rafmacalaba/data-use-mentions-extended huggingface dataset:
a continuation of a panel that started in 2013). At no point in this panel were respondents offered incentives to participate. The introductory letter sent to potential participants in the PPCS also emphasized the importance of providing accurate information.

# _Hypotheses_

H3a: Some people report more severe outcomes than they experience personally because they hope to influence resource allocation toward addressing these problems.

H3b: This effect was stronger in the Facebook survey than in the PPCS because in the Facebook survey the role of the World Bank was more prominent and the incentives to accurately report personal experience were weaker than in the PPCS.

# _Discussion_

Respondents who misreport their experiences in this way seem likely to be people who are not themselves experiencing severe outcomes but who have seen friends, relatives, or other acquaintances experiencing severe outcomes. The effect is perhaps less likely to have contributed to differences in the estimates of aggressive tendencies, as perceived aggression may discourage assistance. Beyond that, it is difficult to separate out this mechanism from other sources of difference in our data.

> 8 Purposeful misreporting and strategic answering behaviors are also explored in De Juan & Koos (2021) and Lund et al (2011).

10
# **Table A6 continued**

|**Category**|**What we look at?**|**Why we look at it?**|**Characteristics**|
|---|---|---|---|
|Company<br>register|Lists of registered (limited liability)<br>companies. The submissions in this data<br>category do not need to include<br>detailed financial data such as balance<br>sheets.This category draws on the work<br>ofOpenCorporates.|Open data from company<br>registers may be used to<br>many ends: enabling<br>customers and businesses to<br>see with whom they deal, or<br>to see where a company has<br>registered offices.|Following data must be online to qualify for<br>assessment:<br>•<br>Name of company<br>•<br>Company address<br>•<br>Unique identifier of the company<br>•<br>Register available for entire<br>country (usually assessed through<br>sample: it is answered with „Yes“<br>if a register indicates companies in<br>|
||||different regions)|
|Land<br>ownership|Maps of lands with parcel layer that<br>displays boundaries. Also a land registry<br>with information on registered parcels<br>of land.The assessment criteria were<br>developed in collaboration with Cadasta<br>Foundation. For more information on<br>land ownership data sets, see Cadasta<br>Foundation's Data Overview.|The Index focuses on<br>assessing open land tenure<br>data (describing the rules and<br>processes of land property).<br>Responsible use may enable<br>
in an Unfamiliar Environment, NBER Working Papers 7053, National Bureau of Economic Research, Inc.

Reis, J.G. and Thomas Farole, 2012. Trade Competitiveness Diagnostic Toolkit Siggel, E. (2006). International competitiveness and comparative advantage: a survey and proposal for measurement _. Journal of Industry, Competition and Trade_ 6:137-159.

World Bank. 2010. “Pakistan Trade Brief.” World Trade Indicators 2009/10: Country Trade Briefs. Washington, DC: World Bank. Available at http://www.worldbank.org/wti World Bank, forthcoming, _Pakistan: Reinvigorating the Trade Agenda_ , Washington DC.

49
a number of studies have found a negative employment effect but no effect on unemployment (e.g., Heckman and Pagés 2000; Nickell 1997; Nickell and Layard 1999).

Not only are the findings on employment impacts mixed, but the results also can be characterized as fragile. Findings are often sensitive to model specification and the treatment of data (Glyn _et al._ 2003, Howell _et al._ 2007). Sometimes researchers in different studies of a common set of countries have found different employment impacts. For example, using crosssectional data for the 1980s and 1990s for Latin American and OECD countries, Heckman and Pagés (2000) found a negative employment effect of job security rules and then in a later study (2004) found no significant employment impact. Using similar data for many of the same countries, Micco and Pagés (2007) found a negative employment effect, especially in volatile sectors.

