rafmacalaba/data-use-mentions-extended huggingface dataset:
effect on MCF estimates in Canada.<sup>4</sup> In addition to the economic effect of informal sectors, including informal sectors avoids a calibration problem. CGE models have sometimes applied ‘effective’ tax rates, calculated as tax revenues divided by sector size. This results in low modelled tax rates. Effective tax rates provide an average between taxpayers who pay tax at something like the legal rate, subject to some under-reporting, and informal producers or consumers who pay no tax. Effective tax rates underestimate the marginal tax rate incurred by those who actually pay tax. Models using effective tax rates are thus likely to underestimate MCFs.<sup>5</sup> Our paper makes several contributions to the literature on MCFs. The first is the development of a simple general equilibrium model that can handle taxes on the five major tax classes, takes account of the existence of informal sectors, can be calibrated with little more than national accounts data, and can be easily reproduced by other researchers for other countries. After a brief review of the existing literature in section 2, we present the model in section 3. It is inspired by the minimal data requirements of the ‘1-2-3 model’ of Devarajan, Go, Lewis, Robinson, & Sinko (1997). The basic 1-2-3 model has one country with two producing sectors and three goods: a domestic good, exports and imports. This model is extended to include production of an informal good, and four factors of production: formal capital, informal capital, formal labor and informal labor. Our definition of an informal good or factor is simply one on which no tax is paid.

Section 4 contains the paper’s second important contribution: application of the model to produce estimates of MCFs for taxes in 38 African countries, vastly increasing the number of developing countries for which MCF estimates exist. Sensitivity testing of the model reveals which elasticities are the most important in determining MCF magnitudes, and suggests that our base case estimates are reasonably robust for purposes
Figure 10. Contribution to Within-Group Inequality by Care Categories <!-- Start of picture text --> 0.08<br>0.07<br>0.06<br>0.05<br>0.04<br>0.03<br>0.02<br>0.01<br>0.00<br>Market Disposable Consumable Final<br>income income income income<br>No dependents Single adults, dependents<br>Other care categories Married couple, dependents<br><!-- End of picture text --> _Source:_ Rodriguez, Wai-Poi, and Woodham (2022).

All told, in Jordan, the receipt of in-kind benefits, primarily education, determines which fiscal or care groups receive positive net benefits from the fiscal system. When focusing only on cash benefits, the fiscal system is very close to neutral for groups. The Jordanian fiscal system does reduce within-group inequalities but has only a very small impact on inequalities between groups which are very low at the outset. Most of the within-group inequality reduction happens when direct taxes and transfers are added to the fiscal system.<sup>39</sup>

# c. Incorporating Time-Use Data

Jordan has one of the lowest rates of female labor force participation in the world at around 15 percent, and during the COVID-19 pandemic, unemployment rates among women rose dramatically. In Jordan, as elsewhere, women bear more responsibility for childcare during school closures as well as care for other family members who have fallen sick. Women ages 15 to 44 spend over thirteen times as much time on chores and nearly five times as much time on childcare as men of the same age group (World Bank 2020).

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# **<mark>Supplementary Materials</mark>**

# **<mark>Appendix A: Methods</mark>**

# **<mark>Study 1: Descriptive Findings: Agency in Context</mark>**

_<mark>Descriptive Surveys in Niger</mark>_ _Participants._ This data was from surveys collected among the full policy experiment sample at baseline (N=4,717) or otherwise from respondents assigned to the control group at the six-month follow-up (N=1,216). The reason for the latter choice was to obtain responses unaffected by the additional psychosocial interventions. The policy experiment sample is described in section ‘Study 2—Policy Experiment’ below.

