rafmacalaba/datause-encoder-data huggingface dataset:
households and 19% were in households where the head was ill or old (1901, p.45.).

Pauperism was a level below most poor households in England at the end of our period, but it was not divorced from general poverty or from the wage economy. The low paid, many of whom comprised the poor, were also those most likely to fall into pauperism in old age or in the event of a crisis such as the illness or death of the prime wage earner.

The 1906 special census includes a breakdown of paupers by past occupations and a separate pauper rate for each past occupation group. Table 18 draws on this information. For adult males the average pauper rate was 213 per 10,000. General laborers and those in arricultural occupations -- the largest two categories of low paid, unskilled workers -- hXd the highest probabilities of falling into pauperism, with pauper rates of 848.6 and 397.3 respectively. If the pattern of past occupations of paupers in 1906 is representative of what went before, it underlines the importance of the returns to different classes of labor in explaining poverty. We discussed what underlies changes in the pay structure in Section II.

What about the U.S.? Hannon gives the composition of paupers in New York State and it is reproduced here as lable 19. The composition of U.S relief recipients looks more like the English poor than the Englisb paupers. But there is no mystery here. New York State poor relief was more generous than its English counterpart so more people were on relief: 6.6% of the population in 1860 and 3.5% of the population in 1895 (see Table 9), compared with pauper rates in England of 4.3% and 2.7% respectively.

52
of governance: the turning point lies at 63 conditions, where the Heritage score (ranging from 0 to 100) is predicted to be 4 points higher on average than with 0 conditions.

The use of the CPIA ratings thus does not appear to create an upward bias in the measured relationship between PSG-related conditions and quality of public sector governance; – nor is it merely reflecting a tautological relationship. Because the CPIA in comparison – with the Heritage Foundation measures or other governance indicators better reflects the Bank’s objectives in strengthening public sector governance, we use it as our preferred dependent variable in the remainder of the study. Note that we use GMM and 2SLS methods in all tests reported below, as a general correction for potential sample selection and endogeneity bias, including any bias that might be produced by CPIA ratings inflation.

# _4.2. Endogeneity of PSG conditionality_

Results for our base GMM specification are presented in table 5. A similar quadratic relationship appears as in table 4, with a stronger effect on the quality of public sector governance: the maximum impact occurs at 80 cumulative conditions, with an average increase in the CPIA cluster D score of 0.247 points (about 0.3 standard deviations) relative to 0 conditions. Note also that here, in contrast to OLS, democratic regimes are significantly associated with higher public sector quality.<sup>13</sup> The test statistics presented at the bottom of table 5 are reassuring. The p-value of the Hansen J statistic does not indicate rejection of the null that instruments are exogenous. The value reported for the Diff-in-Hansen test provides an indication whether the additional moment restrictions necessary for system GMM are met (Bond et al., 2001). With a p-value of 0.625, we do not reject the null that the additional moment conditions are valid.

For about 95 % of the observed cases in the sample, the number of cumulative conditions is below the turning point of 80. For most but not all of the range of observed

> 13‘Political Rights’ varies from 1 (most democratic) to 7 (least democratic), so a negative coefficient implies that more political freedoms are associated with higher CPIA ratings. 15
Table 4 uses our panel data to examine the extent to which baseline business practices help predict subsequent firm survival and firm growth.<sup>8</sup> Columns 1 through 4 use data from Kenya, Nigeria, and Sri Lanka and look at survival over one year. Column 5 uses only the Sri Lanka data to examine a longer-term horizon of 5.5 years. We see from column 1 that baseline business practices do positively and significantly predict one-year survival. A one standard deviation improvement in business practices is associated with a 1.3 percentage point higher likelihood of survival, in a context where on average only 8.8 percent of firms exit in one year. Baseline practices continue to significantly predict survival even after conditioning on initial employment levels (column 2), and on owner and firm characteristics (column 3). However, they lose their significance once we condition on baseline sales and profitability (column 4), suggesting that one of the main channels in helping firms survive is through making them more profitable. Column 5 shows that our business practice measure also predicts survival over longer periods – a one standard deviation increase in baseline practices is associated with 2.2 percentage point higher survival rates over a 5.5 year horizon.

