rafmacalaba/datause-encoder-data huggingface dataset:
|Recently|3<sup>rd</sup>wealthquintile|0.01|(0.03)|2,466|
|---|---|---|---|---|
|~~pregnant~~|4<sup>th</sup>wealthquintile|-0.00|(0.05)|2,466|
||5<sup>th</sup>wealthquintile|-0.01|(0.10)|2,466|
|Recently|Received anyANC|0.06|(0.05)|2,466|
|pregnant|At least 4 ANC visits|-0.02|(0.13)|2,463|
||TimelyANC|0.07|(0.05)|2,164|
|women|ANC atprimarylevel|0.11|(0.10)|2,179|
||Received anyPNC|-0.09|(0.08)|2,466|
||Age|0.01|(0.06)|6,935|
||Male|0.01|(0.02)|6,935|
||Growth monitoringin 6 months|0.10|(0.06)|5,706|
||12-23 months: all basic vaccinations|-0.01|(0.03)|1,668|
|Children|12-23 months: anybasic vaccination|-0.03|(0.03)|1,668|
|d5|24-35 months: All basic vaccination|-0.05|(0.06)|1,370|
|uner-|24-35 months: MMR vaccine|-0.09*|(0.05)|1,290|
||24-35 months: anyvaccination|-0.04|(0.04)|1,370|
||Weight for age z-score|-0.05|(0.12)|5,170|
||Weight for height z-score|-0.18|(0.21)|4,544|
||Height for age z-score|0.34*|(0.20)|5,419|
||Blood pressure measured in past<br>year|0.07|(0.06)|13,494|
|Adults|Elevated bloodpressure|-0.05|(0.03)|9,822|
||Self-reported hypertension|0.00|(0.01)|16,126|
||Prescription for hypertension|-0.03|(0.03)|1,565|
||Took medicine inpast 24 hours|-0.12|(0.08)|1,531|

42
changing each fuel’s share of total generation. Lastly, the pandemic may have disrupted the logistics of resource extraction and fuel imports and exports, causing changes in prices large enough to stifle generation or encourage substitution from one type of generation to another.

This study investigates whether COVID-19 has affected total electricity generation, GHG emissions from the power sector, and electricity supply mix, particularly the penetration of solar and wind. Rather than focusing on a single system operator (Carvalho et al., 2021; Leach et al., 2020; Agdas and Barooah, 2020), this study uses daily electricity generation data from the United States, Brazil, India, and the majority of the European countries from January 2018 to September 2021. According to the latest data from the Emission Database for Global Atmospheric Research (EDGAR), these countries collectively account for 31% of global CO2 emissions in 2019.

To quantify the impact of the pandemic on electricity generation, we regress daily generation in each country on the stringency of the measures adopted to combat the pandemic, controlling for other factors thought to affect generation. We also regress the _share_ of a particular type of electricity (e.g., coal-fired or hydro) out of total generation on the same determinants and measures of the pandemic. Identification comes from the stringency of lockdowns and other public health measures that vary across countries, depending on how early the pandemic hit them and how proactive the governments were in implementing pandemic mitigation measures, as well as within a country over time.

The study finds that a 1-unit increase in the lockdown stringency measure is associated with a 0.473 GWh decrease in the total daily generation. The lockdown stringency is measured using the Oxford COVID-19 Government Response Tracker, which documents and compares worldwide government policy responses to the coronavirus using well-defined and consistent criteria across countries and over time. On average, COVID-19 response has decreased daily electricity generation by almost three percent, increased the share of solar electricity generation by 0.6% in generation mix, which is around 15% increase relative to its pre-COVID level. Regarding fossil fuels, coal-fired share decreased by 1.6%, largely substituted by natural gas. However, the impacts are significantly different across the countries selected in our study. These findings should be interpreted with caution, as changes in the shares may not only have been caused by the COVID19 pandemic – directly or indirectly via disruptions in the markets for fuel inputs to generation – but also by other causes, such as droughts or weak winds, which limit hydropower or wind generation.

4
we have witnessed in practice. In such a case, an upfront haircut for all creditors would lead to more easily attainable fiscal targets (as less official money would be needed).

