rafmacalaba/datause-encoder-data huggingface dataset:
4 electricity. The rural electrification rate is about 70 percent in the world, but less than 15 percent in Sub-Saharan Africa.

By 2030, the International Energy Agency (IEA) estimates that—at current levels of growth in generating capacity—access to power will just keep pace with population growth (IEA 2010b). Forty percent of Sub-Saharan Africa countries will not reach the goal of universal access to electricity by 2050.

**<mark>Table 1. Access to Electricity in the Developing World</mark>**

||**Population without**<br>**electricity**<br>(millions)|**Overall**<br>**electrification rate**<br>(%)|**Urban**<br>**electrification rate**<br>(%)|**Rural**<br>**electrification rate**<br>(%)|
|---|---|---|---|---|
|Africa|587|41.8|68.8|25.0|
|_North Africa_|_2_|_99.0_|_99.6_|_98.4_|
|_Sub-Saharan Africa_|_585_|_30.5_|_59.9_|_14.2_|
|DevelopingAsia|675|81.0|94.0|73.2|
|_China and East Asia_|_182_|_90.8_|_96.4_|_86.4_|
|_South Asia_|_493_|_68.5_|_89.5_|_59.9_|
|Latin America|31|93.2|98.8|73.6|
|Middle East|21|89.0|98.5|71.8|
|**Developing countries**|**1,314**|**74.7**|**90.6**|**63.2**|
|**World**|**1,317**|**80.5**|**93.7**|**68.0**|
|_Source_: IEA 2011.<br>_Note:_World includes Organi|zation for Economic Co-opera|tion and Development(OE|CD)countries and Eastern|Europe/Eurasia.|

The low rates of electrification in rural and urban Sub-Saharan Africa are serious obstacles to the region’s development. Electricity is one of the necessary conditions for development (IEG 2008; World Bank 2002). These low electrification rates result in a loss of significant benefits—such as productivity gains in business, the creation of new jobs, opportunities to study at home, improvements in health, and better communication via television and radio. Because school attendance is improved by access to electricity, deficits in electricity access also may represent a loss in the development of human capital (Khandker, Barnes, and Samad 2009, 2012a, 2012 b).

The distribution of electricity—whether for social or productive uses—is a capital-intensive enterprise, and the cost of providing household connection to electricity service can be quite high. When connection charges are recovered through one-time, up-front fees for new customers, they can constitute a powerful disincentive to people who wish to obtain electricity, no matter how much they might desire the service.

Making it easier for households and small commercial enterprises to gain access to grid electricity without aggravating the financial problems of distribution companies is a delicate balance, but in most countries in the region, it is an achievable goal. If efforts to expand access to electricity are to be aligned with the important goal of ensuring utilities’ financial sustainability and operational efficiency, they must be planned carefully (Barnes 2007;
related to internal migration. However, international migration is significant in El Salvador.

The results suggest that residents of former guerrilla areas were not more likely to migrate abroad or to receive remittances than those in nearby locations, and that—if anything—migration abroad seems more recent. The coefficients are also negative, indicating that people were less likely to migrate internationally. These results provide evidence that former guerrilla areas did not face more “brain drain,” a result consistent with the idea that elites did not live in these locations and left their operations there when their workers stayed. Moreover, it accords with the idea that peasants supported the guerrilla movement and wanted to stay in these locations.

We examine in-migration outcomes in Columns 6–9 using data from the 2007 census. To evaluate if there was more migration into rebel areas, we estimate equation (1) for the share of individuals who always lived in the same location, the share of individuals who lived in the same location as their mothers, in-migration during the Civil War period, and years since arrival. Each variable is A. defined in Appendix There is no evidence of large differences in migration patterns for areas under guerrilla control. Moreover, the coefficient estimates are positive, suggesting areas under guerrilla control were less likely to have more in-migration. Importantly, the magnitude of the estimated coefficients is small and close to zero for all these outcomes. The sign of the coefficients is consistent with the idea that communities in previous guerrilla areas are closed to individuals from outside.

To further examine if there was more migration from FMLN areas by highly educated people, we examine the same outcomes in Columns 6–9 of Table D.21 using the sample of individuals who had finished at least high school by the time the conflict started. The magnitude of all the coefficients in Table D.21 is close to zero and not significant, implying that migration of highly educated individuals may not underpin the effects. Moreover, the sign of the coefficients in Columns 6–8 highlights that, if anything, more in-migration of highly selected individuals occurred.

