rafmacalaba/data-use-mentions-extended huggingface dataset:
|(0.48)|
|Age|34.07<br>|35.90<br>|33.37<br>|33.24<br>|
||(11.82)|(12.21)|(11.84)|(11.10)|
|Compulsory law|0.22|0.01|0.22|0.45|
||(0.41)|(0.10)|(0.41)|(0.50)|
|_N_|13135|3811|5869|3455|

_Note_ : Standard deviation in Parenthesis. Compulsory law indicates the share of respondents born in or after 1991 who were on the verge of starting 7 grade and affected by the 2004 constitution—increased compulsory education from 6 to 9 grade.

Finally, for the economic analysis, we use macro-level historical data on average years of schooling and gross national income per capita in constant 2017 purchasing power parity (PPP) terms from the Human Development Index country profile for Afghanistan (https://hdr.undp.org/data-center/ specific-countrydata#/countries/AFG). We also compile data on population growth, labor force, and female-to-male labor force participation ratio from the World Bank Open Data repository (https://data.worldbank.org).

While these surveys are among the few nationally representative repeated cross-sectional household surveys reporting on the labor activities of adults in the household, there are some potential limitations. For instance, labor market outcomes for those aged 14 and 15 years old are missing for 2007. Additionally, the coverage of women working as salaried workers is limited, corresponding to the lower labor force participation rate of women in the country. Furthermore, the absence of information on the school type and degree of salaried workers restricts our ability to analyze differential returns to education by school type and/or degree, if any. However, we provide the first rigorous evidence on
presents the dataset being used in this section. Section 3.2 evaluates if there are differences between foreign investors and domestic producers in terms of their potential to generate positive spillovers. Section 3.3 examines if there are differences in the extent of FDI spillover potential between different groups of foreign investors, depending on their characteristics.

> 3 Note that the general managers experience in other foreign-owned firms at home or abroad in the strict sense could also be considered part of the transmission channel “labor turnover“.

9
sup> A World Bank Study in 1995 based on documents from CIH showed that 80% of beneficiaries of _régime spécial_ loans declared annual incomes above MDH 60,000, which corresponds to deciles 6 and above in the income distribution scale. Subsidies under _régime spécial_ loans appeared better targeted, all of them targeting the poorest 40% of the population.

In the context of fixed-rate mortgages (FRMs) with fixed repayments applied in Morocco, affordability problems would arise at the beginning of the mortgage repayment period, either because of constraints on the maximum LTV (insufficient savings), or because payments were too high. In time, fixed nominal payments translate into decreasing real payments due to inflation; also, real incomes can increase. Thus, subsidies are needed mainly at the beginning of the loan. Instead, the interest rate subsidy as parameterized in Morocco would apply for the entire life of the loan, which is clearly inefficient. Moreover, under this scenario, the value of the _régime spécial_ subsidy was very high, equivalent to about 40% of the mortgage value.. Combined with other types of subsidies, this resulted in huge subsidies for few fortunate households able to obtain a mortgage. Lastly, this resulted in very high inertia of the system, in the sense that loans approved at any given time would continue to generate liabilities for the State for the next 15 years and thus, flexibility was very low.

As noted above, the main problem facing subsidies at that time was that they were granted only to CIH, _de facto,_ excluding other banks from competing in the mortgage market. However, the

> 45 This tax seems to exist only in Morocco.

> 46 This was amplified by the fact that the concept of value used for administrative purposes, the VIT, was based on administratively pre-set values for land, and on construction costs for dwellings, resulting in differences between the VIT and market value of the dwellings.