What is the impact of official seniority in such a case? Being senior means having the first claim on available primary surpluses remaining after the exit of the short-term creditors. But this irrelevant to the extent that official loans are priced in line with the risk they bear: the key point is the exit of short-term creditors at 100 cents on the dollar. However, seniority could become an issue if official loans are priced above the risk-free rate in spite of their seniority. In a related paper, Gros (2010) argued that the formal announcement of seniority could cause the sort of problems described in Chamley and Pinto (2011). He attributes the problems with the Irish bailout in part to the November 2010 announcement that official financing from the Eurogroup would be considered senior to private loans; but the case of Greece itself as well as that of Russia in 1998 (Kharas, Pinto and Ulatov 2001) convincingly shows that all that matters is a perception of seniority based on precedents. However, it is important to note that seniority _per se_ is unimportant so long as the official loan is priced in line with its risk.

The seniority clause that often included in official loans<sup>4</sup> has been criticized because it could weaken the incentive of private lenders<sup>5</sup> . We focus on this issue and we argue that there may be some misconception in that debate<sup>6</sup> . One should make a distinction between the impact on new loans that are made in conjunction with the IMF loan, and the impact on existing loans with a service due at the time of the IMF loan. We address the first issue in the next section and provide some discussion of the second in Section 4.

> 4 However, loans by the European Financial Stability Facility (EFSF) that was set in May 2010, as a temporary measure to be replaced by the European Stability Mechanism (ESM), were not senior to private loans. Also, the European Central Bank has recently announced (August 2, 2012) that its purchases of euro area bonds will not transform those bonds in “senior bonds”, as was in the case in the past.

> 5 See also the article A 10 of the ESM.

> 6 A number of studies on seniority (Penalver (2004), Bolton and Jeanne (2005), Conesa and Kehoe (2011)), address issues that are different from the ones in this paper.

2
Table 4: Estimation results: Firm type, state-reporting and employment growth

||||**LSDV estimat**<br>|**or**<br>|||**Differe**<br>|**nce GMM esti**<br>|**mator**<br>|||**Co**<br>|**rrected LSDV e**<br>|**stimator**<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Main|Affil.|Indep.|Ministry|Non-Min.|Main|Affil.|Indep.|Ministry|Non-Min.|Main|Affil.|Indep.|Ministry|Non-Min.|
|Lagged growth|0.291|0.070|-0.009|0.088|0.001|0.161|-0.110|-0.021|-0.051|0.004|0.448*|0.198*|0.059|0.200***|0.064|
||(0.388)|(0.066)|(0.019)|(0.066)|(0.027)|(0.259)|(0.072)|(0.036)|(0.047)|(0.049)|(0.254)|(0.112)|(0.038)|(0.061)|(0.044)|
|Lagged employm.|-0.348|-0.447***|-0.596***|<br>-0.493***|-0.656***|-0.342|0.565***|0.912***|-0.683***|-1.013***|-0.394**|-0.438***|-0.603***|-0.494***|-0.667***|
||(0.294)|(0.059)|(0.088)|(0.075)|(0.118)|(0.213)|(0.061)|(0.104)|(0.074)|(0.132)|(0.182)|(0.071)|(0.058)|(0.049)|(0.065)|
|Lagged wages|0.355*|.152***|0.077*|0.045|0.093|0.411***|.173***|0.042|0.038|0.054|0.354*|0.148***|0.078***|0.043|0.093***|
||(0.162)|(0.048)|(0.046)|(0.032)|(0.056)|(0.111)|(0.056)|(0.029)|(0.026)|(0.037)|(0.201)|(0.047)|(0.023)|(0.027)|(0.022)|
|Lagged exports|-0.011|-0.008*|-0.002|-0.005**|-0.003|-0.004|-0.012***|-0.002|-0.005***|-0.003|-0.011|-0.007|-0.002|-0.004|-0.003|
||(0.007)|(0.004)|(0.002)|(0.002)|(0.004)|(0.008)|(0.003)|(0.002)|(0.002)|(0.003)|(0.010)|(0.005)|(0.003)|(0.003)|(0.006)|
|Lagged investment|0.033*|0.000|0.005***|<br>0.003|0.006**|0.036***|0.000|0.004***|0.000|0.006***|0.034|0.000|0.005**|0.003|0.006**|
||(0.014)|(0.002)|(0.002)|(0.002)|(0.003)|(0.008)|(0.002)|(0.002)|(0.001)|(0.002)|(0.021)|(0.003)|(0.003)|(0.002)|(0.003)|
|Lagged capacity util.|-0.158|0.042|0.119**|0.097**|0.086|-0.246**|0.069|0.126***|0.091**|0.135**|-0.177|0.037|0.118**|0.092*|0.082|
||(0.109)|(0.077)|(0.048)|(0.048)|(0.067)|(0.093)|(0.072)|(0.045)|(0.043)|(0.061)|(0.176)|(0.074)|(0.057)|(0.050)|(0.069)|
|Year fixed-effects|8.15**|13.9***|7.84***|17.22***|5.52***|48.84***|52.20***|28.69***|67.42***|16.45***|7.63*|32.16***|53.77***|<br>54.71***|50.24***|
|Observations|28|109|426|281|282|21|81|318|210|210|28|109|426|281|282|

