rafmacalaba/datause-encoder-data huggingface dataset:
where _Yht_ is our outcome variables from the LSMS-ISA, described above, for household _h_ in year _t_ and _Xht_ is a matrix of input variables from the LSMS-ISA.<sup>9</sup> We control for year fixed-effects ( _γt_ ) and include household fixed-effects ( _αh_ ) in some specifications. The function _fj_ ( _Wjht_ ) represents our weather variables of interest where _j_ represents a particular measurement of weather. Finally, _uht_ is an idiosyncratic error term clustered at the household-level.

From this general set-up, we estimate six versions of the model: three linear and three quadratic. For each model, a single weather variable is considered. For the linear specification: For the quadratic specification: All of the regression models are estimated for each permutation of the data (see Table 5). This is a substantial number of regressions, given the number of variables defined (14 rainfall, eight temperature variables), the number of countries (six), the number of remote sensing products (six rainfall, three temperature), the number of extraction methods (ten), and the number of outcomes (two). This gives us a total of 77,760 different regressions: each of our six models on the 12,960 different data sets.<sup>10</sup> By varying both specifications and data, we seek to define a robust set of outcomes by combining the multiple analysis approach of Simonsohn et al. (2020) with the multiverse approach of Steegen et al. (2016).

> 9Where relevant, all continuous variables from the LSMS-ISA are log transformed using the inverse hyperbolic sine.

> 10We also test a series of 51,840 linear combinations of temperature and rainfall data for each of the possible combinations of rainfall and temperature weather products (six data products for rainfall with three data products for temperature). We only test combinations of rainfall and temperature for the same extraction method. For the linear combinations, we estimate each with the linear specification, but estimate two linear combinations with the quadratic specification. These are presented in Appendix F. The 77,760 standard regressions plus the 51,840 linear combinations give us a total of 129,600 regression results presented in this paper.

18
Table 6a: Contribution to the Unemployment Rate and Share of Formal Employment Volatility.

||Unemploym|ent Rate|Share of Formal|Employment|
|---|---|---|---|---|
||Mexico|Brazil|Mexico|Brazil|
|**Job Separations Rates**|**0.40**|**0.37**|**-0.10**|**-0.03**|
|I-U|0.22|0.18|-0.11|-0.08|
|S-U|0.08|0.14|-0.09|-0.14|
|F-U|0.10|0.05|0.10|0.19|
|**Job Finding Rates**|**0.21**|**0.27**|**0.27**|**0.54**|
|O-F|0.03|0.04|0.10|0.28|
|U-F|0.13|0.13|0.17|0.25|
|O-I|0.02|0.02|-0.01|-0.01|
|U-I|0.04|0.11|-0.04|-0.04|
|O-S|0.00|0.00|0.02|0.03|
|U-S|-0.01|-0.03|0.03|0.03|
|**Job Reallocation Rates**|**0.05**|**0.06**|**0.81**|**1.12**|
|I-F|0.03|0.06|0.50|0.87|
|S-F|0.00|0.01|0.13|0.36|
|F-I|0.00|-0.02|0.08|-0.07|
|F-S|0.02|-0.01|0.04|-0.17|
|I-S|0.00|0.02|0.05|0.10|
|S-I|0.00|0.00|0.01|0.03|
|**Exit of the labor force**|**0.02**|**0.00**|**0.03**|**-0.04**|
|I-O|0.00|0.00|0.01|0.01|
|S-O|0.00|0.00|0.00|-0.02|
|F-O|0.02|0.00|0.02|-0.03|
|**Unemployment-Inactivity**|**0.26**|**0.36**|**-0.06**|**-0.09**|
|U-O|0.03|0.08|-0.01|-0.03|
|O-U|0.23|0.28|-0.05|-0.06|
|Total Contributions|0.94|1.06|0.95|1.50|

Notes: The table presents the contribution of the cyclical component of each flow to cyclical volatility of the unemployment rate and the share of formal employment for Mexico and Brazil following Shimer ( 2007). We compute the covariance between the HP de-trended steady state unemployment rate (and share of formal employment) with the counterfactual of the series derived from setting all the possible flows at their average level and allowing only the flow of interest to vary. We present the covariance as a proportion of the variance of the series. O=Out of the Labour Force, U=Unemployment, E=Employment, S=Informal Self-Employed, I=Informal Salaried, and F=Formal Sector. Data for Mexico is drawn from the quarterly National Urban Labor Survey (ENEU) from 1987:Q1 to 2004:Q4. Data for Brazil is drawn from the Monthly Employment Survey (PME), quarterly averaged from 1983:Q1 to 2001:Q2.

