rafmacalaba/datause-encoder-data huggingface dataset:
15 the end of 1996, 85 percent of the producers paid their fees, and by the end of 1997, 7 firms provided extension services to more than 13,000 producers through 102 private technicians and collected 81% of the producers fees (2 firms joined in 8/97). Table 4 presents cost recovery rates by producers for the 7 private companies for the first and second years of ATP2's operation. In light of the increasing number of producers--that creates logistical problems of fee collection, the costrecovery values of 81% on average (with a range between 64%-96%) are quite impressive. The drought conditions that affected coffee production, a major crop in certain regions, accounted for relatively low recovery rates for companies 2 and 5.

As correctly indicated by one reviewer, there is a potential problem of bias in the analysis if producers are selected for participating in paid-extension programs. In such case the impact of paid extension does not reflect real world distribution of producer ability and real willingness to pay. However, for an undertaking such as paid-extension departing from a heavily subsidized public extension, a selection approach such that was chosen in Nicaragua is fully justified.

Table 4: Rates of producers payments by companies in ATP2

|**Company**||**Recovery of extens**|**ion fees**||
|---|---|---|---|---|
||**First Year (8/95-**|**7/96)**<br>**Seco**|**nd Year (8/96-1**|**0/97)**|
||**Recovery rate**(|%)<br>**Billing**<br>**(Cordobas)**|**Actual**<br>**payment**<br>**_(Co rdnhb:t_**|**Recovery**<br>**rate (%)**|
|1|87|391,007|<br>367,546|94|
|2|87|331,828|212,866|64|
|7|N/A|180,049|237,187|82|
|6|N/A|142,255|148,360|86|
|3|83|254,548|237,187|93|
|4|85|278,865|267,710|96|
|5|84|477,420|320,893|67|
|Total|85|2,055,973|1,677,472|81|

Source: Estrada-Rizo and Garcia (1997) for the period 8/95-7/96, and Estrada-Rizo (1997) for the period 9/96-10/97.
- [6] Baldwin, Richard (2012) “Trade and Industrialization after Globalization’s Second Unbundling: How Building and Joining a Supply Chain are Di¤erent and Why it Matters.” in Globalization in an Age of Crisis: Multilateral Economic Cooperation in the TwentyFirst Century. University of Chicago Press.

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- [9] Brandt, Loren, Johannes Van Biesebroeck, Luhang Wang, and Yifan Zhang (2015). “WTO Accession and Performance of Chinese Manufacturing Firms,”Working Paper, University of Toronto.

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- [13] De La Cruz, Justino, Robert B. Koopman, Zhi Wang, and Shang-Jin Wei. (2011). “Estimating Foreign Value-added in Mexico’s Manufacturing Exports,” US International Trade Commission Working Paper 2011-04A.

40
For the full enumeration treatments, we ask the same questions, but with respect to individual use. For example, for the _banked_ indicator, we ask simply: “Do you have a bank account?” Reponses are aggregated across all members of the household to arrive at our measure of household usage. In other words, if one member of the household reports having a bank account, then the whole household is considered banked for the full enumeration treatments.

There is an element of subjectivity that went into the crafting of these questions, and one might worry that slight tinkering with the institution-based questions could increase reported levels of usage. While we recognize this possibility, we worked extensively with staff at the Ghana Statistical Service to adapt these questions to the country context and we did extensive piloting of these questions (both at GSS and in the field) to make sure that the questions were well understood by respondents. We are confident, therefore, that these questions represent a reasonable and fair attempt to gather data on usage of financial services in Ghana via institution-based questions. Moreover, in our first set of comparisons between full enumerations and informants, all respondents were asked the same questions. While the usage levels might be affected by the specifics of those questions, the differences in the data generated by a full enumeration versus the informants are much less likely to be affected.

For informants and the full enumeration, Table 6 reports the percentage of households that use a financial service for each of our seven indicators. For five of the seven indicators – _banked_ , _indirect access_ , _formal non-bank savings_ , _informal savings_ , and _informal credit_ – household usage rates are almost identical when the head of household is the informant or when a full enumeration is undertaken. For _formal credit_ , 18
but only one in eight or one in six cars is designated as reserved space. Both administrative and rider audit data confirm that the reserved space is at least as crowded as the public space (Figure A2).

