rafmacalaba/data-use-mentions-extended huggingface dataset:
.263|0.276|0.138|0.249|0.257|
|Panel B Adj.R<sup>2</sup>|0.100|0.167|0.177|0.135|0.254|0.272|0.119|0.244|0.253|
|Nr. of countries|16|16|16|16|16|16|16|16|16|
|Nr. of hh|134872|134872|134872|134872|134872|134872|134872|134872|134872|

Source: International Urban Poverty Database. Note: *** p < 0.01, ** p < 0.05, *** p < 0.1. Robust standard errors in parentheses. WorldPop 250m is used for the DOU and DB method. The dependent variable is a dummy variable taking the value 1 if a household’s per capita expenditure expressed in PPP and spatially deflated falls below the poverty line. The baseline category is “Rural” in all specifications in Panel A, and “Other rural” in all specifications in Panel B. Demographic control variables include household size and household head’s age, sex, and marital status. Education is a categorical control variable that summarizes the education of the household head in seven categories. Employment is a categorical control variable that summarizes the household head’s labor status and employment sector.

areas. Conversely, 13 percent of the urban non-poor population and 23 percent of the urban poor households are reclassified into the rural non-poor and rural poor populations, respectively. The scale of rural-to-urban reclassification is even more significant when transitioning from the official urban definitions to the DB approach (Panel B), with 47 percent of the non-poor and 56 percent of the poor being reclassified. It is worth noting that spatial deflation primarily reduces poverty in rural areas due to their relatively low prices.

# **5.4 Additional analyses*
# Acknowledgments

The authors would like to thank Harry Patrinos, Georgios Sideridis, and Tarek Mostafa for their valuable comments on a draft version of this paper. The team is also grateful to Laura Gregory, Andreas Blom, and Yisgedullish Amde for their helpful feedback and support. Jee Yoon Lee edited the paper.

# References

- Angrist, Noam, Simeon Djankov, Pinelopi K. Goldberg, and Harry A. Patrinos. 2021. “Measuring Human Capital Using Global Learning Data.” _Nature_ 592: 403–8. https://doi.org/10.1038/s41586-02103323-7.

Autor, David, David Figlio, Krzysztof Karbownik, Jeffrey Roth, and Melanie Wasserman. 2016. “School Quality and the Gender Gap in Educational Achievement.” _American Economic Review_ 106 (5): 289– 95. https://doi.org/10.1257/aer.p20161074.

- ———. 2019. “Family Disadvantage and the Gender Gap in Behavioral and Educational Outcomes.” _American Economic Journal: Applied Economics_ 11 (3): 338–81. https://doi.org/10.1257/app.20170571.

- Bertrand, Marianne, and Jessica Pan. 2013. “The Trouble with Boys: Social Influences and the Gender Gap in Disruptive Behavior.” _American Economic Journal: Applied Economics_ 5 (1): 32–64. https://doi.org/10.1257/app.5.1.32.

- Brozo, William G., Sari Sulkunen, Gerry Shiel, Christine Garbe, Ambigapthy Pandian, and Renate Valtin. 2014. “Reading, Gender, and Engagement.” _Journal of Adolescent & Adult Literacy_ 57 (7): 584–93. https://doi.org/10.1002/jaal.291.

- Buchmann, Claudia, and Thomas A. DiPrete. 2006. “The Growing Female Advantage in College Completion: The Role of Family Background and Academic Achievement.” _American
where no respondent receives information. The decision to omit the no information condition from our research design was based on results from a pilot, in which surveyors found it difficult to re-elicit beliefs when no new information had been provided to the respondent. Since this treatment condition is not necessary to estimate the effects of information, we chose to exclude it from our experimental design altogether.

# **4.3.4 Elicitation of Posterior Beliefs**

The second round of belief elicitation takes place immediately after respondents are provided with information about inflation, the exchange rate, or both. To ensure that the responses are comparable to the elicitation of prior beliefs, the second round of belief elicitation uses the exact same wording as the first. The goal of this second round of belief elicitation is to understand whether individuals incorporate the information provided to them through the information treatments into their expectations.

