rafmacalaba/datause-encoder-data huggingface dataset:
Most research on the division of household tasks has been conducted using timeuse surveys for the United States and Scandinavian countries. Bianchi, Milkie, Sayer, and Robinson (2000) attempt to disentangle the contributions of three previous theories in explaining the evolution of the gender division of household tasks. Using time-diary data from representative samples of U.S. adults, they find more support for the timeavailability and relative-resource models of household production than for the gender perspective, although there is some support for the latter perspective as well.

Evertsson and Nermon (2007) investigate the gender distribution of household tasks in Sweden using data from the Swedish Level of Living Survey for the years 1991 and 2000. The analysis shows that changes in spouses’ relative resources only resulted in a moderate change in women’s share of the housework between 1991 and 2000. The changes that do occur indicate that women’s share of housework decreases if their relative resources, in terms of education level and social status, increase during the time span analyzed. If a woman’s economic dependency on her spouse decreased during these two years, her share of the housework also decreased. The decrease in women’s share of the housework is mainly due to an increase in men’s time spent on housework. However, regardless of access to resources, Swedish men perform less housework than Swedish women.

# **<<A>>The Effect of Gender Policies on Men’s Behavior and Attitudes toward Women**

The evidence summarized in the previous section identifies an important role for men in the well-being of women. Men clearly influence women’s health outcomes by controlling reproductive methods and access to health care facilities. Men can damage women’s health through their violent behavior. There is also evidence that culture and public institutions that favor men and place women in a residual position (patriarchal societies and certain kinship norms) limit women’s autonomy in many ways. In developing countries, these social structures favor the persistence of son preference and have adverse effects on outcomes for girl children. In developed countries, the unequal distribution of household tasks represents an important barrier for gender equality in the labor market.

21
Bacon, 2021; Baker et al., 2022). In our case, entering export markets could have heterogeneous effects on technology adoption over time, especially considering variation in costs and technology diffusion. To address this issue, we take advantage of recent developments in the difference-in-differences literature and apply the multiple periods estimator proposed by Callaway and Sant’Anna (2021). The method breaks down several treatment periods into group-time average treatment effects (the average treatment effect in period _t_ for the group of units first treated in period _g_ ) and aggregates them into meaningful measures of the causal effects.<sup>7</sup> The average treatment effect on the treated (ATT) for a treatment-timing group _g_ is thus: where _Gg_ denotes the time when unit _i_ receives treatment and _Gg_ = _g_ for all firms that receive treatment at time period _g_ . For instance, take the case where there are five groups, each of which gets treated in 2010, 2011, 2012, 2013, and 2014, and the panel ends in 2016. – As a result, the model estimates a total of 15 group-time ATTs 5 ATT(g,t) for the first group, 4 for the second, 3 for the third, 2 for the fourth, and 1 for the last.<sup>8</sup> In most of our discussion, we focus on a weighted average of post-treatment average effects from _t_ to _t_ + 5 with weights proportional to the group size. The model assumes parallel trends of the potential outcome in the absence of treatment, which we relax to hold only conditional on the covariates. In addition to a dummy indicating firms in the services sector, we add the logarithm of employment and average wages as control variables so that parallel trends hold only after conditioning on a vector of pre-treatment covariates. Finally, estimates use the doubly robust estimator based on stabilized inverse probability weighting and ordinary least squares proposed by Sant’Anna and Zhao (2020).

# **5 Results**

Table 3 shows the main results of estimating the impact of entering export markets on the probability of adopting, which are based on the average treatment effect on the treated from

> 7Although data from the Ministry of Trade includes information on the first and last years a given firm exported, the method proposed by Callaway and Sant’Anna (2021) assumes that treated units remain treated during all subsequent periods.

> 8Under the no-anticipation and parallel trends assumptions, group-time average treatment effects are identified in periods when _t ≥ g_ (i.e., post-treatment periods for each group). In practice, we also estimate pseudo group-time pre-trend coefficients (when _t < g_ ), which we can use to test the parallel trends assumption.

11
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Table 5: Differential impact electricity access and reliability on tax compliance

||(1)<br>All|(2)<br>Urban|(3)<br>Rural|
|---|---|---|---|
|Frequency of electricity availability||||
|Level 1|-0.018**|0.009|-0.023**|
||(0.008)|(0.016)|(0.010)|
|Level 2|0.014*|0.028**|0.004|
||(0.007)|(0.011)|(0.011)|
|Level 3|0.017**|0.022*|0.022|
||(0.008)|(0.012)|(0.014)|
|Level 4|0.027***|0.036***|0.026***|
||(0.006)|(0.010)|(0.009)|
|Level 5|0.033***|0.053***|0.010|
||(0.007)|(0.011)|(0.011)|
|Constant|0.192***|0.198***|0.172***|
||(0.035)|(0.055)|(0.045)|
|Observations|43,560|16,365|26,603|
|R-squared|0.037|0.043|0.039|