Observations recorded by our platform observers confirm that, even though the reserved space is designated for women only, substantial numbers of men ride in the reserved space (Figure A3). The presence of officers enforcing the policy varies substantially by station, resulting in variation in presence of male riders (Figure A4). Moreover, the cars are connected internally; it is possible for men to move from public to reserved space after boarding, further complicating enforcement. Figure A3 shows that the average proportion of men in the reserved space is 29 percent compared to 58 percent in the public space. The difference in male presence across public and reserved space at a given time and location also varies substantially (Figure A4).<sup>22</sup>

# **4.2 Riders and Their Experiences**

Table 1, Panel A, shows socio-economic characteristics of the riders and platform survey respondents. Column 1 reports characteristics of our crowdsourcing app users. Most participants are regular commuters: about 70 percent are employed, and the average participant rides the SuperVia 6 times a week. Unemployed participants are the minority, but are overrepresented in the rider sample relative to the representative platform sample; this is likely because participation in the app served as a form of employment or subsidized search costs. The smartphone app also attracted a somewhat younger and more educated pool of participants than the average commuter. Stated use of the reserved space is slightly lower among crowdsourced riders than general women commuters, although both groups report taking close to half of their rides in the reserved space.

> 22In November-December 2016, we worked with the SuperVia authorities to implement an experiment to deploy enforcement staff to experimentally vary enforcement of the reservation rule. However, due to limited numbers of staff, this failed to generate sufficient variation in the presence of men in the reserved space. Therefore, we do not examine the effect of this intervention on downstream outcomes such as harassment. The data for this period are included in all our main analyses, and we include a dummy indicating being part of this pilot in all specifications. More details on the pilot are available upon request.

15
# **Table 6: Skills**

## **OLS estimates, standard errors in parentheses, p-values in braces**

||**Literacy**<br>**[0-100]**<br>**Aged 12**|**Numeracy**<br>**[0-100]**<br>**-17 at BL**|**Literacy**<br>**[0-100]**<br>**Aged 18**|**Numeracy**<br>**[0-100]**<br>**-25 at BL**|**Entrepreneurial**<br>**Confidence [0-100]**<br>**Aged 12-1**|**Financial**<br>**Literacy [0-100]**<br>**7 at BL**|**Entrepreneurial**<br>**Confidence [0-100]**<br>**Aged 18-2**|**Financial**<br>**Literacy [0-100]**<br>**5 at BL**|
|---|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|**Disruption Effect**|-12.1***|-7.34***|.964|-8.01***|.342|-5.00*|.962|-2.24|
||(3.50)|(2.79)|(2.67)|(2.65)|(2.27)|(2.96)|(2.08)|(2.13)|
|**Treatment Effect in High Disruption**|8.85***|7.25***|2.48|7.46***|1.45|4.72|2.69|3.36*|
||(3.26)|(2.70)|(2.51)|(2.08)|(2.12)|(2.98)|(1.90)|(1.84)|
|**Treatment Effect in Low Disruption**|-4.86**|-1.41|2.25|-.596|.968|-1.47|2.59**|.271|
||(2.09)|(1.55)|(1.45)|(1.44)|(1.37)|(1.66)|(1.16)|(1.78)|
|**Difference [β3, p-value]**|{.001}|{.006}|{.934}|{.002}|{.846}|{.073}|{.960}|{.254}|
|**Control Mean at BL**|24.6|42.4|21.3|33.1|60.5|58.8|78.2|63.6|
|**Observations**|2,382|2,382|2,401|2,401|2,381|2,382|2,400|2,400|

**Notes:** ***, ** and * denote significance at the 1%, 5%, and 10% levels. Literacy and Numeracy are self-reported abilities to perform: "Basic counting, simple addition/subtractions, and measurement"; "Working with fractions, multiplying and dividing, doing algebra or basic bookkeeping "; "Reading simple things like labels on containers"; "Reading comprehension, writing complete sentences or longer passages". Answers to the skill self-assessments are then aggregated and rescaled in a measure that ranges from 0 to 100, with the latter indicating more advanced proficiency. Entrepreneurial Confidence is an index that measures respondents' self-reported ability to: run a business, identify business opportunities, obtain credit, save and invest, manage financial accounts, bargain prices, manage employees and search for jobs. Financial Literacy is assessed through 8 simple problems relating to market prices, interest rates, borrowing and budgeting. The number of correct answers is rescaled in an index ranging from 0 to 100. The main results are estimated with an ANCOVA specification. Control variables include: age, PPI score, household size, village size (nr of dwellings), village average PPI score, distances form key facilities (clinic, secondary school and market), a dummy equal to one if the village is a political stronghold (i.e. has a resident paramount and/or section chief), the number of NGOs active within the village before the Ebola outbreak (excluding BRAC), share of Christian households, distance from Freetown and distance from Kailahun. Columns (5) through (8) control also for illiteracy. All specifications include dummies for the randomization strata (district) and errors are clustered at the unit of randomization (village).
- Komatsu, Hitomi, Mahvish Ifrah Shaukat, and Ceren Ozer. 2024. “Unpacking Tax Compliance from a Gender Perspective.”

- Kotikula, Aphichoke, and Wameq Azfar Raza. 2021. “Housing ownership Gender differences in Dhaka, Bangladesh” [in en]. _Land Use Policy,_ Land governance and tenure security at scale, 110 (November): 104983.

