rafmacalaba/datause-encoder-data huggingface dataset:
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**Table 2B. Effects of Market Access on Landlessness (IV Estimates)**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Dep. var.|||ln(Landless 2012)|||
|Estimator|2SLS|2SLS|2SLS|Lasso-IV|2SLS|
|**ln(MA1996)**|**0.974*****|**0.403***|**0.758*****|**0.678*****|**0.662*****|
||**(0.23)**|**(0.21)**|**(0.16)**|**(0.14)**|**(0.14)**|
|ln(Slope)|-0.079|-0.078|-0.078|-0.078|-0.078|
||(0.11)|(0.09)|(0.10)|(0.10)|(0.10)|
|Elevation|-0.106|0.129|-0.017|0.016|0.022|
||(0.50)|(0.41)|(0.44)|(0.42)|(0.42)|
|Crop Suitability|-0.010**|-0.005|-0.008**|-0.008**|-0.008**|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|ln(Rain)|0.120|0.151|0.132|0.136|0.137|
||(0.19)|(0.15)|(0.17)|(0.16)|(0.16)|
|ln(Temperature)|-0.530|0.534|-0.127|0.021|0.051|
||(1.34)|(0.99)|(1.12)|(1.06)|(1.03)|
|ln(Rain CV)|0.575|0.280|0.463|0.422|0.414|
||(0.50)|(0.38)|(0.43)|(0.41)|(0.41)|
|ln(Temp. seasonality)|-0.104|-0.067|-0.090|-0.085|-0.084|
||(0.26)|(0.19)|(0.22)|(0.21)|(0.21)|
|Pop. density, 1961|0.000|0.034|0.013|0.018|0.019|
||(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|
|Constant|-14.063*|-10.750*|-12.808*|-13.660**|-12.255*|
||(7.87)|(5.56)|(6.68)|(6.33)|(6.33)|
|**First Stage**||||||
|ln(km of railroad)|0.114***||0.114***||0.186***|
||(0.03)||(0.03)||(0.07)|
|ln(dist. to GQ)||-0.148***|-0.120**||-0.030|
|||(0.04)|(0.05)||(0.08)|
|ln(dist. to GQ) x||||-0.131***|-0.106*|
|<br>ln(slope)||||(0.04)|(0.06)|
|ln(dist. to GQ) x||||0.119|0.081|
|<br>Elevation||||(0.17)|(0.18)|
|ln(km of railroad) x||||0.002***|-0.001|
|<br>Crop suitability||||(0.00)|(0.00)|
|Weak identification|18.49|11.14|26.96|12.36|41.70|
||0.0000|0.0010|0.0000|0.0000|0.0000|
|Observations|212|212|212|212|212|
|R-squared|0.154|0.427|0.312|0.3535|0.361|

Notes: Columns (1) and (2) are estimated by two-stage least squares (2SLS) using railroad length and distance to the Golden Quadrilateral as instrumental variables, respectively. Column (3) is estimated using predicted Market Access as an instrumental variable. The predicted Market Access are fitted values from a zero stage with including both railroad length and distance to GQ. The Lasso in column (4) is estimated using a parsimonious set of instruments chosen by Lasso from a broad set that includes railroad length, distance to GQ, and each of their interactions with the exogenous control variables as instrumental variables. Lasso selects three instruments which are reported in the first stage in column (4). Column (5) is estimated using predicted Market Access as an instrumental variable. The predicted Market Access are fitted values from a zero stage with railroad length, distance to GQ, and the instruments chosen by Lasso. The predicted Market Access are fitted values from a zero stage including railroad length, distance to GQ, and all the instruments chosen by Lasso. In columns (3) and (5), under the first stage, we report the zero stage estimated coefficients of the instrumental variables. Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.
- 12 -

# C. Structure of Taxes

# 1. Indirect Taxes.

# 1.1. Taxes on International Trade

35. Empirical investigations show that taxes on foreign trade account for 5.6Z of GDP and over one-third of total tax revenues in developing countries. In fact, import duties comprise the most important taxable base in developing countries, given that they do not require a very sophisticated tax administration. In open economies, such as those in Central America, the significance of taxes on foreign trade is larger (Table II). However, the significance of taxes on trade should decline over time as the share of taxes collected on domestic transactions in GDP increases.