38
**Table 2: Divorce and widowhood rates in Sub-Saharan Africa (percentage of evermarried women aged 15-49).**

||Once|Ever-|widowed|Ever-d|ivorced|
|---|---|---|---|---|---|
||||Rid||Rid|
||married|Widow|emarre<br>widow|Divorcee|emarre<br>divorcee|
|Burkina Faso|80|3|3|3|11|
|Niger|76|2|3|3|17|
|Senegal|76|2|4|5|13|
|Mali|81|2|4|2|11|
|Sierra Leone|73|3|7|4|13|
|Nigeria|83|3|2|3|9|
|Cote D’Ivoire|77|4|2|10|8|
|DRC|71|3|3|10|14|
|Congo|63|3|2|17|16|
|Gabon|63|2|2|14|19|
|Lesotho|79|12|1|8|1|
|Swaziland|77|11|1|6|4|
|Mozambique|80|5|0|15|0|
|Namibia|73|6|2|10|9|
|Zimbabwe|69|11|2|11|8|
|Malawi|66|4|3|12|15|
|Uganda|65|6|3|12|14|
|Zambia|68|6|4|11|12|

Note: Samples of ever-married women, Source: Authors’ based on DHS surveys between 2005 and 2011 depending on the country; and for Senegal, DHS 2005.

33
# **Figure 8. Consumer Protection and Financial Literacy under Responsibility of Financial Regulator**

<!-- Start of picture text --> Consumer Protection Financial Capability Consumer Protection Financial Capability<br>100<br>90 79 79<br>80<br>67 67 67 67<br>7060 60 50 52 54 62 50 58 58 62 50<br>% 50 45 42<br>36 36<br>40<br>30<br>20<br>10<br>0<br>EAP ECA LAC MENA SA SSA High Low Income Middle Income High income: OECD<br>income:<br>OECD<br><!-- End of picture text --> Source: CGAP and World Bank Financial Access 2010

**Arab microfinance institutions have developed consumer protection and financial responsibility codes on a voluntary basis** at a MENA regional level, and also in Jordan. MFI members of the regional microfinance network, Sanabel, signed a consumer protection and responsible finance document in 2008, committing themselves to standards of practice. Jordanian MFIs developed and signed a voluntary Code of Ethics covering disclosure and transparency, service standards, and ethical practices.

# _2.5 Financial infrastructure_

**Financial infrastructure underpins financial inclusion.** Moral hazard and adverse selection are reduced as collateral frameworks and credit information systems are improved, and loans can be made - at a lower cost and risk – to clients that previously would have been excluded. Without effective secured transactions laws and judicial (or non-judicial) processes, ideally with unified collateral registries to facilitate lenders‟ use of collateral as a guarantee, micro entrepreneurs may not be able to leverage current assets into access to finance. Low income individuals and microenterprises also need access to low cost and safe payment mechanisms, whether for salaries, benefits, remittances, or for business transactions.

**MENA has the lowest score on the Doing Business Legal Rights index of any region, however** .  As a result banks face higher costs of overcoming information asymmetries and higher levels of credit and default risk for micro lending, and have not yet directly entered the low income consumer or microenterprise market at any great scale. The Legal Rights index assesses the use of movable assets as collateral, the strength of creditor rights, and the availability of out of court enforcement mechanisms – all key areas for improvement for expanding financial inclusion. No MENA country scores above 4 out of 10 on this index.  Banks therefore do not extend lending further into the micro and small enterprise sector. Microfinance providers are forced to rely on solidarity group lending methodologies or on collateral that is