> 47 The subsidy also had a built-in regressive feature (within each bracket, the higher
||year spanned more than one calendar year, the<br>inflation figure is the weighted average over<br>the calendar years covered, where the weights<br>used are the proportion of months covered in<br>the calendar year.<br>Source: World Development Indicators,<br>World Bank.|
|---|---|
|Gross Tertiary Enrollment Rate|Gross enrollment rate in tertiary education.<br>Values of the variable were computed in two<br>steps. In the first step, gross tertiary enrollment<br>rate values for the fiscal year covered by the<br>Enterprise Survey were taken. In cases where<br>the fiscal year spanned more than one calendar<br>year, the enrollment figure is the weighted<br>average over the calendar years covered,<br>where the weights used are the proportion of<br>months covered in the calendar year. In the<br>second step, annual values were taken from the<br>first step and averaged over the three years<br>prior to the (final) year covered by the<br>Enterprise Survey. Three years average was<br>taken to avoid too many missing values of<br>tertiary enrollment rate. The final figure is<br>divided by 100 to avoid too small coefficients<br>in the regressions.<br>Source: World Development Indicators,<br>World Bank.|
|Gross Primary Enrollment Rate|Gross enrollment rate in primary education.<br>Values of the variable were computed in two<br>steps. In the first step, gross primary<br>enrollment rate values for the fiscal year<br>covered by the Enterprise Survey were taken.<br>In cases where the fiscal year spanned more<br>than one calendar year, the enrollment figure is<br>the weighted average over the calendar years<br
life, leading to a higher probability of survival (Arpino et al. 2019). Bellucci et al. (2020) use the European Survey on Health, Ageing, and Retirement to show that war-exposed individuals have higher resilience to shocks and increased perception of uncertainty and uncontrollability of the environment. Cassar et al. (2013) explore the effects of war-related violence on trust and cooperation in Tajikistan and found that exposure to war enhanced prosocial behavior. A study of the effect of violence during Nepal’s civil war found that violence-affected communities had higher levels of prosocial motivation and public good cooperation (Gilligan et al. 2013). Similarly, civic participation increases in the districts of Uganda, where battle events took place (De Luca and Verpoorten 2015). Bellows and Miguel (2009) show that households in Sierra Leone that experience war violence are more likely to join political and community groups and more likely to vote. Bauer et al. (2020) review multiple studies pointing in the same direction: that social cooperation is enhanced by past exposure to war violence.

The second set of studies pertains to the role of social capital and trust in institutions for compliance with the NPI during the 2020 pandemic. Data on real-time mobile phone locations in Italy demonstrate a higher decline in personal mobility in areas with higher social capital (Durante et al., 2020). Similar results are found by Barrios et al. (2020) for a sample of counties in the United States and European countries. In a sample of 84 countries, Elgar et al. (2020) find that trust in government fosters lower COVID-19 mortality, though they also find that (controlling for trust in government) stronger social bonds in a country may facilitate the spread of infection. Bargain and Aminjonov (2020) found that regions with higher trust experienced larger reductions in non-essential mobility following the implementation of containment policies in March 2020. Bartscher et al. (2020) provide evidence from seven European countries that culture and social capital have a considerable impact on
_Published information for specialized MFIs_

In the case of specialized microfinance providers, the situation is somewhat different, given their dual mandate of operating efficiently and reaching many target clients. A large number of country studies and studies of multi-country MFI networks throw light on the scale of outreach. Drawing on such evidence, a group at CGAP, Christen et al.(2004), have been able to put together a database setting out the number of clients reached by a wide range of microfinance suppliers, including not only specialized MFIs, but also other non-traditional intermediaries with large numbers of small clients.

The CGAP inventory aims to determine how many “poor and near poor” clients have savings or deposits at a broad range of “alternative financial institutions” including not only the “new breed” of specialized MFIs, but also for example mutuals, state agricultural and development banks. The CGAP database also includes postal savings systems and other savings banks even though they do not make microloans. As such it sets out to be a maximal data set for whatever could be regarded as alternative in the

> 24 For example, this proble m is likely the source of the huge discrepancy between the aggregate number of deposit accounts reported by Tanzanian financial intermediaries – 2.23 million – and the grossed-up number of households reporting a deposit account in the household budget survey – 0.42 million.

