rafmacalaba/datause-encoder-data huggingface dataset:
29. Of the 204 projects, 23% were rated as unsatisfactory. These ratings are for the projects as a whole; performance was worse on the credit components with 47% being unsatisfactory. Several projects had positive results. They (a) helped increase crop, livestock, and industrial production, b) fostered substantial improvements in assets and c) enhanced the capacities of the implementing agencies. However, there were several deficiencies reported. Project design typically paid little attention to the mobilization of rural savings. Most credit institutions were far from being financially viable caused by factors including high delinquency rates, rapid de-capitalization due to high inflation and negative interest rates. Not enough attention was paid to managing delinquency and developing appropriate criteria for assessing eligibility for loans or debt rescheduling.

**30.** A recent review of the World Bank’s rural finance projects approved during FY94-99 reported a  mixed performance.<sup>19</sup> During FY94-99, the Bank approved 101 projects with rural finance components (either stand-alone or with rural finance components). The rural finance loan amount associated with these projects is $ 4.12 billion, i.e. an average of about 0.7 billion per year, with an increasing trend over time.  Positive findings on the portfolio performance include better performance and better incentive systems related to loan collection and increased attention to setting appropriate interest rates. On the negative side, there is inadequate attention to institutional subsidy dependence and insufficient analysis of financial and non-financial institutional aspects.

# **III. Technical Assistance, Extension, Training and Technology** **<u>Upgrading</u>**

# **A. Technical assistance to private firms**

31. Technical assistance programs for SMEs gained prominence in the 1970s. These programs typically provide support, usually through public sector agencies, to help SMEs build technical, marketing and managerial skills. Often the projects also support capacitybuilding of local institutions, usually in the public sector, that provide technical assistance (TA) to SMEs, and financial institutions that provide credit to SMEs.

32. On balance, the impact of these programs has been limited. A study of World Bank lending for SMEs found that the Government-executed schemes were mostly “slow to get off the ground, costly and ineffective”.<sup>20</sup> Many project development facilities reach only a small number of firms at substantial costs. These often benefit firms that are large by developing country standards. The cost effectiveness of these interventions remains unclear and has not been rigorously evaluated. However, useful findings have emerged from some IFC-sponsored evaluations.

> 19 Yaron and Charitonenko (2000).

> 20 Webster (1991).

10
where _Y_ is the firm performance indicator, and _i, c,_ and _j_ represent firm, country, and local levels. _Y_ could be log labor productivity (or TFP), the labor productivity growth rate, the rate of sales growth, export intensity, or the investment rate.<sup>22</sup> _Firm_ is a set of basic firm-level controls including firm age, state and foreign ownership, and the ownership share of the largest owner. For the sales (labor productivity) growth rate equation we control for initial sales (labor productivity) three years ago to allow for regression toward the mean. _Geography_ includes log country population and a dummy equal to one for landlocked countries. _Infrastructure_ includes the telecom index (measured at the local, that is, the city-industry-size, level). _PoliInst_ represents the category of political and institutional factors, and includes log party years, any conflict in the past ten years, and ethnic fractionalization. _BusiEnvi_ indicates business environment consisting of firm-level foreign ownership, average country-industry foreign ownership, firing difficulty, the local average bribe, country-industry tariff levels, countryindustry competition index, and the average perceived level of crime at the local level. _Finance_ includes the local share of firms having access to an overdraft facility, and the local average for access to trade credit. We also include industry dummies, so that comparisons are between firms within the same sectors across countries. The error term captures unobserved variables and measurement errors.<sup>23</sup> Since the error terms within a country may be correlated due to omitted common factors or shocks, and many of our explanatory variables are aggregate variables, we

> 22 Log labor productivity is trimmed of tail one percent to avoid the outlier issue. Results are similar without trimming.

> 23 Since we have many control variables, and the coverage of both the Enterprise Survey and cross-country sources differ for some countries, dropping the observations with any variable missing would result in the loss of the majority of the sample, and render our empirical exercises mush less representative.  For variables with significant number of missing observations, we thus resort to imputation, using the predicted value based on the following basic controls: the country-level urbanization level, the regional dummies (Africa, East Asia and Pacific, East Europe and Central Asia, South Asia, Middle East and North Africa), the manufacturing dummy, and firm size dummies (10-20, 20-60, and 60+ employees).  Why do we use the manufacturing dummy in imputation when our analysis largely focuses on the manufacturing subsample?  While we largely use the manufacturing subsample for most of analysis, we do use the service subsample in some sensitivity checks.

