rafmacalaba/datause-encoder-data huggingface dataset:
existing literature about why some EPZs have been successful, while most have failed.

# **A. The Indian software sector**

In India, the preconditions for the development of the software sector were high quality education in junior colleges and universities financed by the government. A critical role was played by university graduates who went abroad for further training, remained as expatriates in the high tech sector, and later returned home or interacted intensively with newer Indian firms. The lamented brain drain became, with some lag, a paradoxical source of strength and a critical catalytic input (this was also true in the case of Hsinchu).

In the 1980s there were a growing number of programming graduates and many were underemployed. There were a large number of graduates at levels ranging from post secondary technical schools to those trained at the Indian Institutes of Technology. Almost all of the students trained in programming had been educated in English. The government’s continuing investments in education had resulted in over 1,800 educational institutions and polytechnics producing 70,000 to 85,000 computer science graduates every year.<sup>19</sup> Many Indian graduates also had a second university degree or post-graduate degree from the United States or the United Kingdom, often in computer technology<sup>20</sup> . Other Indian software programmers received training in private software institutes to keep abreast of the latest developments in the software industry and acquire a breadth of software skills. Hence, many were familiar with major computer hardware systems (IBM, UNISYS, DEC, HP and DG<sup>21</sup> ), computer-aided software engineering tools, objectoriented programming, graphical user interface and client networking<sup>22</sup> .

An effort to insure greater local competence in computer production inadvertently contributed to the growth of software. In 1977 increasing pressures to increase domestically controlled computer production led the Indian government to demand that IBM allow Indians to hold 60% of its equity if it wished to continue its operations in India. IBM responded by shutting down its operations in India within 6 months. IBM’s departure turned out to be a boon to the Indian computer software industry if not to the

> 18 An extensive set of references is provided in World Bank, 2004. 19 James (2000) and India Express (2000). 20 Deshmukh (1993). 21

> Lakha (1990).

35
**Figure 3. Country of Origin of Import Shipments Handled by Freight Forwarders**

<!-- Start of picture text --> 80%<br>72% 72%<br>70%<br>60%<br>50%<br>40%<br>67%<br>30%<br>20%<br>11% 11% 9% 10%<br>10% 5% 5% 20%<br>7% 4% 1%1%1% 2%1%2%<br>0%<br>European Cefta Elsewhere in North China Other<br>Union Europe America<br>Small Medium/Large Total<br>Share of freight forwarders<br><!-- End of picture text --> Source: Authors’ calculations based on data from the Survey of Serbian Freight Forwarders. Note: the statistics shown are based on responses by all 153 freight forwarders.

# **III. How Freight Forwarders Operate**

This section describes the types of activities undertaken by firms in the course of fulfilling their specific freight forwarding duties. Respondents were asked how often they consolidate shipments, whether they use multi-modal transport, as well as details about route choices for inter-continental movements of goods.

## **_Larger freight forwarders consolidate products more frequently than small freight forwarders_**

Freight forwarders can organize consolidated shipments that are comprised of different products of the same client, or of similar/different products of different clients.<sup>10</sup> Consolidating shipments has the benefit of lowering shipping costs but has the risk of taking potentially longer time to be delivered due to a higher risk of inspection by customs (and possibly higher cost) and/or due to longer time to organize a full shipment/container.<sup>11</sup> Figure 4 shows that the frequency of shipment consolidation varies with firm size: 75% of medium/large freight forwarders either always or frequently consolidate shipments of different products or clients while only 46% of small freight forwarders always or frequently consolidate shipments and actually a quarter rarely or never consolidate shipments. Moreover, when they do consolidate

> 10 The decision to consolidate may be taken either by the freight forwarder or by the importing firm.

> 11 Customs operations specialists have indicated to us that when there is a mix of products and/or importing firms, the likelihood of a physical inspection by the customs agency increases. Also, if customs clearance for one product/client is taking longer than the others, the entire consolidated shipment is delayed.

