rafmacalaba/datause-encoder-data huggingface dataset:
time, especially with only two time periods. This measurement will then impact “attenuation bias” of the credit coefficients, meaning that the impact estimates will be biased towards zero.

A standard correction for both types of problems (time-varying heterogeneity bias and measurement error bias) is the use of instrumental variables (Deaton 1997). Provided we have instruments, we could reintroduce the instrumental variable (IV) method to the FE method to estimate the credit effects by correcting such biases. The case for fixed-effects instrumental variables (FE-IV) method as opposed to fixed-effects (FE) method is tested by the Durban-Wu-Hausman test.

For the implementation of IV method, let us write the first-stage equation for the stock of credit (suppressing subscripts for male, female, household and village unobserved characteristics) as, where _Z_ is a set of household and village characteristics distinct from _X_ ’s so that they affect _C_ but not household outcomes dependent on _C_ .

Selecting appropriate _Z_ variables is critical. We created household-level male and female choice variables separately for 1991/92 and 1998/99 and used the interactions of the choice variables with all household characteristics and village-level fixed-effects as the instruments.<sup>16</sup> Although the same instruments were used in 1991/92 data analysis with cross-sectional data, the purpose then was to resolve the households’ program placement endogeneity and measurement errors associated with credit variables, and this time it is to control for any measurement and timevarying errors correlated with credit variables. The identification of gender-specific choice variables is achieved by making use of the fact that all program groups are single sex and not all villages have both a male and a female group. The sample includes households from all types of villages. The

> 16 Choice variables are created based on a household’s land-based eligibility to join a program and a program’s placement in a household’s village.

14
**_Energy demand models for policy_** **_<u>formulation</u>_**

The residential demand module forecasts energy demand using a structural model based on housing stock and the appliance stock. The demand is driven by four drivers: economic and demographic factors, structural effects, technology, and market effects. The housing stock and appliance stock information from the Residential Energy Consumption Survey is used to capture the diversity of stock holding and usage patterns across the country. It projects the demand for various end-uses by fuel type.

The commercial sector demand module projects energy demand in the commercial sector by taking into account building and non-building demand. It also captures the appliance stock and technological advancements and their effects on energy demand for three major fuels, namely electricity, natural gas and distillate oil. For the remaining minor fuels, the demand is projected using a simple econometric method. The demand by fuels for various end-uses is projected by the module.

The industrial demand module projects energy demand in the industrial sector using a hybrid approach: it uses the technological representation found in the end-use method and incorporates the behavioral aspects of a top-down approach. The demand is analyzed at a disaggregated level – with a greater focus on energy intensive industries which are analyzed at the 3-digit level of industrial classification. Within each industry, three elements of demand are considered – building, boiler and process/ assembly activities. The demand for each element is estimated separately using a combination of approaches ranging from simple growth rates to more involved methods.

The transport demand module projects the fuel demand in the transport sector by mode and includes alternative energy demand. A disaggregated approach is used in demand forecasting where personal car usage, light truck, freight transport, air transport and miscellaneous transport are considered separately. A nested multinomial logit model is used to predict the vehicle sales by technology. The vehicle miles per capita is estimated based on fuel costs of driving, disposable income per capita and an adjustment for men to women driving ratio. The model captures the regional variation in transport demand as well.

80
- Bowen, B.H., F.T. Sparrow and Z. Yu (1999). Modeling electricity trade policy for the twelve nations of the Southern African Power Pool (SAPP). Utilities Policy, Vol. 8, No. 3, Pp. 183–197.

- Central Electricity Authority (CEA) (2014). Load Generation Balance Report 2014-15, Ministry of Power, New Delhi.

- Central Electricity Authority (CEA), 2012a. Electrical Energy Generation Review 2011-12, CEA, Government of India, New Delhi.

- Central Electricity Authority (CEA), 2012b. Load Generation Balance Report 2011-12, CEA, Government of India, New Delhi.

- Central Electricity Authority (CEA), 2012c. Performance Review of Thermal Power Stations 2011-12, CEA, Government of India, New Delhi.

- Central Electricity Authority (CEA), 2012d. Annual Reports of CEA 2011-12, CEA, Government of India, New Delhi.

- Central Electricity Authority (CEA), 2012e. 18th Electric Power Survey of India, CEA, Government of India, New Delhi.

- Central Electricity Board (CEB), 2012. Long Term Generation Expansion Plan 2013-2032, CEB, Colombo.