22
Table 1: Descriptive Statistics: Manufacturing sector

||Mean|sd|p10|p25|p50|p75|p90|N|
|---|---|---|---|---|---|---|---|---|
|2008|||||||||
|**ICT variables**|||||||||
|Share of labor that uses computers|30.82|35.31|0.00|0.00|15.00|50.00|100.00|11,438|
|Share of labor that uses Internet|25.56|31.68|0.00|0.00|10.00|40.00|85.00|11,438|
|Stock of computer equipment/worker|3.54|4.48|0.34|0.78|1.83|4.41|8.95|11,438|
|**Employment and wages**|||||||||
|Number of white-collar workers|24.33|79.23|2.00|4.00|7.00|17.00|51.00|11,438|
|Number of blue-collar workers|122.92|318.47|11.00|17.00|37.00|96.00|276.00|11,438|
|Annual real wage white-collar workers|107.67|90.35|32.40|50.00|80.50|132.21|218.47|11,438|
|Annual real wage blue-collar workers|52.88|32.44|21.00|31.20|46.24|66.41|91.57|11,438|
|White/Blue-collar workers|0.25|0.22|0.06|0.10|0.19|0.33|0.54|11,438|
|White/Blue-collar wages|2.19|1.42|0.92|1.21|1.76|2.76|4.15|11,438|
|**Firm characteristics**|||||||||
|Capital/worker|194.46|392.10|11.48|33.45|84.44|201.33|433.92|11,438|
|Total employment|151.66|387.31|15.00|24.00|48.00|121.00|334.00|11,438|
|Firm age|23.01|14.83|9.00|13.00|19.00|29.00|41.00|11,438|
|Share of FDI|8.21|26.88|0.00|0.00|0.00|0.00|0.00|11,438|
|2013|||||||||
|**ICT variables**|||||||||
|Share of labor that uses computers|28.27|30.39|0.00|5.00|20.00|40.00|85.00|11,438|
|Share of labor that uses Internet|25.57|29.10|0.00|3.00|15.00|35.00|80.00|11,438|
|Stock of computer equipment/worker|4.28|5.32|0.32|0.80|2.16|5.60|11.36|11,438|
|**Employment and wages**|||||||||
|Number of white-collar workers|27.08|87.54|2.00|3.00|7.00|19.00|56.00|11,438|
|Number of blue-collar workers|126.64|352.21|10.00|17.00|36.00|98.00|265.00|11,438|
|Annual real wage white-collar workers|105.31|85.46|39.04|53.60|79.41|126.27|202.53|11,438|
|Annual real wage blue-collar workers|54.07|31.95|24.82|32.67|46.07|67.48|91.91|11,438|
|White/Blue-collar workers|0.26|0.23|0.06|0.10|0.19|0.33|0.57|11,438|
|White/Blue-collar wages|2.09|1.24|0.96|1.28|1.74|2.53|3.68|11,438|
|**Firm characteristics**|||||||||
|Capital/worker|249.08|696.11|11.98|38.01|102.67|254.98|544.35|11,438|
|Total employment|160.31|424.70|15.00|23.00|48.00|127.00|342.00|11,438|
|Firm age|21.39|13.98|7.00|12.00|18.00|28.00|39.00|11,438|
|Share of FDI|9.18|28.34|0.00|0.00|0.00|0.00|1.00|11,438|

_Source:_ Authors’ calculations with data from the 2009 and 2014 Economic Censuses, INEGI 14
51 reason: the righL does not want to let Garcia<sup>off the hook, doing the hard</sup> and ungrateful work of stabilizing the economy only<sup>to see Garcia come</sup> back for another round. But while there is<sup>apparent stability, the extreme</sup> reduction in living standards and the growing<sup>number of strikes do suggest</sup> that surviving to the elections in April 1990,<sup>will not be easy. The very</sup> depth of the economic collapse in late 1988<sup>caused so sharp a fall in</sup> imports that the external constraint, for a while, was<sup>lessened. The real</sup> exchange rate in the informal market (see Figure 7) actually<sup>declined to</sup> pre-1987 levels.

But the collapse of real wages and activity was devastating.

Table 8 Peru: Real Wages and Real Per Capita GDP:1985-1989 (I-idex July 1985-100)

||Re<br>National Average|al Wages<br> <br>Minimu|...<br> <br>m Wage|P<br> <br>Government|er Capita GDPb|
|---|---|---|---|---|---|
|1985|111||120|113|100|
|1986|126||124|118|107|
|1987|137||131|136|112|
|1988|105||lid|101|110|
|1989a|<br>56||64|43|81<sup>c</sup>|

aJulyl989 bIndex 1985-100 cApril 1989 Source: Peru: Ministry of Economics Official estimates placed the decline in per capita real income, betwwen 1987 and April 1989 at 25 percent! Just how far<sup>the disintegration</sup> of the economy has gone is apparent from a survey in Jamuary<sup>1989 reported</sup> by ADOyO, an economic consulting service in Peru. Middle<sup>and upper income</sup> respondents predominantly indicated that they would buy<sup>dollars given</sup>
- 20 -

