rafmacalaba/datause-encoder-data huggingface dataset:
employed and 17 percent are unpaid family workers. In contrast, only 35 percent of working people are wage and salaried “dependent” workers. Among these, almost half do not hold a written contract of employment.

**Table 1 – Employment by sector and type**

||**Employer**|**Self-**<br>**employed**|**Unpaid**<br>**family**<br>**worker**|**Member**<br>**of co-**<br>**operative**|**Wage**<br>**worker**<br>**without**<br>**contract**|**Wage**<br>**worker**<br>**with**<br>**contract**|**Total**|
|---|---|---|---|---|---|---|---|
|**Household enterprise**<br>**(agriculture)**|0.35%|29.17%|13.32%|.|3.31%|0.01%|46.17%|
|**Household enterprise**<br>**(non-agriculture)**|1.80%|16.07%|3.70%|.|9.46%|0.45%|31.48%|
|**Government and**<br>**public service**|0.02%|.|0.01%|.|0.15%|7.58%|7.75%|
|**State-owned**<br>**enterprise**|0.01%|0.01%|0.01%|0.02%|0.13%|2.57%|2.74%|
|**Domestic private**<br>**firm**|0.32%|0.05%|0.13%|.|1.40%|6.50%|8.42%|
|**Foreign-owned**<br>**private firm**|.|.|.|.|0.07%|3.37%|3.44%|
|**Total**|2.50%|45.31%|17.17%|0.02%|14.53%|20.48%|100.00%|

**Source: World Bank staff calculations based on 2013 labor force survey.**

**Notes: Working people 15 and over. Data are for 2013. State-owned enterprises include cooperatives.**

As Vietnam’s transition continues and market forces play an increasingly dominant role, labor markets can become more important places of economic opportunities for working people. But these new opportunities will be accompanied by new risks as well as by new expectations from citizens about how their government can help to mitigate risks. As a result, managing and mitigating risks will become an ever more important element of the social contract. Combined with other forms of social protection, appropriately designed and implemented labor market policies – labor market regulations, interventions, and institutions – have the potential to help people better grasp and take advantage of economic opportunities in a risky world. More specifically, labor market policies have the potential to help lower an individual’s probability of experiencing adverse events such as job displacement that might result in earnings and consequently welfare losses. When shocks occur, policies can also help reduce the size of earnings losses, provide voice to workers and other labor market actors and strengthen growth by improving access to productive opportunities, as well as by facilitating the movement of labor and human capital to where they are most efficiently deployed. Finally, they can contribute to ensuring equality of opportunity on the labor market and basic protection for those vulnerable to exclusion.

- 3 -
for the baseline survey. Given that all firms were only considering female applicants, the study sample comprises only women.

Study participants were then randomized into treatment and control, with two-thirds of applicants in the treatment group and one-third in the control group using a public lottery method. Once randomized, the treatment applicants were assigned a specific firm to interview with. This stage was complicated by the fact that each of the participating IP firms has slightly different hiring criteria.<sup>9</sup> These differences create multiple distinct applicant groups according to the intersecting hiring criteria. Those treatment applicants who were eligible for multiple firms were asked for a second random draw for firm assignment.

Once firm assignment was determined, the enumerators formally invited the treatment individuals to a firm interview and informed them that they will be given transportation to the firm at the Bole Lemi IP for the interview.<sup>10</sup> As such, both treatment and control individuals only learned about their treatment status after completing the baseline survey but before they left the screening center. On the day of the interview, an enumerator confirmed that only treatment individuals were provided with transport to the firm. After the interview process was completed, all candidates were transported back to the screening center.

The firms conducted interviews with each of the treatment candidates according to their own hiring procedures and decided whether or not to offer the applicant a job. With the additional in-person eligibility verification done at the screening centers, it was anticipated that the firms would select the vast majority of the treatment candidates who participated in an interview. After the interview and before an offer was made, candidates were required to complete any additional hiring procedures—such as a medical exams and/or aptitude tests—at this time. Once they received a job offer, treatment candidates had the option to take up the position and begin work or decline the offer. Nearly two-thirds of the applicants assigned to the treatment group attended a firm interview, and, of those who attended, half started employment at the factories. Applicants who accepted the

> 9 For example, one firm accepts individuals between the ages of 18 and 35, but another firm only accepts those between 18 and 28; with respect to education, one firm accepts individuals who have completed grade 5 and above, but another firm only accepts those with grade 8 and above.

> 10 In the pilot of the evaluation design, one of the primary reasons fully eligible candidates did not attend their scheduled interview was that they had difficulty getting to and finding Bole Lemi I. Providing transportation to the interview was intended to minimize sample attrition at this stage of the hiring process.