- Central Electricity Regulatory Authority (CERC), 2009. Terms and Conditions of Tariff Regulations, 2009, CERC, New Delhi.

- Central Electricity Regulatory Authority (CERC), 2012. CERC Tariff Regulation for Renewable Energy, 2012, CERC, New Delhi.

- Central Statistical Office (CSO), 2012. Energy Statistics 2012, CSO, National Statistical Organization, Ministry of Statistics and Programme Implementation, New Delhi.

- Druk Green Power Corporation Limited (DGPCL), 2013. Annual Report 2013. DGPCL, Thimphu, Bhutan.

- Economic Advisory Council (EAC), 2009. Integrated Energy Plan 2009-2022: Report of the Energy Expert Group, Ministry of Finance, Government of Pakistan, Islamabad.

- Geological Survey of Pakistan (2015), Energy Resources - Coal: The Neglected Energy Source in Pakistan. http://www.gsp.gov.pk/images/energyresourcecoal.pdf (downloaded on April 10, 2015).

- Gnansounou, E, H. Bayem, D. Bednyagin and J. Dong (2007). Strategies for regional integration of electricity supply in West Africa. Energy Policy, Vol. 35, pp. 4142–4153.

India Central Statistics Office (ICSO), 2012.

- Integrated Research and Action for Development (IRADe) (2013). Prospects for Regional Cooperation on Cross-Border Electricity Trade in South Asia. United States Agency for International Development, South Asia Regional Integration Project, New Delhi.

International Energy Agency (IEA), 2013. World Energy Outlook 2013, IEA, Paris.

35
# **1 Introduction**

The double burden many women face combining labor force participation with unpaid domestic labor and care work represents a major impediment to female labor force participation and political participation in the MENA region (ILO 2018; Nazier 2019; Women 2020). Expectations that women will take on the dominant role caring for the home and raising children lead many women to exit the labor force upon family formation (Assaad, Krafft, and Selwaness 2022). At the same time, norms around household task burdensharing are in flux, and active efforts to change norms are underway. In this domain as in others, attitudes and social practice could shift more rapidly than normative expectations of what others believe and value (or _perceived norms_ ). To what extent do attitudes and perceived norms around household roles hinder the emergence of more gender-equal distributions of labor in Morocco?

This study draws on an original online survey of 1,038 Moroccan adults to investigate these and related questions. A convenience sample recruited via Qualtrics in JanuaryFebruary 2023 answered questions about responsibility for household tasks within their homes, their attitudes around gender roles within the home, and their perceptions of what men, women, and their neighbors think about the same issues. The respondents also compared hypothetical ideal household configurations in a conjoint survey experiment that measured preferences over whether men and women each work outside the home and how unpaid labor and care tasks are distributed. An information experiment on the survey also tested whether encountering real data about increases in the time men spent on household tasks during the COVID-19 pandemic altered perceptions about how attitudes around household gender roles would evolve in the future.

Among the survey respondents as among the general Moroccan public, unpaid labor burdens remain highly unequal, yet men’s and women’s perceptions of their relative contributions diverge. I examine responses to questions about who in the respondents’ households is primarily responsible for 13 common tasks. Women’s estimates of the female share of household labor are higher than men’s estimates, with men more likely to claim that tasks are undertaken equally. Married women report near-exclusive responsibility 1
Another popular approach is to plot the forms of PPP against a continuum (see Figure 1) - this model is used for the World Bank’s PPP Resource Center Website<sup>6</sup> , the UNCITRAL model law<sup>7</sup> , PPIAF PPP toolkits<sup>8</sup> and others. Similar to the field discussed above, the continuum endeavors to show the flexibility of PPP, and the lack of clear delineation between different forms of PPP, by demonstrating the movement of PPP structures across the continuum. However, it clearly provides only very rough classification against the general concepts of private sector risk and control. It also implies that the sub-parts of the continuum are subject to clear and exact definition.

Each of the existing structures endeavors to capture the flexibility of PPP while providing the order and structure, despite the lack of clarity. While good pedagogical tools, these tables are not useful for analytical purposes, and would not achieve the aims set out above.

The creation of a practical, descriptive terminology will help

- i) Reinvest PPP with the innovative and creative capacity that it is meant to embody

- ii) Facilitate analysis and comparison across sectors and regions, permitting lessons learned to cross these often confusing barriers

- iii) Decouple terms of art from specific examples whose specificity may influence assessment of other similarly named but fundamentally different structures.

