rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2. Examples of expected impact of decarbonization policies on the demand for labor**

|Policy|Expected impact<br>C|ountryor region<br>Method|Reference|
|---|---|---|---|
|State-level green policy|1% more green jobs|US<br>Historical data|Yi (2013)|
|Regional-level energy|0.24% increase in|EU<br>Simulation|Markandya et|
|shift|employment||al. (2016)|
|Energy efficiency<br>measures and renewable<br>energy targets|4 million+ green job-years by<br>2030|US<br>Simulation|Wei et al.<br>(2010)|
|Investment in<br>renewables and energy<br>efficiency|Around 5 more FTE jobs<br>compared to similar<br>investment in fossil fuels|US<br>Simulation|Garrett-<br>Peltier, (2017)|

State-level green policy refers to market-based tools that provide support for renewable energy and energy efficiency. Energy shift refers to a change from carbon intensive sources toward gas and renewables.

The new jobs that come with the transition to low carbon may require new skills, often referred to as _green skills_ . There is, by our knowledge, very little rigorous evaluation in this area using historical data. One US study finds that changes in environmental regulation are positively related to demand for green skills, in particular for technical and engineering tasks (Vona et al., 2018).<sup>lxvi</sup> Cooling technology in the workplace is an important example of _labor-oriented adaptation_ that may improve worker productivity and labor supply, as well as worker well-being. As mentioned previously, firm adaptation may be less likely for low-productivity jobs (Somanathan et al., 2021). Government subsidies can be beneficial in situations where a change in behavior is considered desirable but faces obstacles, for instance when the cost of early adoption is high but anticipated to fall substantially over time, potentially due to increased demand (Stern, 2022).

Policy makers have been motivated to introduce regulation regarding _occupational health and heat management to protect workers_ from heat-related stress. Preventative measures, such as frequent rest breaks, and hydration and high-sodium intake, can protect workers from heat-related illness and work accidents. These regulations are unlikely to be followed in informal firms and settings with weak enforcement. In China’s manufacturing industries protective measures knew limited implementation by private firms during periods of elevated temperature (Zhang et al., 2018).

30
the IP law to protect their interests in businesses, and actively cooperate with the government in anti-infringement actions.

Generally speaking the IP protection in Beijing and Shanghai is much better than the national average level. Firms from home and abroad are learning how to do businesses with IP rights involved. As to incorporating the awareness of IP rights into the local business ethnic morality, there is still a long way to go.

# **_VI. Survey Findings_**

# **Survey Instrument and Profile of Survey Respondents**

To gain the viewpoints of the enterprises belonging to the creative industries subsector in Beijing and Shanghai on the urban environment for nurturing creativity and innovative capacity, we conducted a small exploratory survey covering 100 firms (50 in Beijing and 50 in Shanghai) during Aug. 2003 – Apr. 2004 using a semi-structured questionnaire designed by the World Bank. It contained a total of 63 questions, many of which were open ended in order to obtain a richer range of responses on various issues. Issues covered were as follows:

- a) Determinants of location choice

- b) Knowledge workers supply

- c) Availability and quality of business services

- d) Cluster economies

- e) Intellectual property law

- f) Views on cities in the knowledge economy

The questionnaire was completed through a combination of self-administration by respondents and face-to-face interviews. Each interview took around 45 to 90 minutes to complete. According to the description provided by the respondents, we grouped the firms interviewed into three categories:

- **High-tech Manufacturing** , including electronics/IT manufacturing and life sciences firms;

- **Knowledge Intensive Business Services** (KIBS), including consultancy, legal, marketing, financial, IT services, etc.;

26
reveals that poor citizens are typically more likely to report a need for all government provided goods and services (Figure 7).