_Notes:_ Robust standard errors in parenthesis. *(*)[***] stands for significance at the 10%(5%)[1%] level. All specifications include firm fixed effects. Year fixed effect test based on chi-squared tests with 3 degrees of freedom.

13
of plants, we develop a one-sector growth model where both formal and informal plants grow by investing in their productivity. Our model is based on Guner et al. (2018) (GPV in what follows) which in turn builds on the Lucas (1978). In the model, agents choose to be workers and entrepreneurs. Our main innovation to their setup is introducing a proportional tax on output that entrepreneurs can escape by operating informally due to incomplete enforcement, in the spirit of Leal (2014). There are two enforcement mechanisms in the model. First, tax officials are not able to enforce taxes to all plants. at the of their face a tax with some Agents, beginning life-cycle, official probability. Second, regardless of whether agents are assigned to an official at the beginning of their life-cycle, informal entrepreneurs are caught if they use capital more than a certain amount. Entrepreneurs must therefore choose whether to operate informally or formally as well as when to transition into formality if they start operating informally at the beginning of their life-cycle.

In equilibrium, smaller and relatively unproductive plants operate informally, while larger plants and those assigned to tax officials early in their life cycle operate formally. Informal plants with higher productivity eventually transition to formal operation as they invest and grow. Consequently, less productive informal plants grow more slowly than formal plants to avoid tax enforcement.

We calibrate the model parameters to match key properties of the plant-size distribution and the growth rates of both formal and informal plants in Mexico. The model is able to successfully match the mean sizes of formal and informal plants as well as the concentration of employment in large plants. In addition, the model is able to capture the markedly different growth rates of formal and informal plants over their life cycle. We first document the effects of changes in the burden of formality, i.e. the tax rate. As the tax rate declines, the formal managers increase their demand for inputs and marginal informal plants decide to operate formally to enjoy higher output and growth potential. This reduces the fraction of informal plant numbers and increases productivity investment, resulting in larger plants and higher aggregate output and growth. Lower taxes also increase the fraction of informal plants transitioning into formality along their life-cycle. Quantitatively, when the tax rate halves, the fraction of transitioning plants rises from 1.9% to 5.8%. This is another factor that contributes to higher growth rate of plants along their life-cycle on average because of the level effect. In other words, the transitioning plants start their life-cycle small and informal; and they don’t have any limit to grow when they become formal. So their growth rates are higher compared to other plants at similar productivity levels.

2
to cross-listing some individuals have a lower utility, while others experience and increase in their utility level.

# 6 Conclusion

We investigate whether contagion is a cost of international cross-listing for emerging market firms. We develop a rational expectations model of contagion where some emerging market firms cross-list their stocks in a developed exchange. We model the fact that markets are segmented along emerging market borders, so that cross-listing is the only way for foreign investors to invest in emerging market stocks and for emerging market firms to access developed capital markets.

We find that contagion is a cost of internationalization as the price response in one emerging country to a shock in another emerging country is significantly greater for cross-listed stocks than for pure domestic stocks. Furthermore, we find that a shock in one emerging market affects the stocks of all the other emerging markets that have cross-listed stocks in the same developed market. These findings support Calvo (1998) and Calvo and Mendoza (1999), that international capital markets act as carrier of emerging market crises. However, the magnitude of contagion that we find is more modest than reported in papers on contagion like Kodres and Pritsker (2002) that assume that all markets are perfectly integrated. The discrepancy in the magnitude of contagion found suggests that market segmentation in emerging markets may reduce the transmission of shocks from other markets and that some previous studies may have overestimated the magnitude of contagion.