11
Figure 2: Trends in Korean Outward FDI and Employment of Foreign Affiliates <!-- Start of picture text --> China ASEAN<br>USA & EU15 Other Countries<br>(a) Number of New Foreign Affiliates<br>2014 2015 2016 2017 2018 2019<br>China ASEAN<br>USA & EU15 Other Countries<br>20<br>15<br>10<br>5<br># of New Foreign Affiliates (in thousand)0<br>1989-92 1993-96 1997-00 2001-04 2005-08 2009-12 2013-16 2017-20<br>2,000<br>1,500<br>1,000<br>500<br># of Total Employees (in thousand)<br>0<br><!-- End of picture text -->

- (c) Total Employees in Foreign Affiliates

<!-- Start of picture text --> China ASEAN<br>USA & EU15 Other Countries<br>(b) Net Foreign Investment Amount<br>China ASEAN USA & EU15 Others<br>2014 2015 2016<br>2017 2018 2019<br>150<br>100<br>50<br>Net FDI Amount (in Billion US$)<br>0<br>1989-92 1993-96 1997-00 2001-04 2005-08 2009-12 2013-16 2017-20<br>10<br>8<br>6<br>4<br>2<br>Share of Korean Employees (percent)<br>0<br><!-- End of picture text -->

- (d) Share of Korean Employees in Foreign Affiliates

_Notes:_ Values in each bar in both panels (a) and (b) are aggregated over the four-year period. Investment in countries frequently regarded as tax havens is excluded from the calculation. Statistics in panels (c) and (d) are based on foreign affiliates of Korean MNCs that satisfy all of the following criteria: (i) total investment by the Korean headquarters firm is at least 2 million US dollars, (ii) classified as a firm in the non-financial sector, and (iii) reliability of the survey response is verified by the Korea Export-Import Bank, the administrator of the survey. About 60-70% of all foreign affiliates subject to data collection are included in the sample each year because of the third criterion, which requires caution in interpretation. All data come from the Korea Export-Import Bank Foreign Direct Investment Database.

9
Hindu or Muslim, a full set of birth order indicators interacted with the relationship of the child’s mother to the head of the household, twinship indicators, and month-of-birth indicators.<sup>15</sup> Results are presented with and without controls and fixed effects to verify robustness. The mean PSU studied here contains 10 children under 3 used in these regressions.

# **3.2 Regression results**

Table 6 presents results from estimations of equation 2, with simple OLS in Panel A and district fixed effects in Panel B. Districts which saw greater differences in sanitation also present greater differences in child height.

District-level open defecation rates do not statistically significantly predict child height once village-level open defecation is included, which plausibly suggests that villages are much nearer than districts (which are much larger than villages) to capturing the geographic extent of sanitation externalities. Village-level open defecation predicts child height with or without district fixed effects and with or without individual controls.<sup>16</sup> The coefficient on village open defecation is smallest in absolute value with fixed effects and controls,<sup>17</sup> although it is not statistically significantly different from the other estimates; this could reflect the well-known attenuating bias of fixed effects, if much of the important variation in sanitation that is causing variation in height has been captured by other controls, leaving noise remaining.<sup>18</sup> 15If the survey rounds were conducted in different places in different times of year, different children would be under 36 months old. Month of birth is correlated with early-life human capital inputs ( _cf_ . Doblhammer and Vaupel, 2001, about developed countries).

> 16If, instead of omitting observations with height-for-age _z_ -score beyond _±_ 6, a cut-off of _±_ 10 is used, then results are very similar. For example, the coefficient in column 1 of panel A becomes -0.768 (0.222); the smallest coefficient in absolute value, column 3 in Panel B, becomes -0.292 (0.138). If the log of height in centimeters is used as the dependent variable instead of the _z_ score, moving from 0 percent to 100 percent open defecation is associated with an approximately 2 percent decrease in height ( _t ≈_ 4, analogously to column 2 of panel B).

> 17Would any village-level (instead of household-level) asset or indicator of well-being have the same effect as sanitation? Adding village electrification and water averages to the most controlled regression, column 3 of panel B, changes the point estimate on open defecation only slightly, from -0.35 to -0.33 ( _s.e._ = 0 _._ 12); these two village level variables have _t_ -statistics of 1.15 and -0.59, respectively, with a joint _F_ -statistic of 0.73.

> 18For readers concerned about this possibility, regressing height on village-level open defecation with no district or time fixed effects produces an estimate of -0.700 ( _t ≈_ 4 _._ 5) and of -0.0501 ( _t ≈_ 4 _._ 8) with all the controls.

17
interval of (-NZ$63, +NZ$71) for total remittances over 6 months – which is small both in absolute terms, and when compared to the median annual income of NZ$20,000-30,000 for Pacific Islanders in our sample.