19
Table 11: Analysis of Indonesia standards on children's EDI (raw scores)

||DV: Physical health<br>& well-being|DV: Social<br>competence|DV: Emotional<br>maturity|DV: Language &<br>cognitive development|DV: Communication<br>& general knowledge|
|---|---|---|---|---|---|
|**Observed classroom quality**||||||
|Indonesia standard|0.107*|0.121**|0.088|0.167*|0.133*|
||(0.058)|(0.062)|(0.062)|(0.092)|(0.072)|
|**Teacher characteristics**||||||
|% of teachers w/ sr. secondary edu.|0.288|0.813**|0.184|-0.127|0.911*|
||(0.383)|(0.342)|(0.413)|(0.538)|(0.481)|
|% of teachers w/ post secondary edu.|0.285|1.078***|0.269|-0.180|1.188**|
||(0.392)|(0.358)|(0.433)|(0.563)|(0.489)|
|Mean years of teaching|-0.015**|-0.008|0.009|-0.006|-0.045***|
||(0.008)|(0.009)|(0.008)|(0.012)|(0.012)|
|% of teachers w/ no ECED experience|-0.058|0.053|0.052|0.236|-0.025|
||(0.099)|(0.125)|(0.100)|(0.170)|(0.138)|
|% of teachers w/ no training|0.019|-0.110|0.114|0.041|0.073|
||(0.133)|(0.155)|(0.128)|(0.215)|(0.178)|
|**Structural characteristics**||||||
|Student-to-staff ratio|0.013*|0.002|0.001|0.017|-0.004|
||(0.007)|(0.009)|(0.007)|(0.011)|(0.012)|
|Hours of operation per week|0.007|0.027*|0.000|0.036*|0.004|
||(0.012)|(0.015)|(0.012)|(0.020)|(0.018)|
|Constant|3.876***|2.192***|3.781***|-1.524*|0.014|
||(0.518)|(0.562)|(0.502)|(0.778)|(0.682)|
|Observations|7,984|7,957|7,946|7,958|7,981|
|R-squared|0.157|0.143|0.119|0.270|0.150|
|No. ofclusters (centers)|565|565|565|565|565|
|First-stageF-statistic ofexcludedinstrument|842.486|842.505|842.323|862.420|842.419|
|**Control included:**||||||
|Child characteristics|Y|Y|Y|Y|Y|
|Kindergarten dummy|Y|Y|Y|Y|Y|
|District dummy|Y|Y|Y|Y|Y|

* p<0.1,  ** p<0.05, *** p<0.01

Note: Robust standard errors clustered at the center level in parentheses. Child characteristics include age, age-squared, gender, whether child has special needs (reported by teacher), and mother's highest education level. Kindergarten dummy is 1 for kindergartens or Islamic kindergartens and 0 for non-project playgroups or project playgroups. District dummies are included for the 8 districts where centers and children are located. Models 1-4 are OLS regressions. Models 5 and 6 are two-stage least squares model to <u>correct for measurement error in the measure of center quality. The F-statistic of the first stage is reported.</u> 30
20 higher tax morale and a lower size of the shadow economy (Torgler, Schneider & Schaltegger, 2009).

Taking into account such results, it may make sense to take a closer look at how governance and institutional quality has changed over time. We therefore use the International Country Risk Guide (ICRG) data set that provides yearly values that are measured in a consistent manner over time. The ICRG has a special emphasis on aspects affecting private foreign investment decisions. The rating comprises 22 variables in three subcategories of risk: political, financial, and economic. We will focus on the political risk component (see _Figures 1_ and _2_ ). The political risk rating provides a standard basis from which to compare political stability by using 12 different measurements that cover both political and social attributes. Five key sub-components that measure governance and institutional quality are: bureaucratic quality<sup>3</sup> , corruption<sup>4</sup> , democratic accountability<sup>5</sup> , government stability<sup>6</sup> , and law & order<sup>7</sup> . A higher number of points indicates a lower potential risk and therefore higher scores represent a higher institutional and governance quality. We are going explore data between 1985 and 2011. _Figures 1_ and _2_ presents the results of our 10 countries. _Figure 1_ shows a substantial improvement of governance and institutional quality in the 1990s until 1998 for the countries Bulgaria, Czech Republic, Hungary, Poland and Romania. After that point we observe a decrease without substantial improvements in the first decade of the new century. A similar picture is found for the remaining countries in _Figure 2_ , namely Estonia, Latvia, Lithuania, Slovenia, and Slovakia. The peak point for Latvia and Lithuania, after

