rafmacalaba/datause-encoder-data huggingface dataset:
Table A21: Impact on mechanism outcomes—with double-LASSO selected controls

||(1)<br>SRH<br>knowledge<br>(15 yo+)|(2)<br>Gender<br>attitudes|(3)<br>Aspirations|(4)<br>SES|
|---|---|---|---|---|
|T1 (safe spaces)|0.265**|0.242***|-0.119|-0.011|
||(0.118)|(0.093)|(0.101)|(0.111)|
|T1 + Leaders|0.155|0.087|-0.140|0.259**|
||(0.104)|(0.115)|(0.123)|(0.113)|
|T2 (safe spaces + IGA)|0.257**|0.172**|0.049|0.226**|
||(0.110)|(0.077)|(0.105)|(0.094)|
|T2 + Leaders|0.058|0.063|-0.078|0.301***|
||(0.119)|(0.104)|(0.092)|(0.098)|
|T3 (safe spaces + husbands’ clubs)|0.433***<br>(0.098)|0.206***<br>(0.077)|-0.030<br>(0.113)|0.181**<br>(0.092)|
|T3 + Leaders|0.176|0.155**|0.014|0.443***|
||(0.158)|(0.077)|(0.149)|(0.108)|
|T4 (control spillover)|-0.015|0.053|-0.041|0.108|
||(0.115)|(0.074)|(0.095)|(0.098)|
|T1 = T1 + L|.308|.208|.882|.0433|
|T2 = T2 + L|.0925|.299|.287|.486|
|T3 = T3 + L|.0806|.509|.796|.0222|
|T1 = T2|.947|.448|.179|.0481|
|T2 = T3|.0697|.649|.557|.659|
|T1 = T3|.0973|.704|.5|.0981|
|T1 + L = T2 + L|.388|.857|.647|.733|
|T2 + L = T3 + L|.465|.378|.555|.235|
|T1 + L = T3 + L|.891|.549|.381|.162|
|N|3,002|3,003|2,995|3,003|
|Control mean|0|0|0|0|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, standard errors in parentheses clustered at the locality level. IGA = income-generating activity; SES = socio-emotional skills; SRH= sexual and reproductive health; yo = years old. The table presents estimated impacts of our 6 interventions’ combinations and the control spillover relative to the pure control group. The p-values of the difference tests between the treatments are in the second part of the table. All regressions control for stratification fixed effects and the double-LASSO-selected variables within a set of baseline values that include the outcome variable at baseline, the adolescent individual characteristics and her household’s. When the baseline value is missing, it is imputed and if it is selected by the double-LASSO procedure a dummy indicating whether the variable is imputed or not is added as control.

53
Espitia, A., Ruta, M., Rocha, N., Winkler, D.E., Mattoo, A., 2021. Pandemic Trade : Covid-19, Remote Work and Global Value Chains (No. 9508), Policy Research Working Paper Series, Policy Research Working Paper Series. The World Bank.

Feenstra, R.C., Inklaar, R., Timmer, M.P., 2015. The Next Generation of the Penn World Table. American Economic Review 105, 3150–3182. https://doi.org/10.1257/aer.20130954 Javorcik, B., 2020. Global supply chains will not be the same in the post-COVID-19 world, in: COVID-19 and Trade Policy: Why Turning Inward Won’t Work. CEPR Press.

Kilic, K. , Marin, D., 2020. How COVID-19 is transforming the world economy. VoxEU, 20 May 2020.

Lund, S., Manyika, J., Woetzel, J., Barriball, E., Krishnan, M., Alicke, K., Birshan, M., George, K., Smit, S., Swan, D., 2020. Risk, resilience, and rebalancing in global value chains. McKinsey Global Institute.

Mayer, T., Zignago, S., 2011. Notes on CEPII’s Distances Measures: The GeoDist Database (SSRN Scholarly Paper No. ID 1994531). Social Science Research Network, Rochester, NY. https://doi.org/10.2139/ssrn.1994531 Martin J., I. Mejean, M. Parenti, 2021. Relationship Stickiness, International Trade, and Economic Uncertainty, Working Papers ECARES 2021-03, ULB -- Universite Libre de Bruxelles.

Rodrik, D., 2008. The real exchange rate and economic growth. Brookings papers on economic activity 2008, 365–412.

UNCTAD (2020), World Investment Report 2020: International Production Beyond the Pandemic, New York and Geneva: United Nations.

