rafmacalaba/data-use-mentions-extended huggingface dataset:
>Madagascar<br>2.7%<br>6.6%<br>Malawi<br>3.9%<br>10.6%<br>Mali<br>2.1%<br>7.9%<br>Mauritania<br>2.1%<br>6.8%<br>Mozambique<br>3.3%<br>19.0%<br>Niger<br>2.0%<br>8.5%<br>Rwanda<br>2.7%<br>13.7%<br>Sao Tome and Principe<br>2.3%<br>18.4%<br>Sierra Leone<br>3.2%<br>10.3%<br>Tanzania<br>2.4%<br>8.8%<br>Uganda<br>3.3%<br>7.6%<br>Zambia<br>2.9%<br>10.2%<br>Sample mean<br>2.7%<br>10.1%|

23
*10**|**63**|**2**|**39**|**1**|**9**|**10**|**2**|**15**|**2**|**1**|**2**|**9**|**2**|**27**|**197**|
|FY08-FY10 (MU- %)|100%|8%|0%|41%||0%|0%|0%|57%|0%||0%|40%|0%|0%|21%|
|FY11-FY13 (MU- %)|25%|17%|0%|32%|100%|33%|33%|0%|75%|0%|0%|100%|100%|100%|27%|34%|
|**Grand Total(MU-%)**|**40%**|**11%**|**0%**|**36%**|**100%**|**22%**|**20%**|**0%**|**67%**|**0%**|**0%**|**50%**|**67%**|**50%**|**15%**|**27%**|
|Candor Gap Set #**|4|17|#N/A|19|1|5|4|#N/A|8|#N/A|#N/A|1|3|1|12|77|
|Candor Gap Set
At the other end of the spectrum is evidence which finds causal impacts between precipitation and agriculture (Auffhammer _et al._ 2006; Rowhani _et al_ . 2011; Fishman 2016; Lesk et al. 2016; Zaveri, Russ and Damania 2018); industry and firm performance (Islam and Hyland 2018); urban growth (Desbureaux and Rodella 2018), and health (Maccini and Yang, 2009; Hyland and Russ, 2018).

Here, we argue that spatial aggregation of weather data at the country level explains why impacts of precipitation on GDP are found to be fragile and inconsistent at the macroeconomic level and yet are found to have powerful economic effects at the level of individual sectors. We demonstrate that highly aggregated statistical models may underestimate the impact of rainfall due to measurement error, since rainfall is spatially heterogenous, and much more so than temperature (Lobell and Asseng, 2017). Aggregation masks heterogeneity of impacts across regions (Barrios _et al._ 2010) -- a problem termed Modifiable Areal Unit Problem, or MAUP in the economic geography literature (Briant et al. 2010). Our results cast light on this issue by suggesting the need for spatial disaggregation to capture local heterogeneity. This remains an area that warrants further research.

Rainfall and water availability exhibit spatial variability – that is considerably higher than that of temperature. Indeed, the wettest areas in Alaska (4,880mL year<sup>-1</sup> ) received 4,830mL more precipitation on average per year between 1990 and 2014 than the Mojave Desert (52mL year<sup>-1</sup> ). Such differences are also observed in the major urban centers of the United States, with a difference of 1,800mL year<sup>-1</sup> near Miami, Florida and 72mL year<sup>-1</sup> in the Coachella Valley, California. And even higher spatial variability between the driest and wettest areas is found in India (10,083mL year
focused in particular on expensive and unreliable power supply as a source of development bottleneck in India (World Bank 2004). In China, reliability of power supply has also been a concern.

