rafmacalaba/data-use-mentions-extended huggingface dataset:
*

Colombia is the main recipient of Venezuelan migrants. According to data from the United Nations Refugee Agency, approximately 2.5 million Venezuelan migrants had arrived in Colombia by February 2022, with the vast majority arriving since 2016. This number does not include undocumented migrants who escaped detection by authorities.

> 4A related literature studies effects of migrant amnesties on crime in host communities. See Baker (2015) for the United States, Mastrobuoni and Pinotti (2015) for the European Union, and Pinotti (2017) for Italy. 5Other papers have also studied PEP’s impacts on labor outcomes (Bahar et al., 2021), political outcomes (Rozo et al., 2023), firm outcomes (Bahar et al., 2022), and inequality (Lombardo et al., 2021).

5
provides similar tabulations for the 2005 and 2007 data that follow the GQ upgrades. Columns 2-4 provide aggregates of manufacturing activity within each spatial grouping, averaging the two surveys, and Columns 5-7 provide similar figures for young establishments less than four years old. Column 8 provides means of labor productivity across plants in the range. One important observation from these tabulations is that non-nodal districts in close proximity to the highway networks typically account for around 40% of Indian manufacturing activity.

Table 1b provides some simple calculations. Panel A considers the log growth in activity from 1994/2000 to 2005/2007, combining districts within spatial range. Panel B instead tabulates the change in the share of activity accounted for by that spatial band. Share changes in Panel B are calculated separately for distances from the GQ and NS-EW networks such that they sum to zero for each group. Accordingly, we do not present a totals row in Panel B. Share of labor productivity is also not a meaningful concept.

11
complementarities. Karakaplan (2021) finds that conventional small business loans of up to $1 million went up on the order of magnitude of an additional dollar of conventional loans for a marginal dollar of PPP loans, particularly for small banks. Berger, Karakaplan, and Roman (2021) similarly find strong positive effects especially for smaller banks and smaller businesses. Marsh and Sharma (2021) also find significant increases in lending by small banks, while Lopez and Spiegel (2021) find increases in small business and farm lending by all bank sizes. In contrast, another study using the Federal Reserve’s Y-14 data on conventional loans over $1 million suggests strong substitution effects. Chodorow-Reich, Darmouni, Luck, and Plosser (forthcoming) find firms that receive PPP loans obtain fewer conventional credits over $1 million from very large banks. Thus, there may be key differences by firm and bank size that need more research to obtain a clearer picture of the answer to this important question.<sup>29</sup> Some research focuses on the financial institutions that issued the PPP loans. Several studies find that small, community banks played an outsized role in PPP, disbursing outsized shares of the PPP loans (e.g., Levine, Lin, and Xie, 2020; James, Lu, and Sun, 2021). Another study finds that FinTech firms also played a key role in distributing PPP funds, providing greater roles in local markets with fewer bank branches, lower incomes, larger minority shares of the population, in industries with bank small business lending (Erel and Liebersohn, 2020).

There may also be some long-term costs of PPP to the extent that the funds were misused or were allocated to unproductive firms that should otherwise not be kept alive. One theoretical study emphasizes the potential for misallocation of the PPP funds to firms by banks (Joaquim and Netto, 2021). Some empirical evidence suggests that some PPP funds were not employed as

> 29 Although it is not our focus here,
in other communities who had watched Shuga. These video-clips were assembled using material from the pilot screenings and included interviews of youth condemning negative behaviors and praising positive ones after watching Shuga, as well as ‘smart graphs’with statistics. The intent was to raise awareness about how common certain beliefs and attitudes are among other participants, and how willing they may be to change them. T2 thus embeds a first type of ‘social referents’, namely young people from other communities who look similar to our respondents and who watched MTV Shuga.

