rafmacalaba/data-use-mentions-extended huggingface dataset:
From the EIT center, we also obtained school averages disaggregated by gender. Note that, based on descriptive statistics, girls do better than boys, on average, on all subjects, except for Geography, with the gender gap to be most substantial on the Ukrainian exam.

When running regressions by gender, we use the average score of the female students, respectively male students, of a school and regress it on the same set of explanatory variables, with the exception that our language variable is now the percentage of exams (other than the Ukrainian exam) taken in Ukrainian by the female students of a school rather than by all students.

We find that for the Ukrainian exam (table 9a and 9b)

- Like for the total sample, school size has a positive effect on the school‟s mean Ukrainian exam score for both males and female students. Again, the effect is clearly stronger for urban schools.

- Class size has a negative effect for male and female students and this effect is statistically significant for male students and almost significant for female students. The size of these effects is bigger for males than for females but in both cases moving from the 25<sup>th</sup> to 75<sup>th</sup> percentile does not change the mean by more than 1 point.

We also find that

- Student-teacher ratio has a consistently negative effect but like for the whole sample, for females when dis-aggregating the sample into urban and rural schools, this effect is not significant. For boys there is a significant negative non-linear effect, but the size of this effect is very small.

- Evening schools perform substantially less well (more so for girls than for boys), urban schools do better than rural schools and there are substantial oblast specific effects.

- Gymnasia and Lycea score 5 to 10 points higher than other school types.

- The gender composition matters little for girls but is significant for boys: more boys among the test takers reduces the average school scores of boys
districts to work.<sup>16</sup>

# **5 Results**

## **5.1 Overall Enrollment**

In order to evaluate the effect of MGNREGA on enrollment, we estimate equation 1 and present the results in Table 2. Column (i) presents the basic difference-in-difference specification with school and year fixed effects. This result is robust to controlling for state specific time trends as reported in Column (ii). Both specifications control for district level controls that influenced the roll-out timing. The study controls for the Scheduled Castes and Scheduled Tribes population from the Census of India 1991, agricultural wage in 199697, and output per agricultural worker in 199093, interacted with time indicators. This accounts for differential trends in districts with the backward district status that influenced selection into the program. In addition, we control for trends in districts level total population, percentage of urban population, population growth rate, overall literacy rate, and women’s literacy rate in a similar manner.<sup>17</sup> The school-level controls include any attendance scholarships being offered at the time, uniform, books, stationery and other such subsidies offered to girls, the number of classrooms, the number of classrooms in good condition, availability of common toilets, girls toilets, drinking water facilities, electrification status, number of male teachers, and number of female teachers.

The coefficient in Columns (i) and (ii) is -2.23 and is statistically significant at the 5 percent level. Overall, enrollment in this period is increasing and thus this coefficient indicates that introduction of MGNREGA results in a smaller annual increase in school

> 16Also, anecdotal evidence suggests that beneficiaries use the employment guarantee in summer months. One concern might be that schools are already closed for vacation. However, schools in India generally close for only around 40 days and the timing varies spatially ranging from mid-May to end of June in North to mid-June to end-July in the South. Also, Imbert and Papp
estimate the indirect impact. Resulting poverty is expected to increase overall by 2.9 percentage points – 1.9 percentage points higher than the direct impact alone. That is, the ripple effect of the higher prices on other goods and services due to higher gasoline prices will be large. Inequality, measured by the Gini, is expected to increase by 0.5 point (compared to just 0.1 point through the direct impact only).

Table 7. The total (direct and indirect) impact on poverty and inequality after increase in gasoline prices

|Pre-Ref|Post-Ref|Change|
|---|---|---|
|Welfare (per capita), (IRR)<br>310,157|290,836|-19,321|
|Poverty (%)<br>11.2|14.17|2.9|
|Inequality (%)<br>39.7|40.3|0.5|
|Source: Authors’ calculation using forecasted HEIS 2018/2019.|||
|Note: Inequalityis based on welfare aggregate,not spatiallyadjusted.|||

## 4.3 Impact on government spending without mitigation efforts

The increase in gasoline prices would generate substantial savings for the government but would not be enough to eliminate subsidies fully as shown in Table 8. In particular, the savings will be around IRR 439 trillion. This is about 38 percent of current government spending on subsidies. However, part of these savings could be spent on mitigating the impact on poverty, which is examined in the next section.