_Procedure_ . For the descriptive data on models of agency _,_ we explored women’s understanding of drivers of and barriers to economic success through two questions asking them to rank four factors in order of their importance as drivers of women’s economic success and as barriers to women’s economic success. For both questions, two options were more independent factors taken from Western goal setting and entrepreneurial interventions (Campos et al., 2017). For the drivers of success question, these included “having self-initiative” and “hard work” and for the barriers question, these included “not planning for the future” and “not being persistent.” Other factors were derived from qualitative interviews. For the question on drivers, the options included “peacefulness” and “good social connections,” and for the question on barriers, these included “not respecting others” and “tension in the household.” In the question on drivers, participants were asked “of these 4 factors, which is the most necessary for the success of women in their economic activities?” and then asked to rank the subsequent qualities. The four options were: <mark>working hard, having peacefulness, showing personal initiative and being strategic, and having good social connections
any subsequent team meetings, and the focus on gender was never broached. Even if agents thought the experiment was about gender, it is unclear how this would affect their behavior as the name assignment changed daily without their knowledge.

# **2.3 Data**

The analysis relies on two sources of administrative data. The first dataset records every purchase made by customers, including the sale amount. The second dataset contains the agent-customer chat interactions: the full chat transcript, a timestamp for each message, and the customer’s country.<sup>14</sup> The sales data were matched to the chats using date and customers’ IP address.<sup>15</sup> To measure overall purchases, we include purchases directly made by customers, which are observed in administrative sales records, and also purchases made by agents on behalf of customers. Agents may input customer details and purchase products on their behalf, which we capture by reading through the chat records and flagging instances where agents send final purchase confirmation details to customers. Customers then pay separately or at the time of receiving the order. When agents make purchases, we cannot measure purchase values. We therefore only measure the total value of purchases using the administrative sales records.

From the chat transcripts, we create objective and subjective outcome measures. Objective measures do not require human interpretation—for instance, whether a purchase occurred. Subjective measures represent outcomes that require human interpretation of the chat content—e.g., the overall tone, whether customers bargained with agents (e.g., asking for a discount), or whether customers harassed agents. Enumerators familiar with the cultural context hand-coded these subjective outcomes; 20% of the observations were double coded to ensure consistent measurement.

Agents’ jobs involved several sales-related activities, including assisting customers via online chat and phone. Each agent worked the chat interface six weekdays per month on average. On days when agents responded to chats, they spent 2.9 hours on
, likely as a result of time spent on discipline management. It is unclear how much time is lost in MENA classrooms that have a high occurrence of classroom disruption. However, international evidence shows

> 9 Jordan, Tunisia Qatar, United Arab Emirates, Algeria, Lebanon.

> 10 Using TIMSS data, it is possible to relate school safety and students’ mathematics performance. Considering gender, age, the use of the language of instruction at home, as well as educational possessions, it is possible to compute a socioeconomic index tailored to the survey. That index is averaged at the school level to take into account peer effects. As students’ performance highly depends on their peers’ ability, students tend to benefit from being enrolled in a class with affluent peers. A linear model explaining students’ mathematics performance is estimated for MENA and non-MENA countries. The disciplinary climate is indeed measured at the individual level while the school safety index is measured at the school level. As the aggregation reduces the variance of the indicator, the variance of the disciplinary climate is indeed about 3 times as large as the variance of the school safety indicator. As one TIMSS point is grossly comparable to one PISA point, it appears that improving the school climate by 3 standard deviations – which corresponds to an increase of the disciplinary climate of one standard deviation – roughly increases math performance by about 25 points.

9
surveys, and section 5 concludes.

# **2 Background on ECD in Nepal**

Nepal has made large investments in early childhood education in recent years, with enrollment in early childhood education and development (ECED) centers increasing from 12% in 2000 to 86% in 2017 (Poudyal et al. (2019)). In Nepal, ECE centers are either privately run or government run. The government-supported centers are either community-based or school-based. Our sample includes teachers in school-based centers, which are located near and connected to primary schools in the area. These centers work to promote the cognitive, physical, emotional, social, and language development of children in them, as well as educate parents about these realms of development. The constitution of Nepal states that "every child shall have the right to early childhood development" and Nepal has adopted a goal for every child to receive education in an ECED center by 2030.

