rafmacalaba/data-use-mentions-extended huggingface dataset:
Among the host countries in our sample, regulations for Croatia, Hungary, and Poland discriminate the most against foreign branches. Most notably, the first two countries place restrictions on the type of activities that branches can undertake relative to subsidiaries. With the exception of Mexico (where branches can only operate with non-residents), such restrictions appear to be uncommon in Latin America.

# **III.   EMPIRICAL METHODOLOGY**

We model a bank’s organizational form according to equation (1) below: _Organizational Formi,j,k=_ α _0 +_ β _1Parent Bank Characteristicsi,j +_ β _2Home-Country Regulationsj+_ β _3Affiliate Bank Characteristicsi,j,k +_ β _4 Host-Country Factorsk+_ ε _i,j,k_ (1) where _Organizational Formi,j,k_ is a dummy equal to 1 if parent bank i from home-country j has a branch in host-country k, and 0 if it operates a subsidiary in that country.

Among the _Parent Bank Characteristics_ we control for the size, business orientation and international strategy of each parent bank. _Parent Bank Size_ refers to the log of the total parent bank assets. This data comes from Bankscope. _Parent Bank Business Orientation_ is a dummy that equals one if the bank has a retail orientation and zero if the bank is primarily engaged in wholesale or investment bank activities. The sources for this classification are Bankscope and the home banks’ WebPages. _Parent Bank Internationalization Strategy_ refers to the ratio of branches to total foreign operations that the parent bank owns. This variable helps determine whether the 9
the context of financial markets and corporations. The consensus among many studies is that religiosity is negatively correlated with risk-taking, and that both religiosity and risk aversion have an influence in the corporate sector (Hilary and Hui, 2009; Blau, 2017), the banking sector (Adhikari and Agrawal, 2016), and even influence the decision-making process of venture capitalists (Chircop et al., 2020). Further, recent work reports a positive correlation between religiosity and investor loyalty (i.e. lower fund flows volatility) in the mutual fund industry (Peifer, 2011; Rao et al., 2015). Taken together, these results suggest that religious investors prefer funds with lower risk, and are less likely to switch away once they undertake an investment decision. Additionally, firms located in areas with high levels of religiosity are less likely to experience future stock price crashes (Callen and Fang, 2015; Blau, 2017; Chourou, 2020).

A common factor among these studies is that religiosity of companies and fund managers are proxied by the level of average religiosity in the local geographical area<sup>9</sup> (where they have their headquarters). Specific to the mutual fund industry, ex-post transactional data are employed in the analysis. The findings of this literature are derived with secondary data, which make it difficult to disentangle pecuniary from non-pecuniary motives in investor behavior.

> 7 In this paper, we refer to sustainable investments, socially responsible investments or ESG investments as synonyms, although we are aware of potential differences among them. In this respect we take the approach of Hartzmark and Sussman (2019), who employ ESG ratings and ESG funds to discuss the interest of investors in sustainable investing. 8 Among many, the very recent work of Hartzmark and Sussman (2019) found that investors largely value sustainability using the introduction of the ESG rating by Morningstar in 2016 as a natural experiment to investigate asymmetries in the fund flows of mutual fund with different ESG rating scores.

> 9
body of papers finds that financial market development, and in particular the ability of firms to access long-term credit, have positive growth effects (see King and Levine, 1993; Levine and Zevros, 1998; Levine, Loayza, and Beck, 2000; Beck, Laeven, and Levine, 2000; Rajan and Zingales, 1998; Demirgüç-Kunt and Maksimovic, 1998).<sup>2</sup> Earlier literature has also established that macroeconomic stability is positively related to financial market development and long-term debt use (see Beck, Demirgüç-Kunt, and Maksimovic, 2008; Demirgüç-Kunt and Maksimovic, 1999; Fan, Titman and Twite, 2012). We add to this literature by establishing that financial market development that increases the availability of long-term finance reduces firm-level economic volatility, potentially by reducing liquidity risk.

