rafmacalaba/datause-encoder-data huggingface dataset:
Page 10

**K**

Although all of the London Club agreements still carry market rates of interest (Libor plus a margin) and most are for relatively short terms (under 10 years), several borrowers have rescheduled several times, others have interrupted their payments, and a few arrangements have totally broken down. Nevertheless, some smaller borrowers have performed well on their restructured debt, e.g. Malabi, Madagascar, Senegal, Togo and Gambia.

There are few instances where short-term debt has been rescheduled (Nigeria, Madagascar, Sudan, and Zambia), but interest arrears have been rescheduled in just about every rescheduling country. Most countries that have had continued access to short-term trade finance facilities have performed well, with notable exceptions in countries such as C6te d'Ivoire and Congo whose local banks have experienced severe liquidity crises. However, the commercial banks' response to liberalized exchange controls, well-funded auctions and other improved payments systems in countries such as Nigeria, Madagascar, Guinea and Ghana has generally been disappointing, as much from lack of attention as from lack of confidence.

# B. NATURE OF COMMERCIAL BANK LENDING

The various forms of commercial bank cross-border lending are best categorized by the type of risk assumed, the type of borrower, the term of the loan and the nature of the underlying activity being financed. Four fundamental risk categories are briefly characterized below, with an indication of recent trends in the region: unsecured lending, exporc-secured lending, asset-based finance and secured lending.

While individual transactions often combine different forms of risks, all of the categories are typically counted as cross-border loans in the World Bank Debt Tables.

# 1. Unecured Lending

Unsecured lending encompasses loans that are extended on the basis of the borrower's ability to service the loan from resources generated within the borrowing country and transferred to the lending bank through the country's normal foreign exchange allocation system. The lender assumes both the "commercial" risk, i.e. the borrower's solvency and the 'political" risk, i.e. principally that of foreign exchange availability.

Under this category, the three most prevalent forms of lending to Africa have been the following: a) Short-term revolving facilities extended to central and commercial banks for the advance settlement of obligations to offshore suppliers of goods and services, typically through confirmation and negotiation of letters of credit or through short-term advances. As these advances are repaid by foreign exchange transfers from the borrowing country, new availability for further advances is created within the limits of the facility.
- 54 -