Table 8. The impact on Government annual spending and revenues after increase in gasoline prices, trillion IRR

|Pre-Ref|Post-Ref|Change|
|---|---|---|
|Subsidies<br>1,170|731|-439|
|Transfers, universal<br>0|98|98|
|Net total budget<br>1
of the two over longer periods, often with a focus on the direction of causality. Chen, Kuo, and Chen (2007), for example, study 10 newly industrializing and developing Asian countries and find a bi-directional long-run causality between real GDP and electricity consumption and a uni-directional short-run causality running from economic growth to electricity consumption. Ferguson, Wilkinson, and Hill (2000) find that correlations between electricity consumption and GDP are close to one. In Appendix A, we use a sample of 123 countries to update these correlations between economic growth

> 11The weighting is done at the regional level (according to the POSOCO classification) where Chennai represents the South, Delhi the North, Kolkata the East and North-East, and Mumbai the West.

8
.1|2.5|3.5|3.5|
|UNIQA|FIN||5.9|5.4|4.8|3.8|3.3|2.9|
|Aryankor|INDEP|11.0|5.8|3.8|3.6|3.4|2.9|2.7|
|Életút|INDEP|9.0|5.7|3.7|2.4|1.8|1.9|1.8|
|Évgyűrűk|INDEP|11.3|8.7|5.1|4.3|3.3|3.1|2.6|
|Weighted Average|MKT|12.7|7.0|4.5|3.8|3.2|3.1|2.9|
|Weighted Average|EMP|12.5|6.1|3.8|3.0|2.4|2.3|2.1|
|Weighted Average|FIN|12.9|7.0|4.6|3.9|3.2|3.2|2.9|
|Weighted Average|INDEP|11.1|7.6|4.6|3.9|3.3|3.0|2.6|

Source: HFSA raw data 44. Total fees in the Hungarian second pillar still compare favorably with most other systems with the same period of implementation, especially when expressed as a share of contributions. As shown in Figure 5, the ratio of total fees to contributions has increased but is still one of the lowest, with the exception of Bolivia.<sup>14</sup> The ratio of total fees to assets is roughly equal to the average of other reforming countries, as shown in Figure 6. The difference between the two results
equation for potential GDP to WW<sup>�</sup> = (1 −h)WW ∗ ∗ K �(1 −h � , ∗ YY αα ββ γγ where is trend TFP, is structural employment and YYtt = (1 + DD∗ωωVVGGDD)AAtt tt)CC is the share of agriculture in GDP. The first tt KKtt−1YYEE,tt part, , was discussed above in the agricultural damages section. YY tt tt VVGGDD AA CC ωω YY VVGGDD 1 + DD∗ωω

# 3.8.6 Damages to physical capital from flooding

Pakistan is already affected strongly by flooding. Serious floods caused extensive damage in almost every year between 1985 and 2018 (Figure 11), with between one thousand and 20 million people being affected annually. Further data on the World Bank’s Climate Change Knowledge Platform shows that between 1900 and 2018, a total of 93 flood events were recorded in Pakistan, which is equivalent to 45% of all natural-hazard events in Pakistan in that time interval.

> 22 Full description: “Potential annual daylight work hours lost for work at 300W, %; based on a business as usual scenario (RCP 8.5, average of HADGEM2 and GFDL models) current (1995) and up to 2085”, see UNDP (2016).

50
We use ICRG’s Quality of government as a governance indicator, with higher values of the variable indicating better governance. The interaction term between the number of police and the quality of government is positive and significant at 10% as shown in column 2 in table 2. A one percent increase in the police force is associated with a 0.65 percent reduction in crime losses at the mean level of the Quality of Governance index, significant at 1%. This result is magnified at the minimum level of governance whereby the elasticity doubles to 1.23 %. These results seem to indicate that police expansion is more strongly associated with crime reduction in countries with a low level of governance. It is also important to note that the inclusion of governance indicators or institutional or political controls in the estimations, as indicated in the robustness section, do not affect the significance of the police size - crime losses relationship. This finding is consistent that expanding the police force may be effective in reducing crime under more crime-enabling environments.