We therefore proxy for the quality of physical infrastructure that a firm faces in the city where it is located by the city average of the _negative_ of the proportion of annual sales lost due to power outages that other businesses in the city report in the World Bank surveys. By excluding the firm’s own report, we hope to minimize simultaneity biases caused by a likely reverse causation where a more productive firm is better equipped to deal with power outages, by, for example, investing in onsite power generating capacity. On average, sample Indian firms report 9 percent in lost sales against 2 percent in the China sample (table 3). The median difference is also 7 percent and statistically significant. Thus, China leads India in the quality of power supply.<sup>14</sup> An alternative measure of power supply is the city share of firms having their own generators, which is also available from the data set. Using own generators is obviously a more expensive way to supply electricity for normal operations. In general, own generating capacity is often used as a source of emergency backup power. It would be more costly to rely on it for normal usage, and would require more capital expenditure to scale up when the firm expands production. In addition, a firm that uses continuous production processes would find any disruption in power supply unacceptable and would therefore invest in onsite emergency power even if external power supply is deemed reliable. The installation of emergency generating capacity by firms may not be a clean indicator of poor reliability of power supply from the grid. It is unfortunate that the surveys did not make a distinction between investing in own generating capacity as a substitute for electricity supply from the grid or as an emergency backup in the event that power transmission from the grid is interrupted

> 14 Some argue that roads (
**MONGOLIAS INFORMAL SECTOR -**

**Reasons for Growth**

**20**

entrepreneurs when they attempted to establish an insurance company a few years ago: the state registry told them they must first get permission from Mongoldaatgal, the state monopoly insurance company.' 48. In addition to the registration as an economic entity, formal enterprises are subject to other regulations that informals may avoid, including the labor regulations discussed below. Many of the regulations (e.g., health regulations, safety inspections, environment protections, industry-specific licensing) are not unreasonable. The difficulty with registrations and regulations is not that there are too many, but that there is frequently too much discretion (and interpretation of the meaning of the laws) permitted on the part of the implementing authorities6' Such discretion adds to the uncertainty of formal sector entrants, leads to delays, and hinders formal sector development.

# **Informals Make Their** Own Labor Arrangements

49. A notable difference between the informal sector and the formal sector is the manner by which labor relations (wages, working hours, holidays, etc.) are settled. For the informal sector, in which family ownership is the norm, working arrangements are made informally. (The survey of kiosks, for example, found that for over 80% of kiosks there was no pre-defined mechanism for compensation; profits simply went into the family coffers.<sup>2</sup> Enterprises in the formal sector, on the other hand, must adhere to Mongolia's regulations on the labor market, particularly those imposed by the _Labor Law._ _50._ Mongolia's _Labor Law_ was enacted in 1991, and is very Coverage Under **Social and Health Insurance in** ambitious in its designs to protect the Mongolia's **Informal** Sector Mongolian worker. It seeks to ensure healthy and safe working conditions, The informal sector surveys provide some indication of the degree healthy and safe working conditions, to which
MP. In regression form, I use two approaches to estimate the effect of receiving on bed net on approval of local politicians. I first estimate the reduced form effects of the bed net distribution campaign by estimating the following OLS model: This model is analogous to the intent-to-treat (ITT) analysis of a randomized experiment.<sup>15</sup> . However, simply being surveyed after the ITN campaign does not predict perfectly receipt of a net. Therefore in a separate set of estimates, I use a post-campaign survey date as an instrument for receipt of a net. In this model, I estimate the effect of post-campaign survey date on net receipt in a first stage regression: Then we estimate a structural model using two-stage least squares (2SLS): For the IV estimation to be valid, several conditions must hold. First, there must be a strong and highly significant relationship between the instrument (post campaign survey date, _post_ ) and the endogenous variable ( _any_ _~~f~~ ree_ _~~n~~ et_ ). As Figure 1 shows, being surveyed after the ITN campaign took place ( _post_ =1) increases net ownership by 30 to 40 percentage points for any definition of net ownership, receipt, or use. The resultant first stage _F_ statistic, ranging from 19 to over 100, in all cases comfortably exceeding the threshold value of 10 (Staiger and Stock 1997). Second,

> 15Robust standard errors are clustered at the district level. In additional models we add zonal fixed effects, and a linear time trend and the time trend interacted with the _post_ variable.