As was noted above, much of the analysis of EPL impacts has been carried out on OECD countries. There have been numerous studies designed to econometrically test the relationship

> 33 A third, but less used, approach is to categorize a country’s EPL according to qualitative data based on assessments by managers regarding the flexibility of hiring and firing arrangements (e.g., Di Tella and MacCulloch 2005; Feldmann 2009).
.9_**|**_157.3_**|
|**_Total Disbursements All Creditors_**|**_237.6_**|**_461.0_**|**_584.2_**|**_576.4_**|
|**_Loan-Weighted Average Terms of New Commitments_**|||||
|Interest Spread Over 20 Yr US TreasuryBill Rate(%)|-2.6|-4.7|-2.0|-2.1|
|Grace Period(Years)|6.9|5.9|6.0|6.4|
|Maturity (Years)|25.2|22.9|21.4|22.6|

Note: This table provides summary statistics on disbursements on loans from official creditors to developing countries captured in the Debtor Reporting Statistics (DRS) database of the World Bank. Disbursements are measured in billions constant 2005 $US. "Other Multilaterals" includes all other multilateral creditors with loans recorded in DRS. "Other DAC Bilaterals" and "Other Non-DAC Bilaterals" include all other bilateral creditors with loans recorded in DRS. DAC refers to membership in the OECD's Development Assistance Committee as of 2012. Data on terms in the last three lines are calculated on a loan-weighted basis, weighting by initial loan commitment. The interest spread refers to the nominal interest rate on the loan less the 20-Year US T-Bill rate, except for the <u>period 1987-1992 when no 20-Year T-Bills were issued: during this period the 30-Year rate is used instead.</u> 35
programs; and (6) increasing the regressivity of income by excluding non-taxpayers, which include some of the poorest groups in society; Our analysis of equity of Polish tax expenditure programs is based on the data provided by the Ministry of Finance from tax returns and the estimates of the 18 personal income tax expenditure programs. The first observation is that most low income taxpayers were in most cases not able to access the benefits of the 18 tax expenditure programs. Table 1 below provides the evidence by the number of individual taxpayers in each income tax bracket applying for reductions in their tax liabilities. In the first income bracket, only 39% of the taxpayers applied for tax reductions, compared to over 80% in the second and the third income tax brackets. Two factors appear to account for lower income taxpayers benefiting less from tax expenditure programs. Lower income tax payers do not reach the expenditure threshold needed to apply for tax exemptions and deductions. Also, lower income tax payers do not have the time or access to the professional advice needed to benefit from the opportunities provided in tax laws and regulations.

**Table 1:** Personal Income Taxpayers Applying Deductions in 1997

|Income brackets|Total number<br>Of taxpayers|Total number<br>of taxpayers applying<br>deductions|(3:2)|
|---|---|---|---|
||1 2|3|4|
|I|22,210,454|8,606,610|38.75|
|II|1,038,069|839,546|80.88|
|III|237,206|212,246|89.48|
|Total (I+II+111)|23,485,771|9,658,402|41.42|

**Source:** Polish Ministry of Finance

7
work for different household members, _a f_ ( _K − K_ ). Taken together, these results show that gender norms around suitability of working with oxen lead to a concentration of household plot activities under men in the household, with women shifting to non-agricultural work.

One concern with the above heterogeneity analysis is the potential endogeneity of women’s involvement in oxen tasks. While we define this using endline data from control group households, the phase-in design means that many of these households also received the oxen and may have changed behaviors in response to treatment. With this in mind, we demonstrate the robustness of the heterogeneity results to using districtlevel differences in women’s decision-making power, shown in Figure 3, as an alternative construction of gender norms. Appendix Table B6 presents equivalent heterogeneity results using pre-program DHS data to characterize districts in the sample as either more or less conservative based on women’s decision-making power. Since, as discussed in Section 3.2, the two measures of district norms are highly correlated (0.75), this alternative way of analyzing heterogeneity correspondingly yields a similar pattern of results.