45
[Bollard et al. (2013)] Albert Bollard, Pete Klenow, and Gunjan Sharma. India’s Mysterious Manufacturing Miracle. _Review of Economic Dynamics_ , 16: 59–85, January 2013.

[Chamarbagwalla and Sharma (2011)] Rubiana Chamarbagwalla and Gunjan Sharma. Industrial Deregulation, Trade Liberalization, and Skill Upgrading in India. _Journal of Development Economics_ , 96 (2): 314–336, 2011.

[Fernandes and Pakes (2010)] Ana M Fernandes and Ariel Pakes. Factor Utilization in Indian Manufacturing: A Look at the World Bank Investment Climate Survey Data. 2010.

[Fernandes and Sharma (2012)] Ana M Fernandes and Gunjan Sharma. Determinants of Clusters in Indian Manufacturing: The Role of Infrastructure, Governance, Education and Industrial Policy. _Mimeo_ , 2012.

[Ghani 2011] Ejaz Ghani, edited, 2011, Reshaping Tomorrow—Is South Asia Ready for the Big Leap, Oxford University Press

[Ghani et al. (2011a)] Ejaz Ghani, William Kerr, and Stephen O’Connell. The Exceptional Persistence of India’s Unorganized Sector. December 2011.

[Ghani et al. (2011b)] Ejaz Ghani, William R. Kerr, and Stephen D. O’Connell. Spatial Determinants of Enterprenuership in India. _NBER Working Paper 17514_ , 2011.

[Government of India (2012)] Ministry of Finance Government of India. _Economic Survey of India 2012-13_ . Government of India, Ministry of Finance, 2012.

[Grimm and Gunther (2005)] Michael Grimm and Isabel Gunther. Inter- and Intra-Household Linkages Between the Informal and Formal Sector: A Case Study for Burkina Faso. _WIDER Research Paper No. 2005/14_ , April 2005.

[Harrison et al. (2013)] Ann Harrison, L. Martin, and S. Nataraj. Learning Versus Stealing: How Important are Market-Share Reallocations to India’s Productivity Growth? _World Bank Economic Review_ , 27, June 2013.

[Hasan and Jandoc (2010)] Rana Hasan and Karl Jandoc. The Distribution of Firm Size in India: What can Suvey Data tell us? _ADB Working Paper 213_ , 2010.

27
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were provided with had recently become available from international organizations, such as the IEA. In addition, respondents were asked about their beliefs about energy subsidies before the information treatment; this arrangement somewhat naturally concealed the purpose of the treatment because it could be seen as feedback on whether the respondents’ beliefs were accurate (Haaland et al. 2023). Further, given the extensive analysis in prior work to illustrate that experimenter demand effects are not likely to be present in this type of randomized survey experiment (e.g., see Kuziemko et al. [2015] on a related topic), it is unlikely to be an issue in this study.<sup>10</sup> Indeed, experimenter demand effects are not likely to be a concern in experiments like this one, where respondents are anonymous, the survey is online, no information is provided about the research hypotheses, the treatments are framed in a matter-of-fact way, and the analysis is between subjects (Haaland et al. 2023; de Quidt et al. 2018). To address any concerns that experimenter demand effects may still exist, additional robustness checks were conducted based on respondents’ answers to questions about their views on whether the survey is biased.

The complete survey instrument (including the information treatments) for Indonesia is provided in English in Appendix B. The survey instrument was translated into the following languages: Spanish (for use in Argentina, Bolivia, and Ecuador), Arabic (for use in Egypt), Bahasa (for use in Indonesia), Vietnamese (for use in Vietnam), Russian (for use in Kazakhstan), Kazakh (for use in Kazakhstan), Bengali (for use in Bangladesh), Urdu (for use in Pakistan, where the English version was also offered), and Continental Portuguese (for use in Angola). Before the full launch of the study, the survey instrument was piloted sequentially in Ghana, Nigeria, and then Indonesia (see Appendix A for details about the piloting process).

> 10Some of the earlier studies using randomized survey experiments provided follow-up surveys to respondents to address concerns about experimenter demand effects and to check on the persistence of treatment effects (e.g., see Kuziemko et al. [2015]). They typically found that treatment effects existed in follow-up surveys, so many of the more recent studies do not take this step (Haaland et al. 2023; Stantcheva 2022).

22
the threshold. Third, the data has information on different levels of geographic units. This allows one to test for the appropriate level of geographic aggregation in analyzing the effects of location.

