rafmacalaba/datause-encoder-data huggingface dataset:
5–19 employees (small), 20–99 employees (medium), and 100+ employees (large). These types of firms are also quite distinct from those with fewer than five employees (micro); in particular, it is expected they do not experience the same extent of fluctuations in sales and employment levels. A shortcoming of this approach is that the VAT returns do not include information about the number of employees of each firm. As such we merge the VAT and PAYE return data and can only report on the subset of firms that provided both types of returns in a given month. Once again this is likely to mean the results may not be generalizable beyond firms that share these characteristics.

The final part of the descriptive analysis we conduct is to examine the trends from both data sources based on characteristics other than firm size. The three most notable characteristics are in terms of sector, location, and firm age, as this information is available for all firms in both data sources. The sector analysis is decomposed into three categories that are identical across the survey and tax data (manufacturing, retail and wholesale services, and other). The location analysis in both data sources focuses on a simple binary split between firms in Lusaka and firms everywhere else.

# **3.2.2 Determining the covariates of recovery**

Both data sources, but particularly the survey data, provide the opportunity to explore what firm characteristics are associated with experiencing an initial reduction in sales and employment, as well as a recovery by 2021. This analysis complements exploring the descriptive trends because through conducting econometric analysis we can identify what firm characteristics appear to be most closely correlated with firm performance. It is important to emphasize that this regression analysis is not causal as there is no exogenous variation. We cannot rule out, among other things, reverse causality and omitted variable bias. For example, on the former, if we find a positive relationship between receiving government support and recovering from the pandemic, we cannot be sure whether this is because firms that received support were better placed to recover anyway or whether government support actually aided their recovery. An example of the latter is that both data sources (partic15
_Notes:_ r esults are restricted to the sample of individuals aged between 15 and 24 in 2010 and between 23 and 32 in 2018. “Lower” and “Upper” refer to the two bounds on mobility; for immobility, “Lower” is the upper bound and “Upper” is the lower bound. Upper-bound estimates are based on 50 replications. Individual-level sampling weights are applied.

Some further validation checks were undertaken by means of comparison between transition matrices—both for the overall population and by population subgroups—using true panel data and the estimated synthetic panels for each of the consecutive year-pairs of data (2010/2011 to 2017/2018).<sup>11</sup> As we expected, the lower-bound estimates underestimate mobility—understating movements into and out of the NEET condition, and overstating the extent to which people remain NEET or non-NEET—while the upper-bound estimates overestimate mobility. Encouragingly, the “truth” (true rate) tends to lie about midway between these bounds. We find, thus, that our approach does indeed present bounds within which the “truth” can be observed.

# 6. Discussion and Conclusions

Several decades ago, Morocco embarked on a long-term process of political, social, economic, and environmental reforms, culminating in the adoption of the New Constitution in 2011. Because of these reforms, progress has been made, in particular, in the reduction of absolute poverty, better access to basic public services, and the considerable development of public infrastructures. The social landscape, however, remains marked by challenges yet to be met, particularly in terms of social cohesion. These challenges could pose the risk of exclusion of the most fragile components of Moroccan society, in particular women and young people. Young people unemployed, outside the school system and not undergoing any training—the so-called NEETs—form about 30 percent of the Moroccan population between ages 15 and 24. That number looks even grimmer when compared to NEET rates in the MENA region, already characterized by particularly high rates of NEETs (ILO, 2019).

Subsequent rounds of Moroccan labor force surveys have already presented the opportunity to develop a clearer profile of NEETs in the country. What is missing, however, are the data to examine how the circumstances of people in the NEET condition have evolved over a reasonably long period of time. Our adaptation of the synthetic panel methodology to this specific issue enables us to overcome the data limitations and to provide helpful insights into NEETs dynamics in the last decade.

The first part of our analysis showed the key determinants of the NEET condition. As expected, individual characteristics play an important role. The probability of becoming NEET is higher for

> 11 For space considerations, we just summarize the findings here; the full set of results is provided in the Appendix.

24
# **2.  Methodology and Data Sources for 2007**

In this section we briefly describe the latest update of the WGI. Our methodology for constructing aggregate governance indicators has not changed from past years, and a detailed description can be found in Kaufmann, Kraay, and Mastruzzi (2004), as well as in a technical Appendix D to this paper.

