rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Prediction 4: Heterogeneous Effects on Number of Female Candidates by Gender of Past Winner

|**Panel A**|**# Female **|**candidates**|**Incumbe**<br>**candidate**|**nt party**<br> **is female**|**Non-incu**<br>**candidat**|**mbent party**<br>**e is female**|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
||Previous<br>winner<br>is female|Previous<br>winner<br>is male|Previous<br>winner<br>is female|Previous<br>winner<br>is male|Previous<br>winner<br>is female|Previous<br>winner<br>is male|
|_Treat_|0.261|-0.217|0.111|-0.045|0.035|0.064|
||(0.111)**|(0.236)|(0.049)**|(0.037)|(0.017)**|(0.052)|
||[0.075]*|[0.219]|[0.075]*|[0.156]|[0.075]*|[0.069]|
|**Panel B**|||**First **|**Stage**|||
|_Cut-of_|-0.200**|-0.201**|-0.206**|-0.204**|-0.202**|-0.179**|
||(0.084)|(0.085)|(0.084)|(0.085)|(0.084)|(0.090)|
|Untreated _y_ Mean|0.055|0.405|0.021|0.025|0.004|0.038|
|Observations|1,497|1,605|1,614|1,618|1,596|1,387|
|State FE|x|x|x|x|x|x|
|Year FE|x|x|x|x|x|x|
|State*Year FE|x|x|x|x|x|x|
|Clusters|202|216|217|218|215|187|
|Bandwidth|11.705|12.490|12.789|12.831|12.455|10.961|

_Notes: y_ refers to the dependent variable. Estimates are for unreserved seats only. Columns (1) and (2) show impact estimates on the number of female candidates contesting a seat for elections where the previous winner was a woman or a man, respectively. Columns (3) and (4) show impact estimates on the probability that the incumbent party that won the seat previously runs a female candidate when the previous winner was a woman or a man, respectively. Columns (5) and (6) show the corresponding estimates for non-incumbent parties that lost previously. Robust standard errors clustered by the running variable in parentheses. Anderson (2008) q-values in square brackets. Bandwidth is reported in female literacy rate percentage points around *** ** * treatment cut-off of 39.3 percent. _p <_ 0 _._ 01 ; _p <_ 0 _._ 05 ; _p <_ 0 _._ 10.

fielded the winning female candidate in the previous election) as well as for other parties (whose candidates lost to a female candidate in the last election).<sup>17</sup> The probability that the incumbent party and other parties field a female candidate increases, respectively, by five times and nine times in DPEP districts relative to non-DPEP districts if the winner in the previous election was female. Thus, all political parties field more female candidates in DPEP districts after the previous election was won by a woman.<sup>18</sup>

> 17Similar to the results in column (2), there is no effect from a male candidate’s win for either incumbent or non-incumbent parties in columns (4) and (6).

> 18In a similar manner, we investigate whether exposure to female leaders at the village level differentially changes the impact of the DPEP on the probability of women winning elections at the state level. We exploit the variation in exposure to village-level female leaders induced by the mandated reservation of one-third of village council seats and headships for women in combination with the differential timing of village council

22
- Narang, Neil and Jessica A. Stanton. 2017. “A Strategic Logic of Attacking Aid Workers: Evidence from Violence in Afghanistan.” International Studies <u>Quarterly 61(1):38–51.</u>

- Naylor, Sean. 2008. “Insurgents in Afghanistan Have Mastered Media Manipulation.” Armed ForcesJournal .

- NCTC. 2023. “Afghan Taliban.”.

**URL:** _https://www.dni.gov/nctc/groups/afghantaliban.html_

- PBS. 2006. “Flying Down to Kabul, Women in Afghanistan: Education.”.

- **URL:** _https://www.pbs.org/wnet/wideangle/uncategorized/women-in-afghanistaneducation/2200/_

- Pourghadiri, Aryan. 2020. “The Roots of Public Distrust to the Afghan Government.” East Studies: Institute for East Strate ic Studies . <u>g</u>

- Roggio, Bill. 2017. “SIGAR: ‘Afghan government district control deteriorated’.” FDD’s Long War <u>Journal .</u>

- **URL:** _https://www.longwarjournal.org/archives/2017/10/sigar-afghan-government-districtcontrol-deteriorated.php_

- Ruiz, Isabel and Carlos Vargas-Silva. 2013. “The Economics of Forced Migration.” The <u>Journal</u> of Development Studies 49(6):772–784.

