rafmacalaba/data-use-mentions-extended huggingface dataset:
4,388|3,903|

_Source_ : Authors’ analysis from student surveys.

_Note_ : Dependent variable is an index of related questions. All components of the indices are standardized to mean 0 and standard deviation 1, based on the sample frame at baseline. Standard errors clustered at the school level in parentheses. *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1. _Knowledge of symptoms and causes of STI subindex_ : Respondent knows STI symptoms include: (a) Abnormal discharges from the penis/vagina; (b) lesions/sores in genitals; and (c) painful urination; respondent knows (d) vomiting and headache are not STI symptoms; (e) HIV can be transmitted by having sexual intercourse without a condom; (f) HIV can be transmitted by a contaminated blood transfusion; (g) HIV transmission does not depend on hygiene; (h) HIV cannot be transmitted via food sharing; (i) clothes sharing; or (j) being in a pool with an HIV-positive person. Respondent knows that (k) HIV is not transmitted if a condom is used while having sexual intercourse with an HIV-positive individual. _Sexual violence knowledge subindex_ : Respondent identifies (a) nonconsensual touching of genitalia, buttocks, breasts, inner thigh as abusive sexual contact; (b) forcible sex by husband on his wife as a form of sexual abuse; (c) having sex with a person who is impaired due to alcohol as a form of rape; (d) if an individual changes his/her mind about sex even at the last minute, sex is nonconsensual and hence a form of sexual abuse; (e) the use of threats to obtain sex is a form of sexual abuse; respondent knows: (f) sexual abuse is more often than not perpetrated by a known person not a stranger. _Prevention of STI knowledge subindex_ : Respondent knows one of the safest methods to prevent an
We complement these data with national accounts data for EMDEs compiled by Haver Analytics. We remove the top and bottom 1 percent of

> 10Panel (a) in figure B.1 in the appendix shows that the threshold of zero reduces the volatility of the sum of nighttime lights in Sierra Leone and ensures that the sum is always positive.

> 11Threshold values greater than zero yield very similar results.

> 12As shown in Panel (b) in figure B.1 in the appendix. The calibration occurred at 14:18 Coordinated Universal Time on January 12, 2017. For more details, see `https://ncc.nesdis.noaa.gov/ VIIRS/` .

> 13Since changes in recorded nighttime lights are at the pixel level, there is a stronger increase in lights of larger countries. However, this is of no concern for growth rates of nighttime lights.

> 14We find that not winsorizing nighttime light data at all leads to poor correlation between nighttime light growth and GDP growth. However, winsorizing the tail end of the distribution from 1 to 10 percent yields similar results.

7
.94952|6.90287|5.241257|
|Personal Cost(X3)|22.49096|23.00957|27.17094|14.67552|
|**Standard deviation**|||||
|Sales (Y)|9214.583|1604.977|1536.927|3170.787|
|Materials Cost (X1)|5205.379|856.9301|664.3403|1578.464|
|Energy Cost (X2)|595.0032|87.42706|104.0559|154.6308|
|Personal Cost(X3)|641.1238|154.4191|427.9902|445.9945|
|**Maximum**|||||
|Sales (Y)|88827.03|8673.232|13709.87|30116.26|
|Materials Cost (X1)|52686.09|5364.427|5982.488|15777.23|
|Energy Cost (X2)|6747.266|587.511|875.7089|1700.407|
|Personal Cost(X3)|5275.788|773.6096|3373.633|4774.785|
|**Minimum**|||||
|Sales (Y)|2.154254|0.509635|0.2548175|0.3011479|
|Materials Cost (X1)|0.4787231|0.2162088|0.1235479|0.1389914|
|Energy Cost (X2)|0.2393615|0.0386087|0.0154435|0.0154435|
|Personal Cost(X3)|0.4015306|0.1081044|0.0540522|0.0540522|

Source: Authors‘ calculation from the Investment Climate Survey Databank, World Bank Having found that there exists wide variation in technical efficiency among the sample firms it is important and useful to understand the impact of the overdraft facility policy.
implemented many strategies to address low compliance by firms. Identification of unregistered firms includes drawing on data from business registries, licensing regimes, and banking systems. To identify tax evasion among registered firms, DGT takes steps to determine the accuracy of tax returns, including implementing a compliance risk management model that predicts the likelihood of non-compliance. These predictions are partly based on tax audit results covering around 2 percent of registered firms each year. However, there is no public reporting on the scale of misreporting identified by these audits, nor any estimates of total tax losses if rates of identified misreporting were to be extrapolated.

