rafmacalaba/datause-encoder-data huggingface dataset:
We focus the analysis on the investment climate surveys of India, Turkey, South Africa and Tanzania because they represent almost all the situations regarding the structure of missing data we may find.<sup>12</sup> For India, in the complete case we lose 35% of the original sampling frame, while after replacing we only lose 16%. Turkey and Tanzania lose a similar percentage of observations, 70.9% and 60.7% respectively. South Africa only loses 29.2%.

Table 1.2 looks in depth at the description of the missingness problem of the four countries selected. In this case, for the computation of the observations available in the complete case, we use all those IC variables included in the survey likely to be used in a regression analysis framework. This means using more than 115 variables in India, 90 in Turkey, 168 in South Africa and 162 in Tanzania. For each country we consider two benchmark cases: the first one includes both PF and IC variables in the computation of the complete case, while the second only considers the IC variables. In the extreme case, when we consider all those IC variables, the complete case reduces to 0% of the complete case in all the countries; it doesn’t matter whether we include PF or not. Note that the observations available in the complete case increase as we exclude from the computation of the complete case those IC variables with the largest proportion of empty cells reported. In order to have a large enough number of observations we would need to exclude from the analysis those IC variables with a response rate lower than 95%. Even in this case, and also considering the PF variables, we should be forced to exclude 41.1% of the interviews in India, 76.9% in Turkey, 60.2% in South Africa and 66.2% in Tanzania. The evidence concerning the size of the problem of missing information we have to deal with is overwhelming.

> materials, capital and employment), although all we say about imputing missing information in production function variables can be easily extended to any other IC variable. 12 These datasets have in turn been analyzed in the following works: Escribano, Guasch and de Orte (2009) for India, Escribano, Guasch, de Orte and Pena (2008b and c) for the case of Turkey and Escribano, Guasch and Pena (2009) for South Africa and Tanzania.

6
# **<u>Table 8: Principal Components Analysis of Level 1 variables</u>**

||Eigen-<br>Sum<br>|Diff-<br>mary Stati<br>|stics<br>||Compon<br>three pr|ent loading<br>incipal com|s for first<br>ponents|
|---|---|---|---|---|---|---|---|
||value|erence|Prop.||Comp1|Comp2|Comp3|
|**SDI (N=268**|**)**|||||||
|Comp 1|**2.86**|1.65|0.48|Share of time teaching and learning|**0.49**|0.09|-0.02|
|Comp 2|**1.21**|0.40|0.20|Good Teacher Demeanor|**0.52**|-0.02|0.04|
|Comp 3|0.80|0.35|0.13|Good Pedagogical Practise|**0.50**|-0.12|-0.07|
|||||Classroom environment|0.06|**0.70**|**0.70**|
|||||Materials and infrastructure|0.08|**0.69**|**-0.70**|
|||||Use of materials|**0.48**|-0.15|0.08|
|**Stallings (N=**<br>|**277)**<br>|||||||
|Comp 1|**1.32**|0.31|0.44|Share of time teaching and learning|**0.69**|-0.22|**0.69**|
|Comp 2|**1.00**|0.32|0.33|Materials and infrastructure|**0.71**|0.02|**-0.71**|
|Comp 3|0.68|.|0.23|Classroom environment|0.14|**0.98**|0.17|
|**CLASS (N=1**|**48)**|||||||
|Comp 1|**2.94**|2.25|0.74|Emotional Support|**0.54**|-0.20|**0.62**|
|Comp 2|0.69|0.49|0.17|Classroom Organisation|**0.39**|**0.89**|0.12|
|Comp 3|0.20|0.03|0.05|Instructional Support|**0.52**|-0.41|0.08|
|||||Student Engagement|**0.54**|-0.05|**-0.77**|
|**Teach (N=15**|**1)**|||||||
|Comp 1|**1.48**|0.46|0.37|Share of time teaching and learning|0.23|**0.85**|**0.42**|
|Comp 2|**1.02**|0.17|0.26|Classroom culture|**0.60**|-0.21|**0.41**|
|Comp 3|0.85|0.20|0.21|Instruction|**0.47**|**0.31**|**-0.81**|
|||||Socioemotional Skills|**0.60**|**-0.36**|0.06|

Note: Eigen values greater than 1 and component loadings greater than 0.3 (in absolute value) are in bold.