15
cantly more likely to interact with citizens on a daily basis (column 3). This finding holds when using a standardized measure of interaction frequency (column 4), as illustrated in Figure 3, panels (c) and (d). These results suggest that bureaucrats under newly elected leaders are more proactive in engaging with constituents. Notably, however, these interactions appear to occur outside formal settings, as we find no evidence that turnover increases attendance at village assemblies ( _Badan Permusyawaratan Desa_ or BPD) after a turnover (RD estimate = 0 _._ 0003, p-value = 0 _._ 941).

**Robustness Checks.** Appendix Tables A.6 and A.7 consider alternative specifications for the estimates in Table 2 and Table 3, respectively. The first row of each table reports our baseline estimate. In the second row, we remove from our baseline equation (1) the controls for election-year dummies and the 4. survey experiment treatment indicator described in footnote In the third row, we include region fixed effects in addition to dummies for pairs of election years (2015-2016, 2017-2018, etc.). In the fourth row, we use a third degree polynomial in the running variable to construct the RD point estimate, instead of the local linear regression in our baseline. In the fifth, sixth, and seventh rows, we vary the RD bandwidth to half, three-fourths, and two times the MSE-optimal bandwidth from Calonico et al. (2014), respectively. In the eighth row we also report RD estimates when excluding 27 villages with at least one bureaucrat appointed under a Suharto-appointed district mayor (Martinez-Bravo et al., 2017). The ninth row reports the fuzzy RD specification described above, instrumenting for leadership changes in our survey data with the victory dummy from equation (1). Overall, these specification changes leave unchanged the main takeaways from Tables 2 and 3.

# **4.2 Reduced Nepotism**

In many villages, entrenched nepotistic practices sustain the
single and married individuals, and married individuals were issued invitations for their spouse to join the study as well (in the same intervention arm).

Regarding age group, we initially sought to enroll 15-24 year olds (a particularly high risk age group), but due to ethical concerns we decided not to enroll minors, thus our final target range was 18-30 year olds (plus all spouses ages 16 or older).

For recruitment we chose a random sample of village residents from the demographic surveillance site computer registry, and went to each of their houses to explain the study and invite them to participate by reporting to a study station set up in their village the following week. Of individuals who were reached in their houses for recruitment, approximately 70% came to the study station and enrolled. Thus the enrolled sample is highly representative of the general population of 18-30 year olds in these villages. Table 3 further shows that only 6.5% of enrollees attrited or were lost to follow-up during the one-year intervention. Attriters were younger and more likely to be single, but overall means of those choosing to complete the study were quite similar to attriters, except for the fact that attriters were substantially more likely to be HIV positive at baseline (with 21% of HIV positives attriting, possibly due to confusion about continued study eligibility). With this exception, the study sample was highly representative of the underlying population.

# **_Conditionality Design_**

A primary goal of the study was to reduce risky sexual behaviors. But because these were not directly verifiable, we instead opted to link cash payments to objective measures – STI test results – that could serve as (imperfect) proxies for risky sexual behavior. Ideally we wished to condition on only STIs which have been incontrovertibly linked to solely risky sexual activity. We also needed to balance cost considerations with the imperative to have enough tests so as to sufficiently capture risky behavior. In addition, we needed to consider local knowledge of STIs, testing capacity, and availability of
This paper explores the religious divide in sanitation practices in India, considering in particular the role of location-specific factors in driving observed differences between Hindus and Muslims. To do so, the analysis uses data from three nationally-representative surveys: the Indian Human Development Survey (IHDS), the District Level Household and Facility Survey (DLHS-3), and the National Family Health Survey (NFHS-3). Each data set provides detailed information on latrine ownership or latrine use at the household level.

The study examines the role of household religion in predicting differences in household sanitation practices. If differences in household sanitation practices are being driven by religion itself, then household sanitation practices should continue to differ substantially by religion when comparing Hindus and Muslims within the same geographic areas.<sup>4</sup> The estimates show unconditional differences between Hindus and Muslims in latrine ownership and latrine use, similar in magnitude to Geruso and Spears (2018), but these differences are reduced by approximately two-thirds after controlling for measures of urbanization and other location characteristics. When replacing these location characteristics with location fixed effects, the analysis estimates a similarly large reduction in the HinduMuslim gap. Latrine ownership remains 5.3 percentage points lower in Hindu households than in Muslim households, using IHDS data in the preferred specification (95% confidence – interval: 1.5pp lower 9.1pp lower). This estimate is similar across specifications, once controlling for local characteristics or location fixed effects, and are similar in DLHS-3 data and NFHS-3 data. While there remains some difference in latrine ownership and latrine use, the unconditional differences by household religion largely reflect differences in household location.