10
26 Table 8: Regressions of GDP per worker growth including growth of human capital and enrollment rates at the same time.

|Growth Physical<br>capital|.501<br>(9.49)|.458<br>(8.57)|.485<br>(9.07)|.452<br>(8.35)|
|---|---|---|---|---|
|Growth of human<br>capital|-.117<br>(2.04)|-.025<br>(.369)|-.08<br>(1.31)|-.01<br>(.139)|
|Ln(initial GDP<br>per worker)|-.0008<br>(.491)|-.006<br>(2.75)|-.004<br>(1.53)|-.0076<br>(2.89)|
|Primary<br>enrollment, 1960||.022<br>(2.92)||.021<br>(2.65)|
|Secondary<br>enrollment, 1960|||.016<br>(1.64)|.010<br>(1.11)|
|N|79|77|77|76|
|R-Squared|.558|.626|.573|.632|

Notes: Absolute values of t-statistics in parenthesis. Physical and human capital data are from N-D and N-S-D.

# 2) Gr(wth and the level **of** wchnoling

That the level of education is significant in a growth regression is an interesting finding, but one that is more puzzling than is generally acknowledged. If one were going to use the level of human capital an augmented neoclassical production function would suggest regressing the level of output on the level of physical and human capital. The level-on-level regressions results in table 9 show, analogous to the growth-on-growth regressions above, that physical capital is very strong (definitely too strong in this case) and always significant. In contrast the human capital estimate is much smaller and is only statistically significant in 1985 using B-L data. There are many well known reasons why these coefficients (for both types of capital) will be biased upwards in levels on levels regressions. If the educational capital results are biased upward by as much as the physical capital results appear to be (relative to growth-growth regressions or national accounts estimates) that is, by between .1 and .2, then the negative levels from the
**Table 3. Distribution of the Top and Bottom 100 Districts across States on Various Education**

**Development-related Indices**

||**Total**<br>**No. of**|**INPUT**|**INDEX**<br>**Bottom**|**OUTCOM**|**E INDEX**<br>**Bottom**|**EQUITY**|**INDEX**<br>**Bottom**|**EDI I**|**NDEX**<br>**Bottom**|
|---|---|---|---|---|---|---|---|---|---|
||**Dis-**<br>**tricts**|**Top 100**<br>**districts**|<br>**100**<br>**districts**|**Top 100**<br>**districts**|<br>**100**<br>**districts**|**Top 100**<br>**districts**|<br>**100**<br>**districts**|**Top 100**<br>**districts**|<br>**100**<br>**districts**|
|AP|23|1||7|||17|3||
|Assam|23|5|||5|1|2||2|
|Bihar|37||37||35||25||37|
|Chhattisgarh|13||||2||3||3|
|Gujarat|25|14|||1|6||4||
|Haryana|17|1||2||2|1|3||
|HP|12|12||10||10||12||
|Jharkhand|20||17||17||11||18|
|Karnataka|27|13||20||7||20||
|Kerala|14|14||14||14||14||
|Maharashtra|34|13||9|2|18|1|12|1|
|MP|45||8|5||4|2|2|1|
|Orissa|30||||5|6|6||2|
|Punjab|17|10||9|||8|8||
|Rajasthan|32|3|2||8|2|2||7|
|TN|29|1||23||17||19||
|UP|70|1|29||22|1|20||26|
|Uttaranchal|13|11||1||7||2||
|W. Bengal|19|1|7||3|5|2|1|3|

# **Socio-economically disadvantaged districts and EDIs**

To understand the relationship between social and economic disadvantage and educational development, an analysis of the EDIs (also other related sub-components, especially the Input Index) of districts with significantly higher proportion of Scheduled Caste (SC), Scheduled Tribe (ST), Muslim and below poverty line (BPL) population was undertaken.

Districts with sizable SC population are highly dispersed across EDI and Input Index quartiles and percentiles. In the case of districts with sizable ST population<sup>11</sup> , the Input Index distribution shows an equitable distribution, indicating that these districts are not deprived specifically of inputs (access, infrastructure and teachers). However, in the Outcome Index and EDI quartile distribution, more than 80 % of these ST districts belong to the lowest 2 quartiles, indicating that while inadequate infrastructure (input) is not a serious problem in these districts, translating these inputs into outcomes has remained a challenge. Similarly, more than 2/3rds of the districts with high Muslim concentration are in the lowest 2 quartiles of the Input Index, and more than 77% of them are in the lowest 2 quartiles of Outcome Index and EDIs. See Graph 1 below.

