rafmacalaba/datause-encoder-data huggingface dataset:
# **Appendix 1: The Governance Database**

|A1.1 Business Environment Risk Intelligence (BERI) ....................................................... 29|
|---|
|A1.2 The Wall Street Joumal Central European Economic Review (CEER) .............................<br>31|
|A1.3 Standard and Poor's DRI/McGraw-Hill<br>(DRI) .......................................................<br>32|
|Al.4 European Bank for Reconstruction and Development (EBRD) ...................... ..................<br>34|
|A1.5 The Economist Intelligence Unit (EIU) .......................................................<br>36<br>|
|A1.6 Freedom House (FHFW, FHNT) .......................................................<br>40|
|A1.7 Gallup International<br>.......................................................<br>41|
|A1.8 World Economic Forum (GCS, GCSA) .......................................................<br>10|
|A1.9 Heritage Foundation/Wall<br>Street Joumal (HFWSJ) ....................................................... 46|
|Al1.10 Political Risk Services, Intemational Country Risk Guide (PRS/ICRG)<br>.<br>.<br>48|
|Al .11 Political Economic Risk Consultancy (PERC) ....................................................... 50|
|A1.12 Institute Management Development (WCY)<br>.........................................<br>52|
|Al .13 World Bank/University<br>of Basel (WDR) ......................<br>..<br>............................... 53|

28
- Jansen, Henrica, Charlotte Watts, Mary Ellsberg, Lori Heise, and Claudia Garcia-Moreno (2004). “Interviewer Training in the WHO Multi-Country Study on Women’s Health and Domestic Violence.” _Violence against Women_ 10 (7): 831–849.

- Jeong, Dahyeon, Shilpa Aggarwal, Jonathan Robinson, Naresh Kumar, Alan Spearot, and David S. Park (2021). “Exhaustive or Exhausting? Evidence on Survey Fatigue in Long Surveys.” Working Paper.

- Lensvelt-Mulders, Gerty J. L. M., Joop J. Hox, Peter G. M. van der Heijden, and Cora J. M. Maas (2005). “Meta-Analysis of Randomized Response Research: Thirty-Five Years of Validation.” _Sociological Methods & Research_ 33 (3): 319–348.

- Newman, Jessica Clark, Don C. Des Jarlais, Charles F. Turner, Jay Gribble, Phillip Cooley, and Denise Paone (2002). “The Differential Effects of Face-to-Face and Computer Interview Modes.” _American Journal of Public Health_ 92 (2): 294–297.

- Park, David S. and Naresh Kumar (2021). “Reducing Intimate Partner Violence: Evidence from a Multifaceted Female Empowerment Program in Urban Liberia.” Working Paper.

- Tourangeau, Roger and Ting Yan (2007). “Sensitive questions in surveys.” _Psychological Bulletin_ 133 (5): 859–883.

- WHO (1996). “Violence against women : WHO consultation, Geneva, 5-7 February, 1996.”

- (2012). “Understanding and addressing violence against women: Intimate partner violence.” World Health Organization Report.

- (2016). “Ethical and safety recommendations for intervention research on violence against women.” World Health Organization Report.

18
_Figure 1: Tolerable Rates of Missingness in GPS-Based Plot Areas Above a Given Distance Threshold for Plot Area & Plot-Level Yield Analysis_ <!-- Start of picture text --> Tolerable rates of plot area missingness<br>Malawi (1km threshold) Malawi (500m threshold)<br>82 93 45 52<br>0 20 40 60 80 100 0 20 40 60 80 100<br>% of plots missing beyond threshold % of plots missing beyond threshold<br>Ethiopia (1km threshold) Ethiopia (500m threshold)<br>56 73 36 48<br>0 20 40 60 80 100 0 20 40 60 80 100<br>% of plots missing beyond threshold % of plots missing beyond threshold<br>Plot area Land Productivity<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>identical to the 'Truth' identical to the 'Truth'<br># Imputations Statistically 0 # Imputations Statistically 0<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>identical to the 'Truth' identical to the 'Truth'<br># Imputations Statistically 0 # Imputations Statistically 0<br><!-- End of picture text --> First, at low rates of missingness, the distribution of each outcome variable in each of the 50 imputed datasets are statistically indistinguishable from the observed counterpart. As the rate of missingness increases, this count starts to decrease until only a small number (between 0 and 10) of the imputations appear to have distributions that are not statistically different from the true distribution. Second, the tolerable rates of missingness are lower with the 500-meter distance threshold in comparison to the 1- kilometer counterpart. Third, plot-level agricultural productivity is more sensitive to missingness than plot area (i.e. the tolerable rate of missingness is reached earlier in the case of the latter).