46
To examine the activities of these ethnically-based business networks and see how network externalities confer advantages on members of the group, we focus on a subset of the RPED sample that includes entrepreneur-owned firms, as these firms constitute the SMEs in our sample. Four countries provide the backdrop for the investigation – Kenya, Tanzania, Zambia and Zimbabwe. It was in these countries that the surveys gathered data on ethnic business networks.

# **_Estimates of the Effect of Network Externalities on SME Performance_**

How might networks affect enterprise performance? In circumstances where there is a high degree of economic uncertainty, market imperfections are endemic, and formal legal institutions are weak, networks could aid entrepreneurs in entering the market. Members of a network, for example, might be expected to start larger firms, because the network provides connections and credible multilateral enforcement capabilities that facilitate access to supplier credit and other inputs, aid the flow of information on technologies and markets, or ease access to equity investments. Similarly, a network’s impact on enterprise productivity or growth could be significant where there are information barriers and credit constraints and firms have difficulty enforcing property rights and business contracts. Networks could provide positive externalities that help firms to overcome such problems, improving value added and growth prospects. Where problems of asymmetric information and enforcement are less important one would expect the value of network externalities to be smaller. But, as we know from the literature, relation-based governance via stable and cohesive networks can be found even in advanced countries with well-developed information and legal infrastructures (see for example Bernstein 1992). These issues are examined here.

Tables 1 through 3 present descriptive statistics that are used in the analysis. Table 1 describes the ethnic distribution of firms in the RPED sample. It shows that the majority of SMEs in manufacturing in Tanzania and Zambia are indigenous-African owned, while in Kenya and Zimbabwe minority ethnic groups are more important: Indian-Asians dominate in Kenya, and represent a significant share of ownership in the other three countries, and Europeans are the most important entrepreneurial group 12
in 1981 (Appendix Table A.1). The strong growth in female enrollment in primary school during the 60. and the trend to continue on to secondary and higher education are particularly evident. The proportion of females who reported primary school as their highest level attended rose from 38 percent in 1960 to 42 percent in 1972; and by 1972, 24 and 18 percent of all males and females, respectively, had attended secondary school compared to 6 and 3 percent in 1940. By 1981, these proportions each rose by 10 percentage points reflecting comparable enrollment growth for males and females. A similar pattern is observed for higher education.

# Educational Policy Context

The particularly strong upward trends observed during the **508** and 609 reflect the commitment of Presidents Prado (1956-1962) and Belaunde Terry (1963-1968) to expand public education. Under their administrations, schools, teachers, and government expenditures on education grew at unprecedented rates. Between 1955 and 1965, the number of primary schools rose from just 12,000 to 18,500 and the number of teachers nearly doubled (Appendix Table A.3). Higher education grew the fastest, offering not only more student places but also a wider variety of programs.31 The number of universities mushroomed from 9 in 1955 to 27 ten years later.

3/ Many new post-secondary non-university institutions known as sinstitutos de educacion tecnica superior' (IETS) were established to meet the demand for higher education that lead to a professional title and to provide an alternative to students unable to obtain admission to a university.

6
# **The Gender Impact of Pension Reform:** A **Cross-Country Analysis**

> The majority of old people are women and poverty among the old is concentrated among very old women. Therefore, in designing a pension system and pension reform it

> is essential to take account of the gender impact. Employment-based old age systems pose a problem in this regard, since women are less likely than men to work in formal labor markets and earn lower wages when they do. Recent multi-pillar pension reforms include a defined contribution (DC) pillar that tightens the link between payroll contributions and benefits, which leads critics to argue that they will fail to protect

> women. In contrast, supporters of these reforms argue that multi-pillar systems remove

> distortions that favored men and permit a more targeted public pillar that will help

> women. They hypothesize separating the redistributive and earnings-related parts of the systems into two pillars-one DC and one DB-makes the new DB smaller, more transparent and more redistributive toward low earners such as women.

In order to test these conflicting claims about multi-pillar reforms, this paper examines the differential impact on the two genders of the new and old systems in three Latin American countries-Chile, Argentina and Mexico. Based on household survey data, we simulate the wage and employment histories of representative men and women, and the pensions that these are likely to generate under the new and old rules.' Our basic intent is to examine the gender impact of alternative pension system designs, with policy implications that would apply more broadly. While both efficiency and equity considerations are involved, our emphasis is on relative rather than absolute changes, on distributional rather than efficiency effects.

We ask four basic questions:

1. What are the relative monthly and lifetime benefits of men versus women under the new systems?

2. What are the relative gains or losses of men versus women, due to the shift from the old to the new systems?

3. Which sub-groups within each gender benefit or lose the most from the reform and from redistributions under the new systems?

