rafmacalaba/datause-encoder-data huggingface dataset:
the youth and young adult population. The shape of Tunisia’s demography in 1994 was characterized by the clear signs of a youth bulge, as depicted by the largest share of the population ages 5–9 and 10–14.<sup>2</sup> In the intervening 20 years, and thanks in large part to a decline in fertility rates, this bulge transitioned to the 25–29 and 30–34 age groups (Assaad, Ghazouani, and Krafft 2017).

The pressure created by this new labor supply was not matched by an increase in the number of formal jobs that matched the qualifications and expectations of educated workers. Weak economic growth limited job creation in the formal private sector, which in turn was too low to absorb new entrants to the labor market. Because of the delays in implementing reforms after the 2011 revolution, as well as security-related incidents, social unrest, and regional instability (including in neighboring Libya), economic growth has averaged only 1.4 percent since the revolution (compared with 4.4 percent during the five previous years). The entry rate for the small firms that create formal sector jobs has been low, and, of those firms created, only a few have been able to survive and grow (World Bank 2017). As a result, a majority of those who do work are in lowproductivity and low-quality jobs. Indeed, as much as 77 percent of Tunisia’s workforce is employed in low-productivity sectors,<sup>3</sup> while high-productivity service sectors have absorbed only 7.7 percent of total employment.

Important for this study is that the pressure on the labor supply is particularly acute for women, whose ranks among graduates have been growing at a faster rate than those of men. In 2013 women made up 67 percent of higher education graduates, and that share persisted at 65 percent in 2017. Assaad et al. (2018) argue that the lack of increase in opportunities in the formal private sector has disadvantaged women more than men. This is especially true for educated women for whom informal or nonwage employment is not a viable option. Women’s higher reservation wages<sup>4</sup> mean they are more able and likely to remain unemployed until they can locate good-quality jobs that match their expectations (Assaad and Kraftt 2016).

> 2 A youth bulge is a common phenomenon in many developing countries and is typically the outcome of a reduction in infant mortality and a stability in fertility rates.

> 3 _Low-productivity sector_ refers to those with below-average productivity such as agriculture, textile, construction, public infrastructure, commerce, and manufacturing (see World Bank 2015a).

> 4 A <mark>reservation wage is the lowest wage rate at which a worker would be willing to undertake a particular kind of job.</mark>

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**9 Tables**

Table 1: Summary Statistics

||**Firms **<br>Mean|**in the E**<br>Median|**ntity List**<br>Std. Dev.|**Oher F**<br>Mean|**irms in P**<br>Median|**ATSTAT**<br>Std. Dev.|
|---|---|---|---|---|---|---|
|Total Patents|81.46|6.00|501.43|14.08|2.00|108.95|
|Domestic Patents|79.87|5.00|486.94|13.90|2.00|108.54|
|EPO/USPTO/PCT Patents|39.60|0.00|339.22|1.65|0.00|18.23|
|High-Tech Patents|46.59|1.00|324.80|4.21|0.00|39.59|
|Triadic Patents|3.51|0.00|37.41|0.19|0.00|2.27|
|Patent Stock|120.77|4.87|898.56|23.10|4.43|187.56|
|Patent Quality|1.28|0.91|1.38|1.64|1.00|3.31|
|Priori Ave. Patent App.|0.79|0.00|8.20|0.23|0.00|2.97|
|Patent Age|9.08|9.00|3.60|12.16|10.00|7.22|
|No of Observations|2,138|||347,521|||

Table 2: Post-Sanction Innovation Performance among Sanctioned Firms

||(1)<br>Total|(2)<br>International|(3)<br>Triadic|(4)<br>High-Tech|
|---|---|---|---|---|
|**Panel A: Patent **|**Count**||||
|Treatment_×_Post|-0.104***<br>(0.026)|-0.171***<br>(0.012)|-0.077***<br>(0.013)|-0.036<br>(0.037)|
|**Panel B: Quality**|**-Adjusted **|**Patents**|||
|Treatment_×_Post|-0.151***<br>(0.05)|-0.193***<br>(0.012)|-0.092***<br>(0.024)|-0.102*<br>(0.053)|
|No. Obs.|6,334|6,334|6,334|6,334|

Note: We estimate the coefficients in columns (1) to (5) using OLS. Sector-year and firm FEs are included. Robust standard errors clustered at the 2-digit ISIC sector level are in parentheses. ***, **, and * indicate significance at levels 1 percent, 5 percent, and 10 percent, respectively.