This paper proposes a categorization model, i) a snap shot of the most important characteristics of a PPP project, while ii) maintaining the simplicity necessary for it to function effectively. These twin functions necessitate including only the absolutely critical characteristics of PPP in the model.

> 6     www.worldbank.org/inflaw

> 7    www.uncitral.org

> 8    www.ppiaf.org

13
<!-- Start of picture text --> Figure 22: Selected LAC countries: Current account deficit as a share of GDP, boxplot (percent)<br>(a) 1980-1999 (b) 2000-2004<br>Argentina Bolivia Chile Colombia C. Rica El Salv. Mexico Peru Uruguay Argentina Bolivia Chile Colombia C. Rica El Salv. Mexico Peru Uruguay<br>5 10<br>0<br>5<br>-5<br>0<br>-10<br>Current account deficit (% GDP) Current account deficit (% GDP)<br>-15 -5<br><!-- End of picture text --> Note: Boxplot is a graph of the five-number, in which the central box spans the 25<sup>th</sup> and the 75<sup>th</sup> percentiles, the line in the box marks the median, lines extend from the box out to the minimum and the maximum observations and “dots” represent outliers. Source: WDI.

Macroeconomic improvements coupled with relaxation of the investment regime and measures to develop securities markets will contribute to the improved asset allocation of pension funds in the future. Greater foreign diversification will be necessary to compensate for the developing nature of local capital markets.

# **V. The Approach to Pension Supervision**

Consistent with the restrictive investment regulation approach discussed above, most supervisor authorities of mandatory DC pension systems in the LAC region were designed as proactive agencies expected to closely monitor the performance of pension funds and pension fund managers. A similar approach was adopted in some Eastern European countries. Specialized pension supervisors were generally set up to safeguard the stability and resilience of the new pension systems. Over time, some countries, including El Salvador, Hungary, and Peru, moved towards a more integrated supervisory approach of financial services, consolidating the supervision of all financial entities under a single agency.<sup>60</sup> Consolidation can facilitate the supervision of financial conglomerates and ensure better use of scarce supervisory resources, especially in small countries.<sup>61</sup> By contrast, most OECD countries follow a reactive approach towards pension supervision, although a new paradigm has emerged. For instance, Australia, Denmark, and the Netherlands moved towards a risk-based supervision model.

The monitoring activity by pension supervisors in LAC has generally been intense. But the effective surveillance capability of supervisors has remarkably varied across the region, with some agencies requesting more information than they can effectively analyze (Acuña 2005). Enforcement tools, such as power to issue warnings,

> 59 See Appendix 8.

> 60Argentina, Chile, Costa Rica, Dominican Republic, and Mexico have specialized pension supervisors.

> 61 See Taylor and Fleming (1999) and Abrams and Taylor (2000) for discussions on lessons from integrating financial supervision and issues related to the unification process.

36
– Table 4: Impact on Social Conformity Outcomes (Group and Position Effects) Asch Line Match 1 Game

|**_Social Conformity - Line Match Game 1_**|**_Private_**|**_Correct Line_**<br>**_(B D)_**|**_Match_**|<br>**_Change in_**|**_Weak Conform_**<br>**_Public from B_**|**_ity_**<br>**_to D or D to B_**|**_Str_**<br>**_Change in Pub_**|**_ong Conform_**<br>**_lic from B or_**|**_ity_**<br>**_D to incorrect_**|
|---|---|---|---|---|---|---|---|---|---|
|||**_or_**||**_(P_**|**_ositions 2-5_**|**_only)_**|**_(Po_**|**_sitions 2-5 o_**|**_nly)_**|
||All|Boys|Girls|All|Boys|Girls|All|Boys|Girls|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|CHOICES|-0.019|0.002|-0.038|0.024|0.028|0.023|-0.004|0.004|-0.023|
||(0.02)|(0.03)|(0.03)|(0.04)|(0.06)|(0.05)|(0.03)|(0.03)|(0.04)|
|Number of CHOICES peers in team (0-4)|-0.020|-0.052***|0.003|-0.002|-0.013|0.012|0.010|-0.009|0.033|
||(0.01)|(0.02)|(0.02)|(0.02)|(0.02)|(0.02)|(0.01)|(0.02)|(0.02)|
|Mixed-gender teams|-0.005|0.021|-0.005|-0.100**|0.056|-0.107**|-0.023|0.074|-0.020|
||(0.04)|(0.04)|(0.04)|(0.05)|(0.05)|(0.05)|(0.05)|(0.05)|(0.05)|
|Boys only team|-0.045|||-0.158**|||-0.077|||
||(0.06)|||(0.07)|||(0.07)|||
|Response of position before is other correct answer||||0.226***|0.184*|0.271***||||
|||||(0.07)|(0.10)|(0.10)||||
|CHOICES # Response of position before is other correct answer||||0.010|0.112|-0.091||||
|||||(0.08)|(0.12)|(0.13)||||
|Response of position before is an incorrect answer|||||||0.331***|0.131|0.481***|
||||||||(0.10)|(0.14)|(0.12)|
|CHOICES # Response of positions before is an incorrect answer|||||||-0.004|0.195|-0.172|
||||||||(0.10)|(0.15)|(0.14)|
|Control group mean|0.90|0.90|0.91|0.26|0.22|0.29|0.21|0.16|0.24|
|Adjusted R-Squared|0.02|0.05|0.00|0.08|0.08|0.07|0.18|0.22|0.18|
|Observations<br>Notes: * significant at 10% level ** significant at 5% level *** significant at 1% l|994<br>evel|491|503|697|351|346|697|351|346|