Columns 6 through 8 show that there is a strong positive association between initial business practices and subsequent sales growth (conditional on survival).<sup>9</sup> Sales growth here is measured as the change in log sales. This positive association holds over the one year horizon (columns 6 and 7), and in our Sri Lankan sample over the 5.5 year horizon (column 8). A one standard deviation increase in business practices is associated with 7 percent higher growth over one year (column 7), and with 15 percent higher growth over 5.5 years (column 8).

# **3.5 Discussion of Business Training Experimental Literature**

The above analysis shows that there exists a strong positive association between our measure of business practices and business performance and growth. In order to show that this relationship is causal, we would ideally experimentally change business practices and measure their impact on firm growth. A growing experimental literature, summarized in McKenzie and Woodruff (2014), has attempted to do this. However, much of this literature has struggled to find significant impacts

> 8 Note in our survival probits we control for industry classification at the level of manufacturing, trade, and services, in order that the probit does not drop observations from industries with small numbers of firms in our sample and in which all the firms survive or all fail.

> 9 Since sales growth can be noisy, for robustness we also considered sales growth truncated at the 5th and 95th percentile, and using this truncated growth measure also results in a positive and statistically significant association.

14
# **ABSTRACT**

The paper examines the diverse prospects of innovative sectors in Beijing and Shanghai using available indicators and data collected for this study through surveys. Beijing is the first choice for companies locating in China but foreign employees prefer Shanghai for living convenience and cultural amenities. While Shanghai lags behind Beijing in knowledge creation and the generation of startup companies in the innovative sectors, it takes the lead in the commercialization of technological innovations and the development of creative cultural industries. The municipal authorities of Beijing and Shanghai have improved the innovation environment of the cities, but certain elements still stunt the growth of innovative industries, which cannot be removed easily. Three kinds of knowledge-intensive enterprises included in innovative sectors in the survey are high-tech manufacturers, knowledge intensive business services and creative content providers. The survey found that the clustering of the firms arose from the attraction of preferential policies and the purchase by governments or state-owned enterprises of information technology products. The survey shows that interaction among firms is inadequate in the knowledge-based industrial clusters in both Beijing and Shanghai. Hence, it may be some time before clustering leads to substantial gains in collective efficiency for innovative industry in Beijing and Shanghai.

The authors also are grateful for the helpful comments provided by the participants of the workshop organized for this study held in Bangkok, February 22-23, 2004.

ii
# **2 Implementation and update of the 2019 BRSS**

The fifth round of the World Bank - Bank Regulation and Supervision Survey was completed in 2019. The survey encompasses information on 160 jurisdictions listed in Appendix I, including two monetary areas (Euro Area and the West African Economic and Monetary Union), 66 high-income countries and 93 emerging and developing economies.<sup>3</sup> These include all G-20 countries and countries from all the World Bank developing regions, as reported in Figure 1, Panel A and B.

**Figure 1: Number of countries by income group and region – BRSS 2019**

**a. Number of countries by income group b. Number of countries by regional group**

Source: Wave 5, BRSS.

Note: In Panel A, we use classifications drawn from The World Bank - World Development Indicators (WDI). In Panel B, we use a combination of classifications from the WDI, the Organisation for Economic Co-operation and Development (OECD) and the Financial Stability Board (2010). Off-shore financial centers are excluded from Panel B. For further detail on country groupings in the HI OECD category and the World Bank developing regions, please see Appendix I. The developing region definitions are based on World Bank country classifications as described in Anginer and Demirgüç-Kunt, (2014a).