The manufacturing firm survey covers a range of basic information on these firms such as sales, production, exports, investment, number of permanent and temporary workers, sector, year of establishment, legal status and location. There are 15 sectors corresponding to the ISIC 3.0 level.<sup>9</sup>

# _3.2 Variables: Definition and Construction_

# _3.2.1 Firm-Level Variables_

A number of firm characteristics are controlled for, include firm size (i.e. number of full-time employees), firm age, and legal status (e.g. sole proprietorship, corporation etc.). Additional firm-level variables are used in the firm exit analysis to control for the firm’s estimate of its persistent profitability, described in Section 2. These include the share of casual workers in the firm;<sup>10</sup> export propensity (defined as the percentage of sales exported); foreign ownership share (defined as the percentage of a firm’s equity owned by foreigners); capital intensity (defined as the log of capital to workers)<sup>11</sup> ; and

> 9 “Food & Beverages”; “Tobacco”; “Textiles”; “Clothing”; “Leather & Shoes”; “Wood Products”; “Paper Products”; “Publishing & Printing”; “Coke, Refined Petroleum & Nuclear Fuels”; “Chemicals”; “Rubber & Plastics”; “Non-Metallic Mineral Products”; “Basic Metals”; “Fabricated Metal Products”; “Machinery & Equipment”; “Office, Accounting & Computing Machinery”; “Electrical Machinery & Apparatus”; “Radio, Television and Communication Equipment”; “Medical Instruments”; “Automobiles”; “Transport Materials”; and “Furniture”.

> 10 The total workers figure is a summation of permanent and temporary workers. The temporary worker information is expressed in days worked per year. To transform this into a permanent employee equivalent, this figure is divided by 256, which allows for 52 weekends and 6 public holidays in a given year. 11 Deflated net book value of total assets is used as the measure of capital. Capital estimates were only available for 2001 and had to be predicted for the other years. See Hallward-Driemeier et al. (2009) for further details.

11
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key parameters underlying learning in Section 7.

# **3.3 Data**

We collected four rounds of detailed household surveys, complemented by monitoring data on demonstration plots, to support our analysis; we now discuss these data and their application to our analysis.

**Household surveys** Between 2012 and 2015, we administered four rounds of household surveys of farmer group members across our experimental sample. Each survey round was timed to immediately follow the harvest of the dry season; Figure 1 provides a detailed timeline of these survey rounds. Each survey round collected detailed records of agricultural production and technology adoption during the dry season, including adoption of the new flood-saline-resilient seeds promoted by the project. We leverage data from these surveys at the level of the village, household, household-survey wave, and household-crop-survey wave. Appendix A provides a detailed description of all variables used in our analysis and details their construction, and Appendix Table A6 shows how our sample size changes across survey waves and all sample restrictions used in our analysis.

**Baseline survey** The baseline survey was administered on a sample of 1,407 IAPP farmer group members within 78 randomly selected villages of the 110 villages in our experimental sample.<sup>14</sup> In addition to household characteristics, and the dry season agricultural data collected in each survey wave, the baseline survey included a module on social network linkages, in which we asked each household questions about their interactions with all other farmers in their farmer group (sampled or not). In our analysis, we consider a household to be in another household’s social network if they report speaking to them, or were reported being spoken to by them, about farming in the last three months; to ensure consistent measurement across households, for all regressions leveraging social network relationships we restrict our sample to farmers and villages surveyed at baseline.

> 14The baseline survey covered 279 of the 386 eventual demonstrators, including 24 of 41 regular demonstrators, 81 of 115 shared demonstrators, and 174 of 230 decentralized demonstrators.

17
households. Second, school distance becomes insignificant, even for urban girls. This suggests that in urban areas there is less variation in presence of school or school distance.

# **7. Discussion**

This paper uses cross-sectional, time-series data to examine economic growth, child school, and work decision in Pakistan. The relationship between wealth and the child activity decision is quite similar across different years for children above the poverty line, except for rural girls. Wealth plays an insignificant role in determining rural girls’ activity decisions in the multinomial logit model. However, rural girls’ school enrollment has increased significantly over the years across all the expenditure percentiles. This implies that other factors, such as presence of schools for girls, distance to school are more critical in determining girls’ school enrollment. This finding echoes the findings from the Learning and Educational Achievements in Punjab Schools (LEAPS) study(The World Bank Group 2008) and other papers (Bhalotra 2007).

