rafmacalaba/data-use-mentions-extended huggingface dataset:
our estimation results are similar whether zero-valued links are considered (PPML) or not (OLS), if not larger in the latter specification. For example, the OLS regressions show that South-to-South portfolio investment grew on average at 8.8 p.p. per year faster than North-to-North investment, whereas the PPML regressions estimate an implied growth differential of 5.5 p.p. For FDI, this growth differential is estimated at 7.3 p.p. in the OLS regressions and 4.4 p.p. in the PPML regressions. Similar patterns emerge for the South relative to the North as source and destination of international investment. In section 4, we analyze in depth the dynamics of international investment along the extensive margin.

As discussed in section 2, when merging UNCTAD and CDIS data, there was a large increase in the number of reported counterparties in 2009, especially for South countries (appendix, section A.3). Hence, the regression estimates in table 3 might overestimate FDI growth over our sample period because of improvements in coverage. Table 4 reports a robustness test eliminating the possible effect of the change in coverage in 2009. In particular, we add to all the specifications a dummy that equals 1 from 2009 onward interacted with region-to-region dummies, thereby assigning all the growth in FDI positions from 2008 to 2009 to the change in coverage. These new estimations still show that South-to-South investment grew faster than North-to-North investment and the South expanded faster than the North as source and destination of international investment.

# **4. Evolution of International Financial Connectedness**

# **4.1. Bilateral Links and the Extensive Margin**

In this section, we examine the growth of the South through the pattern of links between countries. We analyze the share of active links in reported links, within and between the North and South.<sup>16</sup> We

> 16 In network analysis, this simple measure of connectedness is known as density. Active links are
<!-- Start of picture text --> Average Nightlight Data (2021)<br>4.0<br>43,5<br>43.0<br>42.5<br>42.0<br>415<br>en<br>23 24 25 26 27 28<br>SS10 20 » 40 — Ee50<br>Average Nightlight Data (2021)<br><!-- End of picture text -->
magnitudes in question are much smaller. Last, in the first survey in Bangladesh (1993-94) the presence of primary, and primary and secondary schools, have positive and significant effects on the enrollment of boys but an insignificant effect on girls. Again the magnitudes in question are relatively small and, perhaps more importantly, had been wiped out by the time of the second survey (1996-97).

VII) Conclusions This paper set out to document and analyze gender disparities in education. The results confirm prior studies that there are some countries where a female disadvantage in education outcomes is a major problem. This disadvantage appears to be less related to measures of a country's income level, income growth, or spending on primary education than to a fairly strict regional breakdown, although it is somewhat related to the level of income inequality within a country. The large female disadvantage exists in only few countries outside of the Western and Central Africa, North Africa, and South Asia regions. Moreover, the extent of the female disadvantage varies by the wealth of the household.

Even in countries with a relatively small gender gap there might be large inequalities. In many of the countries with a very small female disadvantage (or even with a small female advantage) the gaps between outcomes for the rich and the poor can be very large. Moreover, in some countries wealth and gender interact to create a very large female disadvantage among the poorest in society (for example in India). This study highlights the necessity to consider wealth and gender gaps simultaneously.

regression instead of the wealth groups as derived from the asset index (Annex Table lOa).

39
young children (born between 1970 and 1979) had a 10 percent probability not to survive until the end of 1979.

Figure 3 further emphasizes that point by plotting the death probability during the 1975-1979 period by birth cohort and gender. Mortality is much higher among males, but this is only true for adults. The fact that prime adult males are, by far, the most likely to die during the Khmer Rouge period indicates that violent deaths made a major contribution to excess mortality.<sup>6</sup> The result of this differential mortality by birth cohort and gender is that there are serious imbalances in the current population structure of Cambodia, including an excess number of females especially for certain age groups.