- Shemyakina, Olga. 2011. “The effect of armed conflict on accumulation of schooling: Results from Tajikistan.” <u>Journal</u> of Development Economics 95(2):186–200.

- Singh, Prakarsh and Olga N. Shemyakina. 2016. “Gender-differential effects of terrorism on education: The case of the 1981–1993 Punjab insurgency.” Economics of Education Review 54:185–210.

- Swee, Eik Leong. 2015. “On war intensity and schooling attainment: The case of Bosnia and Herzegovina.” European <u>Journal</u> of Political Economy 40:158–172.

32
77

# **Table A3a Dependent Variable: Growth Rate of Real GDP Per Worker Information and Communications Technologies (ICT) Pillar Indicators**

|**Years : 1960 - 2000**|**Reg A3a.1**|**Reg A3a.2**|**Reg A3a.3**|**Reg A3a.4**|**Reg A3a.5**|**Reg A3a.6**|**Reg A3a.7**|**Reg A3a.8**|
|---|---|---|---|---|---|---|---|---|
|**Growth of Capital**<br>**per worker**|**0.2350*****|**0.2293*****|**0.2298*****|**0.2360*****|**0.2119*****|**0.2298*****|**0.2354*****|**0.2324*****|
|**Initial GDP (1960)**|**-2.5410*****|**-3.5813*****|**-2.9514*****|**-2.9244*****|**-3.7103*****|**-3.3504*****|**-2.3082*****|**-2.6036*****|
|**HC15**|**4.6508****|**3.9910*****|**5.2399****|**5.7432*****|**4.4661****|**3.9538***|**4.3988****|**4.5494****|
|**Log (Educational Quality)**|**0.7044**|**0.5363**|**0.4788**|**0.6516**|**0.3719**|**0.5727**|**0.6775**|**0.6134**|
|**Sq (HC15)**|**-1.0271****|**-1.0697*****|**-1.2780****|**-1.3677*****|**-1.0965****|**-0.9356***|**-0.8424**|**-1.0317****|
|**Log (Patents)**|**0.2640*****|**0.2474*****|**0.2586*****|**0.2636*****|**0.1956*****|**0.1707***|**0.2894*****|**0.2510*****|
|**Computers**||**0.0086*****|||||||
|**Internet Users**|||**0.0084*****||||||
|**Hosts**||||**0.0017*****|||||
|**Phones**|||||**0.0040*****||||
|**TV**||||||**0.0037***|||
|**Radio**|||||||**-0.0011***||
|**News**||||||||**0.0010**|
|**Constant**|**-6.2701*****|**-4.4327****|**-5.7535*****|**-6.8771*****|**-4.5304****|**-5.0121****|**-6.0569*****|**-5.7971*****|
|**R squared**|**0.5556**|**0.6165**|**0.5779**|**0.5687**|**0.5912**|**0.5712**|**0.5663**|**0.5587**|
|**Number of Countries**<br>*, **, *** denotes significance at the 9<br>Robust standard errors obtained usin|**85**<br>0, 95 and 99 perce<br>g White's correctio|**81**<br>nt confidence level r<br>n for heteroscedasti|**85**<br>espectively<br>city.|**85**|**85**|**85**|**85**|**85**|
# **Childcare and Maternal Employment: Evidence from Vietnam**

Hai-Anh H. Dang<sup>a</sup> Masako Hiraga<sup>b</sup> Cuong Viet Nguyen<sup>c</sup> Keywords: Gender equality, child care, maternal employment, women’s empowerment, Vietnam.

