rafmacalaba/data-use-mentions-extended huggingface dataset:
The FUS was conducted on all youth from treatment and control groups who could be successfully re‐ contacted, resulting in sample sizes as presented in Table 3. Only about 45% of treatments and controls were successfully re‐contacted, due to the high rates of mobility among the youth, and a very challenging security environment in which this survey work has taken place. To assess the extent of attrition bias, we regressed our main outcome of interest variables at baseline on an indicator for attrition. This analysis, which is further elaborated below, reveals no significant overall difference in baseline outcomes between attrition and non‐attrition individuals. The section bellows describes in detail how various samples used for evaluation were created, and how these samples compare to each other in terms of the basic characteristics at the baseline.

**Table 3: Sample sizes for various groups used in evaluation**

||Eligible|Treatments|Re‐interviewed<br>Treatments|Controls|Re‐interviewed<br>controls|
|---|---|---|---|---|---|
|Intake 4|1,199|381|168|270|110|
|Intake 5|1,014|362|170|299|140|
|Total|2,213|743|338|569|250|

Source: Authors’ calculations based on survey data.

# _4.3 Sample Balance_

## 4.3.1 Sample Balance (participants vs. eligible non‐participants)

The youths who were selected into the program were randomly drawn, with stratification by gender, from a larger sample of eligible applicants. As noted in Table 3, 743 individuals were accepted into the program (381 in intake 4 and 362 in intake 5) out of a total of 2,213. Since the baseline screening survey was administered to all applicants, a logical first step is to verify that the baseline characteristics of program participants look similar to all
consequences of violent conflict at the local level. Dercon (2006) and Verwimp (2005) analyze the effects of local violence on household wellbeing in Ethiopia and Rwanda. Again, important spatial differences emerge. For instance, Yanagizawa-Drott (2012) finds evidence that in Rwanda radio stations encouraged people to participate in the genocide. But the reach of these stations varied depending on the terrain, resulting in considerable dispersion in violence across geographic areas.

Some of the studies in this strand of the literature refer specifically to Afghanistan. Using household data from 2007, D’Souza and Jolliffe (2013) find robust evidence that conflict and food security are negatively correlated. They attribute this relationship to disruptions of local markets leading to spikes in food prices. Callen et al. (2014) show the persistence and intensity of the conflict affect household expectations, with the poorest sections of the population having a more negative perception of their future wellbeing. Consistent with this perception, Ciarli et al. (2015) find that conflict reduces employment opportunities by making business investment less attractive, while boosting self-employment in activities that have low returns.

There are also empirical studies focusing on how foreign aid affects economic growth and household wellbeing in conflict-affected countries. During a conflict, aid can enhance state legitimacy and service delivery. After a conflict it could help implement peace agreements and support reconstruction and development. However, aid may also fuel violence because it might exacerbate tensions among local communities. Although the literature has yet to come to a consensus, findings so far suggest a positive impact. Girod (2012) finds that aid improves development outcomes after a civil war. Chandy (2011) reaches a similar conclusion for fragile countries, while pointing out that targeting, design and delivery are critically important.

The allocation of aid is also driven by political and security considerations. Douglas (2012) shows that in Afghanistan, foreign aid is positively correlated with conflict at the provincial level. This is so not only in the case of security assistance but also—albeit to
those that could not have been exposed to pre-primary education. We create a placebo dummy that equals one for the 1990 and 1991 cohorts in the beneficiary communities, and zero otherwise. The placebo indicator would measure any differences in trajectories for the variable of interest prior to the construction of pre-primaries. The results for this specification are presented in columns (1) and (2) of Table 8. The point estimates for the placebo indicator are close to zero and not statistically different to zero at conventional levels.