# **3.4 Impact on Remitting Channel**

Even if immigrants do not change their frequency of remitting or the amount remitted, they may still benefit from the training if it causes them to change the method they use to remit. In Table 6 we examine whether migrants in our samples use a different method of remitting in any of the follow-up surveys to that which they had used in the 12 months prior to the baseline survey. We see that in the Pacific Island sample, 16 percent of the control group use a different method at least once during the four follow-up surveys, and financial literacy training leads to a significant reduction of this, halving the rate of switching to new products. Recall that the training introduced these migrants to a new product, the Westpac prepaid express card, but we see no increase in usage of this among those treated. Instead, the main effect appears to be fewer individuals switching from Western Union or Melie Mei Langi to use either Mana or Epokifo’ou, two less used methods. Mana is a church based money transfer method with a low fixed fee ($5-8) but an unfavorable exchange rate, so that for the median remittance it is the most expensive of the money transfer operators. Moreover, Mana is not included on the _sendmoneypacific_ website. Epokifo’ou is a money transfer organization which had very similar costs to Melie Mei Langi for much of our sample period, and which was slightly cheaper than Western Union. So the main effect seems to be to stop people from switching to methods which provide little or no benefit to switching, or for which it is less easy to track costs.

We find no significant impact on switching methods for the other two groups. For the East Asians, this in part reflects the low frequency of remitting overall, meaning that there are a small number of transactions to look at switching over. For the Sri Lankans, most immigrants were using a relatively cheap method at baseline, implying relatively little to be gained from switching (and also we have the caveat of high and unbalanced attrition).

17
Table 2 presents the results of Komives, et al (2005) in relation to these types of subsidies. Besides the errors of exclusion, the table presents the Ω parameter introduced in Section 6. It represents the percentage of total benefits accruing to the poor divided by the percentage of the poor in the general population. This parameter will be above 1 when the subsidy has a progressive impact on the income distribution and a value below 1 when its impact is regressive.

It can be seen from the table that out of the 25 real world subsidies analyzed not one of them was distributed progressively. In all cases —except in Gujarat India where the subsidy had a neutral impact on the income distribution— benefits accrued proportionally more to the non-poor than to the poor. This implies that the poor would be made better off if these subsidy were eliminated and the financial resources thus liberated were distributed randomly across the population.

**Table 2: Distributive impact of different quantity based subsidies in the electricity** **<u>and water sectors</u>**

|**Country, city**|**Type of subsidy**|**Ω**|**Error of exclusion**|
|---|---|---|---|
|_Electricity cases:_||||
|Guatemala|VDT|0.20|55.4|
|Honduras|VDT|0.49|56.0|
|Peru|IBT|0.82|59.9|
|Cape Verde|IBT|0.48|75.6|
|SaoTome andPrincipe|IBT|0.41|76.8|
|India: average|IBT|0.70|21.0|
|India, Kerala|IBT|0.65|14.5|
|India, Andhra Pradesh|IBT|0.78|16.4|
|India,TamilNadu|IBT|0.53|15.3|
|India, Karnataka|IBT|0.74|18.6|
|India, Maharashtra|IBT|0.66|13.8|
|India,MadhyaPradesh|IBT|0.70|12.4|
|India, Gujarat|IBT|1.00|21.6|
|India, Orissa|IBT|0.71|40.1|
|India,Punjab|IBT|0.91|13.4|
|India, Haryana|IBT|0.66|15.4|
|India, Rajasthan|IBT|0.84|20.7|
|India,Delhi|IBT|0.57|9.1|
|India, Uttar Pradesh|IBT|0.66|25.8|
|India, West Bengal|IBT|0.62|30.5|
|India,Bihar|IBT|0.43|47.7|
|_Water cases:_||||
|Nepal, Katmandú|IBT|0.56|53.0|
|India,Bangalore|IBT|0.66|60.5|
|Sri Lanka|IBT|0.83|69.5|
|Cape Verde|IBT|0.24|89.7|

_Source_ : Table 5.1, Komives, et al (2005). _Note_ : Real subsidies reported. Simulated cases not shown.