This paper is also related to a recent literature that examines the effects of shocks in the availability of credit on firm-level investment. Several papers, in particular, use the recent financial crisis as a source of exogenous variation in credit availability. Among these, Duchin, Ozbas, and Sensoy (2010) show that the impact of the crisis on corporate investment was greatest for firms with low cash reserves or high short-term debt. Along similar lines, Almeida, Campello, Laranjeira, and Weisbenner (2011) show that firms with a larger fraction of long-term debt maturing immediately after the third quarter of 2007 reduced their investment more than firms with longer remaining maturities. Vermoesen, Deloof and Laveren (2013) find qualitatively similar results for a sample of Belgian firms. Furthermore, Campello, Graham and Harvey (2010), and Campello, Giambona, Graham and Harvey (2011) use survey data to show that firms that were financially more constrained were more likely to change their investment plans, while Chodorow-Reich (2014) and Duygan-Bump, Levkov and Montoriol-Garriga (2015) find evidence that credit constrained firms reduced

> 2 See Levine (2005) and Beck (2012) for detailed overviews of the literature on finance and growth .

4
services” (that is, services subsectors other than WRT services). For firm-level data, we use two rounds of censuses of firms for Ghana: the 2003 National Industrial Census (NIC) and the 2014 Integrated Business Establishment Survey (IBES).

We examine the spatial and temporal pattern of the mining shock by exploiting the variation in the intensity of mining activities between the north and the south and between 2003 and 2013. Differences in regional intersectoral linkages are measured using the I-O tables for the north and the south in 2004 and 2013. Between 2004 and 2013, mining’s output as a share of gross domestic product (GDP) or the Domar weight increased by 16 percentage points, and the increase in the Domar weight for mining was 1.7 times larger in the south than in the north. The sectoral total factor productivity (TFP) ratio between mining and “other industry” in the south (0.532) is more than twice the size of that in the north (0.204). We also find larger growth in the share of intermediate use in mining and the downstreamness index 3
<br>|rruption<br>||
|Change in government investment|4.764|1.661|1.686|1.965|2.005|
||(4.312)|(3.069)|(3.010)|(2.449)|(3.164)|
|Observations|1,394|1,340|1,287|1,234|1,180|
|F-statistic|1.580|1.378|1.658|2.049|1.617|
|KP Underid stat|0.196|0.225|0.183|0.140|0.186|
|No. of countries|70|70|70|70|70|

_Notes_ : *** p <0.01, ** p <0.05, * p <0.1. Dependent variable is change in private investment to lagged GDP. Robust standard errors in parentheses are Driscroll-Kraay standard errors to control for the presence of error cross-sectional dependence a ́ la Boehm (2020). All estimations include country-fixed effects and year effects and one year lagged dependent variable. “KP” is Kleibergen and Paap underidentification test.

To examine for the impact of corruption on the ability to crowd-in private investment, we divide the sample (that is, country-year pairs) into those above (high corruption control) and below (low corruption control) the median outcome for corruption control from the Worldwide Governance Indicators (Kaufmann et al. 2011). The control of corruption indicator measures perceptions of the extent to which public power is exercised for private gain.

We find that the crowding-in effect of public investment on private investment is only statistically significant in the high control of corruption group, with the cumulative impact increasing up to four years ahead (table 4). When control of corruption is weak, the estimated coefficient across the different horizons, while generally smaller in magnitude, is
estimation of equation (1) in Panel A, and it is evident that, after controlling for base year health insurance and pension participation, the average effect of assignment to the information intervention is zero (model 1), and the effect does not change as base period individual controls and firm-level controls are added in models (2) and (3), respectively. As the RUMiC survey is sampled through workplaces, standard errors in all models are clustered at the workplace of the household head.

**Heterogeneity of Treatment with Contract Status.** This average zero impact, however, masks considerable heterogeneity in the effect of the information intervention on individuals with and without written employment contracts. To examine this difference, Panel B of Table 2 presents the OLS estimates of equation (2). The bottom line of the panel shows the treatment effect on individuals without contracts by adding the coefficient on treatment ( _β_ ) to that on the interaction between treatment and the indicator for not having a contract

15
- Krakah, T., Rankin, N., and Teal, F. (2014). Report on the 1987 and 2003 industrial censuses in ghana.

- Li, W., Mengistae, T., and Xu, L. C. (2011). Diagnosing development bottlenecks: China and india*. _Oxford Bulletin of Economics and Statistics_ , 73(6):722–752.

