rafmacalaba/datause-encoder-data huggingface dataset:
were more likely to have joined U-report recently than individuals registered with U-report that did not respond (see Appendix A5 for details). Over 10,000 respondents claimed to have already received a COVID-19 vaccine and consequently were not eligible to participate in the survey experiment. Of the remaining unvaccinated participants, 2,331 provided at least one valid response to the questions sent via SMS message, of which 1,957 had information available on the U-report database from previous surveys about their age, gender and region. A further 238 did not provide valid answers to every question in the SMS survey, which meant that there was complete information for 1719 participants. We present the weighted results for the 1,957 respondents with information about their age, gender and region in the body of the paper and conduct robustness checks with the samples of 2,331 (that provided at least one valid response) and 1719 respondents (that provided valid responses to every question) (see Appendix 6 Tables A6.3 and A6.4). The results are qualitatively similar.

Survey experiment participants were randomly allocated into three equally sized groups (two treatment groups and the control group). The randomization was stratified by the characteristics of U-report users, when this information was available (e.g., age, gender, region, when they joined U-report and their last interaction with U-report). This ensured there were very few statistically significant differences between treatment and control groups across demographic characteristics (see Table A4.1 in Appendix). As such, we present graphs showing treatment effects by comparing means across groups (with survey weights) in the body of this paper. In Appendix A4, we present tables showing the regression analysis of the treatment effects with controls added. Results from both methods are effectively the same.

# **5.2 Findings**

## **5.2.1 Descriptive results**

Similar to the in-person and online surveys, the descriptive findings of the SMS survey illustrate the close relationship between the attitudes of other household members and an individual’s willingness to get vaccinated. Figure 11 shows the results of the equivalent 31
Thus, the system is based on the following factors:

* "wage points" that are determined by comparing a person's income to the average income each year and taking account of the years of contributions

* two adjustment factors, for retirement age (I for regular retirement, lower or higher for early or late retirement) and for type of pension (I for old age pension and general disability, less for occupational disability and survivors' pensions)

> * the "pension value". This value was calculated in December 1991, immediately before the pension reform, and corresponds to the pension entitlement an average income worker earned by contributing for one year according to the old pension formula.The pension value is adjusted annually to the growth of net wages. Pensions in payment are also adjuste~d annually by applying the new current pension value; until 1992, pensions were adjusted to gross wages.

The pension for an average income worker with a contribution period of 45 years, net of pensioner's contributions to the statutory health insurance, replaced 68.6 percent of net earnings in 1993. With respect to gross wages, the replacement rate of gross pensions was 48.9 percent in the same year.

Since these figures relate only to a worker who earns the average income over the entire contribution period, it is interesting to look at the actual replacement rate which is shown in Table 2. This ratio is obtained by dividing the total pension expenditure by the number of pensions awarded (insured' and widows' pensions) and relating it to the average income. In 1993, the effective gross replacement rate was 36.3 percent (gross pensions/gross wages) and the net replacement rate (pensions net of health insurance contributions/ net wages) 50.9 percent.

**Table 2: Replacement Rates in Germany 1993** (in % of earnings)

||Gross<br>Rate|Replacement|Net<br>Rate|Replacement|
|---|---|---|---|---|
|Average income<br>w/ 45 years of contributions|48.9||68.6||
|Average income<br>w/40 years of contributions|43.4||60.9||
|-Average replacement rate|36.3||50.9||
|Source: Verband Deutscher Renten|versicherung|straeger,<br>1994|||

9
# (d) Promotions

The research found only indirect evidence of parallel systems in promotion processes. However, the findings show promotion processes are irregular and that performance, however defined, is not a criterion for promotions. Promotion processes follow broader Civil Service Conduct Rules in both states. Eligibility for promotion in both states requires at least 12 years of service, completed annual confidential reports (ACRs) and absence of disciplinary actions. As is the case across the Indian public service, seniority is the principal if not only factor determining promotion to a higher cadre.<sup>16</sup> Table 4 displays the findings of the perception surveys for promotions in each state. The main difference relates to the seniority criterion. Physicians in State A consider that promotions are based on seniority (63 percent) while only a minority of their State B counterparts (29 percent) shares this perception. Only 17 percent of State B respondents consider that promotion criteria are followed.

