rafmacalaba/datause-encoder-data huggingface dataset:
37 at 54.4%. In Togo and Cameroon **these numbers were** 25% and 14.5% for savings **respectively,** and 33.3% and 32.4% for loans **respectively.** In all threo **countries** credit union **savings** and loans grow at significantly **higher** rates than **the** national average (Cuevas, 1988) 6 The proposition that credit cooperatives which rely heavily on members' voluntary savings **as** funds for their loans generally **fare** botter in terms of loan recovery can be confirmed with. examples from Honduras, **the** Dominican Republic, Cameroon, South Korea, Taiwan **and** others.- A credit cooperative pilot project in **the** Dominican Republie **emphasized** technlcal assistance, savings mobllzation and educatlonai campaigns **and a slgnifLcant increase** in real **interest** rates, **and** resulted **in** a substantial **rLse** of membership. As a result, savings and concurrently loans grew signlficantly faster in partLeLpating unions than **in others.** And most Lndlcatlve of all, loan delinquency rates dropped markedly, to below 10% (from rates as high as 50% in certain cas-s) (Poyo, 1988). 7 A survey of 18 credit cooperatives in Honduras revealed that the flnanclal health of these organizatlons was dlrectly related to Lnterest rate pollieLs. Credit cooperatlves with higher rates benefitted from higher deposlts and lower loan delinquencies 6 Although it must be borne in mind that these figures pertain to a national average of all credit unions, it can be concluded that union savings and loans grew significantly in rural areas, as rural credit unions outnumber urban unions. 7 An interestlng fact about the increased interest rates in the Dominican project is, tb effective interest rates remalned basically ionstant despite a doubling in nominal rates. Credlt unions required share accounts as compensatlng deposits. When interest rates **were** low and funds scarce this balance amounted to up to 67% of **the** value of **the** loan, thus slgnificantly increasing the effectlve interest rate on the loan. Wlth lncreased funds available from higher savings, this balance was significantly reduced allowlng malntenance of constant effective rates deospit doubling nominal rates.
repeated values in the independent variables (which have annual frequency) without necessarily introducing new information<sup>17</sup> .

To compare firm level annual shipments with product level annual shipments (reported in Comtrade), the former are aggregated by product, year and destination. This procedure allows observing that the Tanzanian firm level datasets reports from 87 to 100 percent (depending on the year considered) of the exports recorded in Comtrade at the product level. For this reason, it is not necessary to clean or reduce the database.

For the analysis, the firm’s price is proxied by the fob unit value of every annual shipment, which is the value of shipment divided by quantity of shipment, at the firm-product-destination level. More ∑valuefpdt specifically, the fob price is calculated as: ~~.~~ This proxy for price has been largely used in ∑quantityfpdt previous literature, as actual prices are typically not observed. Since the value of the shipments in the dataset is fob value, it should be seen as the “farm gate” price, not including the costs for shipping, handling, storage, marketing, or the tariff paid in the final destination. Therefore, the analysis is conducted under the assumption that this price should only include the mark-up applied by the firm and its marginal costs<sup>18</sup> .

With regard to the firm, value, quantity and prices are the only information available. The dataset is then merged with destination and product specific characteristics such as, annual GDP, GDP per capita (from the Word Development Indicators Database); distance (from CEPII); and the Rauch classification<sup>19</sup> (to classify products as homogeneous and differentiated goods).

Before discussing the econometric analysis, some preliminary statistics are presented. Table 4 shows that the number of firms and products has increased constantly over the 2003-2009 period, while the number of destinations oscillates somewhat, but remains quite stable overtime. Also the average number of products exported per firm is quite stable, while the number of destinations per firm decreases overtime. This last observation might indicate a decline in geographical experimentation and that exporters may limit their trials to a limited number of (maybe more secure) destinations. The number of products and destinations served by firms indicates that firms experiment more with products than with destinations. Also the number of firms per product is quite small, indicating a low degree of competition among firms

> 17 Manova and Zhang (2012)

> 18 The assumption that marginal costs are constant within a given firm across destinations allows attributing differences in unit value to differences in mark-ups.