# **3 Data**

The data used in this analysis comes from the 2023 WBES in Indonesia, which interviewed the top managers or owners of 2,955 firms. This sample was nationally representative of firms possessing a Company Registration Certificate (TDP) or Business Identification Number (NIB) with five or more employees with at least 1 percent of private ownership and who do not have legal status as cooperatives. Firms are selected through stratified random sampling from the 2016 Economic Census conducted by the Central Agency of Statistics of Indonesia. Stratification is based on sector, firm size (employment),<sup>7</sup> and location. The sectoral distribution covers firms from the manufacturing and major services sectors. The survey covers all 38 provinces of Indonesia, though some are aggregated, resulting in a total of 22 regional strata. The sample design ensures no more than 7.5 percent margins of error and 90 percent confidence intervals at each of the stratification levels: size, sector, and region. All interviews were conducted face-to-face with top managers or business owners in Bahasa Indonesian through Computer-Assisted Personal Interviews (CAPI) using tablets and Survey Solutions as the software for data collection. Data collection started in December 2022 and concluded in September 2023, achieving a response rate of 41.2 percent, similar

> 7The stratification by firm size is defined using the number of employees in the firm. The definitions are
. Given relatively low level of poverty in the Kurdistan region, almost no one from the region will qualify for the government program when targeting for the MoLSA CT is set at bottom 18 percent nationally (Table 5). As a consequence, there are no PPMT referral thresholds for the region at high confidence levels.<sup>12</sup> For practical purpose, these cutoff values can be interpreted as the referral threshold ( ), below which humanitarian caseloads would be referred to MoLSA for screening. For a given program size i.e. targeting mm WW bottom 18 percent nationally, changes with the level of referral confidence. However, there is a tradeoff between the confidence level and the number of referrals – for each program size number of mm WW household that would be referred to the government program decreased with confidence level. For example, if the PPMT score referral threshold is increased to 4.7 (VM-based score) or 4.8 (SEVAT-based score) in the North, it will entail screening more households but the referred households will have a 50 percent chance of being eligible for the government’s cash transfer program.

For a given targeting rate, the PPMT referral thresholds corresponding to different confidence levels can be calculated for households in different regions of the country. Figures A1 to A6 in Annex A show the probability of referral to the MoLSA program (targeting the bottom 18 percent nationally) based on the VM- and SEVAT-based PPMT scores for the North, Central-South, and Kurdistan. These thresholds can be adjusted according to the capacity of the national SP system to screen referred households and absorb them into the system. We repeat the exercise for several other targeting rates namely: poorest 15 percent, poorest 25 percent, poorest 30 percent, and poorest 35 percent nationally. The PPMT cut-off World Bank (2019) for more. Hence, it is possible that, in addition to the smaller set, pseudo-PMT may be employing inferior proxies.

> 10 Refer to Box A 1, which presents
# **Data Access**

Due to the confidential nature of mobile data, data access is perhaps the most daunting and time-consuming task of most mobile data-based projects. For CDRs or any mobile network- generated data, there are several ways to approach data access: _a) Directly contacting with MNOs:_ Depending on the relationship and connections, as well as the MNO’s familiarity with the topic, some teams have reported success in accessing data by directly contacting MNOs. This approach is as simple as it sounds: it simply involves directly reaching out to one or more MNOs and requesting their willingness to collaborate on a project.

_b) Leveraging the convening power of telecom regulators:_ In most countries, there is a regulatory body that oversees the MNOs’ work and sets the rules to which MNOs have to adhere, which in theory means the MNOs are obliged to work closely with the regulators and be on their good side. Most of the organizations that have had success with accessing mobile data have done so by going through regulators. The advantage of using regulators is that it is relatively easy to deal with all the MNOs in a country at once rather than communicating with separately with many MNOs.