**Table 4:  Promotion in States A and B Perception Survey Results 2011,** **<u>(in percentages)</u>**<sup>**a**</sup>

||**% Agr**<br>|**eeb **<br>|
|---|---|---|
|**Summary Statements**|**State A**<br>**N=273**|**State B**<br>**N=266**|
|The Departmental Promotion Committee (DPC) is constituted regularly|26.7|6.4|
|Time-bound promotions are being given|23.4|N/A<sup>C</sup>|
|Promotions are done based on the departmental seniority lists|62.7|29.1|
|The promotion process follows the promotion criteria of the department.|N/A<sup>d</sup>|17.2|
|The Confidential Reports (CRs) are being filled in annually|32.3|57.7|
|Ultimately, it is the MOs, who have to ensure that their updated CRs are sent to the|||
|DH, at the time of promotions|68.4<br>|57.5|
|MOs have to oblige in unofficial payment to department staff to get their personal|N/A<sup>d</sup>|77.2|
|filesprocessed faster.|||

aPercentage based on valid responses (exclude missing data) bCombines responses for “agree” and “completely agree” cTime bound promotions not specified in State B rules d Statement not included in State A survey.

In both states, the promotion process suffers several shortcomings. Securing documents for promotions is challenging. Only 32 percent of respondents in State A, and 58 percent in B, agreed that ACRs are completed regularly, and the majority agree that physicians themselves must ensure completion and submission of the reports. Further, only 27 and 6 percent of State A and B physicians respectively agreed that the Department Promotion Committee (DPC) is constituted regularly.

14
<!-- Start of picture text --> urban<br>national<br>rural<br>1990 1994 1998 2002 2006 2010<br>Time<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br><!-- End of picture text --> Figure 1: Estimated and projected adult HIV prevalence rates in Ethiopia, 1990-2010 (MoH, 2006).

40
Table 17. Host Community Near the Syrian Border, 2013 & 2014 Means Comparison Test

||mean(2014)-|two-sided|2014-|2013-|
|---|---|---|---|---|
||mean(2013)|means test p-<br>value|mean|mean|
|N children*|-1.284|-|-|1.28|
|N adults|0.050|0.0006|2.29|2.24|
|N old|0.024|0.0017|0.60|0.58|
|**Characteristics of the head of household**|||||
|0-14|0.0%|.|0.0%|0.0%|
|15-24|-0.1%|0.3616|1.4%|1.5%|
|25-39|-0.3%|0.5363|27.4%|27.6%|
|40-54|0.7%|0.1266|36.1%|35.4%|
|55+|-0.3%|0.4658|35.2%|35.5%|
|1-Male, 0-Female|2.4%|0.0000|85.0%|82.5%|
|None|-0.3%|0.4101|23.3%|23.6%|
|Primary|1.3%|0.0054|42.9%|41.6%|
|Secondary|-0.6%|0.1661|23.9%|24.5%|
|Post-Secondary|-0.2%|0.4396|10.1%|10.3%|
|**Al workers in the household**|||||
|Share of Employed Adults|11.0%|-|36.5%|25.5%|
|Share of Unemployed Adults|1.5%|0.0000|3.8%|2.4%|
|Total Hours Worked in the Whole Family|0.190|0.3610|51.50|51.31|
|Mean Hours Worked out of all working individuals|0.251|0.1554|50.70|50.45|
|Share Workers White Collar|-0.7%|0.2275|33.8%|34.4%|
|Share Workers Blue Collar|-0.1%|0.8321|45.8%|45.9%|
|Share with Permanent Contract Type|-1.1%|0.0273|35.1%|36.2%|
|1-9 employees|1.6%|0.0042|45.9%|44.3%|
|10-49 employees|-3.4%|0.0000|14.2%|17.5%|
|50+ employees|0.9%|0.0512|19.4%|18.5%|
|Share Agriculture, forestry, fishing-Mining and quarrying|1.4%|0.0002|13.6%|12.2%|
|Share Manufacturing Industry|-0.2%|0.6378|13.0%|13.2%|
|Share Electricity, Gas , Steam, Water and Sewerage|0.1%|0.4251|1.1%|1.0%|
|<br>Share Construction|-1.2%|0.0005|8.5%|9.7%|
|Share Commerce and services|-1.3%|0.0113|22.3%|23.6%|
|Share Others|0.4%|0.4353|21.1%|20.8%|
|Registered with Any SS institution related to your main job|-1.1%|0.0785|62.5%|63.5%|
|Share of workers registered with any social security<br>|-0.8%|0.1496|51.6%|52.4%|
|institution related to your main job|||||

