rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 6: The determinants of the use if temporary workers used (Marginal effects from the Bivariate Probit model)**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|_Perceptions (No obstacle)_||||||
|Minor obstacle|0.038<br>(0.022)+|||||
|Moderate obstacle|0.051<br>(0.026)+|||||
|Major obstacle|0.089<br>*|||||
||(0.035)|||||
|_Age (Less than 5 years old)_||||||
|5 to 15 years old<br>16 or more|0.052<br>(0.017)**<br>0.080|0.047<br>(0.018)**<br>0.086|0.047<br>(0.018)**<br>0.078|0.048<br>(0.017)**<br>0.086|0.052<br>(0.017)**<br>0.093|
||(0.044)+|(0.042)*|(0.042)+|(0.039)*|(0.038)*|
|Ownership (Public)|0.041|0.025|0.040|0.028|0.031|
||(0.036|(0.034)|(0.037)|(0.031)|(0.030)|
|Innovator|0.048<br>(0.028)+|0.061<br>(0.026)*|0.055<br>(0.027)*|0.068<br>(0.024)**|0.053<br>(0.024)*|
|_Size (Less than 20 employees)_||||||
|20 to 100 employees|0.066<br>(0.015)**|0.054<br>(0.017)**|0.069<br>(0.017)**|0.058<br>(0.016)**|0.064<br>(0.016)**|
|More than 100 employees|0.032|0.018|0.039|0.022|0.029|
||(0.038)|(0.041)|(0.038)|(0.040)|(0.037)|
|Sector (Manufacturing)|0.094|0.105|0.092|0.097|0.092|
||(0.044)*|(0.045)*|(0.049)+|(0.047)*|(0.046)*|
|_Country income level (Low income)_||||||
|Lower Middle income|0.047|0.063|0.053|0.064|0.095|
|Upper Middle income or high income|(0.077)<br>0.177|(0.076)<br>0.207|(0.081)<br>0.182|(0.077)<br>0.203|(0.076)<br>0.199|
||(0.073)*|(0.073)**|(0.081)*|(0.077)**|(0.081)*|
|_Labor regulations indexes_||||||
|Regular employment||0.319<br>(0.140)*||||
|Temporary employment|||-0.070<br>(0.126)|||
|_Regulatory mix – median (Low TER and lo_<br>High TER and High RER|_w RER)_|||0.056<br>(0.070)||
|Low TER and High RER||||<br>0.066<br>(0.079)||
|High TER and Low RER||||-0.117<br>(0.117)||
|_Regulatory mix – mean (Low TER and low_|_RER)_|||||
|High TER and High RER|||||0.070<br>(0.080)|
|Low TER and High RER|||||-0.006<br>(0.072)|
|High TER and Low RER|||||-0.165|
||||||(0.092)+|
|Observations (number of countries)|9676 (38)|||||
|Predictedprobabilityat the mean|0.424|0.426|0.426|0.426|0.425|

Standard errors in parentheses (and adjusted for clustering on country). Where relevant, base categories of dummies are indicated in parentheses in italics. + = significant at 10%; * = significant at 5%; ** = significant at 1%.Data source: ICS.

29
11 controls for change in productivity due to the economic environment and the inflation rate to control for macroeconomic stability. The inflation rate is measured by the consumer price index and captures the annual change in the prices of a basket of goods and services. The quality of the regulatory environment captures the ability of the government to formulate and implement sound policies that permit and promote private sector development. This variable is a proxy of the institutional framework in which firms operate. Also, this variable helps to control for the fact that the average institutional quality may affect the initial reform targets, reform implementation as well as the productivity. The average GDP growth and inflation level data are from the World Development Indicators database while the average quality of the regulatory environment is from the World Governance Indicators.<sup>11</sup> The macroeconomic variables and fixed effects help to minimize potential endogeneity issues. They control for the country’s business cycle and aggregate productivity trend, the institutional capacity, and the ambitiousness of the authorities, the political situation and several other factors such as the availability of development partners in providing technical assistance.

# **III.   EMPIRICAL STRATEGY**

The empirical analysis first presents the estimation strategy of productivity at the firm level. The subsection discusses the estimation strategy that helps assess the effects of reforms at the macroeconomic level on productivity at the firm level.

