especially in land-poor households. However, severe drought reduces women’s short-distance and predominantly marriage-related mobility.

Another study by Karanja Ng’ang’a et al. (2016) shows that climate change influences the livelihoods of shepherds in arid and semi-arid lands in Kenya. By analyzing data from a survey of 500 rural households in northern Kenya that relates adaptive family behavior to family migration, their analysis suggests that migration and local innovation are complementary mechanisms to ensure resilience to adverse shocks. In addition, families with at least one migrant member can employ high-cost agricultural innovations through remittances, thus improving their self-protection against weather shocks. Mueller, Gray and Hopping (2020) use census data on the migrations of 4 million individuals over 22 years to estimate the climate effects on migration in Botswana, Kenya, and Zambia. Their results for Kenya show that temperature had limited effects on migration, whereas a one standard deviation increase in precipitation caused a 10% reduction in migration. In Botswana, mobility decreases by 19% with a one standard deviation increase in temperature, and an equivalent change in rainfall causes an 11% decrease in migration. The effects of temperature appear more severe among poorly educated individuals. Rainfall shocks increase mobility in Zambia, while an increase in temperature does not affect mobility in the region. Decreases in inactivity and unemployment coincide with increases in migration, which suggest that the perspective of new job opportunities may act as a driver of climate-induced migration.

Mastrorillo et al. (2016) also argue that agriculture may function as a primary channel through which adverse weather conditions influence migration. They combine South African census data with climate data on spatiotemporal weather variability to examine South African bilateral inter-district migration flow patterns and determinants during the periods 1997-2001 and 2007-2011. The results reveal that precipitation scarcity and higher temperatures act as push effects for migration. However, the importance of the effect of climate on migration varies greatly depending on migrant characteristics, including ethnicity. In particular, the flows of black and low-income migrants in South Africa are strongly influenced by climate variables, while white and high-income migrants are weakly or not affected.

Focusing on Uganda, Agamile, Ralitza and Golan (2021) assess gender-differentiated reactions of smallholder farmers to droughts, finding that adverse weather conditions are an occasion for women to enter the commercial crop market by exploiting land from subsistence for income-generating crops, while relatively wealthier and better-educated people, especially men, are among those who benefit most from 14
- 14 -

# D. DEVELOPMENTIN ELECTED **S** SUBSECTORS

25. Industrial regulatory reforms during the 1980s have introduced more competition between domestic firms by relaxing regulations on capacity licensing, production levels, and prices. These reforms have had important effects in a number of subsectors where realized protection has been lower than that afforded by the trade regime. At the same time, both the firm level EPRs and model simulations outlined in the previou **s** ections indicate that the core input supplying sectors of the economy are highly protected and relatively inefficient. This section first presentr " few examples where liberalization of the regulatory environment has led to increasing efficiency. In the second part, a more detailed analysis of the core sectors, i.e., steel, petrochemicals and capital goods is undertaken to identify more precisely the sources of inefficiency in these subsectors. It should be pointed out that the information for these subsectors is highly uneven across different product groups. Therefore, the analyses are partial and focus on product groups where the information is available.

26. One successful example of liberalization has been the cement subsector. Prior to 1982, this subsector was highly regulated, ex-factory prices were controlled, and plants were obliged to sell a part of their production to the public sector at a below-market "levy" prices. As levy prices failed to keep pace with rising input costs (e.g., energy), the price controls had an adverse impact on the subsector's profitability. Moreover, investments in cement were constrained by restrictive capacity licensing policies, particularly towards MRTP companies, and by the levy sale exemptions granted to mini cement plants. In addition the freight equalization scheme, introduced in 1956 to permit uniform cement prices throughout the country, distorted decisions regarding plant location.