18
14 2) The second model assumes that the variable is stationary with a change in the mean after 12/16/94. This is suggested by inspection of the plot of MEXDIS. The variable can be described by a decreasing trend with a change in the constant after the December crisis. We estimated the following model, where the dummy _dl_ takes value one after 12/16/94 and zero before that date: _4 MEXDIS = c + a t + dl + E±_ 7 _. MEXDIS(-i) i=_ (2) The results are: _LS // Dependent Variable is MEXDIS Sample: 6/15/90 - 7/12/96 Included observations: 318_

|Variable<br>Coefficient|Std.|Error|T-Statistic|Prob.|
|---|---|---|---|---|
|C<br>78.52782|19.|52125|4.022684|0.0001|
|T<br>-0.253105|0.07|5648|-3.345822|0.0009|
|DI<br>72.04601|18.|44906|3.905132|0.0001|
|MEXDIS(-1) 1.186698|0.05|6149|21.13472|0.0000|
|MEXDIS(-2) -0.204627|0.08|5975|-2.380075|0.0179|
|MEXDIS(-3) -0.185268|0.08|5210|-2.174256|0.0304|
|MEXDIS(-4) 0.118871|0.05|4331|2.187901|0.0294|
|R-squared<br>0.966|303|Mean|dependent var|657.8306|
|Adjusted R-squared 0.965|653|S.D. d|ependent var|282.6279
a vehicle for foreign aid delivery, the resulting evidence should not be interpreted as contributing to the long-standing empirical debate on the growth effects of aid. In contrast with the aid-growth literature, which has primarily been concerned with the medium- to longrun effects of aid on growth, my interest in this paper is in the short-run cyclical effects of increased

> 4 A key practical difficulty with implementing this approach in developing countries is the relative scarcity of highfrequency data in these countries.  In a notable effort to fill this gap, Ilzetzki, Mendoza and Vegh (2010) assemble quarterly data for a sample of 20 developed and 24 emerging markets, and use this to implement BlanchardPerotti (2002)-style estimates of spending multipliers.  There are however only 11 countries in common between their emerging-market sample and the sample of 102 low- and middle-income countries used in this paper. The countries included in their 24 country sample but not mine are primarily richer emerging-market economies that rely little on borrowing from official creditors.  Conversely, my paper covers 91 developing countries not covered in Ilzetzki, Mendoza and Vegh (2010), where this source of financing of government spending is important.

6
that play such a crucial role in the models of heterogeneous firms and trade pioneered by Eaton and Kortum (2002) and Melitz (2003) do not seem to be very large in practice, or at least appear to play a less dominant role than uncertainty about export success.

# **_5. Export Entrepreneurship across LAC Countries along the New Product Dimension_**

Export entrepreneurship is present also when incumbent exporters export new products. To examine this dimension of entrepreneurship, we focus on incumbent exporters (as defined in Table 1) in each country and consider products defined at the 6-digit HS disaggregation level.<sup>24</sup>

> 23 The decomposition of the contribution of new exporters during the crisis period is not presented due to the lack of data for 2010 for most LAC countries which would be necessary to calculate the survival of new exporters into the year following 2009. 24 Brazil, New Zealand, and Spain are not used in this section given that we do not have access to the raw exporter-level customs data for those countries necessary to compute the measures used here. Egypt is also not used as a comparator given that its exporter-level customs data is provided with a 4-digit HS (instead of a 6-digit HS) level of disaggregation.

21
presented in Figure 8(a) and (b). In all scenarios, energy related CO2 emission in Romania would be lower in years between 2015 and 2050 from the level in 2005. If energy efficiency measures are not implemented, total energy related GHG emissions in 2030 and 2050 under the baseline scenario would be, respectively, 9% and 2% lower from 2005 level. It is very important to note that historically energy related CO2 emissions in Romania are dropping from the level of 2005. For example, according to emissions data published by IEA (IEA, 2014b), Romania’s 2012 emissions are 16% lower compared to 2005 level. If efficiency measures in the demand side are implemented, the baseline emissions in 2030 and 2050 would be 16% below from the 2005 level in both years. Under the green scenario, total energy activity related CO2 emissions would be 26% below the 2005 level in both years. Under the stringent super green scenario total national CO2 emissions from energy activities in 2030 and 2050 would be respectively, 47% and 43% below the 2005 level.