World Bank, 2020. World Development Report 2020: Trading for Development in the Age of Global Value Chains, World Development Report. The World Bank. https://doi.org/10.1596/978-1-4648-1457-0 Zhu L, K Ito, and E Tomiura, 2016. Global Sourcing in the Wake of Disaster: Evidence from the Great East Japan Earthquake. RIETI Working Papers 2016.

22
is driven by a few factors such as topographic indicators and parents’ age in 1988, but in 2002 a number of other factors are also important for heteroskedasticity including ethnic minority status and county fixed effects.<sup>32</sup> An important question is whether the instrument based on heteroskedasticity is strong enough to identify the causal effect. To check the strength of the instrument, we report the Kleibergen‐Paap F statistics. The results in Table 4 show that in seven out of the eight cases reported, the Kleibergen‐ Paap F statistic is higher than the Stock et al. (2002) rule of thumb of 10 for one endogeneous variable. The only case where the instrument is relatively weak (Kleibergen‐Paap F=7.43) is for daughters in 1988 when a limited set of variables is used in the heteroskedastic probit model (called specification K‐V2 in the Tables). One might wonder how much of the first stage variation is due to heteroskedasticity as opposed to the nonlinearity of the normal CDF in the probit model. This is important because identification that relies on the nonlinearity of the normal CDF in a probit model is generally deemed not credible. To check the role played by the nonlinearity of the normal CDF, we use simple probit model to generate the instrument and see how much it explains the variation in parental education, especially in comparison to the instrument generated by heteroskedasticity (i.e, from heteroskedastic probit). The Kleibergen‐Paap statistics for the instrument derived from simple probit model are reported in Table 4. The estimates clearly show the value of heteroskedasticity for identification; the highest F statistic for the simple probit based instrument is 5.14 and in two of the other three cases the F statistic is less than 2. To appreciate the contrast better, consider the case of daughters in 2002. The Kleibergen‐Paap F is 1.23 when we rely exclusively on the nonlinearity of normal CDF for identification, but with heteroskedasticity, it increases to 16.79 in KV2 specification and to 92.23 in the KV1 specification in Table 4.

> 32 As noted before, we use an index of county fixed effects derived from univariate probit, as heteroskedatic probit fails to converge with the county dummies. For a similar approach, see Emran and Shilpi (2011).

27
# **Figure A.10: Standard Deviation of CLASS Scores Across Dimensions**

<!-- Start of picture text -->
1.6<br>1.4<br>1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Pilot Study Elementary School Pilot Study Middle School<br>Secondary MyTeaching Partner Measuring Effective Teaching (4‐6th)<br>Measuring Effective Teaching (7‐9th) Understanding Teaching Quality<br><!-- End of picture text -->

Source: Guangdong class observations; authors’ calculations; Teachstone.

32
Table 5: Determinants of probability of transition into the high-productivity convergence Club 1: Logit model marginal effects – covariates average value, 1980-1990

||_EMDE m_|_Dependent_<br>_embership o_|_variable:_<br>_f convergen_|_ce club 1_|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|(5)|||||
|Years of education|0.10***|0.09**|0.06***|0.09***|
|Economic complexity index|0.20**|0.33***|0.30**|0.32**|
|Initial productivity|-0.17***|-0.22***|-0.24***|-0.22***|
|Trade openness||0.15|||
|WGI: Government efectiveness|||0.24*||
|Investment in percent of GDP||||-0.68|
|FDI in percent of GDP|||0.02|0.06|
|Observations (All)|54|47|53|48|
|Pseudo R<sup>2</sup>|0.32|0.39|0.46|0.54|
|0.49|||||

_Note: ∗_ p _<_ 0.1; _∗∗_ p _<_ 0.05; _∗∗∗_ p _<_ 0.01 Note: Marginal effects of a one unit increase in each variable on the probability of an EMDE joining convergence ‘Club 1’ relative to other EMDEs. Derived from a logit model, with standard errors calculated using the delta-method. Average years of schooling for males and females from Barro and Lee (2015). Economic complexity index of Hidalgo and Hausmann (2009). Exports and imports as a percent of GDP. Government effectiveness survey from the World Bank’s Worldwide Governance indicators, defined as: perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies. A higher index value indicates greater political stability. Gross fixed capital formation and FDI are measured in percent of GDP.

37
into the categories above and also declare work-related characteristics such as earnings and hours in self-employment and whose pension and/or health contributions are paid.