JEL codes: J13, J16, J22, H42

-----------------------------------------------------

> aDang <u>(hdang@worldbank.org; corresponding author), World Bank, Indiana University, and Vietnam’s</u> Academy of Social Sciences; bHiraga (mhiraga@worldbank.org), World Bank; cNguyen (cuongwur@gmail.com; corresponding author), Mekong Development Research Institute, and National Economics University, Vietnam. We would like to thank Ousmane Dione, Gary Fields, John Gibson, Ha Nguyen, Tomas Rau, Cliff Waldman, and participants at a training workshop organized by General Statistical Office (Hanoi), Society of Government Economists conference (Washington, DC), and IZA-WB-NJD Jobs and Development conference for helpful comments on earlier versions. We are grateful to the UK Department of International Development for funding assistance through its Trust Fund for Statistical Capacity Building (TFSCB) and Strategic Research Program (SRP).
1998, the average catch-up rate of a service-based<sup>index reached a level of 16.5 percentper</sup> annum. This suggests that even though an average<sup>bank in the region might have reacheda</sup> satiation point in terms of its ability to improve<sup>upon revenue performance(either because it</sup> knew a lot to start with or further improvements<sup>are outside of their control, or both), it had a lot</sup> to leam in terms of reaching out and delivering<sup>transactionservices to its clients.</sup> Second, although experiencing less of a lag compared<sup>to banks in CE and SEEin terms</sup> of revenue generating capacity, banks in CIS<sup>are clearly in a more disadvantageousposition if</sup> judged by the service-based index. However, it<sup>tums out there are dramatic differences between</sup> the rate of catch-up in a service-based index<sup>acrosssub-regions:the DEA2 index for CIS</sup> countries grew on average 30 percent per<sup>annum,followed by almost16 percentin SEE</sup> countries, and only 11 percent in CE countries.

Third, with the exception of 1997, banks in Central<sup>Europeare more efficient in terms of</sup> both revenue generating capacity as well<sup>as theability to provide services to their clients.</sup> A skeptical reader should note that the presence of<sup>the sample bias -resulting because</sup> countries with more advanced banking systems<sup>may have a largershare of their banks (i.e.,both</sup> strong and weak banks) presented in the sample<sup>-does not run infavor of CE banks.On the</sup> contrary, eliminating the bias (by ensuring equal<sup>representation from allcountries)is likely to</sup> make the difference between CE and CIS look<sup>larger.</sup> Overall, we conclude that the trends described<sup>aboveare largelyconsistent with one's</sup> expectations about banking sectors in various<sup>transition countries,thereby indicating that the</sup> DEA analysis could be successfully applied<sup>in studies of banking in transition economies.</sup> Despite concems about the quality of data, the<sup>analysis providesa somewhat plausible outcome</sup> in terms of the relative ranking of countries<sup>and sub-regions.Thus confident in the quality of the</sup> indicators, we proceed to the next chapter where<sup>we evaluatethe effectsof various institutional</sup> and policy settings on these indicators.

# Chapter 4. Second Stage Regression Results

The purpose of this section is to take a step further<sup>in analyzing banking sector</sup> productivity indicators by looking at their<sup>potentialdeterminants. In doing so let us assume that</sup> 15
**Table 5: Trade flows observed at the Ras Ajdir border crossing**

||Number<br>of<br>vehicles<br>observed|Number<br>of<br>vehicles per<br>day|Average<br>quantity*|Average cost<br>of load at<br>point<br>of<br>h**|Average cost<br>of load at<br>point<br>of<br>l**|
|---|---|---|---|---|---|
|||||purcase|sae|
|Foodproducts||||||
|Cars|5|21|185|1618|1758|
|Trucks and vans|4|14|7520|2333|3860|
|Householdgoods||||||
|Cars|5|21|28|1090|1290|
|Trucks and vans|3|10|35|3600|4250|
|Othergoods||||||
|Cars|1|4|.|2000|2800|
|Trucks and vans|2|7|1000|10000|15000|
|Bananas||||||
|Cars|1|4|393|600|920|
|Trucks and vans|17|59|3,117|4,618|5,408|
|Shoes||||||
|Cars|0|0|0|0|0|
|Trucks and vans|2|7|1,500|11,250|13,000|
|Whitegoods||||||
|Cars|19|81|20|1,026|1,100|
|Trucks and vans|9|31|48|2,822|3,353|
|Electronic equipment||||||
|Cars|7|30|13|884|1,041|
|Trucks and vans|3|10|75|12,667|13,600|
|Fuel||||||
|Cars|58|246|72|25|72|
|Trucks and vans|3|10|50|12|27|
|38-ton trucks|0|200|954|330|954|
|Tires||||||
|Cars|1|4|50|400|480|
|Trucks and vans|1|3|200|10,000|11,000|
|Apples||||||
|Cars|1|4|0|300|350|
|Trucks and vans|6|21|2,400|4,317|4,626|
|Carpets||||||
|Cars|5|21|25|1,090|1,220|
|Trucks and vans|2|7|30|1,800|1,900|
|Tea||||||
|Cars|0|0|0|0|0|
|Trucks and vans|3|10|803|9,800|11,700|
|Clothing||||||
|Cars|25|106|76|1,245|1,522|
|Trucks and vans|10|35|188|3,000|3,465|