- Kresch, Evan Plous, Mark Walker, Michael Carlos Best, François Gerard, and Joana Naritomi. 2023. “Sanitation and property tax compliance: Analyzing the social contract in Brazil.” _Journal of Development Economics_ 160:102954.

- Lauletta, Maximiliano, and Felipe Montano Campos. 2023. “Is the forgiveness of a tax amnesty divine? Evidence from Argentina.” _International Tax and Public Finance,_ 1–20.

- López-Luzuriaga, Andrea, and Carlos Scartascini. 2023. _Willing but Unable to Pay?: The Role of Gender in Tax Compliance._ IDB Publications (Working Papers) 12983. Inter-American Development Bank.

- Luttmer, Erzo F. P., and Monica Singhal. 2014. “Tax Morale.” _Journal of Economic Perspectives_ 28, no. 4 (November): 149–68. https://www.aeaweb.org/articles?id=10.1257/jep.28.4.149.

- Maloney, William, Jorge Andres Zambrano, Guillermo Vuletin, Guillermo Beylis, and Pablo Garriga. 2024. _Taxing Wealth for Equity and Growth._ Latin America and the Caribbean Economic Review. World Bank, Washington, DC. http://hdl.handle.net/10986/42001.

- Mascagni, Giulia. 2018. “From the Lab to the Field: A Review of Tax Experiments.” _Journal of Economic Surveys_ 32 (2): 273–301.

- OECD. 2022. _Tax Policy and Gender Equality._ 52. https://www.oecd-ilibrary.org/content/publicat ion/b8177aea-en.

- Porto, Alberto, Diego Fernández Felices, and Jorge Pablo Puig. 2019. “Análisis de las tasas municipales en la Provincia de Buenos Aires.” _Documentos de Trabajo del CEFIP,_ http://sedici.unlp. edu.ar/handle/10915/123807.

- Scot, Thiago, Tatiana Flores Ibarra, Davi Moura, Javier Feinmann, and Roberto Rocha. 2023. _Gender and Property Taxes in São Paulo._ Equitable Growth, Finance and Institutions Notes Washington. D.C.: World Bank Group.

- Spicer, Michael W., and Rodney E. Hero. 1985. “Tax evasion and heuristics: A research note.” _Journal of Public Economics_ 26 (2): 263–267.

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TABLE 3. Regression estimates, effects of childcare coverage on women’ employment.

||Hou|sehold|Indivi|dual|Moth|ers|Other|women|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|_Employed_|||||||||
|Coverage rate|0.419||0.174||0.264*||0.034||
||(0.246<br>)||(0.11)||(0.14)||(0.09)||
|Treated||0.0811**||0.033||0.038||0.025**|
|||(0.0336)||(0.02)||(0.03)||(0.01)|
|Mean|0.376|0.376||0.186||0.206||0.160|
|Individual controls|||Yes|Yes|Yes|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Region FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|3305<br>2|33052|61423|61423|35079|35079|26344|26344|

_Note:_ The analysis is estimated among working age women living with young children aged 1 to 6, excluding households with infants under the age of 1. For household-level analyses, the outcome variable is the number of women in the labor force and the number of women employed. Standard errors in parentheses, clustered at regional level;* _p_ < 0.10, ** _p_ < 0.05, *** _p_ < 0.01 _Source:_ World Bank:Household Budget Survey data 2015–2022; Uzbekistan Statistical Agency: data on children preschool participation rate; own calculations.

_Different measurements of treatment._ In Appendix Table 3, we present regression estimations using various measurements of treatment variables. For the second treatment variable, we defined a dummy variable equal to 1 if the district coverage rate increased by more than 25% during the study period and 0 otherwise, following the approach used by (Kleven 2021). Our third policy variable takes the value of 1 if the district’s relative coverage rate is higher than the median coverage rate of 2021, considering data from 2018 onwards. Additionally, we included the baseline coverage rate as a control to account for regional differences before the policy implementation. Remarkably, all the measurements of policy variables yielded statistically significant estimates in our analysis.

_Placebo test._ Finally, in Appendix Table 4, we provide the estimation of the model for women without young children and for male respondents living with young children. These analyses are made because the policy is anticipated to have a more pronounced impact on women with young children, as they are commonly considered the primary caregivers. In line with our expectations, we did not find any consistent significant 18
and relevant for exporters. Based on the previous literature we argue that exporters rely on two main sources of finance. First, exporters are likely to need to finance their investments as much as domestic firms and even more as confirmed by studies pointing to the importance of fixed costs and investment in order to succeed in export markets (Iacovone and Javorcik 2008). Furthermore, given the larger volumes of production that exporters have to generate in order to serve export markets they are also likely to be heavily reliant on working capital and trade finance. These are going to be our main variables that will capture the reliance on external finance and in the next section we will discuss more in details which proxies we use to capture them.

