rafmacalaba/data-use-mentions-extended huggingface dataset:
the currency depreciated. In 2012, the government legally unbundled EVN to create a separate Electricity Power Trading Company that purchases power both from state owned generators and IPPs. The government’s vision for unbundling EVN was to trigger further private sector investment into the sector, including privatization of the state-owned generators. Under this market design private sector investment increased. By 2023, private players were estimated to own 42% of installed capacity (Thu and Ha 2023). EVN retains monopoly in the transmission segment but changes to the Electricity Law in 2022 now allow private sector investments in the grid. One private company in 2023 has taken advantage of this change in the law by building the transmission line from its solar plant to the grid (Thu and Ha 2023).

**_Prepare for a transition to a competitive wholesale market structure:_** A few countries created independent separate single buyers to gain experience in power trading before transitioning to a competitive wholesale market structure. For example, Oman Power and Water Procurement Company (OPWP) was an integrated power and water VIU that functioned as a single buyer purchasing power from IPPs starting in 1996. In 2005, the government unbundled the company creating a separate Oman Electricity Transmission Company as an independent system operator while OPWP became the market operator. OPWP envisaged moving to a more competitive wholesale market structure as the long-term PPAs with IPPs gradually expired. It began operational trials of a gross pool wholesale spot market in 2019. On January 2022, Oman formally transitioned to a gross pool market structure operated by OPWP. Currently, all generators that do not have existing PPA obligations are mandated to sell on the pool. The generators submit daily offers via a single unified portal. They can access market data such as schedules and prices and receive payment for electricity sold through the spot market. The market has created new revenue opportunities for generators especially for solar and wind projects (Al Arabiya English 2022).

27
participant choices, following the previous literature, we examine whether those who underestimated the level of support in the community were more greatly impacted by the interventions (than those who estimated the level of support correctly or over-estimated it). Figure 5 shows the extent of misperceptions in the three domains of the interventions. The red line shows the actual level of support in the community (as measured in our first survey or the World Values Survey for older women’s attitudes). All three figures show that there was substantial, and varying, misperceptions in all three domains.

Table 5 presents results where we interact treatment with the extent to which respondents underestimated or overestimated the extent of community support for the social norms relevant to their treatment arm. For respondents in treatment arm 1 we allow the treatment impact to differ with the extent of over or underestimation of support for working women. For respondents in treatment 2 we allow for participants’ under/overestimation of support for working women and for shared childcare to affect the treatment

> 30These heterogeneity tests were not pre-specified. The pre-analysis plan specified tests of heterogeneity of treatment effects in relation to gender (presented in the main results above); education; religiosity; female respondents’ and male respondents’ wives’ pre-intervention labour force participation; preintervention attitudes to working women and shared childcare; and reasons the woman in the couple is not working. No heterogeneity was found along these dimensions. Results available on request.

16
of continued learning during the pandemic, but not the heterogeneity within countries due to unequal engagement with learning during school closures for children from different types of households. These results essentially act as a benchmark scenario showing the impact of school closures on intergenerational mobility in education globally _if_ the learning losses were distributed uniformly within every country.

-

- (ii) <u>Scenario 2 (Distributionally sensitive simulations): simulations account for the heterogeneity in</u> learning losses not only across countries but also within countries, by adding the information from HFPS on learning engagement for every child (and their parental/household characteristics) to estimate a distribution of national learning losses (obtained in Scenario 1) across socioeconomic groups for each country. Limited by the availability of HFPS data, this simulation can be conducted for 30 countries in our HFPS sample.

-

- (iii) <u>Scenario 3 (Global distributionally sensitive simulations): these simulations extrapolate the</u> distributionally-sensitive losses from Scenario 2 to other countries that are not in our HFPS sample, to obtain global estimates of changes in educational mobility that account for differential learning experiences within countries.

