rafmacalaba/datause-encoder-data huggingface dataset:
While foreign investors only participated in **8** operations in 1988, they were involved in 200 by 1992 and a total of 392 over the whole period. A total of $18.5 billion in foreign exchange was generated during this time, with a peak of $8.8 billion in 1991, representing 40 percent of the total revenue from privatizations that year.

Figure 6 FOREIGN PARTICIPATION IN DEVELOPING COUNTRY PRIVATIZATIONS, 1988-1992 <!-- Start of picture text --> By Number of Transactions<br>150 . Europe & Central Asia<br>El Latin America & Carbbean<br>* East Asia & PacAic<br>100 QSouthAsia...<br>* Sub-Saharan Africa<br>sO North Aftca & Middle East . _<br>50 -<br>1988 1989 1990 1991 1992<br>By Amount of Foreign Exchange Generated<br>US$ Billion<br>10<br>* Europe & Central Asia<br>8 E Latin America & Caribbean<br>1 East Asia & Pacific . .<br>0 O South Asia<br>4 Sub-Saharan Africa<br>O North Africa & Middle East ...... l..<br>2<br>0<br>1988 1989 1990 1991 1992<br><!-- End of picture text --> Due to the lack of domestic savings, _Eastern Europeani countries_ were especially dependent on the participation of foreign investors, who paid a total of $5.3 billion for 194 enterprises during 1988-92 which amounts to over 90 percent of the total revenues generated. 1<sup>8</sup> _Latin America_ clearly was the region that managed to attract the largest amount of foreign exchange through its privatization effort since 1988, amounting to 64 percent of foreign exchange from privatizations in the developing world. 1991 was the peak year for the region with almost $6.8 billion in foreign exchange, primarily due to the privatization of large telecommunication units in Argentina, Mexico, and Venezuela. On average, Is Including Portugal and Turkey, 207 companies were privatized in Europe and Central Asia with a foreign exchange content of _$5.9_ billion, amounting to 56 percent. Also note that the share of foreign exchange in total rcvenue in Eastern Europe is slightly exaggerated by the fact that revenues from smallscale and mass privatizations are excluded.

18
report their data in all the years under analysis, leading to missing data in a non‐random way. Thus, arriving at the average performance in a year and comparing it to another year can be misleading given the difference in the composition of the service providers in the two years. The methodology to compute the trends in this paper uses a nested panel approach, such that “balanced panels” for two consecutive years are nested together across the years.<sup>1213</sup> Additionally, a survey of pourashava officials and subscribers in 80 randomly chosen pourashavas provided qualitative data on water supply services as a means to investigate the quality of services to poorer areas. The survey enquired about the number of hours of water supply per day and the number of hours elapsed between the lodging of a complaint and the response of the water supply department of the pourashava. There were 61 class A, 17 class B, and 2 class C pourashavas in the sample.

The paper qualitatively analyzes the performance of pourashavas compared to the performance of the rest of Bangladesh, i.e. the WASAs (Dhaka, Chittagong, Rajshahi, and Khulna) and five city corporations (Comilla, Rangpur, Sylhet, Barisal, and Gazipur) and global service provider performance. In addition, the paper also analyzes the performance levels among the pourashavas to provide the “aspirational” level for the lower performing pourashavas to benchmark against the better performing ones.

# Pourashava Performance Indicators

Pourashavas were measured on several indicators to benchmark their performance against that of the WASAs and city corporations and the performance of global service providers. These indicators are grouped into three categories – efficiency, availability, and financial sustainability, as described below.

**1. Efficiency**

- a. _Non‐revenue water (NRW):_ The difference between water supplied and water sold as a percentage of the former. This indicator helps assess the loss of saleable water.

- b. _Staff productivity:_ The number of connections per employee. A higher number suggests a greater level of staff efficiency.

**2. Availability**

- a. _Quantity:_ Water consumption in liters per person per day (lpcd). Higher levels of consumption imply improved service delivery.

- _b. Continuity:_ The average number of hours of service per day.

**3. Financial Sustainability**

- a. _Operational cost recovery:_ The ratio between operating revenues and operating costs. A ratio greater than 1 implies that a service provider is able to recover its costs and generate profits.