# **A.   Calculation of Firms’ Productivity and Structural Reform Indexes**

We first compute productivity at the firm level and then an index of structural reforms. The literature has proposed several measures of productivity at the firm level. The common ones are the labor productivity, value-added per worker and the total factor productivity (TFP). In this paper, we focus primarily on labor productivity growth for two main reasons. First, the labor productivity growth measured as sales per worker has the advantage of being dynamic and is computed over the period covered by structural reforms, an advantage that might allow

> 11 The World Governance Indicators proxy the quality of governance at the macroeconomic level over six dimensions including the quality of the regulatory framework. The indicators rely on perception-based governance data from a set of 31 sources, including survey of firms, households, non-governmental organizations, and multilateral organizations, and other public sector bodies. See Kaufmann, Kraay and Mastrizzi (2011) for more detailed information on the methodology.
# Figure A7. Impulse Responses to a Positive Oil Price Shock

<!-- Start of picture text --> CPI Inflation Output<br>0.03 0<br>-0.1<br>0.02<br>-0.2<br>0.01 -0.3<br>-0.4<br>0<br>5 10 15 20 5 10 15 20<br>Interest Rate Oil Prices<br>0.08 0.15<br>0.06<br>0.1<br>0.04<br>0.05<br>0.02<br>0<br>0<br>5 10 15 20 5 10 15 20<br><!-- End of picture text --> Notes: The impulse responses are based on the economic model introduced in the Appendix A. The impulse responses follow a one-standard deviation of a positive oil price shock.

50
depreciated cumulatively by 26.2 percent between 2000 and 2005, and the exchange rate vis-a-vis the dollar depreciated by 52.2, while consumer prices rose by 27.6.

The key question I address in this section is the effect of the large depreciation during 2003 on disaggregated consumer prices. Table 2 reports the cumulative growth rate between July 2000 and June 2005 of the disaggregated components of the consumer price index that I have for Egypt.<sup>5</sup> A quick glance at this table reveals that price changes have varied considerably across expenditure items, and to a lesser extent across regions. Most striking is the behavior of food prices, which increased faster than the overall consumer price index. Taking a simple average across regions, overall consumer prices increased by 28 percent, but food prices increased by 38 percent, implying a 10 percent increase in the relative price of food. In the remainder of this section I investigate in detail the contribution of the depreciation of the Egyptian pound to these absolute and relative price changes.

# **2.1 Empirical Framework**

As shown in Table 2, I have monthly data on the consumer price index disaggregated into 31 goods and services, for 8 regions in Egypt. Because of difficulties in mapping the expenditure items in the CPI to the household survey, I will work with a somewhat more aggregated set of 20 of these expenditure items that correspond to expenditure categories in the household survey. I model the consumer price of item i in region r in month t as follows: where P<sup>N</sup> denotes the price of the non-traded component and P<sup>T</sup> denotes the price of the traded component of that item. To simplify notation, we can think of the non-traded component as capturing both purely non-traded goods within this item, as well as nontraded distribution costs associated with the traded goods. Accordingly we can think of P<sup>T</sup> as the price of imported goods "on the dock" in Egypt. Concretely, one of our disaggregated items is fruit. P<sup>T</sup> would therefore be the price of imported fruit "on the

> 5 We would like to thank the staff of CAPMAS for kindly assembling this dataset.

4
identified clerical and professional job categories as being more exposed to the risks of automation with GenAI (Cazzaniga et al., 2024; Gmyrek et al., 2023; Ozdeneron, Hakki, 2023; WEF, 2023), with pre-GenAI regional assessments also classifying female-held jobs in LAC as being at a higher risk of automation from digital technologies (Egana-delSol et al., 2022).

In accordance with the technical documentation of ISCO-08, such differences in the occupational structures also correspond to varying levels of skills and educational attainment, as clerical support workers, technicians and professionals are typically classified in the mid- to high-skill level brackets (ILO, 2023b). Given that educational attainment and earnings gaps across skills groups have been important drivers of income inequality in LAC (Azevedo et al. 2013), the impact of GenAI that follows the existing labor market structures would likely also have an effect on the overall income inequality. In the best-case scenario, GenAI would boost the productivity of lowerskilled workers in the exposed occupations, allowing them to access higher incomes and therefore leading to a more broad-based income distribution. In the worst-case scenario, the technological transition could result in the automation of largely female-held jobs in the clerical, technical and professional occupations, while the opportunities for new GenAI-augmented jobs could be limited, given the high concentration of current employment in elementary occupations and in the informal sector, where technology adoption and private sector investment are low. To better understand how the first-order effects of GenAI may affect inequality, this study provides a detailed profile of the socio-economic groups most exposed to this technology.

