rafmacalaba/data-use-mentions-extended huggingface dataset:
Different types of adaptation options, including hard and soft options, are often hard to compare to one another in a quantitative manner, in particular softer solutions that do not reduce asset risk. Here, we have quantified the wider benefits of hard infrastructure solutions, while our framework also allows complementary interventions such as flood-proofing critical infrastructure and improved disaster recovery efforts. Although not done in this work, putting a dollar value on experienced service disruptions, or temporary MDP incidence, one could include such wider welfare impacts in the CBA, allowing one to compare and contrast different interventions in a more comprehensive way. As such, the framework presented provides the basis for evaluating how portfolios of interventions can reduce both asset risk as well as benefit people in terms of welfare, underlining that a holistic portfolio including hard and certain soft interventions (only those affecting service disruptions, not those related to consumption such as direct payments), is required to achieve both. In this work, we have only evaluated a limited number of interventions, and did not run combinations of interventions. In particular, evaluating the benefits of complementary portfolios of interventions allows looking at the synergies and trade-offs between interventions and is a promising avenue of future research.

The methodological advances presented in this working paper are driven by the recent developments in terms of constructing synthetic population datasets of entire countries, as done for Bangladesh here, including realistic, but synthetic, locations. The entire methodology (except the flood hazard maps) relies on publicly available data sources, making it (in theory) possible to replicate such analysis in other regions globally. However, extending the risk analysis from asset damages alone to the welfare implications of individual households increases the complexity, data requirements, and computational resources considerably. For instance, constructing the synthetic household data relies on detailed Census data, which is not available everywhere, as well as being very computationally intensive for large countries. As such, the quality of the synthetic household output, including which variables can be considered, is dependent on the level
of every household and to tailor the transfer received by each household perfectly) is provided by the FGT1 measure of poverty (weighted by the poverty line and the total population). Thus, we can calculate from our poverty mapping database the hypothetical cost of eliminating poverty if it were possible to target the poor perfectly (and there were no behavioral responses). We can then take this budget and target it, instead, geographically, at the lowest level of geographic disaggregation that we feel that the poverty map can support. How far are we from having eliminated poverty when our transfer occurs at this geographic level rather than having been tailored to the precise circumstances of each poor household? In rural Ecuador, on the basis of the lower poverty line, optimal parroquia-level geographic targeting of this budget reduces the FGT2 from 0.028 to 0.0177, only a 37 percentage point decline. At the higher poverty line, optimal geographic targeting of the budget that, in principle, could eliminate poverty altogether, reduces the FGT2 from 0.070 to 0.032, a 54 percentage point decline. Very similar results obtain in Madagascar and Cambodia (for example, see Table 6 for Cambodia).

Why does optimal geographic targeting on the basis of a detailed poverty map fall so far short of the ideal? In a companion paper, Elbers et al (2004) analyze evidence on the level and variation of inequality within poor communities. They show that in three countries, including two of the countries examined here (Ecuador and Madagascar), within-community inequality varies widely across communities. Some communities exhibit levels of inequality as high, or higher, than at the national level, while others are significantly more equal. An important conclusion from this study is that there should be no presumption of lower levels of inequality in poor communities. In fact, in the three countries studied by Elbers et al (2004), median inequality is highest amongst the bottom quintile of communities (ranked in terms of 27
without any specific leadership or management preparation. Second, school actors have limited information on _who_ is at risk of dropping out. Data on mandatory “dropout risk flags” reported by school principals and teachers show that they identify on average fewer than 5% of the students who end up dropping out in the transition from primary to lower secondary.<sup>1</sup> Third, there are several parallel demands and multiple tasks that compete for the attention of principals and teachers, which may prevent dropout prevention from being top-of-mind, particularly since dropout in the transition from primary to lower secondary occurs after students have left their primary schools and are no longer under their official responsibility. For instance, surveys of school principals show that reducing dropout is not a top priority for them, as they rank it third on average out of five options (below teaching/curriculum and internal administrative tasks).