We define governance broadly as the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them. The six dimensions of governance corresponding to this definition that we measure are:

1. _Voice and Accountability (VA)_ – measuring perceptions of the extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media.

2. _Political Stability and Absence of Violence (PV)_ – measuring perceptions of the likelihood that the government will be destabilized or overthrown by unconstitutional or violent means, including politically-motivated violence and terrorism.

3. _Government Effectiveness (GE)_ – measuring perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies.

4. _Regulatory Quality (RQ)_ – measuring perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development.

5. _Rule of Law (RL)_ – measuring perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence.

7
## **Table 2.4 Leading Emerging Bancassurance Markets**

|Region/coun<br>try|GDP<br>per<br>Capit<br>a ($)|M2/GD<br>P (%)|Bank<br>Branch<br>es<br>per<br>100,00<br>0<br>Adults|Life<br>Premiu<br>m as %<br>GDP|Non-<br>Life<br>Premiu<br>m as %<br>GDP<sup>a</sup>|Life<br>(Estimated<br>%<br>Bancassuran<br>ce)|Non-Life<br>(Estimated<br>%<br>Bancassurance)|
|---|---|---|---|---|---|---|---|
|Latin<br>America||||||||
|Brazil|8,360|66|12|1.3|2.0|77|12|
|Chile|9,484|79|15|2.5|1.6|35|28|
|East Asia||||||||
|China|3,739|167|n.a.|2.4|1.2|48|n.a.|
|Malaysia|6,920|134|12|2.7|1.5|32|7|
|Thailand|4,151|110|11|2.4|1.2|31|10|
|North Africa||||||||
|Morocco|2,884|106|12|0.9|1.9|60|3|
|South Asia<br>||||||||
|India<sup>~~b~~</sup>|1,077|70|9|3.7|0.6|7|35|
|EU<br>Accession||||||||
|Hungary|12,83<br>7|60|17|1.6|1.6|20|n.a.|
|Poland|11,29<br>6|53|33|1.9|1.9|43|4|

_Sources:_ World Bank Development Indicators, IMF database, Axco Reports, 2008– 2009 data. _Note:_ Unadjusted Life insurance percentages tend to favor the bank distribution system because of the higher proportion of single-premium business sold through bank branches. n.a. = not available. a. Including Health and Accident.

b. Bancassurance accounts for 19 percent of non-LIC Life sales in India.

# **Latin America**

## **Brazil**

_Regulation:_ No limit is placed on insurer/bank cross-ownership, except for government banks, which may hold no more than 49.9 percent of an insurer. In practice all insurance transaction must be intermediated through a registered broker, and Bancassurers normally nominate in-house officers to be registered as brokers. In addition all insurance premiums must be collected via a banking institution. This unusual requirement has actually had an important impact on the development of Bancassurance in Brazil.

35
Table 9: Results with national economic condition indicators as control variables

|Dependent Variable|Log(Mo|nthlyWage)|||
|---|---|---|---|---|
|0b.pot<br>~~e~~xp#c.indepvar|0.522|0.0442|0.0278|0.0169|
|1.pot<br>exp#c.indepvar|0.217|-0.000762|0.0614|0.0227**|
|2.pot<br>exp#c.indepvar|-0.187|-0.0494*|-0.0550|0.00980|
|3.pot<br>exp#c.indepvar|0.539**|0.0581***|0.0682*|0.00162|
|4.pot<br>exp#c.indepvar|0.282|0.0391**|0.0875**|-0.00457|
|5.pot<br>exp#c.indepvar|0.370|0.0457**|0.0867**|-0.0149**|
|6.pot<br>exp#c.indepvar|0.300|0.0548**|0.0898***|-0.0146**|
|7.pot<br>exp#c.indepvar|0.0182|0.0493***|0.0908***|-0.0110**|
|8.pot<br>exp#c.indepvar|-0.118|0.0452**|0.0828**|-0.00271|
|9.pot<br>exp#c.indepvar|-0.279|0.0487**|0.0721***|-0.00585|
|10.pot<br>~~e~~xp#c.indepvar|-1.943***|-0.0834**|-0.176***|-0.0284***|
|indepvar|0.00145|0.0404*|0.0746**|-0.0186***|
|cohort|0.115***|0.113***|0.111***|0.110***|
|# obs|13,415|13,415|13,415|13,415|
|R-squared|0.884|0.885|0.885|0.885|
|Independent Variable|youth<br>~~e~~mployment|log<br>~~o~~ilprice|log<br>~~w~~heatprice|Gdp<br>~~g~~rowth|

Notes: Log of the individual’s monthly wage is regressed in each column on country level economic condition indicators at the year of graduation: youth employment rate (15-24 years old), oil price, wheat price and gdp growth; interacted with potential years of experience 0 to 10. The potential experience is captured by the difference between the individuals’ age at the year of the survey and their age at the year of graduation.