37
For decomposition analysis, we obtain value added of manufacturing at the subsector level from United Nations Industrial Development Organization (UNIDO) Yearbook of Industrial Statistics (INDSTAT4) (2011). INDSTAT4 reports manufacturing value added at the three-digit level of ISIC Rev.3 for selected ECA countries. We match IEA (2012) with INDSTAT4 (2011) to calculate subsector energy intensity at the ISIC Rev.4 two-digit level. INDSTAT4 reports value added in current prices, denominated in US dollars. We use the GDP deflator of each country to convert values of different years into 2000 prices and then to constant international dollars using 2005 PPP rates.

Per capital GDP (PPP based), the capital-labor ratio of manufacturing and the share of exports in total manufacturing value added are all obtained from WDI (2012). Energy prices are obtained from Energy Regulators Regional Association’s tariff database and Eurostat.

# **IV. Empirical Strategy and Results**

In this section, we present three types of empirical analysis to identify the sources and mechanisms of energy intensity change in the manufacturing sector of ECA. First, we conduct a cross-country convergence analysis to test whether backward countries are catching up with efficient ones. Second, using an index decomposition method, we investigate the extent to which the change in energy intensity is induced by structural shifts vs. efficiency improvements. Finally we explore the determinants of energy intensity, the structural mix and the efficiency levels based on regression analyses.

# _A. Energy Intensity Convergence_

Convergence analysis is typically used in the context of economic growth and goes back to Barro and Sala-i-Martin (1992). There are different definitions of convergence in the literature. The reduction in cross-sectional variance in productivity is called σ-convergence. The tendency of less productive countries to grow faster is called β-convergence.

To measure σ-convergence in the context of the energy intensity analysis, we look at the trend of cross-country variance over time. The mean and standard deviation of energy intensity for an unbalanced panel of 28 countries in ECA during 1998-2008 are shown in Figure 3-a. To avoid selection bias, we also present the same statistics for a balanced panel of 23 countries.<sup>6</sup> For both samples of countries, the average energy intensity had declined steadily on average at a rate of 3.2 percent per year. The group of countries as a whole was converging towards more efficient economies (EU-15) with the gap in manufacturing energy intensity between ECA and EU15 average more than halved over the sample period.

The standard deviation of energy intensity had also reduced, indicating that the gap between the less efficient and the technology leaders were narrowed over time. The reduction in cross-section

> 6 Value added data are not available for Albania during 2004-2006, Romania in 2002, Russia during 1998-2001, Serbia during 1998-1999 and Turkmenistan during 2005- 2008.

5
# **4 Concluding remarks**

This paper explores the link between jobs, finance, and exposure to informal competition for firms in MENA countries. Using longitudinal data from the WBES survey, we document that job creation is facilitated by access to finance, which - in turn - is reduced when the firm is exposed to competition from informal firms. We provide suggestive evidence that a possible mechanism explaining this result is that firms that are more exposed to competition from informal firms have worsen expectations for future sales growth that in turn make them less likely to apply for a loan, depressing job creation.