Considering these gaps, the government designed ENTRE to combine three key components: a brief training on actionable, evidence-based strategies to support students to stay in school; information on sixth-grade students at high risk of dropping out; and small behavioral nudges to try and keep dropout prevention top-of-mind for principals and teachers.

# <u>Component 1: Short training on actionable strategies to prevent dropout</u>

The first component consists of a half-day training for school principals and sixth-grade teachers on specific, evidence-based methods to help students make the transition from primary to lower secondary – in essence, providing them more information on _how_ to help students stay in school. The training was complemented by a short and user-friendly guidance manual (13 pages of text and informative illustrations in the main manual, plus 13 pages of additional resources in annex) that summarizes the training content, a copy of which was given to each participant at the beginning of the half-day training. Along with the guide, the school principals received a personalized letter from the Minister of Education that emphasized the importance of every Guatemalan child completing at least nine years of basic education and
Springer Science+Business Media. DOI 10.1007/978-1-4614-7138-7 Jolliffe, I.T. 2002. Principal Component Analysis. 2nd Edition, Springer-Verlag. https://doi.org/10.1007/b98835 Kabeer, N. 2005. Gender equality and women’s empowerment: A critical analysis of the third millennium development goal. _Gender and Development_ , 13(1), 13–24.

Kaiser, H. F. 1960. The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20, 141–151. http://dx.doi.org/10.1177/001316446002000116 Ketchen, D. J., and C.L. Shook. 1996. The application of cluster analysis in strategic management research: An analysis and critique. Strategic Management Journal, 17(6), 441–458.

Kleven, H., C. Landais, J. Posch, A. Steinhauer, and J. Zweimüller. 2019. Child penalties across countries: Evidence and explanations. _American Economic Association Papers and Proceedings_ , 109: 122-126 Latinobarometro Corporation (2023). Latinobarómetro 2023 [dataset]. Retrieved from https://www.latinobarometro.org/latContents.jsp Lee, N. T., P. Resnick, and G. Barton. 2019. Algorithmic bias detection and mitigation: Best practices and policies to reduce consumer harms. Brookings Research. Washington, DC: Brookings Institution.

Liu, H., W. Wang, Y. Wang, H. Liu, Z. Liu, and J. Tang. 2020. Mitigating gender bias for neural dialogue generation with adversarial learning. arXiv:2009.13028.

21
003<sup>***</sup>|0.008<sup>***</sup>|-0.006<sup>***</sup>|0.002<sup>***</sup>|
|<br>_(initial level, logs)_|(0.000)|(0.011)|(0.000)|(0.000)|(0.001)|(0.000)|
|ICRG Political Risk Index|0.004<sup>***</sup>|0.000|0.004<sup>***</sup>|0.003<sup>***</sup>|-0.000|0.004<sup>***</sup>|
|_(principal components)_|(0.000)|(0.734)|(0.000)|(0.000)|(0.491)|(0.000)|
|Credit to private sector|-0.017<sup>***</sup>|-0.009<sup>***</sup>|-0.011<sup>***</sup>|-0.019<sup>***</sup>|-0.010<sup>***</sup>|-0.012<sup>***</sup>|
|_(% of GDP)_|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Government consumption|0.011<sup>***</sup>|0.016<sup>
of formerly sponsored men, and they may also reflect that a husband’s income has a stronger influence on the type of dwelling in adulthood.

Impacts on consumer durables are virtually identical between formerly sponsored men and women, where estimations indicate a strong and significant impact on cell phone ownership for both genders but no effect on ownership of a bicycle, motorcycle, car, or land.

Another measure of wealth in adulthood relates to where an individual resides as an adult. Does a grown adult remain in her parents’ home, in a rented home, or in an owned home? Typically living in a home apart from parents is desirable after a certain age, especially for married couples, but this is not always economically feasible. Baseline values among the untreated show 46.8% living in the parents’ home, 23.8% living in a rented home, and 29.4 living in a home owned by the individual or jointly owned with a spouse. Table 12 shows multinomial logit estimations indicating that formerly sponsored individuals are much less likely to remain living in their parents’ home (the base category). There is some indication that women are more likely to live in an owned home, but marginal effects are insignificant. Instead of living in their parents’ home as adults at the time of the survey, sponsored individuals are 4.8 percentage points more likely to live in a home rented themselves, about two percentage point higher for men than for women.