_c) Third–party organizations:_ In this context, we use the term _third-party organization_ to refer to a private organization or firm which deals with mobile data. Examples of such firms include Flowminder, OPAL, and the University of Tokyo. These organizations often have, on their own initiative or through being contracted by other organizations, worked with MNOs and/or regulators to access mobile data. They often play the role of a consulting firm that handles the negotiations with either MNOs and/or regulators to access data. They also provide technical support to store, process and analyze the data. The way one would use these third-party organizations to access data is by engaging them as consultants who do all the work of negotiating with either MNOs and/or regulators in a specific country to access the data and
0.07)<br>-0.010<br>(0.19)<br>0.446<br>(0.23)_∗_<br>9.388_∗∗∗_<br>IV-1<br>3<br>37<br>0 U.S. dollar<br>1999–2003 (c<br>rd errors are<br> in the regre<br>ndicates sign|
|regressions for fun<br>8, with real GDP g|**BF2**<br>**BF3**<br>2.037<br>0.659<br>(0.94)_∗∗_<br>(0.23)_∗∗∗_<br>-0.396<br>-0.198<br>(0.15)_∗∗∗_<br>(0.13)<br>-0.135<br>-0.015<br>(0.09)<br>(0.03)<br>2.289_∗∗_<br>5.750_∗_<br>2.478<br>75.080<br>-1.636<br>IV-2<br>S-GMM<br>3<br>82.000<br>75<br>349<br>m GDP data in real 200<br>y fve-year averages for <br>correlation-robust standa<br>cifcations) were included<br> percent level, and _∗∗∗_i|
|nchmark <br> 1968–200|**BF1**<br>1.364<br>(0.51)_∗∗∗_<br>-0.344<br>(0.14)_∗∗_<br>-0.071
**Treatment effect on components of the Livelihoods Coping Strategies Index, continued...**

||**Control**||**Lower bounds**||**Unadjusted**||**Upper bounds**||**IV**|
|---|---|---|---|---|---|---|---|---|---|
||**mean**<br>**(standard**<br>**deviation)**|(1)<br>Lee(-)|(2)<br>(+/-).25 SD|(3)<br>(+/-).1 SD|(4)|(5)<br>(-/+).1 SD|(6)<br>(-/+).25 SD|(7)<br>Lee(+)|(8)|
|Reduced foo|d expenditur|es||||||||
|6 months|0.80|-0.09***|-0.12***|-0.09***|-0.07***|-0.05***|-0.02|-0.02|-0.10***|
||(0.39)<br>|(0.02)<br>|(0.0: 1)<br>|(0.01)<br>|(0.02)<br>|(0.01)<br>|(0.01)<br>|(0.02)<br>|(0.03)<br>|
|12 months
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

||Unit|**Sudan**<br>Resource-rich<br>countries<br>**2009**<br>**Mid-2000s**|Low-income<br>countries<br>**Mid-2000s**|Middle-income<br>countries<br>**Mid-2000s**|
|---|---|---|---|---|
|Open defecation|% pop|43<br>28|38.3|11|
|||**2005**<br>**Mid-2000s**|**Mid-2000s**|**Mid-2000s**|
|Domestic water consumption|liter/capita/day|53<br>115|50.9|196|
|Revenue collection|% sales|64<br>60|94.1|99|
|Distribution losses|% production|40<br>40|34.8|29|
|Cost recovery|% total costs|62<br>67|89.5|86|
|Operating-cost recovery|% operating costs|86<br>94|125.2|121|
|Labor productivity|connections per employee|93<br>96|175.9|203|
|Total hidden costs|% of revenue|121<br>194||67|
|||**Scarce water**<br>**resources**<br>**S**|**udan**|**Other**<br>**developing**<br>**regions**|
|Average effective tariff|U.S. cents per m3|60|57|3–60|

_Source:_ Demographic and Health Survey (DHS) and AICD water and sanitation utilities database (www.infrastructureafrica.org/aicd/tools/data).
</sup> � = 1,500 people per km<sup>2</sup> and T<sup>�</sup> � = 50,000 with the areas that result being labelled _“High Density Clusters”_ (HDC) (Dijkstra and Poelman, 2014).

# **_Approach #4: Thresholding of nighttime lights (NTL) data_**

The use of nighttime lights (NTL) data to identify metro areas, and, more generally, urban settlements, originated in the remote sensing literature with early applications including Imhoff _et_

> 9 Namely, CIESIN’s Global Rural – Urban Mapping Project (GRUMP) Settlement Point layer (http://sedac.ciesin.columbia.edu/data/set/grump-v1-settlement-points-rev01).