32
# FIGURE 2: MONEY AND WATER TRANSACTIONS **IN** ONITSHA, NIGERIA(PER DAY)- Dry Season

<!-- Start of picture text --> .. V 51 -- _ 2031 0 tS4D<br>3 TAKE TRC ." |.11 -u-L REALl1S C<br>V .- _ fi000 BUSIoESaoo<br>D _~~~~~~~~~~~~<br>SHALLOWELLS 0.31 | 26J0. uW.<br>L 16C- StUXLLOW 0EU S o.os 0.31"" _ 1 0.3111D 0.11"C- 0.VENDOR_ °<br>R u~~~~~~~~~~~~~~~~~~~~~<br>E + +<br>H<br>0 10.05181 1 E."|l<br>FULC SYTE i -4 ° R BUSINESSE S H<br>bysaL reMaALvndosETAmIeLeH<br>PUBLIC SYSTEM *UD1.C- N5.00<br>* ae nu snteult 0.0ater4 aerotu eas ml muto ois cosue g<br>by small themselves.retail vendors<br><!-- End of picture text -->
Gregson, Simon, Tom Zhuwau, Joshua Ndlovu, and Constance A Nyamukapa. 2002. “Methods to Reduce Social Desirability Bias in Sex Surveys in Low-Development Settings: Experience in Zimbabwe.” _Sexually Transmitted Diseases_ : 568–575.

- Gutiérrez, Juan Pablo, Sergio Bautista, Paul Gertler, Mauricio Hernández, and Stefano M. Bertozzi. 2004. “External Evaluation of the Impact of the Human Development Program Oportunidades:” Mexico City.

- Handa, Sudhanshu, Carolyn Tucker Halpern, Audrey Pettifor, and Harsha Thirumurthy. 2014. “The Government of Kenya’s Cash Transfer Program Reduces the Risk of Sexual Debut among Young People Age 15-25.” _PloS One_ 9 (1) (January). doi:10.1371/journal.pone.0085473.

Harvey, Paul. 2007. “Cash-Based Responses in Emergencies”. London.

- Harvey, Paul, and Kevin Savage. 2006. “No Small Change. Oxfam GB Malawi and Zambia Emergency Cash Transfer Projects: A Synthesis of Key Learning”. London.

- Haushofer, Johannes, and Jeremy Shapiro. 2013. “Household Response to Income Changes: Evidence from an Unconditional Cash Transfer Program in Kenya”. Massachusetts Institute of Technology.

Hoddinott, John, and Lawrence Haddad. 1995. “Does Female Income Share Influence Household Expenditures - Evidence from Cote-Divoire.” _Oxford Bulletin of Economics and Statistics_ 57: 77–96. doi:10.1111/j.1468-0084.1995.tb00028.x.

- Houthakker, HS. 1957. “An International Comparison of Household Expenditure Patterns, Commemorating the Centenary of Engel’s Law.” _Econometrica_ 25 (4): 532–551.

- Humphreys, Rowena. 2008. “Evaluation of the Cash Transfers for Development Project in Vietnam”. London.

- IEG. _Evidence and Lessons Learned from Impact Evaluations on Social Safety Nets_ . Washington, DC: World Bank.

Ikiara, Gerrishon K. 2009. “The Political Economy of Cash Transfers in Kenya”. London.

Inman, Robert P. 2008. “The Flypaper Effect”. Cambridge, MA.

Jones, Nicola, Rosana Vargas, and Eliana Villar. 2007. “Conditional Cash Transfers in Peru: Tackling the Multi-Dimensionality of Childhood Poverty and Vulnerability. Social Protection Initiatives for Families, Women and.” In _Forthcoming Chapter in Alberto Minujin et Al. (ed.) 2007. Social Protection Initiatives for Families, Women and Children: An Analysis of Recent Experiences_ . New York: New School and UNICEF.

Khera, Reetika. 2013. “Cash vs In-Kind Transfers: Indian Data Meets Theory.” 18
24 Table 2.3: External Shocks

