rafmacalaba/datause-encoder-data huggingface dataset:
If e-commerce participation is random across the population, one may use the following model to assess the causal impact of e-commerce participation on household income: (1) where is household income per capita of household i, ii ii is a constant term, ii takes value one if YY = αα+ γγPP + εε household _i_ participates in e-commerce and zero otherwise, gives the difference in average ii ii household income per capita between the e-households and non-e-households, YY αα PP contains both the observed and unobserved (to an econometrician) characteristics of household γγ _i_ . Under the ii εε assumption that e-commerce participation is random, is independent of . As a result, captures the average treatment effect on the treated (ATT) of e-commerce participation on ii ii PP εε γγ household per capita income.

However, our survey data are collected in an un-experimental setting, where e-commerce participation is not random but determined by household _i_ ’s choice based on . The dependence between and introduces selection bias in the estimate of . In other words, the choice of ii ii PP εε treatment and control groups simply based on e-commerce participation is far from scientifically ii ii PP εε γγ rigorous. In addition, the linearity assumption in equation (1) can be strong and could lead to serious bias if specified inaccurately (Imbens 2015).

To remediate the un-experimental setting, we use propensity score matching (PSM). Rosenbaum and Rubin (1983) developed the use of the propensity score—the probability of receiving treatment conditional on covariates—to reduce the dimensionality of the matching problem. Since then, PSM has been widely employed in empirical economic studies (Heckman et al. 1997, Dehejia and Wahba 1999, and Davies and Kim 2003), and many other fields where there is a treatment, a group of treated individuals, and a group of untreated individuals. After matching, PSM does not assume any parametric relationship between outcome variables and participation decisions to calculate the treatment effects, which is another reason PSM is more reliable than the model in equation (1).

# **_3.1 Assumptions and caveats_**

Identification using the PSM method rests on two assumptions: unconfoundedness and common support. The application of PSM and its results regarding the size of the causal effects are subject to several caveats.

# **_<u>Unconfoundedness</u>_**

The assumption of unconfoundedness states that: YY(0), YY(1) ⊥PP | XX

where ⊥ denotes independence, that is, given a set of observable covariates X that are not affected by treatment, potential outcomes are independent of treatment assignment.<sup>10</sup> This implies that all variables that influence treatment assignment and potential outcomes simultaneously must be

> 10 In plain English, unconfoundedness means participation is a random event among people after controlling for the set of observed characteristics.

11
data. Based on these two assumptions, we split the original �0c�� based on the following formula: 3. To set the initial value for �0��, total domestic demand for goods and services supplied by firm group _g_ in sector _i_ (i.e., the sum of private consumption, government spending, fixed capital investment, and inventory changes), we use the following formula: Notice that we implicitly assume that the supply of intermediate products/inputs for domestic use from each firm type in a sector is proportional to their gross output in that sector. To make the model fully initialized and operational, we also need the relative shares of different firm types in the country’s total exports and imports for each of the 42 sectors. Such information is readily available in the disaggregated trade statistics from China’s Customs.

# **4. Estimating Indirect Contribution to Value-Added Exports by Firm Size and Ownership Type**

# **4.1 Main Results**

# **4.1.1 Relative Importance in the Aggregate Economy**

Based on the estimates of the model described in Sections 2 and 3, we portray the domestic segment of GVC in China. Table 1 shows that SOEs account for 19% and 9% of value added and employment of China in 2008, respectively. The relatively small shares of SOEs are partly due to years of economic reforms led by the Chinese authorities to privatize and let go SOEs, especially the small ones in downstream sectors. SOEs’ contributions to gross exports and value-added exports (VAX) in 2007 are 12% and 21%, respectively. The large difference between SOE’s contributions to value added and gross exports suggests that SOEs have a higher share of indirect exports through other firms, compared to other firm ownership types. Notice that while SOEs’ gross export share declined significantly from 12% in 2008 to 9% in 2010, their share in value added exports actually increased. We will focus on analyzing these opposite trends in greater detail below.