# **5 Conclusion and discussion**

In this paper, we provide experimental evidence on the short-term impacts of mechanization on household production and intra-household labor allocation among cotton farmers in Cˆote d’Ivoire. We find positive impacts on cotton production–a male-dominated cash crop–and intensification of agriculture, along with an increase in the value of complementary agricultural inputs used. Though our results are short-term and we do not detect a significant increase in household income in our survey time-frame, second season outcomes indicate that households expand their overall agricultural cultivation and not merely cotton. Moreover, our data indicates that animal traction can lead to yield–not just production–improvements, as farmers were able to increase their cotton production after land cultivation decisions were made.

Our results shed additional light on four stylized facts related to agricultural mechanization identified in observational studies. First, earlier observational studies analyzed by Pingali et al. (
of papers use ARC2, including Arslan et al. (2015), Amare et al. (2018), Asfaw and Maggio (2018), Asfaw et al. (2019), Coromaldi (2020), Alfani et al. (2021), and Arag ́on et al. (2021).

A second data product, Tropical Applications of Meteorology using SATellite data and groundbased observations (TAMSAT), similarly makes use of rain gauge information from the GTS and Meteosat thermal infrared (TIR) data (Tarnavsky et al., 2014). Input gauge data are supplemented by collection from local sources and the relationship of rainfall to cloud cover duration (CCD) derived from TIR is optimized for spatio-temporal calibration zones. Data are produced at 0 _._ 375<sup>_◦_</sup> resolution. Eberhard-Ruiz and Moradi (2019), Mamo et al. (2019), and Morgan et al. (2019) are some of the papers which rely on TAMSAT data.

Finally, the Climate Hazards group InfraRed Precipitation with Station Data (CHIRPS) data set is a global product that builds on the same blending approach of ARC2 and TAMSAT (Funk et al., 2015). Enhancements include use of a phased approach, incorporating additional climatological products, and ingestion of gauge data from national sources. Daily data are produced at 0 _._ 05<sup>_◦_</sup> spatial resolution. A large number of papers utilize CHIRPS, including Michler et al. (2019), Shee et al. (2019), Jagnani et al. (2021), and Martey and Kuwornu (2021).

# **2.1.2 Assimilation Models**

Assimilation models combine a large number of observations from different sources (e.g. satellites, weather stations, ships, aircraft) to produce a model of the global climate system or a particular atmospheric phenomenon. Reanalyses are assimilation models applied retrospectively over a given time period,
well as practical reasons, it is useful to test the validity of _ex ante_ predictions from the BFL model using the Mexican National Household Survey (ENIGH). This is done by estimating the key parameters of the BFL model ( _αj_ ’s, _M_ and _D_ ) for all children of age 12-17 years with ENIGH 1996 (the round that was the closest to the launch of _PROGRESA_ ), and comparing the estimated parameters with those estimated with a similar model specification for the same age group of children from the ENCEL survey.

The estimated parameters from ENIGH do not differ significantly from those estimated with the baseline survey (Table 3). For all key parameters, the 95 percent confident interval generated by ENIGH and ENCEL overlap each other and can thus be considered statistically similar. However, 23
- How large errors am I willing to tolerate?

- How pervasive is missing data in my indicators of interest?

- Is the probability of a country not having data likely correlated with the indicator of interest?

- [If producing time series] How much do the global statistics change from year to year and do the same countries consistently have missing values?

- [If missing data is imputed] How confident am I in the precision of the imputations?

- [If producing sub-global statistics] How large are the groups and how much of the variation happens between subgroups rather than within subgroups?

Jointly answering these questions should afford an approximate slope of the error as a function of the population coverage, as well as a ceiling on how large an error one is willing to tolerate. Judging from the table comparing standard deviations with original units, our (admittedly, subjective) take is that errors should never on expectation exceed 0.25 standard deviation. Even in the less optimistic cases we presented, this roughly corresponds to not publishing statistics when data for less than half of the relevant population is available. For certain purposes, such as comparing statistics over time, it is likely that much lower errors are needed. A corollary of these recommendations is that it is always optimal to use the share of the global population covered rather than the share of countries covered as the coverage threshold. A corollary of this is that rather than conditioning on data being present for populous countries, it would be better to increase the coverage threshold.