Notes: Robust standard errors in parenthesis and clustered at the PSU level. Estimations are done using OLS. The survey sampled across 7,137 communities/towns (4,435 in rural areas, 2,702 in urban areas). The independent variable of interest is the availability and reliability of electricity for those who are connected. Responses ranged from 1 to 5, where 5 represents the highest level of availability. The reference category is no access to electricity. All the regressions in the table control for individual/household, community, and country level variables. Individual/household-level variables include the respondent’s gender, age, education, quality of housing, employment status, size of the household, and ownership of a TV or mobile phone. Community-level variables include the presence of piped-water systems, paved roads, schools, and hospitals. Country-level variables include total population, GDP PPP, share of the urban population, population density, value added of agriculture and service as a share of GDP, total natural-resources rents (% of GDP), IIAG score, and ethno-linguistic fragmentation score. The dependent variable is tax morale. The index on tax morale is an average of responses on whether the respondent agrees that people must pay taxes, sees refusing to pay taxes as a legitimate protest action, thinks the government should have the right to collect taxes, and trusts tax officials or administration. _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level _∗∗∗_ Significant at 1 percent level 27
# **Table 10.  Sector Imports as a Percent of Total Imports of the Regional Market**

|Good|Central Fa|r East|North|Siberia|South|Urals|Moscow|St.<br>Petersb.|Tumen|North-<br>west|
|---|---|---|---|---|---|---|---|---|---|---|
|AGR|3|3|2|3|4|3|1|1|2|2|
|AIR|0|0|0|0|0|0|0|0|0|0|
|CHM|9|8|8|13|7|10|9|6|6|9|
|CNM|2|1|1|2|2|2|2|2|1|2|
|COL|0|0|1|1|0|0|0|0|0|1|
|CON|6|8|10|7|7|8|3|3|20|4|
|CRU|1|1|1|2|1|2|0|1|1|0|
|ELE|0|0|0|0|0|0|0|0|0|0|
|FIN|1|1|2|2|1|1|1|1|2|1|
|FME|7|2|2|2|6|4|3|3|4|7|
|FOO|13|14|9|10|13|9|15|26|5|19|
|GAS|0|0|0|0|0|0|0|0|0|0|
|HEA|1|1|1|1|1|1|1|0|1|0|
|HOU|2|3|3|3|3|3|2|1|3|2|
|MWO|25|28|23|13|22|23|38|35|25|27|
|NFM|3|4|4|9|3|4|2|2|3|3|
|OIL|3|2|3|3|3|3|1|1|4|2|
|OTH|1|1|1|2|1|2|1|1|2|1|
|OTI|1|1|1|1|1|1|0|0|1|0|
|PST|0|0|0|0|0|0|0|0|0|0|
|RLW|0|0|0|0|0|0|0|0|0|0|
|SCI|0|0|0|0|0|0|0|0|0|0|
|TMS|1|1|1|1|1|1|0|0|1|0|
|TPP|2|1|3|1|3|1|4|5|0|7|
|TRD|1|1|1|1|1|1|0|0|2|1|
|TRK|0|0|0|0|0|0|0|0|1|0|
|TRO|0|0|0|0|0|0|0|0|0|0|
|CLI|17|20|22|23|21|21|16|9|15|13|
|Total|142|52|35|142|140|268|528|140|81|41|

See Tables 1 and 2 for sector and region definitions.

Source: Roskomstat unpublished surveys and authors' calculations.

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<!-- Start of picture text --> GBI-EM Broad Diversified Index Weights by Region<br>SSA<br>SAR 8.0%<br>10.0% és<br>0.2% 31.1%<br>MNA<br>LAC<br>28.2%<br>ECA<br>22.4%<br>Source: JP Morgan<br><!-- End of picture text --> <!-- Start of picture text --> GEM Index Weights by Region<br>SSA<br>12.0% EAP<br>SAR 21.5%<br>8.4%<br>2.1%<br>MNA<br>Lac ECA<br>28.1% 27.9%<br>Source: Markit<br><!-- End of picture text -->
Fernández, R. 2007. _Culture as Learning: The Evolution of Female Labor Force Participation over a Century._ NYU Working Paper.

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Fogli, A., and L. Veldkamp. 2010. _Nature or Nurture? Learning and the Geography of Female Labor Force Participation._ CEPR Working paper.

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33
3 geographic characteristics, rather than the endogenous location of within-commune broadcasters, determine intra-commune variation in village access to within-commune radio stations.

