rafmacalaba/data-use-mentions-extended huggingface dataset:
# **B Data Appendix: Details on Survey Design**

We conducted a survey of village officials and citizens in Indonesia between March and August 2022, in partnership with the Indonesian Ministry of Home Affairs (MoHA) and the World Bank. The survey took place in 852 villages spread across 23 districts in 17 provinces. The primary targets were active village officials as well as 8 to 12 adult citizens residing in the same villages. The survey aimed to gain a better understanding of village governance and to provide a new window into the level of village development as perceived by both officials and citizens. As a result of the restrictions associated with the Covid-19 pandemic, we conducted all surveys over the phone. Below, we describe the sampling procedures we used to select villages, village officials, and citizens.

## **B.1 Sampling of villages**

We constructed a large representative sample of villages spanning each of Indonesia’s major islands. Since the survey was designed as the baseline of a future digital training intervention, this sample was restricted to districts with relatively high internet coverage. We first randomly selected districts after stratifying by region, and then randomly selected a fixed proportion of villages within each district.

Our initial goal was to recruit a sample of 1,000 villages from a set of eligible villages in 20 districts. Given surveys were conducted over the phone, we expected a low consent rate. We thus sampled from a pool of around 1,700 villages across 20 districts and later added another 3 districts in order to reach a final target sample of 1,000 villages. Among these, we were able to administer the survey in 852 villages spread across the islands of Sumatra, Java, Bali and Nusa Tenggara (NT), Kalimantan, and Sulawesi.

Contact details for village heads and BPD chairpersons were obtained directly from MoHA. We started data collection by conducting a listing process to verify these phone numbers, obtaining village heads’ consent. If a village was successfully listed, the survey
according to their quartile in the wage distribution of destination (i.e., the wage distribution of formal workers). The “share in new formal” is defined as the share of those who formalize who were originally informal employees and who were originally self-employed. The sum does not add to 100 percent, as some unemployed and workers out of the labor force at the beginning of the panel also formalized during the 12-month window considered. See supplementary online appendix S1.2 for details on the construction of the panel data sets.

22
contribute to increased skill and economic activity in rural areas, will be important additional questions for the near-term research agenda.

# **References**

Adamopoulos, T., Brandt, L., Leight, J., Restuccia, D., 2022. Misallocation, Selection, and Productivity: A Quantitative Analysis with Panel Data From China. Econometrica 90, 1261–1282. https://doi.org/10.3982/ECTA16598

- Adams, R.H., Cuecuecha, A., 2010. Remittances, Household Expenditure and Investment in Guatemala. World Development 38, 1626–1641. https://doi.org/10.1016/j.worlddev.2010.03.003

- Akay, A., Giulietti, C., Robalino, J.D., Zimmermann, K.F., 2014. Remittances and well-being among ruralto-urban migrants in China. Rev Econ Household 12, 517–546. https://doi.org/10.1007/s11150013-9208-7

- Akguc, M., C. Giulietti and K.F. Zimmermann. 2014. “The RUMiC Longitudinal Survey: Fostering Research on Labor Markets in China,” IZA Journal of Labor and Development, 3(5).

- Akgüç, M., Liu, X., Tani, M., Zimmermann, K.F., 2016. Risk attitudes and migration. China Economic Review 37, 166–176.

- Bai, Y., Zhang, L., Liu, C., Shi, Y., Mo, D., Rozelle, S., 2018. Effect of Parental Migration on the Academic Performance of Left Behind Children in Northwestern China. The Journal of Development Studies 54, 1154–1170. https://doi.org/10.1080/00220388.2017.1333108

- Bao, S., Bodvarsson, Ö.B., Hou, J.W., Zhao, Y., 2009
. Within a country, there might be spill-overs from violence in affected municipalities to municipalities nearby.