> 19 <u>http://www.macalester.edu/research/economics/PAGE/HAVEMAN/Trade.Resources/TradeData.html#Rauch</u>

10
due to terms-of-trade effects and the benefits and costs accruing from trade creation and diversion. Figures 2a and 2b show the sources and destinations of US imports and exports by its trading partners for the year 2007 constructed from the GTAP 8 database. The figures show that India accounts for around 2 percent of US imports and exports. The U.S.’s North American Free Trade Agreement (NAFTA) partners, namely Canada and Mexico combined, are the largest sources and destinations of U.S. trade flows (representing 24 percent of imports and 26 percent of exports), followed by the 27 member European Union (EU) in both directions (21 percent of US imports and 25 percent of exports). Figures 3a and 3b show that the U.S. is much more important as a trading partner for India, accounting for 9 percent of imports and 18 percent of exports, respectively. The EU is the most important import source and export destination for India, accounting for 20 percent and 29 percent of India’s imports and exports, respectively.

.

5
Table A.8: Correlation between overall performance on experimental task and screening for dangersigns in direct clinical observation

|||Across cases||Ac|ross respon|ses|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Screened for all fve danger signs|7.17*|||9.57***|||
||(3.82)|||(3.58)|||
|Screened for at least one danger sign||7.72**|||8.59***||
|||(3.20)|||(3.00)||
|Number of danger signs screened|||2.52***|||2.97***|
||||(0.76)|||(0.71)|
|Constant|40.38***|37.26***|36.74***|40.58***|37.45***|36.53***|
||(1.77)|(2.49)|(2.21)|(1.66)|(2.34)|(2.06)|
|Observations|339|339|339|339|339|339|

* p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01. OLS models. The five dangers signs to screen for are: fever and severe fatigue; headache and blurred vision; vaginal bleeding; swelling; convulsions. The screening is conducted by the health worker asking the pregnant woman if she has experienced the danger sign in the current pregnancy or in any previous pregnancy.

54
**<u>Table 2.3. Sectoral Water Use in Turkey</u>**

||Irriga|tion<br>|Dome|stic<br>|Indu|stry<br>|Total use<br><sup>3</sup>|
|---|---|---|---|---|---|---|---|
||Use<br>(hm<sup>3</sup>)|% of<br>total|Use<br>(hm<sup>3</sup>)|% of<br>total|Use<br>(hm<sup>3</sup>)|% of<br>total|(hm)|
|1990|22,016|72|5,141|17|3,443|11|30,600|
|2000|29,300|75|5,800|15|4,200|10|39,300|
|2004|29,600|74|6,200|15|4,300|10|40,100|
|2023<sup>a</sup>|72,000|64|18,000|16|22,000|20|122,000|

Note: Both the sectoral and total use varies depending on the source. We have presented the most recent findings.<sup>a</sup> Target mentioned in DSI (2007) implies full utilization of all usable water supplies. _Sources: SPO, 2007; DSI, 2007._ The total irrigated area was 5 million hectares in 2007 (Table 2.4) with 75 percent of the water allocated to irrigation. The irrigated area has already reached 60 percent of the total “economically irrigable” area of 8.5 million hectares. Water consumption per hectare amounts to more than 6,000 m<sup>3</sup> . With respect to geographical regions used in this study, more than 40 percent of the total irrigated area is located in the Western region.

12
Jorgensen, Steen, Margaret Grosch, Mark Schacter (1992), _Bolivia's Answer to Poverty, Economic Crisis, and Adjustment: the Emergency Social Fund,_ IBRD Regional and Sectoral Studies.

Khadiagala, Lynn S. (1995) "Social Funds: Strengths, Weaknesses, and Conditions for Success." World Bank Education and Social Policy Discussion Paper #52.