22
## **7.4 Public School Closings**

Unlike public schools, charter schools must close if they are unable to cover their costs. To investigate the effect of this rule on public schools, in the absence of public school cost data we simulate a counterfactual (“Close Public”) whereby a public school must close if its enrollment loss is greater than 40 percent during the sample period. As column 6 of Table 8 shows, this leads to closing approximately 49 public schools, affecting about 14 percent of all K-12 students. Note that DCPS actually closed 33 schools between 2008 and 2012; most closings affected elementary or K-8 schools in the Northeast or Southeast. Our counterfactual captures these aspects.

In the simulations, about 64, 29 and 7 percent of the displaced students switch into (other) public, charter of private schools respectively, consistent with the observed reallocation of students after the 2008 closings (documented in www.21csf.org). Because the schools that are predicted to close lag behind in proficiency, quality and value added, most student groups enjoy proficiency gains (see Appendix Tables 11 and 12). For instance, middle-school black students attain an average proficiency gain of 3.17 percentage points. Nonetheless, Appendix Table 13 shows that most students suffer welfare losses from the elimination of school options. Thus, although the closings lower total social costs (see Table 11’s row 6), they lower total social benefits even more. Our estimated per-school net social loss from these closings is between $603,000 and $755,000 depending on cost assumptions. This loss, however, is lower than the per-school loss of closing charter schools (approximately $1,000,000).

By simulating a closing rule strictly based on enrollment rather than the multiple factors actually considered by DCPS for closings (such as student travel time and facility condition), we likely overpredict closings and hence welfare losses. Nonetheless, the counterfactual suggests in the short run students would be less hurt by the closing of a low-enrollment public school than of an average charter. Further, losses from public school closings might be reversed in the long run through the entry of new charters serving the displaced students.

# **8 Conclusion**

In this paper we develop and estimate a rich yet tractable model of charter school entry and household school choice. We model the equilibrium sorting of households across schools as well as regulator behavior. We estimate the model using data on the full choice set of schools in Washington, D.C. between 2003 and 2007. Our estimates indicate that charter schools have generated net welfare gains in the city by providing new options to serve a heterogeneous population. Welfare gains have been particularly large for middle school- and for non-white, low-income students, whose options before charters were quite limited. According to our counterfactuals, raising the supply 37
generally occurs most frequently in the lowest quintiles of the consumption distribution. The odds of employment in casual non-farm wage labor are less than one in the top quintiles and greater than one in the bottom three quintiles. There is little evidence of systematic change in these patterns over time.

Non-farm self-employment activities tend to be more evenly distributed over the consumption distribution, indicating that both poor households as well as rich households are involved in such activities (Table 5c). On balance, however, the odds of selfemployment in the non-farm sector are slightly higher in the top three quintiles of the consumption distribution suggesting that such activities are more frequent among the relatively better off. There is some suggestion that in the 2004/5 survey year the gradient between self-employment in the non-farm sector and consumption quintiles has weakened somewhat. While in this year the percentage of self-employed in the non-farm sector is still lowest in the bottom quintile, the odds of participation are higher than in the other survey years and analogously, the odds are somewhat lower in the top quintile.

The patterns of employment observed across the consumption distribution also tend to be repeated in terms of other dimensions of well-being such as education and caste status (Kijima and Lanjouw, 2005). Education levels and social status are generally highest among those with regular non-farm employment while casual non-farm employment is more common among the illiterate and scheduled caste and scheduled tribe households. Over time there is some suggestion that casual non-farm employment has become slightly more strongly correlated with higher education levels. This is consistent with gradually rising education levels in rural India over time, and a tendency for those with some education to crowd out the uneducated in casual non-farm employment (see further below).