The number of ECED centers was over 36,000 in 2017, serving almost a million children (World Bank (2020)). The ECED gross enrollment ratio (GER) has increased from 63.4% in 2007 to 84.1% in 2017, and the percentage of grade 1 students with ECED experience has doubled from 33% in 2007 to 66.3% in 2017. While ECD expenditure has been increasing, the budget for ECD services is still less than 0.1% of GDP and is between 1-3% of Nepal’s expenditure on education (Unicef (2019)). Despite the rapid expansion of ECED access, quality remains a concern. In 2014, less than one third of Nepalese children aged 36-59 months were developmentally on track in literacy and numeracy and only 64% were on track in the social-emotional domain according to UNICEF’s Early Child Development Index (Unicef (2019)). ECED facilitators are required to have completed grade 10 and ECED customized teachers’ training curriculum was released in 2018; this training was developed as a refresher curriculum
, 2011). One of the benefits of decentralized decisionmaking and localized participatory development is to take advantage of idiosyncratic information at the local level (e.g. Alderman, 2002). Other goals include improving accountability, democratization of local politics by giving voice to previously marginalized groups, increasing social capital, and improving governance (Mansuri and Rao, forthcoming). These are broader goals that may well lead to a substantially improved allocation of social assistance funds in the

> 16 Ravallion (2000) finds that the improvement in the targeting performance of Trabajar II was in part due to the incentives provided by the center to the provinces for reaching poor areas. Baker and Grosh (1994) and Elbers et al. (2007) both find that targeting smaller administrative units, by using “poverty maps” or similar data, would produce large gains in poverty reduction.

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# **2.1 Land tenure security and investment: Conceptual and measurement issues**

The empirical link between greater tenure security and transferability of land and incentives for investment and efficiency of resource use rests on three arguments. First, security of rights, i.e. a reduction in the probability of being evicted or otherwise losing land rights has been shown to provide land users with greater assurance that they will be able to enjoy the fruits of their labor, thus encouraging them to make long-term investments and manage land in a sustainable fashion (Besley 1995). Having land rights defined clearly and accessible enforcement institutions available reduces the probability of unproductive spending on conflict, something that has been shown to have far-reaching impacts on productivity as well as equity (Deininger and Castagnini 2005). Second, adding the right to transfer land to others, either through rental or sale, encourages investment as it makes it easier to liquidate such investment in case of an exogenous shock (Deininger _et al._ 2005). It also constitutes an essential precondition for transactions that can help bring land to more efficient uses, thus maximizing output and allocative efficiency and creating the preconditions for labor to move from agriculture to non-agricultural pursuits in the broader context of economic development (Kung 2002). With rapid economic transition, e.g. at the urban fringe or in areas of rapid commercial expansion, and the associated increase in the number of transactions, ways of ensuring the legitimacy of land transfers and eliminate incentives for opportunistic behavior by one of the contracting parties have been shown to be critical to (Lavigne Delville 2002). Finally, having a formalized and low-cost way to unambiguously identity land owners without the need to physically inspect the parcel, enquire with neighbors, or conduct an extensive search of land records, allows the use land as collateral. This will, in turn, be critical to facilitate financial market development and the emergence of more sophisticated financial instruments that draw on the abstract representation of land rights provided by titles (de Soto 2000
## **4.1 Data and Descriptive Statistics**

In our analysis we construct a unique longitudinal dataset which covers the universe of refugees and migrants in Switzerland over 1998-2018. It allows us to follow refugees over the life-cycle for 20 years, and allows us to follow them even after they change residence permit and status.

To construct this longitudinal dataset we are combining three administrative datasets. The first dataset, that covers all asylum seekers is AUPER (Automatisierte Personen Registratursystem) which is provided by the State Secretariat for Migration. The data includes information about the residence permit, year of arrival, country of origin, canton of allocation and socio-demographic characteristics. Once an asylum seeker has obtained a residence permit other than permit N (asylum seekers) or F (provisionally admitted foreigners), he or she is registered in ZAR (Zentrales Ausländerregister). This longitudinal dataset is also coming from the SEM. The ZAR dataset includes all foreigners residing in Switzerland and contains similar information than AUPER. We are combining these two datasets with the yearly population census data that includes ZAR and AUPER starting from 2010. By combining these three datasets we can follow refugees and migrants over 1997-2018 and natives over 2010-2018.

