rafmacalaba/datause-encoder-data huggingface dataset:
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- Yang J, Avralt-Od P, Li S. forthcoming. The Marginal Cost of Traffic Congestion and Road Pricing: Evidence from a Natural Experiment in Beijing. _Am. Econ. J. Econ. Policy_

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42
**Table 8: Labor tax wedge for a one-earner couple with two children by level of average wage 2008**

||**Leve**<br>|**l of average wage**<br>||
|---|---|---|---|
||**33%**|**50%**|**100%**|
|Sweden|41.8%|44.6%|47.9%|
|**Hungary**|**39.5%**|**43.4%**|**54.1%**|
|Bosnia – Federation(1)|37.8%|37.8%|37.9%|
|Serbia(1)|36.7%|38.0%|39.3%|
|Finland|35.5%|38.0%|44.9%|
|**Bulgaria**|**35.1%**|**35.1%**|**35.1%**|
|Greece|34.4%|34.4%|39.8%|
|**Lithuania**|**34.4%**|**37.8%**|**41.1%**|
|**Poland**|**33.7%**|**33.7%**|**33.7%**|
|Germany|33.4%|33.4%|42.8%|
|**Romania**|**32.9%**|**35.3%**|**40.9%**|
|**Slovenia**|**32.9%**|**32.9%**|**35.8%**|
|France|32.5%|34.1%|45.1%|
|Italy|31.5%|31.5%|40.2%|
|**Slovak Republic**|**31.4%**|**31.4%**|**33.2%**|
|Bosnia - Republika Srpska(1)|30.6%|30.6%|33.0%|
|Macedonia(1)|28.5%|30.9%|33.2%|
|Portugal|28.1%|28.1%|31.0%|
|Spain|28.0%|28.0%|32.0%|
|**Estonia**|**26.9%**|**26.9%**|**31.3%**|
|**Latvia**|**26.7%**|**26.7%**|**34.5%**|
|Netherlands|26.4%|33.4%|43.1%|
|Belgium|23.6%|35.7%|47.0%|
|Japan|22.6%|22.5%|26.1%|
|Austria|22.0%|32.3%|44.7%|
|Norway|20.9%|26.8%|35.3%|
|United Kingdom|19.9%|26.6%|32.8%|
|**Czech Republic**|**17.7%**|**23.7%**|**31.0%**|
|United States|14.9%|11.9%|17.9%|
|Switzerland|12.6%|15.2%|32.9%|
|Ireland|7.8%|12.2%|18.5%|

1Values refer to 2009. _Source:_ OECD Tax and Benefit model.

42. The transition from unemployment into formal work is not only affected by labor taxes that have to be paid on earned income but also by the loss of unemployment benefits which may, however, be partly compensated by in-work benefits. The average effective tax rate (AETR) of taking up a job is defined as the sum of income tax and social contributions, which have to be paid if the worker takes up a formal job minus the (net) social benefit, 20
Our explanatory variables include three indicators of the business environment, which vary over time. First, we proxy the barriers to entry by the number of procedures to start a business and the rigidity of employment index, and by an indicator of governance. We control in all analyses for policy stability, the ratio of domestic credit to GDP, and GDP per capita.

**Table 1:  Definitions and Summary Statistics, Panel of 76 Countries, 2003-2005**

|Variable|Obs|Description|Mean|Std. Dev.|
|---|---|---|---|---|
|ENTRY|197|New registered corporations during year t divided by<br>existing stock of corporations as of end of year t-1|0.09|0.04|
|ENTRY PER CAPITA|197|New registered corporations during year t divided by<br>population (000s)|34.43|33.51|
|BUSINESS DENSITY|197|Stock of corporations as of end of year t divided by<br>population (000s)|3.25|4.06|
|ENTY PROCEDURES|197|Log of number of entry procedures (Doing Business)|2.07|0.49|
|RIGIDITY OF EMPLOYMENT|197|Rigidity of employment index (Doing Business)|36.97|16.85|
|GOVERNANCE|197|Average of governance indicators (Kaufmann et al.)|0.33|1.00|
|DOMESTIC CREDIT (% GDP)|197|Domestic credit divided by GDP (WB statistics)|62.01|53.69|
|GDP PER CAPITA|197|Log of GDP per capita, PPPs, 2000 int’l dollars (WB)|9.01|1.13|

Source: World Bank Group Entrepreneurship Database and World Bank (2007).

Table 2 shows the correlation matrix, with asterisks identifying statistical significance. These univariate tests show that business density (Column 3) is significantly related to all country characteristics; however, entry rates are more sensitive to the business environment and governance. We also find large and significant correlations among our dependent variables.