# Table II: Share of Taxes on International Trade in Total Central Government Revenues, 1975, 80, 84 (2)

||1975|1980|1984|
|---|---|---|---|
|World|4.8|5.1|4.4|
|Industrial Countries|1.8|1.5|1.4|
|Developing Countries|14.1|16.1|14.0|
|Non-Oil Developing||||
|Countries|15.5|17.4|14.6|
|LAC|10.8|14.4|8.3|
|Asia|19.5|20.1|20.1|

Source: IMF, UNDP.

# 1.1.1. Import Tariffs

36. Empirical investigations show that import duties contribute about 4.52 of GDP and over one-fourth of total tax revenues in developing countries, although there is considerable variation in their reiative importance among the countries. It is also a known fact that all the great users of taxes on imports make little use of taxes on domestic transactions and conversely virtually all the great users of domestic transaction taxes on goods and services make lit.le use of import duties.

37. Import tariffs are used mostly to generate revenue. But those taxes are also utilized to provide protection to domestic industries, reduce imports and discourage consumption.

38. In open economies, high tariff protection could result in the misallocation of resources in the economy. Resources could move into
- 35 -

# Table 16: Percentage Distribution of Client Objectives, Three Time Periods: By Mode

# Flscal Year of SAR

|Type of<br>Objective|Se<br>63-70|em^da<br> 71-7|ry<br>8<br>79-87|Po<br>63-70|st-Se<br>71-78|codar<br>79-87|v<br>No<br>63-70|nfor<br>71-78|mal<br>79-87|Tea<br>63-7|cher<br>Tr<br>0 71-7|<br>aining<br>8 79-87|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Marvower<br>Expansl|on36|<br>43|<br>35|<br>50|48|48|50|<br>32|29|*|46|<br>50|
|Improve<br>Training<br>QuaIty|36|<br>41|<br>35|<br>50|43|48|50|<br>30|30|-0-|46|<br>39|
|LUOgade<br>Worker<br>Skills|09|<br>05|<br>14|<br>-0-|-0-|-0-|-0-|17|17|-0-|-0-|11|
|Improve<br>Access<br>of<br>Groups|-0-|<br>08|<br>10|-0-|05|05|-0-|08|13|-0-|09|<br>-0-|
|TOTALS (X)|81|<br>97|<br>94|100|86|91|100|87|89|-0-|91|<br>100|

* No teacher training components financed Inthis period In the sample.

# Linkages Between TraininR and Employment

3.38 Articulation between vocational training and employment is widely accepted as a crucial factor in the cost-effectiveness of training. It can facilitate the match between the supply of trained persons and job opportunities, and increase the relevance of vocational curricula to job skill requirements. It can also improve the ability of training institutions to adjust both curricula and the volume of training to rapidly changing labor markets.

3.39 The pattern of linkage arrangements in the completed components in the sample indicates that more attention was given to developing linkages for nonformal training institutions than for post-secondary irstitutions and secondary vocational schools. Moreover, the linkage arrangements for nonformal components have been much stronger in establishing the information flows and training services relationships that facilitate demand-driven training that is responsive to changing labor market requirements. -
# **B.3. Random Effect Model Estimation Results**

## **Dealing with Extreme Coefficients**

Extreme deviations from the main sample of estimates are problematic for statistical analyses. They usually compress the variation of the rest of the sample. To deal with this issue, we use the Grubbs test (NIST/SEMATECH 2004) as our criterion to exclude from our estimation extreme outliers. This test calculates, _G_ , the maximum deviation from the sample mean, divided by the standard deviation (SD), calculated including that observation. If ��� �<sup>�</sup> , the observation is deleted. �<sup>∗</sup> is the critical value for _G_ at a ��max � �� �� �<sup>∗</sup> �<sup>�</sup> confidence level α and is computed by �<sup>∗</sup> ����1� , where _T_ is the critical value of a _t_ - <u>��������</u><sup>�</sup> � � distribution with ( _N_ − 2) degrees of freedom and a confidence interval of ~~.~~ We start with _N_ = 149 observations �� (estimated coefficients) and set ��0.001, which yields �<sup>∗</sup> �5.086. The Grubbs procedure eliminates one outlier at a time, recalculating �<sup>�</sup> and SD with each iteration. We apply this procedure and this led to the removal of the coefficients of eight outliers. As a result, the mean and standard deviation changed drastically from 0.18 to 0.54 and from 111.3 to 1.7, respectively.