27. The reforms adopted in 1982 included gradual reduction and elimination of the levy cement obligation, a concomitant complete decontrol of prices, elimination of the freight equalization scheme, significant relaxation of Investment licensing and entry of MRTP firms, and easing in foreign technology transfers and collaborations. The response of the cement subsector to liberalization was impressive. Investment and production accelerated, price increases in deficit regions brought further investments in these regions, and
and Delgado found that about 80 percent of their respondents paid their dental treatment in Hungary out-of-pocket, while only 20 percent were partially reimbursed (2006: 134). These numbers suggest that the majority of cross-border cases take place outside the EC regulatory framework and Austrian reimbursement provisions. However, the exact number of this category of patients remains unknown.

UNIQA, a private insurance company, provided data on in-patient treatment of Austrian patients abroad (see Table 5). In 2003, UNIQA reimbursed 692 persons, world-wide, for the total amount of 2,349,754 Euro. In 2005, the numbers were still rather low and increased only modestly (937 persons; 2,985,412 Euro).

**Table 5: UNIQA: In-patient Treatment Abroad, 2003–2005**

||**2003**|**2004**|**2005**|
|---|---|---|---|
|Switzerland|60|81|75|
|Germany|271|266|293|
|Europe|229|201|305|
|Rest ofthe world|132|155|264|
|Total|692|703|937|
|Amountin Euro|2,349,754|2,033,337|2,985,412|

Source: Data provided by courtesy of UNIQA, assembled by the author.

# **4.2 Data for Hungary**

Within the regulatory structures of the EC, about 42,500 Austrian insured persons received Hungarian health treatment in 2005 for the total amount of 732,558 Euro (see Tables 6 and 7). These numbers cover several different groups: Austrian pensioners resident in Hungary, regular cross-border commuters, co-insured family members, and persons who stayed in Hungary only for a short term (see Table 6). The majority of cases concerned medical aid and visits paid to a general practitioner. Half of the overall costs were caused by expensive benefits in kind (see Table 7).

**Table 6: Treatment in the Framework of EC Regulation 1408/71, 2005**

|**Group of persons**|**Form**|**Number of**<br>**cases**|**Type of calculation**|**Granted benefits**|
|---|---|---|---|---|
|Pensioners (insured in<br>Austria, resident in<br>Hungary)|E121|40|Lump-sum|All benefits|
|Regular cross-border<br>commuters|E106|1,592 (registered<br>in Hungary, plus<br>co-insured)|Actual costs|All benefits|
|Co-insured family members|E109|50 (registered in<br>Hungary)|Lump-sum|All benefits|
|Short term stay|EHIC|About 41,000|Actual costs|Medically<br>necessary<br>treatment|

Source: Data provided by courtesy of the Hungarian NHIF, assembled by the author.

17
incomes on staple foods (Cranfield, Preckel and Hertel 2007). On the other hand, the incomes of farm households—frequently one of the poorest groups in low-income countries—may be increased by higher commodity prices (Hertel, Ivanic, Preckel and Cranfield 2004). However, the benefits of higher food prices to poor farm households may be less than they might at first appear, since these benefits depends not on what they produce, but on their net sales of these goods.

In this study, we attempt to address the main implications of higher food prices on poverty following the methodologically simple yet data-intensive approach of calculating the short-run impacts on households’ income and costs of living following the changes in food prices. We do so using household surveys containing at least a thousand households in each of nine low-income countries, for which the data on consumption and production of the main food commodities are available. Besides calculating the change in household real income, we also estimate the impact of food prices on poverty rates and poverty gaps.

We consider two experiments in this paper. First we estimate the importance of small changes in the prices of individual commodities on poverty rates in each of our sample countries. We do so by conducting a stylized simulation in which we increase individual prices by 10 percent to assess the effect of a small change in the price of each commodity. Second, we estimate the impact of the actual food price changes between 2005 and 2007 on poverty in our sample of countries. In the initial analysis, we assume that changes in international prices are fully transmitted into domestic markets, and consider only the direct impacts of commodity price changes. We also consider scenarios under which commodity price changes lead to changes in the wage rate for unskilled labor—the other key source of income for most low-income households identified in Ravallion (1990).