4. What are the key policy choices that determine these gender outcomes?

**2**
_growth rate are taken from China Statistic Year book 2017 and UNDESA(2015) with very small modification based on our understanding about labor quality development tendency of China in the future._

# **4. Results from Model Simulations**

In this section, we discuss the allocation of initial allowances, and results of our CGE model on the impacts of ETS to meet China’s NDC. Although we present the detailed results in several tables, the focus of discussions would be on intuition and implications of the results rather than quantitative information provided by the results.

## **4.1 Emission Allowances, Trade and Reduction**

## **4.1.1 Allocation of allowances**

There are many possibilities for allocating the initial allowances across the sectors or emitters, including free allowances based on historical emissions, outputs, emission intensity, or auctioning of emission allowances (Cheng, 2018; Pang and Duan, 2016; Wu et al. 2016). For example, Wu et al. (2016) consider five alternative approaches (GDP based, populationbased, emission based, based on ability to pay and based on government’s five-year plan) to freely allocate national emissions to provinces. In this paper, we do not allocate the emission allowances to the provinces. Instead we directly allocate allowances to emitters (here production sectors) in each province. There are 496 emitters (16 sectors in 31 provinces) participating in the national ETS. We considered two approaches for allocation or distribution of allowances: free allocation (or grandfathering) and auction. Under the free allocation, we considered two criteria for the allocation of the national emission allowances in a given year based on: (i) their baseline emissions, (ii) their baseline outputs. Two cases are also considered under auctioning based on schemes used to recycle the auction revenues to the economy. These are: (i) use of the auction revenue by the government for public consumption or public investment, and (ii) recycling the revenue to households as a lump-sum rebate.

The national emission allowance in 2030 is set in a way that it meets China’s NDC, meaning that China’s emission intensity (CO2 emission divided by GDP) would be 65% below its 2005 level. CO2 emissions and GDP are projected by the CGE model in the baseline. The 34
**Consumption** The consumption module of the TUP survey captured information on household food consumption for the week prior to the interview and non-food expenditures for the month or year prior to the interview. These are used to construct monthly _per capita_ consumption values, as detailed in Bedoya et al. (2019). Based on these data, we measure the logarithm of _per capita_ monthly consumption, using the same approach that the Afghan government used to determine the national poverty line. This monthly consumption aggregate thus captures a short-term (weekly) measure of food consumption during one of the planting seasons, as well as a medium-term (monthly and annual) measure of non-food expenditures (Deaton, 1997; Ravallion, 1998).

**Asset Index** We use survey data on household assets to construct a wealth index for each household, which provides an indication of each household’s wealth relative to others in the survey. Specifically, we calculate the first principal component of variation in household asset ownership based on the sixteen items listed in Table S1, across the 2,814 households with complete asset data, after standardizing each asset variable to zero mean and unit variance. This wealth index explains 25.3% of the variation in asset ownership. Figure S1 shows the distribution of the underlying asset index components and Table S1 shows the direction of the first principal component. Broadly, we expect that the asset index will provide an indication of each household’s long-term economic status, relative to other households in the survey.

**Other Variables** The TUP surveys collected several other covariates that we use in subsequent analysis. These include a food security index (composed of variables relating to the skipping and downsizing of meals, separately for adults and children), a financial inclusion index (composed of access to banking and credit, knowledge of banking and credit, and savings), and a psychological well-being index for the primary woman (standardized weighted scores on the Center for Epidemiological Studies Depression scale, the World Values Survey – happiness and satisfaction questions, and Cohen’s Stress Scale) see Bedoya et al. (2019). The survey also collected data from each household on mobile phone ownership. Nearly all (99%) households with a cell phone provided their phone numbers and consented to the use of their call detail records for this study.

**Sample Representativity** Portions of our analysis are restricted to the 535 households from the TUP survey with phone numbers that match to our CDR (see Section 2.3). Table 1 and S2 compare characteristics of these households to the full survey population. There

6
Merton, R. C. 1974. “On the Pricing of Corporate Debt: The Risk Structure of Interest Rates”. _The Journal of Finance_ 29 (2): 449‐470.

Panetta, F., T. Faeh, G. Grande, C. Ho, M. King, A. Levy, F. Signoretti, M. Taboga, and A. Zaghini. 2009. “An assessment of financial sector rescue programmes”. BIS Papers No. 48. Bank for International Settlements. Basel, Switzerland.

