rafmacalaba/data-use-mentions-extended huggingface dataset:
been validated using a range of crime data,<sup>5</sup> provide a framework with systematic data management capacity that enables user input decisions as sufficiently fair, credible, and acceptable. A second is ‘social inclusion’ which is the extent to which access to basic services and markets as well as political, social, and cultural spaces are enabled in order for participation in society with agency and dignity (Cuesta Leiva 2022). To measure the four (social inclusion, social resilience, social cohesion, and process legitimacy) components of ‘Social Sustainability’ they cite, (1) for ‘social inclusion’, labor participation, financial inclusion, public service accessibility, and political participation; (2) for ‘social resilience’, the ability to save, income diversity, shock-responsive assets, food security and migratory mobility; (3) for ‘social cohesion’, interpersonal and institutional trust, violence levels, and democratic and civic participation, and; (4) for ‘process legitimacy’, rule of law measurements, corruption levels, separation of power, objectivity and equality (including accessibility) of law/regulation application, and physical security.

4 See analytical approaches employed by the World Bank: https://www.worldbank.org/en/topic/governance/brief/justice-rights-and-public-safety.

> 5 The current version of MCBT, with crime-related data as examples, can be found at:

> <u>https://dmm.anu.edu.au/CD-AV/. New examples are regulalry uploaded to demonstrate the capability of the tool</u> to be adopted in different contexts. For access, please contact <u>matthew.manning@anu.edu.au.</u>

4
crime surveys conducted in Germany and found that interviewers were accountable for 77 percent of the total clustered variance with interview areas only accounting for 23 percent.

# _Overview of research_

These findings, which are typically not accounted for in survey designs, mean that surveys are likely underpowered. This paper sets out to quantify interviewer design effects in the context of a developing country household survey conducted with CAPI. To do this, we attempt to isolate the portion of the variance attributable to interviewers from the portion attributable to interview areas. We also control for interviewer – respondent interaction effects that may be associated with the gender of interviewer and respondents. We make recommendations for survey design and data analysis to mitigate the effect of interviewer design effects.

The paper proceeds as follows. Section 2 describes the data, including sampling and the variables selected for this study. Section 3 describes our analytical approach, while section 4 presents and discusses the results. Finally, section 5 presents a brief conclusion and recommendations.

# **2. Data description**

The data used in this study is from the Agricultural Production Survey (APS), a pilot survey conducted by the Central Bureau of Statistics of Sudan in collaboration with the World Bank in March-April 2018. The survey was conducted in Kassala State of Sudan. The APS questionnaire was designed largely based on the World Bank’s Living Standards Measurement StudyIntegrated Surveys on Agriculture (LSMS-ISA). The questionnaire included four modules: (1) the household module, (2) the agricultural planting module, (3) the agricultural harvest module, and (4) the livestock module. For this study, we only use the household module because this was the only module completed for all households and therefore provides the largest sample size. The data was collected with tablets using the Survey Solutions CAPI program.

# _Sampling_

The sample was drawn using a two-stage cluster design. First, the simple random sample size was calculated with the standard parameters of a 5 percent error margin, a 95 percent confidence level and a
To test our hypothesis, we take advantage of a unique longitudinal survey on the National Public Services (NPS) covering over 6,000 households throughout Sierra Leone and use multilevel models to exploit variation over time and within and across sub-national units in Sierra Leone. Our analysis shows that the quality of decentralized service delivery is positively associated with views of local government authorities as trustworthy. We also find that the perceived honesty of councillors is positively correlated with councillors’ trustworthiness and that this finding is stronger in more remote communities. These results are robust to controls for alternative determinants of trust including ethnicity, the history of war violence, NGO service provision, councillors’ outreach efforts, social capital, and political party affiliation. Our findings speak to the possibility that decentralization reforms aiming to improve local service provision will help to overcome long-lasting patterns of popular discontent and distrust toward political authorities and to enhance the prospects of political stability in Sierra Leone.

The paper is organized as follows: Section 2 outlines the theoretical framework and reviews the empirical literature on the links between government performance, administrative competence and honesty, and trustworthy government. Section 3 describes the advantages of the Sierra Leone case-study while Section 4 describe our data, measures and methods. In Section 5, we analyze the sources of beliefs about the trustworthiness of local government officials and discuss the results. Finally, in Section 6, we consider the relevance and policy implications of our findings for the ongoing efforts to support local governments and build accountable institutions in post-conflict and fragile settings.