- b. _Average revenue:_ Operational revenue per cubic meter of water sold.

- c. _Collection ratio:_ Cash income as a percentage of billed operational revenue.

The paper draws upon the data from the IBNET database to assess the efficiency, availability, and financial sustainability of the pourashavas. The key variables described above are summarized in Table 1 below for the years 2010 and 2016.

> 12 (Andres L. A., 2005)

> 13 (Andres, Guasch, Haven, & Foster, 2008)

4
**Table 18: Annual value added (in million LCU) in presumed comparative advantage industries by size group of enterprises (weighted estimates of totals  at 2000 prices) , UBI 2002 and UBI 2009‐**

|<br>**_industries_**<br>|**_A. All comparati_**|**_ve advantage_**|% sha|re in sector|**_E. Comme_**|**_rcialfarming_**|||
|---|---|---|---|---|---|---|---|---|
|**Size Group**|Annual val|ue added|<br>total||Annual val|ue added|% share|in sector total|
|**(number of workers)**|2002|2009|2002|2009|2002|2009|2002|2009|
|less than 5|18,866|305,614|2.4|10.0|340|29,547|0.2|16.0|
|5‐20 workers|59,139|1,448,219|7.4|47.2|10,780|42,566|13.8|23.0|
|21‐100 workers|94,443|406,874|11.8|13.3|2,769|34,766|3.5|18.8|
|More than 100|629,905|906,487|78.5|29.6|64,297|78,008|82.2|42.2|
|Total|802,353|3,067,194|100.0|100.0|78,186|184,886|100.0|100.0|
||**_B. Compar_**|**_ative advantag_**|**_eprimary _**<br>% sha|**_industries_**<br>re in sector|**_F. Food an_**|**_d beverages in_**|**_dustry_**||
|**Size Group**|Annual val|ue added|total||Annual val|ue added|% share i|n sector total|
|**(number of workers)**|2002|2009|2002|2009|2002|2009|2002|2009|
|less than 5|931|34,793|1.1|15.8|7,905|125,318|9.7|9.7|
|5‐20 workers|13,542|62,486|16.5|28.3|27,098|280,266|7.5|21.8|
|21‐100 workers|3,064|45,201|3.7|20.5|63,826|169,733|17.7|13.2|
|More than 100|64,738|78,008|78.7|35.4|262,056|712,511|72.6|55.3|
|Total|82,275|220,488|100.0|100.0|360,886|1,287,828|100.0|100.0|
||**_C. Compar_**<br>**_industries_**|**_ative advantag_**<br>|**_e manufa_**|**_cturing_**|**G. Thegar**|**ments industry**|||
||||% sh|are in sector|||||
|**Size Group**|Annual val|ue added|total||Annual val|ue added|% share i|n sector total|
|**(number of workers)**|2002|2009|2002|2009|2002|2009|2002|2009|
|less than 5|14,068|263,390|3.0|14.6|6,155|134,532|78.2|57.9|
|5‐20 workers|32,610|398,261|7.1|22.1|1,594|96,823|20.3|41.6|
|21‐100 workers|84,379|313,625|18.3|17.4|122|1,118|1.5|0.5|
|More than 100|330,582|824,502|71.6|45.8||52|0.0|0.0|
|Total|461,639|1,799,779|100.0|100.0|7,871|232,525|100.0|100.0|
||**_D. Compar_**<br>**_services_**|**_ative advantag_**|**_e_**||**H. Land t**<br>**services**|**ransport**|||
||||% shar|e in sector|||||
|**Size Group**|Annual val|ue added|<br>total||Annual val|ue added|% share i|n sector total|
|**(number of workers)**|2002|2009|2002|2009|2002|2009|2002|2009|
|less than 5|3,867|7,431|1.5|0.7|3,752|1,660|3.1|3.1|
|5‐20 workers|12,987|987,472|5.0|94.3|12,838|14,924|15.1|27.4|
|21‐100 workers|7,001|48,048|2.7|4.6|7,001|33,869|8.2|62.2|
|More than 100|234,584|3,977|90.8|0.4|61,349|3,977|72.2|7.3|
|Total|258,440|1,046,927|100.0|100.0|84,940|54,431|100.0|100.0|

54
# **_4.1 Key limitations of the LSMS-ISA data sets_**

Given the features of the LSMS-ISA program and based on our previous critical overview, we identify the following as the main limitation of LSMS-ISA data collection to empirically disentangle the climatemigration nexus: questionnaire design; sample size and statistical challenges; and respondents’ issues. In the rest of this section, we focus on each of these areas in turn.