Our results have some clear policy implications. The first concerns which policy to implement to favour employment creation in MENA countries. Because job creation is influenced by the functioning of the finance sector, our findings indicate that reducing the _disconnectedness_ which characterizes firms in MENA countries is a possible strategy to foster employment growth. Our results provide a novel demand-side view on the determinants of _disconnectedness_ , documenting that the presence of informal competition negatively influences the demand for finance. This implies that increasing the supply of credit and easing the access to credit should not be considered the only possible strategies to increase the use of finance by firms in MENA countries. Second, our results provide a novel motivation for reducing informality. Reducing informality would benefit the overall economy by inducing formal firms to increase loan applications and thus employment in response to a lower competition threat from informal firms. Third, our results show that policy interventions to support firms should be designed taking into account that the _perception_ of a constraint is as important as the existence of an actual constraint in driving firms’ behavior. As we document in our analysis, a firm’s decision not to apply for a loan is influenced by the perceived competition threat from informal firms, which is not necessarily correlated with an existing actual threat.

# **References**

- Abadie, A. and G. W. Imbens (2011). Bias-corrected matching estimators for average treatment effects. _Journal of Business & Economic Statistics 29_ (1), 1–11.

- Amin, M. (2021). Does competition from informal firms hurt job creation by formal firms? evidence using firm-level survey data. _World Bank Policy Research Working Paper 9515_ .

- Amin, M. and C. Okou (2020). Casting a shadow: Productivity of formal firms and informality. _Review of Development Economics, 24_ (4), 1610–1630.

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economic changes that may influence migration. Additionally, province‐year effects ���� are included to control for province‐wide macroeconomic shocks, and village fixed effects, �� , which are interacted with a time trend _T_ to allow for village‐specific trends in migration. Finally, there may be unobserved individual characteristics, ��� and ���� is an idiosyncratic error term.

Evident in this specification are both observed and unobserved sources of heterogeneity that we must consider in examining the effects of reallocation risk on migration decisions. The detailed information available in the RCRE household and village panel data from 1995 to 2003, and the supplementary survey data collected in 2004 are of tremendous use, and allow us to include a large set of control variables in the regression analysis. More specifically, included in ���� are individual gender, age, years of schooling. Recognizing from Figure 1 that there are important trends associated with age and gender, ���� includes age‐ and gender‐specific time trends. As previous analysis has shown that parent health status affects the migration decision of adult children and that the impact varies with the number of siblings (Giles and Mu, 2007), individual regressors ( ���� ) also include controls for whether father and mother are still alive and number of siblings, regardless of their current residence in the household, thus reducing bias associated with endogenous household composition. Household characteristics ( ���� ) include land per capita, consumption per capita and working‐age residents (aged 16‐60) as a share of household members (excluding individual � ). In addition, we control for the number of young women (aged 19‐24) and the number of young men (aged 21‐26) in the family, which proxy for potential household demographic changes caused by marrying‐out or marrying‐in, and which may be correlated with both land reallocation risk and a household member’s migration decision. Among village characteristics variables ��� are village population (controlling for scale effects), average village income per capita, land per capita, the number of village cadres (controlling for features of governance), share of village cadres with high school education or above (quality of governance), a village land per capita _Land Tenure Insecurity and Migration, Page 16_
The remainder of this paper is organized as follows. Section 2 provides an overview of the spread of COVID-19 and the government measures in Kenya. Next Section 3 describes the dataset used in this paper and discusses the empirical approach. Section 4 presents empirical results on the gender differences in household coping strategies, including the main results, potential reasons for the difference, as well as sensitivity analyses. Finally, Section 5 concludes.

# **2 Background**

In Kenya, it is estimated that about one-third of the population (20 million people) live under the poverty line (The World Bank, 2021 _b_ ). The COVID-19 pandemic reached Kenya in early 2020, with the first case reported on March 13, 2020. Since then, there have been more than 210,000 confirmed cases, with 3,595 deaths as of June 29, 2021.

The Kenyan government adopted several containment measures following the outbreak of COVID-19. These included promotion of social distancing practices, restrictions on curfews and limits on public gatherings, night public transport passenger capacities. Schools were closed from March 15, 2020, and the re-opening was done in phases, with schools fully re-opening on January 4, 2021. Another lockdown and renewed school closures were put in place in late March 2021. Based on the Oxford Stringency Index (Hale et al., 2021), a composite measure of the severity of policy response in nine areas, Kenya has consistently had more stringent policies in place than other countries in SubSaharan Africa.