# **Figure 7: Need for Goods and Services, By Income** Percent of adults

<!-- Start of picture text --> 85%<br>80%<br>75%<br>70%<br>65%<br>60%<br>Driving License* Register Public Utilities* Public Schools* Healthcare*<br>Land/Property*<br>Wealthy Poor<br>Likelihood of Reporting a Need<br><!-- End of picture text --> _Note_ : This figure shows the likelihood that the wealthy or the poor would express a need for a good or service. For example, of every 100 respondents coded as wealthy, 72 are likely to report a need for a driver’s license. For those coded as poor, the corresponding likelihood is 74 of every 100. An asterisk indicates a statistically significant difference between the wealthy and the poor.

# **_<u>4.2 The Second Stage: Measuring Gaps in Access by Gender and Income Level</u>_**

Table 4 presents the Stage 2 results of the Heckman Two Stage Regression. Stage 2, as discussed above, measures whether “access” to government provided goods and services varies by gender or income level.

The preceding section noted that women are more likely than men to express a need for goods such as education or health care. By contrast, men are more likely to express a need for services, such as a driver’s license, land or property registration, and access to public utilities, such as water, electricity and gas. This section shows that regardless of their expressed needs, women are less likely than men to be able to access government provided goods and services. In Table 4, the coefficient for the indicator variable “women” is negative and statistically significant across all 25
**Method 1.** The assumption here is that the behavior of a given cohort will be similar to what happened with individuals of other cohorts, (that can be observed in other PNADs), when they were the same age.  For example, in 2006 a given percentage of individuals aged x were in the formal sector. In 2011, this cohort will be 5 years older.  We postulate that the percentage of individuals from the cohort who would be in the informal sector is a weighted average of percentages found for the cohort aged x +n in the previous PNADs.  The weighting gives greater importance to more recent years.  For each cohort and for each group, the procedure is repeated. So we have:

where PCTxij is the percentage of people who was in the group i, aged x, in the year j. The terms a, b, c and d represent the weights of each year in the equation.

**Method 2** .  In this case, for each cohort, we estimate the relationship between the percentages of individuals in each group found in each pair of consecutive PNADs.  For instance, in the first PNAD there is a given percentage for people aged x that is in category y.  In the following PNAD, we take into account the age group x +n (i.e., the same cohort) and look at the percentage still in category y.  We compute the growth rate RG:

After this, we calculated the mean rates for going from age x to age x+5 for every category. We used these means to input values for the distribution of the cohorts in the years when we do not observe them. The results for each group are normalized in such a way that the sum of the four groups is always 100.

Methods 1 and 2 give similar results for workers with _carteira_ (see Table). For workers without _carteira_ the second method seems to overestimate this group, especially for older workers. For the unemployed and the retired the same thing occurs, the difference is more important for the latter. In the analysis we therefore opted for the first method.

|**Age (2006)**|**Method 1**|**Method 2**|**Longitudinal data**|
|---|---|---|---|
|31-35|52,56|52,56|52,56|
|36-40|52,54|49,53|51,49|
|41-45|51,64|45,28|51,66|
|46-50|48,90|39,04|49,44|
|51-55|41,86|30,47|43,10|
|56-60|30,46|21,19|30,93|
|61-65|17,09|12,35|16,33|
|66-70|4,80|4,29|4,35|
|71+|0,93|0,95|0,87|

_Source_ . Authors’ calculations.

43
_L. Fox and A.M. Oviedo_ _Are Skills Rewarded in Sub-Saharan Africa?_

**Table 7: Average years of education of employees**

||Anglophone|Francophone|
|---|---|---|
||Byfirm size||
|Small (<20)|10.7|9.1|
|Medium (20-99)|10.8|11.3|
|Large(100+)|12.3|12.7|
|Total|11.1|10.6|
|By|worker occupation||
|Management|13.7|12.7|
|Professional|14.4|15.5|
|Skilled production|11.2|11.2|
|Unskilled production|10.0|8.3|
|Non-production|10.5|10.4|
|Total|11.1|10.6|
||Byworker age||
|<20 years old|9.2|7.2|
|20 to 29 years old|11.0|9.5|
|30 to 39 years old|11.5|11.2|
|40 to 49 years old|11.0|11.6|
|50 to 59 years old|10.8|10.7|
|60years old and older|<br>9.9|10.8|
|Total|11.1|10.6|
|Source: Enterprise Sur|veys||