(Insert Table 1 here) 20
treatment status for a concentration was jointly identified using two primary sources: [1] the Kankor exam selection sheet produced by the administering authority as an example, is shown in Figure A1.2, and [2] the Kankor matriculation dataset, which explicitly denoted whether an applicant was admitted into the treated units. If there were any discrepancies between the two sources, the author contacted a specific university and the MoHE to identify the treatment status for these concentrations. For example, four religious’ studies units were admitting applicants based on gender before 2016 in the matriculation dataset. Nonetheless, these units historically enrolled students in a gender-specific manner for cultural reasons, and were not subject to the gender quota. Hence, these concentrations were not considered treated units because of the gender quota policy in the analytical sample.

The yearly descriptive statistics of Kankor participants—the share of female students and that of female students admitted into 4-year public IHEs are reported in Table B1.1. The number of concentrations increase over time, as shown in Table B1.1, which does not threaten the identification strategy, as I controlled for the concentration fixed effects. This increase was driven by the splitting of enrollment levels from general admission into several concentrations in some fields of study, the enlargement of academic programs, and the establishment of universities for further details (see Appendix A.4).

Moreover, I obtained monthly nightlight data as an average of the Day/Night Band radiance values in nanoWatts/cm2/sr units from Stray Light Corrected VIIRS composited at the district level for 2014– 2015<sup>18</sup> . I excluded pixels with zero values across all images to increase the accuracy of the zonal statistics computations at district level. The use of nightlight as a proxy for economic development and measure of economic activities (Basihos, 2016; Beyer et al., 2018; Galdo et al., 2020; McCord & Rodriguez-Heredia, 2022; S ̈anger et al., 2022) is becoming popular. High economic development, which correlates with enhanced service provision, will enable women to access better educational services. Conversely, a low level

> 18The Visible Infrared Imaging Radiometer Suite (VIIRS) Stray Light Corrected Nighttime Day/Night Band Composites Version 1 data were not available prior to 2014.

15
# **Table 2: Mortality as a function of per capita income and period, by age group**

|||Sub-Saharan<br>|African countrie<br>|s<br>||Countries i<br>|n other regions<br>||
|---|---|---|---|---|---|---|---|---|
||Ages<br>15-24|Ages<br>25-34|Ages<br>35-44|Ages<br>45-54|Ages<br>15-24|Ages<br>25-34|Ages<br>35-44|Ages<br>45-54|
|GDP pc (ln)|-0.132|-0.045|0.054|0.026|-0.258|-0.274|-0.243|-0.214|
||(0.037)**|(0.042)|(0.051)|(0.064)|(0.081)**|(0.083)**|(0.073)**|(0.106)*|
|1980-84|-0.100|-0.000|0.120|0.130|-0.180|-0.135|-0.194|0.639|
||(0.102)|(0.117)|(0.146)|(0.232)|(0.195)|(0.199)|(0.175)|(0.252)*|
|1985-89|-0.014|0.048|0.173|0.112|-0.335|-0.335|-0.270|0.179|
||(0.103)|(0.118)|(0.146)|(0.230)|(0.195)|(0.199)|(0.175)|(0.252)|
|1990-94|0.107|0.207|0.444|0.266|-0.335|-0.330|-0.171|0.282|
||(0.104)|(0.120)|(0.148)**|(0.231)|(0.195)|(0.199)|(0.176)|(0.254)|
|1995-99|0.221|0.507|0.628|0.651|-0.463|-0.399|-0.255|0.345|
||(0.108)*|(0.124)**|(0.153)**|(0.235)**|(0.219)*|(0.223)|(0.197)|(0.285)|
|2000-04|0.360|0.725|0.852|0.849|-0.479|-0.234|-0.119|0.604|
||(0.120)**|(0.137)**|(0.169)**|(0.249)**|(0.237)*|(0.242)|(0.213)|(0.308)|
|Observations|180|179|175|147|68|68|68|66|
|R-squared|0.23|0.25|0.19|0.18|0.35|0.34|0.37|0.38|

Note: Standard errors in parentheses. * significant at 5%; ** significant at 1%.