24
families have lower educational attainment than other children, as the boys of the migrants are more likely to opt for migration themselves (implying decreasing returns to education) while the domestic workload of the daughters increase (McKenzie & Rapoport 2006). Adolescents left behind are also commonly overrepresented in adapting risky behavior, and absence of mothers has been found to be associated with the involvement of children with violence: 80 percent of children in conflict with the law in Jamaica had their mothers absent, while this was the case for only 30 percent of other children (D’Emilio _et al._ 2007). Also the abuse of drugs and alcohol as well as reduced school attendance has been observed among children left behind by migrants. On the other hand, recent evidence from Mozambique suggests that migration could also strengthen social networks as the higher income from remittances reduces the cost for the migrant-sending household to participate in these networks (Gallego & Mendola 2010). This closer inter-family collaboration can, to some extent, remedy the absence of withinfamily cohesion and safety nets.

Even though migration is usually a voluntary and planned choice of the individual, the reality might turn out to be very different from the original expectations. Too often the intended aspirations of the migrants do not materialize but many are trapped in trafficking. Young women are exploited as sex workers abroad, and in many cases they have been promised legitimate work at the destination but then forced into prostitution upon arrival (see e.g. Kebede 2001). Also children are commonly victims of trafficking that can lead to life-long trauma. The abuse of migrants by the middle men or the recruitment agencies is a problem that is growing in magnitude as the migration flows increase and the phenomenon becomes more commercialized. The problem expands from pure trafficking into working in slave-like conditions as the international market for labor allows for trading with workers’ contracts leading to abuse of individual migrants.<sup>12</sup> The abuse of the migrant workers has lead to calls for further regulation of the recruitment agencies across migration corridors (see e.g. Agunias 2010).

> 12 For instance, over 50 percent of migrant domestic workers surveyed in Kuwait reported physical (including sexual), psychological and verbal abuse at their work place. The female workers are also more vulnerable to sexual abuse by their male employers, who are often also their visa sponsors. (Chammartin 2005)

10
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Educating women is often viewed as the single most effective policy lever for improving incomes and impacting a wider set of human development outcomes in low-income countries. In their roles as mothers, women also pass on additional benefits of education to their children. This paper contributes to our understanding of inter-generational causal links in three ways. First, causal studies have focused on countries with high levels of female education; the typical margin studied has been the additional impact of secondary schooling or college education.<sup>1</sup> However, in much of the developing world, levels of female education are abysmally low. The average woman aged 25 and over in 2000 reported 3.2 years of education in India and Kenya, 0.5 years in Niger, 2.6 years in Guatemala and 1.2 years in Pakistan (Barro and Lee, 2000). We study whether the benefits of maternal education for child outcomes extend to such low levels of education. To isolate causal effects, we employ an instrumental variables approach where our instrument affects only maternal education, rather than the education of both fathers and mothers. Second, we broaden our outcome measures to child-learning in addition to educational attainment; to our knowledge, this is the first study in low-income countries to do so. Third, we demonstrate the importance of maternal and child time-use patterns in understanding the unique role mothers play in their children’s lives.

This paper uses unique primary data from rural Pakistan—a country characterized by low maternal levels of education—to address the link between maternal education and their children’s educational outcomes. We examine the difference between mothers with no education (75 percent of mothers in our sample) relative to those with some education reported by the remaining 25 percent (10 percent report higher than primary education and 15 percent primary schooling or less).<sup>2</sup> We develop causal estimates of the affect of maternal education by taking advantage of the gender segregated nature of schooling in the country and use the availability of girls’ schools in the mother’s birth village as an instrument for her education. Since boys cannot attend female schools, this instrument affects only the mother’s education levels rather than the joint education levels of mothers and fathers. We present detailed maternal time-use, child time-use and child learning as evidence for the causal impact of maternal education on child educational outcomes.

> 1 See Black and others, 2005, Currie and Moretti, 2003 and Berhman and Rosenzweig, 2002.

> 2 The average years of education in our sample of mothers is 1.34 years which accords well with the 1.21 years reported in Barro and Lee (2000) dataset for females above the age of 25 in Pakistan.