* per vehicle

** per vehicle, in TND

Source: Authors' calculations based on fieldwork 16
fuel type in the column heading. Consequently, magnitudes of the own-price elasticities of demand are smaller in this table than in the previous table, which uses price changes of individual vehicles to compute elasticities. In Table 7, the cross-price elasticities of demand indicate that hybrid vehicles are closer substitutes for electric vehicles than are gasoline vehicles. Intuitively, consumers regard electric vehicles as closer substitutes for hybrid vehicles than for gasoline vehicles. The reasonableness of the consumer substitution patterns helps validate the demand model.

Table 7: Own- and cross-price elasticity of demand by fuel type

|Fuel Type|Electric<br>Hybrid|Gasoline|
|---|---|---|
|Electric|-4.0<br>0.0|2.6|
|Hybrid|0.1<br>-3.9|2.5|
|Gasoline|0.0<br>0.0|-2.1|

Note: This table shows the own- and cross-price elasticities by fuel type. Elasticities are the percent change in market share of the fuel type caused by a 1 percent increase of the prices of all vehicles belonging to the fuel type in the column heading.

# 4. Policy Simulations

## 4.1 Scenarios

We compare a baseline no-policy scenario that removes the preferential sales and import taxes against scenarios that include one or both of the preferential taxes. We use data from 2019 and 2020 for the simulations and report results separately for each year. Simulating outcomes for 2019 allows us to characterize the equilibrium before the COVID-19 pandemic, which likely disrupted Colombia’s new vehicle market as the economy contracted. The market shares of hybrids and electrics increased threefold between 2019 and 2020 (see Figure 1), and it is interesting to consider the effects of the tax policies when market shares reached historical highs. The baseline or ‘no-policy’ scenario includes the pre-reform status quo of 19 percent sales tax and a 30 percent import tariff for all vehicles. In the first policy scenario, we reduce the sales tax to 5 percent for hybrid and electric vehicles. In the second policy scenario, we reduce the import tariff to 5 percent for hybrids and 0 for electrics; we maintain the 30 percent import tariff for gasoline vehicles and the sales tax at 19 percent for all vehicles. The second scenario is the actual 22
31 Di Tella, Rafael, and Emesto Schargrodsky. 2001. Using a Terrorist Attack to Estimate the Effect of Police on Crime. Center for Research on Economic Development and Policy Reform Working Paper No. 90.

Dodson, Belinda. 2002. Gender and The Brain Drain from South Africa. The Southern African Migration Project. Migration Policy Series No. 23.

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Elbers, C., P. Lanjouw, J. Lanjou'w, and P.G. Leite. 2001. Poverty and Inequality in Brazil: New Estimates from Combined PPV-PNAD Data. Processed.

Elbers, C., P. Lanjouw, and J. Lanjouw. 2002. Micro-level Estimation of Poverty and Inequality. _Econometrica._ Forthcoming.

Fafchamps, Marcel, and Christine Moser. 2002. Crime, Isolation, and the Rule of Law. Processed.