# **3 Data and Empirical Strategy**

We use the first five waves of the data from a rapid response phone survey conducted with Kenyan households by the World Bank from May 2020 to June 2021 (The World Bank, 2021 _a_ ). It includes samples of Kenyan nationals as well as refugees. The Kenyan national sample was drawn from two sources: (i) all households that were part of the 2015/16 Kenya Integrated Household Budget Survey Computer Assisted Personal Interview pilot and provided a phone number, and (ii) households selected using 3
**Table 5.a Representation of renewable energy, substitutions and emissions for General Equilibrium**

|**Model**|**Renewables and Intermittency**|**Functional Structure for Energy**<br>**System Substitution**|**Emissions**|
|---|---|---|---|
|**MERGE-ETL**|Includes levelized generation costs.<br>Constraints due to intermittent<br>production are described by setting a<br>maximum share on electricity<br>production from wind and solar|Substitution between equivalent technologies<br>with dynamic constraints and share<br>constraints|GHG (CO2 and NO2)|
|**MESSAGE**|Links intermittent power with the<br>availability of flexible generation (e.g.,<br>hydro, gas, other thermal) and back-<br>up/storage capacity|Substitution between equivalent technologies<br>with limitations due to system integration,<br>etc.|GHG and other pollutants|
|**ReMIND**|Requires storage and curtails wind and<br>solar depending on the share of wind<br>and solar in power generation|Hybrid: CES at the level of final energy,<br>linear substitution with adjustment costs and<br>capital stocks at the primary and secondary<br>energy levels|GHG and other pollutants|
|**WITCH**|Cost penalty for wind with increasing<br>share in electricity to capture spinning<br>reserves, backup capacity, and<br>discarded electricity|Constant elasticity of substitution (CES)|GHG (CO2 and NO2)|
|**C3IAM**|The evolution of the power mix is<br>calibrated to energy models (i.e.<br>PRIMES for EU and PROMETHEUS for<br>the world).|Substitution occurs through a nested CES<br>function|GHG and other pollutants|
|**BET EMF33**|The power generation sub-module in<br>BET takes into account capacity<br>constraints and the lifetime of each<br>generation plant. In addition, it<br>considers the load duration curve,<br>discretized into four sections, ranging<br>from year maximum hours, peak<br>hours, shoulder hours, to bottom<br>hours. BET has a generation<br>constraint on solar and wind, which<br>are intermittent renewables|Linear choice (lowest cost)|CO2|
|**AIM-CGE**|Requires additional storage for<br>intermittent renewables|Energy system cost minimization|GHG and other pollutants|
|**GEM-E3-ICCS**|The evolution of the power mix is<br>calibrated to energy models (i.e.<br>PRIMES for EU and PROMETHEUS for<br>the world).|Substitution occurs through a nested CES<br>function|GHG and other pollutants|
|**IMACLIM**|Production is distinguished between<br>base and peak demand in a load curve.<br>Constraints due to intermittent<br>production are described by setting a<br>maximum share on wind and solar<br>capacities which depends on the<br>segment.|In the short run static equilibrium, the fixed<br>technologies (Leontief coefficients) prevent<br>substitution among factors. These<br>substitutions are treated between two<br>equilibria in sector-specific dynamic modules.|GHG and other pollutants|
|**POLES**|Links intermittent power with the<br>availability of back-up capacity (hydro,<br>gas, other thermal)|Logit sharing depending on costs and<br>political choices|GHG and other pollutants|
|**TEA**|As COFFEE|As COFFEE|As COFFEE|