The second test takes advantage of the entire sample and, in addition to our preprimary access dummy, we add a placebo indicator that takes the value of 1 for cohorts born one or two years prior to the first exposed cohort in beneficiary communities. For example, if the 1995 cohort was the first to be exposed to a pre-primary in a certain community, then the 2-year prior placebo indicator would take the value of 0 for cohorts 1990 to 1992, 1 for the 1993 and 1994 cohorts and 0 for cohorts 1995 and 1996. Columns (3) through (6) present the results for this test. The point estimates on the placebo indicators are similar to those obtained in the first two columns, albeit more precisely estimated given that we are including the entire sample. Note that the estimates for pre-primary access remain unchanged.

Finally, we use the 1994 and 2002 Population censuses to test whether the change in community characteristics between these two points in time is different between beneficiary and comparison communities, which could potentially bias our results. We present the estimates for this test in Table 9. We find that the change in several socio-economic characteristics is, in almost all cases, not significantly different between beneficiary and comparison communities, again suggesting that our main identification assumption is valid.

# **7 Concluding remarks**

Many developing countries have recently expanded (or are considering to expand) preprimary coverage as an instrument to stimulate human capital accumulation. Evidence from the United States, Uruguay and Argentina suggests that these investments yield
presence of a clinic) and facilities (eg. adequate sanitation) are often a characteristic of a whole village or neighbourhood. However the costs of carrying out intensive or follow-up studies on a widely dispersed sample would probably be prohibitive. It might be necessary to conduct them in a localised area or in a subsample of clusters, rather than individuals, included in the main survey.

# 3.4 Reneat surveys

If the proposal to repeat country health studies after several vears is adopted, attention will have to be given to this in the overall design of the WHS. It is probably impractical to attempt to conduct a panel study, that interviews the same households, after such a long interval. Some of the advantages of this could be obtained by conducting the second survey in the same sampling areas which were used for the initial study. For example, changes in community-level variables could be examined directly. To ensure that this approach was feasible, attention would have to be given to the production and preservation of the necessary documentation. Second, there is evidence that the results of health interview surveys are sensitive to even minor changes in methodology and changes in people's perceptions of illness over time (Ross and Vaughan 1986). The need for consistency will conflict with the desire to update the questionnaire to reflect changes in circumstances and improvements in methods. Explicit attention will have to be given to the resolution of this conflict of interests during preparation for the second round of fieldwork.

# 5.5 In-depth studies

Section 4 of this paper outlined the wide range of information on health and its determinants that can be collected in a single-round survey. Other kinds of information can only be collected prospectively or by conducting medical examinations. Many possibilities can be envisaged and the priority that should be assigned to them will depend on the country concerned. One theme that could be particularly valuable, both for assessing the benefits from health expenditure and for clarifying the determinants of health, would be follow up studies 39
- Ahmad, F., Shah, G.M., Pradhan, B.B., Dixit, R., 2022. Towards an Environment-and-Worker Friendly Brick Kiln Sector in Punjab, Pakistan. Working Paper. International Centre for Integrated Mountain Development (ICIMOD), Kathmandu.

- Bhattarai, J., Ghale, D.B., Chapagain, Y.P., Bohara, N.B., Duwal, N., 2018. Study on the physical and mechanical properties of ancient clay brick samples of Kathmandu Valley, Nepal. Tribhuvan University Journal 32, 1-18.

BiofuelCircle, 2022. Can Coal be Replaced with Biomass Briquettes? BiofuelCircle, Pune.

- Budhathoki, P., Duwal, N., Bhattarai, J., 2018. Investigation on physico-sintering and mechanical properties of clay tiles available in Kathmandu valley of Nepal. Asian Journal of Chemistry 30, 2515-2520.

- Budhwar, R., Bihari, V., Mathur, N., Srivastava, A., Kumar, S., 2003. DNA-protein crosslinks as a biomarker of exposure to solar radiation: a preliminary study in brick-kiln workers. Biomarkers 8, 162-166.

- Build Better Nepal, 2018. Competition - Interlocking Bricks (CSEB) vs Fired Brick - Building 1m2 Wall. Build Up Nepal, Kathmandu, https://www.youtube.com/watch?v=wU2spFktjAA.

- CBS, 2014. National Population and Housing Census 2011. Volume 04, NHHC2011. Central Bureau of Statistics, Kathmandu.