Fajnzylber, Pablo, Daniel Lederman, and Norman Loayza. 2000. What Causes Violent Crime? Forthcoming in _The European Economic Review._ Fajnzylber, Pablo, Daniel Lederman, and Norman Loayza. 2001. Inequality and Violent Crime. Forthcoming in _The Journal of Law and Economics._ Glaeser, Edward L., and Bruce Sacerdote. 1999. Why is There More Crime in Cities? _The Journal of Political Economy._ Volume 107, Issue 6, Part2, S225-S258.

Gould, Eric D., Bruce A. Weinberg, and David B. Mustard. 2002. Crime Rates and Local Labor Market Opportunities in the United States. _The Review of Economics and Statistics,_ 84(1): 45-61.

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Lederman, Daniel, Norman Loayza, and Ana Maria Menendez. 2001. Violent Crime: Does Social Capital Matter? Forthcoming in _Economic Development and Cultural Change._
Many countries that started their industrialization process as exporters of textile and apparel products, subsequently diversified into different sectors that offered more development potential. Over time, as they overcame transaction costs to serve international markets and as the capabilities of domestic firms deepened, countries tend to transition and gradually specialize in more sophisticated sectors. This upgrading patterns has many dimensions, as discussed in Van Assche and Van Biesebroeck (2018). Industrial upgrading, moving activities from less to more sophisticated industries, is one such aspect and there is weak evidence that it coincides with functional upgrading, meaning that domestic firms become responsible for a broader range of activities.

In Figure 2 we show the evolution of textile exports for four countries that have already completed more of a development trajectory than any country in sub-Saharan Africa. In each case we see textile exports accelerating sharply, but after some years export growth tailors off. At that point these countries started to experience export success in products where they are able to capture more value added domestically.

In the case of Mexico, the growth in textile exports after the NAFTA agreement came into effect in 1994 was extremely fast. But Mexican textile exports declined quickly once its firms started to integrate in the North American production structure performing activities where timely delivery of inputs was highly valuable, as in the automotive industry (Sturgeon, Van Biesebroeck and Gereffi, 2008). Similarly, textile exports from Malaysia grew by 150% between 1988 and 1996, but subsequently its manufacturing sector shifted focus to production of electronic equipment. Growth in textile exports from Turkey and China was also very rapid, but only took off as quotas were relaxed in the gradual elimination of the MFA, mostly between 2001 and 2005. Subsequently, their manufacturing sectors also oriented away from textiles and by 2012 growth in textile exports also stalled for these countries.

**Figure 2:  Evolution of total exports of textiles and apparel (first year = 100)**

<!-- Start of picture text --> 800 1200<br>700 1050<br>600 900<br>500 750<br>400 600<br>300 450<br>200 300<br>100 150<br>0 0<br>1989 1993 1997 2001 2005 2009 2013 2017<br>Mexico Turkey Malaysia China (right index)<br><!-- End of picture text --> Source: Calculations based on data from UN Comtrade. Nominal export growth rate in current USD. All indices are normalized at 100 in the first year shown. Values for the first three countries are shown on the left scale and for China on the right scale.

6
Learning from Developing Country Power Market Experiences: The Case of Philippines <!-- Start of picture text --> MW<br> 9,000 8.0%<br> 8,000 7.0%<br>6.9%<br> 7,000 6.0%<br> 6,000 4.9%<br>5.0%<br> 5,000<br>4.0%<br> 4,000<br>3.0%<br> 3,000<br>2.0%<br> 2,000<br>1.5%<br> 1,000 1.7% 1.0%<br>0.6%<br> ‐ 0.0%<br>Coal Oil Gas Hydro Non‐hydro<br>renewables<br>2005 2017 2005‐2017 CAGR<br><!-- End of picture text --> Figure 5‐7 Evolution of technology‐wise generation operational capacity in Philippines.

Source: Department of Energy.

Investments in new generation capacity are primarily backed by bilateral contracts (about 70%‐80%) to provide revenue stability and bankability. Given the crucial impact of bilateral contracts in generation investment decisions, key elements for power market outcomes include (1) the incentives and structure of the distribution and retailing market (including horizontal and vertical integration); and (2) the role of the regulator (ERC) in reviewing and approving Power Supply Agreements. Neither of these issues was extensively analyzed in this case study, since the focus of the paper is on wholesale markets at the generation level. In this regard, it is worth noting that market‐driven spot prices in the Philippine’s WESM largely reflect supply and demand conditions, thus providing complementary signals for generation investments (despite not being the primary signal for investment decisions). Indeed, a few merchant power plants have been recently commissioned, and these projects are thus fully exposed to the volatility and uncertainty of spot prices.<sup>22</sup> Despite the recent trends of investment in new generation capacity, it is worth noting that supply security and the overall market performance hinge on the adequacy of both transmission and distribution infrastructure. However, these issues are out of the scope of this case study.