46
# **3. Data**

# **3.1 POEA Micro Data**

The data are from the Philippine Overseas Employment Administration’s (POEA) database of departing OFWs. Created in 1982, POEA is a Philippine government agency within the Department of Labor and Employment. POEA has a multifaceted agenda: it monitors recruitment agencies, monitors worker protection, and conducts a variety of other tasks relating to the oversight of the overseas worker program. Further, as a final step prior to departure, all OFWs are required to receive POEA clearance. Since all OFWs are required to pass through POEA, the agency has a rich dataset composed of all migrant departures from the Philippines. This is the first paper to utilize this rich data resource.

Since all OFWs must pass through POEA, the dataset contains data on departures for all land-based new hires leaving the Philippines between 1992 and 2009 for temporary contract work. New hires are defined as OFWs who are starting a contract with a new employer. These migrants may have previously worked overseas, but the contract that they are presently departing on is new, rather than renewed. For each OFW departure from the Philippines, the database includes name, birthdate, gender, civil status, destination, employer, recruitment agency, contract duration, occupation, date deployed, and salary. Typical contracts are of one or two year durations, with an average duration of 17.7 months over our sample period. Female workers account for 60.6 percent of new hires during this period. The most common occupations are in production (e.g., laborers, plumbers), services (domestic helpers, cooks) and professional occupations (nurses, engineers, entertainers).

To study the flows of migrants in response to fluctuations in GDP, individual migration records are grouped by year and destination country and combined to create a count of the number of migrants to each destination country annually between 1992 and 2009. Table 1 displays the top twenty OFW destinations averaged over the sample period, along with their average annual flow. Saudi Arabia is the most common destination, accounting for 33% of new hires. It also shows the average monthly wage in US dollars by destination, showing wide differences in the wages Filipinos earn in different locations. Since the micro data contain a few outliers on wages, we trim at the 1<sup>st</sup> and 99<sup>th</sup> percentiles before taking means.

10
<!-- Start of picture text --> iC} 10<br>x services, low developing|,<br>= a<br>z 5 =<br>Er)<br>pan<br>= eencneees ... Sefvices, developed<br>> sini<br>30 7<br>2 we industry, developed<br>a industry, low developing<br>5<br>1990 1995 200 2005 2010<br><!-- End of picture text -->

<!-- Start of picture text --> a<br>3f) 20<br>5 modern services<br>g 15<br>6 ld<br>r=<br>- nn goods<br>c<br>‘s 10 een<br>> ehieneecener**"" traditional services<br>0 5 wenee<br>ea 1% 19% 2000 2005: 2010<br><!-- End of picture text -->
istry was open between April 6 and June 8 of 2018, meaning that migrants who arrived in Colombia after June 8 could not register in RAMV and thus were ineligible for PEP. Furthermore, the RDD takes advantage of the fact that PEP was enacted unexpectedly, was available to all migrants registered in RAMV, and was not paired with other eligibility requirements or policies, which enabled us to rule out behavioral and anticipatory effects as well as simultaneous treatments that have precluded the analysis of similar programs. Specifically, the fuzzy RDD compares eligible and ineligible migrants on each side of the RAMV cutoff date under the following two-stage specification: Equation 1 models the likelihood of receiving PEP based on whether Venezuelans migrated to Colombia before the RAMV registry closed, while equation 2 models the effects on the outcomes of interest _Yij_ as a function of the predicted likelihood of having PEP. Specifically, 1[ _PEPi_ = 1] is an indicator variable that takes the value of one for migrants with PEP, _Ti_ and _T_ ̄ are the date of migration to Colombia and the date when the RAMV registry closed, respectively, and 1[ _Ti < T_<sup> ̄</sup> ] is an indicator variable for whether the migrants arrived in Colombia when the registry was still open. Therefore, the treatment is equal to one for migrants who arrived in Colombia before RAMV closed and had the opportunity to register in RAMV and, subsequently, in PEP. As many PEP benefits are defined for the individual holder (e.g., the right to work), the PEP treatment variable 1[ _PEPi_ = 1] is defined at the individual level for the majority of outcomes. For other outcomes, however, the treatment is defined at the household level because access is legally defined at this level (e.g., access to Sisb ́en and government transfers), or because outcomes were measured at the household level (e.g., consumption) in the survey.

