rafmacalaba/datause-encoder-data huggingface dataset:
detailed in Table 2. High (skill) level occupations include managers, professionals and technicians. Mid (skill) level occupations include clerks, services, crafts and machine operators. Elementary (skill) level are elementary occupations, including domestic workers.

Table 2: Skill levels in SASCO 2003 and ISCO-88 major code (one digit) occupations

|Code|Majorgroup|Skill level|Equivalent education|
|---|---|---|---|
|1|Legislators, senior officials, and managers|*|*|
|2|Professionals|4|Postgraduate|
|3|Technicians and associate professionals|3|College/tech degree|
|4|Clerks|2|Complete high school|
|5|Service workers and shop/market sales workers|2|Complete high school|
|6|Skilled agricultural and fishery workers|2|Complete high school|
|7|Craft and related trades workers|2|Complete high school|
|8|Plant and machinery operators and assemblers|2|Complete high school|
|9|Elementaryoccupations|1|Primary/no schooling|

Note: Major code 0 (Armed forces with 20 four-digit occupations) is not included in this table and is also excluded from our sample. The *asterisks mean that there is no skill reference for this occupation. In this paper we combine this occupation with professionals and technicians therefore assuming a skill level of 3 and 4. Source: SASCO 2003

- 3 Description of Green Jobs in the South African Labor Market

3.1 Which Occupations Are Green?

In this section, we provide a description of green jobs using both the strict and broad definitions of green tasks. Using the strict<sup>24</sup> definition of green tasks, we find that 46 out of 428 occupations in ISCO-88 can be classified as strictly green, meaning that their strict-GTI is greater than zero.<sup>25</sup> When using the broad definition of green tasks, we count 137 occupations can be classified as broadly green; the additional 91 occupations are those that do not include any strictly green tasks but do include tasks on the broad green tasks list. See Tables A1 and A2 in the Appendix for the full lists of strictly green and broadly green occupations.

24 The strict definition of green tasks is measured by the total number of strictly defined green tasks in a 4-digit occupation divided by the total number of tasks in that occupation. A job is classified as green if it has a green task intensity (GTI) greater than zero, meaning it has at least one strictly defined green task.

25 Our original list had 47 strictly green occupations, including ISCO code 5169 (protective services not elsewhere classified) with a GTI of 10 percent. We chose to shift this occupation to the list of broadly defined green occupations. While this occupation contains jobs such as park rangers and wardens that would be classified as green (park rangers are classified as green in the DHET (2013) OFO skills list), the majority of the workers in this occupation are security officers whose tasks cannot be classified as strictly green. Because this occupation accounts for a very large share of jobs (over 500,000 employees), removing it from the strictly green occupations list reduced the share of strictly green employment in South Africa from 9 percent to 5.5 percent.

11
# Policy Research Working Paper Series

|**Title**|Author|Date|Contact<br>for paper|
|---|---|---|---|
|WPS1033 Latin America and the Caribbean<br>Region (and Northern America)<br>Population Projections, 1992-93<br>Edition|My T. Vu<br>Eduard Bos<br>Ann Levin|November 1992|0. Nadora<br>31091|
|WPS1 034 Revising Financial Sector Policy<br>in Transitional Socialist Economies:<br>Will Universal Banks Prove Viable?|David H. Scott<br>|November 1992|K. Waelti<br>37664|
|WPS1035 How Import Protection Affects the<br>Philippines' Motor Vehicle Industry|Wendy E. Takacs|November 1992|D. Ballantyne<br>37947|
|WPS1 036 Output Decline in Hungary and<br>and Poland in 1990-91: Structural<br>Change and Aggregate Shocks|Simon Commander<br>Fabrizio Coricelli|November 1992|0. del Cid<br>35195|
|WPS1037 Vocational Secondary Schooling,<br>Occupational Choice, and Earnings<br>in Brazil|Ana-Maria Arrlagada<br>Adrian Ziderman|November 1992|C. Cristobal<br>33640|
|WPS1038 Determinants of Expatriate Workers'<br>Remitances in North Africa and<br>Europe|Ibrahim A. Elbadawi<br>Robert de Rezende Rocha|November 1992<br>|A. Maraiion<br>31450|
|WPS1039 Education, Extemaliies, Fertiliy,<br>and Economic Growth|Martin Weale|November 1992|PHREE<br>33680|
|WPS1040 Lessons of Trade Liberalization in<br>Latin America for Economies in<br>Transition|Jaime de Meb<br>Sumana Dhar|November 1992|D. Ballantyne<br>37947|
|WPS1041 Family Planning Success Stories in<br>Bangladesh and India|Moni Nag|November 1992|0. Nadora<br>31091|
|WPS1042 Family Planning Success in Two<br>Cities in Zaire|Jane T. Bertrand<br>Judith E. Brown|November 1992|0. Nadora<br>31091|
|WPS1043 Deriving Developing Country<br>Repayment Capacity from the Marke<br>Prices of Sovereign Debt|Stijn Claessens<br>t George Pennacchi|November 1992|R. Vo<br>33722|
|WPS1044 HospRtal Cost Functions for<br>Developing Countries|Adam Wagstaff<br>Howard Barnum|November 1992|0. Nadora<br>31091|
|WPS1 045 Social Gains from Female Education:<br>A Cross-National Study|Kalanidhi Subbarao<br>Laura Raney|November 1992|M. Abundo<br>36820|
|WPS1046 World Bank Project-Financed<br>Research on Population, Health,<br>and Nutrition|J. Price Gittinger<br>Carol Bradford|November 1992|0. Nadora<br>31091|
Policy Research Working Paper 7668