The questionnaire builds on previous rounds while adding new questions on recent regulatory developments that characterized the period 2011-2016 such as the Basel III capital and liquidity requirements, bank resolution mechanisms and macroprudential supervision.<sup>4</sup> Specifically, compared to the

> 3 The 2019 Bank Regulation and Supervision Survey and previous iterations are available at: <u>http://www.worldbank.org/en/research/brief/BRSS. Notice that questionnaires have been received from 159 jurisdictions and the</u> European Central Bank (the ECB). Since October 2014, the ECB has overseen directly significant banking institutions in the Euro Area. 4 The description and implementation of the previous rounds of the survey appear in Barth et al. (2001, 2006 and  2008) and Čihák et al. (2012). For a comprehensive view of all four rounds of the survey, see Barth et al. (2013).

4
In all three of these cases the relationships we observe are less stark than in the LMICs in our sample. This is consistent with differential patterns of residential sorting in the United States – – in particular, greater suburbanization in the United States leading to lower pollution exposure<sup>8</sup> driving a different relationship between pollution exposure and wealth. It is also consistent with a relatively weak relationship between income and ambient pollution exposure in the United States.<sup>9</sup>

# **SI-1.4 Robustness of Results**

Our results are robust to a variety of alternative specification and data choices. Measuring pollution as the median across months and years, rather than the average, in our sample results in broadly similar results (Table SI-1). The choice of weighting scheme appears to matter more when pollution is measured as the median and the magnitude of the correlation is somewhat smaller. In particular, weighting by grid-cell population eliminates the correlation in the pooled sample when using the median. This suggests there are a few grid-cells with very high populations that do not exhibit any correlation between wealth and median pollution but do exhibit a correlation when pollution exposure is measured using the average level of pollution. We believe that using average pollution is a more appropriate measure than median pollution because the average is sensitive to extremely high values of pollution that may be particularly harmful (e.g. spikes in Delhi during the crop burning season).

Alternatively, we can calculate the average level of pollution by month for each grid-cell in our sample and assign the average value of the month with the highest average level of pollution to each grid-cell. This approach focuses heavily on the spikes that might occur as a result of seasonal crop burning or forest clearing or due to seasonal increases in dust storms. Using this approach yields results that are qualitatively similar to using the average across the full sample but the magnitudes of the correlation are larger in the maximum approach (Table SI-1). Comparing the country specific estimates of the correlation between wealth and pollution exposure, Figure SI-4, indicates broad agreement in the estimated correlation when exposure is measured as median or average levels of pollution. Most countries fall on or near the 45<sup>_◦_</sup> line.

There is less agreement between the maximum measure and the average measure however. At the extremes the qualitative patterns seem to persist - Nigeria for example has a negative correlation with both measures and has the strongest negative correlation in the sample. The notable exception is India and Pakistan (which are treated as a single unit in the RWI data, a choice we preserve), which has a substantially higher positive correlation using the maximum measure rather than the average measure. This is likely driven by very high levels of pollution in some North Indian cities during December. One possible explanation for these somewhat divergent results using the maximum measure rather than the average measure is that the 23
income tax law implemented in China in 1986. So conceptually, these eamings are equal to the aftertax income in the United States.

45. The earnings of medical professionals in China were estimated by including the base and duty salaries of doctors and other allowance **s** uch as bonuses, benefits and labor insurance (including medical care insurance, and fiinge benefits for birth control, death, care for dependents, poverty, entertainment, bathing, and haircuts) and housing allowances. The base and duty salaries are diferent for various levels of medical professionals, but the allowances are the same. The estimated earnings for medical professionals inChina are presented in Table 5.

# Table 5: EARNINGS OF PHYSICIANS, DENTISTS AND NURSES, ETC. (YUANIYEAR)

|Category|Base and<br>duty<br>salary|B<br>Bonuses|onuses, bene<br>Benefits<br>and labor<br>insurance|fits and allowan<br>Housing<br>allowances|ces<br> <br>Total|Total<br>earnings|
|---|---|---|---|---|---|---|
|Physicians|1,616|432|236|_1,051_|1,720|3,335|
|Dentists|1,616|432|236|1,051|1,720|3,335|
|Nurses, etc.|788|432|236|1,051|1.720|2,507|

Notes: 1. In China dentists are not distinguished from physicians as a different profiession. 2. Earnings of physicians and dentists are geometric means of eamings of the various levels of physicians and densts.