- CBS, 2022a. National Industrial Survey 2019/20: National Report. Central Bureau of Statistics (CBS), Government of Nepal, Kathmandu.

CBS, 2022b. Statistical Yearbook 2021. Central Bureau of Statistics (CBS), Kathmandu.

CEN, 2009. Baseline Study of Brick Kilns in Nepal. Clean Energy Nepal (CEN), Kathmandu.

- Chapagain, Y.P., Sapkota, S., Ghale, D.B., Bohara, N.B.
data include producers’ physical outputs, q� , along with their respective prices, p� . This allows us to distinguish between revenue‐ based measures of total factor productivity (TFPR) and those based on physical outputs (TFPQ). As is standard in the literature, we define TFPR as the value of revenue �p�q�� per input unit �x�� and TFPQ as the number of physical units produced per unit of output �q�/x��. TFPQ measures the technical efficiency of a plant.

> 1 This panel is compiled from 10 years of the Ethiopian Survey of Large and Medium Scale Manufacturing Industries which, despite its name, is a census of all manufacturing firms with 10+ employees.

> 2 Data from 2005 are dropped because a survey was conducted during that year rather than a census.

3
|16|
|_Foreign Owners_|1-2|1-2|1-2|4-5|8|
|_StateAgencies_|12-20|11-13|9-11|7-14|3|

1. No survey undertaken in 1998.

_2._ Includes investment funds, all other firms (including holding companies and financial-industrial groups), and banks.

private investment funds. Similar critique by the same author is mentioned in John Nellis, "Time to Rethink Privatization," _Finance and Development_ 36, 2 (1999): 16-19.

13 Estimates vary considerably depending on when, during the course of the year, the survey was taken, and on the sample size. Figure ranges are taken from the following: for 1994, Blasi and Shleifer, "Corporate Governance in Russia," Radygin, "The Russian Model of Mass Privatization: Governmental Policy and First Results," in _Privatization, Conversion, and Enterprise Reform in Russia_ (Boulder, Col.: Westview, 1995); for 1995, Blasi, "Corporate Ownership and Corporate Governance in the Russian Federation," in Ira Liebermjan, Stilpon Nestor, and Raj Desai, eds. _Between State and Market: Mass Privatization in Transition Economies_ (Washington, D.C./Paris: World Bank/OECD, 1997), Radygin, "Securities Market Development and its Relationship to Corporate Governance," paper presented at OECD conference on Capital Markets, 1996; for 1996: Blasi _et al, Kremlin Capitalism: Privatizing the Russian Economy_ (Ithaca, N.Y.: Cornell University Press, 1997), Radygin, S. Aukutsionek, _et al.,_ "Dominant Shareholders and Performance of Industrial Enterprises," _Russian Economic Barometer 1_ (1998): 8-41, Institute for Economies in Transition, _Annual Report 1996_ (Moscow: IET Press, 1996); for 1997: Simeon Djankov, "Business as Unusual," World Bank, 1998; data from IET; 1999 figures are from Radygin, "Ownership and Control of Russian Industry." 6
Low returns to human capital were in line with a planned economy offering free education, centralized allocation of labor, and the ideology of the dictatorship of the proletariat; a similar picture was observed in other contemporaneous socialist countries (see, for example, Münich, Svejnar, and Terrell 2005).

However, an even earlier attempt to establish the contribution of education to productivity took place during early Soviet times. Strumilin (1924) showed that those who were more educated contributed more in terms of productivity. He even calculated earnings benefits, and though his calculations did not discount earnings, the estimates of educational returns were high, at about 17 percent in 1919 (Strumilin 1924).