2
||**Table**<br>|**17: Wage deter**<br>|**mination by ag**<br>|**e groups**<br>|||
|---|---|---|---|---|---|---|
||A|ll|Anglo|phone|Franc|ophone|
||≤32|32+|≤32|32+|≤32|32+|
|Education (years)|-0.011|0.007|-0.003|-0.026|-0.036*|0.043*|
||[0.58]|[0.50]|[0.08]|[1.59]|[1.68]|[1.70]|
|Education years squared|0.002***|0.001|0.002|0.002**|0.003***|-0.001|
||[2.61]|[1.06]|[1.36]|[2.17]|[3.18]|[0.71]|
|Vocational degree|0.004|0.320***|-0.111|0.309***|0.349***|0.303***|
||[0.06]|[4.56]|[1.20]|[3.13]|[4.46]|[3.32]|
|Junior/senior secondary degree|0.006|0.210***|-0.009|0.316***|0.120**|0.079|
||[0.12]|[4.02]|[0.12]|[3.86]|[2.41]|[1.24]|
|Tertiary/university degree|0.407***|0.660***|0.246**|0.708***|0.704***|0.539***|
||[5.58]|[9.49]|[2.31]|[6.73]|[9.06]|[5.81]|
|Tenure|0.016***|0.010***|0.012|0.005|0.023***|0.016***|
||[2.67]|[3.24]|[1.41]|[1.11]|[3.02]|[4.35]|
|Previous experience in industry|0.015***|0.009***|0.013|0.007*|0.020***|0.011***|
||[2.59]|[3.46]|[1.40]|[1.74]|[3.62]|[3.90]|
|Permanent, full-time worker|0.249***|0.218***|0.227***|0.276***|0.320***|0.116|
||[4.84]|[3.06]|[3.57]|[2.84]|[3.77]|[1.39]|
|Manager|0.731***|0.928***|1.022***|1.090***|0.177|0.744***|
||[5.57]|[9.12]|[5.71]|[7.22]|[1.36]|[7.33]|
|Professional|0.537***|0.631***|0.737***|0.747***|0.270***|0.566***|
||[7.94]|[9.20]|[7.42]|[4.64]|[3.28]|[7.35]|
|Skilled production worker|0.150***|0.264***|0.191***|0.306***|0.089*|0.240***|
||[3.40]|[5.42]|[3.09]|[4.78]|[1.83]|[3.62]|
|Non-production worker|0.063|0.192***|0.08|0.229***|0.046|0.164***|
||[1.24]|[4.17]|[0.95]|[3.52]|[1.03]|[2.64]|
|Belongs to union|0.048|0.027|0.015|-0.136**|0.094*|0.192***|
||[1.00]|[0.54]|[0.24]|[2.10]|[1.85]|[2.76]|
|Related to owner/manager|-0.096*|-0.171***|-0.088|-0.129|-0.092|-0.227***|
||[1.80]|[2.71]|[1.07]|[1.37]|[1.48]|[2.62]|
|Unionized firm|0.081*|0.05|0.140**|0.172**|-0.023|-0.084|
||[1.75]|[0.91]|[2.19]|[2.15]|[0.45]|[1.23]|
|Constant|4.189***|4.232***|4.804***|5.401***|4.340***|4.185***|
||[21.46]|[21.85]|[16.37]|[23.90]|[18.37]|[15.99]|
|Observations|4760|4684|1983|1731|2777|2953|
|R-squared|0.4|0.4|0.28|0.34|0.59|0.51|

**Notes:** Robust t-statistics in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%. Wages are converted to PPP US dollars (see Table 19). All regressions include country, size, industry, and exporter dummies, hours worked per week, and dummies for female, and permanent full time workers.  All observations are weighted by the country's GDP.

<u>Countries:</u> Benin, Botswana, Burkina Faso, Burundi (excluded), Cameroon, Cape Verde, DR Congo, Gambia, Ghana, Guinea, Kenya, Madagascar, Mali, Mauritania, Namibia,Rwanda, Senegal, Swaziland, Tanzania, Uganda.

<u>Industries:</u> Food & beverages (excluded), Chemicals, paints & pharmaceuticals, Construction materials, Metals, Paper & printing, Plastics, Textiles, garments & leather, Wood, Other manufacturing.