# **2 Theoretical Framework**

## **2.1 Trustworthy Government: Sources and Consequences**

The majority of governments, especially those in transitional and post-conflict societies, are struggling to establish regimes of widespread compliance based on legitimacy and not solely upon coercion. A government perceived as legitimate can expect widespread public cooperation for such voluntary acts as voting, adherence to public health recommendations (Lieberman, 2007) and participation in community problem solving, as well as quasi-voluntary compliance with taxes (Levi and
Figure 2: Services GVC participation, Gross Exports and Services GVC participation by Type <!-- Start of picture text --> GVC participation and gross exports SERVICES<br>Services<br>IRL 0.2<br>0.2<br>SGP<br>0.1<br>VNM BEL<br>EST CYPSVK HUNCZE MYSTHA HKG 0.1 0.1 0.1<br>KHM TUNCOLISLKAZLTUPERLVACHLSVNHRVBGRCRIMARGRCNZLZAFSAUPRTARGTURMEXROUIDNDNKNORFINPHLCHNAUSTWNBRARUSPOLAUTISRKORITASWECANESPJPNNLDCHEROWFRAINDDEUGBR USA 0.0 0.0 0.1 0.0 0.1 0.0 0.1 0.0 0.1 0.0 0.0 0.1<br>MYS KOR DEU IND GBR BRA MEX CHN USA<br>6 8 10 12 14 Backward Forward<br>Gross exports log (2015)<br>Source: OECD; own calculations<br>Figure 3: GVC Participation for IT and Information Services<br>GVC participation and gross exports IT AND INFO<br>Backward GVC participation and gross exports IT AND INFO Forward GVC participation and gross exports IT AND INFO<br>LUX<br>IRL LUX SGP<br>IRL<br>LUX<br>KHM KAZBRN THAPERTUNCOLSAUMLTTURMEXCYPLTUHRVISLCHLSVNIDNLVAGRCVNMZAFNZLESTSVKPRTCRIMARBGRHKGTWNBRAARGCZENORHUNAUSDNKMYSPHLCHNRUSROUKORJPNCANAUTPOLITAFINSGPBELFRAISRROWESPNLDSWEGBRCHEDEUUSA IND KHM KAZBRN THAPERTUNCOLSAUMLTTURMEXCYPLTUHRVISLCHLSVNIDNLVAGRCVNMNZLZAFESTSVKPRTCRIMARBGRHKGTWNBRAARGCZENORHUNAUSDNKMYSPHLCHNRUSROUKORJPNCANAUTPOLITAFINSGPBELFRAISRROWESPNLDSWEGBRCHEDEUUSA IND KHM KAZBRN THAPERTUNCOLSAUMLTTURMEXCYPLTUHRVISLCHLSVNIDNLVAGRCVNMNZLZAFESTSVKPRTCRIMARBGRHKGTWNBRARGCZENORHUN A DNKMYSPHLUSCHNRUSROUKORJPNCANAUTPOLITAFINBELFRAISRROWESPNLDSWEGBRCHEDEUUSAIRLIND<br>0 5 10 0 5 10 0 5 10<br>Gross exports log (2015) Gross exports log (2015) Gross exports log (2015)<br>80<br>60<br>40<br>20<br>0<br>.8<br>.6<br>.4<br>.2<br>0<br>.6<br>.4<br>.2<br>0<br>2<br>1.5<br>1<br>.5<br>0<br>.2<br>.15<br>.1<br>.05<br>
, both from a physical and institutional perspective. Indeed, the combination of Transnet and privatized road freight service plays a key role in determining the transport logistics choices made by the private sector. This has resulted in freight flows increasingly dominated by road transport. At the time of the first State of Logistics Survey in 2004 road already accounted for 84 percent market share in volume flow. This has increased to close to 90 percent in 2013 (CSIR, 2014). This modal imbalance, combined with poor performance of South Africa’s ports, is central to the transport logistics challenge undermining export competitiveness.