The choice of this way of implementing T2 represented a compromise between a number of different imperatives. Announcing the post-screening average —which is what we opted for—is the right thing to do if the population expects that the rest of their peer group will also see Shuga eventually and they want to be close to that post-Shuga consensus. This is a plausible case since MTV is a well-known brand and our viewers may have correctly expected that Shuga would be shown on television eventually. Consistent with this interpretation, below we show 13
behaviors and home environments suggest that, in spite of their large magnitude, the medium-term impacts on language outcomes are plausible, because they are mirrored by sustained changes in parenting behaviors and home environments for participating households. Table 6 shows that NEP-I significantly improves the HOME index in the medium term by 0.66 SD (also robust to multiple hypothesis testing), and decreases negative discipline by 0.33 SD. NEP-I also significantly improves the Affection index measured by the PBC nurturing scale, by 0.36 SD. These are substantial impacts.

Regarding parental beliefs, we observe a positive and large impact of NEP-I on parental selfefficacy of 0.411 SD and on parental perceived impact of own behavior on child development by 0.446 SD (both robust to multiple hypothesis testing). We also observe an improvement of perceived social support of 0.37 SD. There are no significant changes in parental styles.

Finally, parents receiving the NEP-I treatment expect their children to reach particular developmental outcomes at a younger age than those in the control group about the age at which children. This is especially true in a “low-investment” home scenario (by 0.39 SD).

Again, our IV results in parental outcomes are robust to the inclusion of additional controls such household socio-demographic characteristics, caregiver endowments and outcomes at baseline, as shown in Table A9 in Appendix 2.

The estimates of the impacts of NEP-I on parental behaviors and home environments (in particular, on the HOME index) are as remarkable as the estimates on child language reported above, precisely for the same reasons already discussed: they are large in magnitude (especially when we take into account the low costs of the program), and they are long lasting. The fact that we observe 19
estimates of informal trade are accounted for it is implausible to characterize the South Asian countries as “natural trading partners” based on their trade volumes.

# _Trade Intensity and Geographical Proximity_

It is argued that countries in relative geographical proximity tend to trade more with each other than with more distant countries owing to lower transport and communications costs (Deardorff and Stern, 1994). Wonnacott and Lutz (1989) also refer to transport costs, suggesting that geographical proximity between countries tends to increase trade between them and reduce trade diversion. Regional trade in South Asia tends to be centered around India, which, for most of the smaller countries, is the closest neighboring country. However, as demonstrated in Annex Table A1, there exists very little or no trade among the smaller countries, such as Bhutan, Maldives, and Nepal, which are in relative proximity compared with the more distant industrial countries with whom they trade more. This suggests that there may be other factors—such as transport and logistical impediments that give rise to high transaction costs, or the lack of trade complementarities—which account for the apparent low levels of trade. Taneja (2000) points out that high transaction costs induced by inefficient institutional designs are the premier factors diverting trade between India and its smaller neighbors into informal channels, circumstances that may also explain the low levels of trade between the landlocked countries and others in the region. This might imply that tariff preferences alone would not be sufficient to stimulate regional trade.

While the preceding analysis, based on broad trade trends, has shed some light on prospects for further regional integration, it would be informative to evaluate whether the South Asian countries, given the size of neighboring markets, export to the region as much as could be expected and whether regional trade, from this perspective, has changed over time. The trade intensity index (TII) is a useful tool for evaluating these questions in the context of South Asia.

11
**Figure  2.   Variation in Actual 2008 Performance and 2008 Sec. of  Education Target in Portuguese Proficiency,  5**<sup>**th**</sup> **Grade Schools - Minas Gerais, Brazil**

<!-- Start of picture text --> 0 1000 2000 3000 4000<br>Total Number of Students 2007<br>0 50 100 150 200<br>School Participation Rate in PROEB 2008<br>100 150 200 250 300<br>School Average Proficiency Score in Portuguese of 5th Grade Students, 2007<br>100<br>50<br>0<br>-50<br>Difference 2008 Actual - Sec. Ed Target<br>-100<br>100<br>50<br>0<br>-50<br>Difference 2008 Actual - Sec Ed Target<br>-100<br>100<br>50<br>0<br>-50<br>Difference 2008 Actual - Sec. Ed Target<br>-100<br><!-- End of picture text --> 8
**3**