Reinhart, C., and K. Rogoff. 2008. “The Forgotten History of Domestic Debt”. NBER Working Paper 13946. National Bureau of Economic Research. Cambridge, Massachusetts. April.

Reinhart, C. and K. Rogoff. 2009. _This time is different: Eight Centuries of Financial Folly._ Princeton University Press. New Jersey. USA.

Schnabel, I., and U. Schuwer. 2016. “What Drives the Relationship between Bank and Sovereign Credit Risk”. Working Paper 07/2016. German Council of Economic Experts. November.

Van Rixtel, A., and G. Gasperini. 2013. “Financial crises and bank funding: recent experience in the euro area”. BIS Working Papers No. 406. Bank for International Settlements. Basel, Switzerland.

World Bank. 2018. _Global Economic Prospects, June 2018: The Turning of the Tide?_ World Bank, Washington DC.

Yu, S. 2017. “Sovereign and bank Interdependencies ‐ Evidence from the CDS market”. _Research in International Business and Finance_ 39: 68‐84.

42
# **TABLE 13** (continued)

||1989-90|1990-91|1991-92|1992-93|1993-94|1994-95|1995-96|1996-97|1997-98|1998-99|1994-99|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Asia|**7.37**|**-12.78**|**-25.59**|**_-5.36_**|**18.44**|**34.24**|**32.68**|**-12.22**|**-8.22**|N.A.|N.A.|
|Korea|59.15|-44.25|-34.92|-36.59|57.69|121.95|117.58|-65.66|7.35|110.96|275.61|
|Taiwan|-23.33|10.14|-43.42|-51.16|9.52|91.30|-4.55|2.38|16.28|82.00|295.65|
|Hong Kong|-34.85|102.33|-28.74|0.00|180.65|189.66|-13.89|-34.79|-23.32|-17.97|2.30|
|Israel|9.69|-23.72|14.02|-44.92|-97.09|266.67|-9.09|200.00|-40.00|111.11|1166.67|
|Japan|8.03|-17.60|-36.10|-11.73|43.00|-2.20|42.29|-17.01|-26.38|-8.70|-22.38|
|Singapore|200.00|12.12|181.08|25.96|-48.85|158.21|35.84|64.68|16.02|6.90|616.42|
|China|N.A.|600.00|-68.25|125.00|-6.67|-11.90|2.70|21.05|130.43|18.87|200.00|
|Other|-10.32|-9.35|3.97|67.18|-10.05|21.32|70.29|0.00|12.04|N.A.|N.A.|
|**Africa**|**-16.44**|**14.75**|**-40.00**|**-66.67**|**14.29**|**193.75**|**72.34**|**48.15**|**-21.67**|**N.A.**|**N.A.**|
|**Oceania**|**7.55**|**33.33**|**-25.00**|**-1.75**|**23.21**|**8.70**|**0.00**|**17.33**|**39.77**|**N.A.**|**N.A.**|
|Australia|-2.63|37.84|-3.92|-2.04|12.50|16.67|-7.94|31.03|43.42|N.A.|N.A.|
|Other|25.00|25.00|-68.00|0.00|87.50|-20.00|41.67|-29.41|16.67|N.A.|N.A.|
|**Rest of the World**|**-73.68**|**-100.00**|**N.A.**|**N.A.**|**136.36**|**-19.23**|**-40.48**|**140.00**|**-13.33**|**N.A.**|**N.A.**|

SOURCE: State of the Nation Report from 1989-98, Secofi for 1999, both with Banxico data.

*Exports includes transportation and insurance expenses.

**Figures may not add up due to rounding off.

40
49 7. More detail is found In Leigh (1990:Ch. 3).

8. The Buffalo program Is one of siX geographically scattered demonstration sites that<sup>received</sup> funding from the U.S. Department of Labor in the Dislocated Worker Demonstration Project. The projects were carried out between October 1, 1982 and September 1, 1983. The Buffalo program was selecteu for a net Impact analysis primarily because it offered a true control<sup>group for the</sup> majority of workers recruited for program participation.

9. Results for the nontarget plant sample" are considered less reliable because program participants were not randomly selected.

10. The large difference between the demonstration cost of the JSAltraining treatment shown In Table 6 ($491) and the cost of CT and OJT appearing In Table 5 ($2,723 and $1,960, respectively) Is that the former Is calculated over all claimants assigned to the skill training treatment, while<sup>the</sup> latter is specific to those members of the treatment group who actually participated in a training activity.