**Table 8: Average years of education of employees, 1990s and 2000s**

|Burundi (1992-1995)*|4.9|
|---|---|
|Burundi (2006)|8.72|
|Ghana (1992-1995)*|9.5|
|Ghana (2007)|10.47|
|Kenya (1992-1999)*|8.6|
|Kenya (2003)|11.52|
|Tanzania (1992-1995)*|8.2|
|Tanzania(2006)|10.55|

Source: Enterprise Surveys (workers' module). (*) Source: Benhassine _et al._ (2006) from RPED 34
Policy Research Working Paper 6697

# **Abstract**

Little is known about which of the skills that make up workers’ human capital contribute to higher earnings. Past empirical evidence suggest that most of the return to schooling is generated by effects or correlates unrelated to the skills measured by the available tests. This paper uses the International Adult Literacy and the Adult Literacy and Life Skills surveys to obtain multi-country estimates of the components of the return to schooling. The results reveal considerable heterogeneity and a dichotomy between two groups of countries. For a subgroup of educationally advanced countries, nearly half of the return to schooling can be attributed to labor marker-relevant functional literacy skills associated with schooling, while for a subgroup of less educationally advanced countries, such skills account for just over 20 percent of the return to schooling, while the return to schooling mostly reflects the signaling value of schooling.

This paper is a product of the Education Unit, Human Development Network. 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 authors may be contacted at hpatrinos@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
girls, in the control group report that the statement “I am a person who works hard in school” describes them fully, while for 24% of the boys and 23% of the girls the statement describes them a lot.

The major concern when using self-reported measures in a context like ours is the possibility of _social desirability_ bias: students in the treatment group might be more likely to reply in a way that will be viewed favorably by the others. The measure of self-reported effort that we use was collected almost three years after the intervention as part of a standard nationally administered survey, and it is therefore unlikely that students could bias their response as result of the information treatment. In general, self-reported data might capture poorly the actual level of effort. In order to boost confidence in our measure, we use data from the control group to measure the correlation between the self-reported level of effort and the 2012 ENLACE results in math and Spanish. One standard deviation increase in self-reported effort leads to a 0.11 _σ_ increase in math and 0.12 _σ_ in Spanish. Both correlations are statistically significant at conventional levels (results available upon request).

We use eq. 3 to study the impact of the information treatment on self-reported levels of effort. In order to simplify the interpretation of the results, we standardize the categorical variable using the mean and the standard deviation observed in the control group. Results are presented in Table 7. In column 1 we present the results for the entire sample. Overall the treatment group reports a level of effort that is 0.24 _σ_ (statistically significant at 1% level) higher than for the control group. In column 2, we consider the effect by gender and we find much larger impact for girls (0.35 _σ_ ) than for boys (0.11 _σ_ ). We can marginally reject the null hypothesis of no differential effect by gender (p-value=0.07). In column 3 we study how the effect on self-reported effort varies with the level of ability. We do find increases in effort for all three subgroups discussed above (high- and low-ability students and those with no 9th grade ENLACE). The increase among low-ability students is larger in size (0.29 _σ_ ) than among high-ability students (0.16 _σ_ ). However, when testing whether the effect varies across the three subgroups, we cannot reject the null hypothesis of no differential effect. In column 4 we show how the effect varies with household income. Both for low and high income students we find large effects of the information package and also in this case we can not reject the null hypothesis of no differential effect.

Although the self-reported nature of the data requires a cautious interpretation of the results presented in this section, the information intervention seems to have improved students’ intrinsic motivation, irrespective of their ability level and their income level. The fact that learning outcomes only increase among high income students, although both high and low income students report higher self-reported effort, is consistent with the hypothesis that 17
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effect. However, together with the results of specifications (1) and (3), the model in column (6) supports a hypothesis that joining a PTA and being a part of a larger common market are both important channels linking preferential liberalization and net FDI inflows—but market size is probably more important.