# **4.3.5 Elicitation of Consumption Plans**

While the main goal of our experiment is to estimate the effect of information on _actual consumption_ , as measured objectively in administrative data, we also asked a series of questions on respondents’ _consumption plans_ . These responses were collected after the elicitation of posterior beliefs and serve two purposes. First, they allow us to test whether our information treatments affect intended behavior. With that goal in mind, we measure expected future spending in the main consumption categories highlighted by the theoretical framework (durable goods, tradable goods, and credit card debt), as well as other categories that act as useful proxies or benchmarks. Second, these questions allow us to confirm that survey responses have predictive content by testing whether predicted consumption correlates with actual future consumption.

The first of these questions elicit respondents’ expected change in total credit card expenditures (which corresponds to the total expenditures we observe in administrative data). Specifically, respondents were asked: “Do you expect your credit card spending to go up, stay the same, or go down during the next 3 months?” We code
cost) and the expenses incurred for the rent-seeking activity (indirect cost). As we discuss in the next paragraph, just the direct costs can be substantial and larger than the expenses on electricity. Meeting these expenses is more costly for firms that are financially constrained. For instance, we argued above that corruption hurts firms because it diverts resources from productive activity to rent-seeking activity. Firms that have easy access to finance can compensate for the loss of funds by borrowing. As financial constraints rise, the option of borrowing becomes increasingly more costly and less feasible.

Before going to the formal empirical analysis, we provide some descriptive analysis about the financial burden that corruption imposes on firms. The Enterprise Surveys (ES) data that we use for our empirical analysis provides some insights into the magnitude of the bribe payment visà-vis other cost elements. We focus here on firms that report paying bribes (about 20 percent of the sampled firms), as the role of access to finance in our model is conditional on payment of bribe. For these firms, Figure 1 shows our main corruption measure, that is the amount of bribes firms pay public officials to “get things done” as a percentage of firms’ annual sales. For comparison, the figure also shows the electricity costs (as percentage of annual sales) for the same firms. All averages shown are median values. Reported bribes are higher, equaling 2.77 percent compared with 0.13 percent of electricity cost. We confirm that the qualitative nature of the finding reported here holds even when we exclude from the sample firms with no electricity cost. Thus, the direct cost of corruption (bribes paid) is significant and likely to put a strain on firms’ financial condition. Information on the indirect monetary cost of corruption is not available.

Another piece of evidence comes from firms’ perception of access to finance as an obstacle to firms’ current operations. If corruption imposes significant costs on firms, then firms that pay bribes should find access to finance as a
# **1. Introduction**

The majority of firms in most developing countries are informal. In Sri Lanka, for example, only one-fifth of firms operating without paid workers are registered with any government agency. Even among firms employing paid workers, the majority are unregistered with one or more pertinent agencies. Policymakers are concerned about high levels of informality for two reasons. First, informality is associated with lower tax collection, restricting the government’s ability to finance public services (Levy 2008). Second, the coexistence of formal and informal firms means that firms competing in the same industry face different marginal production costs (e.g., labor costs and taxes), leading to an inefficient allocation of resources in the economy (Hsieh and Klenow 2009; Levy 2008). How can informal firms be induced to formalize?

A firm’s decision to become formal depends on the costs and benefits of formality. There are two prevailing views of informality, dubbed by Perry et al. (2007) as _exclusion_ and _exit_ . The exclusion view focuses on the costs of registering. This view is most notably associated with the work of de Soto (1989), who argued that burdensome entry regulations prevent small firms from becoming formal. These firms suffer a loss in productivity as a result of remaining informal. The natural policy response is then to remove the burdensome regulations. Spurred in part by the World Bank/IFC _Doing Business_ project, governments around the world have in recent years streamlined the process of becoming formal. Indeed, since 2004, 75 percent of the countries included in the _Doing Business_ survey have adopted at least one reform making it easier to register a business (IFC, 2009).

But is streamlining the registration process sufficient to spur formality? The exit view focuses more attention on the balance between the costs and benefits of formality. The decision to become formal is comparable to any other investment decision taken by the firm. Each firm compares its perceived costs of being formal—including both initial
/MT)|Trade<br>balance<br>(Rice)<br>(US$Mn)|Employment<br>(Numbers)|
|---|---|---|---|---|---|---|---|
|**Survey data (2016)**||**1,029,709.0**|**695, 255.0**|**334,454.0**|**770.00**|**(257.5)**|**732,461.0***|
|**Model baseline**|1,328.3|1,030,210.9|694,394.4|335,816.5|768.6|(258.6)|669,340.0|
|**% Deviation**||0.05%|0.12%|0.41%|0.18%|0.43%|8.62%|

Note* Number of heads of agricultural households.