> The analysis then extends to consider other sanitation practices and does not find differences between Hindus and Muslims in reported handwashing or observed fecal material near

> 4Note that religion may influence average sanitation practices in locations, which would be absorbed into location fixed effects, whereas the main analysis focuses on how religion differentially affects households of that religion. Using a machine learning technique (LASSO), this paper also
_Figure 10: Justification of violence toward children by officials_ <!-- Start of picture text --> a. “From parents” b. “From teachers”<br>100% 100%<br>90% 90%<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Never Something in between Always Never Something in between Always<br><!-- End of picture text -->

# Source: CGD–World Bank survey 2022 microdata.

The questions were: “Parents beating children...always/never/something in between...justified” and “Teachers beating children...always/never/something in between...justified”.

_Figure 11: Justification of violence toward children from teachers and/or parents_ <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>IDN LAO MNG PHL VNM EAP average Non-EAP<br>Always justified for both teachers & parents<br>Always justified for either parents or teachers<br>Sometimes justified for either parents or teachers<br>Never justified, neither for parents nor teachers<br><!-- End of picture text --> Source: CGD–World Bank survey 2022 microdata.

15
better access to finance. These are firms that are larger, more productive, faster growing, exporting firms, and firms with all male owners.

We organize the remainder of the study as follows. Section 2 describes the data, main variables used, and the estimation methodology. Section 3 provides the base regression results. Robustness checks are discussed in section 4. In section 5, we provide results for endogeneity checks based on heterogeneity in the corruption-finance relationship. Section 6 contains results for the mediating effect of firm performance measures and gender composition of owners on the corruption-finance relationship. We summarize our main findings in section 7 and suggest scope for future work.

# **2. Data and methods**

# 2.1 _Data description_

In this section, we discuss the data and estimation method used in the empirical analysis. The main data source is Enterprise Surveys (ES), firm-level survey data collected by the World Bank. The ES are nationally representative surveys of non-agricultural and nonfinancial private enterprises with 5 or more full-time permanent workers. The surveys use a common sampling methodology, stratified random sampling, as well as a common questionnaire across all countries.<sup>4</sup> Stratification is done by firms’ size, industry, and location within the country. Sampling weights are provided in the ES and used in all our regressions so that the sample is representative of the private sector in the country. We complement the ES with other data sources such as World Development Indicators (WDI), World Bank, and Doing Business (DB), World Bank.

> 4 Details of the sampling methodology and other survey related information are available at <u>www.enterprisesurveys.org.</u>

7
Charges**|||||||
|Container handling ($ per TEU)|150|67.5|275|110–243|100–320|80–150|
|General-cargo handling ($ per tonne)|10|6.5|13.5|11–15|8–15|7–9|
|Container handling ($ per TEU)|-130|-54.5|-248|110–244|100–321|80–151|

_Source:_ AICD ports database. _Note:_ TEU = 20-foot equivalent unit.

A steady increase in containers handled at Port Sudan has created serious port congestion problems, adding significant delays to the movement of freight. Several of the region’s ports are beginning to experience serious capacity constraints (figure 10) due to burgeoning demand associated with increases in container traffic and dry-bulk cargo. Port Sudan in particular is already operating at 80 percent capacity at least, a level of intensity that creates problems in terms of congestion. There is some scope for easing capacity constraints by improving the efficiency of port performance, although ultimately new investments will be needed (see figure 10).

Labor requirements at the port lead to overstaffing, poor productivity, and higher costs. In the North Port, the equivalent of a dock labor board exists and there is a legal requirement to use labor from a pool of 25,000 workers. In the container terminal, the SPC provides the labor and acknowledges that it is overstaffed by 7,300 workers (World Bank 2008).

