rafmacalaba/data-use-mentions-extended huggingface dataset:
Within our community sample, more than 97% of communities above the cutoff received the program as intended, while no communities having point totals below the cutoff received the program. Although our analysis is based on an intent-to-treat analysis, the high levels of compliance with the program cutoffs for both treatment and control groups means that our ITT estimates are similar to (local) average treatment effects.

# _Tests for Discontinuities of Covariates_

To assess the covariate discontinuity and therefore validity of the RD design, this section presents a series of visual and parametric tests using the satellite and survey data. While the assumption that no determinants of the outcomes jump discontinuously at the threshold is untestable for unobservable characteristics, **Figures 5** and **6** show kernel regressions of several observable characteristics, recentered by subtracting their year and state mean, on the point scores. **Figure 5** shows these graphs for the survey data, while **Figure 6** contains those for the environmental data. In neither case do we observe substantial visual discontinuities in key observable environmental and social characteristics. Parametric tests of discontinuity for these same covariates generally confirm the visual inspection. These tests are presented in **Tables 3a and 3b** for the environmental polygon data set and the survey data set respectively. We do reject the null of no discontinuity for some indicators, but we do not think that these differences drive our results for two reasons. First, most of the differences are small in magnitude with respect to the control means.<sup>14</sup> Second, when we include poverty and the other controls in our final regressions, we find that the results are unaffected, even when we allow for differential slopes on either side of the discontinuity. Furthermore, the correlation between the poverty index and our social capital outcomes, controlling for state effects, is 0.005-0.009 across the cohorts and is not statistically significant.

**Table 4a** shows summary statistics for participant and non-participant
country.<sup>19</sup> Table C1 and Table C2 in Appendix C show detailed information about the list of goods and services considered for our study and associated budget shares (calculated based on GLSS7).

**Table 3. Price data in Ghana**

||GLSS7 consumption module|Market price survey|CPIprice data|
|---|---|---|---|
|Geographic<br>coverage|• 1,000 clusters<br>• 214 districts<br>• 10 regions|• 398 clusters (urban 209;<br>rural 189)<br>• 174 districts<br>• 10 regions|• 44 markets<br>• 10 regions|
|Food items|109 items|96 items in GLSS7<br>consumption module<br>matched|67 items in GLSS7<br>consumption module<br>matched|
|Non-food items|Paid rents and imputed rents<br>available with quality<br>information|Limited housing<br>information|Limited housing<br>information|

We compare price distributions of major food items in the market price survey and CPI price data. Figure 3 shows price distributions of the following food items: smoked herring and salmon, bread, smoked fish, maize (ground or dough), imported rice, fresh tomatoes, water, yam and water yam, local rice, and large onions.<sup>20</sup> Median price values between the market price survey and the CPI database are overall similar with smoked fish and water as exceptions.

> 19 Prices of about 307 items are regularly collected using the 2018 Classification of Individual Consumption According to Purpose (COICOP).

> 20 For each item, observations are treated as outliers and removed if their log prices are above or below 2.5 standard deviations.

14
MK 500.<sup>10</sup> After the household member was assigned the lottery ticket, the number was recorded and the price

> 8 This road was identified as in a good condition for buses to run (during the dry season).

> 9 The first Phase included conducting a listing of all households in the village cluster area and randomly sampling 100 households in four villages to participate in the first phase of the project which did not randomize the price, rather randomized overall accessibility.

> 10 All households that participated in the first phase (randomly selected) were allocated MK 0.