We define as refugees, all foreign-born individuals that went through an asylum process, and as migrants all other foreign-born individuals. Natives are defined as individuals born in Switzerland. In the final sample, we are drawing a random sample of 6% of the native population.

For the main outcome variables used in the descriptive analysis, we are first adding Swiss social security data provided by the Federal Compensation Office. This data collects information about every Swiss resident that contributed to old age provision (i.e., the old-age and survivor’s insurance OASI or AVS in French). We know the size and nature of the contribution made by individuals (from paid work, independent work, voluntary contribution or other kinds). This data is available from 1998-2018. Our main outcome variables measuring economic integration include employment, earnings and
# **Figure 2: Hypothetical scenarios leading to IPC4**

These two scenarios illustrate how two entirely different situations of food insecurity lead to the same classification under the 20% rule, where a region or country is classified based on which IPC classification the 80th-percentile of its population finds itself in. Our framework addresses this shortcoming by using a compositional transformation and leverages the full distributional information in the data, thereby accurately reflecting the more detrimental situation in Scenario 2.

<!-- Start of picture text --> Scenario 1 Scenario 2<br>1.00<br>Threshold (80%)<br>0.75 Population share<br>(cumulated)<br>0.50 Population share<br>0.25<br>0.00<br>IPC1 IPC2 IPC3 IPC4 IPC5 IPC1 IPC2 IPC3 IPC4 IPC5<br><!-- End of picture text --> in the respective IPC phases over all districts.2 The reported IPC values are net of humanitarian assistance impacts as the model is intended to estimate outcomes if urgent action is not taken.

As previously outlined, food insecurity is driven by complex interactions between conflict, poverty, extreme weather, climate change and food price shocks (D’Souza & Jolliffe, 2013; Headey, 2011; Misselhorn, 2005; Singh, 2012). This means that a large number of possible covariates may explain dynamics in the food insecurity distribution.

Agronomic and weather-related variables reflect the agricultural conditions. Historically, crop failures and fluctuations in crop yields have been a key reason for worsening food conditions. We use satellite data to calculate stress indicators and anomalies in vegetation, humidity and soil moisture. To highlight anomalies from historical norms, we also calculate differences with respect to long-term averages for a particular month.

Closely related are prices of staple food items, whose composition may differ between countries. We constructed a staple food price index to capture the market conditions for food commodities. Volatility in food prices are seen as
when payments come due. On the other hand, a debt maturity longer than asset life may create problems of finding new assets to support debt service."<sup>6</sup> Finally, another firm characteristic not suggested in the literature on agency cost is size. As argued **by** Demirgfii-Kunt and Maksimovic (1994), firm size may be relevant for financial structure, because access to financial markets may be a functior. **of** size.

The empirical literature on financial structure in developing countries emphasizes the influence of firms' characteristics, institutional factors, and the development of financial markets on the financing choices of firms. Aivazian, Booth, Demirgiiq-Kunt, and Maksimovic **(1999)** use a firm-level database to study whether the capital structure theory is relevant for developing countries with different institutional characteristics.<sup>27</sup> They study capital structure in ten developing countries and fccus on three main issues. First, they study the possibility that corporate leverage decisions differ between developed and developing countries. Second, they analyze the factors that affect cross-sectional variability within each country. Finally, they examine the possibility that capital structure models add insights to just knowing the nationality of the company. They focus on book and market debt ratios.<sup>8</sup> They regress these ratios against: (a) firms characteristics (tax rates, assets tangibility, business risk, size, returns on assets, and market to book ratios), **(b)** dummy variables for the different countries, and (c) aggregate financial indicators for each country (for cross country regressions).