# Klugman, Jeni, (1999) "Social Safety Nets and Crises." Mimeo, IBRD PREMPOV

IBRD (1999) "Managing the Social Dimensions of Crises: Good Practices in Social Policy" mimeo.

Lustig, Nora (1997), The Safety Nets which are not Safety Nets: Social Investment Funds in Latin America, mimeo, IDB.

Ministerio de Trabajo y Seguridad Social (1999) "Evaluacion Ex-Post de los Proyectos Financiados por el Programa Trabajar II," Unidad Coordinadora Central del Programa Trabajar II, Buenos Aires, Argentina.

Morduch, Jonathan (1995) "Income Smoothing and Consumption Smoothing," _The Journal of Economic Perspectives,_ Vol. 9, Number 3, Summer 1995.

Ravallion (1991) "Reaching the Rural Poor Through Public Employment: Arguments, Evidence, and Lessons from South Asia." _World Bank Research Observer_ 6:2 153-175.

# Ravallion, Martin (1998) "Appraising Workfare Programs" mimeo, IBRD

Ravallion, Martin and Quentin Wodon (forthcoming), "Does Child Labor displace Schooling? Evidence on Behavioral Responses to an Enrollment Subsidy, _Economic Journal._ Ravallion, Martin (2000), "Are the Poor Protected from Budget Cuts: Theory and Evidence for Argentina." Mimeo, Regional Study on Risk, LAC.

Schady, Norbert R. (2000) "The Political Economy of Expenditures by the Peruvian Social Fund (FONCODES), 1991-1995, _American Political Science Review_ 94:2 289304.

Squire, Lyn and Herman G. van der Tak (1995), _Economic Analysis of Projects,_ Baltimore: Johns Hopkins University Press.

Stewart, Frances and Willem van der Geest (1995) "Adjustment and Social Funds: Political Panacea or Effective Poverty Reduction? Employment Papers 2, Geneva: ILO (employment department).

23
**Table 1 : Incentives for Information Sharing from the Perspective of the Home and the Host Supervisor**

|||**Parent Bank**<br>|||
|---|---|---|---|---|
|**_Host country_**<br>**_entity_**|**Systemic**in h|ome country|**Non systemic**i|n home country|
||Foreign operations<br>**significant**to<br>parent|Foreign operations<br>**insignificant**to<br>parent|Foreign operations<br>**significant**to<br>parent|Foreign operations<br>**insignificant**to<br>parent|
|**_Systemic_**|(a) High|(b) High|(c) High|(d) High|
||priority for|priority for|priority for|priority for|
||both|host|host|host|
|**_Non systemic_**|(e) High<br>priority for|(f) Low<br>priority for|(g) Low<br>priority for|(h) Low<br>priority for|
||home|both|both|both|

_Source_ : Herring (2007) p. 15 From the home country perspective, the most challenging situations occur where the foreign operations are not regarded as systemically important by the host country but are a significant part of a systemically important bank in the home country (case e). From the host country perspective, cases (b) and particularly (d) are the most challenging. These situations arise most frequently in Africa, central and eastern Europe and Latin America.

 _The stage of supervision_ Rosengren (2007) as well as Claessens, Herring and Schoenmaker (2010) have found that incentives between home and host supervisors get magnified as problems get worse. Hence, divergences between these sets of incentives will also get magnified. As a consequence, incentives for communication and cooperation will further diminish as the health of the bank deteriorates because each jurisdiction will give priority to addressing its own problems and will not communicate with others.

The analysis developed in the next section builds on the work done by Herring (2007) but takes into account this time dimension as reflected in the stage of supervision. It also analysis the conflicts in incentives in three scenarios: preventative supervision, deterioration in the health of the parent bank and deterioration in the health of the subsidiary bank.