||:1981-84|compared|to1970-80|**1985-89**|compared t|o 1970-80|**1935-89**co|mpared<br>**to**|**1981-84**|
|---|---|---|---|---|---|---|---|---|---|
||Terms<br>of<br>Tradede<br>-<br>-|Real<br>;t<br> <br><br>Rate|<br>.ont.<br>'Shock|Terms,<br> <br>o<br>Trade|Real<br>''Rate|<br>oTota<br>Total<br>Shock|-.Terms.<br>l<br>-<br> <br>f<br>Trade|Rea<br>nt<br>Rate|l<br>,Shock|
|EIAL<br>||||||||||
|LIC<br>|-10.6<br>|-2.0<br>|-12.6<br>|-14.0|-3.2|-17.2|1.6|-0.5|<br>1.1|
|SSA|-13.6|-1.8|-15.4|-13.4|-3.4|-16.8|1.7|-0.6|<br>1.1|
|OAL<br>||||||||||
|LIC<br>|-8.6<br>|-1.6<br>|-10.2|-9.7|-3.6|-13.3|-1.7|-0.8|-2.5|
|SSA|-6.9|-1.4|-8.3|-9.8|-3.2|-13.0|-2.5|-0.8|-3.3|
|NAL<br>||||||||||
|LIC<br>|-18.7<br>|-0.9|-19.6|-9.5|-2.0|-11.5|<br>3.1|-0.|6<br>2.5|
|SSA|-15.3|-1.1|-16.4|-8.5|-2.3|-10.8|6.7|-0.7|6.0|

Notes: The total effect of external shocks as % of GDP is computed as the sum of real interest rate effect and the terms of trade effect. The interest rate effect is calculated as -(rrba s)*(debt/GDP)bg,g where r is the real interest rate computed as (i-dp/p)/(1+dp/p); rba e is tt.e average reaf interest rate of the base period, it is the ratio of interest payments to total debt; interest payments are calculated byadding public interest payment to private interest payments; private interest payments are proxied by multiplying private debt by L (L equals three-month annualized LIBOR plus one percent); the private debt is estimated by subtracting public and publicly guaranteed dett from total debt- dp/p is "world" inflation (proxied by the percentage change of the GNP deflator of the USJ, and (debt/GDP)beg is the ratio of debt to GDP of the year precedina the beginning of the end period. Debt data correspond to total disbursed guaranteed and nonguaranteed debt. The effect of terms of trade is computed as ((PX/PXbyse)-1)*(X/GDP)be ]-[(P/PMba e)1)*(M/GDP)b a], where PX and PM are the averag **e** xport and'import price indices deflatea by the US GNP WNflator, respectively; PX and PMbase are the average price indices of the base period; X and M are exports of GNF5 and imports of GNFS; and (X/GDP)bea and (M/GDP)bea are the ratios of X and M to GDP respectively of the year preceding the beginning the en period. All the variables are denominated incurrent US dollars.
> - 32 -

# |Hox 12 Settlement an Securities Administration in Korea

The Korea Securities Settlement Corporation ("KSSC") was created in December 1974 to operate as the sole central depository for securities and to serve as the clearing house for the settlement of transactions conducted on the floor of the Korea Stock Exchange (KSE). The KSSC also acts as transfer and paying agent for issuing companies listed on the KSE and the OTC marken. In its role as central depository, the KSSC provides safekeeping for more than 60% of all listed shares. The services offered are:

- safekeeping of deposited securities

- book-entry delivery of securities on deposit

- book-entry pledges of securities on deposit

- collection and payment of dividends on deposited securities

- book-entry delivery of securities between participants

- separate safe custody service(Individual Custody)

- collection and payment of principal and interest on deposited bonds

- checking of securities deposited against list of stopped securities

- physical receipt and delivery of securities from/to participants

- settlement of transactions made on the floor of the KSE

- administration of beneficial owner's rights

- exercise of various rights associated with the consecutive deposit of securities

- transfer agent for registered shares

- sub-conversion agent for Korean overseas securities

- nominee function

- collection of securities transfer tax

- conversion of convertible bonds

The KSE provides an automated comparison system for its members who are counterparties of a trade, so that they may confirm the details of their trading commitments just after contracting trades. As there are very few unmatched or rejected trades under this system, the KSE has little need for a secondary trade comparison system. Additionally, the Data Correction System checks the overall format of all input thereby identifying major errors so they can be corrected immediately.

The KSE proceeds with multilateral netting based on the integrated trade data determined after the matching process is complete. The traded quantities are netted to a final long or short position by issue and securities companies who are counterparties to a trade.

The netting data is forwarded to the KSSC via magnetic tape to enter into its owr computer. Then, after identifying the position in the participant's account book, the KSSC distributes details in a report to the securities companies, concerned for settlement by the time limit for settlemeut. The KSE has entrusted the settlement of funds to the KSSC which executes delivery against payment, simultaneously effecting the delivery of securities and the payment of the corresponding proceeds.