Table 11. Consolidated Budget Infrastrcture Expenditures: Summary of Results

|Hy<br>Variable<br>Re|po4beaized<br>lationship|Estimated<br>Relationship|Effect<br>Effect I unit Inrea|Peak or<br>Trough|Ehaticity<br>at Men|
|---|---|---|---|---|---|
|Stock of lnfr_tuure||||||
|FCRNFPA|?|>0|$0.02|-|0.25|
||||(S 0.02)||0.25)|
|Population Denity||||||
|<br>DENS|t|<0|S-0.06|-|-0.34|
||||(S-0.06)||(-0.33)|
|Udranization||||||
|URBAN4|<0|<0|_$-0.5_||-1.1|
||||(S-0.55)||_(-1.15)_|
|Sectoral Imbalance||||||
|MIGZ & MIGZSQ|>0|Inverted U|_$4.45_|5.52|0.37|
||||($4.30)|_(5.51)_|(0.38)|
|Labor Force Participation||||||
|<br>LFPER|?|<0|S-0.79<br>|-|-1.48|
||||($-0.79)||(-1.47)|
|Level of Development||||||
|GDPCAPK|>0 /Inverted U|>0|$0.03|-|1.83|
||||($0.03)||(1.S)|
|Internl Balance||||||
|BBGDPL|?|<0|S-0.04|-|0.01|
||||(-0.08)||(0.01)|
|INFL4 & INFL4SQ|?|U Shaped|S-0.12|241|-0.12|
|External Balance||||||
|TBWIGDPL|?|N.S.|-|-|-|
|BLACK|?|<0|S-0.04|-|-0.07|
|DOLLAR4|?|>0|S 3.29|-|16.81|
|Size Poreign Sector||||||
|TVALX2|>0|>0|S 21.68<br>|-|0.25|
||||(5 22.24)||(0.25)|
|Termc of Trade Shock||||||
|TOTS|>0|_N.S._|-|-|-|
|Debt Obligations||||||
|DSGDP|<0|_N.S._|-|-|-|
|Insitutional Development||||||
|GASTIL|?|U Shaped|$ 0.22|4.26|0.05|
||||(5 0.30)|(4.23)|(0.07)|
|Level Foreign Funding||||||
|<br>NFFCAPIK|>0|>0|$ 0.03|-|0.09|
||||(S 0.03)||(0.10)|
|Mix of Foreign Funding||||||
|NOFGDPI||_N.S._|||-|
|<br>NCBGDPSQ|?|<br>U Shaped|S0.79|-|0.48|
||||($ 0.76)||(0.47)|
|DFIGDP & DFIGDPSQ|<br>>0|U Shaped|<br>S-2.25|51.47|-0.08|
|Povert Comnitment|||(S-2.22)|($1.41)|(4-08)|
|y<br>PCOM|?|_N.S._|-|-|-|
|||**Grade**|**4**||**Grade**|**8**|
|---|---|---|---|---|---|---|
||**Male**|**Female**||**Male**|**Female**||
|**Class/school-level variables**|**%**|**%**|**_φ/φc_**|**%**|**%**|**_φ/φc_**|
|_Teacher major area of study (science teacher)_|||0.245***|||0.167***|
|education and science (grade 4)|40.9|15.8|||||
|science but not education (grade 4)|49.1|66.9|||||
|science and science education (grade 8)||||27.2|10.3||
|science but not science education (grade 8)||||65.6|85.0||
|all other majors|10.0|17.3||7.2|4.7||
|_Professional development hours on mathematics_|||0.326***|||0.196***|
|none|10.5|4.6||11.9|3.8||
|less than 6 hours|20.1|5.4||14.2|12.2||
|6–15 hours|34.0|28.0||34.4|26.1||
|16–35 hours|19.2|27.1||20.9|31.3||
|more than 35 hours|16.2|34.9||18.5|26.7||
|_Professional development hours on science_|||0.238***|||0.324***|
|none|18.6|8.7||13.5|0.2||
|less than 6 hours|12.4|13.0||12.8|8.9||
|6–15 hours|32.9|28.1||35.2|26.0||
|16–35 hours|21.1|16.9||16.9|34.4||
|more than 35 hours|15.0|33.3||21.5|30.6||
|_Professional development on mathematics content_|||0.007|||0.098***|
|yes|55.3|55.0||42.0|50.0||
|no|44.7|45.0||58.0|50.0||
|_Professional development on science content_|||0.131***|||0.182***|
|yes|43.5|56.1||43.6|62.3||
|no|56.5|43.9||56.4|37.7||
|_Professional development on mathematics pedagogy_|||0.075***|||0.081***|
|yes|56.8|61.8||60.5|67.1||
|no|43.2|38.2||39.5|32.9||
|_Professional development on science pedagogy_|||0.053**|||0.172***|
|yes|46.3|47.1||57.2|76.8||
|no|53.7|52.9||42.8|23.2||
|_Principal highest level of education_|||0.112***|||0.027*|
|ISCED Level 6 — bachelor’s or equivalent level|82.4|92.9||90.9|93.7||
|ISCED levels 7 & 8 — master’s or doctorate degree|17.6|7.1||9.1|6.3||
|_Principal qualification in educational leadership_|||0.168***|||0.233***|
|yes|25.6|14.8||28.5|8.5||
|no|74.4|85.2||71.5|91.5||
|_School library_|||0.187***|||0.026|
|yes|69.5|49.4||74.7|72.4||
|no|30.5|50.6||25.3|27.6||

_Note:_ * _p_ < .05; ** _p_ < .01; *** _p_ < .001.

13
|||**Particula**<br>**te matter**|**Sulphur**<br>**dioxide**|**Carbon**<br>**monoxid**<br>**e**|**Nitrogen**<br>**oxide**|**Hydrocar**<br>**bons**|**Volatile**<br>**organic**<br>**compone**<br>**nts**|
|---|---|---|---|---|---|---|---|
|||**PM**|**SO2**|**CO**|**NOx**|**CnHm**|**VOC**|
||transport; Supporting and auxiliary transport activities;<br>activities of travel agencies|||||||
|LO|Public administration and defense; compulsory social security;<br>Other community, social and personal service activities|2,15|1,05|2,98|0,49|10,18|1,05|
|K|Real estate, renting and business activities|5,85|24,30|46,87|12,60|3,68|13,78|

_Source: Authors’ calculations based on SUST-RUS data_ 102
OZIER - REPLICATION REDUX – p. 39 of 41 <mark>Stanley, T. D., 2001. Wheat from chaff: Meta‐analysis as quantitative literature review.</mark> _<mark>Journal of economic perspectives</mark>_ <mark>,</mark> _<mark>15</mark>_ <mark>(3), 131‐150.</mark> Taylor‐Robinson, D.C., Maayan, N., Soares‐Weiser, K., Donegan, D., and Garner P., 2015 “Deworming drugs for soil‐ transmitted intestinal worms in children: effects on nutritional indicators, haemoglobin, and school performance (Review).” _Cochrane Database of Systematic Reviews_ . 7.