**Table B.2. The Grubbs Procedure to Eliminate Extreme Coefficients**

|**Coefficient**|**N**|**Mean**|**SD**|**IQR **|**P5**|**P95**|**Min**|**Max**|
|---|---|---|---|---|---|---|---|---|
|**Before**|149|0.18|111.27|0.44|-0.42|6.49|-917.3|854|
|**After**|141|0.54|1.7|0.42|-0.23|3.22|-7.27|8.61|

Note: SD = standard deviation; IQR = Q3-Q1; p5 = 5<sup>th</sup> percentile; p95 = 95<sup>th</sup> percentile min= minimum; max = maximum.

As can be seen from table B.2, there is a large amount of variance in the estimated reduction in transportation effects. The fifth percentile is −0.23 and the 95<sup>th</sup> percentile is 3.22. The standard deviation is 0.54 and the interquartile range is 0.42. The vertical line show that the mean is skew to the right, which is consistent with the descriptive section where the mean paper finds a positive (beneficial) and significant impact.

## **Testing for Sampling Error**

Our coefficients of transportation effect are based on a sample of countries and years studies. It is therefore important to investigate the role of sampling error. To do so, we estimate the _I_<sup>2</sup> statistic provided by Higgins et al. (2003). The _I_<sup>2</sup> tests for the variation in the transportation coefficient attributable to heterogeneity. Specifically, let �� represent an individual estimate of the transportation effect and �̅ represent an estimate of the population mean. Define �� by: 52
- Chelsky, J., Morel, C., and Kabir, M. (2013). Investment financing in the wake of the crisis: The role of multilateral development banks. _World Bank - Economic Premise_ , (121).

- Corsetti, G., Guimaraes, B., and Roubini, N. (2006). International lending of last resort and moral hazard: A model of IMF’s catalytic finance. _Journal of Monetary Economics_ , 53(3):441–471.

- Cortina, J. J., Didier, T., and Schmukler, S. (2018). Corporate Borrowing and Debt Maturity: The Effects of Market Access and Crises. Discussion Paper 13008, Center for Economic and Policy Research (CEPR).

- Dasgupta, D. and Ratha, D. (2000). What factors appear to drive private capital flows to developing countries? and how does official lending respond? Policy Research Working Paper Series 2392, The World Bank.

- Dobbs, R., Pohl, H., Lin, D.-Y., Mischke, J., Garemo, N., Hexter, J., Matzinger, S., Palter, R., and Nanavatty, R. (2013). Infrastructure productivity: how to save 1 trillion a year. _McKinsey Global Institute_ , 88.

- Dreher, A., Fuchs, A., Parks, B., Strange, A. M., and Tierney, M. J. (2017). Aid, China, and growth: Evidence from a new global development finance dataset. Working Paper 46, AidData, Williamsburg, VA.

- Dreher, A., Fuchs, A., Parks, B., Strange, A. M., and Tierney, M. J. (2018). Apples and dragon fruits: the determinants of aid and other forms of state financing from China to Africa. _International Studies Quarterly_ , 62(1):182–194.

- Eichengreen, B., Kletzer, K., and Mody, A. (2006). The imf in a world of private capital markets. _Journal of Banking & Finance_ , 30(5):1335–1357.

- Eichengreen, B. J. and Mody, A. (2000). What Explains Changing Spreads on Emerging Market Debt? In _Capital Flows and the Emerging Economies: Theory, Evidence, and Controversies_ , pages 107–136. National Bureau of Economic Research.

- Eichengreen, B. J. and Mody, A. (2001). Bail-Ins, Bailouts, and Borrowing Costs. _IMF Staff Papers_ , 47:155–187.

- Engen, L. and Prizzon, A. (2018). A guide to multilateral development banks. Overseas Development Institute.

20
It also possible to decompose the consumption variance due to idiosyncratic and covariate shocks. Columns four and five of Table 4 present the estimates of the percentage of households that would fall below the poverty line from an idiosyncratic shock and the percentage of households that would fall below the poverty line from a covariate shock. The estimates reveal that a higher share of the households are vulnerable due to either idiosyncratic shocks or covariate shocks in the droughtprone lowlands. Covariate shocks play a larger relative role in drought-prone lowlands than in the other agroecological zones.