> <sup>18</sup>
Bayesian Impact Evaluation with Informative Priors: An Application to a Colombian Management and Export Improvement Program<sup>∗</sup> Leonardo Iacovone (World Bank) David McKenzie (World Bank) Rachael Meager (LSE) _JEL Classification: C11; C93; F14; O12; O14_ _Keywords:_ Bayesian Impact Evaluation; Prior Elicitation; Randomized Experiment; Management

> ∗We thank: Darío Rodríguez Pérez for his collaboration on the initial stages of this project; staff at the Colombian government agencies PTP and DNP; the academic and policy experts who provided priors for this project; Andrew Gelman for feedback on our pre-analysis plan; seminar audiences at BREAD, Chicago, Dartmouth, DeNeB, Fudan, Maryland, St Andrews, and Yale; and Juan Sebastián Leiva, Daniela López, Sofía Jaramillo, Marta Carnelli, Maria Juliana Otalora and Kyle Holloway of IPA Colombia. We gratefully acknowledge funding from the Knowledge for Change Program and the World Bank’s CIIP Trust Fund. This study was pre-registered in the AEA Social Science Registry on June 26, 2018 ( AEARCTR-0003109) and was accepted as a stage 1 registered report at the Journal of Development Economics.
**In Bulgaria, the income thresholds determining MSA eligibility vary by household type** . Table 2 Panel A shows the differentiated minimum income of each family member before and after the reform (in 2020 currency). The coefficient for different individual types has experienced some changes, while the new benchmark has also been adjusted. Previously, the benchmark was based on the GMI indicator (75 BGN), but it has now shifted to 30% of the at-risk poverty threshold, estimated at 504.3 BGN per month using SILC 2021 data (income year 2020).<sup>20</sup> This represents substantial increases in the value of the threshold. Table 2 Panel B below shows the differentiated minimum income of the family (DMIf) for different family types, both before and after the reform. To exemplify these rules, the monthly income eligibility threshold for a family composed of a single parent and a child younger than 3 was 158 BGN before the reform. It increased to 332 BGN after the reform. Before the current MSA reform, these nominal thresholds were constant and not adjusted by inflation, so they have decreased in real terms, becoming more restrictive over time.

**Table 2. Differentiated Minimum Income (before and after reform)**

Panel a. Differentiated minimum income of each individual in the family (DMI),

|**Pre-reform(Percentage of GMI Indicator)**||**Post-Reform(Percentage of 30% AROP Poverty **|**Line)**|
|---|---|---|---|
|Persons over 75years of age livingalone|165|||
|Persons over 65years of age,livingalone|140|Person living alone (no age cut)|165|
|Person upto 65years of age,livingalone|73|||
|Persons over 65years of age|100|||
|Adults in multi-adult households|60|Peole cohabitin with other (no ae cut
explained by the financial and economic crisis which has generally lowered GDP growth during 2007 through 2009. The coefficients are consistently positively signed, indicating that a better investment climate is associated with higher economic growth. Except for model 1, which only controls for initial log GDP per capita, the DBI coefficient is statistically significant at the 1% level of higher. The results are robust to controlling for initial GDP per capita, the initial deviation from the GDP deflator, to civil war, FDI, and exports.

> 9 Civil conflict, as in Djankov _et al._ (2006), is defined as internal conflict with more than 1,000 casualties.

> 10 Data were also obtained for primary school enrollment and secondary school enrollment. As in Djankov _et al._ (2006), these variables are consistently insignificant – they do, however, significantly reduce the sample size. They will thus be omitted from the analysis presented here.

> 11 Djankov _et al._ (2006) also use an instrumental variable approach to test the robustness of their results. Generally, their results uphold with slightly larger coefficients and larger standard errors. Thus, endogeneity bias seems to be relatively negligible. To test the robustness of the results in this paper, the same instruments were obtained, including a country’s legal origin, its principal religion, the percentage of its English speaking population, and its absolute latitude. As in Djankov _et al._ (2006), the results generally uphold. This paper will thus focus on the presentation of the OLS findings.

9