The other frequent approach to instruments is the prior refugee or migration stock in the area, based on Altonji and Card (1991) and the idea that previous migrants attract new migrants (network effect). Borjas and Monras (2017), for example, instrument for the refugee shock with prior migration to that region. Rozo and Sviatschi (2018) use the settlements of Syrians in Jordan before the start of the war in Syria in 2011. Hunt (1992) uses the share of early (1954-1962) repatriates as a share of the 1962 population to instrument the 1962-1968 repatriates as a share of the labor force. Like in the case of distance, this is often combined with (proxies for) outflow numbers. Morales (2018) uses an instrument for inflows of IDPs in municipalities that combines outflows with immigrant stock. A common criticism of the migrant stock instrument is that the settlement of previous immigrants or refugees may be correlated with economic conditions across these locations that may persist until today. To confront this criticism authors either use migrant stock data from a number of years before the influx they study or argue that the settlement of previous (forced) migrants 9
**Figure 1** : Distribution of inpatient beds* by facility type (hospital vs. non-hospital) in Kenya, Sierra Leone, and Tanzania

<!-- Start of picture text --> Kenya Sierra Leone<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Inpatient beds Inpatient beds<br>Hospitals Non-hospitals Hospitals Non-hospitals<br>Tanzania<br>0 20 40 60 80 100<br>Inpatient beds<br>Hospitals Non-hospitals<br>.15<br>.04<br>.03 .1<br>Density .02 Density<br>.05<br>.01<br>0 0<br>.04<br>.03<br>.02<br>Density<br>.01<br>0<br><!-- End of picture text -->

* For clearer visualization, the x axis has been truncated at 100 beds for these figures, but all three distributions of hospital beds have a long right tail

While simple bed counts are one measure of bandwidth, inpatient bed occupancy rates are a de facto metric of surge capacity. Specifically, the inpatient bed occupancy rate indicates whether facility beds are full under normal circumstances, which can begin to shed light on the extent to which the facility can accommodate more inpatients during a pandemic. Combining information from the SDI on the number of inpatient beds in the facility with information on the number of inpatient bed-days spent in the facility over the past three months (Appendix Table 2) yields such an estimate of the occupancy rate. Overall, occupancy rates of inpatient beds in facilities are low: at the national level, estimates from Sierra Leone suggest a 30% occupancy rate of inpatient beds, 20% for Tanzania, and 22% for Kenya. Figure 2 converts these estimates into average numbers of occupied and unoccupied beds in a given facility on a given day. While the sample of hospitals from Tanzania includes
including these prices in the structural participation probit. Our results are robust to this specification.

Finally, we explore the validity of the lunar calendar as an instrumental variable. As previously discussed, there is an extended literature that links lunar phases with fish availability. However, it is also possible that the lunar calendar affects the marginal dis-utility from working, thus violating the exclusion restriction. One reason is night brightness: boat owners might prefer to fish during nights with full moon because getting oriented and conducting all fishing-related activities might be easier when there is more light. The fact that we observe lower likelihood of participation in full moon nights mitigates this concern.<sup>34</sup> An additional link between the dis-utility to work and the lunar phase might be driven by tide variation. Every day, there are two high tides and two low tides. When there is full moon, high tides are very high and low tides are very low while this difference decreases during quarter moons. Therefore, sea conditions might also affect the dis-utility of going fishing if boat owners prefer not to sail under cycles with a higher sea tide variance. In order to test for this possibility, Column 4 of Table 7 explicitly incorporates dummies for the different lunar phases as regressors of the structural probit. As we can see, the results are unaffected.

# **6 Discussion and Conclusions**

Most of the previous literature on labor supply has studied the intensive margin of participation decisions—how many hours to work—rather than the extensive margin—whether to work on a given day. However, the studies that are able to measure inter-temporal work elasticities in both margins, find that they are much larger in the extensive margin.

In this paper we use a dataset on daily labor force participation of Indian boat owners that we observe for 7 years. We develop an empirical strategy that allows us to estimate extensive margin elasticities. We find that a 10 percent increase in expected wages increases the likelihood of participation by 8 percent. The
Financial Regulation and Government Revenue We advance this literature and contribute to the calculation of government revenue gains by tracking the micro-level evidence through which financial regulation leads to government revenue gains. We propose a direct calculation of such savings based on credible counterfactuals. In this paper, we focus on a specific case study: the introduction of a financial regulation policy in April 2011 by the National Bank of Ethiopia (NBE) on all private banks. This regulation is ideal for the purpose of our study for a variety of reasons. First, the policy was announced and implemented with short notice (in mid-March 2011 and April 2011, respectively) and banks were largely surprised by this. Second, the magnitude of the regulation was substantial, as it imposed the purchase of 0.27 bonds issued by the central bank (NBE bills) for every unit of loans extended to the private sector. Third, banks would not be willing to hold these bills in absence of the regulation, because these present a fixed nominal rate of 3% and deliver an effective negative net return<sup>1</sup> . Therefore, both the regulated mandatory quantity of bond purchase and their price are fixed.