# **Abstract**

A survey of participants in a large-scale business plan competition experiment, in which winners received an average of US$50,000 each, is used to elicit beliefs about what the outcomes would have been in the alternative treatment status. Participants are asked the percent chance they would be operating a firm, and the number of employees and monthly sales they would have, had their treatment status been reversed. The study finds the control group to have reasonably accurate expectations of the large treatment effect they would experience on the likelihood of operating a firm, although this may reflect the treatment effect being close to an upper bound. The control group dramatically overestimates how much winning would help them grow the size of their firm. The treatment group overestimates how much winning helps their chance of running a business, and also overestimates how much winning helps them grow their firms. In addition, these counterfactual expectations appear unable to generate accurate relative rankings of which groups of participants benefit most from treatment.

This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dmckenzie@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
group of women in Muslim-majority Indonesia, only 4% reported being concerned that others will view them as not following their religious traditions.<sup>18</sup> Our findings are similar to those of Bernhardt et al. (2018) who found that in India the husband’s social status was the main concern for husbands if their wives worked outside the house. The main concern for women in India, however, was that they were perceived as being disobedient to their husband.

# **3.2 Misperceptions about Social Norms Pertaining to Married Women Working**

Figure 3 shows the level of support reported for women with children under 12 working for pay outside the home and the estimated (perceived) level of support amongst peers. Female (male) respondents were asked to think of women who are similar to themselves (their wife) in terms of education level and to estimate “Out of 100, how many of these women do you think are supportive of wives with children under 12 years working for pay outside the home?”. The right hand set of bars show that while the actual level of support reported among female respondents was 76%, the mean (incentivized) level of women’s support as estimated by female respondents was 67% - an underestimate of 9 percentage points; and men estimated that only 59% of women are supportive - an even larger underestimate of 17 percentage points.

The left hand set of bars in the same figure show men’s support for women working for pay outside the home. Both men and women estimate this level of support among men (62%) relatively accurately. The average estimate of the level of male support among male respondents is 59% of men, an underestimate of just 3 percentage points. Women overestimate the level of support among men by 2 percentage points.

These findings are consistent with men’s views being more widely known and hence more accurately perceived, while women’s views are more closely held. They suggest that there is scope to influence gender norms in favor of working women by providing information about the greater than expected support among women. There is however little scope for an intervention to influence gender norms by providing information on men’s support for working women (a la Bursztyn et al. (2020). This result underscores the importance of formative research to ensure intervention design reflects the cultural context.

> 18A similar pattern was found for respondents who reported that their mothers were not supportive of women working outside the home - 50% (46%) of women (men) reported that if the woman worked their mothers would think that the husband was not able to provide for the family or that the family was in financial need. Of the female respondent, 21% were worried that their mother would think they were neglecting their family.

9
Reflecting this systemic approach, the Study analyzes three specific dimensions of implementation efficiency:

- **_Programmatic Efficiency_** —by looking at issues such as: (i) priority populations (including size and coverage); (ii) type of programs (prevention, diagnostics, treatment, care, support); and (iii) the degree of integration and coordination between the existing schemes.

-

- **_Budgetary Efficiency_** by looking at issues such as: (i) current expenditures on priority groups and programs; (ii) sources of funds (public, private, international); and (iii) financial sustainability.

- **_Service Delivery Efficiency_** —by looking at issues such as: (i) delivery modalities; (ii) providers (public, private, NGO); (iii) facilities (formal care and lay care); and (iv) the basket of services.