(ii) Governorate and potential experience fixed effects are included in each regression. Controls for eudcational attainment are also included.

(iii) Sample of males who graduated between 1990-2019. Data are constructed from LFS 2006-2019, and the economic conditions indicators are extracted from World Bank data.

(iv) Cluster-robust standard errors in parentheses (clustering on cohort and governorate of residence).

(v) * , ** and ***refer to significance at the 10, 5 and 1% levels respectively.

21
The average effect sizes across these pooled outcome categories in Table 7 provide suggestive evidence that pre-primary interventions can have greater impacts on more disadvantaged populations. For instance, for outcomes related to school participation and progression, estimated effects are 0.096 sd higher on average for children coming from lower socioeconomic backgrounds, compared to their less disadvantaged peers, although this advantage in gains is not statistically significant. However, average effect sizes are 0.05 sd significantly higher for disadvantaged populations when it comes to skills.

# **Economic analysis**

In Table 8, we present our findings from an economic evaluation of a subset of studies in low- and middle-income countries that reported sufficient information for us to compare costs per child to an estimate of the gain in lifetime earnings the child would receive from an improvement in cognitive skills. Panel A reports calculations for a discount rate of 3 percent, while Panel B presents results from a more conservative rate of 5 percent. Ganimian et al. (2021) report a range of benefit-to-cost ratios following the same method for a discount rate of 3 percent, which we directly report in Panel A.<sup>30</sup> In Panel A, our smallest benefit-to-cost ratio is 3.5 when we use the smallest estimate of the returns to cognitive skills (4.3 percent per standard deviation) to translate the smallest estimate of gains in cognitive skills to gains in earnings for the Spier et al. (2020) study, which assessed the impact of adding an additional year of preprimary education for four-year old children on top of the mandatory year prior to the start of first grade in Bangladesh. Our largest benefit-to-cost ratio is 103.5 when we use the largest estimate of the returns to cognitive skills (13 percent per standard deviation) and the largest estimate of the returns to social-emotional skills (15 percent per standard deviation) to translate the smallest estimated average treatment effects into earnings for the Wolf et al. (2019a) study. This study assessed the impact of an in-service teacher training program in Ghana that sought to help teachers transition to a more holistic, child-centered curriculum.<sup>31</sup> Although both estimates of the benefit-to-cost ratio are quite high, it is

> 30 Appendix Table 4 documents the assumptions underlying these calculations as well as data sources for parameters such as the labor force participation rate and growth in real wages.

> 31 Though poor children did show positive and significant gains in cognitive skills in Brinkman et al. (2017), the average treatment effects for all cognitive skills in the full sample were not statistically distinguishable from zero. However, even in the full sample, there were average gains in social-emotional skills. If we

30
**Figure 4.5: Outage frequency and lightning strikes**

<!-- Start of picture text --> 250 250<br>200 200<br>150 150<br>100 100<br>50 50<br>induced outages 2000-2017 0 0 Number of lightning strikes<br>Average number of days of storm<br>January February March April May June July August September October November December<br><!-- End of picture text --> Source: _authors based on BREB electricity performance data and_ NASA Tropical Rainfall Measuring Mission Lightning Imaging Sensor (LIS)

**Figure 4.6: Time series for outages and lightnings.**

<!-- Start of picture text --> 400 16<br>350 14<br>300 12<br>250 10<br>200 8<br>150 6<br>100 4<br>50 2<br>0 0<br>Lightning count<br>Days with outages<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> _Source: authors based on BREB electricity performance data and_ NASA Tropical Rainfall Measuring Mission Lightning Imaging Sensor (LIS) To better understand the correlation between power outages (caused by natural shocks) and severe local convective storms, we further disaggregate the lightning data by Dewan et al. (2017) using seasonal maps for the period 1998–2014 (Appendix 5). Seasonal weights were assigned to divisions, depending on the average number of lightnings each of these divisions experienced over the considered period, thus allowing us to disaggregate the LIS lightning data to the division level. Table 4.3 presents these correlations. Overall, lightning strikes are highly correlated with power outages. The correlation is highest in the divisions of Dhaka (75%), Rajshahi (72%), and Chittagong (49%), which together account for 86% of all storm induced outage days.