The running variable in the fuzzy RDD is _di_ , the distance measured in days between the mi18
Learning from Developing Country Power Market Experiences: The Case of Colombia <!-- Start of picture text --> 6,000.0<br> 5,000.0<br> 4,000.0<br> 3,000.0<br> 2,000.0<br> 1,000.0<br> -<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>Unrelieved constraints (BCOP)<br>AGC commercial responsibility (BCOP)<br>Volume of energy transactions on pool market (BCOP)<br>BCOP<br><!-- End of picture text --> Figure 6-7 Evolution of total volume of transactions in the Colombian pool, considering energy, reserves (Automatic Generation Control) and transmission constraints.

Source: Own elaboration based on data collected by the World Bank.

As described previously (see section 3.2), the day-ahead pool in Colombia is a single-node market which disregards transmission congestions and any other system constraints. Although these costs are not reflected directly in spot prices ( _“Bolsa”_ prices), these constraint costs do pass-through to final retail tariffs (as described in section 3.2). Constraint costs are highly dependent on hydrology and particular operating conditions. Yearly unrelieved constraint costs in the day-ahead pool increased by 71% in real terms over the past decade, from 285 BCOP in 2005 (expressed in 2015 COP) to 487 BCOP in 2015. As a share of total pool trades, constraint costs have varied from 8% to its highest level of 54% in 2011. Considering the importance of these constraint costs, it has been argued that significant operating cost savings could be achieved by implementing a more efficient congestion management mechanism in Colombia, based on a market clearing more reflective of the physical realities of the transmission grid, and based on Locational Marginal Prices instead of a single-node price (Mcrae & Wolak, 2016). It is worth noting that the rules for transmission grid expansion and management have proved ineffectual to reduce congestion in the main electric link between the center (mostly hydro) and the north (mostly thermal) regions of the country, which translates into gas pipeline congestion.

42
# **Figure 15: Location explains the largest share of inequality in access to basic infrastructure**

<!-- Start of picture text --> 8<br> 22<br> 15 32<br> 51<br> 9<br> 15<br> 9 19<br> 38<br> 11 14<br> 3<br> 45<br> 19<br> 25<br> 22<br> 6<br> 2<br> 7 6 8 9<br> 1 1 1 2 0<br><!-- End of picture text --> _Source:_ DHS 2000 and 2008; HIECS 2000 and 2008 used for the imputation of household consumption.

# **Education**

Four main educational indicators are used to assess inequality of opportunity in access to education. The first two are related to enrollment separately for compulsory and non-compulsory levels: (a) the probability of enrollment for children in the age group 6-14 years old in primary and preparatory levels; and (b) the probability of enrollment for children in the age group 15-17 years old in noncompulsory secondary school education. The third and fourth indicators are related to educational attainment: (c) the probability of completion of 6th grade on time; and (d) the probability of completion of 9th grade on time (preparatory).

The HOI for enrollment in compulsory primary education and non-compulsory secondary education have both improved over the 2000s. The HOI for enrollment in the age group of 6-14 years old increased from about 85 to over 92 percent between 2000 and 2008 (Figure 16). The enrollment rate and the HOI indicators for the older age group (15-17 year olds) are lower, increasing from about 61 to 66 percent between 2000 and 20008. At the secondary school level (ages 15-17), the 30
# Table 6. The impact of different methods of recycling carbon revenues

(R2CUT vs. R2LUMP, cases with high and rising carbon tax)