6
After the baseline data was collected and the randomization performed, students participated in the entrepreneurship track between February 2010 and June 2010, when they graduated. Followup data were collected through face-to-face interviews between April and June 2011, approximately nine to twelve months after the end of the academic year.<sup>9</sup> The instrument included questions related to socio-economic characteristics and a detailed labor module. The survey also collected a range of other indicators including information on aspirations for the future, personality traits, and behavioral skills related to entrepreneurship. Thorough tracking procedures led to low non-response rates at follow-up<sup>10</sup> : 92.8 percent of the 1,702 applicants were tracked, a low attrition rate given the high mobility of the population of graduates.<sup>11</sup> Importantly, attrition was balanced and uncorrelated with treatment status.<sup>12</sup> Table 2 presents the average baseline characteristics of the treatment group (assigned to the entrepreneurship track) and the control group (assigned to the standard curriculum), as well as differences between the two at baseline.<sup>13</sup> Randomization achieved good balance. There were few systematic differences between participants and non-participants and the differences were quantitatively small.<sup>14</sup> Empirical analysis will control for the few characteristics that are statistically different between the two groups at baseline.

Administrative records from ANETI show imperfect compliance with assignment to the treatment group. Of the 856 students who applied and were randomly assigned to the entrepreneurship track, 67 percent completed the business training at the local employment office, and 59 percent completed both business training and coaching. Overall, 41 percent dropped out of the program prior to completing both training and coaching. The magnitude of

> 9 In October 2010, qualitative data were collected to gather students’, coaches’, and professors’ perceptions on the implementation of the intervention.

> 10 Detailed contact information was collected in the baseline surveys.
1997-1998, 2001, and 2008. Due to sample restrictions, among bond funds we only observe the drop in assets in 2008.

Fund assets may grow because of higher returns of their investments or because of injections to the fund by the underlying investors. In fact, the growth rate of fund ’s total assets, , can be trivially written as where is the (net) return to fund at time , and is the injection to the fund expressed as a fraction of the fund’s initial assets . While injections are not directly observable, we can estimate them. To do so, we compute individual fund returns on a given month using data from Bloomberg and Datastream and obtain injections from the difference between the change in total net assets and individual returns. More formally, where is the gross rate of returns to fund at time , computed as , with being the fund price or net asset value (NAV), adjusted by dividend payments.<sup>19</sup> The evolution of returns and injections for the median fund is shown in Figure 5, Panels B and C, and summary statistics are reported in Table 2. For the median equity and bond funds, both returns and injections experience significant fluctuations. For equity funds fluctuations in fund returns are much more volatile than those in injections (standard deviation of 6.23% and 2.05%, respectively), while for bond funds the volatility of these components is similar (standard deviations of 2.53% and 2.05%, respectively). This is consistent with equity returns being more volatile than those of fixed income securities (Schwert, 1989; Andersen et al., 2007). Both components also exhibit a similar time pattern, which also coincides with that of the growth rate of assets, suggesting that the components do not cancel each other, but instead reinforce themselves. Both returns and injections expand during good times and experience severe contractions during periods of financial turmoil. Across types of funds by target
.5°C of warming is −0.1 to 1.7% GDP, at 4°C of warming is 1.5 to 5.6% GDP, and at 8°C warming is 6.4 to 15.7% GDP annually.” Economic losses arising from the implementation of climate mitigation measures needed to provide a two‐thirds (66 percent) chance of achieving the 2 percent goal are estimated at between 2.9 and 11.4 percent of consumption by 2100 (NGFS 2019a).

There is, however, increasing recognition that existing models may significantly underestimate the economic impact of climate change and overestimate the cost of mitigation measures (Stern, 2016). In particular, most current models fail to consider the catastrophic and irreversible impacts of unmitigated climate‐change which could be triggered by the crossing of global and local tipping points (Box 1). The mounting evidence on the impact of climate change on factors of production is either not or only inadequately included in IAMs through damage functions. Therefore, IAMs are not able to capture direct economic growth impacts of climate change. Similarly, most IAMs do not account for the distributional impacts of climate change and their potentially severe consequences in terms of migration and conflict. There is also considerable debate on the appropriate discount rate, where higher discount rates give less weight to the welfare of future generations. In addition, accumulating micro‐evidence provides a better understanding of the impacts of climate change on productivity. For example, natural hazards and higher temperature have a negative impact on children’s physical and cognitive development (through undernutrition and stress) and educational achievement (because learning is more difficult in high temperatures and because children tend to drop out of schools in disaster aftermaths and in response to reduced agricultural yields). There is also a direct link between temperature and outdoor labor productivity (e.g., for agriculture or the construction industry) (Hallegatte et al., 2016).