# **IV. A Schematic Presentation of Supervisory Incentives for Information Sharing**

Consider an integrated banking group with centralized liquidity, capital and risk management where the subsidiary is dependent on the parent bank for capital, funding and liquidity. The banking group has a home and a host supervisor, which also act as agents to their respective governments. Assume that this is a systemically important bank in the home country. The consolidated banking group is headed by the parent bank and the consolidated banking group is supervised by the home supervisor.<sup>13</sup>

> 13 Annex A shows the analysis in this section in more general terms.

14
7 Table 1.4. Mean Interest Rates on Positive Interest Rate Loans and the Proportion of Loans with Non-zero Rate to Total Loans (1990)

||**Loan**<br>**Institutio**|**s from**<br>**nal Sources**|**Loan**<br>**Frien**<br>**rela**|**s from**<br>**ds and**<br>**tives**|**Loan**<br>**Nonins**<br>**Informa**|**s from**<br>**titutional**<br>**l sources**||**Other**|
|---|---|---|---|---|---|---|---|---|
|**AntQuinC**|**Mean**<br>**interd**<br>**Rate**|**Share**of<br>**total**<br>**loam**|**Mean**<br>**intend**<br>**rate**|**Share**of<br>**total**<br>**han**|Mean<br>**interest**<br>**Rate**|**Sareof**<br>**total**<br>**loam**|Mean<br>**Intren**<br>**Rate**|Share of<br>**totl**<br>**l0MM**|
|Lowest|11.1|66.7|10.0|0.5|30.0|0.2|10.7|0.5|
|**Secod**|11.5|53.8|20.0|0.4|24.8|1.0|13.9|1.8|
|Third|11.9|73.9|0.0|0.0|IS.0|0.3|12.0|3.1|
|Fourth|12.6|73.1|0.0|0.0|57.2|0.7|13.0|7.8|
|Highed|12.9|81.9|0.0|0.0|31.3|1.5|13.1|19.1|
|AllGroups|12.8|76.8|14.3|0.2|32.9|0.7|13.0|5.7|

Source: IFPRL Pakistan Panel Survey, Round 13, 1990, cited in Von Braun, Malik and Zeller (1993).

# Institutional and Noninstitutional Credit Sourcs Serve Different Needs

Most (94 percent) of institutional credit is for agricultural production and investment (Table 1.5). Nearly half (47 percent) of noninstitutional credit is for consumption. The requirement fcr nonfarm busminess credit remains unsisfied, according to the 1985 rural credit srvey. Projecting their future credit requirements, borrowers estimated that their credit requirement for farm actMties vwxld constitute 56 percent of total need; their requirement for diret consumption, 24 percent; and their requirement for credit to finance nonfarm business imvestments, 20 perent

**Table 1.5.** histitutional and Noninstitutional Borrowing by Purpose. 1985 (percentage of total borrowing)

|...............................<br>..................................................<br>Source|...............<br>Farm|.................................<br>Agriculture<br>Nonfarm|....................<br>Business|..........<br>..................<br>Industry|........<br>Consump-<br>tion|
|---|---|---|---|---|---|
|.....................................................................................................................<br>|.....|..................|..................................................................|...................................|.......................|
|In~fli~nn1<br>9 4|94.|2<br>..............|.....<br>..|....|3|
|(Rs 7,472 million)||||||
|<br><br>|39<br>|3<br> <br>|10<br>|..<br>|47<br><br>|
|N o n in stitu t<br>039<br>R .....<br>(.Ru. 20 519 million)|....................|3T<br>..............|1...........|..........**......**......|............................................<br>4|

Sotuce: 1985 Rutal Credit Survey. Note: The figures do not add up to 100 because of rounding
tariffs (tariffs of 5% or below). These account for more about 70% of imports and 27% of total revenues. The remaining tariff lines are relatively evenly distributed across the rest of the bands.