<u>Other excluded categories: <20 empl (firms), <32 years old (workers), unskilled workers, no education/primary degree.</u> 44
Table 2: Descriptive Statistics for Mother’s Labor-Supply in Albania (2002 - 2012)

|Variables<br>Observations|Mean|Standard<br>Deviation|Min|Max|
|---|---|---|---|---|
|**Mother’s Labor Supply**|||||
|Worked in last 7 days<br>7445|0.444|(0.496)|0|1|
|Worked of-farm<br>7445|0.163|(0.369)|0|1|
|Worked on-farm<br>7445|0.217|(0.412)|0|1|
|Self-employed<br>7445|0.066|(0.249)|0|1|
|Second occupation<br>4903|0.014|(0.120)|0|1|
|Hours/week (total)<br>4903|16.60|(21.24)|0|112|
|Hours/week (of-farm)<br>4903|7.17|(16.20)|0|70|
|Hours/week (on-farm)<br>4903|7.52|(15.64)|0|70|
|Hours/week (self)<br>4903|2.35|(11.15)|0|112|
|Monthly labor income<br>4903|50166.4|(114456)|0|1217661|
|**Other characteristics of mothers**|||||
|Age<br>7480|35.04|(5.691)|20|49|
|Age at frst birth<br>7480|23.22|(3.489)|14|41|
|Years of education<br>7404|10.47|(3.135)|0|21|
|Rural<br>7480|0.482|(0.499)|0|1|
|Household size<br>7480|5.188|(1.478)|1|16|
|Extended family<br>7480|0.381|(0.485)|0|1|
|Marital status (married)<br>7480|0.980|(0.137)|0|1|
|Muslims<br>7480|0.816|(0.387)|0|1|
|Catholics<br>7480|0.081|(0.273)|0|1|
|Orthodox<br>7480|0.067|(0.251)|0|1|
|Coastal region<br>7480|0.264|(0.440)|0|1|
|Central region<br>7480|0.282|(0.450)|0|1|
|Mountain region<br>7480|0.291|(0.454)|0|1|
|Tirana region<br>7480|0.161|(0.367)|0|1|

_Notes:_ The data used are the 2002 LSMS, the 2005 LSMS, the 2008-09 DHS and the 2012 LSMS 35
<!-- Start of picture text --> ∑ ai(t)<br>E d<br>B<br>Es<br>t time<br>/yrGtCO2<br>Emissions<br><!-- End of picture text -->

**Figure 1:** An illustration of the climate constraint. The cumulative emissions above _Es_ are capped to a carbon budget _B_ (this requires that the long-run emissions tend to _Es_ ). Dangerous emissions _E_<sup>_d_</sup> are measured from _Es_ .

For instance, if each vehicle is replaced by a zero-emission vehicle, all the abatement potential in the private mobility sector has been realized.<sup>3</sup> The sectoral potential may be roughly approximated by sectoral emissions in the baseline, but they may also be smaller (if some fatal emissions occur in the sector) or could even be higher (if negative emissions are possible). We make the simplifying assumption that the potentials _a_ ̄ _i_ and the abatement cost functions _ci_ are constant over time and let the cases of evolving potentials and induced technical change to further research.

# _2.2. Carbon budget_

The climate policy is modeled as a so-called _carbon budget_ for emissions above a safe level (Fig. 1). We assume that the environment is able to absorb a constant flow of GHG emissions _Es ≥_ 0. Above _Es_ , emissions are dangerous. The objective is to maintain cumulative dangerous emissions below a given ceiling _B_ . Cummulative emissions have been found to be a good proxy for climate change (Allen et al., 2009; Matthews et al., 2009).<sup>4</sup> For simplicity, we assume that all dangerous emissions are abatable, and, without loss of generality, that doing so requires to use all the sectoral potentials. Denoting _E_<sup>_d_</sup> the emissions above _Es_ , this reads _E_<sup>_d_</sup> =<sup>�</sup> _i_<sup>_a_ ̄</sup><sup>_i_.Inotherwords,( ̄</sup><sup>_ai −ai,t_)standsforthehigh</sup> carbon capital that has not been replaced by low carbon capital yet in sector _i_ , as measured in emissions. The carbon budget reads:

> 3 This modeling approach may remind the literature on the optimal extraction rates of non renewable resources. Our maximal abatement potentials are similar to the different deposits in Kemp and Long (1980), or the stocks of different fossil fuels in the more recent literature (e.g, Chakravorty et al., 2008; Smulders and Van Der Werf, 2008; van der Ploeg and Withagen, 2012). Our results are also similar, as these authors find in particular that different reservoirs or different types of non-renewable resources should not necessarily be extracted at the same marginal cost.

> 4 Our conclusions are robust to other representations of climate policy objectives such as an exogenous carbon price, e.g. a Pigouvian tax; or a more complex climate model.