- Melitz, M. J. (2003). The impact of trade on intra-industry reallocations and aggregate industry productivity. _Econometrica_ , 71(6):1695–1725.

- Midrigan, V. and Xu, D. Y. (2014). Finance and misallocation: Evidence from plant-level data. _American Economic Review_ , 104(2):422–58.

- Oberfield, E. (2013). Productivity and misallocation during a crisis: Evidence from the chilean crisis of 1982. _Review of Economic Dynamics_ , 16(1):100–119.

- Restuccia, D. and Rogerson, R. (2008). Policy distortions and aggregate productivity with heterogeneous establishments. _Review of Economic Dynamics_ , 11(4):707–720.

- Restuccia, D. and Rogerson, R. (2013). Misallocation and productivity. _Review of Economic Dynamics_ , 16(1):1–10.

- Sandefur, J. (2010). On the evolution of the firm size distribution in an african economy.

- Van Biesebroeck, J. (2005). Firm size matters: Growth and productivity growth in african manufacturing. _Economic Development and Cultural Change_ , 53(3):545–583.

- World Bank (2015). _Cˆote d’Ivoire: From Crisis to Sustained Growth Priorities for Ending Poverty and Boosting Shared Prosperity_ . The World Bank.

# **APPENDIX**

33
productivity parameters were defined to generate internal rates of return of 17-18 percent for government investment in these areas.

Finally, in order to capture the determination and impact of urbanization, elasticities were needed for: (a) the urban population share with respect to the agricultural employment share; (b) the agricultural VA share with respect to GDP per capita; and (c) TFP in individual sectors with respect to the urban population share. On the basis of cross-country regressions covering low- and middle-income countries, (a) and (b) were set at -0.35 and -0.20, respectively. Due to a lack of easily accessible data, (c) was not estimated. On a priori grounds, it is also questionable whether urbanization per se leads to higher productivity; urban concentration may be more relevant and it may depend on the context in which urbanization takes place (for example, the impact may be absent if natural resource rents are driving the process but present if it is driven by expansion of industry and services (Henderson 2003, pp. 49-50; and Jedwab 2013, p. 4). In the MAMS database, the TFP elasticities used for agriculture and non-agricultural sectors were 0.1 and 0.2, respectively. Given limited urbanization, this mattered very little to the findings.

32
in year _t_ . Using the coefficients estimated from these regressions, we then predict real GDP per-capita for 2023-2030 for each country. Then, using this GDP per-capita series (the MPO series up until 2022 plus our predicted series from 2023-2030), we calculate country specific growth rates in GDP per-capita out to 2030. These growth rates, combined with the latest observation of mean income in each country from PovcalNet, allow us to predict mean

> 17. For population measures, we use predicted country populations for 2022 generated by the Macro and Poverty Outlook (MPO). See `https://www.worldbank.org/en/publication/ macro-poverty-outlook#:~:text=Next-,Overview,Group%20and%20International%20Monetary% 20Fund.` . The predictions we use are from the vintage dated June 9, 2020. 18. Specifically, we use the vintage dated June 9, 2020. 19. We estimate an MA(2) model as opposed to the simpler log-linear specification so as to smooth out shocks when forecasting GDP per-capita. Note, the MPO projections take into account the anticipated COVID-19 shock.

23
treatment, and 31% to community greening. 79% of the respondents thought the dialogue topics were on extremely urgent issues they were facing.

35% of the respondents were from Chongqing, 23% from Tianjin, 26% from Chifeng, and 15 from Shenyang. 51% of the respondents were male and 49% were female. About 60% of the participants were 40 years old and older; 11.5% were younger than 30. More than 60% of the respondents received education higher than high school. The monthly income of the respondents was mostly between 500-3000 yuan.

# **5.3. Dialogue Design and Implementation**

Most participants thought the roundtable dialogues were well prepared in the aspects of pre-announcement, provision of background materials, location choice, representative selection, selection of moderators, and definition of rights and responsibilities. Less than 10% rated the preparation work as poor. Most participants regarded the community public announcement board as the best channel to pre-announce the dialogue meetings. There is no convergence with regard to the best way to select resident representatives. The favorite way is self-nomination plus striking a balance between districts or sectors.