3. Earnings of nurses etc. are a geometric mean of eanings of various types of non-doctor medical professionals.

4. Benefits and labor insurance are computed by dividing tot expenditures on benefits and labor insurance by the number of employees.

5. Housing allowances are computed by the shadow rent estimated in this paper. The formula is: Housing Allowance = 7.65 yuan/sq.m.mon x 6.36 sq.m. (average housing floor per person in urban region) x 1.8 (1 dependency ratio) x 12 mon.

46. To calculate the eamungs of physicians, denfists, and medical professionals, the data on fte number of doctors and national health expenditu in the _StuiticalAbslract of he United States_ **_were_** 20
(iii) The dataset should include information on labor force and earnings histories as well as demographics, because earnings and employment dynamics are key predictors of decisions to enter training programs.

Different studies face different kinds of data limitations and the choice of estimator should take into account the particular limitations that need to be overcome. For example, if participants and nonparticipants are drawn from different local labor markets or if the survey questionnaires used to gather the data are different across groups, then difference-in-difference approaches tend to be more reliable than cross-sectional methods, because they allow for fixed unobservable differences between groups. (See Heckman, Ichimura and Todd, 1997, and Smith and Todd, 2005.) If there is good reason to believe that selection in the program is based on unobservable attributes, then control function methods, instrumental variables, or behavioral modeling that explicitly allow for program selection on unobservables may be most appropriate.<sup>6</sup>

# **3  Employment Creation and Job Training Strategies**

## **3.1  Active Labor Market (ALMP) Policies**

In this section, we review the evidence on the effectiveness of a variety of Active Labor Market Policy (ALMP) programs in affecting the employment, wages and poverty status of participants. Two predominant types of ALMP programs are wage subsidy programs that subsidize wages in either public or private sector jobs, and training programs that provide formal training designed to help participants develop occupational skills. Other types of ALMP programs are basic education programs, or short-term interventions that enhance job search skills. ALMP programs may also provide other sorts of benefits, such as work clothing, childcare and transportation expenses.

There are multiple channels through which ALMP programs might be expected to influence workers‟ employment and earnings outcomes. One is that they may increase the productivity of the worker and therefore her offered wage by augmenting her skill set or by providing opportunities to gain work experience (e.g. through internships). ALMP programs may also affect the process by which workers match with firms, for example, by reducing the costs of searching for a job or by increasing the arrival rate of job offers. Wage subsidy programs could

> 6 See Heckman, Lalonde and Smith (1999) for a discussion of the use of control function methods in program evaluation.

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Page No.

TABLES

|Table 1:|Real Deposit Rates in Korea,||
|---|---|---|
||1960-1986 ...............................|.......<br>6|
|Table 2:|Korea: Sources of Funds for Housing||
||Purchases by Household Income||
||Level for All Households, 1986 ...........|15|
|Table 3:|Korea: Strategies Followed by<br>Households to Purchase Housing:<br>All Households**....**<br>**0.............**|**..........**<br>17|
|FIGURES|||
|Figure 1:|Korea: Financial Deepening<br>**.............**|**....**7|
|Figure 2:|Levels of Financial Development**............**|7|
|Figure 3:|Korea: Household Portfolio<br>**..............**|**..**11|
|Figure 4:|Korea: Curb Market Rates, 1963-86**.........**|13|
|Figure 5:|Korea: Dual Interest Rate Structure........|<br>13|
|Figure 6:|Korea: New Housing and Ml,<br>1966-1983 ..........................|..............<br>23|
|Figure 7:|Korea: Share of Housing in GNP<br>**............**|23|
|Figure 8:|Korea: Housing Units per Household**........**|25|
**In Bulgaria, the income thresholds determining MSA eligibility vary by household type** . Table 2 Panel A shows the differentiated minimum income of each family member before and after the reform (in 2020 currency). The coefficient for different individual types has experienced some changes, while the new benchmark has also been adjusted. Previously, the benchmark was based on the GMI indicator (75 BGN), but it has now shifted to 30% of the at-risk poverty threshold, estimated at 504.3 BGN per month using SILC 2021 data (income year 2020).<sup>20</sup> This represents substantial increases in the value of the threshold. Table 2 Panel B below shows the differentiated minimum income of the family (DMIf) for different family types, both before and after the reform. To exemplify these rules, the monthly income eligibility threshold for a family composed of a single parent and a child younger than 3 was 158 BGN before the reform. It increased to 332 BGN after the reform. Before the current MSA reform, these nominal thresholds were constant and not adjusted by inflation, so they have decreased in real terms, becoming more restrictive over time.