Table 2 Summary: Imnact of economy-wide wolicies on the performance of social and nonsocial orolects

||Pe|rcentage<br>of unsati|sfactory<br>projects|
|---|---|---|---|
|Economy-wide||Social<br>proiect|s<br>Nonsocial|
|policy variables|AlI|Education|Health<br>projects|
|Number of prolects<br>Percentage<br>unsatisfactory|259<br>20.1|228<br>17.5|31<br>1488<br>38.7<br>21.6|
|Trade restrictiveness||||
|Low<br>High|0.0**<br>28.1**|0.0*<br>23.9*|INSF<br>16.3**<br>INSF<br>24.9**|
|Black market<br>premium<br>Low<br>High|17.4***<br>37.0"**|13.6***<br>39.0***|42.3<br>19.5***<br>20.0<br>36.7""*|
|Fiscal<br>deficit<br>Low<br>High|11.3**<br>29.2**|11.1<br>22.6|12.5**<br>14.3***<br>58.3**<br>25.4***|
|Price distortions||||
|Low<br>High|14.8**<br>25.7**|11.9**<br>22.9**|28.6*<br>18.1***<br>6?.5*<br>25.3***|
|Real interest<br>rate<br>Positive<br>Negative|17.5<br>29.4|13.7<br>23.3|33.3<br>16.4<br>75.0<br>17.4|
|Growth<br>of GDP||||
|High|14.4*|11.4*|33.3<br>16.9*"*|
|<br>Low|23.8*|21.3*|60.0<br>28.3***|

Note: This is a summary table based on information given in Tables 4-5. For definitions of variables, and benchmark for each variable see Tables 4-5 and Appendix Table A-1. INSF stands for insufficient number of observations.

Significance leveLs based on Chi-squares for each crosstab: ** Represent **s** ignificant at the 99 percent confidence level. * Represent **s** ignificant at the 95 percent confidence level. * Represent **s** ignificant at 90 the percent confidence level. No asterisk: Significance Less than 90 percent.
each center at the same time. Both raters were present in the room with the class they were observing for three hours and followed this group if they left the room for outdoor play. Raters did not interact with staff or students during their observation. The two raters scored each center on a seven-point Likert scale, which ranged from inadequate (1), minimal (3), good (5) to excellent (7). For each center, rater one and rater two’s scores are averaged to construct a mean ECERS-R score.<sup>13</sup> We then computed village level averages of this ECERS-R score. In the analysis we divide the 310 villages in our sample in terciles – based on their average ECERS-R score. These are reported in Table 1 and show that girls and boys are equally likely to be found in villages with low, medium and high quality preschool centers.

# _Parenting practices_

The primary caregivers of the children in our sample (usually mothers) were asked to answer a series of questions about their parenting practices. These practices were measured using 24 items describing parent-child relationships adapted from the Longitudinal Study of Australian Children (LSAC) (Zubrick et al. 2008). The questions covered a range of possible practices that reflect three domains: parental warmth, consistency, and hostility.

Caregivers were asked how often they used each of a number of different parenting practices. A total positive parenting score was given to each child’s caregiver by adding together scores for each of the three parenting dimensions (with the negative items reversed). The higher the score, the more likely it is that parents have high levels of warmth and consistency, and low levels of hostility toward their children. These are reported in Table 1. On average, girls have a slightly higher total parenting practices score than boys but the magnitude of this difference is very small (0.91 points). In our analysis, we normalized this total parenting practices score to have a mean of 0 and a standard deviation of 1.

# **5. Gender gaps in early childhood development**

We examine gender differences by comparing means between girls and boys by age group. Using ordinary least-squares (OLS), we regress each outcome of interest on gender. For

> 13 These assessments focused on the seven subscales of the ECERS-R: Space and Furnishings, Personal Care Routines, Language-Reasoning, Activities, Interaction, Program Structure, and Parents and Staff. All averages were done first by sub-scale and then overall to construct the center’s ECERS-R score.