11
# **Table A.2: Overview of key variables**

**<u>Variable</u>** Definition **Personal characteristics** Age Age of an individual in full years Female 1=household head is female, 0=male Kyrgyz, Uzbek, etc 1=household head is Kyrgyz (Uzbek,..), 0=otherwise Years of schooling Reported years of schooling Internal migrant Number of years a person lived in community Risk attitude Self-assessed risk-taking attitude in a scale from 1 to 10 Trust questions Self-assessed trust level in a scale from 1 to 4 **Household characteristics** Household size Number of members listed by a household; external migrants and students are excluded Migrants abroad Number of adults in labor migration Income per capita Household income from all sources/household size, in thousand Soms Land Size of owned land in hectares Cars Number of cars/vans a household owns Livestock Livestock units in sheep equivalent; 1 cow = 5 sheep Asset index An index calculated by means of the first principal component analysis based on 13 household assets and access to services. The mean index at household level is 100. **Location and community** South oblast 1=if Batken, Jalalabat, or Osh oblasts, 0=Chui, Issyk-Kul, Naryn or Talas oblasts Population Population in the community, in thousands Frequent energy interruptions Energy interrupted several times per week

Sources: LiK 2012 and 2011

# **Table A.3: Responses to local budget questions by rural and urban individuals**

|**Indicator**|LiK 2012 rural|LiK 2012<br>urban||
|---|---|---|---|
|Informed about the local budget|34.5|27.3|***|
|Interested in how the local budget is spent|51.4|60.7|***|
|Knows that local government should provide info about<br>local budget|31.0|15.4|***|
|Information on local budget is accessible|42.5|43.4||
|An ordinary citizen can influence budget process|11.7|13.2|**|
|Me/my household members tried to influence local budget<br>process in the last 12 months|3.5|2.5|**|
|Knows the date of the last budget hearing in the local<br>government|5.8|3.2|***|
|Participated in budget hearing of Aiyl Aimak during the<br>last 12 months|4.0|1.6|***|
|No. of observations|5,145|3,022|***|

No. of observations Source: LiK 2012.

The mean differences in responses of rural and urban respondents are tested using t-test. Significant differences are indicated by * p<0.1, ** p<0.05, *** p<0.01.

24
These results can be confirmed running additional non-parametric mean comparison tests to see if the differences between the fluency performance groups are statistically significant or not. For the dichotomic variable (45 words per minute) we use the U Mann Whitney test, a non-parametric equivalent to the t test that evaluates whether two independent samples are from the same population.<sup>40</sup> For the ordinal variable we use the Kruskal-Wallis one-way analysis of variance by ranks, a non-parametric method for testing equality of population medians among groups.<sup>41</sup>

**Table 13. Mean comparison between minimally fluent and non-fluent readers with Rasch scores (U-Mann Whitney test)**

|||**Rasch Sco**<br>|**res**<br>|
|---|---|---|---|
||**Obs**|**Rank Sum (mean)**|**Expected Rank Sum**|
|Under 45 wpm|159|26,189 (165)|37,842|
|Over 45 wpm|316|86,861 (275)|75,208|
|Combined|475|113,050 (238)|113,050|
|Unadjusted variance|||1,993,012|
|Adjustment for ties|||-10.82|
|Adjusted variance|||1,993,001.18|
|z|||-8.254|
|Prob>|z||||0.000|

The Mann-Whitney test shows that there are statistically significant differences in written comprehension scores between students who read fluently and those who do not (Table 13). What is more, such difference is not only significant but also large, as it is equivalent to nearly one standard deviation.<sup>42</sup>

**Table 14. Differences in reading comprehension between fluency groups (Kruskal-Wallis test)**

||**Fluency Groups**|**N**|**Mean Rank**|
|---|---|---|---|
|**Ranks**|0-29 wpm|85|134.22|
||30-49 wpm|127|206.30|
||50-69 wpm|117|261.62|
||70+ wpm|146|307.07|
||**Total**|**475**||
|**Test Statistics**|Chi-Square||95.789|
||df||3|
||Asymp. Sig.||0.0001|

Breaking down the sample by quartiles of fluency we also find large and significant differences (Table 14): the mean rank in the bottom quartile is 134 while the mean rank in the upper quartile is 307. In other words, more fluent readers consistently rank higher in the written test than less fluent readers, and the probability that this would be due to chance is less than 0.0001 percent.

> 40 It is more powerful than the median test since it uses the ranks of the cases. Requires an ordinal level of measurement. U is the number of times a value in the first group precedes a value in the second group, when values are sorted in ascending order.