# _Non-Farm Employment Probabilities_

Of course, education and social status are not just of interest as intrinsic indicators of welfare, but are also likely to play an instrumental role in determining income or consumption levels – influencing for example, individuals’ _access_ to non-farm opportunities. The relationship between occupational choice and household characteristics is explored more systematically in Appendix Tables 3-7 on the basis of 12
of evidence from which to evaluate the behavioral ramifications of displacement in the contexts in which internal displacement is most prevalent.

Our research contributes to a growing body of evidence on the consequences of displacement for political and social behavior. Our results are consistent with Denny, Dow, Levy & VillamizarChaparro (2021), who show that experiences with extortion increased downstream participation in Guatemala. We also demonstrate that in certain cases and across some indicators displacement experience increases subsequent political and social engagement, but we caution that our results suggest that contextual factors structure these relationships and that different manifestations of engagement may have different relationships with displacement experience. In line with the findings in Pham, O’Mealia, Wei, Bindu, Makoond & Vinck (2021), who show that hosting displaced persons differentially impacts perceptions of social cohesion based on the type of displacee hosted and based on the gender of the host, we highlight the complexity and heterogeneity of the relationships between displacement and social cohesion.

The rest of the paper is divided into 5 parts. In Section 2, we describe the contexts of displacement that our surveys capture; in Section 3, we situate the experience of forced displacement in the broader literature on the behavioral ramifications of experiences of trauma (Section 3.1) before presenting a theory of why the experience of forced displacement can increase perceptions of social and political empowerment (Section 3.2). We then describe our research design in Section 4 before presenting our results in Section 5. Finally, we conclude by discussing the implications of our findings for policy and programs seeking to build social cohesion in forced displacement contexts in Section 6.

# **2 Contexts**

In addition to differences in personal experiences driven by chance, gender, age, and other demographic characteristics, an individual’s experience of displacement likely varies according to the institutional and regional context in which their displacement occurs. In this section, we describe the contexts in which we conducted surveys and the analytical opportunities and challenges that these contexts provide.<sup>6</sup> We analyze the experiences of displaced persons in three contexts: Mosul, Iraq; Central Mindanao, Philippines; and Acholi, Uganda. Table 1 provides descriptive characterizations of the contexts that we analyze at the time of enumeration.

> 6In this section, we focus on the broader context of violence, displacement, and politics, not the specifics of the survey design. Additional details on each surveys’ sampling scheme and enumeration procedures are available in Section 4 and in the Appendix, Section A.1.

3
Bellak, C. 2004. How Domestic and Foreign Firms Differ and Why Does it Matter? _Journal of Economic Surveys_ 18(4): 483-514.

Beverelli, C., Fiorini, M., and Hoekman, B. 2017. Services Trade Policy and Manufacturing Productivity: The Role of Institutions. _Journal of International Economics_ 104: 166-182.

Blalock, G., and Simon, D.H. 2009. Do All Firms Benefit Equally From Downstream FDI? The Moderating Effect of Local Suppliers’ Capabilities on Productivity Gains. _Journal of International Business Studies_ 40(7): 1095-1112.

Chaudhry, T.T., and Faran, M. 2016. Same Jeans, Same Stitch? A Comparison of Denim Production Across Three Factories in Punjab, Pakistan. _The Lahore Journal of Economics_ 21: 211.

Chen, W. 2011. The Effect of Investor Origin on Firm Performance: Domestic and Foreign Direct Investment in the United States. _Journal of International Economics_ 83(2): 219-228.

Choudhary A., Lemos R., and Van Reenen J. 2018. Management in Pakistan: Performance and Conflict, IGC working paper F-89459-PAK-1.

Crespi, G., Criscuolo, C., Haskel, J.E., and Slaughter, M. 2008. Productivity Growth, Knowledge Flows, and Spillovers (No. w13959). National Bureau of Economic Research.

Defever, F., Imbruno, M. and Kneller, R., 2020. Trade liberalization, input intermediaries and firm productivity: Evidence from China. Journal of International Economics, vol 126, p.103329.