2
<!-- Start of picture text --> EGRA<br>LLECE<br>PASEC<br>PIRLS<br>PISA<br>SACMEQ<br>TIMSS<br>0 5 10 15<br>Standard error for Original Score<br>Standard Error for HLO Score<br>SUPPLEMENT FIGURE II<br>Standard Errors by Test<br><!-- End of picture text --> Notes: We decompose standard errors on the overall HLO score versus the original test. This reveals the source of uncertainty derived from sampling variation from the original test relative to variation introduced from the production of the linking function.

Finally, we supplement the primary approaches to link regional to international assessments with alternative linking methods. We compare results with learning scores using Item-Response Theory, often considered one of the most reliable methods in the psychometric literature (Kolen and Brennan 2014). IRT models the probability a given pupil answers a given test item correctly as a function of pupil and item-specific characteristics (Mislevy, Beaton, Kaplan, and Sheehan 1992; Holland and Dorans 2006). This methodology is used to construct the underlying tests we use. However, to use it to compare learning _across_ assessments would require enough overlap in the test items across each assessment. This is not true for a large enough set of tests and time periods to create a globally comparable panel data set. For example, TIMSS 1995 and SACMEQ 2000 included overlapping math items, but only had three items to make this comparison. When this overlap is small, standard maximum likelihood estimates will reflect both true variance and measurement error, overstating the variance in the test score distribution. Das and Zajonc (2010) elaborate on the various challenges of estimating IRT parameters with limited item-specific overlap.

While IRT might not be a reliable approach when there is limited item-by-item overlap, we conduct a few comparisons where overlap is larger. We compare our results to the _Linking International Comparative Student Assessment_ (LINCS) project which uses IRT methods and has significant overlap in items for a subset of international studies focused on reading at primary school (Steinmann, Strietholt **,** and Bos 2014). We find that our database can produce similar results to IRT methods for average scores where there is overlap with a correlation coefficient above 0.98 for primary reading scores.

28
Relative to other household surveys, unusual effort went into obtaining accurate data on consumption and income. While the community, individual and project activity surveys used conventional one-time interviews each year, the household surveys were quite different. The surveys were closely modeled on NBS’s Rural Household Survey (RHS) (which is described in detail in Chen and Ravallion, 1996). This is a good quality budget and income survey, notable in the care that goes into reducing both sampling and non-sampling errors. Similarly to the RHS, sampled households maintain a daily record on all transactions plus log books on production. Local interviewing assistants visited each household at two-three weekly intervals to monitor compliance and check questionable data entries or inconsistencies found at the local (countylevel) NBS office. Other trained interviewers also visited at regular intervals to collect additional data. This intensive interviewing method is in marked contrast to most surveys in which the respondent is visited only once or twice.

The consumption aggregate was built up from very detailed data on cash spending on all commodities and imputed values of consumption from own household production, valued at local selling prices. Living expenditures exclude spending on production inputs (which are accounted for in net income from own-production activities).<sup>12</sup> The income aggregate includes cash income from all sources and imputed values for in-kind income. Income is measured net of all production costs, including interest on debt (including loans from the SWP). The outmigrating workers were not tracked, although the income aggregate includes remittances received from family members who migrated, including those supported by the SWP. Remittances are expected to be the main means by which the out-migration component reduced poverty in the short run.

For the 2004/05 follow-up survey, exactly the same survey instrument was used as for the prior surveys. However, toward the end of the period, a rapid-appraisal module was designed by the author and refined based on field testing. The Chinese and local language versions of the module were refined on the basis of field tests in poor villages in a number of locations.<sup>13</sup> For the

> 12 Living expenditures exclude transfer payments, although these only account for a small share of total spending (3.7% over the whole sample in 1996).

> 13 In the development stage for the module, the first field testing was done over two days in two selected poor villages in Jiangxi, and then revised. The module was then fields in 12 villages in Jiangxi, and further refined. The Jiangxi work was supervised by Solveig Buhl (GTZ staff member assigned to the provincial poor-area program

8