7
31 : The earlier discussion predicts that the “Number of years from first year of crisis to year of next election” should be positively related to the size of fiscal transfers: the further in the future that citizens can use the vote to punish political actors who cater excessively to special interests, the larger are the benefits to special interests. The variable has the predicted positive sign in all of the regressions of Table 1, but is significant only in Regression 3.5° However, the sample includes countries where elections, even when they are held, are not necessarily competitive and therefore pose less of a threat to incumbent politicians. When regressions 1 and 2 are run only on the 21 countries for which the variable Executive Index of Electoral Competitiveness (E/EC) from the Database on Political Institutions is at its highest value (seven), the coefficients on the proximity of elections variable rise substantially and are more significant.>! For example, in regression 1, the coefficient rises from 1.6 to 2.7 (p ~0.10). The £- coefficient of 0.29 in the sub-sample indicates that a 1.3 year increase in the time to the next election is associated with a 2.6 percentage point increase in fiscal transfers.

The two control variables are not significant and have little effect on the other coefficients. Income per capita is highly insignificant in regression 2: the wealth of countries apparently has no effect on the fiscal costs of banking crises as a fraction of national income. Moreover, although the number of veto players (checks) and the size of the financial sector are positively correlated with income per capita (at approximately 0.23 in this sample), all of the terms from regression 1 retain the predicted signs and approximately the same statistical and economic significance in regression 2.32 30 It is not the specification in Regression 3, which controls for terms of trade shocks, but rather the particular sub-sample, that is responsible for this result,
The coefficients oif the error-correction process are not all well-determined, but sometimes there is also an indication of a stronger impact of world interest rates after the break. All iin all, it the empirical patterns detected seem to confirm the a _priori_ belief that this method would capture a significant date in the liberalization of wholesale interest rates.<sup>23</sup> The same approach was extended to 8 of the 10 money market rates (Argentina and Brazil excluded because of the difficulty of defining a satisfactory smoothed inflation series). Here a further three break points were detected as shown in Table 4, again with high volatility ratios. The indications were that most of the remaining countries may have crossed that threshold before 1980.24 Also included in Table 4 are the liberalization dates provided for these countries by other recent studies.<sup>25</sup> The differences between the dates reflect differences in the concept of liberalization date being used. They should thus be considered as complementary to the dates obtained bv approaches (a) and (b) here. Galbis (1993) study uses dates at which preferential lendiing rates, or controls on key bank deposit or lending rates were removed. Demirguic-Kunt and Detragiache (1998) use deregulation of bank interest rates as the observable policy change to date liberalization. Williamson and Mahar (1998) are looking at a wider concept of financial liberalization and provide two dates: 'start of liberalization' and 'largely liberalized'.

> 23 We also carried out the same exercise for the thirteen industrial countries. Break points within the sample period were also found for four of these. (An unusual situation arose for one country, Ireland, where a break existed, but the post-break residual variance was lower than the pre-break. In fact the identified break was in this instance related to the EMS crisis of 1992-93 rather than to liberalization.)