By having access to data on monthly balance sheets of all Ethiopian banks, to a disaggregated survey of branch installation costs, and to annual reports, we are able to verify how the policy affects bank behavior and to calculate the government revenue gains induced by the policy. This paper advances this body of literature through two fundamental innovations. First, we provide a countryspecific government revenue calculation based on a clear policy, for which we can verify its effect on the regulated entity (banks and their balance sheets) and calculate the revenue gains. Secondly, we benchmark this policy against three alternative and credible ways of raising revenue through the banking system. Despite methodological changes, our results are essentially in line with the literature<sup>2</sup> , however rather than a macroeconomic approach we follow a micro-oriented calculation as suggested by
.14|0.71|0.07|0.48|0.70|0.26|0.66|0.70|0.65|
|Employed in Industry|0.08|0.14|0.05|0.15|0.09|0.05|0.12|0.06|0.05|0.05|
|Employed in Services|0.26|0.55|0.12|0.58|0.30|0.13|0.46|0.16|0.13|0.15|
|**Access to basic services**|||||||||||
|Improved water|0.69|0.84|0.62|0.88|0.75|0.61|0.82|0.63|0.63|0.60|
|Improved sanitation facility|0.24|0.40|0.17|0.46|0.27|0.16|0.37|0.15|0.20|0.19|
|Access to electricity|0.48|0.82|0.32|0.85|0.53|0.33|0.69|0.36|0.39|0.34|

Source: International Urban Poverty Database. Note: WorldPop 250m is used for the DOU and DB methods.

find that the gaps in household characteristics between urban cores and other areas are narrower. On several indicators, people in rural towns appear to be worse off compared to those in other rural areas. Specifically, compared to other rural areas, households in rural towns have lower education levels and are more likely to work in agriculture.

Using our microdata approach, we can also analyze the profile of the extreme poor based on location. The comparison of characteristics
access to electricity (DHS 99)|% population<br>3.9|**26.6**|31.8|
|Growth in access to electricity|% population/year<br>3.3|**3.3**|1.5|
|Revenue collection|% billings<br>33.6|**66.1**<br>**88.0**|100.0|
|System losses|% production<br>40.0|**17.5**<br>**23.4**|10.1|
|Cost recovery|% total cost<br>100.0|**100.0**<br>**80.6**|100.0|
|Total hidden costs as % of revenue|%<br>442.5|**62.8**<br>**136.5**|0.1|
|U.S. cents|**Côte d’Ivoire**|Predominantly thermo<br>generation<br>Oth|er developing regions|
||**2005**<br>**2009**|Mid2000s|Mid 2000s|
|Power tariff (residential at 75 kWh)|**11.9**<br>**9.6**|14.5|5.0 – 10.0|
|Power tariff (commercial at 900 kWh)|**16.9**<br>**18.5**|18.8||
|Power tariff (industrial at 50,000 kWh)|**10.7**<br>**9.3**|14.2||

* Based on Enterprise Survey 2009 ** Based on DHS 1999 Survey and benchmarks for nonfragile low-income countries.
# **Table A.3. Share of energy efficient appliances in the new stock of appliances (%)**

|Technology|Baseline<br>scenario|Mitigation<br>scenario|Annual rate at which existing appliances are<br>replaced withefficient appliance|
|---|---|---|---|
|Refrigerator|20%|25-82%|0.0%|
|TV|20%|25-82%|0.0%|
|Washingmachine|20%|25-82%|0.0%|
|Bulbs|5%|10-86%|2.8%|
|Plastic double-<br>glazed windows|80%|100%|5.0%|
|Roof insulation|0%|0-50%|1.0%|
|Wall Insulation|0%|0-50%|0.3%|

# **Commercial Sector**

Commercial sector includes commercial buildings as well as street lighting. The assessment of MACs for commercial sector was performed based on space area of commercial sector that uses gas for heating and number of bulbs used for street lighting. There are no statistics about the space amount of commercial sector of Georgia, so it was calculated based on energy audits of several commercial buildings performed in different cities of Georgia (Sustainable Dvelopment Centre Remissia, 2015). As a result, the average commercial area that is heated by gas was assessed as around 5 mln sq.ms. The growth rates than were based on GDP growth projections from commercial sector and elasticity from MARKAL-Georgia (MARKAL-Georgia, April 2015).