As an application of our model, we investigate whether the contagion of the 1998 Brazilian crisis to Mexico and Chile could be spread by the action of international buying and selling stocks cross-listed in the U.S. We calibrate our model using stock price data of Brazil, Mexico, Chile, and the U.S.. We find that our model can indeed explain some of the transmission of the Brazilian crisis to Mexico and Chile and the model further indicates the crucial determinants of contagion in that case, were negative liquidity shocks and country specific shocks.

Our initial results suggested that emerging markets firms should cautiously consider internationalization to developed exchanges as cross-listing is associated with more exposure to cross-market contagion. Against this backdrop, the welfare analysis of internationalization versus autarky concludes that the benefits of internationalization in terms of reducing information asymmetry and increasing market efficiency are greater than the costs of contagion.

34
**Table 6: ITT panel sample:  Weight for age z-scores by mother’s education** **<u>and village characteristics</u>**

||<br>**Mother:**<br>**Unschooled**|**Mother:**<br>**Primary**<br>**education**|**Mother:**<br>**Secondary**<br>**education or**<br>**higher**|
|---|---|---|---|
|_Poverty_||||
|Lowest Tercile|0.275*|0.202**|0.301**|
||_(0.151)_|_(0.096)_|_(0.119)_|
|Middle Tercile|0.048|0.059|0.098|
||_(0.146)_|_(0.097)_|_(0.113)_|
|Upper Tercile|0.009|0.098|0.197*|
||_(0.147)_|_(0.089)_|_(0.110)_|
|_Provincial Road_||||
|Present|0.363***|0.198***|0.300***|
||_(0.119)_|_(0.075)_|_(0.099)_|
|Not Present|-0.054|0.063|0.160*|
||_(0.109)_|_(0.080)_|_(0.088)_|
|_National Road_||||
|Present|0.174|0.133*|0.218**|
||_(0.119)_|_(0.070)_|_(0.098)_|
|Not Present|0.075|0.139|0.284***|
||_(0.122)_|_(0.086)_|_(0.084)_|
|_Secondary school_||||
|Present|0.111|0.161**|0.205***|
||_(0.117)_|_(0.064)_|_(0.075)_|
|Not Present|0.167|0.050|0.442***|
||_(0.124)_|_(0.091)_|_(0.157)_|
|_Hospital_||||
|Present|0.505***|0.173|0.259**|
||_(0.177)_|_(0.109)_|_(0.119)_|
|Not Present|0.044|0.128**|0.224***|
||_(0.096)_|_(0.062)_|_(0.083)_|
|_Electricity_||||
|Present|0.507*|0.177|0.122|
||_(0.272)_|_(0.142)_|_(0.126)_|
|Not Present|0.101|0.128**|0.312***|
||_(0.089)_|_(0.058)_|_(0.078)_|

Note: Each line reports the coefficients from a separate regression. Below in parentheses are standard deviations based on errors clustered at the village level. All reported results are PS weighted estimates. Significance level: .01 - ***; .05 - **; .1 - ** 27
|ADD:Machinga|-0.047|0.005|-0.055|||-0.053|
|---|---|---|---|---|---|---|
||[0.023]**|[0.027]|[0.020]***|||[0.025]**|
|ADD:Blantyre|0.004|0.023|0.003|||-0.012|
||[0.021]|[0.025]|[0.018]|||[0.022]|
|Observations|9752|5140|4612|1415|3819|4518|
|R-squared|0.44|0.26|0.21|0.43|0.45|0.44|

Robust standard errors in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% 47
26 Singapore. The bottom 5 countries in terms of average financial intermediary development over the 1986-1993 period are Colombia, Pakistan, Philippines, Turkey, and Mexico." _D. Stock Market Development and Financial Intermediary Development_ Armed with the financial intermediary indicators and financial intermediary conglomerate indexes we can now shed some empirical light on the question, _Do countries with well-developed stock markets also have well-developed banks and nonbank financial_ _intermediaries?_ The answer that emerges from Tables 15, 16, 17, and 18 is yes.