> 24 Liberalization is not irreversible; the data from Malaysia (late 1981), and to a lessor extent India (early 1984)
/j.apenergy.2017.10.089

Palmer, M., & Truong, Y. (2017). The impact of technological green new product introductions on firm profitability. _Ecological Economics_ , _136_ , 86-93. <u>https://doi.org/10.1016/j.ecolecon.2017.01.025</u>

- Pan, X., Ai, B., Li, C., Pan, X., & Yan, Y. (2019). Dynamic relationship among environmental regulation, technological innovation and energy efficiency based on large scale provincial panel data in China. _Technological Forecasting and Social Change_ , _144_ , 428-435. <u>https://doi.org/10.1016/j.techfore.2017.12.012</u>

- Papadopoulos, S., Kontokosta, C. E., Vlachokostas, A., & Azar, E. (2019). Rethinking HVAC temperature setpoints in commercial buildings: The potential for zero-cost energy savings and comfort improvement in different climates. _Building and Environment_ , 155, 350-359. <u>https://doi.org/https://doi.org/10.1016/j.buildenv.2019.03.062</u>

Park, W. Y., Shah, N., & Phadke, A. (2019). Enabling access to household refrigeration services through cost reductions from energy efficiency improvements. _Energy Efficiency_ , 12(7), 1795-1819. https://doi.org/10.1007/s12053-019-09807-w 51
is a test of human capital externalities. Dist is a vector of the two distance variables‐ distance to Accra and distance to Kumasi. We used the inverse hyperbolic sin function since distance is then zero for industries in Accra and Kumasi and so a log transformation is not possible. W is a vector which contains the level of exports and imports in each industry.

Table 6 shows the OLS regression results. In column 1 we control only for population density, one of the standard ways of testing for agglomeration economies. The effect is small, negative but not statistically significant. In the other columns we gradually add in further regressors. Looking across the other regressions as we add in further independent variables the coefficients on population density are positive, small (between 0.02 and 0.04) but never statistically significant. As a comparison Collier et al (2018) used average wages as their dependent variable and firm‐level data for developing countries, finding an elasticity with respect to density of between 0.06 and 0.08 across 51 countries. When we experimented with employment density from either the population or firm census we obtain very similar results‐ coefficients are small and not statistically significant.

One response to these results is that the regressions should mechanically imply and find that density leads to employment growth. The argument is that with large scale migration to Accra and other more urban districts with higher density there is mechanically going to be more employment growth in those areas, and thus it is surprising that our results do not find this. But in limiting our analysis to firms with 5 or more persons engaged in the firm censuses we believe we mitigate this concern. In using the firm census data we are asking whether increased density resulted in employment growth 13
.

- Burke, M., Hsiang, S.M., Miguel, E., 2015. Global non‐linear effect of temperature on economic production. Nature advance online publication.

- Dell, M., Jones, B.F., Olken, B.A., 2009. Temperature and Income: Reconciling New Cross‐Sectional and Panel Estimates. Am. Econ. Rev. 99, 198.

- Dell, M., Jones, B.F., Olken, B.A., 2012. Temperature shocks and economic growth: Evidence from the last half century. Am. Econ. J. Macroecon. 66–95.

- Dell, M., Jones, B.F., Olken, B.A., 2014. What Do We Learn from the Weather? The New Climate–Economy Literature. J. Econ. Lit. 52, 740–798.

- Deryugina, T., Hsiang, S.M., 2014. Does the environment still matter? Daily temperature and income in the United States. National Bureau of Economic Research.

- Deschênes, O., Greenstone, M., 2007. The Economic Impacts of Climate Change: Evidence from Agricultural Output and Random Fluctuations in Weather. Am. Econ. Rev. 97, 354–385.

- Deschênes, O., Greenstone, M., 2011. Climate Change, Mortality, and Adaptation: Evidence from Annual Fluctuations in Weather in the US. Am. Econ. J. Appl. Econ. 152–185.

- Deschênes, O., Greenstone, M., 2012. The economic impacts of climate change: evidence from agricultural output and random fluctuations in weather: reply. Am. Econ. Rev. 102, 3761–3773.