See also Caprio and Demirgib;-Kunt **(1997).**

> There exist some case studies. Jaramillo and Schianl:arelli **(1996)** study the case of Ecuador, Schiantarelli and Srivastava **(1996)** and Samuel **(1996)** cover the case of
exogenous) to be correct. Overall, the estimated effects are slightly higher than our original returns, except for the aggregate output index where the return is lower. The results are fairly robust and in almost all cases retain significance with our earlier estimates.<sup>21</sup>

# **5. Distribution of benefits of road investment**

While the above analysis allows for a broad range of controls in road project evaluation to generate average returns to road investment, it still imposes a single response coefficient γ on all households and communities conditional on other factors, such as the household distance to a paved road. The estimates of the earlier section are based on the assumption that distance does not matter in terms of gains from a road development project. This may not be the case, however. That is, households located in a roadside village may benefit more from road investment than households living away from a paved road. Testing this hypothesis requires differences in household distance from road, which is very minimal

> 20 The returns to consumption seem to be higher for rural electrification than rural road investment (as high as 23 percent for electrification as compared to 11 percent for rural road development). 21 The only exceptions are boys’ schooling and female labor in the RDP sample.

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for example, Ades and Di Tella (1997), Svensson (2003), and Holcombe and Boudreaux (2015) and Fisman and Golden (2017).

The present paper contributes to the above literature in several ways. First, as Fan et al. (2009) and Svensson (2003) note, most previous studies have used perceived corruption indices that rely on the aggregated perceptions of businesspersons or country experts, many of whom may have formed impressions—perhaps subconsciously—based on common press depictions of countries or conventional notions about what institutions or cultures are conducive to corruption. Thus, the use of these indices raises concerns about perception biases. The _Manual on Corruption Surveys_ prepared by the UN (UNDOC, UNDP and UNODC-INEGI 2018) argues that corruption measures based on the actual experience of individuals with corruption (obtained from survey data) are far superior than the corruption measures based on opinions and perceptions of the experts. Thus, we depart from the literature by using firms’ experience with corruption instead. We do so for the overall corruption and petty corruption that arises in conducting a specified list of public transactions.

Second, for regulation, we use the actual regulatory burden experienced by the firms rather than rules on the books. This is an important departure from the literature which is almost exclusively focused on regulations on the books. The only exception we found is Duvanova (2014). While our study uses the same measures of regulation and corruption as Duvanova (2014), ours differs in several ways. For instance, Duvanova (2014) employs an older data set (1999 to 2005) and is confined to 25 post-communist economies. In contrast, our sample includes 131 countries (baseline sample) surveyed over 2006 to 2018. Further, we explore the corruption and regulation relationship for the overall corruption and petty corruption. The study by Duvanova 4
costs have a significant effect on the geographic concentration of economic activity, no matter whether we consider plants, employment, or sales to measure that concentration.

Table 6: Estimation results for specification (5), robustness checks.

|Variables|Dependent<br>**(Model 8)**|variable is the<br>**(Model 9)**|cdfat50km<br>**(Model 10)**|
|---|---|---|---|
|Ad valorem trucking costs (residual)|-0.245<sup>_a_</sup><br>(0.082)|-0.198<sup>_b_</sup><br>(0.083)|-0.192<sup>_b_</sup><br>(0.086)|
|Asian share of imports|-1.119<sup>_a_</sup>|-1.035<sup>_a_</sup>|-1.041<sup>_a_</sup>|
||(0.387)|(0.398)|(0.401)|
|Input distance|-0.337<sup>_a_</sup>|-0.238<sup>_a_</sup>|-0.232<sup>_a_</sup>|
||(0.056)|(0.055)|(0.056)|
|Output distance|-0.324<sup>_a_</sup>|-0.292<sup>_a_</sup>|-0.298<sup>_a_</sup>|
||(0.041)|(0.039)|(0.038)|
|Average minimum distance|-0.264<sup>_a_</sup>