The power sector will experience a large reduction of CO2 in all scenarios. Since it is an ETS sector and mandated to reduce emission even in the baseline, its emissions in the baseline scenario 20
cases where power purchases are not cost-reflective requires far more data collection and detailed examination of the data collected, and is beyond the scope of this study.

- If the seller is a state-owned generation or transmission utility, the seller’s financial statements are examined to identify expenditures on loan repayments to isolate capex. The distribution company’s opex is adjusted accordingly to avoid double counting capex costs, assuming that capex of the upstream company is built into the purchase price. In cases where the financial statements were not available for upstream state-owned sellers (Republic of Congo, Ghana, and Mozambique), power purchase costs were assumed to be fully cost-reflective including capex costs, and to avoid double counting calculated capex estimates exclude the assets of the companies with missing information.

- Where the main utility is selling to other utilities performing distribution activities to end-users including exports, such as those in group 4 in figure 1, the sales to these other utilities are treated as end-user sales for the main utility.

- Where there is horizontal unbundling in distribution as well as vertical unbundling, as in Ghana and Uganda, the calculated capex is scaled to the share of the total electricity generated or transmitted that is purchased by the distribution company in question. For Nigeria, this study did not follow this procedure and instead used information from the Multi-Year Tariff Order (see below for more detail).

Treating all payments to IPPs or SPVs for power purchase as opex means that opex and capex are not comparable across countries. As an (extreme) illustrative example, consider two utilities, both of which rely 100 percent on hydropower generation. The first utility is vertically integrated, so that capex is high and opex is low. The second utility is a distribution company which buys all of its electricity from SPVs running hydropower plants assumed to be operating on a commercial basis. The second utility would then have much higher opex and much smaller capex than the first.

Lastly, one of the questions this study asks is whether the power system in a given country
**Roadmap** The paper is organized as follows. Section 2 describes the data, before Section 3 presents the method for identifying the unexpected component of demand shocks at the firmlevel. Section 4 describes the econometric model, while 5 reports the corresponding empirical results. Section 6 concludes the paper.

# **2 Data**

The empirical analysis in this paper combines and examines several sources of panel data from Portugal spanning the period 2006-2018. We provide a brief description of each data source in this section and give further details in Appendix A.3.

**Employer-employee data** : _Quadros de Pessoal (QP)_ [Personnel Records] is a high-quality compulsory census run by the Ministry of Employment covering the population of firms with wage earners in manufacturing and services. Each firm is required by law to provide information on an annual basis about its characteristics and those of each individual that comprises its workforce. Firm-level information includes annual sales, number of employees, industry code, geographical location, date of constitution and share of capital that is foreign-owned. The set of worker characteristics includes wages (monthly base wage, overtime pay, and other components of pay), gender, age, schooling, date of starting, detailed occupation and hours worked. A worker may also be matched to the firm in which he is employed. Extensive checks have been performed to guarantee the accuracy of worker and firm data. After these checks, we kept for analysis full-time wage earners working at least 100 hours a month, and aged between 20 and 60 years old.

**Firm census** : Using common unique firm identifiers, we supplement the firm-year data from QP with information from _Sistema de Contas Integradas das Empresas (SCIE)_ [Enterprise Integrated Accounts System], a yearly census of firms run by National Statistics Institute (INE). The main objective of SCIE is to characterize the economic and financial behaviour of firms. This data set includes information on total sales, investment, employment, value added,
differences between the two groups. Propensity scores are often estimated to address this issue of noncomparability. A propensity score is the probability of selecting a sample household or individual into the treatment group from the population, conditional on covariates. If a set of assumptions is satisfied (see Rosenbaum and Rubin 1983 and 1984 for details), a comparison of the weighted average of the outcome indicators between the control and treatment groups provides an unbiased estimate for the treatment effect.