Duggan, V., Rahardja, S., and Varela, G. 2013. Service Sector Reform and Manufacturing Productivity: Evidence from Indonesia. World Bank Policy Research Working Paper (6349).

Fernandes, A.M., and Paunov, C. 2012. Foreign Direct Investment in Services and Manufacturing Productivity: Evidence for Chile. _Journal of Development Economics_ 97(2): 305-321.

Foster, L., Haltiwanger, J., and Syverson, C. 2008. Reallocation, Firm Turnover, and Efficiency: Selection on Productivity or Profitability? _American Economic Review_ 98(1): 394-425.

Goldberg, P.K., Khandelwal, A.K., Pavcnik, N., and Topalova, P. 2010. Imported Intermediate Inputs and Domestic Product Growth: Evidence from India. _The Quarterly Journal of Economics_ 125(4): 1727-1767.

Goldsmith-Pinkham, Paul, Isaac Sorkin, and Henry Swift. 2020. Bartik Instruments: What, When, Why, and How. _American Economic Review_ 110(8): 2586-2624 Government of Pakistan. 2011. “Pakistan: Framework for Economic Growth.” Planning Commission, Islamabad.

Grossman, G.M., and Helpman, E. 1991. Trade, Knowledge Spillovers, and Growth. _European Economic Review_ 35(2-3): 517-526.

Haseeb, M., and Chaudhry, T.T. 2014. Resource Misallocation and Aggregate Productivity in Punjab.

24
Indonesia, and lower-income ASEAN economies (primarily because of large landmass and population size). There is substantial demand for upgrading and maintenance of infrastructure in other regional economies, including Malaysia, the Philippines, and Thailand.

_Infrastructure upgrades and challenges._ Despite some remarkable successes, providing adequate transport networks, power, water, and other facilities remains a challenge across much of the region (Figure 7).

_Infrastructure projects underway._ Extensive construction activities are underway in the region (BMI 2016). Transport, especially rail, accounts for the largest share. The aim is to integrate the region's transport networks, and to accommodate rapid urbanization.<sup>5</sup> These projects are supported by government initiatives such as the China’s One Belt One Road.

- _China’s_ highway network more than doubled in size between 2004 and 2014, and the share of high-speed railways was boosted from 33 percent to 50 percent of total railway kilometers. Yet, transport density still falls far short of that in advanced economies. Infrastructure needs vary considerably across Chinese regions, and range from high-profile projects (such as high-speed railways) to installing basic municipal infrastructure and pollution-reducing or -reversing technologies (World Bank 2013a, World Bank and DRC 2014).

- Lack of adequate infrastructure are the main cause of _Indonesia’s_ high logistics costs (around 17 percent of companies’ total expenditure). Transport costs are high. About onequarter of the population of Indonesia remains without electricity.

- In _Lao PDR_ , _Cambodia_ , and _Vietnam_ , investment in basic road infrastructure is a priority (World Bank and Vietnam Ministry of Planning 2016).

- In _Malaysia_ , high-profile projects like the expansion of the public transport system in Kuala Lumpur, and airport and port upgrades, are anticipated to proceed through 2020. Middle-income ASEAN countries in general, such as _Malaysia and Thailand_ , are still investing heavily in the rail and public transport systems.

- The _Philippines_ is particularly weak with regard to transport and trade-related infrastructure. It continues to rank above 100 globally in the overall state of its infrastructure (World Economic Forum 2015), with particularly low rankings for the quality of its seaports and airports. About one-quarter of the population remains without electricity.

- In many East Asian countries, about one-third of the population lives in substandard housing.

> 5 For example, in addition to 170 cities in China with populations exceeding 1 million, China is expected to gain 292 million city-dwellers by 2050 (World Economic Forum 2015).

13
approaches, the reference group defining the international line changes, and the resulting line would no longer be based on the same 15 countries that produced the $1.25 line.