_Notes_ : *In 2014, the LFS no longer captures information about children 14 and under. N2013 = 21,467 for most variables, N2014=22,791 for most variables. Variables are at the household level. _Source_ : LFS 2013 & 2014. World Bank author’s calculations.

32
Table 2. Characteristics of labor force participants by primary sector of participation, all adults. Values are bold where hypothesis of similar means between another sector and agriculture is rejected at _p <_ 0 _._ 05.

||ETHIOPIA<br>2011-12|MALAWI<br>2010-11|TANZANIA<br>2010-11|UGANDA<br>2010-11|
|---|---|---|---|---|
|**_Agriculture_**|||||
|Education (yrs)|1.27|4.41|5.46|5.12|
|(sd)|(2.58)|(3.79)|(3.73)|(4.28)|
|Age<br>|39.6<br>|39.9<br>|41.0<br>|40.5<br>|
|(sd)|(10.95)|(11.63)|(10.88)|(11.04)|
|Female (sh.)|0.50|0.58|0.55|0.601|
|N|3,971|2,535|3,513|2,494|
|**_Industry_**|||||
|Education (yrs)<br>|1.40<br>|**7.183**<br>|**8.337**<br>|**8.108**<br>|
|(sd)|(2.76)|(4.81)|(3.48)|(4.88)|
|Age|**36.61**|**38.24**|**37.7**|**37.82**|
|(sd)|(9.79)|(10.02)|(9.66)|(10.12)|
|Female (sh.)|**0.577**|**0.286**|**0.269**|**0.345**|
|N|346|311|209|242|
|**_Services_**|||||
|Education (yrs)|**3.955**|**9.209**|**8.651**|**9.779**|
|(sd)<br>|(5.25)|(4.38)|(4.24)|(5.50)|
|Age|**35.33**|**36.35**|**37.56**|**37.52**|
|(sd)<br>|(9.08)|(9.39)|(9.78)|(9.55)|
|Female (sh.)|0.52|**0.337**|**0.393**|**0.385**|
|N|572|1,004|1,115|983|
|**_Unknown_**|||||
|Education (yrs)|**1.886**|4.52|||
|(sd)|(3.25)|(3.82)|||
|Age<br>|**37.31**<br>|**37.43**<br>|||
|(sd)|(9.09)|(10.56)|||
|Female (sh.)|**0.362**|**0.406**|||
|N|193|589|||
|**_None_**|||||
|Education (yrs)|**1.044**|**6.806**|**7.529**|**7.654**|
|(sd)|(2.45)|(4.63)|(4.19)|(5.37)|
|Age|39.45|**35.25**|**37.25**|**37.66**|
|(sd)|(11.69)|(10.96)|(10.72)|(11.60)|
|Female (sh.)|**0.823**|**0.748**|**0.615**|**0.508**|
|N|826|446|1,640|816|