> 3 Institutional strength and quality of the bureaucracy: “High points are given to countries where the bureaucracy has the strength and expertise to govern without drastic changes in policy or interruptions in government services. In these low-risk countries, the bureaucracy tends to be somewhat autonomous from political pressure and to have an established mechanism for recruitment and training. Countries that lack the cushioning effect of a strong bureaucracy receive low points”.

> 4 Assessment of corruption within the political system. Lower scores indicate "high government officials are likely to demand special payments" and that "illegal payments are generally expected throughout lower levels of government" in the form of "bribes connected with import and export licenses, exchange controls, tax assessment, police protection, or loans. "

> 5 Measures how responsive the government is with  its people.

> 6 Assessment of the government‟s ability to carry out its declared program(s), and its ability to stay in office. (subcomponents: government unity, legislative strength and popular support).
**Figure 13. Example of achievement indices for under-five mortality**

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_Source:_ Woild Bank (2001).

This formulation of the achievement index hinges, of course, on the particular set of weights chosen. It embodies the weights implied by the concentration index itself. But it and the concentration index can be generalized by introducing a parameter indicating the degree of aversion to inequalities in health between the poor and the better-off (Wagstaff 2001). If one proceeds along these lines, the achievement index becomes the mean times the complement of the so-called _generalized_ concentration index. The more averse the policymaker is to health inequalities between the poor and better-off, or equivalently the bigger the weight the policymaker wanted to attach to the health of the poor, the more the achievement index focuses on the health of the poor. In the extreme, the distributionally sensitive measure of population health reduces simply to the health of the poor, or the poorest group. But this would clearly be a very extreme position to take.

# **7. Measuring the Impact of Anti-Inequality Policies**

From the point of view of measuring the impact of anti-inequality policies, the challenge-whether comparing different countries at a point in time or a given country at different points in time-is the same, namely how to separate out the effects of policies from the effects of other differences or other changes. Clearly, we cannot simply compare inequalities across countries, or over time, since the headline levels of health inequality in a cross-section and changes in it over time will reflect many differences and many changesespecially those relating to income.

_29_
A comparative analysis of some important socio-demographic indicators of these UAs is helpful in getting a better idea about their fiscal health. In this study, the analysis has been carried out for the central cities (Table 2) and the surrounding ULBs in the non central cities (Tables 3 and.4) separately in order to compare the two categories of ULBs in an agglomeration and each category across agglomerations.

The main observations on central cities (Table 2) can be summarised as follows:

- According to Census 2001, the population of central cities range between 2.5 million to 10 million; The number of households varied from 500,000 to 3.2 million;  The area varied from 173 sq. kms to 1397 sq kms, the population growth during the period from 1991 to 2001 varied from a mere 4 percent in Kolkata Municipal Corporation (KMC) to 62 percent in Pune Municipal Corporation (PMC); density of population per sq. km area varied from 6,000 persons to 25,000 persons and literacy rates from 76 percent to 85 percent..

- In terms of area, the Municipal Corporation of Delhi (MCD) is the largest followed by Pune (PMC), while Kolkata (KMC), Chennai (COC) and Hyderabad (MCH) corporations are almost of the same size.

**Table 2 Socio Demographic Characteristics: Central Cities**

|Central City|Area (Sq.<br>Km.)|Households|Population|Population<br>Growth<br>1991-2001<br>(%)|Population<br>Density<br>(Persons per<br>Sq Km)|Literacy<br>Rates<br>(%)|
|---|---|---|---|---|---|---|
|Municipal Corporation<br>of Hyderabad|173|660,363|3,658,510|20|20,917|78|
|Corporation of<br>Chennai|174|962,213|4,343,645|13|24,963|85|
|Kolkata Municipal<br>Corporation|186|931,402|4,580,546|4|24,596|83|
|Municipal Corporation<br>of Delhi|1,397|3,247,838|10,679,152|48|7,643|82|
|Pune Municipal<br>Corporation|430|555,771|2,538,473|62|5,903|76|

**Source: Census of India, 2001, Authors’ Computations**

9
set of characteristics to define the sector makes it difficult to design surveys in order to systematically collect data on the sector (Chen, Sebstad, and O’Connell 1999).