Basic education services in northern Benin are also particularly appropriate to distinguish the accountability effects of media influence, since they are almost exclusively provided by the government. Only 15 villages out of our sample of 210 report having a functional private school; only 1 percent of households report sending their children to a private school. The national government‘s budgetary support is critical to fitting these village public schools with the inputs needed to deliver quality education. The schools are also required to have Parent Teacher Associations (PTAs) to facilitate local agency in improving the quality of education provided. All of the schools in our sample report having a PTA. In principle, therefore, communities have vehicles through which they can hold government accountable for its funding and management of public schools.

In the years immediately prior to our survey in March 2009, there were large changes in government education policy with increases in public spending on education—specifically, abolishing (previously required) tuition payments, and recruiting more teachers (World Bank, 2009). In this setting, the effects of radio access on collective action to demand more or better public services from government should be evident in more inputs and better quality in schools located in villages with greater radio access. Education also provides a better opportunity than many other government services to identify the private behavioral channels through which media access might affect public good performance. Information conveyed by media can influence the extent to which households take advantage of public schools. In contrast, household private behavior has little effect on many other measures of public service performance, such as whether a village has a paved road.
(which in theory reflects productivity and hence wages), and the presence of other women in the household.<sup>7</sup> To explore the role of values in shaping women’s labor market decisions, we use two complementary data sources. The first is the Arab Barometer, which measures political, societal, and religious attitudes. The questionnaire distinguishes “Employed,” “Inactive,” and “Unemployed” individuals. While it contains basic demographic characteristics, it does not include some important data, such as household size, region, rural/urban household, or occupation. Importantly, it includes a question asking the level of agreement with the claim that “A married woman can work outside home if she wishes,” which we will use as a measure of the respondent’s attitudes toward women’s work. Interviewers conduct face-toface interviews in the respondent’s residence. There are four waves that include Morocco that are available: 2006-2009, 2012-2014, 2016-2017, and 2018-2019. The first three waves have a sample size of around 1,000 observations. The fourth one contains 1,560 women aged between 18 and 64.<sup>8</sup> The second data source is the World Values Survey, which aims to understand changes in beliefs, values, and motivations of people throughout the world. Morocco is included in three of the waves—2001, 2007, and 2011—each of which has about 1,200 observations, equally split between men and women, for a total of 3,648 individuals aged 18 or older. Questions measure cultural values, attitudes, and beliefs about diverse variables (such as gender, family, religion, poverty, education, health, and security). The surveys contain a labor status variable

> 7 Verme et al. (2016), facing the same limitation, argue that in informal economies earnings play a less relevant role in shaping labor supply decisions than in formal economies.

> 8 The unavailability of the variable regarding attitude toward female work in the 2018-2019 wave renders it useless for our purposes.

10
Table S.6: Impacts on children’s educational achievement - Bengali: heterogeneity <u>(OLS)</u>

|Dependent variable:||||Bengali|: GPA z-sc|ores||||
|---|---|---|---|---|---|---|---|---|---|
|_X_:|Age|Education|Male|Household<br>size|No. of<br>male<br>members|Head<br>age|Head<br>education|Head<br>male|Land<br>size|
||(1a)|(1b)|(1c)|(1d)|(1e)|(1f)|(1g)|(1h)|(1i)|
|**December in 2013**||||||||||
|All products|-0.208|0.184|-0.115|0.359|0.274|0.361|-0.036|0.289|-0.048|
||(0.777)|(0.312)|(0.105)|(0.280)|(0.188)|(0.405)|(0.095)|(0.258)|(0.092)|
|One product|-0.005|0.284|-0.013|0.195|0.064|-0.581*|0.046|0.222|0.017|
||(0.800)|(0.300)|(0.111)|(0.283)|(0.199)|(0.330)|(0.086)|(0.224)|(0.094)|
|All products _× X_|0.013|-0.050|0.161|-0.082|-0.132*|-0.010|-0.008|-0.368|0.000|
||(0.060)|(0.059)|(0.176)|(0.052)|(0.071)|(0.009)|(0.036)|(0.265)|(0.002)|
|One product _× X_|0.002|-0.057|0.082|-0.035|-0.015|0.014*|-0.018|-0.217|0.001|
||(0.065)|(0.064)|(0.148)|(0.053)|(0.073)|(0.007)|(0.031)|(0.227)|(0.003)|
|R-squared|0.257|0.258|0.258|0.259|0.261|0.262|0.257|0.259|0.257|
|No. of obs.|850|850|850|850|850|850|850|850|850|
||(2a)|(2b)|(2c)|(2d)|(2e)|(2f)|(2g)|(2h)|(2i)|
|**December in 2014**||||||||||
|All products|-0.491|-0.185|-0.015|-0.644*|-0.106|0.107|-0.106|-0.483|-0.109|
||(0.541)|(0.292)|(0.088)|(0.348)|(0.172)|(0.375)|(0.092)|(0.355)|(0.089)|
|One product|0.054|0.385|0.073|0.175|0.142|0.237|0.074|-0.020|0.029|
||(0.712)|(0.340)|(0.091)|(0.310)|(0.195)|(0.494)|(0.104)|(0.334)|(0.101)|
|All products _× X_|0.033|0.021|-0.163|0.113*|0.009|-0.005|0.017|0.436|0.002|
||(0.044)|(0.058)|(0.188)|(0.062)|(0.061)|(0.009)|(0.030)|(0.343)|(0.003)|
|One product _× X_|0.000|-0.071|-0.037|-0.026|-0.037|-0.004|-0.017|0.085|0.003|
||(0.057)|(0.069)|(0.200)|(0.059)|(0.082)|(0.012)|(0.034)|(0.361)|(0.004)|
|R-squared|0.114|0.116|0.115|0.119|0.114|0.114|0.115|0.117|0.115|
|No. of obs.|846|846|846|846|846|846|846|846|846|
|Baseline control|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each school-grade. (3) The baseline controls include age (years); education (years); gender (dummy); household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number).