Welch, V.A., Ghogomu, E., Hossain, A., Awasthi, S., Bhutta, Z.A., Cumberbatch, C., Fletcher, R., McGowan, J., Krishnaratne, S., Kristjansson, E., and Sohani, S., 2017. “Mass deworming to improve developmental health and wellbeing of children in low‐income and middle‐income countries: a systematic review and network meta‐ analysis.” _The Lancet Global Health_ , 5(1):e40‐e50.

World Health Organization, 1987. Prevention and Control of Intestinal Parasitic Infections. Report of the WHO Scientific Group. WHO Technical Report Series: 749. Geneva:WHO.

World Health Organization, 2017. Guideline: preventive chemotherapy to control soil‐transmitted helminth infections in at‐risk population groups.
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where _τ_ is the pension contribution rate. Household income for a single household, _yt_<sup>_j_,is</sup> defined similarly.

Formal labor earnings net of pension contributions and private savings returns are subject to a progressive income tax. Taxes due at period _t_ are denoted by _T_ ( _At, wF,t_<sup>_m, w_</sup> _F,t_<sup>_f, d_</sup> _t_<sup>_m, d_</sup> _t_<sup>_f_),</sup> and depend on the household’s stock of private savings, formal sector earnings offers and labor force participation decisions.<sup>28</sup> Net borrowing and borrowing against pension savings are not allowed. It is assumed that individuals working in the informal sector do not pay taxes on their labor income.

# **4.5 Separation and mortality**

In each period, the a probability of the man or woman (whether in a couple or single) surviving to the next period, _π_<sup>_sj_</sup> = _π_<sup>_sj_</sup> ( _at_ ), is assumed to be exogenous with respect to the other aspects of the model.<sup>29</sup> Widows inherit a portion of their former spouse’s pension funds to finance a survivorship pension.

Household separation (for reasons other than widowhood) is modeled as an exogenous event. Conditional on both spouses surviving, the probability of becoming separated in period _t_ is assumed to depend on the man’s level of education ( _e_<sup>_m_</sup> ), and the ages of the spouses ( _a_<sup>_m_</sup> _t_<sup>_, a_</sup> _t_<sup>_f_).Until2004,divorcedidnotexistinChile.Forsimplicitywetreatdivorce,</sup> marriage annulment and de facto separation as equivalent in the model. The separation probability is specified as a logistic model, Upon separation, a couple’s non-pension assets _At_ are split evenly between the two individuals who then become single households.

Recall that one feature of the pension reform was a change in the rules governing pensions upon divorce. Prior to the reform, divorce could lead to a loss of rights to a spouse’s pension benefits. After the reform, in the event of a divorce or annulment, a judge can rule that

> 28See table B8 for the actual bracket values and corresponding marginal tax rates.

> 29We obtain these probabilities from life tables that are specific to Chile and are conditional on age and gender (RV-2004, from Circular 1314, published by the Superintendencia de Pensiones).

15
# **The Data Source**

For the analysis of private transfers, we use data from household and village surveys conducted by the Research Center for Rural Economy (RCRE) at the Ministry of Agriculture and a supplementary survey conducted in 55 villages of four provinces from August to October 2004 in collaboration between Michigan State University and RCRE.<sup>11</sup> All 3999 households in the 2003 wave of the RCRE panel for these four provinces were enumerated, allowing us to match villages and households from the 2004 supplemental survey with a historical panel of villages and households that RCRE surveyed annually from 1986 to 2003.<sup>12</sup> One unique feature of the supplemental survey is that it enumerated the educational attainment, birth year, current occupation, work and migration history, and residence location for all children and other current and former residents (including deceased former residents) of survey households. Using a calendar-based history to collect this information, the supplemental survey allows one to better characterize the individual characteristics of both household members and non-resident family members in all survey rounds from 1995 to 2003. This information is crucial for reducing biases that may be introduced when lacking information on the size and characteristics of the network of non-resident family members, which affect the quality of the transfer network.

Of the 3999 households that were resurveyed, 494 had at least one resident over age 60 in 1995 and this rose to 816 by 2003. These households comprise our analysis sample, which we split into an early period (1995 to 1998) and late period (2000 to 2003) in order to pick up

> 11The Supplemental Rural Household Social Network, Labor Allocation and Land Use Survey was carried out in collaboration with Michigan State University in 2004. Details on the supplemental survey protocol and survey instruments can be found at www.msu.edu/~gilesj/.