# 5.3 Sustainability

Philippine’s generation fleet is largely dominated by fossil‐fueled power plants, accounting for 69% of installed capacity in 2017 (see Figure 5‐8). Renewables in turn accounted for 31% of installed capacity in 2017, down from 33% in 2006. Such trend of renewable capacity is a mix of the slow decline of large hydro

> 22 BPI, 2015, “Financing Energy Projects”. Available online: <u>https://www.doe.gov.ph/sites/default/files/pdf/e_ipo/04_bank_of_the_philippine_islands_financing_facility_for_ene rgy_projects.pdf</u>

39
**Table 7. Effect of preschool availability on female’s occupation**

||(1)<br>Professional|(2)<br>Manager|(3)<br>Clerk|(4)<br>Sales|(5)<br>Service|(6)<br>Agricultural|(7)<br>Production|
|---|---|---|---|---|---|---|---|
|**Panel A: Public**||||||||
|Public * Eligible|-0.002|-0.001|-0.001|-0.007|0.013|0.023**|0.006|
||(0.005)|(0.001)|(0.004)|(0.008)|(0.009)|(0.010)|(0.007)|
|Public Preschools|-0.003|-0.000|-0.004|-0.007|-0.012|0.022*|0.002|
||(0.004)|(0.001)|(0.004)|(0.007)|(0.007)|(0.013)|(0.007)|
|Eligible Child|-0.003*|0.000|-0.009***|-0.004|-0.011***|0.007**|-0.005**|
||(0.001)|(0.001)|(0.001)|(0.003)|(0.002)|(0.003)|(0.002)|
|FDR q-value|0.884|0.884|0.884|0.884|0.821|0.108|0.884|
|**Panel B: Private**||||||||
|Private * Eligible|-0.000|0.000*|0.000|0.001|-0.001|-0.001|0.002***|
||(0.000)|(0.000)|(0.000)|(0.001)|(0.001)|(0.001)|(0.001)|
|Private Preschools|0.000|-0.000|-0.000|0.000|-0.004***|0.003**|-0.000|
||(0.000)|(0.000)|(0.000)|(0.001)|(0.001)|(0.002)|(0.001)|
|Eligible Child|-0.002|-0.001**|-0.010***|-0.008|-0.006|0.013**|-0.011***|
||(0.002)|(0.001)|(0.002)|(0.005)|(0.004)|(0.006)|(0.003)|
|FDR q-value|0.860|0.399|0.860|0.860|0.860|0.860|0.055|
|Observations|226,400|226,400|226,400|226,400|226,400|226,400|226,400|
|Mean|0.046|0.001|0.030|0.143|0.104|0.177|0.073|

Notes: Sample is restricted to females aged 19-45 in at least two IFLS rounds. We infer preschool data in-between PODES years. Each column regresses the dummy for being employed in a certain occupation category indicated in column heading on the density of public (Panel A) or private (Panel B) preschools, defined as the number of preschools divided by the population of children aged 3-6 in each district, a dummy for having a preschool-aged eligible child, and their interaction. Comparison group includes all mothers of children of the wrong ages and non-mothers. All regressions include mother’s age fixed effect, urban dummy, district, year, and individual fixed effects. Robust standard errors clustered at district level. Stars denote statistical significance at 1, 5, and 10 percent levels based on unadjusted p-values. FDR q-values for the interaction coefficient of preschool density and eligible child are computed over all 7 outcomes and are shown in the last row of each panel. FDR q-values indicate the probability of false positives among _significant_ tests. Number of female-year observations and means of dependent variables are indicated in the last two rows. See Table 6 for a list of top3 occupations per category.

52