In these simulations, the key difficulty with imputing losses in learning-adjusted years of schooling (LAYS) into the GDIM data on which inter-generational mobility estimates are based, is that the cardinal measures of years of schooling are not exactly the same. In the GDIM, we observe self-reported completed years of education for those born between 1980 and 1989. In contrast, in the World Bank/UNESCO data, COVID-related changes in the learning-adjusted expected years of schooling (LAYS) are based on the most recent School Census and relate to the expected (but yet unobserved) years of schooling for the current education cohort. Likewise, the stock of, and changes in, learning-adjusted years of schooling take account of both the quantity and the quality of schooling, combined into a single metric, whereas the GDIM makes no quality adjustments.

Expected years of schooling (EYS) is a cohort measure
# **3. DATA**

# **(a) The Rural Investment Climate Pilot Surveys**

The World Bank’s Rural Investment Climate Pilot Surveys conducted in Bangladesh, Ethiopia, Indonesia and Sri Lanka are matched household-enterprise-community surveys that collect information on both enterprise and non-enterprise owning households, the non-farm enterprises they operate, and the local investment climate in the communities in which they are located. For the purpose of these surveys, a rural nonfarm enterprise was defined as any income generating activity (trade, production, or service) not related to primary production of crops, livestock or fisheries undertaken either within the household or in any nonhousing units. Moreover, any value addition to primary production (i.e. processing) was considered to be a rural nonfarm activity.<sup>7</sup> The matched nature of the data is a key advantage. The surveys constitute an improvement over traditional household surveys by collecting very detailed information on enterprise characteristics, inputs and outputs, the local economic environment and various dimensions of performance. For example, by virtue of containing information on the capital stock and inputs they enable us to estimate production functions and assess how productively labor is employed in these off-farm firms. Conversely, they complement the existing investment climate surveys, which are typically urban-based, focused on relatively large manufacturing firms, and lack information on the household characteristics of firm managers. They also allow for an analysis of labor force participation patterns since they contain information on activities, age and education of all household members.

Since the surveys were very similar, they facilitate a cross-country comparison. However, data coverage, variable definitions, and sampling frames can vary from country to country. For example, labor input in Sri Lanka was measured in terms of number of workers only, whereas in Ethiopia it was measured in terms of days worked per employee, while in Indonesia and Bangladesh 8
quality‐wealth relationship, suggesting that even conditional on the specific facility chosen, wealthier women receive higher quality of care.

To understand within‐facility differences in the quality of care, we use detailed exit interview data to analyze the specific services provided and prices charged to patients from various socioeconomic backgrounds. As expected, we find that the overall consultation price is positively correlated with the number of services provided, but also, conditional on quality, with the patient’s socioeconomic status. The positive association between wealth and price could be interpreted as evidence of cross‐ subsidization or price discrimination for profit maximization. If facilities charge wealthier women higher prices to subsidize service provision to the poor, we would expect the difference in prices paid by women of different wealth levels to decrease in the ratio of wealthy‐to‐poor, because each wealthy woman would need to subsidize fewer poor women. But we observe the opposite: the price differences 3
## **data**

My measure of conflict intensity is based on data provided in the annual Human Rights Yearbooks published by the Informal Sector Service Center (INSEC), a Nepali non-governmental organization that monitors human rights issues in the country. INSEC reports the number of deaths due to Maoist conflict in each of the districts in the country. This dataset is identical to the one used by Do and Iyer (2010) in their study of the determinants of the conflict. I use conflict related deaths that occurred in the district between 1996 and 2001 as my measure of conflict intensity for year 2001. For year 2008, I use conflict related deaths that occurred in the district between 2001 and 2006. I set conflict intensity to zero for year 2010. I normalize the conflict deaths by dividing by the population of the district in 1991.

To convert conflict into monetary terms, I do an exercise similar to that with rainfall using three cross-sections of consumption surveys conducted between 1995 and 2010. I find that increase in conflict intensity of 1 death per 1,000 population is correlated with a consumption loss of Rs. 3,490 (10.6 percent of consumption in 2010). This correlation is much stronger for higher end of the wealth distribution, consistent with the nature of the conflict and the assumption in the theoretical framework (Appendix Table B.1 and Appendix Figure B.1)<sup>16</sup> .