**_Questionnaire design._** Although the LSMS-ISA questionnaires are usually highly standardized across surveys and countries, the information is sparse, and there are inconsistencies not just across countries, but also across different surveys within the same country. The lack of consistency in terms of the set of questions included to detect migration and/or define migrants is certainly the first element to be considered for a revision of the LSMS-ISA collection aimed at improving data collection on migration. It also explains why there are only a couple of cross-country investigations on the climate-migration links which uses LSMS-ISA, the analysis by Mueller et al. (2020) and the one by Di Falco et al. (2022). In both cases, due to comparable and precise information about migration not being available for all the countries, the studies have to rely on proxies to define different types of migrations. Specifically, Mueller and colleagues exploit questions regarding the absence of individuals during the follow-up survey to define temporary migration as whether an individual present at baseline reported migrating for at least one month in the previous twelve. Of course, without knowing, in most cases, either the destination or the reasons for migrating, they had to make a strong assumption on the equivalence between temporary absence from the household and outmigration. See below (subsection 4.2) for further discussion on this point. The study by Di Falco and colleagues focuses on long-term migration instead, and thus individuals are proxied as migrants if households report them to have left between two visits or waves of the survey and who were not observed to return to their household during the time of analysis. In this specific case, the assumptions made are even stronger than those implied by Mueller et al. (2020), as the definition of migrants includes individuals that left the household because they married.

**_Sample and statistical issues._** Migration is, statistically speaking, a rare event. As reported below, the only five climate-migration studies using LSMS-ISA data have very small samples. This is unsurprising, as in a normal clustered sample design typical of multi-topic surveys, the expected number of households associated with emigration may be very low (Carletto et al., 2014). To better identify rare events, two potential approaches are disproportionate sampling and two-phase sampling. However, both sample designs require some prior knowledge of migration in the population and are not easy to implement as

24
Table A.1: Under-5 mortality rates by time-period; pooling firstborn-girl and firstborn-boy families

||Male|Female|EFM|
|---|---|---|---|
||(1)|(2)|(3)=(2)-(1)|
|Pre-ultrasound: 1973-1984|11.72|13.09|1.37|
|N|35,021|32,428||
|Post-ultrasound: 1985-1994|9.53|10.44|0.91|
|N|84,207|78,018||
|Post-ultrasound: 1995-2005|7.41|8.01|0.60|
|N|51,594|46,804||

NOTES: This table reports the percentage of second- and higher-order children, by child’s gender, who suffered from mortality within 5 years of birth, over the three time-periods in our sample. Column (3) reports the difference between the mortality numbers in columns (1) and (2). Data: NFHS.

Table A.2: Age distribution of missing girls in 2000

||No. miss|ing (in 000s)|% of all|missing women|% of und|er-5 missing girls|
|---|---|---|---|---|---|---|
|Age group|India|China|India|China|India|China|
||(1)|(2)|(3)|(4)|(5)|(6)|
|At birth|184|644|11%|37%|37%|83%|
|0-1|146|109|9%|6%|30%|14%|
|1-4|164|23|10%|1%|33%|3%|
|_<_ 5|494|776|29%|45%|||
|5-14|93|2|5%|0%|||
|_≥_15|1125|947|66%|55%|||
|Total|1712|1727|||||

NOTES: This table is based on Anderson and Ray (2010) and reports the number of missing girls for various age groups (columns (1) and (2)), missing girls as a percentage of all missing women (columns (3) and (4)), and missing girls across age groups as a percentage of all under-5 missing girls (columns (5) and (6)), separately for India and China. It shows that India and China differ in the age distribution of missing girls. In China, the imbalance in the sex ratio for under-5 children in primarily at birth (83 percent), while there is a more even spread across early childhood in India. In particular, the contribution of post-infancy EFM to the number of missing girls is 33 percent in India but only 3 percent in China.