Why does rail matter in this context? Why not simply rely on what is a fairly efficient road freight network? There are several reasons why it is important to ensure exporters (and in fact all shippers) have access to an efficient and competitive freight rail network. First, for a substantial subset of transport journeys, rail will be the most cost efficient mode (see Box 3); and for a wider set of journeys some combination of rail and road (intermodal) will be most cost efficient. Thus, if some exporters (particularly smaller and less centrally located ones) are forced to settle for a less cost efficient transport mode, their competitiveness will suffer. Second, the existence of rail as a credible alternative to road has a disciplining effect on the competitiveness of road freight.<sup>5</sup> Third, given the fixed cost nature of rail there exists a downside ‘tipping point’ whereby declining competitiveness contributes to lower incentive to invest in the network, which undermines competitiveness still further, leading to a downward spiral. Finally, rail freight minimizes many of the negative externalities posed by road freight which are borne by the state and road users, including: increased congestion; more road accidents; higher harmful emissions; higher road maintenance requirements; and higher logistics cost on poor roads.

> 5 In many countries, the road freight sector displays cartel behaviour (World Bank, 2011a)
# **4.2 Findings**

## **4.2.1 Descriptive results**

Similar to the in-person survey, the descriptive findings of the online survey also reflect the prominent role the attitude of other household members has on an individual’s willingness to get vaccinated. Figure 6 shows the results of the equivalent regression analysis described in relation to the in-person survey in Section 3 (equivalent of Figure 4) but using data from respondents to the Facebook advertisements in the control group, who were not randomly allocated to receive a treatment (see Table A4.2 in Appendix for details). An individual was around 30 percentage points more willing to be vaccinated if others in their household were also willing. This factor has by far the strongest association with whether a respondent is willing to get a COVID-19 vaccine. All other factors had a negligible correlation. It is important to note this means that being involved in household decisions was not clearly associated with willingness to get a COVID-19 vaccine.

## **4.2.2 Main findings from the online experiment**

The overall results of the experiment illustrate that a message about the benefits of COVID19 vaccines can substantially increase willingness to get vaccinated and decrease unwillingness. Figure 7 shows that both treatments increased the share of respondents who were willing to get vaccinated by around 10 to 11 percentage points and decreased the share who were unwilling to get vaccinated by around 7 and 5 percentage points (individual benefit and household benefit respectively). Both of the treatment effects are statistically significant at conventional levels. After controlling for the characteristics of respondents, the treatment effects were still large and significant (see Table A4.3 in Appendix).

21
|Constant|0.46***|0.88***|0.06***|0.01**|
|---|---|---|---|---|
||(0.03)|(0.02)|(0.01)|(0.01)|
|**E. WEALTH INDEX**|||||
|_Reference Category: Bottom 20%_|||||
|Second Quintile|0.08***|-0.00|-0.01|0.00|
||(0.02)|(0.01)|(0.01)|(0.00)|
|Third Quintile|0.11***|-0.01|-0.00|0.00|
||(0.02)|(0.01)|(0.01)|(0.00)|
|Fourth Quintile|0.16***|-0.01|-0.02**|-0.00|
||(0.02)|(0.01)|(0.01)|(0.00)|
|Fifth Quintile|0.15***|-0.03**|-0.04***|-0.00|
||(0.02)|(0.01)|(0.01)|(0.00)|
|Constant|0.51***|0.87***|0.08***|0.01***|
||(0.01)|(0.01)|(0.01)|(0.00)|

Notes: Standard errors in parentheses. * Significant at
primary activity of the head of household (Ravallion and Huppi, 1991), or in the very common consumption regression analyses, if the 7 day recall variable is used, measurement errors are introduced which will result in biased or inconsistent estimates on the role of household economic activities in poverty status.

**Table 8: Comparison of primary employment status in 2009/10: 7 day vs 12 months recall period, UNHS, individual**

||**Same primary activity**<br>|**in 12 month recall?**<br>|
|---|---|---|
|**_Primary activity- last 7 days_**|**_No_**|**_Yes_**|
|Private wage - non agriculture|8.6|91.36|
|Private wage – agriculture|10.3|89.71|
|Government wage|4.5|95.54|
|Own account - non agriculture|8.1|91.06|
|Own account – agriculture|5.8|94.23|
|Family worker- non agriculture|8.3|91.72|
|Family worker – agriculture|5.3|94.68|
|Total|100.0||

_Source: Authors calculations from the UNHS 2009/10. Note: Table based on a direct question asking the main 12 month activity which has 3 responses, i) the same as the 7 day main activity, 2) 7 day secondary activity and 3) other activity._