# **3.4 Demand for migrant workers**

## **Demand data**

Another shock that drives migration in the theoretical framework is the increased demand for workers in the destination countries, which increases migrant income. Since most migrant workers from Nepal work in construction and manufacturing industries, I use a measure of demand that reflects growth in construction and manufacturing industries in the key destination countries. Specifically, I use the growth rate in the levels of carbon dioxide emissions attributed to construction and manufacturing industries in India, Malaysia and the Gulf countries (Qater,
.016|0.016|0.015|0.042|0.042|0.042|
||(0.015)|(0.016)|(0.014)|(0.031)|(0.030)|(0.031)|
|Mean of Control Group|0.977|0.977|0.979|0.902|0.902|0.902|
|Sample Size|678|678|723|726|726|727|
|P-value: all treatments zero|0.029|0.294|0.029|0.653|0.601|0.605|
|P-value: all treatments equal|0.049|0.215|0.049|0.876|0.880|0.785|

Notes: All regressions control for stratification batch fixed effects. Robust standard errors in parentheses. *, **, and *** denote significance at the 10, 5, and 1 percent levels respectively.

First follow-up survey conducted 2-9 months post-intervention; second follow-up survey 14-21 months post-interventions. Base specification uses data from current survey round only, and includes no other controls.

PDS Lasso uses post double-selection lasso with baseline variables as inputs. No covariates were selected in either wave. Cross-wave imputation assumes that firms that have missing survival status in round 1, but are observed open in round 2, were also open in round 1; and that firms which are closed in round 1, and that have missing survival status in round 2, remain closed.

# **Appendix 4: Measurement of Key Outcomes**

The survey instruments and data will be made available in the World Bank’s open data library: Nominal Naira were converted into real (January 2018) Naira using the Consumer CPI of the Central Bank of Nigeria.

Key outcomes are measured as follows:

# **Table 4:**

**Index of
<!-- Start of picture text --> Figure 1. Global Growth<br>A. Growth B. Potential growth<br>Percent Percent<br>8 8 Potential growth<br>1998-2017 average Actual growth<br>6<br>6 1998-2017 potential growth<br>4<br>4<br>2<br>2 0<br>0<br>World AEs EMDEs World AEs EMDEs<br>Sources: Penn World Tables, World Bank.<br>A. Sample of 181 countries.<br>B. Based on production function approach, GDP-weighted averages for a sample of 30 advanced<br>economies and 50 EMDEs.<br>2003-07 2008-12 2013-17 2018-27 2003-07 2008-12 2013-17 2018-27 2003-07 2008-12 2013-17 2018-27<br>2010 2012 2014 2016 2018 2010 2012 2014 2016 2018 2010 2012 2014 2016 2018 1998-2002 1998-2002 1998-2002<br><!-- End of picture text --> 19
further validate the randomization. Annex Table D15 shows the results of this exercise, where the dependent variable measured in July and August 2020 is regressed on the treatment indicator. The table provides strong evidence in favor of a correct randomization: all coefficients are small and statistically insignificant. Moreover, there is no evidence of heterogeneous treatment effects across dwelling type. Fourth, we measured individuals’ concerns about getting COVID-19 in the next year and used this as a placebo outcome, since the campaign should not directly affect it. Consistently, Annex Table D16 shows no impact on risk perceptions about getting COVID-19. This alleviates the potential concerns that our results are driven by reporting bias.