# _3.6 Voter Turnout and Fractionalization_

Social disorganization theories indicate that factors that diminish the effectiveness of informal social controls increase criminal activity (Kelly, 2000). Here we consider two indicators of social disorganization – voter turnout in elections, and religious fractionalization. A higher voter turnout may indicate lower social disorganization. Similarly, larger religious fractionalization would make social organization more difficult due the difficulty of interactions and building consensus among diverse groups. Thus for economies with higher levels of voter turnout, implying lower social disorganization, an expansion of the police force may be more effective in lowering crime. Similarly, under low levels of religious fractionalization, which may imply lower social disorganization, police expansion may be more effective in reducing crime.

17
# **9. References**

Andrade, E. B., & Ariely, D. (2009). The enduring impact of transient emotions on decision making. _Organizational Behavior and Human Decision Processes_ , _109_ (1), 1‐8.

Ariely, D., Loewenstein, G., & Prelec, D. (2003). “Coherent arbitrariness”: Stable demand curves without stable preferences. _The Quarterly Journal of Economics_ , _118_ (1), 73‐106.

Arkes, H. R., & Blumer, C. (1985). The psychology of sunk cost. _Organizational behavior and human decision processes_ , _35_ (1), 124‐140.

Ashraf, N., Fink, G., & Weil, D. N. (2010). _Evaluating the effects of large scale health interventions in developing countries: The zambian malaria initiative_ (No. w16069). National Bureau of Economic Research.

Ashraf, N., Karlan, D., & Yin, W. (2006). Tying Odysseus to the mast: Evidence from a commitment savings product in the Philippines. _The Quarterly Journal of Economics_ , _121_ (2), 635‐672 .

Bernard, T., S. Dercon, K. Orkin, and A. S. Taffesse. 2014. “The Future in Mind: Aspirations and Forward‐ Looking Behaviour in Rural Ethiopia.” Paper presented at Centre for the Study of African Economies conference on economic development in Africa, Oxford, UK, March 25 Bernard, T., S. Dercon, K. Orkin, and A. S. Taffesse. 2015. “Will Video Kill the Radio Star? Assessing the Potential of Targeted Exposure to Role Models through Video.” _The World Bank Economic Review_ , Volume 29, Issue suppl_1, 1 January 2015, Pages S226–S237 Berg, G and Zia, B (2017), “Harnessing Emotional Connections to Improve Financial Decisions: Evaluating the Impact of Financial Education
and Empirical Evidence.” _Journal of Economic Integration_ 16 (1): 106–27.

- ———. 2004. “Public Expenditure, International Specialisation and Agglomeration.” _European Economic Review_ 48 (4): 851–81.

- Cernat, Lucian, and Zornitsa Kutlina-Dimitrova. 2016. “TTIP and Public Procurement: Going beyond the Tip of the Iceberg.” _CEPS_ (blog). March 9, 2016.

- ———. 2020. “Public Procurement - How Open Is the European Union to US Firms and Beyond?” _CEPS Papers_ , CEPS Papers, , March.

- Chen, Hejing, and John Whalley. 2016. “The WTO Government Procurement Agreement and Its Impacts on Trade.” In _The Economies of China and India_ , 93–117. WORLD SCIENTIFIC.

- Cole, Matthew T., Ronald B. Davies, and Todd Kaplan. 2017. “Protection in Government Procurement Auctions.” _Journal of International Economics_ 106 (May):134–42.

- Conte, Maddalena, Pierre Cotterlaz, and Thierry Mayer. 2022. “The CEPII Gravity Database.” Egger, Peter H., and Filip Tarlea. 2015. “Multi-Way Clustering Estimation of Standard Errors in Gravity Models.” _Economics Letters_ 134 (September):144–47.

- European Commission. 2017. _Measurement of Impact of Cross-Border Penetration in Public Procurement: Final Report_ . LU: Publications Office of the European Union.

- Ferraz, Claudio, Frederico Finan, and Dimitri Szerman. 2015. “Procuring Firm Growth: The Effects of Government Purchases on Firm Dynamics.” _NBER Working Papers_ , NBER Working Papers, , May.

- Fronk, Jared. 2014. “Do Procurement Agreements Work?” Working Paper, mimeo.