# **4.4 Decomposition by income level and time period**

In this section, we attempt various decompositions of our sample by geographic region, income level, and time period to determine whether our aggregate results mask some important sub-trends. In addition, the estimates of this section will serve as extra sensitivity tests to assess the robustness of the aggregate relationship between preferential liberalization and FDI in the smaller sub-samples. We begin with some decompositions by income level, with the results shown in Table 7. Column (1) of this table repeats the reference estimates for the full sample (the same as column (1) in Table 6) for ease of comparison with alternative specifications.

Table 7: Estimation results by income level: net FDI inflows

||(1)|(2)|(3)|
|---|---|---|---|
|**Variable**||**Coefcient**||
|GDP|0.899<sup>_∗∗_</sup>|0.850<sup>_∗∗_</sup>|0.988<sup>_∗∗_</sup>|
||(0.017)|(0.021)|(0.052)|
|OPEN|0.941<sup>_∗∗_</sup>|0.946<sup>_∗∗_</sup>|2.596<sup>_∗∗_</sup>|
||(0.083)|(0.085)|(0.971)|
|GDPGRO|0.256<sup>_†_</sup>|0.311<sup>_∗_</sup>|0.231|
||(0.142)|(0.148)|(0.995)|
|CPIGRO|-0.008<sup>_†_</sup>|-0.008|0.460|
||(0.005)|(0.005)|(0.849)|
|RERGRO|-0.336<sup>_∗_</sup>|-0.331<sup>_∗_</sup>|-1.045|
||(0.146)|(0.149)|(1.448)|
|WLDGRO|-0.677<sup>_†_</sup>|-0.898<sup>_∗_</sup>|0.589|
||(0.391)|(0.418)|(1.832)|
|WLDFDIadj|0.373<sup>_∗∗_</sup>|0.400<sup>_∗∗_</sup>|0.443<sup>_∗∗_</sup>|
||(0.039)|(0.042)|(0.096)|
|PTAGDP|0.060<sup>_∗∗_</sup>|0.080<sup>_∗∗_</sup>|-0.082|
||(0.018)|(0.019)|(0.068)|
|DISTA|-0.155<sup>_∗∗_</sup>|-0.233<sup>_∗∗_</sup>|0.331|
||(0.058)|(0.064)|(0.204)|
|N|1541|1320|206|
|Log-lhd|-1716.811|-1467.937|-250.77|
|_χ_<sup>2</sup>|3146.643|2178.762|412.184|

Significance levels: _†_ 10% _∗_ 5% _∗∗_ 1% _Note:_ Standard errors are in parentheses. The dependent variable is expressed in natural logarithms. Column (1) shows estimates for all countries, column (2) limits the sample to low and middle income countries, and column (3) shows only high income countries.

38
production by 5 percent. We notice that production increases on rainfed land does not compensate for the drop in production of irrigated crops. As a result, prices in all agricultural activities increase at varying rates, while prices in national non-agricultural activities (except for food manufacturing, electricity, gas and government services) fall at varying rates. Particularly, the price of water as a commodity by urban use fell by 45 percent following the transfer from rural activities in the West region.