For our health facility data, we test the robustness of our results to different models, samples, and outcome definitions. First, as shown in Table 5, the coefficients of our impact estimators showed minimal changes with or without district- and subdistrict-level controls. Second, Annex Table D17 shows the sensitivity of our estimates to five choices of the population thresholds used to identify subdistricts to include in our sample. The impact of the ad campaign on urban incidence is stable and mostly significant across all subsamples, with estimated reductions in cases ranging between 20% and 30% of the baseline average incidence rate. Conversely, the sign of the coefficient on rural incidence is inconsistent and remains statistically insignificant. Third, in Annex Table D18 we consider the absolute number of malaria cases as dependent variable, and include the subdistrict 27
(US$43), and showed that only 5 percent of female firms had any paid workers. The most common activities, accounting for 40 percent of the women earning below the median profits, are female-dominated industries of apparel manufacture (sewing) and processed food production sold in local markets. Given this context, our goal was to investigate the effectiveness of business training to increase female labor force participation rates and to raise the income levels of low-earning women already in business. The first step in this process therefore involved putting together samples of these two groups.

# **2.1 Generating a sample**

In selecting the sample we balanced the goals of creating a representative sample with logistical concerns. We chose to sample in the two largest urban areas in Sri Lanka – greater Colombo and greater Kandy.<sup>3</sup> Within each of the two urban areas we selected 5 D.S. divisions in urban and semi-urban areas. We located a training venue in each of the ten D.S. Divisions, thus minimizing the required travel time for project participants. Within each D.S. division we then conducted a door-to-door screening exercise in selected G.N. Divisions.<sup>4</sup> The short screening survey gathered employment information on females aged 25 to 45 living in the household.

For self-employed women, our screening survey identified women who worked more than 20 hours per week in self-employment, were involved in a sector other than seasonal agriculture or fisheries, and had monthly profits of 5000 Rs or less (the median in the general population of female self-employed). We refer to this as our _current business owner_ sample.

The second group of interest was women who were out of the labor force, but who were likely to enter the labor force within the next year. For females out of the labor force and between 25 and 45 years of age, the screening survey asked directly whether the woman planned to enter self-employment in the next year, as
° (~5.5 km)|
|Output<br>information|24-hour aggregation<br>since the beginning<br>of the forecast|mm/per unit of time|Z-values, monthly|WRSI per raster cell;<br>current WRSI data give<br>range of 0–100 percent<br>of total|An index describing<br>the relation between<br>surface soil moisture<br>and profile soil<br>moisture as a function<br>of time(percent)|Vegetation density,<br>monthly|
|Data set source|https://cds.climate.c<br>opernicus.eu/cdsapp<br>#!/dataset/reanalysi|https://data.chc.ucsb.e<br>du/products/CHIRPS-<br>2.0/africa_dekad/tifs/|https://spei.csic.es/|https://edcftp.cr.usgs.g<br>ov/project/fews/dekad<br>al/|https://land.copernicu<br>s.eu/global/products/s<br>wi|https://lpdaac.usgs.g<br>ov/products/mod13c<br>2v061/|

38
consistent with the idea that bank finance sustains firms over longer periods enabling them to expand over time.

In contrast, neither private credit nor stock market capitalization is significantly associated with investment rates, firm size, or employee training within the sample of highincome countries. Further, there is no strong relationship between stock market capitalization and the other firm outcome variables within the sample of low-income countries. These patterns mimic the ones that we found for labor growth and thus support our interpretation of our main finding regarding the beneficial effects of banking sector development in lower-income countries.

# **VII. Conclusions**

We have combined data from detailed firm surveys with indicators of financial development and structure that cover a broader set of countries than in earlier studies in order to study the relationships between firm growth, financial structure, and stage of economic development. In low-income countries, we find evidence that labor growth is swifter in countries with a higher level of private credit/GDP, consistent with predictions from new structural economics. This conclusion holds using two well-established estimation approaches for identifying the causal impact of financial development on growth: instrumental variables regressions and the RajanZingales methodology. The plausibility of the finding of the beneficial role of banks in poor countries is further boosted by our finding that a more developed banking system is associated with higher investment rates, more employee training, and larger firm sizes only in poor countries, which suggests that banks spur both physical and human capital investments besides boosting job growth in such countries. In high-income countries, we find that labor growth rates are increasing in the level of stock market capitalization, which could also be viewed as being consistent with predictions from new structural economics, though we are unable to provide evidence that the association is causal.