50
Page 8 countries. Overall, the BIS reports show the African private sector accounting for 38% of total commercial bank stocks and African comme._ial banks a further 16% in 1988.

# 4. _4mparisons with Other Lenders

Total long-term debt stocks from sources other than the commercial banks rose from $44 billion in 1983 to $96 billion in 1988 (Tables 2-3). This was due in large measure to exchange adjustments, rescheduled interest arrears and other non-flow factors which contributed an estimated $33 billion of the $52 billion increase.

Inclusive of 1984-85 data, not shown in the tables, long-term disbursements from all lenders other than the commercial banks totalled nearly $42 billion from 1983-88, as compared with principal and interest pavments of $23 billion. Net flows totalled $28 billion and net transfers $18.5 billion.

The commercial banks held 18% of total long-term debt stocks by the end of 1988, a percentage which rises to 35% when combined and compared solely with debt stocks of other non-concessional lenders (other than IBRD), such as export credit agencies, suppliers and investment agencies such as the International Finance Corporation and the European Investment Bank. This compares at the end of 1983 with 30% and 51% respectively. The decline reflected the declining share of nornconcessional lenders in total debt stocks.

While moving in tandem in so far as stocks are concerned, the record shows a marked difference in net flows from commercial banks vis-a-vis other quasicommercial lenders. Sub-Saharan Africa experienced a cumulative $5.5 billion net transfer deficit vis-a-vis the commercial banks over the 1983-88 period, while other non-concessional lenders provided marginally positive net transfers in all years except 1985 and 1988, yielding a cumulative net transfer surplus of $646 million for the same period. This suggests that since 1983 the commercial bank category has represented the largest net outflow of funds from Africa.

Nevertheless, since 1986 the WDTs indicate a narrowing in the flows gap between commercial and non-concessional lenders, subject to the overstated figures for C6te d'Ivoire. For instance, in 1986-1988, the commercial banks share of combined disbursements increased to an average annual 55% compared with 45% in 1983. During the same time, the commercial banks' share of debt service payments fell from 70% to 55%. In 1988, commercial banks provided some $1.9 billion in new long-term debt as compared with $1.5 billion by other non-concessional lenders. Representing the fourth consecutive yesr of decline, debt service payments to the commercial banks registered $2.5 billion in 1988, as compared with the $2 billion paid to non-concessional lenders. This resulted in net transfer deficits in excess of $600 million for both categories of lenders in 1988.

The statistical trends presented in the WDTs suggest that the commercial banks in the aggregate do continue to disburse fairly significant medium-term credit
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- 20 -

Entrant bidders are considered a key to enhance competition in an auction and break potential collusive arrangements among incumbent bidders. However, it is debatable whether or not fringe bidders exist in these infrastructure procurement markets. It may also be open to argument how to identify fringe (or incumbent) bidders in empirical works.

Asymmetric auction theory predicts that weak (fringe) bidders would bid more aggressively when they are faced with a strong (incumbent) opponent. Our ODA procurement data are supportive of this. It has been found that in the three infrastructure sectors, entrant bidders submitted systematically aggressive bids in the presence of incumbent bidders. It is also shown that high concentration of incumbents in an auction would be likely to harm auction efficiency, raising procurement costs.