# **_II.3 Selective mortality: mortality by socio-economic status_**

It is generally believed that the Khmer Rouge targeted especially the educated and urban groups in the society, as they were seen as obstacles for the creation of a “new” society. However, this claim, based on numerous anecdotal evidence and survivors accounts has not yet been quantified very reliably. Only Sliwinski (1995) who uses, because of constraints at the time of his survey, a non representative sample of survivors (63 Cambodian households refugees in France, 589 households refugees in Thailand and 644 households in the Phnom Penh area) provides estimates of selective mortality: he indicates that more educated individuals were more likely to die between 1975 and 1979 and especially that some professions like army officers, policemen, managers and the clergy were at very high risk. He also provides estimates of mortality rates by province. Such estimates, however, based on samples of convenience, are not as reliable as estimates from a nationally representative sample.

In the siblings’ data collected in the nationally representative Demographic and Health Survey, there is no direct information on the socio-economic status of the siblings. However, by using information about the respondents, it is possible to have an idea of the socio-economic status of the family. One variable indicates
54

# (a) Trade-reduction index

<!-- Start of picture text --> 50<br>40<br>30<br>20<br>High-income countries<br>10 Europe’s transition econs.<br>Developing countries<br>0<br>1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 2005-10<br>percent<br><!-- End of picture text -->

# (b) Welfare-reduction index

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>High-income countries<br>20 Europe’s transition econs.<br>Developing countries<br>0<br>1960-64 1965-69 1970-74 1975-79 1980-84 1985-89 1990-94 1995-99 2000-04 2005-10<br>percent<br><!-- End of picture text --> Figure 4. TRIs and WRIs among high-income, transition, and developing countries for tradable farm products, 1960–2010 (%).

Source: Lloyd, Croser, and Anderson 2010, based on time-series estimates in Anderson and Croser 2009, and updated using Anderson and Nelgen 2012b.
28

**Table S. 5 – continued from previous page**

||1983|1988|1994|2000|2005|2010|2012|
|---|---|---|---|---|---|---|---|
|dumfrewood|0.07|0.06|0.06|0.06|0.05|0.07|0.07|
|dumanimals|0.06|0.05|0.05|0.04|0.04|0.04|0.03|
|dumcollectfood|0.04|0.03|0.03|0.02|0.02|0.03|0.03|
|dumhuskpaddy|0.04|0.04|0.02|0.03|0.02|0.02|0.01|
|dumgrindgrain|0.06|0.04|0.03|0.03|0.02|0.02|0.01|
|dumdung|0.07|0.07|0.08|0.07|0.06|0.06|0.06|
|dumcwater|0.11|0.10|0.10|0.08|0.07|0.06|0.05|
|dumkitchen|0.02|0.02|0.03|0.02|0.02|0.04|0.04|

_Source_ : Authors’ calculations using National Sample Survey Schedule 10 for rounds 38, 43, 50, 55, 61, 66 and 68. _Note_ : Reported population means use sampling weights.
# 5 Numerical Calibration

To calibrate the model for Brazil, several data sources are used. The World Development Indicators (WDI) database of the World Bank, data from the 2009 National Household Sample Survey (PNAD), produced by IBGE, and data available in published papers, as needed.

On the household side, the annual discount rate is set at 003, as in Glomm and Rioja (2004). This implies that the discount factor is equal to 097 on a yearly basis. Interpreting a period as 25 years in this framework yields an intergenerational discount rate of 097<sup>25</sup> = 0467.

To calibrate women’s bargaining power, as defined in equation (38), requires setting six parameters: , κ ̄ , <sup></sup> , 3, 4, and , and knowing the equilibrium value <sup></sup> . As discussed below, coefficients 3 and 4 are set equal to 07 and 01, respectively, and <sup></sup> = 0185. The parameter  is set to a “neutral” value of  = 1 and  is set at 05, to capture the fact that endogenous “macro” factors play only a limited role in determining women’s bargaining power. It is also assumed that there is some initial bias in mothers’ rearing time allocation toward boys and therefore set <sup></sup> = 06. The initial bargaining power of women κ is calibrated as in Agénor (2012a), by using the relative literacy rate of adult females (ages 15 and above), divided by the sum of literacy rates of adult males and females–which can be viewed as a measure of the relative human capital of women. As noted earlier, this
with falling unemployment).