# References

<mark>Dang, Hai-Anh H., Dean Jolliffe, and Calogero Carletto (2019): "Data gaps, data incomparability, and data imputation: A review of poverty measurement methods for data-scarce environments."</mark> _<mark>Journal of Economic Surveys</mark>_ <mark>33 (3): 757-797. https://doi.org/10.1111/joes.12307</mark> <mark>Dang,
entities and is measured at the 4-digit economic NAICS sector using U.S. Input-Output tables. Higher values indicate firms operating in sectors with greater dependence to government spending. The second one is an indicator that equals one for firms headquartered in states of the North of Mexico, which include the states of Baja California, Baja California Sur, Chihuahua, Coahuila, Durango, Nuevo León, Sinaloa, Sonora, and Tamaulipas.

# **4. Empirical Strategy**

We now describe the empirical strategy we follow to identify the impact of the FD Law on states’ fiscal balance, macroeconomic activity, and bank lending to local governments. We then discuss how we map this methodology to more granular data at the bank, loan, and firm level, 15
OPTIMAL SIMPLE FISCAL RULES 13 For country-specific parameters, we chose to calibrate to Algeria and Trinidad & Tobago (TTO). This is mostly because Algeria is close to a “typical” oil producer, and TTO is close to a “typi— — cal” gas exporter many countries export both as measured by the size of resource exports relative to non-resource GDP. Specifically, BJS2013 (Table 1) lists the 2002-07 average export revenues/non-resource GDP for 21 petroleum exporting countries which have petroleum export — revenues/non-resource GDP above 10% running from Sudan (12%) to Saudi Arabia (82%). The cross-country average is 38%, which is fairly close to Algeria’s 33% oil revenues and so it might be regarded as “typical”. BJS2013 also list five countries with natural gas exports above 10% of non-resource GDP, and TTO’s 20% is very close to the 21% average. For the calibration of the model, we also include TTO’s oil exports (13% non-resource GDP) which bring total TTO resource exports to 33% non-resource GDP. As such we calibrate _Q_ = 1 _/_ 3 (and _Yss_ = 1, _Pss_ = 1) so total natural resource exports are 1/3 of non-resource GDP ( _QP/Y_ = 1 _/_ 3) to reflect the relative size of resource exports to non-resource GDP in Algeria and TTO.<sup>16</sup> However, we also chose these two countries based on a desire for geographic diversity (Middle East/North Africa for Algeria, Latin America/Caribbean for TTO), and diversity of country size (TTO has about 1.3m people with Algeria having 40 million). Finally, we also excluded a number of other countries with idiosyncratic features that would make optimal simple rules difficult to calculate, such as large numbers of migrant workers or political instability.<sup>17</sup> Table 1. Exogenous-Income Model Calibration to Algeria and Trinidad &
# **4.  Projections of Future Mangrove Distributions**

# **4.1 Projecting Salinity in 2050**

Using high-resolution digital maps of aquatic salinity and mangroves, we have estimated salinity tolerance ranges and clustering patterns within those ranges for 14 mangrove species and species mixes. In this section, we explore alternative salinity futures and the associated mangrove distributions. Our aquatic salinity scenarios are drawn from Dasgupta, et al. (2015a), who quantify the prospective relationship between climate-induced changes in sea level, temperature, rainfall, and riverine flows from the Himalayas, and the spread and intensity of aquatic salinization in the coastal zone. Their research takes account of projected land subsidence in the Ganges Delta, as well as alternative levels of upstream freshwater withdrawal.