- Giovanni, Julian di, Manuel García-Santana, Priit Jeenas, Enrique Moral-Benito, and Josep Pijoan-Mas. 2023. “Buy Big or Buy Small? Procurement Policies, Firms’ Financing, and the Macroeconomy.” _FRB of New York Staff Report_ ,
.................................................................67_|
|_Implementation—Institutional Gaps .......................................................................................68_|

# **Annexes**

|Annex 1. Knowledge Base on Housing Tenure Choice ............................................................................ 69|
|---|
|Annex 2. Design Outline for Rental Housing Survey ............................................................................... 85|
|**Figures**|
|Figure 1. Building Structure and Rental Tenure in Some Transition Economies.............
<!-- Start of picture text --> Qatar: Real Credit<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term cycle Real credit growth (y/y)<br>60<br>40<br>20<br>0<br>-20<br><!-- End of picture text -->

# **B- Oil importers**

<!-- Start of picture text --> Jordon: Real Credit Morocco: Real Credit<br>(in percent) (in percent)<br>1967 1977 1987 1997 2007 2017<br>year 1967 1977 1987 1997 2007 2017<br> Medium-term cycle Real credit growth (y/y) year<br> Medium-term cycle Real credit growth (y/y)<br>Lebanon: Real Credit Tunisia: Real Credit<br>(in percent) (in percent)<br>1967 1977 1987 1997 2007 2017 1967 1977 1987 1997 2007 2017<br>year year<br> Medium-term cycle Real credit growth (y/y) Medium-term cycle Real credit growth (y/y)<br>West Bank and Gaza: Real Credit Yemen: Real Credit<br>(in percent) (in percent)<br>1967 1977 1987 1997 2007 2017<br>year 1967 1977 1987 1997 2007 2017<br>year<br> Medium-term cycle Real credit growth (y/y)<br> Medium-term cycle Real credit growth (y/y)<br>60<br>60<br>40 40<br>20 20<br>0<br>0<br>-20<br>-20<br>60<br>30<br>40 20<br>20 10<br>0 0<br>-20 -10<br>-40 -20<br>30
Chart 13 **Argentina: Total Debt** Ratio to GDP <!-- Start of picture text --> 0.34<br>0.32<br>0.30<br>0.28<br>0.26<br>0 .2 4 . _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _<br>1990 1991 1992 1993 1994 1995<br>- ARTOTDP<br><!-- End of picture text --> Source: World Bank debt tables and Central Bank While Argentina has had a strict, fixed exchange rate system, Brazil, in contrast, has had what has been referred to as a dirty peg, or sliding band. And although Argentina has not mimicked the "sterilization" policy of Brazil, both have experienced a remarkably similar pattern in the rate of change in their foreign and total indebtedness, as the following charts will exhibit.

31
defining catastrophic as out-of-pocket expenditures<br>exceeding 40% of consumptionless a deduction for food costs. They<br>reported mean and median rates of catastrophic spending so defined<br>of 2.3% and 1.5% respectively.<br>Wagstaff et al. (2018b) report results for 133 countries across the<br>world, many for multiple years. They estimate the mean and median<br>catastrophic out-of-pocket payment rates at the 10% threshold to be<br>9.2% and 7.1%. They report a positive population-weighted median<br>annual rate of change of catastrophic payment incidence whatever<br>catastrophic paymentincidencemeasureis used.|
|6<br>Inequality in incidence<br>of catastrophic<br>expenditures|Van Doorslaer et al. (2007) look at the distribution by gross<br>consumption of those experiencing catastrophic payments in 10<br>Asian territories. For the most part, they find that catastrophic<br>spending is higher among the better off. Taiwan is the exception:<br>catastrophic spending is concentrated among the poor whatever the<br>threshold.|None|
|7<br>Impoverishment|Van Doorslaer et al. (2006) use data from 11 Asian economies to<br>‘gross’ and ‘net’ poverty headcounts and poverty gaps using the<br>World Bank’s dollar-a-day poverty line (as well as its $2-a-day<br>poverty line). They find that the dollar-a-day poverty headcount is,<br>on average, almost three percentage points higher after deducting<br>out-of-pocket spending from household consumption. In Bangladesh<br>and India, the difference is almost four percentage points. In<br>Malaysia and Sri Lanka, by contrast, the difference is just 0.1 and<br>0.3 percentage points respectively.<br>Alam et al. (2005) compare ‘gross’ and ‘
similarly, both younger and older adults decrease their participation in wage employment while increasing participation in self-employment. Likewise, both types of adults have similar responses in their intensive margins - lower weekly labor supply (Table 7).