> The second method for examining causality uses panel data and exploits the cross-country and time-series dimensions of the data. We assemble a panel dataset, with data averaged over each of the seven _5-year_ intervals composing the period **1960-1995.** We use two Generalized Method-ofMoments (GMM) dynamic panel estimators that correct inherent problems with the purely crosssectional estimator. Specifically, these estimators address the econometric problems induced **by** country-specific effects, endogeneity, and the routine use of lagged dependent variables in growth regressions [Arellano and Bond **1991;** Holtz-Eakin, Newey, and Rosen **1990].** In the first GMM panel estimator, the strategy for addressing possible omitted variable bias created **by** country-specific effects is to difference the regression equation. Thus, we first take differences to eliminate country-specific effects and thereby remove omitted variable bias. Next, we instrument the right-hand-side variables (the differenced values of the original regressors) using lagged values of the original regressors (measured in levels) as instruments. This last step removes the inconsistency arising from simultaneity bias, including biases induced **by** the differenced lagged dependent variable. This _dfference_ dynamic-panel estimator

> has increasingly been used in studies of growth. [Caselli, Esquivel, and LeFort **1996;** Easterly, Loayza, and Montiel **1997].**

The second **GMM** dynamic panel estimator goes beyond the _dfference_ dynamic-panel estimator. The problem with the _difference_ estimator is that it generally suffers from weak instruments, which yields large biases in finite simples and poor precision even asymptotically [Alonso-Borrego and Arellano **1996;** Bond, Hoeffler, and Temple **1977].** Specifically, lagged values of the levels of the original regressors frequently make weak instruments for the differenced values of the regressors used in the dynamic-panel equation. This occurs in the current setting if lagged values of financial development do not forecast _changes_ in financial development. To
across Income Groups. National Tax Journal 68 (1):195-213.

- World Bank (2022a). World Development Data Indicator Database. <u>WWW.worldbank. World</u> Bank, Washington, DC.

- World Bank (2022b). Egypt: Country Climate and Development Report. World Bank. Washington, DC.

World Bank (2023). State and Trends of Carbon Pricing 2023, World Bank, Washington, DC.
Development Bank which includes 61 countries and 35 sectors from 2011 to 2017; and (iii) the World Bank Enterprise survey (WBES) for over 100 countries consisting of detailed data on more than 8,000 firms. The fDi Markets data consist of rich information of the greenfield FDI projects including the source country, the parenting firm, the destination country and the sector, number of projects and the commitment amounts of FDI. The data at the firm level (WBES) and sector (MRIOT) level also allow us to check whether the relationships are robust and hold at the firm and sector

> 1 For example, if Nepal is building a hotel, will it help the tourism/real estate sector as well as construction suppliers?  Will it also be a great boost to international architecture and engineering firms as well as potential tourists?
**_<u>________________________________________________________</u>_**

- Starting a Business

- Dealing with Licenses

- Hiring & Firing Workers

- Registering Property

- Getting Credit

- Protecting Investors

- Paying Taxes

- Trading Across Borders

- Enforcing Contracts

- Closing a Business

# _<u>Investment Climate Surveys (ICS)</u>_

Investment Climate Surveys (ICS) provide both quantitative and qualitative information on a wide range of investment climate conditions and links them to their impact on firm productivity, investment and employment. The ICS -- including the Business Environment and Enterprise Surveys (BEEPS) joint with the EBRD -- report results from surveys of over 30,000 entrepreneurs in over 50 countries.

Investment climate surveys (ICS) measure entrepreneurs' perceptions of the investment climate in their country, and provide indicators of firm productivity and performance. All investment climate surveys are grounded in a <u>standard core module</u> (PDF, 180KB) that covers management’s perceptions of the severity of obstacles to operation and growth of the business. The standard core investment climate survey module also collects information on firm performance.

In addition to the core module, the investment climate surveys may be supplemented with questions from other detailed modules covering finance, labor, small and medium enterprises, and logistics, to name a few. These supplemental modules are used to explore in greater depth issues that may be topical or particularly relevant in any given country. Standard investment climate surveys are administered to the manufacturing sector in urban areas. Other sector-specific investment climate survey instruments are also being developed, including questionnaires for the hotel and tourism industry, retail-trade sector, rural manufacturing, information technology, and informal firms.