27
16

# _2.3.1. Denmark._

In Denmark, rather than reducing aggregate demand and income, disinflation was accompanied by an average growth rate of GDP of 3.7 per cent over the years 1983 to 1986. Growth was driven by domestic demand, with private consumption growing (over the same period) at an average of 4.4 per cent a year and business investment at 13.1 per cent (all data are drawn from OECD National Income Accounts). Figure _5_ shows the spectacular behavior of manufacturing employment during stabilization, and table 2 shows the growth rates of GDP and its components over the same period.

Figure 5: Denmark: Manufacturing Employment and Inflation. Source: IMFIFS.

<!-- Start of picture text -->
*20  0.08<br>110  00.o<br>@0<br>100  . 00<br>V  00D<br>10  el  Ia  I3  04  6s  a  @7  9100  go<br><!-- End of picture text -->

As we can see, there was a quite important rise in GDP growth after stabilization.14 This was mainly led by a spectacular behavior of investment expenditures. There is a cycle as in Latin America.

**14** For Denmark, the inflation series was smoothed using standard exponential smoothing techniques and a value of _ca=0.3._
Table A2. 6: Licenses and permits system

|**Licenses andpermits sy**<br>|**stem**<br>**_Question_**<br>**_weights_**|**Coding of a**|**nswers**|||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
||**_(c k )_**|**_Yes_**|**_No_**|**Bulgaria**|**Romania**|**Croatia**|**Albania**|**OECD**|**EU15**|**Poland **|**Slovak Rep**|
|The 'silence is consent' rule (i.e. that licenses are<br>issued automatically if the competent licensing office<br>has not acted by the end of the statutory response<br>period) is used at all|1/3|0|6|no|yes|no|yes|n.a.|n.a.|no|yes|
|There are single contact points (“one-stop shops”)<br>for getting information on notifications and licenses|1/3|0|6|yes|yes|yes|yes|n.a.|n.a.|yes|yes|
|There are single contact points (“one-stop shops”)<br>for issuing or accepting on notifications and licenses|1/3|0|6|yes|yes|yes|no|n.a.|n.a.|yes|yes|
|**Country scores (0-6)**||kckanswerjk||2|0|2|2|2.2|2|2|0|

45
1 Figure 6. Ireland: Manufacturing Employment and Inflation. Source: IMFIFS.

<!-- Start of picture text --> 12 * 0.0a<br>0 0A<br>0.00<br>0 05<br>0~~~~~~~~~~~~~~~~~~~~~~~~~00<br>0 02<br>001<br>-0 00<br>so 1 92 03 ~~04 es es 07 es<br><!-- End of picture text -->

# _3.3.3 France_

France is an intermediate case, though definitely more on the contraction side of the spectrum.

Contrary to the case of Ireland, GDP never fell. However, its growth rates were considerably below those of the OECD average until 1986. Although consumption growth fell, **it** was business investment that suffered the most dramatic blow, falling an average of 3.1 per cent per annum in the period 1983-84.
# **(iii) Intraindustry trade in agriculture**

If the product in question is purely homogeneous, international trade in the product should theoretically be one-way trade. As described in a traditional textbook of international trade theory, for example, Portugal exports wine to England, and England exports cloth to Portugal in return. However, a brief look at actual trade flow data reveals that agricultural trade, as well as manufacturing trade, is far from one-way trade.

To examine the magnitude of intraindustry trade, we calculated intraindustry trade indices, which were first used by Grubel and Lloyd (1975) for agricultural and manufacturing trade in Europe. As in Grubel and Lloyd, the intraindustry trade index of the i-th industry product _(ITI,)_ is defined as where _Xi_ and _Mj_ are the value of exports and the value of imports, respectively, of the i-th industry good. Suppose that good i is a passenger car, and the value of export of passenger cars from Germany to France is 2 billion ECUs, and the value of import of passenger cars by Germany from France is 1 billion ECUs. Then the intraindustry trade index of passenger cars between France and Germany is calculated as 14
available in the communes rose by 14 and 20% respectively in 2003. Consistent with effects on market and services availability, we find evidence of impacts on employment and livelihood patterns. Improved roads resulted in a small but significant 2 percent decline in households relying on farming as their main source of income. A significant increase in the share of households mainly relying on the service sector (1.7%) hints at what alternative livelihoods these households may have switched to. This is not a trivial impact given that only one percent of households were employed in the service sector in the baseline. The impact on households engaged primarily in trading activities is also positive but small and statistically insignificant. Finally, impacts on the primary school completion rate are sustained over time and have even risen slightly. Moreover, small effects on secondary school enrollments also appear to be emerging.