Standard ILO definitions classify a person as employed if they are presently working or on leave from a job. We further distinguish between the formal and the informal sector workers among the group of employed. We define employees in formal sector paid employment as individuals working in public enterprises and international organizations, as well as those in the private sector whose pension and health insurance contributions were paid. Informal sector employment is comprised of three groups: (a) unpaid supporting family members, farmers on own farm, and workers engaged in other activity, such as sale of agricultural products; (b) workers not employed by public enterprises (or state sector) for which pension contributions are not paid; (c) workers declaring any other out-of-employment activity, but reporting earnings or hours of work. This definition meets previous official classification criteria and informal sector estimates for BiH (World Bank, 2002). By default, the unemployed are those who do not presently have a job, are actively searching for employment, and are able to take a job if it were offered to them. All others of working age are classified as inactive. Individuals who are inactive in all waves are dropped.

For BiH and its two entities, Table 1 presents frequencies for the partitioning of the labor force. Self-employed comprise about 5% of the workforce on average, a figure that is mostly constant over the period 2001-2004 (see Figure 1). This compares to a selfemployment rate of about 10% in OECD countries (Haber, Lamas and Lichtenstein, 1987; Parker, 2005). The percentage of self-employed is somewhat higher in RS, compared to FBiH. Another striking feature of the labor market in BiH is the high share of employment in the informal sector. Our calculations indicate that informal employment accounts for more than one third of total employment. There is an 8 percentage point difference between the entities, with the RS having close to a quarter of the labor force in informal employment. This is a relatively stable figure, while in FBiH the informal sector is lower but increasing in the course of the panel. Finally, with respect to the unemployment statistics, our estimates verify the picture that unemployment-inactivity comprises more than 40% of the workforce, a figure 5
25 Thurow, Lester (1980) The Z:er Sum Sociely, Basic Books Tullock, G. (1987) "Public Choice" in J. Eatwell, M. Milgate and P.<sup>Newman (eds) The</sup> New Paigrave: A Dictionary of Economics, Macmillan World Bank (1994) Adjustment in Africa: Reforms. Results and the Road Ahead,<sup>Oxford</sup> University Press World Bank (1993) Ghana 2000: Accelerated Growth with Povet Reduction World Bank (1990) World Development Report: Poverty World Bank (1991) Symposium Issue on Poverty and Adjustment: The World<sup>Bank</sup> Economic Review. May. 1991
# **1 Introduction**

In recent years, women’s underrepresentation at the occupational and educational level in the science, technology, engineering and math (STEM) sector has gained attention from academics and policy makers alike. In particular, the gender gap in computer science careers is the most salient of all STEM fields (Sax et al., 2017). In Argentina and Colombia, where this study takes place, women represent 57 and 58 percent of all college graduates, but they comprise only 15 and 9 percent of the graduates in the computer science field, respectively (Argentina Ministry of Education, 2015; Colombia Ministry of TIC, 2017). To women put this figure in perspective, this gap in the United States is remarkably similar: represent 57 percent of college graduates but only 19 percent of computer science graduates (National Center for Education Statistics, 2019). This, in turn, translates into a low share of female participation in technology occupations. Even though women represent about 45 percent of the workforce in Argentina and Colombia, the share of female employees in technology occupations is 22 and 20 percent, respectively (International Labor Organization, 2014).<sup>1</sup> There are several reasons why this underrepresentation of women in technology is concerning and needs attention. From an economy-wide point of view, this implies a potential misallocation of talent across sectors that could impede aggregate growth (Hsieh et al., 2019). From an employer point of view, firms miss a diversified workforce, potentially impeding productivity gains (Sax et al., 2017). Finally, and most relevant for our study, from the worker point of view, this implies that women are missing in occupations that pay higher wages (Blau and Kahn, 2017) and that rank the best in terms of amenities (Goldin, 2014). The most salient amenities in the technology sector are related to job flexibility: these jobs are better suited for flexible working hours, work-from-home, and part-time arrangements. These are particularly relevant for women of childbearing age, who are the focus of this study.

In the present paper we use a randomized control trial to analyze whether digital courses can have the to women with the skills to ac- training potential equip necessary cess the _tech_ sector. We evaluate the causal effects of a high-quality, intensive part-time coding bootcamp on skill acquisition, employment and educational outcomes on a sam-

> 1In the United States, the share of females in the labor force is 47 percent, versus 25 percent in Computer Science related occupations (U.S. Bureau of Labor Statistics, 2019).

1
FSI analyses.

As a proxy for financial development, we use the FD index, a summary measure of how developed financial institutions (banks, insurance companies, mutual funds, and pension funds) and financial markets (stock and bond markets) are in terms of their depth (size and liquidity of markets), access (ability of individuals and companies to access financial services), and efficiency (ability of institutions to provide financial services at low cost and with sustainable revenues, and the level of activity of capital markets). The FD index was developed by Sahay et al. (2015) (see also Svirydzenka, 2016) to overcome the shortcomings of single indicators (such as ratio of private credit to GDP) as proxies of financial development, which is essentially a multidimensional process. To that end, a number of sub-indices are created for financial institutions and markets across depth, access, and efficiency, culminating in the final index of financial development.