> 41 It is an extension of the Mann-Whitney U test to 3 or more groups.

> 42 Mean differences are also significant at other key cutpoints such as 60 and 75 words per minute.  The differences using the 75 wpm cutpoint are higher than with the 60 wpm cutpoint, but both are lower than with the 45 wpm cutpoint (see Annex 7.10).

41
# **3.1 Household Survey**

We use farm survey data that were collected in two rounds to represent the whole country. The Center for Chinese Agricultural Policy carried out the surveys in December 2000 (collecting data for the year 2000) and early 2009 (collecting data for the year 2008). The data set for 2000 includes information from 60 randomly-selected villages in 6 provinces representing China’s major agricultural regions. The selected provinces are Hebei, Liaoning, Shaanxi, Zhejiang, Sichuan and Hubei. A total of 1,200 households was sampled in the following way. For each province, five counties were selected. Two villages were randomly selected from each county. Twenty farm households were chosen from each village. We judged that the data of 1,189 households of the 1,200 initial sample households were complete. In the 2009 survey, we went back to the same villages that were surveyed in 2000. There were two exceptions. Because of the 2008 earthquake in Sichuan, we were not able to repeat the survey in two of the villages. As a consequence, the sample size (including those without complete records in 2001) was reduced from 1,200 to 1,160. Among the remaining 1,160 households surveyed in 2000, we were able to re-investigate 1,046 households in 2009. Of the 114 households that we could not find in the village, 89 had moved out of the village and were reported to be living in an urban area. The other 25 households either disappeared or were living in the village but were not engaged in farming activities (18 households—mostly because they were too sick to farm).

With special attention to crop production for this study, we constructed a panel data set of households who were engaged in crop production. In the year 2000, among 1,194 sample households, around 90% of households (1,071 households) were engaged in crop production. Some households exited from crop production to allow more off-farm employment (Weiss, 1997; Kimhi, 2000). In the end, we use the panel data set on crop production consisting of 905 households in the study.<sup>13</sup>

# **3.2 Agricultural Production, Landholding and Machines**

> 13 Potentially this would lead to underestimation of rented-out land since those who stopped farming could rent out their land to other farmers.
Industry Restructuring and Regulation - Building a base for sustainable growth

### These, and other key lessons, are discussed in detail in Section 4.

Other single-country evidence was presented in the Galal and Shirley (1994) book. Galal considered the impact of restructuring and reform on the electricity and telecommunications companies. Three companies were considered, Chile Telecom (responsible for 95% of local telecommunications), Chilgener and Enersis (a major electricity generating company and the distribution company for the capital). In all three cases there were overall net benefits to the country through the privatization process. These are shown in Table 6.

||**Tab::e 6:20**<br>**Ipact0y of'0 priv**<br>**an**<br>|**tinChile**|
|---|---|---|
|_Company_|_Primary sources of benefit_|_Main groups benefiting_|
|Chilgener|Productivity|Buyers (excluding workers)<br>Foreigners|
|Enersis|Output diversification<br>Price changes|Buyers<br>Consumers|
|Chile Telecom|Output diversification<br>Investment|Consumers<br>Foreigners<br>Government|

More update detail on some of these specific companies is provided later in this paper.

## **2.2 Sector specific**

Much more prevalent than general equilibrium type studies are those that focus on just one sector, sometimes on a multi-country basis, but more frequently on a single-country basis. Several such studies have been undertaken in the past few years and the results for two of the key sectors, electricity and telecommunications, are presented below. Some evidence for the water sector is also presented, this is, however, much less prevalent than for the other two sectors.

# _2.2.1 Electricity_

One of the areas where the greatest impact has been seen is the electricity sector. An overview of the liberalization of electricity markets is provided by a paper by Pollitt (1997). Table 7 summarizes the international experience with restructuring and privatization of the electricity industry.

|_Geographic area_|_Number only:_||||_Total_|
|---|---|---|---|---|---|
||_Restructuring_|_Privatizing_|_None_|_Both_||
|OECD|11|0|4|9|24|
|Transition|3|1|2|_5_|11|
|Latin America|_5_|0|2|8|_15_|
|Asia|4I1||1|6|12|

As can be seen from the table reform is widespread. Pollitt's paper also highlights the six different approaches that can be adopted to studying the impact of reform and classifies 18 studies according to the approach utilized.'<sup>3</sup> Two of the studies are specifically about Latin _13_ The six approaches that are identified are: Simple comparisons of prices and costs; Simulation models; Financial and physical indicators; Labor and total factor productivity; Frontier analysis; and Social cost benefit analysis.

10
-40-

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