- i1. A fourth option--the Direct Purchase Option--Is not examined In the evaluation report. The Direct Purchase Option funds the purchase of classroom vocational training from provincial educational Institutions.
**Table 1: Summary Statistics**

||**CHINA**|**IND**|**IA**|**INDO**|**NESIA**|
|---|---|---|---|---|---|
||**Mean**<br>**SD**|**Mean**|**SD**|**Mean**|**SD**|
||<br>|**Full S**<br>|**ample**<br>|||
|Father's Edu|4.124<br>4.370|3.633|4.540|6.213|3.854|
|Children’s Edu|7.521<br>4.430|6.501|5.168|9.524|4.154|
|Observations|N=94159|N=8|6748|N=1|8356|
||<br>|**Ur**<br>|**ban**<br>|||
|Father's Edu|5.089<br>4.662|5.233|5.035|7.717|4.023|
|Children’s Edu|9.376<br>4.173|8.446|5.048|10.836|3.876|
|Observations|N=35308|N=3|1216|N=5|919|
|||**Ru**|**ral**|||
|Father's Edu|3.546<br>4.078|2.734|3.962|5.497|3.554|
|Children’s Edu|6.408<br>4.202|5.408|4.907|8.899|4.136|
|Observations|N=58851|N=5|5532|N=1|2437|
|||**So**<br>|**ns**<br>|||
|Father's Edu|4.086<br>4.373|3.689|4.495|6.177|3.839|
|Children’s Edu|8.169<br>4.143|7.557|4.972|9.822|4.022|
|Observations|N=46791|N=4|6701|N=8|558|
|||**Daug**|**hters**|||
|Father's Edu|4.163<br>4.367|3.569|4.590|6.245|3.867|
|Children’s Edu|6.880<br>4.609|5.269|5.120|9.264|4.249|
|Observations|N=47368|N=4|0047|N=9|798|

_Notes_ : (1) The data are from children who were born between 1950 and 1989 using the CFPS 2010 round for China, the IHDS 2012 round for India, and the IFLS 2014 round for Indonesia.

25
<u>Table 4: Robustness checks.</u> <u>No Quota NoHigh NoU-Mid NoL-Mid NoRTA No ACP</u> Ln(Stds) -0.638** -0.642* — -0.424 -0.698 -0.728** — -0.621** [0.290] [0.328] [0.368] [0.485] [0.341] [0.277] ISO/Stds 2.054*** 2.256*** 2.148*** 3.021*** 2.406*** 1.948*** [0.441] [0.548] [0.671] [0.986] [0.500] [0.510] Ln(GDP) 0.463 1.155 0.973 -3.467 0.057 0.826 [2.278] [2.969] [2.640] [3.494] [1.066] [0.949] Ln(GDPPC) — -0.049 -0.679 -0.322 3.913 0.315 -0.220 [1.991] [2.746] [2.523] [3.645] [0.837] [1.424] Ln(1+t) -0.152 -0.708 -1.857 -5.721 0.302 -0.280 [1.224] [1.452] [1.625] [3.942] [1.619] [1.395] ATC2 0.059 0.108 0.319 0.272 0.116 -0.029 [0.200] [0.246] [0.215] [0.347] [0.225] [0.252] ATC3 0.017 0.085 0.274 0.662 0.015 -0.067 [0.133] [0.211] [0.285] [0.396] [0.144] [0.126] Constant -9.68 -18.222 = -19.502 38.929 -1.603 -17.986 [26.209] [33.497] [53.348] [45.206] [20.707] [19.590] Obs. 3265 2696 2005 992 2894 1548 R2 0.66 0.59 0.34 0.63 0.66 Exog. (fF) 14.92*** = 17.38*** 19.12*** 9 .01*** 17.21*** 37.61 *** Overid. (x7) 0.98 1.348 0.2 0.69 0.00 0.868

1. All models include fixed effects by exporter, sector, and year. Estimation is by TSLS (first stage results available on request). Robust standard errors adjusted for clustering by sector-year appear in square brackets under the coefficient estimates. Statistical significance is indicated using * (10%), ** (5%), and *** (1%).

2. Exogeneity and overidentification tests are the regression-based tests outlined in Wooldridge (2002, pp. 118-124).

3. Column 1 excludes countries subject to quotas under the ATC, columns 2-4 exclude (in addition) high income, upper middle income, and lower middle income countries. Column 5 excludes countries having a regional trade agreement with the EU, while column 6 excludes (in addition) all members of the African, Caribbean, and Pacific group of countries.

31