(1) CHOICES=1 if adolescent was assigned to CHOICES only or CHOICES+EMB treatment groups; 0 if in control group.

(2) Standard errors are clustered at the session-team level and are reported under the coefficient in parentheses. All OLS regressions includes district fixed effects and controls for whether household has multiple adolescents, the age of respondent, and peer effects given by the number of CHOICES adolescents in the adolescent's team excluding the respondent (0-4). Regressions control for randomized team gender composition (mixed-gender, boys only, and girls only where girls only is the omitted category), and enumerator-gender fixed effects.

(3) Outcomes Weak Conformity and Strong Conformity are restricted to the adolescents in positions 2-5 who correctly matched the line when responding in private. 90% were correct in private. (4) Position effects for the outcome "Weak conformity" in columns (4) to (6) are estimated by the influence of the response in the position before i being the other correct answer (i.e. B if you said D in private, or D if you said B in private). "Strong conformity" position effects in (7) to (9) are estimated by the response of the previous position being an incorrect answer (A or C).
Table 1. Estimation Results for eq. 1.

|**Variable**|**Parameter**<br>**Estimate**|**Standard**<br>**Error**|**t Value **|**Pr > |t|**|
|---|---|---|---|---|
|τ**_i_**|0.03852|0.01842|2.09|0.0366|
|τ**_i_**<br>**_2_**|-0.00187|0.00109|-1.72|0.0862|
|**_t*i _**τ**_i_**|-0.00305|0.00131|-2.33|0.0199|
|**_t*i _**τ**_i_**<br>**_2_**|0.00000511|0.00003560|0.14|0.8859|
||**F Value**|**Pr > F**|||
|**Test of****_HS_**|4.34|0.0132|||
|**Test of****_HL_**|2.48|0.0842|||
|**R-Square**|0.6943||||
|**No. of obs.**|1471||||

Coefficients for 60 monthly dummies and 30 individual-specific fixed effects for catumeran-operating fishermen as well as 42 individual-specific fixed effects for fibre boat-operating fishermen not reproduced
**Table A3.2. Macro indicators in 2012 and for base simulation by** **<u>period (% of nominal GDP)</u>**

||**2012**|**2017**|**2030**|**2013-17**|**2018-30**|**2013-30**|
|---|---|---|---|---|---|---|
|**Absorption**|166.5|126.8|111.9|145.1|114.1|122.7|
|**Consumption - household**|75.7|65.7|60.8|70.4|60.3|63.1|
|**Consumption - UNMIL**|29.5|0.0|0.0|9.5|0.0|2.7|
|**Consumption - government**|23.5|27.1|26.6|26.9|27.8|27.5|
|**Fixed investment - private**|22.7|22.4|15.6|24.0|15.0|17.5|
|**Fixed investment - government**|15.0|11.5|8.8|14.3|11.0|11.9|
|**Exports**|37.1|56.6|80.9|48.7|73.1|66.3|
|**Imports**|-103.5|-83.4|-92.8|-93.7|-87.1|-89.0|
|**GDP at market prices**|100.0|100.0|100.0|100.0|100.0|100.0|
|**Net indirect taxes**|10.2|9.8|9.9|10.4|9.5|9.8|
|**GDP at factor cost**|89.8|90.2|90.1|89.6|90.5|90.2|
|**GNI**|88.5|85.6|87.2|86.2|87.2|86.9|
|**GNDI**|147.8|109.8|106.5|123.5|108.2|112.5|
|**Foreign savings**|18.6|17.0|5.4|21.5|5.8|10.2|
|**Gross national savings**|19.1|17.0|19.1|16.8|20.1|19.2|
|**Foreign government debt**|12.1|27.0|30.0|23.5|28.5|27.1|
|**Domesticgovernment debt**|17.6|17.4|21.2|18.5|19.5|19.2|