9
Table 3: Balance for Gender

||(1)<br>Male|(2)<br>Female|(3)<br>(1)-(2)|
|---|---|---|---|
|Greater Kuala Lumpur|0.63|0.64|0.01|
||(0.48)|(0.48)|(0.02)|
|Small Firms|0.45|0.44|-0.02|
||(0.50)|(0.50)|(0.02)|
|Salary (USD)|748.22|762.60|14.38|
||(293.76)|(287.83)|(13.44)|
|Days to Process|16.82|16.65|-0.17|
||(9.55)|(9.26)|(0.37)|
|Pre-scan Questionnaire|0.53|0.57|0.04**|
||(0.50)|(0.50)|(0.02)|
|Number of questions|1.84|1.89|0.05|
||(2.23)|(2.22)|(0.08)|
|Business degree|0.49|0.50|0.00|
||(0.50)|(0.50)|(0.02)|
|Engineering degree|0.26|0.26|0.00|
||(0.44)|(0.44)|(0.02)|
|Number of job applicants|144.20|148.69|4.50|
||(251.38)|(240.67)|(9.05)|
|Applicants with more education|6.64|7.30|0.66|
||(12.62)|(13.47)|(0.48)|
|Applicants with higher exp. salary|66.24|70.38|4.14|
||(122.53)|(133.26)|(4.71)|
|Number of foreign job applicants|5.94|6.16|0.22|
||(15.27)|(13.84)|(0.63)|
|Observations|1508|1487||

_<u>Note</u>_ <u>: This table reports descriptive statistics for the sample by gender and</u> balance tests. Columns 1 and 2 report the mean and standard deviation for each gender across different set of job and company characteristics. Columns 3 presents the difference between column 1 and 2.

17
Table 1.9.

Distribution of Unemployed by Age. Marital Status, Work Experience, and Duration of Unemployment, 1985, percents (Labor Force Survey, 1985, Age 12 and above, Oct.-Nov.)

|||Total|Male|Female|
|---|---|---|---|---|
|Age, < 24 yearsb|Turkey|62.9|_53.3_|<br>71.6|
||Urbama|60.1|55.4|69.2|
||Rural|66.1|6*.1|75.6|
|Marital<br>status,<br>singlesb|Turkey|59.8|57.5|65.1|
||Urbana|59.4|57.3|61.q|
||Rural|60.?|57.7|68.5|
|Without<br>work experienceC|Turkey|64.0|54.4|86.7|
|Duration,<br>1 year and abovec|Turkey|56.0|54.4|<br>58.8|

a Cities with 10,000 inhabitants and more b Active and inactive unemployed e Only active unemployed Source: 1985, Household Labour Force Survey, Results, (Turkey - 5 Region), Prime Ministry, SIS, Tables 2A, 4, 16 and 40.

-31 -
-35 -

Table 8 Probit: Imi2act of Policies on the Rerformance of education prolects Dependent Variable: Dummy for unsatisfactory projects

||Model|1|Model 2||
|---|---|---|---|---|
|Variable|Coefficient|t|Coefficient|t|
|Currency over-valuation<br>Siecification<br>1|||||
|Constant<br>|-1.4734|-5.63|-1.4614|-4.47|
|M2BLACK<br>|0.0058|3.73|0.0048|2.97|
|MSM2BLK<br>|-0.4759|-0.93|-0.6608|-1.19|
|MGDPZ|||-0.0457|-1.37|
|MSGDP|||0.3906|1.38|
|Africa|0.4587|1.57|0.5511|1.69|
|EMENA<br>|0.3179|0.94|0.4159|1.82|
|LAC|0.1848|0.56|0.1890|0.53|
|Log likelihood||-<br>96.34||-93.63|
|Currencv over-valuation<br>Specification<br>2|||||
|Constant|-1.4285|-5.51|-1.4207|-4.35|
|LNMBK1OA<br>|0.1430|2.98|0.1136|2.30|
|MSLNB1OA|-0.4498|-0.87|-0.6748|-1.20|
|MGDPZ|||-0.0495|-1.49|
|MSGDP|||0.4399|1.57|
|Africa<br>|0.3738|1.30|0.4918|1.52|
|EMENA<br>|0.2821|0.84|0.3945|1.13|
|LAC|0.0547|0.17|0.0828|-0.23|
|Log likelihood||-98.81||-95.38|
|Fiscal deficit<br> <br>|||||
|Specification<br>1|||||
|Constant<br>|-1.0220|-3.50|-0.8006|-2.23|
|BALANK1|14.9290|1.71|15.2840|1.79|
|BALANK2|1.3225|0.40|2.4165|0.71|
|MSBALAN|-0.1530|-0.57|-0.5169|-1.72|
|MGDPZ|||-0.0816|-2.42|
|MSGDP|||0.4639|1.64|
|Africa|0.3382|1.20|0.4590|1.45|
|EMENA<br>|0.2824|0.84|0.4596|1.31|
|LAC|0.2186|0.69|0.1934|0.56|
|Log likelihood||-103.23||-27.26|
Household credit and firm credit are generally used for different purposes, mortgage lending being a large part of household credit in many countries. In order to take a closer look at how credit to different types of agents may be related to the composition of output, a set of regressions are estimated with growth in real manufacturing value added as the dependent variable. Household credit is generally not significantly associated with manufacturing sector growth, and when it is, it enters with a negative sign (Table 8a). In contrast, credit to firms does have a robust relationship with manufacturing sector growth in all types of estimations used. Moreover, the relationship with manufacturing growth is nonlinear. The marginal impact of credit on growth becomes negative for large financial sectors. As discussed earlier, this can be explained by investors are going towards less productive investments (which are safer) or if the opportunities to finance high productivity activities “run out”.