> 12A detailed discussion of a larger nine-province sample from the RCRE panel dataset, including discussions of survey protocol, sampling, attrition, and comparisons with other data sources from rural China, can be found in the data appendix of Benjamin, Brandt and Giles (2005). This paper makes use of village and household data from the four provinces where the authors conducted follow-up household and village surveys, which are Shanxi, Jiangsu, Anhui and Henan.

17
of US$10,000. The reason for focusing on developing countries is that we want to have a common support with regard to a country’s wealth. If a rich country shows outstanding results, perhaps it may be of interest to other rich countries which do not do as well, but it is hardly of great interest to a poor country. But if a poor country does very well, and stands out from the pack of poor countries that mostly do poorly in PISA, readers from poor countries want to know what can explain such a phenomenon, since it clearly cannot be attributed to the wealth of the country (as captured albeit imperfectly by per capita GDP). We start by looking at the Mathematics scores, with the identical approach being used for the other two PISA disciplines; Reading and Science.

# **3.1 Mathematics**

We estimate a weighted least squares regression of student level test scores as follows:<sup>8</sup> A key estimate of interest is _γ_ , the coefficient on _V IETNAM_ , a 0 or 1 dummy variable. Regressions are run in a sequence, starting from one without any covariates in _X_ , and then adding variables in groups to expand _X_ in consecutive columns in Table 10. Column (1) in Table 10 shows that the Vietnam dummy has a coefficient of 128.05, when no other covariates are added. By construction, this is the absolute difference in means between Vietnam and the Dev7 countries. Next, we want to see the extent to which observable variables included in the PISA dataset can help to explain this large gap of 128.05.<sup>9</sup> The first set of variables included in the regression reported in column (2) concern the students themselves. The student characteristics were - if students went to pre-school, repeated a grade in the past, and how often they are late for school (ST08Q01) or skipped classes (ST115Q01). With these variables included, the coefficient on the dummy or “the Vietnamese advantage” or “gap”, comes down by nearly 20 points, or roughly 0.2 standard deviation units, to 108.91. In other words, one key reason that the Vietnam gap is so high is because of these student related variables - this result was hinted at in the endowment comparison presented earlier in Section 2. Note that of the four student variables used in column (2), only two are

> 8This is a simplification, used to present our main idea. In PISA, the test score is not provided as a single value but as a set of five plausible values for each student, and complex algorithms have to be used for weighting based on a method called Balanced Repeated Replication (BRR) using Fay’s variant. Details are provided in the PISA technical manual [OECD, 2014a]. In this paper, we utilize the R _intsvy_ package for implementation.

> 9For explanatory variables not discussed in the previous sections but used for the regressions here, please see Appendix Table A1 for a comparison of mean values.

20
# **<u>Table III.11 Coffee Costs of Production in Central American Countries</u>**

|||**Consultants**|**CEPAL**|**LMC**|
|---|---|---|---|---|
|||US$/quintal|US$/quintal|US$/quintal|
|**Guatemala**||51|74|74|
|**Costa Rica**||77|86|100|
|**El Salvador**||48|57|84|
|**Nicaragua**|||70|67|
||_Traditional_<br>_Semi-technified_<br>_Technified_|53<br>58<br>70|||
|**_Honduras_**|||62|65|
||_Traditional_<br>_Semi-technified_<br>_Technified_|36<br>46<br>58|||

**_Source:_** The sources of data for this table are explained in Varangis, Siegel, Giovannuci, Lewin (2003, p.12).

52
capital flows, and private sources have increasingly dominated external financing in recent years. At the early stage of transition, official funding and guaranteed capital increased sharply, with bilateral and multilateral sources accounting for most of the flows. In 1992, once some of the CEE countries regained access to international credit markets, private capital flows begun to exceed official flows, and by 1996 the former accounted for more than 80 percent of total flows (figure 1). This type of development is not different from what has been observed in other regions (for example, East Asia and Latin America), except that in CEE, the speed at which official-to-private conversion has taken place has been much more rapid.

Figure 1. CEE Countries: Net Capital Flows by Source (percentage of total) <!-- Start of picture text --> 100 i<br>90<br>80<br>70-<br>60-<br>50-<br>40-<br>30<br>20-<br>10<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997<br>KOfficialFlows MPrivateFlows<br><!-- End of picture text --> _Source:_ World Bank, _Global Development Finance_ 1998.

Second, there is a strong concentration of private capital inflows in a relatively few countries.