In the baseline simulation, all the results show absolute differences of less than 1 percent from the 2016 survey data, except for the employment data. The model simulated employment is 63,121 less (8.62 percent) than the survey data of 732,461. It is not clear whether this is a lower employment bias from the model since the survey data is only recording the number of heads of agricultural households. It may be possible that such household heads may in fact be employed outside of the rice sector while their rice farms remain fallow. Furthermore, since the model is ‘efficient’ in the allocation of labor resources, the difference may be truly reflective of the underemployment in the rice sector.

To allow the model to fully optimize the objective function in a
# 6 Conclusions

This paper aims to estimate to what extent violence explains the extremely low female labor force participation (FLFP) rates in South Asia.

This is relevant for both academics and policy makers as FLFP is well-known to contribute to development. In addition to providing extra labor input and therefore increasing gross domestic product, women having earning power has been shown to increase investment in children, including education, which promotes future GDP growth (Duo [2012]).

The direction of causality between violence and FLFP and its implications for identication represents a crucial challenge. One the one hand violence could increase FLFP through the added worker eect whereby women increasingly work outside the home to help support households during a crisis. On the other hand violence may increase the cost of working for women, either directly due to the risk of violence or indirectly by reducing economic opportunities. Therefore, economic theory does not oer a clear prediction about the relationship.

At rst glance, the evidence supports the latter rather than the former eect, because in a cross-section of countries or regions a negative correlation between violence and FLFP emerges; that is, FLFP is lower in areas where violence is higher. Thus, looking at aggregate data shows little evidence for the added worker eect.

We evaluate the validity of this argument using disaggregated data that track FLFP rates over repeated crosssections. On the "extensive margin" of labor supply - that is, on women's decision whether to work or not work - we nd that one extra-attack reduces FLFP by 0.008 percentage point. This results in a widening of the gender labor participation gap as the eect of violence on men's labor participation is up to seven times smaller in our study. On the intensive margin - that is, on women's decision regarding how many total hours they work - the presence of violent attacks encourages married women to exert more working hours, but when the environment gets more risky, as the number
complete the field, such as free text entry, dates, numeric values, or controlled vocabularies (multiple choice options).<sup>2</sup> The proposal organizes fields into thematic modules and includes whether a field should be optional or mandatory. A core objective of this proposal is standardization. Standardizing the documentation of specific types of data allows harmonization, aggregation, and cross-referencing. Data repositories that follow a standardized schema can be easily made searchable by internet search engines such as Google Dataset Search. As much as possible, our schema therefore conforms with common data description standards (such as those of the Data Documentation Initiative (DDI)), and with existing schemata and catalogs for experiments as well as survey data from the social sciences (such as the World Bank’s Microdata Catalog, the AEA RCT Registry, and ClinicalTrials.gov).

In addition, we added fields that we considered particularly useful for secondary research. Datasets documented with the schema can be easily searched or filtered by many different criteria, from the type

> 1We use the term RCT to describe an experiment that uses randomization to assign some intervention or treatment to participants but is conducted outside of a controlled environment (such as a laboratory).

> 2We follow the definitions in ISO (2021).

2
This paper contributes to the gender gap literature by examining whether gender differences in cognitive and non-cognitive skills emerge in the first few years of primary school. Using data from rural Indonesia, we document the early emergence of gender gaps and show that a combination of early schooling and family environment explain the observed difference between girls and boys. From a policy standpoint, these early-emerging gender differences in rural Indonesia highlight the important role both schools and families play in equally supporting the needs of girls and boys starting from the early years. Differences in the magnitude of these roles at various levels of quality underscore the importance of ensuring the quality of education services – starting with preschools.

21
the probability of temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the logarithm of hourly wages upon return. For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use the diplomatic exchange, which is the diplomatic representation of each country of destination of Egyptian migrants in Egypt at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange and is equal one if there is evidence of diplomatic exchange at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of hourly wages, unconditional on return migration. The predicted logarithm of hourly wages for non-migrants, illegal migrants, and legal migrants are reported in the last row.

40
Experiment as part of the CGD–World Bank survey 2022. See probit model results in the annex. The probabilities have been computed for a similar “average” respondent in all countries. About 50 percent of respondents select school feeding programs outside of the EAP region, which lies outside of the uncertainty range of the selection probability in the EAP region.