Table 15 presents the correlations between individual stock market indicators and individual indicators of financial intermediary development. Table 16 provides rank correlations. Instead of describing the statistics in detail, we simply highlight three points. First, stock market size and liquidity (as measured by total value traded/GDP) are positively correlated with all of the financial intermediary indicators and significantly correlated with all of the financial intermediary indicators except INPE/GDP (the assets of private insurance and pension companies divided by GDP). Second, volatility is significantly negatively correlated with all the financial intermediary indicators except for PNB/GDP (private nonbank assets/GDP). Thus, countries with well-developed financial intermediaries, large banks, and large private insurance companies and pension funds tend to have less volatile stock markets. Third, countries with

**13** We prefer FINDEX-3 to the other financial intermediary indexes because it combines information on particular financial intermediaries: banks, nonbanks, insurance companies, and pension funds. The other INDEXES mix informnation on particular intermediaries with information on intermediary liabilities and the measures of liabilities span across different types of intermediaries.
The purpose of defining a structural revenue is to smooth out transitory fluctuations in actual revenues. As discussed in section III, structural prices are usually set at their (perceived) long-term levels. When production is not the focus of the analysis, to keep the model simple, we set structural production equal to actual production or a moving average. However, the LTGM-NR toolkit provides other specifications, such as using baseline as reference production.<sup>16</sup> **Fiscal rules.** The marginal propensity to invest, , captures the pro-cyclicality of fiscal policy. The user can choose any value of , although more common values θθ range between 0 and 1. This range nests three popular fiscal rules: θθ • captures a SSR (Structural Surplus Rule), as cyclical resource revenues are saved (when prices are high) and do not affect public investment.<sup>17</sup> θθ= 0

> 14 The LTGM-NR spreadsheet also allows the parameter to vary over time. 15 Capturing, for example, royalties from the concessions of exploration of natural resources, taxreceipts from private extractive enterprises and profits from state-owned companies. θθ 16 An equilibrium is defined as a collection of 15 endogenous trajectories

> 0 , R 0 D � where with each endogenous variable specified as a function of the exogenous paths ii ii jj ii jj gg RR RR tt tt tt tt tt tt tt tt tt tt tt tt tt tt tt �YY , QQ , RRh , KK , KK DDPP , YY , ii , ii , ii , MMRRPPKK} and initial conditions , MMRRPPKK �  �ii , zz , zz̆ jj∈{0,1, ... , NN} ttAAjj 0 tt ttNN0 ttωω0 ttρρ0 ttii0 ttii 0 pptt 0ttgg 0ttii 0ttii� that satisfy equations (1)—(15) for all _t_ . 17{gg̅ In the LTGM-NR-default the SSR and SSR-HR are very similar (given that spending is mostly , gg̅ , gg̅ , gg̅jj , gg̅ , DD�jj , pp̅ii , ıı̅ii , ıı̅ , pp� , QQ� constant). Hence, we focus on the SSR in the applications of the Default submodel. �RRDDPP , RRDDPP , KK , KK , RR , pp , NN , ωω , ρρ

12
WPS3945 for the enrollment sample).

The results are somewhat surprising. First, female headship has no additional effect on school enrollment for either boys or girls. Second, while boys in female headed migrant households are signi fi cantly less likely to drop out as compared to boys in male headed migrant households, with dropout rates declining from .18 to .06, girls in such households do much worse than girls in male headed migrant households, with dropout rates increasing from .27 to .50. In fact, girls in female headed migrant households fare little better than girls in non-migrant households! Moreover, while female headship has no effect on completed grades for boys, it has a large negative effect on girls. In fact, girls in female headed migrant households do about as well in progressing through school as girls in non-migrant households!

Finally, both boys and girls work substantially more in female headed households (24 days as compared to about 16 days for children in male headed migrant households). This is consistent with the hypothesis that "male absence" can increase the work burden of children in migrant households. However, there is no differential increase in the work burden of boys, male absence appears to affect both boys and girls equally, in this respect. These results are essentially unchanged if we use the less restrictive de fi nition of work.

In sum, our results suggest that girls do much better in male headed migrant households. This may be because the higher opportunity cost of child time in female headed households, falls disproportionately on girls. Our estimates suggest that girls work about as much as boys on income related activities, but this implies a much greater total work burden since our measures of work do not include housework or child care, activities in which girls are much more likely to be engaged.

# 5 Conclusions

The paper fi nds evidence of a signi fi cant positive effect of migration on school attainment and child labor market activity in rural Pakistan. The evidence suggests that children in migrant households are not only more likely to attend school, they are also more likely to stay in school and accumulate more years of schooling in comparison to their counterparts in non-migrant households in the same village. They are also less likely to be involved in economic work and report working for substantially fewer hours.

The paper also fi nds support for large gender differentials in the gains from migration, with relative gains for girls outstripping those for boys by a good margin, with a substantial 22