Finally, we acknowledge that the final outcomes of the technological transition process will also be largely dependent on the existing and future policy frameworks in the region. While the analysis of country-level polices and legal frameworks is beyond the scope of this regional study, the detailed country-level statistics that we make publicly available alongside this publication can serve as useful inputs to the discussions underpinning such policy responses.<sup>10</sup>

> 10 Access to detailed data at: https://pgmyrek.shinyapps.io/AI_Data_Portal_Research/.

10
**Figure A.4: Differences between Teacher Types**

<!-- Start of picture text --> 7<br>6<br>5<br>4<br>3<br>New<br>2<br>Candidate<br>Backbone<br>1<br>0<br><!-- End of picture text --> Source: Guangdong class observations; authors’ calculations.

Candidate teachers stand out among the three categories, scoring the highest along all three domains. New teachers lag in Emotional Support and Instructional Support, while ranking equal to the other teachers in Classroom Organization. Backbone teachers have a slight edge in Student Engagement, Regard for Student Perspectives, and Quality of Feedback.

**Figure A.5: Differences between Teacher Types in Elementary School**

<!-- Start of picture text --> 7<br>6<br>5<br>4<br>3<br>2<br>New<br>1<br>Candidate<br>0<br>Backbone<br><!-- End of picture text --> Source: Guangdong class observations; authors’ calculations.

27
carbon stock in managed forests. Contrary to equation (B.9), we do not include the minimum age parameter in equation (B.10) as younger tree vintages absorb carbon without giving timber benets (Sohngen et al. 2009a). We obtain the stocking density and the embodied carbon stock, as well as the minimum age for merchantable timber from GTAP Global Forestry Data Base (Sohngen et al. 2009b). We assume that the merchantable timber yield per hectare of forest land with the average tree age _v_ grows linearly across time, adding a constant amount of technology gain per annum (see Figure B.1, panel d): where the parameters _θv,_<sup>_w_</sup> 0<sup>and</sup><sup>_κw_</sup> _v_<sup>correspond to the initial levels and technology</sup> gains to the merchantable timber yield of vintage _v_ . We obtain the data for yield growth in the commercial forestry sector by annualizing the dierence in the average yields from global forest studies of Sedjo (1983) and Cubbage et al. (2010).

We assume that the average harvesting costs per ton of raw timber, are invariant to scale and are the same across all managed forest areas of dierent age. With continuous growth up to vintage _v_ max , the average long-run cost of harvesting per hectare of managed forest land, _c_<sup>_o,w_</sup> _,_ is therefore a declining function of timber output. The average planting costs per hectare of newly forest planted, are invariant to scale and are the same across all vintages. We obtain the average harvesting and replanting costs from GTAP Global Forestry Data Base (Sohngen et al. 2009b). To compute the normalized average costs of replanting, _c_<sup>_p_</sup> _,_ and harvesting, _c_<sup>_o,n_</sup> _,_ we multiply corresponding average costs by their expenditure shares in other primary resources.

Harvest of managed forests and conversion of harvested forest land to agricultural land is subject to additional near term adjustment costs, _c_<sup>_H_</sup> _._ Forest harvesting costs are given by where the parameters _ξ_ 1<sup>_w_and</sup><sup>_ξ_</sup> 2<sup>_w_correspondtoshort-runadjustmentcostsof</sup> harvesting and rotation. We calibrate these short-run adjustment costs to match recent global trends in commercial forestry (FAO 2010).

16
# **Table A1: Survey Response rates by Round and Survey Type**

||Round 1<br>**Sh**|Round 2<br>**ort-Run Su**|Round 3<br>**rvey Roun**|Round 4<br>**ds**|In-person surve|yPhone survey<br>**Long-run Survey**|Either<br>**Round**|Know status|
|---|---|---|---|---|---|---|---|---|
|Personal initiative training group response rate|0.962|0.928|0.938|0.910|0.792|0.746|0.870|0.926|
|Traditional business training group response rate|0.956|0.940|0.938|0.890|0.770|0.678|0.844|0.900|
|Control group response rate|0.940|0.898|0.906|0.882|0.700|0.646|0.786|0.856|
|Total number of observations|1429|1383|1391|1341|1131|1035|1250|1341|
|Overall response response (all groups)|0.953|0.922|0.927|0.894|0.754|0.690|0.833|0.894|
|p-value: PI=control|0.099|0.071|0.059|0.143|0.001|0.001|0.000|0.000|
|p-value: Trad=control|0.238|0.013|0.059|0.676|0.012|0.282|0.015|0.027|
|p-value: PI=Trad|0.642|0.462|1.000|0.290|0.412|0.021|0.254|0.166|

# Notes:

Round 1-Round 4 denote previous survey rounds collected between September 2014 and September 2016, covering a period up to 2.5 years post-training. In-person survey is 7 year in-person follow-up survey collected between September and November 2021. Phone survey is 7-year follow-up survey collected via phone between March and April 2021. Either denotes firm was surveyed in at least one of in-person and phone long-term follow-up surveys.