**Table 2. Differentiated Minimum Income (before and after reform)**

Panel a. Differentiated minimum income of each individual in the family (DMI),

|**Pre-reform(Percentage of GMI Indicator)**||**Post-Reform(Percentage of 30% AROP Poverty **|**Line)**|
|---|---|---|---|
|Persons over 75years of age livingalone|165|||
|Persons over 65years of age,livingalone|140|Person living alone (no age cut)|165|
|Person upto 65years of age,livingalone|73|||
|Persons over 65years of age|100|||
|Adults in multi-adult households|60|Peole cohabitin with other (no ae cut)|100|
|Person, cohabiting with another person (persons) or<br>family,and for each of the spouses livingtogether|66|p g g||
|Children till 16 years of age and if he/she studies – till<br>graduation of secondary education, but not more<br>than 20years of age|91|For each child up to the age of 18, and if studying -<br>until obtaining secondary education, but not|100|
|Orphan child: a child accommodated in a family of<br>close friends or relatives or a receivingfamily|100|exceeding the age of 20||
|For a parent bringing up alone child/children up to 16<br>years of age, and if he studies – till graduating of<br>secondary or professional education, but not more<br>than 20years of age –|100|For a parent raising a child/children alone to the age<br>of 18, and if studying - until obtaining secondary<br>education, but not exceeding the age of 20|120|
|Parents bringing up alone child/children up to 3<br>years of age|120|Parents bringing up alone child/children up to 3 years<br>of age|120|

> 20 At the time of this study, 2022 SILC estimates were not available.

23
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# **Annex Table 1:  Pairwise Correlation Matrix**

||RF|SOA|SOFI|MSHARE|LEV|HER(EARLY)|AP(EARLY)|CR|MIS|ROFI(B)|ROFI(M)|
|---|---|---|---|---|---|---|---|---|---|---|---|
|RF|1.00|||||||||||
|SOA|-0.39|1.00||||||||||
|SOFI|-0.55|0.38|1.00|||||||||
|MSHARE|-0.06|0.17|-0.18|1.00||||||||
|LEV|-0.05|0.06|0.05|0.03|1.00|||||||
|HER(EARLY)|-0.58|0.06|0.20|0.07|-0.07|1.00||||||
|AP(EARLY)|-0.16|0.36|0.17|0.26|0.06|0.00|1.00|||||
|CR|0.42|-0.02|-0.18|-0.09|0.05|-0.44|-0.11|1.00||||
|MIS|-0.22|0.13|0.33|-0.16|-0.11|0.09|0.00|-0.01|1.00|||
|ROFI(B) 1/|0.86|-0.23|-0.37|-0.07|0.05|-0.59|0.00|0.43|-0.15|1.00||
|ROFI(M) 1/|0.92|-0.30|-0.44|-0.08|0.04|-0.68|-0.04|0.47|-0.19|0.97|1.00|
|RES|-0.43|0.52|0.30|0.71|0.09|0.18|0.42|-0.16|-0.01|-0.26|-0.32|

Note (1): ROFI (B) and ROFI(M) are the rates of return on corporate and mortgage bonds, respectively.