**Table 4.10. Change in total production quantity**

|||**Base**<br>|**% Chan**|**ge**|
|---|---|---|---|---|
||**Unit**|**Level**|**Sc. 2.a**|**Sc. 2.b**|
|**Wheat**|Tons|19,000|-1.78|-3.18|
|**Barley**|Tons|8,100|-0.52|-1.00|
|**Maize**|Tons|2,800|-3.32|-5.76|
|**Other grains**|Tons|758|-1.29|-2.33|
|**Pulses**|Tons|1,558|-0.68|-1.27|
|**Tobacco**|Tons|1,528|-1.72|-3.07|
|**Sugar beet**|Tons|12,623|-0.95|-1.87|
|**Cotton (raw)**|Tons|2,295|-3.82|-6.97|
|**Oil seeds**|Tons|2,359|-1.07|-1.78|
|**Potatoes**|Tons|5,300|-0.37|-0.79|
|**Vegetables**|Tons|24,018|-1.34|-2.54|
|**Fruits**|Tons|13,221|-1.62|-2.76|
|**Nuts**|Tons|789|-1.62|-2.88|
|**Other crops**|Tons|2,974|-1.06|-2.00|
|**Milk**|Tons|9,096|-0.79|-1.47|
|**Cattle Meat**|Tons|5,242|-0.33|-0.67|
|**Other Bovine Meat**|Tons|2,333|-0.10|-0.23|
|**Food Industry**|VA, Million|3,646|-0.19|-0.53|
|**Other Animal Prod.**|VA, Million|2,112|-0.77|-1.22|
|**Other Agricultural Prod.**|VA, Million|6,212|-1.05|-1.81|
|**Food Production**|VA, Million|31,969|-1.40|-2.55|
|**Mining**|VA, Million|4,407|0.51|0.64|
|**Consumer Manufac.**|VA, Million|101,370|0.19|0.24|
|**Int. and Cap. Goods**|VA, Million|129,439|-0.65|-0.90|
|**Electricity and Gas**|VA, Million|26,923|-0.04|-0.07|
|**Water**|VA, Million|9,413|18.18|30.18|
|**Construction**|VA, Million|46,351|-1.84|-2.56|
|**Private Services**|VA, Million|219,470|0.20|0.21|
|**Government Services**|VA,Million|69,194|-0.06|-0.09|

_Source: Results from TACOGEM-W._ A production decrease in irrigated land activities, particularly in the West, following a reallocation of irrigation water to urban use, prompts changes in the labor use patterns.

46
of the returns to human capital investment, if the latter is correlated with nonhuman assets.

Second, many empirical studies have not made clear the definition of the self-employed "earnings" measure used -- whether it refers to gross production (sales plus the value of self-consumed output) or net production (gross production less the cost of materials and other inputs). Moreover, although the role of women in family businesses is given due recognition in most discussions of the subject, many empirical studies have excluded women (and children) from the analysis because women and children are often unpaid family workers, reporting zero income from self-employment. Studies parallel to this one by Arriagada (1988a) and Moock and Bellew (1988) have measured the business earnings of Peruvian men by using net production; the study by King (1988) and Arriagada (1988b) have done the same for women in self-employment. Each of these studies, however, has looked only at individuals working alone; none has treated as determinants of income any variables other than the characteristics of the individual worker.

This study presents an analysis of non-farm family businesses in Peru. It uses the enterprise rather than the individual as the unit of analysis, and it incorporates enterprise characteristics (capital, nonlabor inputs, locus of operation) explicitly, and in that respect parallels an analysis of Peruvian farm enterprises by Jacoby (1988). The central question addressed is: does formal schooling make a difference? Women (and children) are included in the analysis since they play an important, if not the preeminent, ro.s in Peru's family business sector. We can thus see whether the payoff, i.e., the private return, to education differs between male and female entrepreneurs, after controlling for other factors.

2
**Table** A5: **Multivariate Cointegration Analysis between the Variables of Table 9:**

|Null<br>Hypothesis|Alternative<br>Hypothesis|Lag: 2<br>With Constant|99%<br>Critical Values|95%<br>Critical Value|90%<br>Critical Value|
|---|---|---|---|---|---|
|,tace<br>test||||||
|r = 0|r>0|224.75**|177.20|165.58|159.48|
|r s I|r> 1|160.82**|143.09|131.70|126.58|
|rs 2|r>2|111.84**|111.01|102.14|97.18|
|r s3|r> 3|75.72*|84.45|76.07|71.86|
|r < 4|r>4|50.29|60.16|53.12|49.65|
|r< 5<br>|r>5<br>|31.26<br>|41.07<br>|34.91<br>|32.00<br>|
|r •6|r>6|14.33|24.60|19.96|17.82|
|r <7|r>7|1.71|12.97|9.24|7.52|

_**_ Significant at the 1% level, * significant at the 5% level. _Source:_ Authors' calculations.

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