# **3.4 The recall period in the measurement of multiple economic activities**

The measurement of multiple activities of individuals and households is influenced by the recall period. Evidence from the various surveys shows that the difference between the short (7-day) and long (12 month) recall period in the number of economic activities measured is significant, with the shorter recall period consistently understating reports of
.009)|0.033***<br>(0.007)|
|ln(GDP/capita)|0.008**|0.005|0.025*|-0.004|0.031|
||(0.004)|(0.011)|(0.013)|(0.012)|(0.021)|
|Adj. _R_<sup>2</sup>|0.492|0.488|0.488|0.426|0.489|
|Number|6,375,668|6,375,826|6,375,826|6,375,826|6,375,826|

_Notes_ : Authors’ calculations using SEDLAC data. Robust standard errors in parentheses. Significance: *** 1%, ** 5%, * 10%.

in his principal job. In column 4, underemployment ( _Ftim_ ) replaces tenure ( _Ten_ ) as a dimension of job quality. Likewise, having a single job is replaced in column 5 with an alternative measure of satisfaction, ( _Sat_ ). A success is recorded under this satisfaction variable if the worker responded that they did not want to work more hours or find another job. In addition to the regressions shown in the tables, other combinations of the nine possible job quality dimensions were investigated and were found to have similar results.

37
In the simplest terms, a poverty line is a threshold expressed in monetary terms below which individuals are deemed poor. This threshold should pertain to the minimum amount of Nepali rupees to satisfy minimum caloric requirements and basic needs for nonfood goods and services. In 1995, the official poverty rate of 42 percent was based on the poverty line of Rs. 5,089 per person per year. In 2003, the poverty line of Rs. 7,696 was used after adjusting for inflation between the two survey rounds.<sup>11</sup> This is to ensure that the same standard of living is used to identify the poor across the two survey rounds so that Rs. 7,696 in 2003 should afford the same basket of goods and services Rs. 5,089 afforded in 1995.

If the original poverty line was used to estimate poverty in 2010 after inflation adjustments, the poverty line would have been Rs. 14,316 and the resulting poverty rate would have been as low as 12.5 percent. This, however, was not adopted as the official poverty rate because of the growing perception and mounting evidence about improving living standards in Nepal. Overall economic well-being in Nepal had improved so much that the definition of what it meant to be poor in 1995 became obsolete by 2010. Some of what used to be luxury became necessity. For example, in 2010, Nepalis on average consumed more expensive food items such as meat and eggs and less on cereals than 15 years ago. They also spent more on nonfood goods and services than they did in 1995.

In order to reflect the evolving way of life and the current consumption patterns in Nepal, a new poverty line was estimated using consumption data from the 2010 NLSS.<sup>12</sup> The new poverty line was set at Rs. 19,262, an increase in real value by 35 percent compared to the original poverty line in 1995/96. Even with the revised poverty line, the poverty rate
early and prime working ages. Second we define skilled individuals as those who have at least a complete post secondary education. Third, we normalize our measure of wages to monthly payments. Fourth, we drop the top and the bottom 1 percent of wages reported within a particular district to omit outliers.<sup>7</sup> Fifth, we identify 10 industries which are merged with the data on greenfield FDI, namely: Agriculture, Extraction, Manufacturing, Construction, Utilities, Commerce, Transport and Communications, Financial and Business Services, Public, and Other Services. The number of sectors was constrained by the maximum disaggregation avaiable in the I2D2 database.

The evolution of the skill premium can be seen in Figure 2. Here the skill premium is defined as the ratio of skilled to unskilled wages, adjusted for years of experience. A striking observation is that for both men and women the skill premium has been declining in most countries in our sample. The general downward trend in the wage premium was also documented by Cruz and Milet (2017).<sup>8</sup> Vietnam is an exception but the last year observed is 2008. In the Philippines it has not changed much over the years. In Ethiopia it has declined for men but increased slightly for women from 2004 to 2012. Hence according to this metric at least these emerging economies have not seen major increases in inequality as measured by the skill premium in the last decade<sup>9</sup> . Another interesting fact is that the skill premium is much higher for women than men. In Ethiopia for example, while skilled women make 3 times more than unskilled women in 2014, down from 5 times in 2004, skilled men make only 2 times more than unskilled men, down from 2.6 times more in 2004. Note that despite the drop in the skill premium, in all countries wages are higher for men relative to women and for skilled relative to unskilled workers. Unskilled women are always at the
### **Beneficiary selection**

Across the 207 localities, individuals interested in the program were invited to enroll before the assignment to treatment or control groups. Enrollment data was collected by the implementing NGO. It was then used to check eligibility and screen applicants by level of vulnerability. Out of 14,880 interested individuals, 12,692 met the eligibility criteria as they were either 18-40 years old, a single mother above 15 years old, a widow or disabled individual (up to 60 years old) and were not benefiting from another program.