# **3.2 Data sources and descriptions**

Exports data, from UN Comtrade, are disaggregated at 4 digits ISIC Rev 2 and cover the period 1980 to 2006. There are 81 industries at this level of disaggregation, however, not all countries have exported in all industries and years and therefore the resulting panel is unbalanced with the number of observations slightly above 30000.

The information on banking crises is obtained from Dell’Ariccia, Detragiache, and Rajan (2008) who identify 48 episodes of systemic financial crises in both developed and developing countries. Because we are only interested in the effect of pure banking crises we exclude all “twin crises” when a currency crisis occurred jointly with the banking crisis. The rationale for this exclusion is that we want to isolate the credit crunch channel from balance sheet effects. During twin crises, when large devaluations occur, firms with high exposure to foreign debt will be hit particularly hard. If these firms are also the firms highly dependent on external finance, the effect of the crisis on exporters that we observe might be a consequence of their own balance sheet problems rather than a consequence of the credit crunch due to the banking crisis. Finally, out of the remaining 32 crisis episodes we only have disaggregated trade data for 23 crises in 21 countries. We use Dell’Ariccia, Detragiache, and Rajan’s (2008) database to identify the start of the crisis but in the estimations the financial crisis dummy is actually a “crisis window”. This is equal to 1 if country i faces a financial crisis in year t as well as in the two following years .<sup>8</sup> The reason of using a crisis window is because we are not only interested in the immediate short run effects of the crisis but also its medium-term effects. Furthermore, given the lumpiness of certain investments it is possible that the impact of the credit crunch due to the crisis may emerge with a lag as firms do not have to finance investment continuously.

The list of countries in the sample and their principal characteristics are summarized in Table 1. The sample is composed of countries of different income levels. Financial crises have affected

> 8As a robustness check we consider also two and four years windows and our results are substantially unchanged.

7
# **4.2. Results for Other Countries in the Region**

To analyze the robustness of our main findings we use data from Argentina and Brazil, the other two countries for which we found (rotating) panel data, to estimate models similar to the ones presented in the previous section. We focus on the results for young men aged 15 to 18 years old.

For Argentina, we use data from the _Encuesta Permanente de Hogares_ (EPH) for 2005-2013. This rotating panel follows individuals every six months and for four periods. For Brazil we use the _Pesquisa Mensual de Emprego_ (PME) for the period 2005-2013. This too is a rotating panel, but follows individuals every month for four months.

Table 5 presents the results of multinomial models similar to the ones presented for Mexico in the previous section (in Appendix Table A1). Overall, we find empirical evidence consistent with the implications of the theoretical model: (i) history matters for current decisions regarding time use (indices show statistically significant coefficients); (ii) income is an important (non-linear) predictor of Nininess; (iii) own and parental education are important in determining the time use decisions of youth (especially in Brazil);<sup>14</sup> and (iv) unlike in Mexico, marriage and cohabitation is not associated with an increased probability of being a Nini.

> 14 Note that having completed secondary education is positively associated with an increased probability of being a Nini in both Argentina and Brazil. This is in contrast with the results from Mexico and it is puzzling. More work is needed to understand the factors behind this result.

29
Commission. Although there are more than 5,000 weather stations in the country, WII only uses a subset since only few attain international standards and have more than 25 years of daily information, necessary to predict rain patterns.

WII was first piloted in five counties of the Mexican state of Guanajuato in 2003. In the following years, it expanded to other counties and states reaching more than 15% of the country’s rain-fed agricultural land in 24 states in 2008 (close to 1.9 million hectares). The first year in which Agroasemex made indemnity payments was 2005 when it reached 15 states.

In 2005, the year previous to the elections for President, 478,000 farmers in 107 municipalities were covered by WII and 115 weather stations were used for rainfall measurement. A total of US $9,553,000 in claims was paid. WII operational guidelines state that the minimum payout is US $82 per hectare for up to 5 hectares of land per farmer, which implies a maximum payout of $410 per farmer.<sup>3</sup>

# **3. Data**

The smallest unit of analysis for which information on drought relief payments and electoral data can be matched is the “electoral section”. An electoral section is a geographical unit grouping poll stations with an average of a thousand voters registered. By using GIS techniques we are able to match electoral sections in municipalities covered by the WII program to rainfall based on the geographic location on the weather stations.

The data used for the analysis come from four main sources. First, we use administrative data from de Ministry of Agriculture regarding WII’s coverage. These data include municipality level coverage information in terms of weather stations used, insured crops (maize, beans, sorghum and barley), number of hectares insured, value of insured production, value of the premium paid, and indemnity payments (in case a drought occurred). It is worth clarifying that this information is available and used at the weather station/municipality levels. To be explicit, we have

> 3 Hazell et al (2010)

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