In short, the service delivery dimension is perceived as the cornerstone that guarantees access to care services for those who need them. Despite not being sufficient by themselves, the programmatic and budgetary dimensions are entirely necessary and should work in conjunction in order to support the service delivery dimension. The graph below synthesizes the proposed analytical framework:

**Figure 1: Framework for Analyzing Implementation Efficiency**

<!-- Start of picture text --> FUNCTIONS<br>Financing Budgetary<br>dimension<br>Resources Service<br>Access<br>Generation Provision<br>Service<br>Provision<br>Programmatic dimension<br>dimension<br>Stewardship<br><!-- End of picture text -->

**Source:** Técnicas de Salud. 2011.

17
Table A.11: Contraceptive use at endline

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|**A: Using any FP metho**|**d**||||
|Own Voucher|0.011|0.005|0.011|0.006|
||[0.050]|[0.050]|[0.050]|[0.051]|
|BAF Voucher|0.009|0.004|-0.004|-0.011|
||[0.048]|[0.047]|[0.047]|[0.049]|
|Observations|608|603|599|599|
|p-value: Own = BAF|0.970|0.975|0.787|0.775|
|Endline control mean|0.382|0.382|0.382|0.382|
|**B: Using modern FP me**|**thod**||||
|Own Voucher|0.063*|0.059|0.054|0.058|
||[0.038]|[0.037]|[0.036]|[0.037]|
|BAF Voucher|0.069*|0.066*|0.049|0.059|
||[0.037]|[0.036]|[0.036]|[0.036]|
|Observations|608|603|599|599|
|Endline control mean|0.123|0.123|0.123|0.123|
|p-value: Own = BAF|0.891|0.881|0.915|0.992|
|Balancing controls|No|Yes|Yes|Yes|
|Other balancing covariates|No|No|Yes|Yes|
|Village FE|No|No|No|Yes|

_Notes:_ Balancing controls refer to whether a woman is currently using family planning, her years of education, whether she wants another child, and the number of peers she mentions in the social networks module. Other baseline covariates are baseline values of woman’s age, indicator variables for Hindu, SC-ST, OBC, whether the woman works, wears ghunghat, and had ever visited a family planning clinic, her mobility score, and her asset index. FP denotes family planning. Variable definitions are in Online Appendix. Robust standard errors are in brackets.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

59
<!-- Start of picture text --> West Bank and Gaza: real credit medium and short term cycle Yemen: real credit medium and short term cycle<br>(in percent) (in percent)<br>1967 1977 1987 1997 2007 2017 1967 1977 1987 1997 2007 2017<br>year year<br> Medium-term credit cycle Short-term credit cycle Medium-term credit cycle Short-term credit cycle<br>syria: real credit medium and short term cycle Libya: real credit medium and short term cycle<br>(in percent) (in percent)<br>1967 1977 1987 1997 2007 2017 1967 1977 1987 1997 2007 2017<br>year year<br> Medium-term credit cycle Short-term credit cycle Medium-term credit cycle Short-term credit cycle<br>C- Western countries and Japan<br>Norway: real credit medium and short term cycle Netherlands: real credit medium and short term cycle<br>(in percent) (in percent)<br>1967 1977 1987 1997 2007 2017 1967 1977 1987 1997 2007 2017<br>year year<br> Medium-term credit cycle Short-term credit cycle Medium-term credit cycle Short-term credit cycle<br>20<br>20<br>10<br>10<br>0<br>0<br>cyclical component cyclical component<br>-10 -10<br>-20 -20<br>20 20<br>10 10<br>0 0<br>cyclical component -10 cyclical component -10<br>-20 -20<br>10<br>10<br>5 5<br>0<br>0<br>-5<br>cyclical component cyclical component-5<br>-10<br>-15 -10<br><!-- End of picture text --> 49
A caveat here is that when we compare number of full-time employees from the IMSS data to our follow-up survey data, the IMSS numbers are lower, suggesting that the enterprises in our sample did not register all their employees with IMSS (the follow-up survey suggests that our enterprises had about 15 full-time paid employees in 2009, and 2010 IMSS data shows about 10 employees). The increase in number of employees in the IMSS data could thus reflect more employees being registered instead of more employees being hired (this still is a desirable outcome though from a societal perspective). However, given that the increase in employees recorded in the IMSS data is long term, not merely a short-term increase after the training, it appears to be a genuine increase in actual employment at these firms.

Not all of the firms in our sample were found in IMSS records, so that the results in Table 4 are based on about 57 percent of our sample. To assess whether there are important compositional changes that influence our key results, Appendix Table 9 reports the main specifications for impact as measured by the follow-up survey, but restricted to the sample of firms for which we have IMSS data. The results are similar.