4
TABLE A15: Impact of leaders’ exposure on ECD outcomes, by baseline social interactions about ECD

||(1)<br>ECD full sample|(2)<br>ECDyoung|(3)<br>ECD Old|
|---|---|---|---|
|ITT Leader|-0,10<sup>_∗∗_</sup><br>(0,04)|-0,08<br>(0,08)|-0,11<sup>_∗∗_</sup><br>(0,06)|
|Social (Had interactions about ECD at baseline)|0,02<br>(0,07)|-0,08<br>(0,09)|0,05<br>(0,09)|
|ITT Leader X Social|-0,02|0,14|-0,06|
||(0,08)|(0,12)|(0,10)|
|Observations|2051|624|1427|
|ITT Leader + ITT Leader X Social|0,07|0,48|0,04|

_Note:_ Social is a binary variable indicating that the household had talked to at least one other community member about ECD in the week before the baseline survey. At baseline, households were asked whether they had talked to other community members about ECD practices in the last 7 days. This includes interaction about ECD practices with the health promoter, pre-school teacher, primary school teacher, other teachers, family members, neighbours, or elected leaders in the village. About half of the households had talked to at least one other community member about ECD practices. All outcome variables are standardized using the mean and standard deviation of control households. In column 1, the dependent variable is the first principal component of the 4 Denver sub-components for children below 36 months old, and the first principal component of all 7 tests for children 36-83 months old. In column 2, the dependent variable is the principal component for the four Denver sub-components for children aged 12 to 35 months. In column 3, the dependent variable is the first principal component of all 7 tests (4 Denver sub-components plus TVIP, Memory and self-control) of children aged 36 to 83 months. All regressions include controls for the household-level treatment, the average level of education of the leaders (the stratification variable for village level leader randomization), as well as the level of education of the main caregiver, whether there is a male caregiver in the household, whether the leader household had access to electricity, child age and gender and test-administrator fixed effects as well as a binary indicator indicating the data was collected in 2016. The standard errors (in parentheses) are clustered at village level.<sup>_∗_</sup> p<0.1,<sup>_∗∗_</sup> p<0.05,<sup>_∗∗∗_</sup> p<0.01 63
establishing an establishment register.<sup>17</sup> The same day Estela also issued a resolution on the voluntary retirement program, in which pecuniary rewards would be based on accumulated points as a function of occupational category and length of service. Those employees eligible to receive pensions would receive an additional bonus of 20% in exchange for surrendering the right to receive their pension.

The SUNAT reform team also believed it necessary to remove SUNAT from the public sector employment regime. In September 1991, responding to SUNAT’s petition, President Fujimori issued a decree creating a new labor regime.<sup>18</sup> The new regime permitted SUNAT to set the salaries of its employees based on their experience and the salaries of comparable jobs in the private sector. Employees also had the option, however, to remain in the public sector regime, which enabled SUNAT to argue that it was respecting workers’ “rights.” Moreover, the decree established a trial period of three months for all new employees (beginning after the six month training period). The decree also provided for dismissal of any employee for “lack of probity” or “abuse of authority.” Salaries, due to the new personnel scheme, increased dramatically. Whereas on average a tax administration employee earned approximately US$50 per month in early 1991, the average salary for the remaining workers increased roughly twenty times to US$1,000 (Haltiwanger and Singh, 1999: 44). The new salary scale ranged from US$400 to US$2,000 ( _The Peru Report_ , October 1991, 5).<sup>19</sup> There is no question that SUNAT’s radical personnel reforms—characterized by retrenchment of two-thirds of the staff and huge increases in salaries—would not have been possible without autonomy, and that the personnel reforms were among the most important for the success of the overall reform.

In Venezuela, on the other hand, it was decided that there would be no involuntary separations. As a result of the voluntary departure and retirement programs, which allowed retirees to collect more than three times the amount that normally would have corresponded to them, about 850 employees left the tax agency (the plan made retirement conditions more flexible, thus expanding eligibility). An additional 350 employees, who were transferred automatically to SENIAT but did not meet the minimum requirements for employment according to new personnel regulations, retired. The voluntary downsizing represented 25.5% of the total staff.

Though the political establishment would not permit Superintendent José Moreno León to undertake radical human resource reform, it did permit him to appoint the entire managerial level of the organization. Thus Moreno León was able to hire nearly 500 new professionals from top private sector accounting and law firms, which resulted in a vast improvement of the managerial ability of the organization. Moreno’s plan was to

> 17 Resolution No. 280-91-EF/SUNAT (6/20/91).

> 18 Decreto Legislativo No. 673, 9/23/91.

> 19 The personnel reform cost SUNAT US$47 million in present value terms (Haltiwanger and Singh, 1999: 44), roughly US$20,200 per worker on average.

15