The key technical and economic characteristics of appliances used in the commercial buildings are summarized in Table A4.

**Table A4. Costs and technical performance of technologies for the commercial sector**

||Unit|CapitalCost<br>Inefficient<br>technology<br>**E**|efficient<br>technolog<br>y<br>*
As a weather indicator, we use the Standardized Precipitation Evapotranspiration Index (SPEI), a multi-scalar drought index developed by Beguería et al. (2014).<sup>10</sup> The SPEI jointly considers precipitation, potential evaporation, and temperature, which provides a distinctive advantage over simple rainfall or temperature indicators as it incorporates the interaction between these variables in determining farmers' agricultural outcomes (Bertoli et al., 2022; Harari & La Ferrara, 2018). It is increasingly used in the literature to capture agriculture-relevant weather shocks and has been found to outperform other indicators in predicting crop yields (Vicente-Serrano et al., 2012). The SPEI is obtained by taking the difference between precipitation ( _P_ ) and potential evapotranspiration ( _PET_ ): _Di = Pi – PETi_ . Then, _Di_ is standardized so that the indicator represents the deviation from the normal water balance (a SPEI of 0 indicates a value corresponding to 50% of the cumulative probability of _D_ , according to a log-logistic distribution). A negative SPEI value is associated with dry events (lower rainfall) while positive SPEI values capture wet events (higher rainfall).

To capture weather conditions that matter for agricultural outcomes and, in turn, migration decisions, we focus only on growing-season SPEI (GS-SPEI), defined as the average monthly SPEI during the growing season months in the period between two waves. The rationale for this choice is provided by previous literature investigating outcomes and settings where impacts tend to be driven by the agricultural channel (Cai et al., 2016; Cattaneo et al., 2019; Harari & La Ferrara, 2018), which is particularly sensitive to weather variability during the growing season. To identify growing periods, we rely on crop calendars—differentiated by region—provided by the Famine Early Warning System Network (FEWS NET) of the United States Agency for Agricultural Development (USAID).<sup>11</sup> Ideally, one could argue that we should attribute to each migrantsending household only the
Figure 5: Effect of Extended Provider Training on Patient Satisfaction <!-- Start of picture text --> Overall Index Testing Sub-Index Medications Sub-Index<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br>-2 -1.5 -1 -.5 0 .5 -2 -1.5 -1 -.5 0 .5 -2 -1.5 -1 -.5 0 .5<br>KS p-value for equality of distributions is p=0.000. KS p-value for equality of distributions is p=0.000. KS p-value for equality of distributions is p=0.011.<br>Basic Training<br>Extended Training<br>Cumulative probability Cumulative probability Cumulative probability<br><!-- End of picture text --> _Notes:_ Patient satisfaction data from home follow-up of the patient survey.
in the United States, on average individuals thought that around 55 people out of 100 in their community would accept a vaccine in November 2020. This figure increased to around 65 people out of 100 by March 2021. In Brazil the increase in believed vaccine acceptance moved from slightly below 70 people out of 100 in the community in November 2020 to around 75 people out of 100 in March 2021. In India, a small but consistent decrease in believed vaccine acceptance in the community was observed during the same period – from slightly above 70 people out of 100 to around 65 people out of 100. The fact that individual vaccine acceptance shows greater variation over time than social norms, which tend to be _stickier_ , suggests that factors other than social norms may be explaining the time variation in vaccine acceptance. Cross-country differences in vaccine acceptance, however, could potentially be explained by differences in norms.