|||2030<br>R2CUT|R2LUMP|
|---|---|---|---|
||Base<br>case|:<br>Cut in<br>VAT, TK|:<br>Lump sum<br>transfer|
|Variable|values|% change|from base|
|GDP (billion yuan 2010)|126,235|-0.74|-1.17|
|Consumption (bil yuan 2010)|67,264|-0.71|-0.77|
|Investment (bil yuan 2010)|43,747|-0.86|-2.13|
|Capital stock (bil yuan 2010)|386,091|-0.51|-1.28|
|Energy use (million tons of sce)|5,958|-13.1|-13.2|
|Coal use (million tons)|4,610|-20.0|-19.9|
|Oil use (million tons)|835|-4.0|-4.5|
|Gas use (billion cubic meters)|381,163|-9.4|-9.4|
|Electricity (billion kWh)|8,951|-7.9|-8.1|
|CO2 emissions(inc cement; mil tons)|12,797|-16.0|-16.1|
|Cumulative CO2 (2015-2030)|184,840|-9.8|-9.8|
|Carbon tax yuan/ton CO2||¥157|¥149|
|Carbon tax as a share of total revenue||5.8%|5.5%|
|Electricity generation|values|% change|from base|
|Coal|5782|-14.2|-14.3|
|Gas|459|-2.2|-2.4|
|Nuclear|678|5.4|4.9|
|Hydro|1132|5.2|4.7|
|Other|271|8.5|7.8|
|Wind|498|0.12|-0.22|
|Solar|130|0.25|0.00|

_Subsidies for renewables and other low-carbon sources of electricity_ In the policy RRsub we subsidize the cost of nuclear, hydro, wind, and solar using the revenues from a fossil fuel tax in proportion to their carbon contents. The subsidies are chosen so that the share of each of these sources in generation is equal to the projection by IEA (2014) under their “New Policies Scenario (NPS)”. We focus on the kilowatt-hours generated rather than the capacity of the various sources which are also projected separately in IEA (2014).

‐23‐
32 students dropping out of school altogether. However, we suspect that this would not be a quantitatively important factor. To see this, note that data from UNESCO (2020a) suggest that in LICs (MICs) 0 _._ 7% (0 _._ 3%) of primary students and 1 _._ 8% (1 _._ 4%) of secondary students were “at risk” of dropping out due to the pandemic (see Web Appx. Table A.5 for the full details). Suppose 2% of students drop out of school due to the pandemic and, to be conservative, assume that they drop out randomly. Average schooling in the LICs (MICs) is 6 (8) years, so random dropouts imply an additional 6 _×_ 0 _._ 02 = 12% (8 _×_ 0 _._ 02 = 16%) of schooling lost, or 6% (8%) if we spread it over the 2 years of the pandemic. For LICs, this moves the magnitude of the schooling shock from 0 _._ 28 to 0 _._ 34. Figure 5(c) shows that this would increase the welfare cost of the COVID pandemic in the LIC from 1 _._ 5 to 1 _._ 6 percent, still far from the 2 _._ 5 percent in the HICs and the 2 _._ 2 percent in the MIC. For MICs, the schooling shock would increase from 0 _._ 33 to 0 _._ 41 and the welfare cost would only increase from 2 _._ 2 to 2 _._ 3 percent (Figure 5(b)).

Likewise, the data from UNESCO (Web Appx. Table A.5) suggest that dropouts are likely to be similar between males and females across the three income groups.

Given that the number of at-risk students exceeds the number of likely dropouts, and that at-risk students were likely not random but rather among a pool of students most likely to leave schooling relatively early, the impact of dropouts is likely much smaller than implied by these calculations. This suggests that including drop-outs in our model would increase the complexity of our framework without affecting the results.

**Selection in the Impact.** Distance learning increases educational inequality between the children of wealthier/more educated families and other families (Fuchs-Sch ̈undeln et al., 2020; Jang and Yum, 2022). If the students from poorer/less educated families are disproportionately more impacted in less developed economies than in more developed economies, the selection effect would be larger in LICs than in MICs and accordingly in MICs than in HICs. Given complementarities between formal schooling and parental wealth/education, an education shock of a given magnitude should then be less impactful in poorer nations (as the impacted students have lower educational potential). Accounting for this type of selection should thus reinforce our results on the asymmetry of the aggregate impact across countries.
_-_ - 55 Table 6 Government Revenue and Population by Major Districts, Albania