5
**Figure 1: Cross-Country Comparison of Income Class Poverty Rates**

<!-- Start of picture text --> 2002/3 2014/15<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>international poverty line lower middle income class poverty upper middle income class poverty<br>line line<br>Poverty Headcount<br>Armenia Georgia Kyrgyz Republic Kazakhstan Russian Federation Tajikistan Turkey Ukraine Armenia Georgia Kyrgyz Republic Kazakhstan Russian Federation Tajikistan Turkey Ukraine Armenia Georgia Kyrgyz Republic Kazakhstan Russian Federation Tajikistan Turkey Ukraine<br><!-- End of picture text --> _Source: Official Survey Data by Country, Author’s calculations_

**Kazakhstan** has achieved large and enduring reductions in poverty since independence. The rate of extreme poverty measured at the international poverty line of $1.9-a-day is now largely indistinguishable from zero. Despite setbacks in 2009 and 2015, poverty measured using all common lines indicate substantially improving welfare over the long-term. The most recent data available suggest that the poverty rate in Kazakhstan fell between 2016 and 2017. Measured against the highest poverty line the World Bank uses – which is usually reserved for upper middle-income countries like Kazakhstan – poverty has fallen from about 65 percent in 2011 down to about 8.5 percent in 2017. But many challenges remain, and Kazakhstan is vulnerable to economic shocks that periodically reverse the country’s progress on poverty reduction. The two most notable cases of this were in 2005 and from about 2013 to 2016. During those spells of low growth or contraction, many people fell into poverty (though not often extreme poverty), and only a return to growth in the following years pulled the poverty rate down again. Indeed, measured at the highest poverty line, Kazakhstan has still yet to return to the low rate it achieved in 2013.

Even
with improved OPVs, which were viewed as more suitable for smallholders. Delayed deliveries, poor weather, and late maize imports led to high prices and increasing food scarcity during the 2001-2 season. A similar scenario occurred in 2005-6.

In response to the 2005/06 crisis, the government initiated the Agricultural Input Subsidy Programme (AISP). AISP provides about 50 percent of farm households with vouchers for 100kg of fertilizer and small quantities of maize (and lately legume) seed, with mainly privately imported fertilizers delivered principally, and in some years exclusively, by two parastatal input suppliers. During the 2005/06 season over one million input coupons were distributed for a fiscal cost of US$32 million. Since then the program has been scaled up each year to reach US$242 million in 2008/09, largely paid by the Government of Malawi. Corresponding to rising fertilizer prices, the subsidy paid 91 percent of fertilizer costs in 2008/09. The program has been perceived as a test case for potential implementation elsewhere in Africa.

Since the policy motivation for governments to subsidize fertilizer is to enable smallholders to attain higher maize yields, establishing positive impacts on productivity is fundamental. In an analysis of three years of plot-level data collected from 450 households in Central and Southern Malawi, Holden and Lunduka (2010a) found that access to subsidized fertilizer had a significant positive effect on maize yields. However, Dorward et al. (2010) concluded that the benefits of the program are difficult to assess due to controversies about national statistics on maize production, which are likely to be overestimated. With reasonable 16
justice systems (4.8)<br>•Score of accessibility of justice systems (4.9)<br>•Score of ability to complain about justice<br>systems’ performance (4.10-4.11)<br>•Score of level of independence of police force<br>(4.12)<br>•Score of confidence in corrupt people facing<br>justice (4.13)|
||**Political**|•HH survey questions 4.14-<br>4.32 also apply at the national<br>level<br>•Percentage of elected<br>representatives in national<br>government per social/ ethnic/<br>religious group<br>•Number of people actively<br>voting in national elections<br>compared to those entitled to<br>vote|•HH survey questions 4.14-4.32<br>also apply at the regional level|•Percentage awareness of local electoral process<br>(4.14)<br>•Percentage interest in local electoral process<br>(4.15)<br>•Percentage entitled to vote in local elections<br>(4.16)<br>•Percentage voting in last local elections (4.17)<br>•Percentage wanting to vote in last local elections<br>(4.18)<br>•Percentage control over their voting choice (4.19)<br>•Frequency of, and impact of, discussion about<br>l<br>l l<br>i<br>did<br>(4 20 4 23)|

a Number in parentheses refers to the section in the national survey where questions corresponding to the indicator can be found. For example, questions about the respondent’s awareness of listed justice systems can be found in survey section 4.1 74
2013). This practice helps align estimates, reduces underreporting in surveys, and contributes to a reduction in the gap between national accounts measures and survey measures. A caveat to this interpretation is that consumption surveys, which do not typically rely on any such administrative data, also show a similar narrowing of the gap among rich countries. In Panel B of Table 1, we also assess the gap between household surveys and GDP. Overall, survey means are on average 47 percent lower than GDP across countries. While this large discrepancy may not be surprising to national accounts and survey experts, it is worth noting that maybe the most commonly cited indicator summarizing living standards in a country – GDP per capita – and often described as ‘income per person’, is on average twice the size of per capita household 16
# **A2. VAR model estimation details**