Among the applicants, 5,116 were selected for the program. The selection was based on an individual proxy score computed using the enrollment data.<sup>21</sup> Selected individuals are those above the vulnerability cut-off, i.e. those considered the most vulnerable. The cut-off is identical for all localities assigned to the same intervention, but varies across the three interventions based on the budget and target number of beneficiaries determined during the program preparation process. As further detailed below, the identification of treatment and spillover effects uses “common support” cut-offs across treatment modalities.

# **3 Estimation strategy and data**

## **3.1 Estimation strategy**

We estimate intent-to-treat treatment effects by taking differences in outcomes between treatment and control groups at endline. For an outcome _Y_ , we estimate the following ordinary least squares (OLS) regression by using the main sample to estimate direct effects, and the sample of non-selected individuals to estimate spillover effects: where i indexes the individual and j the locality. _β_ is the pooled Intention-To-Treat (ITT) estimate of the program’s overall impact. _Tj_ captures treatment assignment in locality _j_ and _Sj_ the randomization strata, i.e. a dummy for rural villages or urban districts within

> 21Table A3 details the composition of the score. The score weighs criteria including disability, marital status, education, employment, economic status, assets and economic responsibility in the household. Weights
reasons. In particular, the data on labor costs may not be fully consistent across all countries, especially in EMDEs. For example, wage data may be derived from surveys (such as establishment surveys) which comprise only part of the employed workforce and the scale of coverage may vary between countries. In EMDEs with a substantial informal sector, the coverage of wage data is likely to be less than comprehensive in terms of its share of the total employed workforce. Also, if wage data are used to compute ULCs, nonwage labor costs will be omitted (often this is unavoidable as the nonwage data are not available). If nonwage labor costs differ across countries (e.g. because of differences in the applicable rates for employers’ contributions to social security funds) direct comparisons of ULCs across countries could be misleading. Finally, ULCs are affected by the capital intensity of production, because the greater is the contribution of capital to output, the larger must be the share of returns to capital in income, ceteris paribus. Therefore, a more capital intensive economy would be expected to have lower unit labor costs than a less capital intensive one, but that does not mean that the former is more competitive than the latter, as competitiveness depends on the total costs of production and not just labor costs (Peters, 2010).

Although these issues could also distort time series measures of ULCs in a single country, they are likely to be less problematic for this use than for cross country comparisons. If care is taken with data sources, it should be possible to obtain time series data which are reasonably consistent over time. Changes in nonwage labor costs are a potential problem, but in most countries they probably do not change dramatically over time. While capital output ratios are not constant, they generally only change slowly over time. Since the question we are trying to answer is whether competitiveness is impacted by remittance levels, we are more concerned with the trends in real unit labor costs over time within countries rather than
.875|0.855|0.02***|
|... education|0.854|0.862|0.844|0.02***|0.737|0.763|0.704|0.06***|
|No. of obs.|12,284|5,390|6,894||11,395|4,978|6,417||

_Note:_ Author’s own calculation from 2014/15 REDS follow-up survey. As discussed in the text, funding constraints required to limit collection of information on intra-household decision to 5 states (Gujarat, Uttar Pradesh, Maharashtra, Orissa, and West Bengal). To test difference in means, p values from regressions with district fixed effects and standard errors clustered by village panchayat are reported in the last column.

19
predictive model, _AIC_ is the Akaike information criterion [Akaike, 1998].

# **2.4 Data and background**

## **The Indonesia context**

Indonesia uses a social registry to implement its PMT for targeting multiple programmes. Much of the country’s population lives clustered above the poverty line and despite marked improvements in welfare, vulnerability remains substantial. In 2018, the poverty rate was 9.8%, but 28% of Indonesians lived below 1.5 times the poverty line. As a result, a relatively small income shock can push around 20% of Indonesian households into poverty. Those living above this vulnerability line but below 3.5 times poverty line 9