In sum, we find some support for TILD. Our results indicate significant average impacts on the development of local markets, both their presence and frequency. The project resulted in households switching from agriculture to non-agricultural, mostly service-related activities, and tailoring and hairdressing services became more commonly available. These impacts were not sharp and short-lived; they took time to emerge, only appearing in 2003, and are thus rising over time. This is all we can say based on our two data points. Of note too are the quicker, sustained and robust impacts on primary school completion rates.

# **_5.3 Heterogeneity in impacts_**

As implied by our theoretical model, the average treatment effects may hide significant heterogeneity across communes. Using the PS-matched DD method and focusing on the 2003 data, we calculate the individual treatment effects for each of the 94 24
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**Table 3: OLS and Tobit for household consumption on natural gas**

||||**Ur**|**ban**|||
|---|---|---|---|---|---|---|
|||**OLS**||**T**|**obit**||
|**Log (naturalgas)**|**Coef.**|**Std. Err.**||**Coef.**<br>|**Std. Err.**||
|North|-0.261|(0.162)||9.513|(5.382)|*|
|Center|-|-||-<br>|-||
|Vehicles|0.059|(0.041)||-0.362|(0.643)||
|Appliances|-0.005|(0.008)||0.106|(0.202)||
|Refrigerator|0.172|(0.072)|**|2.357|(2.509)||
|Stove|-0.199|(0.146)||-3.142|(1.998)||
|Washing machine|0.159|(0.069)|**|4.341|(1.144)|***|
|Fan|-0.069|(0.065)||0.895|(1.101)||
|Air conditioning|-0.075|(0.051)||0.882|(2.085)||
|Heating|0.393|(0.094)|***|11.120|(2.798)|***|
|Owners of housing|0.086|(0.036)|**|2.072|(0.812)|**|
|Log (dormitories)|0.123|(0.071)||4.671|(1.713)|***|
|Housing quality|0.096|(0.042)|**|3.250|(1.956)|*|
|Employment type|0.176|(0.090)|*|0.191|(1.275)||
|Male|0.025|(0.042)||0.143|(0.960)||
|Formal|0.022|(0.044)||0.443|(0.817)||
|Indigenous language|0.524|(0.299)||-2.892|(3.131)||
|Log (household size)|0.078|(0.076)||-5.468|(1.727)|***|
|Skilled|-0.040|(0.070)||0.843|(1.714)||
|Decile 2|0.234|(0.155)||1.265|(2.358)||
|Decile 3|-0.112|(0.168)||2.600|(2.363)||
|Decile 4|0.115|(0.102)||0.602|(1.431)||
|Decile 5|0.252|(0.143)|*|6.149|(3.842)||
|Decile 6|0.176|(0.177)||6.214|(3.412)|*|
|Decile 7|0.268|(0.124)|**|7.555|(2.550)|***|
|Decile 8|0.358|(0.108)|***|8.673|(2.700)|***|
|Decile 9|0.263|(0.107)|**|8.606|(2.977)|***|
|Decile 10|0.497|(0.109)|***|15.845|(5.605)|***|
|Constant|2.993|(0.147)|***|-205.035|(48.298)|***|
|Sigma||||90.573|(17.283)||
|**N**||1008|||7683||
|**F/LR**χ <sup>**2**</sup>|-|-|-|-<br>|-|-|
|**R**<sup>**2**</sup>**/** **Likelihood**<br>Notes: ***, **, * denot<br>Clustered standard erro<br>Sample restricted to hou<br>Sample restricted to mu<br>gas for Tobit|e significan<br>rs at state le<br>seholds co<br>nicipalities<br>0|ce at 1%, 5<br>vel<br>nsuming n<br>with an infr<br>.2548|% an<br>atura<br>astru|d 10 %<br>l gas for OL<br>cture to dist<br>0|S<br>ribute nat<br>.0521|ural|

21