Hungary, the Czech Republic, and Poland-the earlier and faster reformers among the CEE countries3
Causes and of civil strife: consequences Micro-level evidence from Uganda' Klaus Deininger World Bank l The views presented are those of the author and do not necessarily reflect those of the World Bank, its Executive Directors, or the countries they represent. Helpful discussions with and comments by Paul Collier, Ibrahirn Elbadawi, Marta Reynal-Querol, and Jolyne Sanjak are gratefully acknowledged.
# Table 3. Bond Maturity OLS Regressions (Equation (2), excluding Chinese issuance)

|Dependent V|ariable:Ma<br>Push<br>Factor|turityof B<br>_GDPPC_|ond Tran<br>Interaction|che<br>Push Factor|<br>_GROWTH_|Interaction|Push Factor|<br>_EXT_|Interaction|Push Factor|_CAD_|Interaction|Push Factor|<br>_PCRED_|Interaction|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|_VIX_|2.399|||-4.766**|||-4.108|||-3.735*|||-2.003*|||
||(6.849)|||(2.198)|||(2.560)|||(1.869)|||(1.008)|||
|Pull Factor||-0.0849<br>|||-0.416<br>|||-0.0407<br>|||-0.404<br>|||0.184<br>||
|||(2.825)|||(0.412)|||(0.0434)|||(0.404)|||(0.187)||
|Interaction|||-0.747|||0.175|||0.00160|||0.122|||-0.0457|
||||(0.780)|||(0.132)|||(0.0111)|||(0.122)|||(0.0541)|
||_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|
||3,247|60<br>184|2,887|3,247|60<br>184|2,887|3,247|60<br>184|2,887|3,247|60<br>184|2,887|3,247|60<br>184|2,887|
|_RISK_|-0.122|||-5.049***|||-4.708**|||-4.486***|||-3.325***|||
||(3.943)|||(1.873)|||(1.928)|||(1.630)|||(1.089)|||
|Pull Factor||-2.005|||0.0198|||-0.0366|||-0.112|||0.0761||
|||(1.254)|||(0.117)|||(0.0229)|||(0.123)|||(0.0569)||
|Interaction|||-0.531|||0.120|||0.00145|||0.0990|||-0.0352|
||||(0.496)|||(0.115)|||(0.00741)|||(0.103)|||(0.0389)|
||_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|
||3,247|60<br>184|2,887|3,247|60<br>184|2,887|3,247|60<br>184|2,887|3,247|60<br>184|2,887|3,247|60<br>184|2,887|
|_FED_|5.666|||3.432**|||3.399**|||3.649***|||4.318***|||
||(3.871)|||(1.406)|||(1.471)|||(1.365)|||(1.530)|||
|Pull Factor||0.912|||-0.656|||-0.0704|||-0.648|||0.312||
|||(5.961)|||(1.510)|||(0.118)|||(0.995)|||(0.228)||
|Interaction|||-0.241|||0.0552|||0.00255|||0.0431|||-0.0190|
||||(0.454)|||(0.109)|||(0.00797)|||(0.0693)|||(0.0151)|
||_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|
||3,247|60<br>184|2,887|3,247|60<br>184|2,887|3,247|60<br>184|2,887|3,247|60<br>184|2,887|3,247|60<br>184|2,887|
|_LIBOR_|0.154|||-2.102*|||-1.496|||-1.467|||-0.675|||
||(2.437)|||(1.107)|||(1.058)|||(0.883)|||(0.561)|||
|Pull Factor||-2.772|||-0.188|||-0.0371|||-0.220|||0.120||
|||(1.693)|||(0.183)|||(0.0300)|||(0.196)|||(0.0854)||
|Interaction|||-0.206|||0.105*|||-0.00277|||0.0690|||-0.0224|
||||(0.293)|||(0.0616)|||(0.00381)|||(0.0535)|||(0.0196)|
||_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|_Obs._|_#C_<br>_#Id_|_#Bd_|
||2,918|60<br>183|2,595|2,918|60<br>183|2,595|2,918|60<br>183|2,595|2,918|60<br>183|2,595|2,918|60<br>183|2,595|

Robust standard errors clustered on the country-industry level in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Note: Obs. = Observations, #C = Number of Countries, #Id = Number of Industries, # Bd = Number of Bonds. R-squared for all regressions = 0.25; R-squared for regressions with _LIBOR_ = 0.63 42
**Have Remittances Affected Real Unit Labor Costs in the Transition Economies of Eastern Europe, the South Caucasus, and Central Asia?**

Jimmy Apaa Okello,<sup>1</sup> Martin Brownbridge,<sup>2</sup> Sudharshan Canagarajah<sup>3</sup> 1 Bank of Uganda, 2 Independent Consultant, 3 World Bank The authors write in their personal capacity JEL Classification Codes: E24, F24, F66, J30 Key words: remittances, unit labour costs