# **4.2 Process Variables**

In order to investigate the channels that drive the observed treatment effects, we now study how the consulting program changed processes within the enterprise. We measure these processes as follows. First, the surveys asked enterprise owners whether or not they implemented certain changes during the past year, such as developing new products, attracting new investors, and launching a new marketing campaign. Note that if treatment enterprises believed they should please the program by reporting process changes that did not actually occur, these estimates will be upwardly biased.

Table 5 displays the treatment effects on business process variables. We start with an allencompassing standardized index, calculated as per Kling et al. (2007), and are not able to reject the null hypothesis of no change (0.072 standard deviation, s.e. = 0.104). We only find statistically significant improvements in two processes: made a new marketing effort (13 percentage points increase, s.e. = 5.5 percentage points) and the percent of enterprises that keep formal accounts (7 percentage points increase, s.e. = 3 percentage points, where “formal” is defined as using either an accountant or a computerized system as opposed to keeping handwritten records or no notes at all). The finding that the program increased marketing efforts and the use of formal accounting practices is consistent with the case study evidence mentioned above, which suggests that many enterprises worked with their mentors on accounting and record keeping, as well as sales strategy and advertising.

14
||Ilala|PRE Working Pager Series<br>Auor||Contact<br>forpaper|
|---|---|---|---|---|
|WPS388|The Distortionary Effects of Tarfff<br>Exemptions in Argentina|Faezeh Foroutan|March 1990|S. Fallon<br>38009|
|WPS389|Monetary Cooperation in the CFA<br>Zone|Patrick Honohan|March 1990|Wilai Pitayato-<br>nakam<br>37666|
|WPS390|Price and Monetary Convergence<br>in Currency Unions: The Franc and<br>Rand Zones|Patrick Honohan|March 1990|Wilal Pitayato-<br>nakam<br>37666|
|WPS391|An Accounting Framework to Assess<br>Wealth Effects of Voluntary Debt<br>Reduction: Some Applications to<br>Latin America|Daniel Oks|||
|WPS392|Institutional Development at the<br>Sectoral Level: A Cross Sectoral<br>Review of World Bank Projects|Samuel Paul|||
|WPS393|Debt-for-Nature Swaps|Michael Occhiolini|March 1990|S. King-Watson<br>31047|
|WPS394|Threshold Effects in International<br>Lending|Mark M. Spiegel|||
|WPS395|The Rope and the Box: Gambian<br>Saving Strategies and What They<br>Imply for International Aid in the<br>Sahel|Parker Shipton|||
|WPS396|Strategic Trade Policy: How New?<br>How Sensible?|Max Corden|||
|WPS397|Antidumping Regulations or<br>Procartel Law? The EC Chemical<br>Cases|Patrick A. Messerlin|||
|WPS398|Agricultural Extension for Women<br>Farmers in Africa|Katrine A. Saito<br>C. Jean WeWdemann|||
|WPS399|Macroeconomic Adjustment,<br>Stabilization, and Growth in<br>Reforming Socialist Economies:<br>Analytical and Policy Issues|Andr6s Solimano|||
|WPS400|Macroeconomic Constraints for<br>Medium Term Growth: A Model for<br>Chile|Andr6s Solimano|||
_2.4 Whose performance? - different units of analysis_ In addressing the key question about whose performance we are interested in, this analysis slices the public sector in three related ways. First, it examines some performance questions at the whole of government level. Second, it examines what needs to be done to get a particular group of officials to perform more effectively. Third, it asks what can be done to improve the performance of some particular agencies or organizations within the public sector. Disaggregating the public sector by type of official and by type of agency is a pragmatic judgment, and ultimately in this and the country reports the determination was made largely on the basis of custom and practice - how comrnentators refer to units or groups within government in this setting.

However, it is worth exploring briefly the challenges that lie behind this simple classification exercise.

# _Groups of officials_

The terms 'public sector employees' and 'civil service' are often used to describe anyone who is paid by government. However, this is a very heterogeneous group and in reality comprises: an inner core of administrators; members of the armed forces; officials employed by productive enterprises that are majority-owned, directly or indirectly, by the state; health workers of any level of government; primary, secondary, or university teachers paid by government; employees of sub-national governments, etc.