We consider four covariates for the analysis where we cluster countries based on unobserved time-varying heterogeneity while controlling for linear effects of other observables. In particular, drawing from the empirical literature on the determinants of state fragility (see, for example, Akanbi et al., 2021; Simon Feeny and Regan-Beasley, 2015; Blomberg et al., 2011; Carment et al., 2008, 2011), we consider GDP growth ( `gdpgrowth` ), inflation ( `inflation w` , winsorized at 0.5%), military expenditure as a share of GDP ( `militarygdp` ), and total natural resources rents as a share of GDP ( `natresrentgdp` ). Other controls were also investigated in earlier preliminary analyses (e.g. gross capital formation as a share of GDP, life expectancy, changes in effective executive of the country), but none of these led to stable results and their coefficients were typically estimated very close to zero.

There are quite some missing values in the data. Depending on what selection mechanism is used, we therefore end up with a different number of countries and time-series observations in the sample. Table 1 summarizes the main differences. Particularly the requirement that the additional controls exist for all time points can cause substantial sample attrition. Therefore, 11
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles

# **General Calculation Scheme**

<!-- Start of picture text --> Population<br>C ontributors B eneficiaries<br>E conom y<br>P F revenues PS param eters P F expenditures<br>P F balance<br>- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -<br>Im plicit pension debt (IPD )<br>- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -<br>Individual accounts<br><!-- End of picture text --> PROST follows single age/gender cohorts over time and generates population projections, which, combined with labor market assumptions, are used to forecast future numbers of contributors and beneficiaries. These in turn generate flows of revenues and expenditure. The model then projects fiscal balances and calculates the implicit pension debt. The required contribution rates and affordable replacement rates for zero pension fund balance in each year of the simulation period are also calculated. Finally, PROST produces outputs related to individuals – what an individual would contribute to the system and what he/she obtain under PAYG DB and multipillar schemes. This allows both intra- and intergenerational analysis.

Depending on the characteristics of the pension system and data availability, the user can choose the method for calculation of some of the variables. In particular, the number of contributors and beneficiaries can be computed in either “Stock” or “Flow” method. With the “Stock” method, for each year the stocks of contributors/beneficiaries are calculated first and then inflows (new contributors/beneficiaries) are derived as the changes of the stocks: _Inflow(a,t,g) = stock(a,t,g) – stock(a-1,t-1,g) + outflow(a,t,g)_ With the “Flow” method, inflows are calculated first and then stocks are derived as previous year’s stocks in each age/gender cohort adjusted for the net inflow (inflowoutflow): _Stock(a,t,g) = stock(a-1,t-1,g) - outflow(a,t,g) + inflow(a,t,g)_ where a = age, t = year, g = gender As PROST keeps track of contribution years of service accrued by each cohort, the calculated number of new retirees – whatever method is used – is then adjusted so that the total length of service accrued by the cohort is equal to the total length of service

- 98 -
|PO|ModelBI||ModelB2_||ModelBS __||
|---|---|---|---|
||<br>(4.e_)|<br>(35.6.<br>}|<br>|s.e.]|
|In| population|o.122"TM"|—ime€|—).2s0"TM|
|In| population<br>||o.192""<br>|0.945"**<br>|
||||<br><br>|
|<br>In{populationin<br>|magiboriccaodont)—||||<br>0.326"<br> us)|<br>—1.80***<br>oa|
|<br> <br>In{populationin<br>|gibericcaonde)<br>|<br>||<br>|<br>—0.141"<br>wuss)<br>|<br>2.00"**<br>|(ony<br>|
|<br> <br>In{pop.<br>in square}<br>|<br>|<br>|<br>0.0378<br>|
|||||
|<br>In{pop.<br>in neighborhood<br>|<br>[indwianeetwente<br>In{pop.|<br>|||||<br>—0.256"<br>oasr)|
|<br><br>in nelghb.<br>2nel orcler)<br>[Mindtianeetwempted|||||<br>0.635"<br>(om|
|<br>In{pop.<br>in neighb. 3rd order)<br>[nidees<br><br>In||<br>cesw<br>|emp<br>||<br>—0.614TM"<br>tad<br>oss|
|population|<br>U.UTS6**|v.07so"|<br>0.177|
|Injdistance|0.0959*"*|0.156**|ogee|
|In| cliatance|—U.LuT|—U.1l3a"*|—O.174"|
|Border|1.15***|1.410"|1.14°"*|
|Primaryroads||||
|<br>Road type 1:|—).650°TM*|—v.770""|—0.737"|
|Road type 2:|—1.41**|—1.65°*|—1.76**|
|Proximity of previous|6.05***|6.12"|6.02"**|
|Proximity of previous event|—0.973"*|—i.s93""|—).738""|
|Proximity of previous event|—0.0430|—0.0955|—0.245|
|In chstance to|—_430°"|—0 434°"|—U.463*"|
|No of. tailures|2291|2216|2716|
|Il<br>nullmoclel|—s0a71l.d2|—AUaTl.a2|—dU571.32|