**Table 8a: Manufacturing growth with household credit**

|**Dep. Var:**|OLS|OLS|GMM|GMM|2SLS|2SLS|
|---|---|---|---|---|---|---|
|**growth_manuf.**|(1)|(2)|(3)|(4)|(5)|(6)|
|hhcredit|-0.1804***|0.0094|-0.2696***|-0.3488|0.0028|0.6807|
||(0.001)|(0.959)|(0.000)|(0.162)|(0.981)|(0.146)|
|hhcredit<sup>2</sup>||-0.0012||0.0014||-0.0074|
|||(0.396)||(0.390)||(0.105)|
|fdi|-0.0470|0.0340|-0.1702|0.1349|-0.0845|0.4327|
||(0.836)|(0.872)|(0.581)|(0.576)|(0.660)|(0.304)|
|enrolment|0.1545|0.1173|0.1124|0.1245|0.1285|0.1068|
||(0.123)|(0.253)|(0.406)|(0.448)|(0.240)|(0.386)|
|initialgdppc|-11.72***|-11.18***|-11.24**|-9.63|-13.86***|-16.18***|
||(0.000)|(0.000)|(0.016)|(0.134)|(0.000)|(0.000)|
|financialcrisis||-16.28***||-17.68***|||
|||(0.000)||(0.000)|||
|Constant|44.24***|43.47***|52.07***|45.86***|43.76***|44.39***|
||(0.000)|(0.000)|(0.001)|(0.001)|(0.000)|(0.000)|
|# of Obs.|147|145|147|145|147|101|
|R-squared|0.348|0.397|||||
|Prob > F|0.000|0.000|0.000|0.000|0.000|0.000|
|# of countries|||37|36|||

*: in 2SLS, legal origin is used as instrument.

23
would ideally be the government support. Thus, we can test the effect of government support on our outcome variables based on the difference between these two samples. Our matching estimation involves four steps.<sup>10</sup>

# **5.1. Selection of Covariates**

The first step of the matching estimation methods is to select variables related to both outcome variables and government support. We use a long list of firm characteristics from the ES, as discussed in Section 4. In particular, we use: firm size, age, sector dummies, exporting, foreign and female ownership status, top managers’ experience, whether the firm introduced any innovation, labor productivity, being part of a larger firm, financial status, experiencing electricity outages, and whether the firm offered formal training.

For matching analysis, we restrict our sample to the firms for which we have observations on firm characteristics as well as four outcome variables within each country. We further restrict the No-Support group in El Salvador by dropping the firms that are not aware of the support, or are not eligible, or do not need the support, or do not know why they have not received any government support.

# **5.2 Matching Metrics**

The second step of the matching estimation is selecting a metric for matching observations in Support and No-Support groups. We use propensity score and mahalanobis distance metrics for this purpose. The propensity score is the probability of receiving government support given firm characteristics. We estimated propensity scores by logistic regression using survey weights. The mahalanobis distance is a scaled distance between observations as follows:

> 10 Although there is no guideline for how to apply the matching technique, many papers follow these steps, explicitly or implicitly. Please see Abadie & Cattaneo (2018), Rathi & <mark>Vermaak (2018)</mark> Garido et al (2014), Dugoff et al (2014) among many others.

19