**Note: Columns show values for single years or averages for multiple years (as indicated).**

33
The previous subsection found little association between product price variation and participation in most work categories. However, it is possible that the bluntness of participation measures misses important variation in hours worked. In this section, we examine the association between rice price movements and changes in hours worked.

We consider hours worked overall and in wage work, within the household, market work within the household, household production, and market work. In addition, we also consider hours worked by sector in agriculture (wage work, within the household, overall), and in nonagricultural activities (wage work, within the household, overall). Unfortunately, hours worked in agriculture, agriculture within the household, market work within the household, market work, and total hours may be affected by the changes in the questions collecting information on household agriculture (discussed in detail in section 3). Thus, while we present and discuss results for these variables, we are concerned about biases introduced into our discussion because of the changes in the questionnaire. The inclusion of the year indicator in (1) might ameliorate this question design problem, if the error introduced by the question change affects all households similarly in expectation or, more generally if the change in how respondents respond to hours worked questions does not vary with rice prices conditional on the year effect (and other covariates). We know of no way to check these conditions. Thus, we interpret the data on hours worked in agriculture, agriculture within the household, market work within the household, market work, and total hours with hesitation. However, note that variables such as wage work, household production, hours worked in nonagricultural activities (wage work, household enterprise, overall), and wage work in agriculture are not affected.

In examining the link between rice prices and hours worked, we maintain the regression strategy based on equation (1). Namely, we regress hours worked by type of work against rice 31
ranges from less than 3 percent in Bulgaria to over 10 percent in Latvia, Poland and Romania. It is noteworthy, that the incidence of part-time jobs in CEE is substantially below the EU15 average of 18 percent.

**Table 3  Flexible jobs in CEE are less prevalent than in EU**

||Self-<br>employed|Fixed<br>term<br>contracts|Part-time<br>employment|
|---|---|---|---|
|Bulgaria|-|5.3|2.5|
|Czech R.|16.0|8.1|4.9|
|Estonia|8.1|2.7|7.7|
|Hungary|13.7|7.3|3.6|
|Latvia a)|16.9|6.7|11.3|
|Lithuania b)|30.5|6.3|9.7|
|Poland|28.1|15.4|10.8|
|Romania|40.2|1.0|11.8|
|Slovak R.|8.6|4.9|1.9|
|Slovenia|16.0|14.2|6.1|
|**CEE**|19.8|7.2|7.0|
|**EU15**|14.6|13.0|18.1|

- Data not available a) 2000 b) 2001 Source: Eurostat

Although the incidence of irregular jobs has risen in transition economies, it is still low in the comparative perspective. However, one should bear in mind, that the comparison is limited to the formal sector, while the majority of casual and temporary jobs are in the informal sector. Given that the size of the informal sector is much larger in transition economies than in the EU, the actual incidence of irregular or casual jobs is likely to be correspondingly higher, too.

The growing use of fixed-term contracts is a means to lower employment adjustment costs if terminating a permanent employment contract is difficult or costly. Thus, their incidence can be expected to be higher in countries where firing costs are high, although restrictions on their use may hamper this tendency. Croatia and Slovenia, countries where the employment protection legislation is particularly stringent are the cases in point.

To conclude, it is not only the number of jobs but their “quality”, including earnings, that matter. The relevant question is not only the standard one “How many jobs”, but also one that is often overlooked, which is “What kind of jobs”. There is limited but rather plausible evidence that there has been a switch from stable regular jobs towards temporary, casual and precarious jobs, partly in the formal sector, but largely in the informal one.

# **Inter-sectoral shifts and the changing structure of employment**

Economic transition in ECA by its very nature has been associated with the reallocation of labor and jobs across economic sectors. One part of this process has been a shift of resources, including labor, from the public to the private sector. Another part – which is the focus of this section -- has been the movements of labor across industries. Reallocation of labor across industries was meant to redress the misallocation of resources inherited from the previous economic regime. Under central planning industry was overdeveloped while the service sector was underdeveloped. Thus, one would expect, that transition will lead to the increase in the 16