# **III. Lessons from the Qualitative Data:  A Literature Review**

Keeping in mind these measurement difficulties, Table 1 shows that the proportion of the labor force that engages in home-based work ranges from 1.6 percent to 23 percent of the working population across the world. An array of definitions and questions are used to construct these data, thus making comparisons meaningless. Nonetheless, it should be noted that home-based work exists in both developed and developing countries.<sup>5</sup> Table 1 also suggests that this organization of work does not necessarily arise from the globalization process since some of the countries in the table had sizeable home-based work sectors prior to lowering their trade barriers.

# **3.1 Characteristics of home-based workers**

The ethnographic research suggests that home-based workers are primarily individuals who are unable or unwilling to work outside of their homes. Generally, workers who are confined to the home fall into three general categories: women (or men, to a lesser extent) with children or other household responsibilities that require them to be near home throughout the day, those who have physical or mental conditions that limit their ability to travel to a worksite, and individuals who have difficulty entering the labor force due to legal constraints, discrimination, or information asymmetries.

> 5 It should be noted that the conditions of and remuneration for home-based work differs between countries. Thus, a comparison of the incidence of home-based work does not allow us to draw inferences about the quality of the employment nor to pass welfare judgments on the quality of labor in the countries in Table 1.

6
including women’s greater propensity to spend significant periods out of the labor force and women’s higher unemployment rates. The sex variable is used to proxy gender.

The poverty disaggregation is intended to identify if the poor have more difficulty than the non-poor in transitioning from school-to-work or between employment states. Since the three surveys do not include consumption data, we use a crude measure to proxy poverty level of the individual. We calculate total labor income for each household, and we divide the sample into household income quintiles. Once the quintile is assigned to a household, we assign the corresponding quintile level to each individual in our sample.

Seven different sectors are defined in order to better understand where young people were before getting a job and where they go upon leaving it. Three non-working sectors are identified. The “unemployed” are those who are not working, not studying, and report having searched for a job for at least an hour in the week prior to the interview.<sup>12</sup> Those who are “out of the labor force” are not in school, not working, and not looking for a job. “Students” are in school and not working. Three labor market sectors are considered. Those who own a firm and do not have any paid employees are identified as “self-employed”. Those who work for pay are divided into two categories: “formal wage employees” are employees who receive a wage and whose employer also pays into the social security system on their behalf and “informal wage employees” earn a wage but the employers do not contribute to the social security system on the workers behalf.<sup>13</sup>

# _Summary Statistics_

The samples are relatively similar across countries in spite of the different structure of the data sets. In Argentina we have around 90,000 observations of workers under 25 years old, 44 percent of which are age 15-18, while for Mexico there are around 400,000 (43 percent under the age of 19) and for Brazil, around 90 million (Table 1) with 43 percent age 15-18. Young men and women are equally represented across countries. The sample has

> 12 Although we define this category and include it in our transition matrices, we do not analyze the unemployment state in this paper.  For a full discuss of transitions into and out of unemployment, see Cunningham (2009).

> 13 Individuals who self-identify as “employers” are dropped from the sample since few 15-18 and 19-24 year olds identify themselves as such.  The “unpaid” are those who state that they work but do not receive remuneration; they are dropped from the analysis since there are too few of them to estimate meaningful transition patterns.

10
|regulations, export subsidies)|10|
|---|---|
|Review to clarify certain points of the antidumping, subsidies and TRIPS<br>agreements|8|
|Technical assistance<br>• to participate in the WTO or standards-related international bodies<br>• to implement WTO obligations|10<br>4<br>6|
|Reminder that Members have a legal obligation under TRIPS Article 66.2<br>to provide incentives for their enterprises and institutions to promote<br>incentives for their enterprises and institutions to promote<br>technology transfer to least developed countries.|1|
|Total|37|

Source: Tabulated from WTO (2001) MINISTERIAL CONFERENCE, Fourth Session, Doha, 9 - 14 November 2001 Implementation-Related Issues And Concerns, WT/MIN(01)/17, 20 November 2001.