Although outmigration of some family members has similar eect on the intensive margins for wage-work and own work, there is a signicantly dierent eect on the participation rate by household head status. The left-behind household heads are 41.4% less likely to participate in paid work compared to 23.1% of the other household members. Concurrently, outmigration of family members does not aect the household head's decision to participate in self-employment activities but signicantly increases other members' involvement, 29.8% (Table 8). This corresponds well with the intuition that the household heads are likely to have higher bargaining power within the household and are likely to receive remittances sent by the migrants, which in turn, may further increase their bargaining power.

# 6 Discussion and Conclusions

In this paper I use a unique source of nationally representative data during the period that Nepal experienced a boom in outmigration. The data set contains detailed information on time allocation of every individual in the household, which allows me to extend the analysis further and answer the question that previous studies on the topic could not answer in Nepal. Using the NLSS III data set, this paper explores the impact of migration on labor supply of the left-behind household members both on extensive and intensive margins for wage-employment, self-employment, and household activities. The paper also provides an answer to the question, what are the remaining members in the households engaged in instead of work employment?

I nd that having international migrants in the family discourage members staying behind from participation in wage-employment. This is true for both male and female members. However, female members increase participation in self-employment, almost entirely through subsistence farming. The paper also nds that both self-employed and wage-employed adults decrease weekly hours of labor supply and only
2.169**|
|Household Type (Kutcha)-SECC 2013|0.567|0.549|1.073|
|Own Telephone Mobile Phone-SECC 2013|0.402|0.404|0.160|
|Own AnyLand-SECC2013|0.459|0.467|0.487|
|Own Irrigation Equipment-SECC 2013|0.062|0.062|0.072|
|Any Member has a Salaried Job-SECC 2013|0.053|0.061|0.614|
|Mainsource of income of Household-cultivation -SECC2013|0.398|0.399|0.075|
|Main source of income of Household-enterprises-SECC 2013|0.006|0.007|0.363|
|<br>Total Households-Census2011|192.322|250.679|4.977***|
|Total Population of Village-Census 2011|947.727|1,181.799|3.955***|
|Percentage of Female Population-Census 2011|49.811|49.581|3.265***|
|Worker force participation rate of all persons in the Village -<br>Census 2011|0.522|0.523|0.551|
|Worker force participation rate of females in the Village - Census<br>2011|0.489|0.494|0.937|
|Worker force participation rate of females in the Village - Census<br>2011|0.489|0.494|0.937|
|Total Numberof Micro-EnterprisesinVillage2012|14.043|19.894|1.487|
|Access to Pucca Road-Census 2011|0.788|0.796|0.638|
|Status of Agriculture Marketing Society-Census 2011|0.428|0.428|0.015|
|Percentage of IrrigatedLandfrom Total Land-SECC2013|5.894|6.502|1.166|
|Percentage of Households having Source of Light:
, Khaksari, & Tehranian, 2010), stateowned commercial banks (Panizza, 2021), natural monopolies or regulated oligopolies (Vining & Boardman, 1992), and network sectors (Bogart & Chaudhary, 2015). Fewer studies provide a cross-country and cross-sector analysis, especially in competitive sectors, which are particularly relevant for private-sector development. Some studies on competitive sectors include Funkhouser & MacAvoy (1979), Boardman & Vining (1989), and Picot & Kaulmann (1989). Some recent studies with firm-level data across countries include Dewenter & Malatesta (2001), Kabaciński, Kubiak, & Szarzec (2020), Phi, et al., (2019), IMF (2019) and Le (2021).

## **3. DATA SOURCES**

The data employed for the analysis corresponds to a combination of two firm-level datasets: (i) the World Bank Businesses of the State (BOS) database (Dall'Olio, et al., 2022), and (ii) the Bureau van Dijk (BvD) ORBIS financial module.

# 3.1 World Bank Businesses of the State (BOS) database

First, we use the World Bank Global Businesses of the State (BOS) database that provides firmlevel information on companies in which the state holds 10 percent or more direct and indirect participation. Although there is no academic consensus on the definition of state-owned enterprises (SOEs), they are often associated with directly and fully or majority-owned firms by the state.<sup>4</sup> In this paper, we cover a much broader set of entities to capture other channels of state ownership including minority and indirect ownership following the WB BOS definition (Dall'Olio, et al.,

> 4 The SOE definition can vary across countries depending on the different legal forms (e.g., corporatized vs. non-corporatized, level of participation (%)), and local definition of control.

5