Know status denotes firm was surveyed in either long-term round, or owner is dead, or business operating status still known, or migrated abroad.

28
<!-- Start of picture text --> 2007 -EEE 2.000<br>2008 EEE 2010<br>H1 2000 BEES 2011<br>H2 2000 DEES 2012<br>5 20102011 es 2017<br>o 2012<br>+} 2010 es 2018<br>i 2014<br>9005 eee 2021<br>20160 Es 2022<br>2017<br>2018<br>2010 Dee 2023/24<br>2020 ?<br>Estimated year of first oil<br><!-- End of picture text -->
subsidies in addition to the value of subsidies. This may be the case for example of spill-over effects due to improved market functioning of the subsidized product. The total envelope of subsidies in local currency for the product of interest represents the benchmark for assessing changes in budget revenues further in the process. It is also the monetary value that is used to allocate subsidies to households in the incidence analysis that follows.

# Step 3 – Distributional incidence analysis

This step implies a standard welfare distributional analysis. This is a static incidence analysis. There is no “change” to consider but only the existing distribution of subsidies across households. The purpose is to understand whether subsidies are pro-poor or pro-rich and whether subsidies affect the level of poverty and inequality or not. This is an analysis that existing packages like DASP<sup>5</sup> or ADePT<sup>6</sup> are perfectly capable of handling but that will be useful to carry out with the tools provided in this manual so that this step will represent the benchmark scenario for the shock and impact analysis. The output of this step will typically be tables and graphs illustrating the distribution of subsidies across households and welfare statistics such as poverty and inequality.

The only data requirement for an incidence analysis is a cross-section household survey that contains information on household expenditure on the subsidized product and the total amount of estimated subsidies on the product or, alternatively, the amount of subsidies as a share of the subsidized price. With this information, the analyst can spread the total amount of subsidies on households according to the share of household expenditure on the overall expenditure on the subsidized product. Or, equivalently, household expenditure on the subsidized good can be multiplied by the amount of subsidies as a share of the subsidized price.

Note that this step is often mistaken for a policy simulation reform that amounts to the elimination of subsidies where the impact of the reform is measured in terms of the difference between the distribution of income with and without subsidies. This is not the case as changes in subsidies imply household behavioral effects on the demand side (in terms of substitution of the subsidized product with other consumption products and in terms of changes in the quantities consumed) and on the supply side (in terms of price-shift behavior of supply agents). This is the reason why we clearly distinguish in this manual between <u>incidence</u> analysis and <u>impact</u> analysis. Under some specific assumptions, the impact analysis of the elimination of subsidies is equivalent to an incidence analysis of the elimination of subsidies but it is important to clearly distinguish between the two exercises. An impact analysis requires a set of preliminary assumptions on household behavior and can result in very different outcomes as compared to an incidence analysis.

> 5 http://dasp.ecn.ulaval.ca/. 6 http://www.worldbank.org/adept.

10
MINIMUM WAGES IN CAMBODIA 13 <!-- Start of picture text --> All activities<br>Impacts on participation rates<br>All workers<br>Point estimates<br>95% CI<br>Garments Self employment<br>-8 -6 -4 -2 0 2 4 6 -8 -6 -4 -2 0 2 4 6<br>Quarters from minimum wage hike<br>All activities<br>Impacts on participation rates<br>Female workers<br>Point estimates<br>95% CI<br>Garments Self employment<br>-8 -6 -4 -2 0 2 4 6 -8 -6 -4 -2 0 2 4 6<br>Quarters from minimum wage hike<br>.02<br>.01<br>0<br>-.01<br>-.02<br>-.03<br>.01 .06<br>0 .04<br>.02<br>-.01<br>0<br>-.02<br>-.02<br>-.03 -.04<br>-.04 -.06<br>.03<br>.02<br>.01<br>0<br>-.01<br>-.02<br>-.03<br>-.04<br>.01 .06<br>0 .04<br>-.01<br>-.02 .02<br>-.03 0<br>-.04<br>-.02<br>-.05<br>-.06 -.04<br><!-- End of picture text --> Figure 4. Impact of minimum wage hikes on participation rates _Source_ : Author’s calculations from Cambodia SES 2007-2015.