8
**Table 3. Transtions probabilities across sectors between survey waves (age group 15-64 only)**

||||N|ew stat|us||||
|---|---|---|---|---|---|---|---|---|
||1|2|3|4|5|6|7 T|otal|
|Initial status|||||||||
|a. Transition between the 1994 wave and the 1997 wave|||||||||
|1. self-employed|94.7|0.2|0.4|0.2|0.6|1.3|2.5|100|
|2. government worker|0.7|97.3|0.0|0.7|0.0|0.5|0.9|100|
|3. public enterprise worker|1.2|0.0|92.9|1.8|0.0|0.0|4.1|100|
|4. formal private sector worker|0.0|0.5|1.0|94.4|0.0|1.5|2.5|100|
|5. other private sector worker|0.8|0.3|0.5|0.0|95.7|2.8|0.0|100|
|6. unemployed|1.5|0.6|0.3|1.7|2.4|93.2|0.3|100|
|7. out of the labor force|1.4|0.1|0.1|0.4|0.8|1.8|95.5|100|
|Total|10.6|9.4|3.6|4.6|8.9|18.8|44.1|100|
|b. Transition between the 1994 wave and the 2000 wave|||||||||
|1. self-employed|59.4|2.4|0.3|4.4|9.6|4.4|19.5|100|
|2. government worker|2.0|58.6|14.9|6.0|0.8|4.8|12.9|100|
|3. public enterprise worker|8.1|23.4|29.8|7.3|2.4|4.8|24.2|100|
|4. formal private sector worker|11.1|0.9|7.7|46.2|12.0|8.6|13.7|100|
|5. other private sector worker|11.4|2.1|2.1|14.9|40.4|14.2|14.9|100|
|6. unemployed|8.7|5.9|2.5|11.0|11.6|48.7|11.6|100|
|7. out of the labor force|7.9|3.4|1.9|4.5|5.1|19.0|58.1|100|
|Total|13.6|9.9|4.7|8.4|8.5|20.0|34.9|100|
|c. Transition between the 1994 wave and the 2004 wave|||||||||
|1. self-employed|49.3|3.0|1.5|6.3|8.2|7.8|24.1|100|
|2. government worker|3.5|45.4|7.0|9.7|3.5|10.5|20.5|100|
|3. public enterprise worker|3.2|21.3|17.3|10.2|3.2|9.5|35.4|100|
|<br>4. formal private sector worker|8.9|8.9|4.0|37.6|7.9|14.9|17.8|100|
|<br>5. other private sector worker|15.7|4.7|1.2|12.2|32.0|9.3|25.0|100|
|6. unemployed|8.1|9.4|2.4|16.6|10.5|30.4|22.7|100|
|7. out of the labor force|8.5|6.1|2.0|9.0|6.7|20.4|47.3|100|
|Total|12.5|11.2|3.3|11.6|8.8|18.3|34.3|100|
|d. Transition between the 1997 wave and the 2000 wave|||||||||
|1. self-employed|58.3|1.0|0.7|6.2|9.3|5.5|19.0|100|
|2. government worker|2.4|59.4|14.9|6.8|0.8|4.4|11.2|100|
|3. public enterprise worker|3.8|27.4|33.0|9.4|4.7|4.7|17.0|100|
|4. formal private sector worker|9.8|0.0|8.9|44.7|10.6|13.0|13.0|100|
|5. other private sector worker|9.9|3.1|1.9|12.4|40.4|15.5|16.8|100|
|6. unemployed|7.8|5.6|2.0|10.2|11.6|51.3|11.6|100|
|7. out of the labor force|7.2|3.2|1.2|4.0|4.7|16.9|62.8|100|
|Total|12.4|9.3|4.2|8.1|8.3|19.5|38.2|100|
|e. Transition between the 2000 wave and the 2004 wave|||||||||
|1. self-employed|50.0|2.0|1.3|3.7|6.7|9.3|27.0|100|
|2. government worker|3.6|55.7|11.3|6.1|2.4|7.3|13.7|100|
|3. public enterprise worker|2.7|37.3|21.8|10.0|5.5|8.2|14.6|100|
|<br>4. formal private sector worker|5.0|7.7|3.9|45.9|7.2|13.8|16.6|100|
|5. other private sector worker|8.7|8.2|1.4|14.9|26.9|16.4|23.6|100|
|6. unemployed|6.8|8.5|2.9|12.6|10.5|35.7|22.9|100|
|7. out of the labor force|5.9|4.0|1.1|6.3|4.5|16.9|61.4|100|
|Total|10.6|11.0|3.4|10.4|7.5|17.9|39.3|100|

28
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