- Feng, S., Krueger, A.B., Oppenheimer, M., 2010. Linkages among climate change, crop yields and Mexico–US cross‐border migration. Proc. Natl
_− NMAE_ , which is the share of the total absolute variation in the demeaned log price data explained by the imputation model. The _D_ values are averaged as follows.

where _Z_ is the Fischer Z-transformation and _Z_<sup>_−_1</sup> its inverse, and _w_ are the relative weights of the price component in the final price index. The final score from equation 4 roughly has the interpretation of the average _R_<sup>2</sup> of the food price index, using a robust calculation of out-of-sample errors (Andr ́ee, 2021).<sup>4</sup>

> 4The Fischer Z-transformation is used because 1 _−NMAE_ � approaches the R-squared when prediction
Harriet O. and Mark C. Regets. 1994 . “The Elusive Concept of Immigrant Quality: Evidence from 1970-1990.” Discussion Paper PRIP-UI-31, The Urban Institute, Washington, DC. Published in 2002 as IZA Discussion Paper No. 631.

Duleep, Harriet O. and Mark C. Regets. 1997 . “The Decline in Immigrant Entry Earnings: Less Transferable Skills or Lower Ability?” The Quarterly Review of Economics and Finance, 37 Special Issue: 189-208.

Duleep, Harriet O. and Mark C. Regets. 1999 . “Immigrants and Human-Capital Investment.” American Economic Review, 89(2): 182-191.

Duleep, Harriet O. and Daniel J. Dowhan. 2002. “Insight from Longitudinal Data and the Earnings Growth of U.S. Foreign-Born Men. Demography, 39(3): 485-506.

Friedberg, Rachel. 1992. “The Labor Market Assimilation of Immigrants in the United States: The Role of Age at Arrival.” Unpublished manuscript. Providence RI: Brown University.

24
, 97% searched for work in the week before the baseline. In that week, they spent on average 17 hours and 242 South African rand (40 USD PPP) searching. The relatively high search costs suggest that welfare gains for workseekers are possible from improved search targeting. Workseekers submitted on average 10 applications in the preceding month and received 1.2 offers, though the medians for both measures are zero. The job search and application process is somewhat formal: 38% of the candidates employed at endline reported that they submitted written applications for their current job and 47% reported that they had a formal interview.

# **2.5 Assessments**

We conduct six assessments with workseekers: communication, concept formation (similar to a Raven’s test), focus, grit, numeracy, and planning. Firms have demonstrated interest in the results of these assessments, though they obviously also use other information in hiring decisions. Client firms have paid Harambee to screen roughly 160,000 prospective workers using these assessments. Appendix A describes each assessment in detail, their psychometric properties, and how some Harambee client firms use them in hiring.

Each assessment session is led by two or three industrial psychologists, who manage a team of

> 12See Garlick et al. (2019) for an experimental validation of labor market data from phone surveys in this setting.

10
Policy Research Working Paper 9917

# **Abstract**

Building upon the literature on contact theory, this paper explores the role of inter-group interaction in shaping social cohesion between refugees and host communities in East Africa. It draws upon first-hand quantitative (n=16,608) and qualitative data collected from refugees and nearby host communities in urban and camp-like contexts in Ethiopia, Kenya, and Uganda. Focusing on the Uganda data, OLS regressions reveal a positive and significant correlation between refugee-host interaction and the perception of hosts towards refugees. This association disappears when an instrumental variable (IV) approach is used to address endogeneity issues, except when only data from the urban context is used. The analysis of cross-country data highlights further differences in the types of interaction and perception that matter between urban and camp-like contexts. It also suggests that ethno-linguistic proximity between refugee and host populations is associated with more positive attitudes. In all contexts, an important part of attitude formation appears to take place at the intra-group level, within households and immediate neighbourhoods, independently of individual interaction with the out-group. The paper proposes a series of policy recommendations to improve refugee-host social cohesion, with different approaches required in urban and camp-like contexts.

This paper is a product of the Social Sustainability and Inclusion 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 alexander.betts@qeh.ox.ac.uk.

_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 the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are