34
**Table 1: Model Shocks in Baseline Scenario and Tariff-Equivalent Import Price Reduction**

||_ITI in_<br>_2004_|_ITI_<br>_shock%_|_Shock%:_<br>_Tariff_<br>_Equivalent (a)_|_Technical_<br>_Progress_<br>_Contribu_|<br>_Irregular_<br>_Payments_<br>_tion to(a)_|
|---|---|---|---|---|---|
|_AUS_|0.863|0.0|0.0|0.0|0.0|
|_BRU_|_NA_|_NA_|_NA_|_NA_|_NA_|
|_CAN_|0.756|0.0|0.0|0.0|0.0|
|_CHL_|0.743|0.0|0.0|0.0|0.0|
|_CHN_|0.332|15.6|-20.5|-13.1|-7.4|
|_HKG_|0.849|0.0|0.0|0.0|0.0|
|_IDN_|0.388|11.5|-15.0|-12.3|-2.8|
|_JPN_|0.762|0.0|0.0|0.0|0.0|
|_KOR_|0.536|6.2|-8.1|0.0|-8.1|
|_MEX_|0.406|10.2|-13.4|-8.3|-5.1|
|_MYS_|0.445|7.4|-9.8|-7.5|-2.2|
|_NZL_|0.961|0.0|0.0|0.0|0.0|
|_PER_|0.319|16.5|-21.7|-17.4|-4.3|
|_PHL_|0.226|23.9|-31.3|-16.9|-14.4|
|_PNG_|_NA_|_NA_|_NA_|_NA_|_NA_|
|_RUS_|0.000|44.3|-58.1|-43.7|-14.4|
|_SGP_|1.000|0.0|0.0|0.0|0.0|
|_THA_|0.286|19.1|-25.1|-14.4|-10.7|
|_CTP_|0.809|0.0|0.0|0.0|0.0|
|_USA_|0.794|0.0|0.0|0.0|0.0|
|_VNM_|0.103|34.4|-45.2|-34.4|-10.8|

(Note) The second column shows the target percentage change in ITI to achieve the baseline scenario. The third to fifth columns indicate the target percentage change in terms of tariff power (1 + t). See Appendix B for the abbreviations of the member economies.

As a policy shock under the baseline scenario, the importer transparency index (ITI) increases for China, Indonesia, Korea, Mexico, Malaysia, Peru, the Philippines, Russia, Thailand, and Vietnam. The tariff-equivalent shocks in this scenario produce large cost reductions. In particular, the import cost reduction estimated for Russia and Vietnam is above 40 percent and for the Philippines is about 30 percent. These impacts appear large compared, for example, to APEC Trade Facilitation Principle (Shanghai Accord). APEC members adopted the goal of cutting trade transaction costs by only five percent under the Accord. For example, our estimated cost reductions with improved transparency in Thailand, i.e. 25.1 percent equals to 25 times as large as the Shanghai Accord, if the 18
8 returned to the same community after migration were more likely to be reintegrated” (Kuschminder and Koser, 2015) The fact that very few asylum seekers and refugees return home to a developing country is confirmed by the most recent UNHCR and Eurostat statistics – imperfect and incomplete as they are. (Table 1 and 2)

# **Table 1: Refugees, Asylum‐Seekers, And Return Refugees by Country/Territory of Asylum, in 2016**

(A dash ("‐") indicates that the value is zero, not available or not applicable. All data are provisional and subject to change.)