Jolliffe and Prydz (2015) have suggested that a larger set of more recent national lines could be used in setting a new poverty line. They propose a methodology for constructing a global poverty line that is less sensitive to CPI data quality issues, more robust to the smallsample problems (i.e. the 15-country approach), and also significantly increases the coverage of the population of poor people. They use a sample of more than 600 national poverty thresholds estimated from national poverty headcounts. Taking the median of the most recent national thresholds of 32 countries classified by the World Bank as “low income” yields a “Low-Income Country poverty line” of $1.92 in 2011 PPPs.<sup>55</sup> This method also has the attractive feature that the estimated line in 2005 PPPs is exactly $1.25, and as such is comparable with the poverty line used with the 2005 PPPs. Defining a line from 32 low-income countries more than doubles the coverage of the poor relative to the 15-country approach, representing about 25 percent of the population of the global poor in 2011. Once again, although the conceptual pathway is somewhat different, this final line also rounds to $1.90 a day.

Finally, some observers have argued that the most important aspect of holding the poverty line (or at least the ‘goal post’) fixed is to keep the poverty headcount fixed when switching from the 2005 to 2011 PPPs. Sillers (2015) proposed a ‘continuous headcount’ approach in which the global poverty line is updated such that the global headcount is unchanged. His estimate is that the line which would in 2011 keep the global headcount unchanged compared with the $1.25 line, would be $1.78 per day at 2011 PPP. That estimation, however, relies on different rural-urban adjustments for China, India and Indonesia, which are updated in the present analysis. When we use the new adjusted 2011 PPPs, and follow the Sillers methodology, preliminary estimates suggest that the line that would keep the global headcount unchanged in 2011 would be around 1.92 per day.

All in all, most of the alternative approaches that have been proposed for updating the international poverty line to 2011 PPPs end up generating lines that are either exactly or very close to $1.90 a day. We take this as providing reassurance that our approach – deliberately

> 55 As of 1 July 2014, low-income economies are defined as those with a GNI per capita, calculated using the World Bank Atlas method, of $1,045 or less in 2013.

33
20-A Figure 7.a <!-- Start of picture text --> Argentina: Real Commercial and Parallel Exchange Rates<br>Quawity, 19J01 *19U3:3,.<br>XO<br>30<br>1S OU I 1§0 1401§0 1M1191 1§1 1911 list 12 : 1C IM I 1943 1943 1963 1913<br>_. Rol C*mmrcW Excbage Ragc (EP-/P? Rea Plle l Exchange Rae<br>_rK (1*51)<br><!-- End of picture text --> Figure 7.b <!-- Start of picture text --> Official and Parallel RealFExchange Rates<br>Venezuia. 1983U4<br>U<br>23<br>IS<br>e10 1<br>1 2 1 4 3 o T 3 10 11 1: I T 3 4 s X I 10 It 12<br>_ Real Official Exchaz*e Rame CE) -... R:JI Pmnile Exchange Rure<br>_"_m Ws a wow CaNi VAts5<br><!-- End of picture text -->
<!-- Start of picture text --> Figure 8<br>Dominican Republic and Haiti: Credit to the Private Sector, 1963-2007<br>___ Dominican Republic _ _ _ Haiti<br>0.6 0.6<br>Credit to the Private Sector<br>(Unit to GDP)<br>DR<br>0.5 0.5<br>0.4 0.4<br>0.3 0.3<br>HT<br>0.2 0.2<br>0.1 0.1<br>0 0<br>1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007<br><!-- End of picture text --> Source: IMF.

31
**Table 2:  Regression Results: Dar es Salaam Weather Factors in Mean NO2 Concentration**

|**Temperature (C)**|**-0.512**<br>**(2.77)****|
|---|---|
|**Wind Speed (km/hr)**|**-0.254**<br>**(2.66)****|
|**Humidity (%)**|**-0.257**<br>**(3.06)****|
|**Cloud Cover (%)**|**-0.072**<br>**(3.00)****|
|**Constant**|**51.847**<br>**(5.75)****|
|**Observations**|**288**|
|**R-squared**|**0.25**|

Absolute value of t statistics in parentheses * significant at 5%; ** significant at 1%

**Figure 5:  Predicted monthly mean NO2 concentration from weather factors and cloud cover**

<!-- Start of picture text --> 16<br>15<br>14<br>13<br>12<br>11<br>10<br>9<br>8<br>Month_Year<br>Concentration Index<br>Predicted Monthly Mean NO2<br><!-- End of picture text -->