**Table 4. Source and type of vulnerability for different groups in 2016**

|||Poverty<br><br>Poverty<br>induced|versus risk<br>vulnerability<br>Risk<br>induced|induced<br><br>Ratio of<br>Poverty-<br>induced<br>to Risk-<br>induced|Idiosyn<br>Covariate|cratic versus c<br>vulnerability<br>Idiosyncratic|ovariate<br>Ratio of<br>Covariate<br>to<br>Idiosyncr.|
|---|---|---|---|---|---|---|---|
||Tigray|12|20|0.60|19|30|0.65|
||Afar|23|21|1.09|29|43|0.67|
||Amhara|9|19|0.48|14|27|0.53|
||Oromia|20|20|1.03|26|39|0.68|
|ns|Somali|36|24|1.51|45|59|0.75|
|gio|Benishangul-Gumuz|16|19|0.84|23|34|0.68|
|Re|SNNP|22|24|0.95|31|45|0.68|
||Gambella|24|17|1.40|30|41|0.73|
||Harari|13|15|0.90|20|28|0.72|
||Addis Ababa|3|6|0.50|4|9|0.48|
||Dire Dawa|20|16|1.23|27|35|0.78|
||Moisture-reliable<br>highlands|16|20|0.82|22|35|0.64|
|ogical<br>nes|Drought-prone<br>highlands|13|19|0.66|19|30|0.63|
|Ecol<br>zo|Moisture-reliable<br>lowlands|17|21|0.82|25|37|0.67|
||Drought-prone<br>lowlands|34|23|1.54|41|55|0.74|
|Urban -|Urban|7|10|0.69|9|16|0.59|
|Rural|Rural|21|22|0.92|28|42|0.67|
|National||18|20|0.90|24|37|0.66|

Source: Author calculations based on HCES/WMS, 2016.

18
whereas it dropped by 6 percentage points in the WSD group, according to teacher reports. However, the average percent of teachers absent over 5 random days, based on school records, indicates relatively low absenteeism (6%) and no difference between across groups (Table 7).

We found no difference between the two groups in terms of student performance (Table 8). Fourth graders read about 24 words per minute and sixth graders read 41 words. Research suggests that about 45 to 60 words per minute are required for comprehension (Abadzi 2008).

These findings show – unsurprisingly – a higher rate of adoption of the school organization recommended by WSD in the WSD schools and its components within the WSD group compared to the control group. No differences were observed regarding student performance, although it would likely be too early to observe such an effect at that point. At the very least, this indicates that the program was implemented as planned.

# **5.3.2. Two years post-interventions**

In this section, we present the impact of the intervention on student learning outcomes, teaching practices at the school level, and school management two years into the interventions in all three groups.

The estimates of the average treatment effect (Table 9) indicate that neither the WSD nor the Grant-only interventions had any impact on student learning outcomes two years after their implementation. Student performance in all groups remains relatively poor and comparable to baseline levels. This is also true for the control group, which rules out the possibility that the control group may have improved along with the treatment groups over the two years but due to reasons other than the intervention.

Even though we observe no average treatment effect, it is possible that the distribution of performance may have been impacted in a way that would balance out the average effect (e.g., improved performance at the bottom of the distribution and worse performance at the bottom of the distribution). However, the distribution of test scores across groups shows no significant heterogeneity by level of performance except for a small range around the average performance (Figure 6).

Teaching practices improved slightly in the WSD group. As Table 10 shows, the probability that the teacher frequently used the blackboard increased by 7% relative to the control group and teachers were more likely (10%) to call on student by their names (both results significant with 90% confidence). However, we see no evidence that the program affected the confidence of children to participate and ask questions during class. Similarly, the programs did not improve the likelihood that a teacher would prepare for the class with written notes.

14
the different distributions of unit values that these indices imply. Section three outlines some of the desirable properties that a measure of individual diversity should have and tests which Gini satisfies these properties. Section four provides an example of the Gini decomposition by population subgroups using UK data and section five concludes.