|Country/territory<br>of<br>Asylum|Refugees|Asylum<br>seekers<br>(pending<br>cases)|Returned<br>refugees<br>(to<br>country of<br>origin,<br>during<br>2016)|
|---|---|---|---|
|Turkey|2,869,421|245,955|‐|
|Pakistan|1,352,560|4,856|7|
|Lebanon|1,012,969|13,745|‐|
|Iran|979,435|91|11|
|Uganda|940,835|41,880|1,192|
|Ethiopia|791,631|1,964|1|
|Jordan|685,197|35,615|‐|
|Germany|669,482|587,346|‐|
|Dem. Rep. of the Congo|451,956|1,327|13,223|
|Kenya|451,099|43,764|4|
|Sudan|421,466|16,052|37,215|
|Chad|391,251|1,909|28|
|China|317,255|668|‐|
|France|304,546|62,771|‐|
|Tanzania|281,498|8,539|6|
|United States of America|272,959|542,649|‐|
|Yemen|269,783|9,097|1|
|South Sudan|262,560|1,792|1|
|Iraq|261,888|11,458|157|
|Russian Federation|228,990|3,039|56|
|Egypt|213,530|49,877|‐|
|India|197,851|9,219|‐|
from operations, based on the experiences of US firms in the same industries. We find that firms in financially dependent industries are larger at all stages of their life-cycle. Thus, there is no evidence that financial dependence affects growth rates of established Indian firms relative to firms in industries that are not financially dependent.<sup>6</sup> Furthermore we do not see that firms in financially dependent industries are larger or face different lifecycle effects in financially developed states than under-developed states. Using a similar difference-in-difference set-up we also find that firms in large-firm dominated industries are larger at each stage of the life-cycle, both in the case of capital intensive and labor intensive industries.<sup>7</sup> Our findings contrast with the literature that finds significant effect of within-country institutional differences on firm performance. For example, Jayaratne and Strahan (1996) and Dehejia and Lleras-Muney (2007) show that differences in state-level bank regulation in the United States affects financial development. Similarly, Guiso et al. (2004) examine differences in financial development across regions in Italy, particularly on small firms and entrepreneurship and find a significant effect. The explanation for the differences in conclusions is most likely that the state-owned and controlled financial sector in India is not significantly contributing to firm growth. In such a system, the regional differences in financial development as measured by the financial depth of the banking system may be masked by other potentially more important institutional (e.g. infrastructure constraints) and firm-specific factors (e.g. organizational form). The finance literature also shows that in a large cross-section of countries, higher government ownership of banks is associated with slower subsequent financial development and lower

> 6 Rajan and Zingales (1998) show that financially dependent industries grow _less fast_ than financially independent industries using a sample of developed and developing countries.

> 7 Following Beck et al. (2008), we classify industries into small firm dominated and large firm dominated categories using the US industry composition as an instrument. Beck et al. measure an industry’s “technological” composition of small firms relative to large firms as the share of employment in firms with less than 20 employees in the United States (a country with a relatively frictionless financial system) in 1997.  To classify industries into labor vs. capital intensive we follow Hasan and Jandoc (2012)

5
|Armenia|10.7|42.9%|52.3%|9.4|95.7%|18.1%|
|---|---|---|---|---|---|---|
|Kyrgyz*|4.6|21.7%|51.4%|4.6|21.7%|52.2%|
|Georgia|7.6|59.2%|60.1%|6.4|-29.7%|131.2%|
|Moldova*|5.1|105.1%|11.1%|2.2|190.1%|-54.5%|
|**Comparators**|||||||
|**(world)**|||||||
|China|11.5|38.2%|59.2%|7.8|20.5%|71.8%|
|US|1.4|-28.2%|83.1%|0.9|27.2%|45.5%|

Source: Conference Board, Total Economy Database (January 2012) Note: (*) denotes low-income country.

At the break-up of the Soviet Union, fixed capital formation in Eurasia was more than twice that in EAP12. By 2010, it had fallen to 21 percent of GDP, which is well below the level observed in EAP12 (35 percent of GDP). Eurasia fares better when changes in inventories are included (Figure 5). We find that gross domestic investment has been trending upward over the last decade, reaching levels that are comparable to the 25 percent of GDP or more experienced in the fastgrowing Asian economies (Commission on Growth and Development 2008). What has been the decomposition of that investment trend between the private and public sectors?

**Figure 5:** _Gross domestic investment_<sup>1/</sup> (as a share of GDP)

<!-- Start of picture text --> 40%<br>30%<br>20%<br>10%<br>EU12 EAP12 Eurasia<br>0%<br>1990 1995 2000 2005 2010<br><!-- End of picture text --> Source: World Bank, WDI Note:<sup>1/</sup> outlays on fixed assets additions plus net changes in the level of inventories. 2/ overall investment rate in developing countries growing at an annual rate of 7 percent over the last three decades = 25 percent of GDP.