# IV. CONCLUSION

International child sponsorship is a leading form of individual contact and financial assistance between ordinary people in developed countries and the poor in developing countries, yet little has been known about the impact of these programs on the economic outcomes in adulthood of sponsored children. Our more conservative Heckman estimates from a six-country study of 10,144 individuals show that child sponsorship is responsible for increases in monthly income of about $13–17 over an unconditional baseline of $75, or an increase of 17.3–22.9%
3.72|8.26|0.01|2.92|
|Kyrgyz|17.21|58.49|4.00|5.97|6.33|3.20|0.66|7.47|5.24|8.73|2.08|4.83|
|Moldova|25.30|49.17|4.82|22.49|8.18|2.95|0.02|10.32|7.50|7.52|0.34|8.09|
|Uzbekistan|10.74|79.32|1.79|5.03|4.48|4.80|0.04|5.68|2.83|8.70|0.43|2.90|
|Turkmenistan|3.47|37.04|2.83|5.01|1.93|2.41|0.18|6.36|1.75|6.99|0.94|3.77|
|Azerbaijan|4.31|109.94|3.05|8.35|2.61|7.41|2.39|8.48|0.25|7.56|0.09|3.41|
|Armenia|7.40|19.40|2.17|6.68|4.74|1.83|0.00|5.85|2.97|4.94|0.02|2.90|
|Ukraine|18.27|47.18|3.44|19.46|7.70|1.16|0.26|8.83|2.18|7.81|1.63|2.23|

Source: Authors’ calculations using data on tariffs from UNCTAD
, 2020) find that countries which have FDI, even at the broad (horizontal) level, also have greater GVC participation. But the concern tends to be on attracting FDI itself. The evidence seems to be country-specific, sector disaggregation is very broad, and the spillover mechanisms are not always clear.

Farole and Winkler (2015), in a comprehensive study of FDI spillovers in Africa, identify ‘expost’ the salient characteristics of firms that are inserted in GVCs, also using the WBES. They find that firms inserted into GVCs typically have good technology, the host country has good institutions and political stability (for non-resource-rich countries) and thus have the capacity to benefit from FDI spillovers (for example through their superior capacity to absorb new technologies). Winkler
4<br>10<br>12<br>8<br>6 8<br>6<br>4<br>4<br>2 2<br>0 0<br><!-- End of picture text -->

**Quantity of Seeds**

<!-- Start of picture text --> 0.0 0.2 0.4<br>8<br>6<br>4<br>2<br>0<br><!-- End of picture text -->

**Days of household Labor**

**Days of household Labor**

**Days of household Labor**

<!-- Start of picture text --> −0.1 0.0 0.1 0.2 −0.1 0.0 0.1 0.2 −0.1 0.0 0.1 0.2<br>β β β<br>12<br>20 20<br>8<br>10 10 6<br>4<br>5 5<br>2<br>0 0 0<br><!-- End of picture text --> 25
the impact of COVID-19, we focus on Dhaka and aggregate the daily data to monthly frequency. To date, Dhaka has seen much higher numbers of infections than other divisions and accounts for nearly half of all deaths caused by COVID-19. Moreover, electricity consumption in Dhaka is clearly linked to economic production and measured more precisely than in other divisions.<sup>9</sup> During the first national lockdown (end of March to May 2020), electricity consumption was substantially lower than predicted by the model. It dropped 40 percent below normal in April and remained 27 percent below normal in May (see figure 7). Relaxation of the containment measures resulted in subsequent recovery of electricity consumption levels. During the ripple starting in November 2020, electricity consumption declined again. It was around 15 percent lower than usual between November 2020 and February 2021. The deviations in April and May 2020, as well as between November 2020 to February 2021, were statistically significant at least at the 5 percent level.