Within the first two decades of the collapse of the Soviet Union, a group of scholars reported that during the transition period from a planned to market economy in Russia rates of returns to schooling rose sharply (Brainerd 1998; Clark 2003; Vernon 2002; Akhmedjonov 2014). The upsurge in wage premiums to education (especially university education) was asserted to be a pivotal factor that exacerbated wage dispersion: salaries of highly skilled and trained workers had increased in absolute terms and compared to less-educated workers (Fleisher, Sabirianova, and Wang 2005). However, returns to schooling declined for those people who took advantage of higher education expansion in a post-communist Russia (1990-2005) in comparison to youths who obtained university degrees in the preceding periods (Kyui 2016). One researcher exploited data about the average education level at the end of a Soviet period as an instrument and inferred that the growth in the proportion of city dwellers with university degrees was associated with a rise in 4
the Autonomous Energy Efficiency Improvement (AEEI) index. The AEEI is assumed to be 1% per year, following the common assumptions in most CGE models (Chen et al. 2015). The capital accumulation is determined by the current capital stock, depreciation rate, and the new investment.

## **2.2. Data**

In this paper, the benchmark year for the CGE model is 2017. The Social Accounting Matrix (SAM) is developed using the 2017 Input-Output (IO) tables produced by the National Bureau of Statistics of China (CNBS, 2017). The CO2 emission factors of fossil fuels are calculated based on IEA statistical data (IEA,2017a). The tax data are obtained from the State Taxation Administration of China and Financial Yearbook of China (CMF, 2018). The values for the elasticity of substitution are taken from existing literature (Bao et al.2013, Cao et al. 2016). As for the baseline scenario, China's GDP growth rate from 2018 to 2021 is based on National Bureau of Statistic of China (CNBS, 2022), China's energy consumption structure (2018-2019) is based on China Energy Statistics Yearbook (CNBS, 2020) and China Electricity Statistics Yearbook (CEC, 2020). The baseline is consistent with the assumptions and forecasts made in China's Long-term Low-carbon Development Strategies and Pathways Comprehensive Report (ICCSD, 2021).

# **3. Scenario design and incorporation in the model**

## **3.1 Design of Scenarios**

We have developed one baseline scenario and six main scenarios as defined in Table 2. Two main scenarios, Scenario 4 and Scenario 6, are further divided into sub-scenarios (4a, 4b, and 6a, 6b, 6c 6d) for the reasons explained later in this section.

8
**Table 4.2: Estimated effect on hours worked from a US$100 increase in exports per** **<u>worker in Cambodia</u>**

|VARIABLES|**Mean weekly**<br>**hours.**|**Mean weekly**<br>**hours, Male**|**Mean weekly**<br>**hours, Female**|
|---|---|---|---|
|Foreign demand shock|0.0342|0.0197|0.0497*|
|(USD 100), 2014-2009|(0.0221)|(0.0175)|(0.0290)|
|Observations|183|183|183|
|R-squared|0.504|0.520|0.482|
|Foreign demand shock|0.00933**|0.00681**|0.0110**|
|(USD 100), 2019-2009|(0.00459)|(0.00341)|(0.00557)|
|Observations|185|185|185|
|R-squared|0.419|0.516|0.333|
|Foreign demand shock|0.00751|0.00626|0.00848|
|(USD 100), 2019-2014|(0.00528)|(0.00424)|(0.00612)|
|Observations|183|183|183|
|R-squared|0.041|0.169|0.013|

Note: Controls are not exhaustive yet. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 The table contains the results from a single, exactly-identified OLS instrumental variable regression for 2009, 2014, 2019, and 2020 Cambodia Socioeconomic Survey (CSES) data.

In sum
**Table 2: Impacts of mental health on other labor and life satisfaction outcomes**

|Dependent variable:|Labor force<br>participation|Permanent job|Skilled<br>occupation|Work in<br>agriculture|Number of work<br>hours per week|Life satisfaction|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Standardized mental health<br>scores|-0.035***|-0.011|-0.032***|0.056***|-10.146***|-0.602**|
||(0.005)|(0.007)|(0.007)|(0.008)|(1.691)|(0.301)|
|_First stage of 2SLS (depende_|_nt variable is stand_|_ardized mental he_|_alth scores)_||||
|Trauma experience* Year<br>after arrival|-0.023***|-0.039***|-0.039***|-0.039***|-0.058***|-0.058***|
||(0.006)|(0.009)|(0.009)|(0.009)|(0.016)|(0.018)|
|Kleibergen-Paap F stat.|35.644|17.692|17.692|17.692|19.635|79.537|
|AR 95-CIs|[-0.059, -0.025]|[-0.039, 0.006]|[
). Thus we are able to estimate income gains (net of forgone earnings) from NREGS in Bihar, and impacts on poverty. Moreover, we know if household members had enough NREGS working days if they worked on NREGS, or if they did not want to work on the scheme, both during the year ended.