About 20 firm level surveys are being carried out each year, with a 4-5 year cycle. The firm-level survey is complemented with analysis from other studies, as wells as discussions and meetings with key stakeholder groups. For example, indicators on entry regulation estimate the procedures, time, cost and minimum capital requirements
,800.

4 Due to lack of published data available on the total costs of treating HAIs, it is necessary to construct the cost based on the cost per hospital bed day, the additional length of stay due to HAI, and the costs of drugs, procedures and laboratory tests related to the HAI. AMR will also lead to longer length of stay, and additional costs of drugs, procedures and laboratory tests.

5 The highest quality cost studies for different levels of the health systems were from IHME (2014) for Uganda which was extrapolated using PPP to other countries with GDP per capita below US$1,100 (DRC, Ethiopia, Madagascar, Mozambique, Sudan and Tanzania); and IHME (2015) for Kenya, which was extrapolated using PPP to Angola.

6 Gidey et al (2023) for Ethiopia, reflecting health center level - converted to each country using PPP.

7 Aerts et al (2022) for Mozambique, reflecting district hospital level - converted to each country using PPP.

8 Bocoum et al (2019) for Mali, reflecting referral, tertiary, or teaching hospitals - converted to each country using PPP.

9 Based on Ghana data, as follows. Fenny et al (2020) found in a teaching hospital additional average length of stay (ALOS) of 4.6 days for patients with SSI. Otieku et al (2023) found ALOS was 3 days longer between the HAI (non-AMR) group and the control group.

10 Otieku et al (2023) found patients in the AMR cohort stayed approximately 5 more days compared with HAI patients and 8 more days compared with uninfected cohorts.

11 As follows:

|**Variable**|**Angola**|**Cong**<br>**o,**<br>**Dem.**<br>**Rep. **|**Ethiopia**|**Kenya**|**Madag.**|**Mozam
point when polygyny in the communes in which they live rises by one standard deviation (Table 10). This result is consistent with Corno et al. (2020) who find that drought increases child marriage in bride-price countries. As Mali is a bride-price country Corno et al. (2020), polygyny can enable households to have many daughters, whose marriage can become a source of income during hard times. Indeed, our sample’s descriptive statistics show that polygynous households in Mali have nearly twice as many daughters aged 12 ́ 17 as monogamous households (Table 2), putting girls in polygynous households at further risk of lower human capital and individual agency.

# **6 Conclusion**

This paper extends the literature on risk coping in village economies by analyzing polygyny’s resilience effect in the aftermath of a drought shock. We study the interacting effect of polygyny—a marriage practice known to undermine development outcomes— and drought episodes, both current and lagged, in the context of rural Mali—a droughtprone country located on the polygyny belt in the West African Sahel region. For identification, we rely on the plausibly exogenous spatial and temporal variation in drought occurrences across rural Mali communes, accounting for potential serial correlation in our drought indicator and many commune-level as well as time-variant household-level confounders. We use the Standardized Precipitation-Evapotranspiration Index (SPEI) to construct our drought indicator for the rainy season, enabling us to control for a potential spatial correlation of drought episodes due to possible omitted weather variables.

We link the identified resilience effect of polygyny to its positive impact on household fertility documented in the literature. Because polygyny raises fertility, it enables households in drought-prone rural communities to harness the size and composition of the family workforce to their resilience strategies in the context of imperfect rural labor markets. In that context, having a large family workforce, well-balanced in age and gender, enables households to leverage diversification of crop production and income sources as resilience strategies to the occurrence of drought shocks. Furthermore, in bride-price countries like Mali
Why Do Firms in Developing Countries Have Low Productivity?” _American Economic Review: Papers and Proceedings_ 100 (2): 619-623.

Bogetic, Zeljko, and Olasupo Olusi (2013), “Drivers of Firm-Level Productivity in Russia’s Manufacturing Sector” World Bank Policy Research Working Paper 6572.

Calderón, César, William Easterly, and Luis Serven. (2003), “Infrastructure Compression and Public-Sector Solvency in Latin America.” In William Easterly and Luis Serven (eds.), Adjustment Undetermined? Public Deficits and Growth in Latin America, 1980–2000. Princetown, N.J.:Princetown University Press.