> 8 After 18 months of closure, schools and colleges in Bangladesh were re-opened from mid-September 2021. 9 At 9.6 percent, Dhaka has the smallest average annual standard deviation in daily electricity consumption growth among the divisions, followed closely by Chattogram, at 10.7 percent. The standard deviations are larger for the other divisions, varying from 15.9 percent in Sylhet to 47.9 percent in Rajshahi.

14
, and Tomasz Mickiewicz, “Do Institutions Have Greater Effects on Female Entrepreneurs?”, IZA Working Paper 4577 (2009).

- Evans, David, and Linda Leighton, “Some Empirical Aspects of Entrepreneurship”, American Economic Review 79 (1989), 519-535.

- Fernandes, Ana, and Ariel Pakes, “Factor Utilization in Indian Manufacturing: A Look at the World Bank Investment Climate Survey Data”, Working Paper (2010).

- Fernandes, Ana, and Gunjan Sharma, “Together We Stand? Agglomeration in Indian Manufacturing”, Working Paper (2011).

- Field, Erica, Seema Jayachandran, and Rohini Pande, “Do Traditional Institutions Constrain FemaleEntrepreneurship?A Field Experimenton Business Training in India”, American Economic Review Papers and Proceedings 100 (2010).

- Figueiredo, Octávio, Paulo Guimaraes, and Douglas Woodward, “Home-Field Advantage: Location Decisions of Portuguese Entrepreneurs”, Journal of Urban Economics 52:2 (2002), 341-361.

Florida, Richard, Cities and the Creative Class (New York, NY: Routledge, 2005).

23
# **1 Introduction**

The ongoing debate about the extent to which democratization promotes growth (Acemoglu et al., 2019; <mark>Papaioannou and Siourounis,</mark> 2008; Persson and Tabellini, 2006; Rodrik and Wacziarg, 2005) brings into focus the relationship between political systems and capitalism, and the channels through which political contestability affects incentives and conditions for firms to enter, invest and grow. How political institutions shape the trade-off between protecting the property rights and rents of incumbents and promoting entry and demands for redistribution is at the core of this debate (Rajan and Zingales, 2003). Autocracies have been argued to be more prone to corruption and capture by incumbents (Acemoglu, 2008, Treisman, 2000; Chowdhury, 2004). Democratic institutions, in contrast, are often argued to level the playing field and promote entry by constraining public corruption (Adserà et al., 2003; RoseAckerman, 1999) and reducing regulatory protection for incumbent firms (Acemoglu, 2008). Yet, direct evidence on the impact of democratization on competition is limited.

This paper tests whether democratization promotes economic competition by assessing whether the severing of crony ties to President Suharto associated with Indonesia’s democratic transition leads to enhanced competition in sectors disproportionately exposed to cronyism. To this end, we use plant-level manufacturing census data in which firms owned by Suharto family members and cronies are identified. We assess the impact of political turnover on both firm- and industry-level outcomes, exploiting President Suharto’s fall and the removal of district mayors appointed by him as a quasi-natural experiment generating temporal and spatial (e.g. across district) variation in the value of political connections to him. By comparing firm-level impacts of political turnover with industry-level impacts, we aim to quantify both the distributional and efficiency implications of political connections.

Indonesia provides a suitable environment for analyzing the effect of democratization on competition. Suharto’s autocratic regime was marked by state capture. His family had extensive and highly diversified business interests and is
**Government leverage.** If government deficit financing tends to crowd out corporate debt financing through competition for investor funds, a negative association may be expected between government and private leverage. This association is empirically supported by a study based on data for the United States (Graham, Leary, and Roberts 2015).

**Financial Market Efficiency** . Financial market development should reduce the cost of raising capital on the market, thus decreasing reliance on corporate debt and increasing corporate savings (Demirgüç-Kunt and Maksimovic 1999; de Jong, Kabir, and Nguyen 2008). But the relationship between financial market efficiency and cash holdings is unclear. In countries with better developed financial markets, firms can more easily raise funds when needed so that they should not need to hold large amounts of cash.  Past studies have discussed the implication that firms in countries lagging in financial development may be expected to hold more cash (Damodaran 2005). However, more recent studies—(Kalcheva and Lins 2007; Lins, Servaes, and Tufano 2010; Pinkowitz, Stulz, and Williamson 2016)—have found the opposite result, or mixed results, so that the evidence on the effect of capital market development on corporate savings is mixed.