# Education Technology, Gender and Inclusion

## 4.2 Officials overestimate the effectiveness of EdTech and underestimate inequality.

Most EAP respondents agree with the statement that education technology was effective in supporting student learning during COVID-19-related school closures (83 percent, Figure 6). However, the data for EAP and much of the world show that EdTech was usually _not_ an effective support for most children during COVID-19-related school closures, or at least much less effective on average than in-person instruction, particularly for low-income students (Patrinos et al., 2022). Respondents’ views on the impact of EdTech during COVID-19-related school closures are 8 percent more positive in EAP than in non-EAP countries (Figure 6).

9
institutions (such as credit unions, post offices) are often not captured in remittance statistics in developing countries. Only 38% of countries collect data from money transfer companies. Moreover, less than 25% of countries measure remittance transactions that occur through informal channels, such as money carried by migrants themselves to their home countries, money sent through “hawala” operators, and money sent through travel agencies or other informal means.

Given the limited coverage of existing remittance data, current statistics are most probably under-estimating the total value of remittances inflows to developing countries. It is important that developing countries improve their existing statistical tools to measure remittances inflows. Better data on remittances is important for the formulation of macroeconomic policy, because large remittance flows can affect the level of savings, investment and consumption. Furthermore, data on remittances constitutes an important tool to monitor cross-border flows of money and timely detect money-laundering or other illicit activities that may undermine the integrity of a financial system.

While there is a growing need in developing countries to improve remittance statistics, international guidance in this area is required. In particular, there is a need to establish simple and homogeneous reporting requirements across countries that do not undermine the competitive position of market players, especially small institutions. In addition, there is a 30
indicates that those studies ignoring endogeneity are more likely to report a stronger positive effect of financial development on growth. Although it is likely that a part of endogeneity in finance–growth nexus can be addressed by model averaging procedure (reducing ommitted variable bias), we also examine the robustness of our results through specifications that employ the lagged explanatory variables. To the best of our knowledge, this is the first study to combine various characteristics of the financial sector, a rich data set on growth, and an approach that addresses model uncertainty and endogeneity. As a result, our study addresses two main issues in finance–growth literature: (1) causality issues and (2) measurement of financial development.

Using data on real economic growth in 60 countries between 1960 and 2011, we find that bank efficiency is robustly related to long-term growth and exhibits very high PIP. This finding corresponds to the predictions of theoretical model by Pagano (1993), who shows that the efficiency of financial intermediaries is crucial for funneling savings to investment and therefore for increasing real growth. The relevance of traditional variables, such as credit provided to the private sector or stock market capitalization, is weaker. In addition, we also fail to find a nonlinear effect of financial development on growth. Our results are robust to a series of checks such as employing a different sample period, different parameter priors or addressing endogeneity. Therefore, our results highlight that the approach to measuring financial development is crucial for the estimated effect of finance on growth. Our policy implication is that those managing the current worldwide wave of regulatory changes in the financial industry should not underestimate the importance of the efficiency of financial intermediaries for long-term growth.

This paper is structured as follows. Section I provides a literature review on finance and growth. Section II presents the data. We describe Bayesian model averaging in section III. We provide the regression results in section IV. The 5
al. (2012) find that individuals in the US also systematically overestimate gender conservativeness in relation to labor supply of mothers with young children, and that once information on peer beliefs is provided they are more likely to donate towards organizations advocating for women in the workplace.

In this paper we build on the literature examining the role of misperceptions of support among peers. We report the results of two data collection exercises. First, an online survey which was designed to measure and enhance our understanding of social norms around women’s work in Indonesia. Specifically, we collected information from approximately 500 female and 500 male respondents in metropolitan areas across Indonesia on their behavior

> 1Dean and Jayachandran (2019) and McKelway (2021) evaluate interventions in India which provided information on female employment opportunities and/or the benefits of female employment. Both found very little change in the acceptability of women working. Makino (forthcoming) however found that havingparents of young women in Pakistan attend a two-hour lecture which provided information on the safe,female-friendly working environment in large garment factories made parents more positive about theirdaughters working in factories. Interventions targeting adolescents in India have also had some successin changing gender attitudes (see Dhar et al. (2022)).

> 2While not designed to change social norms, Field et al. (2021) show that male attitudes towards female work and their beliefs about community acceptance of women working were shifted by an intervention that resulted in women receiving wages into their own bank account rather than their husband’s account. This shift was hypothesized to come about due to the consequent increase in women’s household bargaining power.

1