Canning, David (1998), “A Database of World Stocks of Infrastructure, 1950–1995.” _World Bank Economic Review_ 12(3): 529–47.

Cole, Matthew A., Robert JR Elliott, Giovanni Occhiali, and Eric Strobl. (2018), “Power outages and firm performance in Sub-Saharan Africa.” _Journal of Development Economics_ , 134: 150159.

18
The Quarterly Journal of Economics 122, 209–241. doi:10.1162/qjec.122.1.209

- Thaler, R., Sunstein, C., 2009. Nudge: Improving Decisions about Health, Wealth, and Loss Aversion. Penguin: London.

The Economic Times, 2011. Honda to cut production at Indian arm by half.

- Tierney, K.J., 1997. Business Impacts of the Northridge Earthquake. Journal of Contingencies and Crisis Management 5, 87–97. doi:10.1111/1468-5973.00040

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UNISDR, 2011. 2011 Global Assessment Report.

- van den Berg, M., Fort, R., Burger, K., 2009. Natural Hazards And Risk Aversion: Experimental Evidence From Latin America. Presented at the International Association of Agricultural Economists Conference, Beijing, China.

- Van Oudheusden, P., Klapper, L., Demirguc-Kunt, A., Singer, D., 2015. The Global Findex Database 2014 : measuring financial inclusion around the world (No. WPS7255). The World Bank.

29
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``` ``` Notes: This table presents four regressions, where each column reports the findings from an OLS regression. The dependent variables, measured in 2011, are: Bank Development equals claims on the private sector by deposit money banks as a share of GDP; Interest Margin equals net interest income divided by total bank assets; Overhead Costs equals total bank overhead costs as a share of total bank assets; and, Nonperforming Loans equals officially reported nonperforming loans as a share of total banks assets. The regulatory indexes are measured in 2011: Capital Regulatory Index measures the extent and stringency of regulatory requirements; Private Monitoring Index measures the degree to which regulatory policies encourage and facilitate private sector monitoring of banks; Official Supervisory Power Index measures the extent to which official supervisory agencies have the authority to take specific actions to prevent and correct problems; Entry Restrictions Index measures the number of specific legal requirements for obtaining a banking license; and Restrictions on Activities Index measures regulatory restriction on the degree to which banks can engage in securities, insurance, and real estate activities plus restrictions on banks owning and controlling nonfinancial firms. Government Owned Banks equals the percentage of the banking system’s assets held by banks that are 50% or more government owned. Though unreported, the regressions also control for the legal origin of each country by including three dummy variables for Common, French civil, and German civil law countries, as well as a constant term. Heteroskedasticity-consistent standard errors are in parentheses under the estimated coefficients. To designate statistically significant benchmarks, *** indicates significance at the 0.01 level,
** at the 0.05 level, and * at the 0.10 level.
```
# Figure 11: Capital income tax revenue increased after 2016

- (a) Number of taxpayers subject to capital income tax

<!-- Start of picture text --> Tax Returns<br>2016<br>50,000<br>Amnesty<br>40,000<br>30,000 Capital income<br>(taxpayers)<br>20,000<br>10,000<br>0<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br><!-- End of picture text -->

- (b) Capital income tax base

<!-- Start of picture text --> Tax base<br>(2015=100)<br>2016<br>600<br>Amnesty<br>Capital income<br>(tax base)<br>400<br>200<br>Rental income<br>Wage<br>income<br>Business<br>0 income<br>2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br><!-- End of picture text --> _Notes:_ this figure compares the number of taxpayers subject to the capital income tax and the capital income tax base in panels (a) and (b), respectively. There is a meaningful increase in reported capital income starting in 2016 when disclosers are required to register income: the number of taxpayers reporting some capital income doubled, and the capital income tax base tripled. By contrast, none of the other three sources of income (wage income, business income, rental income) changed after 2016. These patterns are consistent with foreign and domestic assets, which generated taxable income and were left undeclared before the amnesty, becoming more truthfully reported after the program. Critically, these improvements persisted years after the amnesty program ended.

_Source:_ authors’ calculations using data from AFIP statistical yearbooks