Collected tariff rates are only slightly more skewed toward the lower end of the distribution than statutory rates. Although tariff exemptions seem limited in magnitude, the data reveal that, taking them into account, 1.3% of tariff lines are subject to tariffs close to zero, whereas only 0.1% (two lines) are subject to statutory tariffs within the same band. Overall, the table highlights the fact that, for any effective reform of the Syrian trade regime, it will be crucial to pay close attention to products on which nuisance tariffs are levied, as this is where a large share of the burden of tariff collection ultimately falls. As a check for the consistency of the data set with other published trade data, we compare the 10 most important import partners and products (by 3-digit isic code) for 2007 and 2009 (Table 2), using UN comtrade data for2007 against Customs’ asycuda data for 2009. The data exhibit a significant degree of overlap. 7 of the top 10 partners (8 if we allow the fact that EU imports are mainly from Italy) are common across the two years, as are 7 of the top 10 products. China, the EU, Turkey, and Arab countries such as Egypt and Saudi Arabia are, unsurprisingly, among Syria’s most important trading partners.

One important complication that needs to be addressed in any study of the Syrian import regime is the enormous number of nontariff charges applied at the border. In addition to a spending tax and a revenue tax, there are in excess of a hundred other possible additional charges. This is complicated by the fact that some charges, including both fees and taxes, are levied on the import declaration, while others are levied on the imported item—with complicated rules for what constitutes the appropriate base for each charge.<sup>14</sup> In the simulations that follow, we focus on the three major revenue-generating sources—tariffs, the spending tax, and the revenue tax—and aggregate all additional charges into an “all other charges” category, in order to maintain transparency.<sup>15</sup> Finally, to keep the presentation clean, we also aggregate import values and trade tax revenues across four trading partner groups, consistent with their importance within the Syrian import regime. These are Turkey, GAFTA, the EU, and the Rest of the World

> 14Given that the latter charges are of a small magnitude, we simplify by distributing them evenly across items in a given declaration.

> 15In the Syrian customs regime, tariff revenue is calculated as a percentage of the c.i.f. import value, and both the spending and the revenue tax are calculated as a percentage of the tariff inclusive import value. Some of the other taxes and charges are weighted averages of various different tax bases. For simplicity, we treat the aggregative all other charges category as a percentage of the c.i.f. import value only.

10
# **Unit Cost Analysis of the ZMCM**

To demarcate the natural gas distribution area of Mexico City—in which one or more distribution franchises would be permitted to operate—physical characteristics and economic, political, and social transformation processes were considered.<sup>7</sup> Three demarcation options—Megalopolis, the Valley of Mexico, the ZMCM—were considered, and they all had the same distribution infrastructure (table 1).

**Table 1. Demarcation Options for the Mexico City Natural Gas Distribution Area**

|**Option**|**Approximate**<br>**population**<br>**(millions)**|**Degree of**<br>**connectivity**<br>**between the**<br>**Federal District**<br>**and other states**|**Number ofjurisdictions**|
|---|---|---|---|
|Megalopolis|23|Very low—due to<br>distance|16 “delegations”—Federal District.<br>Metropolitan areas of Toluca, Cuernavaca, Puebla<br>Tlaxcala, and Pachuca<br>91 municipalities—State of Mexico<br>16 municipalities—Morelos<br>29 municipalities—Puebla<br>37 municipalities—Tlaxcala<br>16 municipalities—Hidalgo<br>7 Isolated urban centres (Atlacomulco; Tepeapulco;<br>Jilotepec-Tepeji-Tula; Tepozotlan-Huehuetoca-<br>Zumpango; Piramides-Nopaltepec; Texcoco and<br>Chalco-Amecameca)|
|The Valley of<br>Mexico|18.5|Low—due to<br>distance|16 “delegations”—Federal District<br>57 municipalities—State of Mexico<br>1 municipality—Hidalgo|
|The ZMCM|16|High—due to<br>economic links<br>and physical links<br>(roads)|16 “delegations”—Federal District (Mexico City)<br>28 municipalities—State of Mexico|

The Megalopolis alternative was deemed too extensive and had insufficient economic links among towns and subregions. Population had more than doubled in the Valley of Mexico in 1970–95, posing a challenge for sewerage, drainage, electric power, and transportation systems. Diverse interests, local sovereignty, and the political characteristics of coordination among different jurisdictions have made the existence of two public administrations running the city (the Federal District and the State of Mexico) an obstacle to efficient urban development. The Valley of Mexico was considered too heterogeneous—economically, politically, and socially—to be a viable distribution area.