Table 2: Cox Regression Estimation Resultd]Risk of Conflict Event, Population Size and Concentration Models, Inclucing Distance to Rebel Group Headquarter, 1960-2004
Table 2: **Alternative measures of remote work and internet penetration Panel A. Internet Servers**

||Full<br>(1)|Exporters<br>(2)|Non-Exporters<br>(3)|Full<br>(4)|
|---|---|---|---|---|
|Exporter||||1_._698<sup>_∗∗∗_</sup><br>(0_._559)|
|WfH x digital infra.|0_._644<sup>_∗∗∗_</sup>|1_._072<sup>_∗∗∗_</sup>|0_._590<sup>_∗∗_</sup>|0_._707<sup>_∗∗∗_</sup>|
||(0_._186)|(0_._336)|(0_._237)|(0_._237)|
|WfH x digital infra. x exporter||||_−_0_._090|
|||||(0_._353)|
|Country F.E.|Yes|Yes|Yes|Yes|
|Sector F.E.|Yes|Yes|Yes|Yes|
|R-squared|0_._260|0_._307|0_._239|0_._261|
|Observations|65,742|12_,_910|52_,_832|65_,_742|
|The F-stat testing the equality of coefcients in|columns 2 and|3 is 1.375, with p v|alue 0.241.||
|Estimations also control for (log) frm’s size, qu|arter F.E, and|Google’s Mobility|index. Estimations are||
|weighted using country weights. Standard erro|rs in parenthes|es. Signifcance le|vels ***, **, and *,||
|correspond to p_<_0.01, p_<_0.05, p_<_0.1, respectiv|ely.||||
|**Panel B. Remote w**|**ork defne**|**d atgranula**|**r sector level**||
||Full|Exporters|Non-Exporters|Full|
||(1)|(2)|(3)|(4)|
|Exporter||||1_._930<sup>_∗∗∗_</sup>|
|||||(0_._577)|
|WfH x digital infra.|1_._956<sup>_∗∗∗_</sup>|3_._065<sup>_∗∗∗_</sup>|1_._898<sup>_∗∗∗_</sup>|1_._945<sup>_∗∗∗_</sup>|
||(0_._441)|(1_._054)|(0_._500)|(0_._480)|
|WfH x digital infra. x exporter||||0_._220|
|||||(0_._759)|
|Country F.E.|Yes|Yes|Yes|Yes|
|Sector F.E.|Yes|Yes|Yes|Yes|
|R-squared|0_._267|0_._312|0_._245|0_._267|
|Observations|63,126|12_,_180|50_,_946|63_,_126|

The F-stat testing the equality of coefficients in columns 2 and 3 is 1.001, with p value 0.317. Estimations also control for (log) firm’s size, quarter F.E, and Google’s Mobility index. Estimations are weighted using country weights. Standard errors in parentheses. Significance levels ***, **, and *, correspond to p _<_ 0.01, p _<_ 0.05, p _<_ 0.1, respectively. The amenability of remote work and internet use/penetration do enter the regression individually because the former are absorbed in sector fixed-effects and the latter in country fixed-effects.

about 15 percent of the mean difference between the average changes in the sales of the exporting firms in the two sectors (based on the coefficients reported in Table 2, Panel A, column 4).

The positive and statistically significant coefficient on the interaction between remote work and digital penetration also holds when the amenability to remote work variable is defined at the 4-digit level of classification. (Table 2, Panel B).

# **4.2.2 Endogeneity**

Reverse causality is less of a concern in our empirical specification because our interaction variables remote work and digital infrastructure, respectively defined at the sector and country levels, are exogenous to individual firm performance.

However, omitted variable bias might still be a concern, that is, if additional explanatory variables that vary both with industry and country are correlated with the interaction between amenability to 10
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