The VAR model used to decompose U.S. monetary policy shocks is a modified extension of that used in Matheson and Stavrev (2014) and Arteta et al (2015). The model is estimated using Bayesian techniques and the Minnesota prior with hyperparameters on the first lag coefficients at 0.8, on overall tightness at 0.1, on lag decay at 1.5, on the exogenous variable tightness at 100, and cross-variable weighting at 0.9. A total of 30,000 iterations are run, with the first 5000 discarded and only every 5<sup>th</sup> iteration kept. The model includes 12 lags. The prior autoregressive coefficient on the residual variance (i.e. stochastic volatility), is 0.85 and the prior’s variance is 10000.

Since the period under review includes the COVID-19 crisis (whose unprecedented nature and size presents possible modeling challenges) and focuses on financial data (where heteroskedastic errors are common), the model includes stochastic volatility. Stochastic volatility in the error structure is modelled as in Jacquier, Polson, and Rossi (1994) and a generic version of what is suggested in Lenza and Primiceri (2022).

33
2 This paper examines the regional labour market consequences of the rapid economic growth and the new economic strategy. It focuses on the period of the deregulation when there appears to have been a significant tightening of national labour markets, after real wages stagnated for much of the 1980s, (Manning, 1994; Rosner, 1995). The paper also examines how more recent developments differed from labour market developemnts during the oil boom and subsequent period of slower economic growth.

Two alternative hypotheses can be advanced regarding the relationship between interregional labour market trends, on the one hand, and patterns of economic growth associated with deregulation, on the other. The first, perhaps more plausible scenario is that rapid labour market transition has been primarily experienced on Java - especially in the major areas of non-oil export growth around Jakarta in the west and Surabaya in the east. We would expect the creation of new jobs in manufacturing and associated service sectors to be concentrated in these regions. Both wage growth and employment growth in non-agricultural sectors could be anticipated to have lagged in the Outer Island regions. Many of these - especially in Eastern Indonesia - have not played a major role in the acceleration in growth of manufacturing exports.

A second possibility is that the rapid growth of non-oil exports on Java and in selected Outer Island provinces has provided an impetus for labour market change throughout the archipelago. Inter-regional migration and remittances from workers in the centres of manufacturing growth might have been sufficiently large to stimulate employment growth in non-agricultural jobs and tightening labour markets in most regions outside Java as well. Combined with continued government support and expansion of the dominant agricultural sector, these impulses may have been sufficient to generate a broad-based shift in the balance of labour supply and demand in most regions in Indonesia.

_Data Sources._ The analysis of economic structure is based on national accounts and regional accounts data collected by the Central Bureau of Statistics in Jakarta and in the provinces. The regional data are less reliable in earlier years and even in later years only symptomatic of broad trends.<
educational inequalities in East Africa. Starting from the proposition that household circumstances, school factors and assortative matching (sorting) are _all_ potentially important components of IOE, we seek to parse out their respective contributions to the observed variation in test scores. To do so, we review various variance decomposition procedures and suggest how a partitioned iterative algorithm can be used to estimate the relevant variance components, treating the two effects of interest as fixed latent variables. In order to address technical challenges of estimation, namely the problem of a mechanical negative bias in the correlation between the fixedeffects, we elaborate a unified procedure that controls how the fixed effects are estimated (initialized) and that maps to alternative assumptions about the between-factor covariance.

We apply the approach to rich test score data from East Africa from which three main findings stand out. First, we confirm that how the fixed effects are initialized under a partitioned iterative algorithm matters for subsequent estimates of the variance components.

24
of the high degree of IPRs harmonization within them, the EU and EFTA themselves are counted as IP-related trade agreements in our sample. Finally, panel (d) indicates that there are some 40 IPAs, involving around 60 countries, that do not involve the United States, the EU, or EFTA. Thus, there has been considerable proliferation in such agreements, both over time and across countries.

# **Figure 1: The Number of Legally Enforceable IP-Related Trade Agreements and Number of Countries with Membership in at least One such Agreement by Year, 1990-2015**

> 4 See also the DESTA database described in Dür et al. (2014), which defines IPAs as agreements with an IPRs chapter, regardless of how comprehensive or rigorous.

7