30
(dpi_lipc) or the Executive Index of Political Competitiveness (dpi_eipc) is less than six. In countries where dpi_lipc and dpi_eipc are greater than or equal to six, dpi_checks is incremented by one if there is a chief executive, by a further one if the chief executive is competitively elected (dpi_eipc greater than six), and by a further one if the opposition controls the legislature [...].” _Democ_ : index of democratization (Source: Vanhanen 2011 Variable name: van_index). “This index combines two basic dimensions of democracy – competition and participation – measured as the percentage of votes not cast for the largest party (Competition) times the percentage of the population who actually voted in the election (Participation). This product is divided by 100 to form an index that in principle could vary from 0 (no democracy) to 100 (full democracy). (Empirically, however, the largest value is 49).” _Stability_ : political stability (Source: The World Bank. Variable name: wbgi_pse). “‘Political Stability’ combines several indicators which measure perceptions of the likelihood that the government in power will be destabilized or overthrown by possibly unconstitutional and/or violent means, including domestic violence and terrorism.” 37
- 57 -

equilibrium bid broadly decreases with the number of bidders and that the statistical significance would taper off once the number of bidders reaches seven (Figure 36). About half of World Bank financed projects do not reach this level of competition. Nearly 80 percent of government financed projects are estimated to have insufficient competition (Figure 37). Too small contracts are often less attractive to firms, because of high transaction costs, which would be incurred regardless of the size of projects.

|**Table 1**|**3. Averag**|**e numbe**|**rof **|**bidders**|||
|---|---|---|---|---|---|---|
||Obs|Mean|Std.|Dev.|Min|Max|
|World Bank project|82|7.28||3.78|3|16|
|Governmentproject|74|4.51||3.50|1|15|

**Figure 36. Coefficients of numbers of bidders estimated by partially nonlinear IV regression**

<!-- Start of picture text --> 2.0<br>Estimated coefficient<br>Coef. + 1.6 Std. Err.<br>1.5 Coef. - 1.6 Std. Err.<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>1 2 3 4 5 6 7 8 9<br>Number of bidders<br>Note: The number of bidders equal to 10 or more is used as a baseline.<br><!-- End of picture text -->

**Figure 37. Levels of competition in World Bank or government financed road projects**

<!-- Start of picture text --> 0.40 World Bank financed projects<br>0.35 Government financed projects<br>0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16<br>Number of bidders<br>Fraction<br><!-- End of picture text -->
**<u>Table 3: Households’ knowledge of important legal provisions</u>**

||**Total**|**Free**|**M**<br>**hold**|**ain tenure typ**<br>**Customary**|**e**<br><br>**_M_**|**_ailo_**|
|---|---|---|---|---|---|---|
|**Male knowledge of the land law**|||||||
|Possible to obtain certificates of customary ownership|36.0|35.2||39.2|28.1|***|
|Certificate can be converted into ownership upon payment|31.3|40.8||31.4|28.5||
|_Mailo_tenants are protected from eviction|28.5|52.1|***|18.7|46.5|***|
|_Mailo_tenants can prohibit investments|20.2|50.7|***|12.3|31.6|***|
|Anybody occupying land for 12 years gets a certificate|34.5|56.3|***|29.9|40.2|***|
|Recognition of family land needs application to LC1|26.0|35.2|**|23.5|29.7|*|
|Index|29.4|45.1|***|25.8|34.1|***|
|**Female knowledge of the land law**|||||||
|Possible to obtain certificates of customary ownership|32.2|33.8||33.6|28.1||
|Certificate can be converted into ownership upon payment|29.0|42.3|***|26.3|32.0|*|
|_Mailo_tenants are protected from eviction|27.8|39.4|***|14.8|57.4|***|
|_Mailo_owners can prohibit investments|21.8|60.6|***|9.6|41.4|***|
|Anybody occupying land for 12 years gets a certificate|31.0|43.7|***|25.2|42.2|***|
|Recognition of family land needs application to LC1|31.6|46.5|***|28.1|36.3|**|
|Index|28.9|44.4|***|22.9|39.6|***|
|Number of observations|970|71||643|256||

* significant at 10%; ** significant at 5%; *** significant at 1% for the t-statistic to test the equality of group means using customary tenure as a reference group. _Source:_ Own computation from 2004 WB/Makerere/FASID land tenure survey.

24
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