**Figure 3 The main components of government employment**<sup>**3 5**</sup>

<!-- Start of picture text --> TOTAL PUBLIC S E CITO R<br>rtat.okownd ote GEERm ALee ete eoltet te|rts<br>Fo uther, detil se Cross-N1,AtinlDt nGLrnn mlomn n ae,o h ol<br>em ploy-es ~ ~ ~ ~ ~ _. t P. .- r -- p -Ml<br>Bank Administrativoe an Civilto ~117Y'111111 VERNM1NSe|rvc Reforom websitel at !tolI<br>htot-/wl b--rltban_{ rglpb elicetrtstcvlevbcosh Se als Stvn (1994).|<br><!-- End of picture text -->

35 For ffirther details see Cross-National Data on Governument Employment and Wages, on the World Bank Administrative and Civil Service Reformi web site at http://wwwl.worldbank.org/publicsector/test/civilservice/cross.htm. See also Stevens **(1994).**

**11**
**26**

# **Table 6 : Decomposition of poverty changes**

||_All cou_<br>|_ntries_|||_Middle-i_<br>_countries_<br>|_ncome_<br>|_Low-in_<br>|_come count_|_ries_<br>||
|---|---|---|---|---|---|---|---|---|---|---|
||_Coef_<br>_ficient_|_p-value_|_Coef-_<br>_ficient_|_p-value_|_Coef-_<br>_ficient_|_p-_<br>_value_|_Coef-_<br>_ficient_|<br>_p-_<br>_value_|_Coef-_<br>_ficient_|_p-_<br>_value_|
|||**A**||**B**|**C**|||**D**||**E**|
|**Headcount index (H)**|||||||||||
|GDP/pc growth|-1.68|0.00|-||-||-||-||
|Non agriculture pc growth|||-0.98|0.05|-1.57|0.01|0.12|0.89|0.12|0.91|
|<br>Non-ag pc growth*SSA|||||-|-|-|-|-0.08|0.95|
|<br>Agriculture pc growth|||-9.35|0.02|-21.74|0.00|-6.00|0.05|-12.95|0.06|
|Agric pc growth*SSA|||||-|-|-|-|7.07|0.21|
|Constant|-0.05|0.27|-0.06|0.22|-0.07|0.43|-0.06|0.27|-0.03|0.55|
|Number of observations|222||222||111||111||111||
|R<sup>2</sup>||0.08||0.14||0.26||0.10||0.13|
|Ho: test ag=nag||||0.04||0.00||0.08||0.07|
|Ho: test|||||||||||
|<br>nag+nag*X=ag+ag*X||||||-||-||0.31|
|**Poverty gap index (PG)**<br>GDP/pc growth|-2.03|0.00|||||||-||
|Non agriculture pc growth|||-1.32|0.04|-2.07|0.00|0.05|0.96|-0.05|0.98|
|Non-ag pc growth*SSA|||-||-|-|-|-|0.23|0.89|
|Agriculture pc growth|||-9.99|0.02|-21.15|0.01|-7.33|0.06|-9.99|0.14|
|Agric pc growth*SSA|||-||-|-|-|-|4.79|0.51|
|<br>Constant|-0.10|0.11|-0.11|0.09|-0.13|0.16|-0.10|0.25|-0.07|0.39|
|Number of observations|222||222||111||111||111||
|R<sup>2</sup>||0.07||0.10||0.18||0.08||0.09|
|Ho: test ag=nag||||0.06||0.02||0.10||0.18|
|Ho: test|||||||||||
|nag+nag*X=ag+ag*X|||||-|-|-|-||0.18|
|**Squared poverty gap index**<br>**(SPG)**|||||||||||
|GDP/pc growth|-2.22|0,00|||||||||
|Non agriculture pc growth|||-1.60|0.05|-2.36|0.02|-0.22|0.87|-0.51|0.80|
|Non-ag pc growth*SSA|||-||-|-|-|-|0.78|0.70|
|Agriculture pc growth|||-9.36|0.07|-18.00|0.10|-7.63|0.10|-8.29|0.33|
|Agric pc growth*SSA|||-||-||-|-|1.07|0.91|
|Constant|-0.15|0.05|-0.16|0.04|-0.20|0.10|-0.14|0.18|-0.12|0.26|
|Number of observations|222||222||111||111||111||
|R<sup>2</sup>||0.05||0.06||0.09||0.06||0.06|
|Ho: test ag=nag||||0.16|-|0.16||0.18||0.42|
|Ho: test|||||||||||
|nag+nag*X=ag+ag*X|||||-|-<br>|<br>-|-||0.13|

*10%, **5%,***1% significance All estimations are corrected for heteroskedasticity with robust (cluster) Source: Own calculations based on World Bank data (2005c; 2005d)

However, in the low-income countries (for all measures of poverty—see panel D of Table 6) only agricultural growth appears to affect poverty reduction—and the null hypothesis is therefore rejected. The estimated effect of non-agricultural growth on poverty is not statistically