_Note_ : The figure plots the estimate of _αw_ from Equation (1), with _q_ = _−_ 1 being the reference group. The brown lines show the average of the coefficients 1-8 quarters before the change, and 2-8 quarters after the change. Sample restricted to working age population, and those observations surveyed within 8 quarters of any of the minimum wage hikes. See notes of Table 1 for a full list of controls used in estimation.
**_Energy demand models for policy_** **_<u>formulation</u>_**

the strategic management where it has been used since 1960. In the energy and climate change area, the use of scenarios by the Intergovernmental Panel of Climate Change (IPCC) has played an important role in the policy debate. Similarly, studies by the World Energy Council (WEC), Inter-laboratory Working Group of US and similar studies in Australia have brought the approach to limelight. See for example Jefferson (2000)<sup>15</sup> , Brown et al (2001), Saddler et al (2007 and 2004) and Shell Studies<sup>16</sup> (Shell (2008)), among others. Scenarios are an integral part of the end-use approach as well and accordingly, they are not new to energy analysis.

“A scenario is a story that describes a possible future” (Shell, 2003). In simple terms, scenarios refer to a “set of illustrative pathways” that indicate how “the future may unfold” (Ghanadan and Koomey, 2005). Evidently, they do not try to capture all possible eventualities but try to indicate how things could evolve. It is a particularly suitable approach in a changing and uncertain world (Leydon, et al, 1996).

“Scenarios give the analyst the opportunity to highlight different combinations of various influences, so that alternative future contexts can be sketched out, and the energy implications examined” (Leydon et al., 1996, p.5).

“Scenarios are based on intuition, but crafted as analytical structures...They do not provide a consensus view of the future, nor are they predictions” (Shell, 2003). Clearly, “scenarios are distinct from forecasts in that they explore a range of possible outcomes

> 15 This paper presents a brief history of the WEC efforts in energy understanding the future energy demand and describes the scenarios used in a number of studies. In 1978 study, the Council called for actions to ensure a sustainable future. Until 1989 the Council used two scenarios – high growth and middle course. Since 1993, an ecologically driven scenario was added which were further refined subsequently in 1998 to develop six scenarios – three high growth, one middle course and two ecologically driven scenarios. The fully integrated scenarios present a range of possible rational outcomes and forecast energy and environmental indicators up to 2100.

> 16 Shell was active in using scenario technique for strategic management and planning. It is the ex-Shell planners who have brought this method to the wider public (Ghanadan and Koomey, 2005). Shell produced its first energy scenario studies in the 1992 and produced the catch phrase “There is no alternative” (TINA). This was followed by a number of studies in 1995, 1998, 2002 and 2005 (see <u>http://www.shell.com/home/content/aboutshell/our_strategy/shell_global_scenarios/previous_scenarios/pre vious_scenarios_30102006.html</u> for details). In its latest scenario study, Shell has introduced the new catch phrase “There are no ideal answers” (TAN!A) in 2008.

39
expect the prices of many manufactures to do as well. The 1995/96 NLSS collected information on the quantity and price paid for rice by individual households. From this we compute a unit price per Kg. The log of the district median is used as our price index proxy.

To construct an index of housing costs, we take advantage of a section of the 1995/96 NLSS survey focusing on housing. The survey collected information on hypothetical and actual house rental values of each household together with house characteristics such as square footage, number and type of rooms, quality of materials, and the availability of various utilities. We use these data to construct an hedonistic index of housing costs for each district. Let rs<sup>kbethe</sup> house rental price paid (or estimated) by household h in district s and let x<sup>h</sup> s<sup>denoteavectorof</sup> house characteristics. We estimate a regression of the form: to obtain estimates of � as, the housing cost premium in each district s. Regression results are shown in Table A1 in appendix. Many house characteristics are signi fi cant with the expected sign, e.g., larger, better built houses with better in-house amenities are worth more. District price di ff erentials are large and jointly signi fi cant. Since the dependent variable is in log form, � as measures the housing cost premium in each district.

To the extent that people are mobile, housing price di ff erentials capture, in a reduced form, the e ff ect of location attributes such as proximity to jobs and access to public amenities. It is therefore possible for migrants to be attracted by districts which command a high housing price premium. To further control for access to amenities, we include travel time to the nearest road (a measure of market access) and to the nearest bank (a measure of fi nancial and commercial development).

We include a number of regressors to control for geographical isolation. Fafchamps and Shilpi 19