They develop 27 scenarios for 2050 that incorporate three AR4 global emissions scenarios B1, A1B, A2)<sup>9</sup> ; two estimates of sea level rise by 2050 (27 cm for scenario B1, 32 cm for A1B

> 9 Basic elements of the three scenarios are as follows:

> **B1** : Rapid economic growth with convergence among regions; global population that peaks in mid-century and declines thereafter; rapid change in economic structures toward a service and information economy, with reductions in material intensity and the introduction of clean and resource-efficient technologies. **A1B** : Rapid economic growth with convergence among regions; global population that peaks in mid-century and declines thereafter; rapid introduction of new and more efficient technologies; energy from mixed fossil and renewable sources.

> **A2** : Non-convergent economic development; continuously increasing population; heterogeneous technologies and energy sources.

12
America, Du and Dong 2013 in China, Jain 2016 in India, and Clark et al. 2017 in Kenya) by exploring the relationship between preschool expansion and FLFP in a region with little evidence to date.

After the passing of The National Education System Act (NSEA) in 2003, the Government of Indonesia accelerated efforts to expand preschool education with a medium-term plan to increase enrollment between 2004 and 2009. Block-grants were set up to incentivize private provision, while district-level governments were responsible for the public provision of preschools. In 2004, less than a quarter of children aged 3 to 6 ever attended preschools. By 2016, the gross pre-primary enrollment rate had increased to 60.3 percent (World Development Indicators). We exploit the spatial and temporal variations in preschool access and the exogenous overlap with the time when mothers have a preschool-aged eligible child to infer the causal effect of preschool access on maternal employment in a difference-in-difference-in-differences (DDD) framework. Our constructed panel tracks the same mothers on average for 22 years and allows us to include an individual fixed effect that accounts for unobserved individual career and family preferences, biological dispositions, and cultural traits.

We find that mothers of preschool-aged eligible children increase their work participation by 7.4 percentage points, or 13.8 percent from the mean, if they are exposed to an additional public preschool per 1,000 children. We further find that private preschools do not have a statistically significant effect on work participation, but eligible mothers are more likely to hold a second job. The results further indicate that public preschools increase the likelihood of mothers being unpaid family workers and agricultural workers, while private preschools induce mothers to become artisanal production workers. We do not find any effects on earnings or hours worked. An increase in informal sector employment that does not require a full-time commitment is compatible with the fact that preschools typically only operate 3 hours per day during typical working days. We note that public preschools are likely substitutes to private preschools, although the extent of substitution is likely
Komives, Whittington, and Wu "Infrastructure Coverage and the Poor: A Global Perspective" percent as appropriate, with special emphasis on infrastructure coverage among households in the poorest deciles and quantiles of 5 percent.

# **3. Who has infrastructure services?**

# 3.1. Global infrastructure coverage

Over 65 percent of the households in the pooled cross-national sample had electricity in their homes at the time of the LSMS survey.<sup>12</sup> By contrast, only 38 percent of households had in-house water taps, 36 percent had sewer connections, and 24 percent had telephones.'<sup>3</sup> The distribution of these utility connections among households is highly correlated with our income proxy (i.e. monthly aggregate household consumption): a higher percentage of wealthy than poor households have electricity, in-house taps, sewer connections, and telephones in their homes.

Figure 1 shows how coverage of these services varies by income level in the cross-country pooled sample. Each dot on the graph represents one quantile of 5 percent of households. The dots plot the quantile's median "income" against the coverage of electricity, in-house water taps, sewer connections or telephones within that group.

Aggregate consumption among households in the poorest 5-percent quantile of the pooled sample was less than US$ 1.00 per household per day (US$27 per month, on average). These households came from all countries in the sample, but the majority live in Vietnam, Nepal, and Kyrgyz Republic -- the poorest of the fifteen countries.<sup>14</sup> Electricity was the only service with

> 12 These households may obtain electricity from a utility connection or from an electrical generator. Unfortunately, it is only possible to differentiate between these sources in four of the 15 countries (Panama, Ecuador, Nicaragua, and Nepal). In these four countries, electrical generators for household use are rare. In Nicaragua and Nepal, less than I percent of