At the same time, when we examine the types of firms that appear to benefit from welldeveloped private credit markets in low-income countries, we find no evidence that it is the small-scale firms that benefit most. Rather, labor growth rates are increasing in the level of private credit market development
# **_Annex B:  Estimation of GDP Projections to 2030_**<sup>**_3_**</sup>

# <u>Estimation Method</u>

- Data from 1995 to 2020 are obtained from the World Economic Outlook database.

- Data from 2014 to 2020 are IMF estimates.

- Data from 2020 to 2035 are projections based on the following assumptions:

1. Cobb‐Douglas production function with constant returns to scale: Y = A*K^(1‐ alpha)*L^(alpha)

2. Labor share (alpha) = 1/3 3. TFP growth (A) and Capital growth (K) are constant at steady state 4. Labor force growth is equal to population growth 5. Data on population growth are taken from the UN‐DESA World Population Prospects 2012 (medium fertility assumption)

# <u>Steps</u>

1. Derive the projections for annual growth rates of real GDP ‐‐> (World Economic Outlook data base (WEO_NGDP_RPCH)

2. Rebase the WDI constant GDP series (2011 GDP deflator = 100) in LCU ‐‐> WDI_GDP_constant_LCU2011 3. Derive the projections for real GDP at 2011 prices using projected growth rates (2021‐ 2035) to real GDP estimates from 2020 onward ‐‐> REAL GDP PER CAPITA 4. Apply WPP 2012 population projections based on medium fertility assumption to derive projected real GDP per capita (expressed in million LCU per capita) ‐‐> REAL GDP PER CAPITA 5. 2011 PPP factors are applied to convert constant GDP in LCU to international $ ‐‐> REAL GDP PER CAPITA 6. A multilevel model with country and region random effects is estimated to derive GNI projections from GDP projections ‐‐> REAL GNI PER CAPITA

> 3   GDP estimation for 2030 was prepared by Patrick Eozenou (Health Economist, World Bank) for the working paper by Olusoji Adeyi, Caroline Ly, Patrick Eozenou, Allyala Nandakumar, Ariel Pablos‐Mendez and Timothy Evans, "The economic
coefficients (Table 5). Our data on institutions originally vary with increasing values indicating better institutions, but are rescaled so that increasing values represent worse domestic institutions along with our regulatory variables that have a similar functional scale.<sup>22</sup> Hence, our main motivation for reversing these values is because such a transformed scale would be consistent with our regulatory variable and which could facilitate the ease of interpretation of the results. For instance, if a negative and significant sign is found on the interaction term, it would mean that higher levels of regulations in services are particularly harmful in countries which have weak domestic institutions.

Results in Table 4 show that none of the institutional interaction terms have a significant sign when entered separately. This is quite the opposite however when we look at the other three specifications presented in Table A14, including our second preferred TFP measure from Hsieh and Klenow. They all show that regulatory barriers in institutionally weak countries have an additional negative impact on economic performance. This significant effect is for the overall regulatory index, entry barriers as well as conduct regulations when entered separately. The differential effect using the FDI indicator does not come out significant in any of its individual entries. However, when putting the services linkages together, the only significant effect as part of the interactive effect of institutions using Ackerberg is for FDI restrictions as shown in Table 4. It means that, FDI restrictions are particularly harmful in EU countries with relatively weaker domestic institutions, which would confirm previous work by Beverelli _et al_ . (2015). Yet, as a second very strong result is that also entry barriers are showing to have a significant differential effect for countries with weak institutions in all other three TFP specifications (presented in Table A14) when entering separately as well as together in columns (6), (12) and (18). Throughout all three performance measures, this differential negative impact in institutionally weak countries is quite substantial as the coefficient sizes are large.

Again, we repeat our results