The main time variant factor which this paper is interested in is government policy. When correlating the evolution of vaccine acceptance and government policy response – as measured by the Oxford Government Stringency Index, different patterns emerge. In countries like Brazil, the evolution of vaccine acceptance mirrors the evolution of government restrictions: the more stringent measures are, the higher the level of vaccine acceptance (Figure 3, panel a). In other countries, like Pakistan, vaccine acceptance and the stringency of government response appear to be uncorrelated over time (Figure 3, panel b). Lastly, in some other countries like Argentina (panel c) or Turkey (panel d), it is a particular government action -the start of the vaccination campaign- which appears to increase vaccine acceptance.

7
fees obtained from the migrant household sample vary for each migrant household, the fees obtained from the urban household

> 30 We do not explore the panel feature of the data set between 2008 and 2009 since despite substantial efforts to track individuals over time, the panel data suffer from exceptionally heavy attrition (58.4 percent). This is due to the mobile nature of migrant workers and the consequences of the financial crisis that hit China in 2009 (Akgüç, Giulietti, and Zimmermann 2013). An option is to construct synthetic panel data that can allow dynamic analysis (Dang et al., 2014), but we leave this for future research.

> 31 These fees include tuition, food and accommodation, remedial classes, other fees (e.g. school uniforms and so on) and “sponsorship fees/boarding fees/selecting school fees”. Unless otherwise noted, all numbers are our estimates from the RUMiC survey.

> 32 Figure 1, however, reassuringly indicates that there is no systematic difference between school fees obtained from the rural household sample or the urban household sample. See also Carletto, Larrison, and Ozden (2014) for a detailed discussion on the construction of proper sampling frames for collecting migration data.

21
</sup> Figure 1 also reflects the underlying effect of migration, showing no difference in assets in 1999 but significantly higher assets for migrant families in 2011.<sup>16</sup>

> 12. The main caregiver is the person responsible for nutrition, health, and schooling of a child at the time of the survey.

> 13. In addition, there is a residual category of “other expenditure” for which we find statistically insignificant effects.

> 14. Our dataset does not allow us to compare the differences between migrant households with and without children. Comparisons to other representative data (details on request) reveal that, in households with children, female migration is on average less common. The education level and gender composition do not differ markedly. 15. In reality, the difference could be even wider since migrant households systematically underreport their received remittances and other sources of income (Akee and Kapur <u>2012).</u>

> 16. The asset indexes were constructed by a weighted-sum of the following items: (i.) number of cars, motorcycles, bicycles, washing machines, refrigerators, radios, TVs, computers, and cell phones; (ii.) existence of working phone landline and internet access; and (iii.) number of rooms in the house. For 1999, the last three items were excluded due to a large number of missing values. The weights for the index were obtained from a principal

8
br>(0.023)|||
|_Ideal fraction of sons_||0.052***|0.073***|-0.345***|-0.346***|
|||(0.018)|(0.025)|(0.024)|(0.032)|
|_Firstborn girl_ * _Ideal no. of children_|||0.002<br>(0.016)|||
|_Firstborn girl_ * _Ideal fraction of sons_|||-0.044<br>(0.028)||0.002<br>(0.028)|
|N|118,663|88,475|88,475|88,475|88,475|
|Baseline mean|3.001|3.001|3.001|0.451|0.451|

NOTES: Estimates of specification (3) estimated using OLS regression. The dependent variable in columns (1)-(3) is the number of births at the time of interview and in columns (4)-(5) is excess fertility which equals number of births minus ideal number of children. Baseline means are average of the outcome variable in each column for mothers who had both their first and last birth within 1973-1984. Standard errors in parentheses *** ** * are clustered by state. Data: NFHS. 1%, 5%, 10%.

Table 7: Fertility: Investigating the margin of response by distribution of number of children

|||Number|of children|||
|---|---|---|---|---|---|
||_≥_2|_≥_3|_≥_4|_≥_5|_≥_6|
||(1)|(2)|(3)|(4
) _Access to electricity_ . Available at: https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS?locations=BD.

World Bank (2018) _Enterprise Surveys_ .

World Development Indicators (2018) _World Development Indicators_ , _World Bank_ .

Yamane, Y. _et al._ (2010) ‘Severe local convective storms in Bangladesh: Part I. Climatology’, _Atmospheric Research_ . Elsevier B.V., 95(4), pp. 400–406. doi: 10.1016/j.atmosres.2009.11.004.

- US Energy Information Agency (2018) Electricity Data Base.

https://www.eia.gov/electricity/data/browser 31