Another attraction of the Bihar Survey is that the questions asked allow us to plausibly identify households who are not rationed in the casual rural labor market. These are identified as the sub-sample who report that they do not want more work on the NREGS than what they have already. Since NREGS pays a higher wage rate (about 10% higher in Bihar) than the casual labor

> 9 One difference is that we do not have the split manual/non-manual work in the Bihar survey: we only know if the activity was or was not casual work. This implies higher participation rate in this type of work in the Bihar Survey (21.1%% in the NSS data on rural Bihar versus 39.8% in the Bihar Survey).

8
used a computerized method on English responses, utilizing a dictionary of 27 promotion and 25 prevention words developed and validated by Gamache et al. (2015), and uploaded these dictionaries into Linguistic Inquiry and Word Count (LIWC) software to determine their frequencies. Similarly to Kanze et al., this was not our preferred method, as the dictionary approach leaves LIWC vulnerable to a low detection rate; the software is not sensitive enough to capture intentions that do not directly overlap with the very specific 52 words in the regulatory focus dictionary (Gamache et al. 2015). We use this LIWC method as a robustness check for all responses in English, to verify the direction of results from the research assistants’ qualitative coding. We find the same directional results.

> 8 A small minority of professional investors met startups in multiple programs i.e., in the Middle East and Africa. As we randomized the investor according to the survey they received, 18 of the 276 investors encountered the treatment condition in one program, and the control condition in another program.

18
places were the victims were hiding as a consequence of escaping from the troops, and died for starvation. In addition, since killings are less likely to affect entire families than deaths due to deprivation, there is a lower underreporting bias attached to the former measure relative to the latter one (Silva and Ball 2006). We also exclude disappearances as, according to HRVD data, they do not show enough time and geographical variation in order to identify individuals more or less exposed to the conflict. We believe that the number of killings proxies quite well the intensity of the conflict across time and space as their occurrence largely tracked the movements of the Indonesian military operations. The other two types of violations do not seem to show the same pattern (Silva and Ball 2006). For the same reason, we believe that it proxies quite well the destruction of houses and infrastructure and the displacement of people given the way in which the last wave of violence occurred (i.e. the scorch-earth technique employed by Indonesian troops as they moved towards West Timor).

20
Policy Research Working Paper 10822

# **Abstract**

The world is experiencing a severe loss of biodiversity, highlighting the need for a strong global conservation strategy. Effective conservation depends on accurate information about where endangered species live and the local threats they face. Using data from the Global Biodiversity Information Facility, this paper creates threat and protection indicators for more than 600,000 species, including animals, plants, and fungi. The indicators include habitat size, level of protection, nearby population density, and specific threats like population encroachment for land species and fishing activity for marine species. The paper then uses an ordered logit model to analyze the relationship between these indicators and the extinction risk categories assigned by the International Union for Conservation of Nature. The model is based on 87,731 species in the Global Biodiversity Information Facility database that have been assessed by the International Union for Conservation of Nature. The results are used to predict threat levels for 512,675 species without International Union for Conservation of Nature ratings, revealing many more potentially threatened species and changing the maps of “conservation hotspots.” The paper concludes by noting that its methods can support rapid updates of species maps and threat indicators as the Global Biodiversity Information Facility database continues to grow.

This paper is a product of the Development Research Group, the Development Data Group, Development Economics and the Environment, Natural Resources and Blue Economy Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at bblankespoor@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of