The results suggest that auctioneers should encourage fringe bidders to actively participate in the bidding process while maintaining the quality of the projects to be delivered. It would be conducive to enhancing competitive circumstances in public procurement auctions and improving procurement efficiency.
relative to pre-Ebola mortality rates, as well as lower and upper bound estimates of the percentage change based on the confidence intervals from Speybroeck et al.<sup><u>18</u></sup> The largest effects of health care worker deaths for all three countries are observed on maternal mortality. To date, Guinea, Liberia, and Sierra Leone have lost a total of 78, 83, and 79 doctors, nurses, and midwives to Ebola, translating to decreases of 1%, 8% and 7% in the stock of health care workers. We calculate that this may lead to increases in maternal mortality of 38% in Guinea, 74% in Sierra Leone, and as large as 111% in Liberia, relative to pre-Ebola rates. Even if we take the lower bound estimates, health care worker deaths from Ebola would have increased maternal mortality by 26% in Guinea, 51% in Sierra Leone, and as large as 76% in Liberia, relative to preEbola rates. Effects on infant and under-five mortality are positive, ranging from an increase of 7% to 20% and 10% to 28%, respectively, but in both cases the coefficients used are not statistically significant (in the original Speybroeck et. al. paper), and looking at the 95% confidence interval we cannot rule out an effect of 0% in any of the three countries.

7
<!-- Start of picture text --> Cumulative Forecasting Error j- MCT'<br>0.02<br>0.01 -<br>0<br>De, 97 -98 Feb-98 Mar-98 Apr-98 May-98 Jun-98<br>-0.01 \<br>-0.02 1<br>-0.03 I<br>-0.04 4<br>-0.05<br>< Figure 6 ><br>Cumulative Forecasting Error Loan!<br>Des -97 Jan-98 Feb-98 Mar-98 Apr-98 May-98 Ju 98<br>-0.02 4<br>-0.06-<br>-0.121___________________________<br>< Figure 7 ><br><!-- End of picture text -->

# _Empirical Results on the Real Effect of the December 1997 Episode: Interest Rate Channel vs. Bank Lending Channel_

We now turn to the question of whether the strengths of the money/output and lending/output relationships are different in response to the December 1997 policy shift to a monetary tightening in the aftermath of the financial crisis. This question may be addressed by regressing industrial production _(Y)_ on money ( _M:_ Ml and MCT) and on 10
A survey of business executives, household, and civil servants in Indonesia published in 2000, several years before the Teacher Law of 2005, showed that this view was widely shared: “Respondents were asked to rank the main causes of corruption in society from amongst a list of possible reasons. _The results showed a strong consensus among all three groups with more than one-third of households (36%) and business enterprises (37%) attributing the main cause of corruption to low civil servant salaries._ Public officials were even more strongly of this view with over half of them (51%) putting this reason first.

. . . . almost half of the public officials reported receiving unofficial payments **.** _The argument that low salaries are a cause of corruption assumes that wages are inadequate to meet daily needs_ **,** and thus income has to be supplemented with bribes.” (Partnership for Governance Reform<sup>3</sup> 2000) The report’s authors go on to challenge this assumption, saying “While low salaries as a cause of corruption may be the most widely held belief, the accuracy of this relationship is disputed in the corruption research literature.” Nevertheless, a view that was so prevalent may have contributed to the legislature’s decision to raise teacher salaries.

This argument that higher (unconditional) salaries lead to better civil-servant performance is not unique to Indonesia either. In the case of Cambodia, Korm (2011) argues that: The prevailing opinion is that the low incomes of public servants have led them to pay too little attention to their official tasks and duties as they have diverted their time and effort to obtaining additional sources of income. They have become involved in corruption and „moonlighting‟ in other jobs. Furthermore, it is thought that _public servants have rationalised such behaviour using the argument that low pay justifies their poor performance. Whatever the reason, public service delivery is thought to have suffered significantly . . . ._ In Cambodia, civil servants are paid sums that cannot support a decent standard of living. Securing adequate income may then become the first priority in their minds as they need to meet their necessary costs of living. Chew (1997) emphasised _that if civil servants were well paid in relation to the cost of living, their performance would be good because they could concentrate on their work. When they are paid reasonably, they are happy and they perform to the required standard without being constantly concerned about finding more money to support their living. However, where public servants’ pay is very low in relation to the cost of living, their productivity and quality of performance are similarly low._ As Korm points out, McCourt (2003), in his _Global Human Resource Management_ book, summarizes this situation using the old joke: “you pretend to pay us, and we pretend to work.”

> 3 This partnership included the World Bank, United Nations Development Program, the Asian Development Bank (ADB), and a Governing Board comprising “a number of reform minded individuals including ministers, senior public officials and private entrepreneurs.”

7