As a robustness check, in Table A3 we compare ESS-based proportions of employees working without contracts and proportions of all informally employed persons in total employment for 2004-2006 with similar indicators calculated from the Fourth European Working Conditions Survey<sup>15</sup> conducted in 2005. Cyprus, Greece and Ireland, with very high rates, occupy the top three positions in informality ranking by each of the two criteria in both surveys. Top ten countries by the total prevalence of informal employment are also the same for both surveys; the list repeats the one given above for 2008-2009, excluding Israel (not represented in EWCS) and adding Bulgaria<sup>16</sup> . With regards to work without a contract, nine

> 13 Actual level of informality in the South might be even higher, as seasonal immigrant workers (e.g. fruitpickers) are mostly not covered by ESS surveys. This remark applies also to France, Germany, Ireland and UK.

> 14 Recall that our analysis is restricted to the form of employment relationship, while envelope wages (or _quasiformal employment_ , see Williams (2009)) are not considered; according to _Eurobarometer_ survey on undeclared work conducted in 2007,  Lithuania, Latvia, Hungary, and Estonia are among the countries with  relatively high prevalence of envelope wages, see Riedmann & Fischer (2008), Williams and Renooy (2008). 15 The difference between  the two surveys in handling the contract question is minor: EWCS provides answer options „A temporary employment agency contract‖,  „An apprenticeship or other training scheme‖ and „Other‖ (which we of course do not treat as informal); on average these account for 3.2% of all responses, although this proportion varies between 4% and 6% in six countries and between 6% and 8% in the Czech R. and Greece.

> 16 Romania is missing from the ESS results on 2004-2006 and hence is excluded from the EWCS top ten.

14
mechanization by raising labor wages and leading firms to invest in labor-saving mechanization.<sup>2</sup> However, exogenous innovation in production techniques also leads to labor-saving mechanization that raises labor productivity, with wage increases coming as a consequence of higher productivity (Solow, 1957).<sup>3</sup> I address the problem of simultaneity by measuring how air pollution changes after an exogenous shock to wages generated by India’s Mahatma Gandhi National Rural Employment Guarantee Act (MNREGA), the world’s largest anti-poverty program. After the implementation of MNREGA, low-skill wages increased by between 4% and 8% (Imbert and Papp, 2015) in rural areas of India. I show, using a difference-in-differences framework taking advantage of the staggered roll-out of MNREGA coupled with data on nearly 1 million fires, that agricultural fires increased by between 9% and 21% after implementation of MNREGA.

To explain the mechanism, I outline a simple model showing that rising incomes could lead firms to invest in labor-saving, but polluting, mechanization. My model suggests that farmers responded to MNREGA by mechanizing harvest. This is consistent with both Hornbeck and Naidu (2014) and Clemens et al. (2018), who show that farmers mechanize after shocks to the low-skill labor market in the United States. Mechanization leads to an increase in agricultural fires in India because it leaves between 80% and 120% more biomass on a field relative to manual harvesting (Yang et al., 2008; Jitendra et al., 2017). Biomass must be removed prior to planting and fires are the least expensive way to remove biomass (Ministry of Finance, 2018). I show that fires increase by 27% in districts with the highest probability of mechanization compared to no statistically or economically significant change in districts with low probability of mechanization.

To further isolate the mechanization channel, I test for evidence of two alternative mechanisms: (1) an increase in production that may have led to more fires and (2
entry. Reduction in the constraints on foreign direct investment will induce foreign entry that will typically lead to productivity gains because when more varieties of service providers are available, buyers can obtain varieties that more closely fit their demands and needs (the DixitStiglitz variety effect).

# **VII.  Data of the Model and Evidence for Key Elasticities**

# **Social Accounting Matrix**

The core of the model data is the Social Accounting Matrix (SAM) for 2001 developed by Thurlow and Wobst (2003). The SAM contains 43 sectors, most of which are agricultural and food-producing sectors and only a few are service sectors.

Two sectors in the SAM, “Transport and communication” and "Business and other service activities", account for most business services in Tanzania. Given our focus on services, these sectors are disaggregated into 10 sectors using unpublished national accounts data for the year 2006 from the National Bureau of Statistics of Tanzania.