**Quality of governance** . The quality of a country’s governance and institutions may also affect

corporate savings. A greater risk of expropriation will discourage firms from investing in assets that can be more easily expropriated. Thus, this will tend to reduce cash holdings by firms in countries with high corruption (Pinkowitz, Stulz, and Williamson 2016; Smith 2016). Also to limit expropriation, firms in countries with weaker governance will tend to use more debt (Fan, Titman, and Twite 2012; Smith 2016). Therefore, we would expect a negative association between the quality of institutions and corporate savings.

9
The Journal of Applied Economic Research_ 6, 103–32, 2012. doi:10.1177/097380101200600202

- Chen, S. and M Ravallion, “The Developing World is Poorer than We Thought, But No Less Successful in the Fight Against Poverty,” _The Quarterly Journal of Economics_ , 125, 1577– 1625, 2010. doi: 10.1162/qjec.2010.125.4.1577

- Damgaard, J., T. Elkjaer and N. Johannesen, “What Is Real and What Is Not in the Global FDI Network?,” _IMF Working Papers_ 19, 2019. doi:10.5089/9781513521527.001

- Deaton, A, “Measuring Poverty in a Growing World (or Measuring Growth in a Poor World),” _Review of Economics and Statistics_ 87, 1–19, 2005. doi:10.1162/0034653053327612

- Deaton, A. and V. Kozel, “Data and Dogma: the Great Indian Poverty Debate,” _The World Bank Research Observer_ , 20, 177–199, 2005. doi:10.1093/wbro/lki009

- Deaton, A., and S. Zaidi, “Guidelines for Constructing Consumption Aggregates for Welfare Analysis.” Living Standards Measurement Study, Working Paper no. 135. World Bank, Washington, DC, 2002.

- Engle-Stone, R., C. Sununtnasuk and J.L. Fiedler, “Investigating the Significance of the Data Collection Period of Household Consumption and Expenditures Surveys for Food and Nutrition Policymaking: Analysis of the 2010 Bangladesh Household Income and

31
database. However, billed charges are a misleading indicator of the actual price of the procedure, since they are an inflated price on which provider discounts to the insurer are based. Typically, only uninsured patients are asked to pay the hospital “billed charges”. Thus, while an uninsured patient may be asked to pay a hospital over $13,000 for a hysterectomy, an insured patient’s health plan may end up paying a negotiated discounted rate of only about $4,500 for the same procedure, with the insured patient paying only their $250 deductible and a co-payment. The price for a procedure is made up of the insurer’s payments to the hospital, physician, anesthetist, and other professionals, and the patient’s co-payment. We now turn to the methodology for calculating each of these.

In order to accurately estimate the prices of our selected procedures, we relied on the Medicare provider payment datasets made publicly available by the Centers for Medicare and Medicaid Services (CMS, hereafter). For inpatient procedures, we first identified the relevant Diagnosis Related Group (DRG) code for each procedure from the DRG Expert coding manual, which also lists the national average payment Medicare makes to hospitals.<sup>23</sup> The hospital DRG payment rate varies by hospital, procedure, case complexity, and hospital resource use by the patient. For example, the national average hospital payment rate for a knee replacement (DRG 209) is $9,261, whereas it averages only $4,206 for hernia repairs (DRGs 161 and 162). For outpatient procedures, the hospital/facility fee was obtained from the Medicare Outpatient Prospective Payment System dataset. Unlike inpatient DRGs, outpatient procedures are coded and priced by Current Procedural Terminology (CPT) codes. For example, the facility fee for a toe deformity repair (CPT 29290) performed on an outpatient basis is $1,525, while that for a shoulder joint surgery (CPT 23470, 23472) averages $