|Pr,ipor:on<br>orRevenuem<br>U<br>Pzo<br>1<sup>'ugiiulIP'jiollo</sup><br>,DiL,iicL :<br>*Janumry-NvcmL-er<br>192<br>1990|
|---|
|Tiau<br>24.2<br>11.5|
|Durrea<br>22.7<br>7.7|
|FMc<br>10.9<br>7.7|
|EPun<br>_5.9_<br>7.6|
|Koran<br>5.7<br>6.7|
|Othem<br>30.6<br>58.8|
|TOTAL<br>100.0<br>100.0|

aurce: Ministry of Finance, Goverrment of Albania.
# **Table A 10: Contribution of Each Explanatory Factor to Wage Differentials, Using Dummies for the Highest Degree Completed by Sector 2003-2007**

||**#1**|**#2**<br>**Agricultur**|**#3**<br>**e**|**#4**<br>|**#5**<br>**Manufactur**|**#6**<br>**ing**|**#7**|**#8**<br>**Services**|**#9**|
|---|---|---|---|---|---|---|---|---|---|
||**(Both**<br>**sexes) **|<sup>**(All men)**</sup>|**(All**<br>**women)**|**(Both**<br>**sexes) **|<br> <sup>**(All men)**</sup>|**(All**<br>**women)**|**(Both**<br>**sexes) **|<sup>**(All men)**</sup>|**(All**<br>**women)**|
|**Residual**|**80.4**|**82.6**|**77.0**|**63.8**|**59.2**|**65.9**|**43.7**|**52.3**|**39.5**|
|**Personal Attributes**|**9.3**|**7.1**|**11.2**|**15.8**|**16.6**|**18.2**|**39.3**|**32.3**|**41.3**|
|_Gender_|_2.1_|_--_|_--_|_0.8_|_--_|_--_|_2.9_|_--_|_--_|
|_Experience_|_0.9_|_1.9_|_-0.2_|_0.9_|_2.9_|_0.7_|_3.1_|_4.4_|_2.1_|
|_Highest Degree Completed_|_6.2_|_5.2_|_11.4_|_14.1_|_13.7_|_17.6_|_33.2_|_27.9_|_39.2_|
|**Region**|**4.4**|**4.1**|**6.1**|**9.8**|**12.1**|**7.0**|**4.4**|**5.6**|**3.8**|
|**Occupation**|**5.7**|**6.0**|**5.0**|**10.4**|**11.8**|**8.5**|**12.4**|**6.3**|**15.1**|
|**Year**|**0.3**|**0.3**|**0.7**|**0.3**|**0.3**|**0.3**|**0.3**|**0.2**|**0.3**|

**Table A11: Contribution of Each Explanatory Factor to the Difference in Inequality Index (Gini Index)**

||NCR|Luzon <br>Bo|Visayas <br>th Sexes|Mindanao|NCR|Luzon <br>|Visayas <br>All Men|Mindanao|NCR Luzon <br>Al|Visayas <br>l Wome|Mindanao<br>n|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Gini Index|0.31|0.35|0.40|0.39|0.27|0.32|0.36|0.36|0.36<br>0.39|0.46|0.44|
|**Residual**||**0.04**|**0.16**|**0.09**||**-0.10**|**-0.12**|**-0.12**|**-0.03**|**-0.68**|**-0.97**|
|**Personal Attributes**||**0.16**|**0.44**|**0.64**||**0.23**|**0.54**|**0.81**|**0.31**|**0.77**|**1.27**|
|_Gender_||-0.11|-0.05|-0.08||--|--|--|--|--|--|
|_Experience_||0.04|0.05|0.14||0.12|0.13|0.17|-0.03|0.07|0.32|
|_Dummies for the highest_||||||||||||
|_degree completed_||0.23|0.44|0.58||0.11|0.41|0.64|0.33|0.70|0.95|
|**Sector**||**0.54**|**0.18**|**0.06**||**0.88**|**0.64**|**0.39**|**0.06**|**0.01**|**-0.21**|
|**Occupation**||**0.25**|**0.21**|**0.19**||**-0.02**|**-0.06**|**-0.08**|**0.65**|**0.82**|**0.79**|
|**Year**||**0.01**|**0.01**|**0.02**||**0.00**|**0.00**|**0.00**|**0.00**|**0.09**|**0.11**|

24