The fining also brings us to pay special attention to rural girls. The urban-rural difference and the gender difference in school enrollment are largely driven by low enrollment rate for rural girls. Rather than being active in the labor market, most rural girls stay “inactive” at home and may be substantively involved in domestic works. As illustrated in the paper, the relationship between wealth and rural girls’ schooling and inactiveness are very weak. In Pakistan, many unmarried girls are enforced with _purdar_ norms and their mobility is restricted by not being able to travel without accompany or permission from a male member of the family (Khan 2000; Sathar, ul Haque et al. 2003; Mumtaz and Salway 2005). Thus the presence of girl schools in close distance is crucial for girls to enroll in school, as found in this paper and emphasized in the most recent World Bank Pakistan Gender Report (The World Bank Group 2005). In addition, 18
|Sierra Leone|1|2|1|2||
|---|---|---|---|---|---|
|South Africa|1|1|4|2|1|

# **2.2.2. Transport Questions in Community Questionnaires**

There are many transport questions of interest in the community questionnaires, especially for Ethiopia, Malawi, and Uganda in the standard LSMS collection, and Ghana and Kenya with LSMStagged surveys, where a group of questions on road and public transport are included. For instance, the road questions ask the surface type of the main road accessing the community, the distance to the nearest paved road, whether the main road is passable by vehicles throughout the year and, if not, in which months it is passable. The questions on public transport mainly ask whether public transport passes by the community and, if yes, the main means of public transport, the distance to the nearest public transport station, frequency of public transport stops in the community, as well as the cost of total fare to go to the nearest urban center from the community by public transport.

It is worth mentioning that there are a set of questions on the needs and changes regarding road construction and public transport improvement in the community. The community questionnaires of Ethiopia, Malawi, Niger, Nigeria, and Uganda contain the similar questions on whether the community members approached the leaders to express, discuss or find ways to address the needs for construction and maintenance of roads, bridges, and public transportations during the past five years, as well as the amount of funding raised and results. Some of these countries also have a standalone module regarding the changes in the community, asking respondents whether the access to transportation became better or worse compared to five years ago and to describe the relevant events or programs happened in the community during the past five years. An example of the module of community needs, actions, and achievements is given in the Annex 7.

Even if most LSMS-tagged surveys do not have community questionnaires, several questions are inserted into some of these countries’ household questionnaires to collect the information on transport conditions in communities, for example, there are some questions on roads and public transport conditions in communities in the household questionnaires of Afghanistan, Cameroon, and Sierra Leone.

# _Identifying a set of recurrent questions_

We identify a set of common questions that are recurrent across surveys. There are five topics for which we could identify recurrent transport-related questions: roads, public transport, changes of transportation, governance, and the availability of needs and actions section.

The road topic includes the following questions:

1. Main type of road surface

2. Distance to the nearest tarred/asphalt road 13
# **1. Introduction**

During most of the 2000s, Guatemala grew at a relatively high rate, fertility rates declined and educational attainment of women rose without precedent.<sup>1</sup> However, female labor force participation (FLFP, henceforth) remained the lowest in the Latin America and the Caribbean region (LCR, henceforth) with a rate of only 32% in 2018 according to the population census. Despite its high-middle-income status, Guatemala is today one of the countries in the world with the lowest rates of FLFP (World Bank, 2021).<sup>2</sup> This under-utilization of female labor poses a large cost to the country’s growth rate. Most women are not engaged in paid jobs outside the house, which generally remain low paid, and tend to be the main caregivers for children or the elderly in the household. There is also wide dispersion in FLFP rates within the country with municipalities where not even one out of ten women aged 15 to 64 years old participate in the labor market and some others where more than half of women participate.

This paper investigates the role of different factors in influencing women’s decision to participate in paid employment in Guatemala to understand the forces behind its trend between 2002 and 2018 and the wide within-country dispersion. We assess the role of: (i) individual and household socioeconomic characteristics (such as the level of education or the number of children), (ii) local social norms and attitudes towards women (at home and in the community), (iii) social public policies (such as investments in pre-schools), and (iv) local labor market characteristics (such as the ratio of male to female employment or the composition of employment across sectors of activity). We conjecture that regional differences in these factors influence women’s willingness and ability to participate in paid employment as they all shape incentives and ultimately individual behaviors. Take the case, for instance, of the local labor markets. Municipalities with lower levels of gender discrimination in the labor market or those with higher shares of women in public service may be more attractive to women to participate.

We contribute to a large literature on this topic in developing countries. Goldin (1995) and Mammen and Paxon (2000) argue that at early stages of development, women participate in the labor market doing unpaid work in family businesses or farms. With development, there is a sectoral shift from primary activities to manufacturing and an exit of women from the labor force. This is driven by the income effect of men moving to blue-collar jobs. With higher levels of development, FLFP increases due to a new sectoral shift, now towards services making more

> 1 Between 2000 and 2018, Guatemala grew at an average growth rate of 3.5% compared with 2.7% in Latin America and the Caribbean region. The lower secondary completion rate of women increased from 28.2% in 2000 to 55.2% in 2018, while the number of births per woman declined from 4.6 in 2000 to 2.9 in 2018 (World Bank, 2021). 2 Guatemala’s per capita GDP was approx. $US 9,000 in 2019.

2