Data for gross domestic product for the sectors “Transport”, “Communications”, “Financial intermediation”, and “Real estate and business services” is officially published (National Bureau of Statistics (2007)). Upon specific request, we received a breakdown of these data for the main types of services within each sector. Specifically, the data allows us to disaggregate “Transport and communication” into road transport, railway transport, water transport, air transport, postal services and telecommunication. "Business and other service activities" is disaggregated into insurance, banking, business services and other services.

Within each of the two aggregate sectors, the share of gross domestic product by disaggregate sector is used to decompose the corresponding aggregate sector. It is furthermore assumed that the input output structure for all the disaggregate sectors is identical to the input output structure of the corresponding aggregate sector. Along with the addition of the tourism sector, the resulting table has 52 sectors as shown in table 1.

The Thurlow and Wobst SAM allocate international travel expenditure (mainly international tourism) to the
Kerosene use and its shares of nonfood and total spending by electricity access status and by income group are summarized in Table 2. On average, households without electricity access, use about 3 liters of kerosene per month for lighting while those with grid-electricity access use 2 liters, and with SHS use only 1 liter. The higher kerosene use among grid-connected households compared with households with SHS is likely due to the need to use kerosene for lighting backup as a result of frequent outages of the grid-electricity.

The survey data also show that kerosene use accounts for a large proportion of household nonfood expenditure, in particular among poor households. The average kerosene expenditure accounts for about 9% of total non-food budget among the bottom two deciles, in comparison to 3% for the top two income groups. Clearly, the financial benefits from a reduction in kerosene spending due to SHS installation would be larger for poor households. These benefits are in addition to other benefits, including better quality lighting and reduced health risks (indoor pollution) associated with kerosene lamps.

# 5. **Carbon benefit, affordability and distributional impact of SHS programs**

# **5.1 Quantifying the carbon benefit of SHS**

The carbon benefits of SHS dissemination in rural areas come mainly from the fuel displacement, including kerosene, dry cell batteries, and diesel used for battery charging. In the analysis, we focus mainly on kerosene displacement due to data limitation on other fuel use. The current fuel use for lighting among households without access to grid-electricity is used as the energy baseline. This is consistent with the UNFCCC simplified procedures for small-scale clean development mechanism (CDM), which defines the energy baseline as the fuel consumption of the technology/device in use or would have been used in the absence of project activity.

# 5.1.1 Methodology

Quantifying the CO2 impact from SHS installation involves the estimation of fuel displacement as a result of SHS installation. Two alternative methods are used to estimate the effect of SHS installation on kerosene: the regression
and health, which will have economic impacts in their adulthood. Although gender gaps in school attendance have reduced over the years in Chad, girls’ school attendance remains lower than boys’ (Ngatia et al., 2021). There is an increased risk of girls dropping out of school during the pandemic and never returning, which would exacerbate this gap. Evidence from past pandemics such as the 2014 Ebola Crisis in Sierra Leone reveals long-lasting impacts on girls’ school enrollment, increase in child marriage when girls drop out of school due to school closures, and their adoption of risky behaviors, which leads to early childbearing (Bandiera et al. 2018). Child marriage also increases as households marry off their daughters for dowry during economic stress, as evidenced in Chad (Le Masson et al. 2019).

Chad has notable gender gaps in learning outcomes (Ngatia et al. 2021), and hence women in the workforce are less educated and less skilled. Results from HFPS reveal only 15 percent of households with a child in school before COVID-19 school closures were involved in learning activities at home after closure. With school closures and learning at home, the gender gap in learning outcomes could widen because young girls spend more time helping with increased household chores and gender gaps in access to information and technology required for home-based learning.