51
# Contents

|1|Intr|oduction|3|
|---|---|---|---|
|2|Bac|kground|5|
|3|The|Model|8|
||3.1|Family Preferences . . . . . . . . . . . . . . . . . . . . . . .|. . . . . .<br>8|
||3.2|Home Production . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . .<br>11|
||3.3|Market Production . . . . . . . . . . . . . . . . . . . . . . .|. . . . . .<br>11|
||3.4|Human Capital Formation . . . . . . . . . . . . . . . . . . .|. . . . . .<br>12|
||3.5|Health Status and Productivity . . . . . . . . . . . . . . . .|. . . . . .<br>14|
||3.6|Government . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . .<br>15|
||3.7|Bargaining Power . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . .<br>16|
||3.8|Market-Clearing Conditions . . . . . . . . . . . . . . . . . .|. . . . . .<br>16|
|4|Bal|anced Growth Equilibrium|16|
|5|Nu|merical Calibration|20|
|6|Poli|cy Experiments|26|
||6.1|Investment in Infrastructure . . . . . . . . . . . . . . . . . .|. . . . . .<br>26|
||6.2|Conditional Cash Transfers . . . . . . . . . . . . . . . . . . .|. . . . . .<br>28|
||6.3|Reduction in Gender Bias in the Market Place . . . . . . . .|. . . . . .<br>28|
||6.4|Increase in Women’s Bargaining Power . . . . . . . . . . . .|. . . . . .<br>29|
||6.5|Increase in Mothers’ Time Allocated to Daughters . . . . . .|. . . . . .<br>31|
||6.6|Composite Reform Program . . . . . . . . . . . . . . . . . .|. . . . . .<br>32|
|7|Con|cluding Remarks|34|
|Re|fere|nces|35|
|Ta|ble|1: Benchmark Calibration|38|
|Ta|bles|2 and 3: Experiments|39|
|A|ppen|dix|39|

2
firm size or demand conditions do not drive our estimates of the impact of internationalization events. The other robustness tests help us disentangle the effect of the internationalization process from other financial shocks that could have affected connected and unconnected firms differently around the internationalization period.

The estimates remain significant when including lagged assets and sales growth but are slightly smaller than those in the baseline regression. This could be because the internationalization process also affects these additional controls. For instance, if firms could raise more equity financing through the Stock Connect, they could grow faster and have higher total assets.

One potentially confounding factor is the internationalization of bond markets post-2012, which could have affected equity issuances. The QFII and RFQII programs allowed qualified foreign investors to purchase corporate bonds. Moreover, China implemented a “Bond Connect” program in 2017. However, neither the QFII nor RQFII programs nor the Bond Connect program specifically targeted firms connected through the Stock Connect program. Thus, it is difficult to associate the differential equity issuances between connected and unconnected firms with the internationalization of bond markets. Still, to ensure that bond market events do not contaminate our equity results, we add as control the proceeds from bond issuances per firm and year over 2012 assets. The baseline equity results do not change materially.

A second potential confounding event was the implementation of margin trading, which began in 2010 and expanded in 2013 to some eligible stocks. By allowing investors to borrow to buy shares, margin trading could have prompted equity issuances, potentially explaining the differential behavior between connected and unconnected firms. To ensure this event does not drive our estimates, we exclude margin trading firms (those whose stocks became eligible for margin trading during 2010-17), many of which were also connected. About 32 (11) percent of the connected (unconnected) firms in our PSM sample had eligible margin trading stocks. The estimates of the impact of internationalization become larger when we exclude firms with margin trading stocks.

18