# **2 Ginis**

The Gini index can be written in many different forms (see Xu, 2003 and Ceriani and Verme, 2012 for reviews). In his 1912 book, Corrado Gini first proposed a measure which he called the _average difference between n quantities_ .<sup>3</sup> Gini was particularly keen in showing how any distribution could be seen as a symmetric distribution where each observation had a symmetric counterpart, an aspect that became evident when he published an article that proposed a modified version of his index as a concentration ratio (Gini, 1914).<sup>4</sup> The ratio proposed was equal to his original _average distance between n quantities_ divided by twice the arithmetic mean of _X_ . This is what today is commonly referred to as the _Gini index_ or _one-half of the Gini relative mean difference_ .<sup>5</sup> As the starting point of our analysis, we will then use the two indices introduced by Gini in 1912 but expressed in the more popular form published in 1914. Let us consider a population of _N_ individuals, _i_ = 1 _,_ 2 _, . . . , n_ , _n ∈_ N, _n ≥_ 3, having an income distribution _X_ = ( _x_ 1 _, x_ 2 _, . . . , xi, . . . , xn_ ), where _X ∈_ R<sup>_n_</sup> ++<sup>and</sup><sup>_x_1</sup><sup>_≤x_2</sup><sup>_≤, . . . , ≤xn_.ThentheGini</sup> index can be expressed as: or

> 3 _Differenza media tra le n quantit`a_ , Gini (1912), p. 22, eq. 5.

> 4This is the article where Gini shows the relation of his index with the Lorenz curve.

> 5Gini’s _average distance between n quantities_ was later referred by Dalton (1920) as the _absolute mean difference_ to distinguish it from the _relative mean difference_ defined as the Gini absolute mean difference divided by the mean.

4
# **Figure 14: “In the city or area where you live, are you satisfied with the quality of the school system, water, and healthcare in your area” (by time and ethno-religious groups) (% of adults)**

# **_A: Education or school system_**

<!-- Start of picture text --> 2010-2012 2014-2016 2018-2019<br>63%<br>59% 60%<br>52%<br>50%<br>47%<br>45%<br>40% 39%<br>34%<br>32%<br>30%<br>Baghdad Sunni Shia Kurds<br><!-- End of picture text -->

# **_B: Water_**

<!-- Start of picture text --> 2010-2012 2014-2016 2018-2019<br>74%<br>67%<br>62%<br>47%<br>44%<br>37% 37% 36% 38%<br>28% 27%<br>24%<br>Baghdad Sunni Shia Kurds<br><!-- End of picture text --> 17
# **8. Conclusion**

Our study confirms that digital skills are common in Southeast Asia. The job postings data from Malaysia in 2016-18 reveals that all occupations require at least some digital skills. Highly digital occupations, however, still represent a very small share of employment in the four countries studied: 5 percent in Malaysia, around 2 percent in Thailand and Vietnam, and 1 percent in Cambodia. Instead, the bulk of employment is in very-low-digital occupations (from 40 percent in Malaysia to 66 percent in Cambodia) and low-digital occupations (from 16 percent in Vietnam to 31 percent in Malaysia). These shares are changing very slowly over time, with the exception of Vietnam’s somewhat rapid shedding of very low digital occupations in favor of low and medium digital occupations We find that occupations from all four digital occupations levels need cognitive and socioemotional skills. While the share of intermediate and advanced digital skills differs across digital occupation levels in Malaysia, all four levels of occupations require roughly the same share of basic digital skills (5-9 percent), cognitive skills (8-12 percent), and socioemotional skills (6-12 percent). Cognitive and socioemotional skills are even more important than digital skills for very-low, low-, and mediumdigital occupations (nearly the entire employed population). The share of cognitive and socioemotional skills combined is greater than digital skills: 23 versus 5 percent for very-low digital occupations, respectively, 20 versus 9 percent for low-digital occupations, respectively, and 16 versus 15 percent for medium-digital occupations, respectively. Conditional correlations estimated through regressions show that medium- and highly digital occupations require more thinking skills (cognitive) than very low digital occupations but require similar levels of the other cognitive and socioemotional subsets as do occupations that are less digitally intensive. Similar patterns emerge for Vietnam, Cambodia, Thailand.

When disaggregating cognitive and socioemotional skills, subsets of these skills categories are found to associate more closely with certain levels of digital skills. Using an exploratory factor analysis for Malaysia, we find three clusters of skills from nine digital, cognitive, and socioemotional subsets of skills. Two of these factors are a mix of digital skills and subsets of cognitive and socioemotional skills: 1) basic digital skills with the three subsets of cognitive skills (thinking, communication, and organization), and socioemotional skills related to relationships; and 2) intermediate and advanced digital skills with (to a lesser extent) thinking skills and personal growth. The third cluster does not include digital skills, instead being limited to socioemotional skills, namely with emotions and personal growth. This shows that basic digital skills can be considered a generic skill as much as 35