# 4. Conclusion

In this paper, we used CGE model-based simulations to assess the gender dimensions of the impact of COVID-19 on economic outcomes, i.e., labor force participation, employment, wages, and earnings. We also leveraged the COVID-19 high-frequency phone survey (HFPS) to assess the actual impact of COVID19 on female-headed households, which comprise 23 percent of Chad’s households. To sum up, the COVID-19 pandemic will have a disproportionately higher negative impact on women in urban areas. The CGE simulation results suggest that more women than men working in paid jobs might lose their jobs. The

> 14Gender-Based Violence (GBV), sometimes also referred to as Sexual and Gender-Based Violence (
br>(0%, 72%)|9%<br>(0%, 19%)|2,367<br>(1,060; 3,757)|2.9<br>(2.5, 3.4)|4.3<br>(4.1, 4.7)|4.5<br>(4.4, 4.7)|
|Europe, Asia & Pacific|5|Jun-21 to Dec-21|45%<br>(16%, 64%)|21%<br>(1%, 39%)|12,630<br>(3,948; 18,307)|3.0<br>(2.9, 3.1)|3.9<br>(3.3, 4.2)|4.1<br>(3.8, 4.4)|
|Latin America &|4|Mr-21 t Ar-22|41%|22%|6,997|3.0|4.3|4.6|
|Caribbean||a o p|(0%, 89%)|(0%, 59%)|(3,102; 10,244)|(3.0, 3.1)|(4.1, 4.6)|(4.5, 4.6)|

_Note_ . Experiment start date is the month of Facebook ad launch for the first and last intervention in the region. COVID-19 vaccination rate and GDP per capita show the mean for the region with the minimum and maximum in parenthesis.

16
18 across banks, indicating the existence of other important bank and country-level effects. For example, some individual banks have better strategies and lending technologies, and there are country-level effects and programs that are not easily measurable (e.g. interest subsidies, exemptions on reserve requirements). Even acknowledging these caveats, however, we believe that the results are insightful and help in initiating more research in this important area.

# **6.  Summary of Findings and Policy Implications**

This paper reports the results of a survey of SME lending with unusually high coverage of banks in the MENA region. The paper shows that the average SME loan portfolio in MENA is relatively small amounting to less than 8 percent of total loans. The survey also shows the wide differences in the scale of SME lending between GCC and non-GCC countries (much smaller in the former group), as well as the scope for further SME lending in both sets of countries, revealed by the large difference between the long-run targets and the actual lending levels.

State banks play an important role in SME lending in many MENA countries, to a large extent a compensatory role for the low private bank involvement in SME finance in these countries. Moreover, state banks seem to take more risk than private banks, as indicated by broader selection criteria, more exposure to term lending, and softer collateral requirements. However, they do not seem to have developed sufficient risk management capacity to manage these risks – a smaller share of state banks has introduced dedicated SME units, adopted internal scoring models, and conducted regular stress tests to monitor the risks related to SME lending.

Several MENA countries have introduced credit guarantee schemes, and there is some evidence that these schemes may have contributed to more SME lending. The countries with the largest shares of SME loans in the total loan portfolio are the ones with the largest schemes, and the statistical analysis of the dataset also suggests that credit guarantee schemes may have induced more SME lending, controlling for other factors. However, these
performances in terms of availability and Reliability index, household satisfaction index, and household cost of service index (Table 14). The Jalanidhi schemes perform significantly better than the other community managed schemes in the O&M index, institutional sustainability index and overall performance index. Percentile ranks of Jalanidhi schemes for the O&M index are around 20 percentile points higher than similar community managed schemes (Figure 2). Likewise, when the indexes were aggregated into an overall performance index, percentile ranks of Jalanidhi schemes are on average 19 percentile points higher than comparable community managed schemes. However, the institutional sustainability performance of Jalanidhi schemes is significantly stronger than the other community 20
**Figure 9: Average cost and out‐of‐pocket expenditure on in‐patient care after insurance reimbursement, relative annual income per capita, by urban‐rural typology, 2008**

<!-- Start of picture text --> 12000 70<br>60<br>10000<br>50<br>8000<br>40<br>6000<br>30<br>4000<br>20<br>2000<br>10<br>0 0<br>average total cost average OOP share<br>cost<br>% of annual income p.c.<br><!-- End of picture text --> Source: NHSS 2008 and 2003.

Note: The China MoH urban‐rural socio‐economic typology system is described in the 2006 joint government‐UN review 21