> 7 These criteria are described in _Programa General de Desarrollo Urbano del Distrito Federal; Plan de Desarrollo del Estado de México 1993–1999; Programas Delegacionales de Desarrollo Urbano; Planes de los Centros de Población Estratégico de los Municipios del Estado de México; Propuestas de Divisiones del Área Metropolitana de la Ciudad de México (Secretaría de Desarrollo Social, Instituto Nacional de Estadística, Geografía e Informática);_ and _Planes y Programas Gubernamentales._

8
# **Appendix 5: Propensity Score Matching**

||TableA7.First S<br>(a) All<br>|tage– ProbitEst<br>regions<br>|imation<br>(b) MEN<br>|A region<br>|
|---|---|---|---|---|
|Variables|GVC1|GVC4|GVC1|GVC4|
|Medium|0.436***|0.613***|0.526***|0.612***|
||(0.013)|(0.036)|(0.036)|(0.121)|
|Large|1.048***|1.374***|1.258***|1.396***|
||(0.016)|(0.037)|(0.043)|(0.122)|
|Main city|0.130***|0.077***|0.138***|0.125|
||(0.014)|(0.030)|(0.037)|(0.083)|
|Ln(Age)|0.063***|-0.064***|0.018|-0.222***|
||(0.010)|(0.021)|(0.028)|(0.065)|
|Ln(Gov own)|0.063***|0.049***|0.038|0.074|
||(0.013)|(0.022)|(0.040)|(0.066)|
|Country dummies|Yes|Yes|Yes|Yes|
|Year dummies|Yes|Yes|Yes|Yes|
|Sector dummies|Yes|Yes|Yes|Yes|
|Observations|84,292|77,973|11,446|10,220|

Standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 Note: The intercept is included.

37
**Table 7. Drivers of out-migration**

|Migrated across survey waves (1=mover, 0=stayer)|
|---|
|Baseline variables<br>First stage|
|Log WRSI maize x Aged 15-24<br>-0.031|
|(0.032)|
|Log Kilometers from regional capital x Aged 15-24<br>0.008*|
|(0.004)|
|Log number of fatalities x Aged 15-24<br>0.008**|
|(0.003)|
|Share of one's ethnicity living in urban areas x Aged 15-24<br>0.006|
|(0.056)|
|Head or spouse<br>-0.016***|
|(0.005)|
|Child of head<br>0.031***|
|(0.006)|
|Male child of head<br>-0.012|
|(0.008)|
|Male<br>-0.006|
|(0.004)|
|Unmarried<br>0.003|
|(0.006)|
|Unmarried male<br>0.009|
|(0.007)|
|Age category (reference: aged 10-14)|
|Aged 15-24<br>0.120|
|(0.142)|
|Aged 25-34<br>0.033***|
|(0.007)|
|Aged 35-49<br>0.026***|
|(0.008)|
|Aged 50-65<br>0.013|
|(0.008)|
|Aged 66 plus<br>-0.001|
|(0.009)|
|Number of effective years of schooling completed<br>0.001**|
|(0.001)|
|Sanderson-Windmeijer - F test of excluded instruments<br>6.883|
|Sanderson-Windmeijer - (p-value)<br>0.0000|
|Observations<br>11,338|
|Number of households<br>2,400|
|Linear probability model; Initial Household Fixed Effects|
|Robust standard errors in parentheses|
|*** p<0.01, ** p<0.05, * p<0.1|
|Source: Authors’ calculations with UNPS.|

26