To examine investment trends by the private sector, we use data on private sector participation in infrastructure (PPI). This dataset does not include private investments in the extractive industry and manufacturing sector. We find that PPI was historically low, but that the private sector financed 132 infrastructure projects in the region, equivalent to $143 billion, over the last ten years. This represents an average of $14 billion per year—an amount which is insignificant in view of Eurasia’s investment requirements. It was targeted at only a few sectors—with sixty percent in telecommunications, 34 percent in energy, and transport and water together obtaining less than 10 percent of the total regional inflow. Russia dominated the inflows, by attracting 73 percent of the region’s PPI. This represents, however, less than 5 percent of Russia’s GDP, with a significant share derived from the privatization of state-owned assets rather than new investments (Figure 6). When including the extractive industry and manufacturing sector, total investments by the private sector in Russia accounted for 10 to 15 percent of GDP (Figure 8).

14
that the international community recognize the need for some measure of democratization for a meaningful cooperation between Khartoum and Juba that should go beyond the NCP-SPLM to enfranchise the other popular political parties in the country. In this context the international community should support solutions like the recent initiative by opposition parties, led by the Umma Party, aimed at negotiating with the NCP to forge a national consensus on elections and transition to democracy in return for a “safety net” for the NCP should it lose the election and national commitment to the full implementation of the CPA on the part of the winners to assure the SPLM. Such extensions to the franchise that would increase political competition and break political duopoly inherent in the current situation is certainly more desirable than an election alliance between the NCP-SPLM conditional on full implementation of the CPA, as believed to have been proposed by the SPLM; or the alternative proposition of full implementation of the CPA conditional on an electoral alliance between the two as alleged to be the counter proposal from the NCP (ICG, 2008).

A more credible and positive intervention on the payoffs for Juba and Khartoum would entail a complete package that includes measures to promote deep democratic transformation in North and South, decreases in military dependence and investments in economic interdependence. The international community should help the achievement of a speedy and amicable resolution of the Darfur conflict; support a timely execution of a comprehensive and sound population census (scheduled for April 15-30, 2008), including by providing sufficient number of qualified international observers; and encourage the Sudanese political parties and civil societies to agree on an election law that meets international standards and to commit enough resources to ensure that the multi-levels 45
for example, tenants may invest less than landowners merely because they are less wealthy. The advantage of looking at land tenancy in India (Bell, 1977; Shaban, 1987)—and, as it happens, in Pakistan—is that a given household will often cultivate two or more plots of land under different contractual arrangements, allowing a comparison of behaviors across plots within the same household. A limitation of this approach is that it does not deal with the possibility that the decision to lease out a plot (or to lease it out under a fi xed rent versus a sharecropping contract) may depend on the unobserved attributes of the plot, such as would be the case if there is adverse selection in the leasing market. We use instrumental variables to address this potential endogeneity problem. Our identi fi cation argument, developed below, is that the assets of the owner of a given cultivated plot, which our data set provides even if the plot is leased in, should determine the leasing status of the plot but not its unobservable type.

The next section of the paper provides the context for our study and sets out a simple two-period model of tenancy, moral hazard, and land-speci fi c investment. Section 3 presents the estimation and identi fi cation strategy as well as our evidence for a leasing effect on investment. The analysis of the tenure security effect follows in section 4, along with conclusions and implications in section 5.

# 2 Tenancy and Land Speci fi c Investment

## 2.1 Data and context

Ownership of agricultural land is highly concentrated in rural Pakistan, where about half the population is landless. As a result, land lease markets are quite active. According to the latest agricultural census (2000), about a third of total cultivated area was tenant operated, mostly (roughly two-thirds) under crop-sharing arrangements with the remainder under fi xed rent. Land and tenancy reforms have been attempted at various times, but they have been largely ineffectual, leaving tenant cultivators with little legal recourse in the event of eviction.<sup>4</sup> Land ownership, however, is clearly established in rural Pakistan and the risk of expropriation by the state or by powerful individuals is negligible.

Our empirical analysis draws upon a new nationally representative rural household survey. The Pakistan Rural Household Survey (PRHS), which was completed in early 2002,

> 4 Nabi (1986) reports data from a small 10 village survey in Pakistan’s Punjab in which 46 percent of share-tenants said that their landlords could evict them ‘easily’ and 32 percent said that they or their relatives had experienced eviction within the last two years. At the same time, however, most of these tenants had been leasing land of the same landlord for more than 10 years.

3