Taylor (2000) and Lee (2006) adopted the propensity score matching technique to adjust sampling weights and correct for sampling bias in a web survey. First, they chose a reference survey representative of the population of interest (e.g., the entire population of a country, the urban population, or all refugees in a country). They combined the reference and web surveys and estimated propensity scores using this combined sample. They then divided the combined sample into quintiles based on the propensity scores and adjusted the sampling weights of the web survey to equate the sum of the weights of the web survey with those of the reference survey in each quintile.

Another type of propensity score weighting method is “inverse probability approach,” which uses propensity scores to construct the odds of a sample being selected for a reference survey, and the odds are further used as a sampling weight of a web or phone survey (Morgan and Todd 2008, Schafer and Kang 2008, and Austin 2011). This paper classifies these types of reweighting techniques based on propensity scores as Propensity Score Weighting (PSW) methods. If the assumptions in Rubin and Rosembaum (1983 and 1984) are satisfied, then summary statistics applying PSW-based weights in a web or phone survey become representative of the population of interest.

A second category of reweighting techniques, which we refer to as non-PSW approaches, matches indicators between a phone or web survey and a reference survey at a more aggregate level than at the household/individual level. This group of approaches includes raking, post-stratification, and maxentropy
**Table 8: Identifying the $1.90 poor based on covariates of poverty**

_<u>(Pooled data across countries with equal weights for each country)</u>_

|**Category**<br>**(1)**|**Per capita**<br>**poor only**<br>**(2)**|**Root N**<br>**poor only**<br>**(3)**|**Diff.**<br>**p-value**<br>**(4)**|**Per capita**<br>**poor**<br>**(5)**|**Root N**<br>**poor**<br>**(6)**|**Diff.**<br>**p-value**<br>**(7)**|**Obs**<br>**(8)**|
|---|---|---|---|---|---|---|---|
|Years of schooling|-0.007***|-0.133***|0|-0.158***|-0.284***|0|683,533|
|Asset index|-0.041***|-0.139***|0|-0.380***|-0.478***|0|431,436|
|Asset ownership|-0.007***|-0.033***|0|-0.095***|-0.121***
1975<sup>7</sup> (secondary schools were closed under the Khmer Rouge), their schooling levels can be considered as a good indicator of the social and educational status of their family (only a minority of Cambodian girls went to secondary schools in the 1960s and early 1970s).

Table 2 implements this strategy in order to look at the mortality differentials between 1975 and 1979 by socio-economic status. The comparison between the two first columns indicates that members of a family that lived in an urban area before 1975 were more likely to die, and that this difference exists for both genders. The two last columns establish that individuals who had a sister who attended secondary school prior to 1975 were more likely to experience excess mortality. This difference is however much larger, and only statistically significant, for males.<sup>8</sup> In the lower right corner, the table establishes that the difference by educational status of the sister is much larger and only significant among families of urban origin. Figures 4 and 5 further examine these differentials by birth cohort. Figure 4 establishes that, although the differential is higher for adults, the excess mortality among families of urban origin is present for all birth cohorts. This might be due to the fact that, as part of the Khmer Rouge “Year Zero” experiment, all major cities were evacuated by force and the urban population was relocated in the countryside. On the other hand, figure 5 shows that the differential by educational achievement of the sister prior to 1975 is concentrated among adults<sup>9</sup> , suggesting that educated adults have been especially targeted. Generally, in most developing countries, in the absence of violence targeted at them, it is expected that the urban and the educated experience a lower mortality.

# **_II.4 Infant and under-five mortality_**

In Table 1, constructed from sibling’s mortality data, the last row of each column shows the probability to die within the next five year