All meetings were completed within 3 hours, with 24% less than one hour. More than 60% of the respondents thought the time allocation between different activities during the dialogue was reasonable. Most people (more than 80%) thought that representatives of each stakeholder should be less than ten. 42% thought the best choice of dialogue moderators were community leaders; 26% thought it should be independent NGO people.

About 80% thought the dialogue meetings had given the representatives enough time to discuss about the issues in concern. 19% of the respondents did not say anything in the meetings that they participated.

# **5.4. Impacts**

The survey statistics show that more than 80% of government officers who participated in the meetings felt that after the dialogue process they had better understandings in the issues involved, including enterprises’ measures on the
|
|Other: Market Research|0.62|0.45|
|Other: Digital Marketing/Sales|0.61|0.46|
|<br>Other: Sales Tactics|0.61|0.45|
|Other: MarketingTactics|0.59|0.51|
|Source: administrative data from dashboard on activities submitted by insourcing workers and outsourcing s<br>Proportions are conditional on taking up the intervention.|ervice providers.||

38
# **6.3 Global Economic Activity during the 2020 Pandemic**

During the early phases of the pandemic, it was widely reported in the media and noted in scientific studies that NO2 was falling globally, and that this was indicative of declines in economic activity (Carlton, 2020; Venter et al., 2020; Liu et al., 2020; Mesas-Carrascosa et al., 2020; Zhao et al., 2020). We now use the composite estimate to understand to what degree these measurements overturn officially reported GDP during this unprecedented year. This is done for the full subsample of 81 countries for which the World Bank had compiled 2020 GDP at the time of writing.

Table 8 presents the results, broken down into instances of understatement (Panel A) and overstatement (Panel B). In each panel, the countries are listed from largest absolute magnitude difference to smallest. For each country, we compute a percentile of how many other years in the 2005-2020 sample had larger absolute differences; thus, smaller percentiles indicate that the difference reported for this country is particularly large, and may be deemed an unusually sizable error.<sup>20</sup> Finally, we also compute a normalized difference as though every country were grade C (*). Here we show just the top 10 countries and selected others in each panel. Full expansions of both the understatement and overstatement lists to show all countries, as well as a visual depiction of the extent of normalizations versus the baseline are illustrated graphically in Online Appendix B.4.

**Understatement.** Table 8 Panel A lists the largest instances of understatement, in order. Beginning at the top of the list with #1, we find that grade C Belize’s reported -15.05% contraction in GDP was not a fully accurate depiction of the downturn, which was likely more muted at -10.55%. The percentile of 0.00 indicates that this was the greatest difference between reported GDP
Table 9, the coefficients on the interaction terms are 10 to 15 times greater in magnitude. Since the interaction term captures the association between foreign exchange reserves specifically in times of financial contractions (when _Lag financial contraction_ dummy takes on the value 1), this indicates that the positive effect of reserves on real sector growth is especially prominent during the episodes of sharp financial contraction. This finding suggests that countries in which the severity of the financial shock is magnified by financial openness rely on foreign exchange reserves to mitigate the adverse impact of such capital flight on the real sectors. The non-linear impact of reserves is most prominent in the sectors identified as most vulnerable to financial contractions: for construction sector, a 1 percentage point higher reserves to GDP ratio is associated with 0.2 percent higher value added growth rate on average, but a 2.8 percent higher growth rate in times of financial contractions (hence partially offsetting the effect of financial contractions).<sup>7</sup>

# Figure 2 [about here]

Figure 2 illustrates the degree to which foreign exchange reserves have served to mitigate the impact of sharp financial contractions in financially open economies on real sector value added growth rates. The four panels in the figure focus on sectors most affected by financial contractions: construction, manufacturing, wholesale and retail, and transportation. The figure plots predicted contribution of financial openness (solid line) and financial openness plus reserves (dotted line) to real value added growth rates of each sector during financial contractions episodes. Predicted contribution is calculated by multiplying the coefficients from panel regression in the full specification reported in Table 9 by the values of financial openness and reserve to GDP ratio (conditioning on financial contraction dummy taking on the value of

> 7 Regression coefficients on financial openness and the reserves-to-GDP ratio have different interpretations because of the way the data have been normalized: reserves-to-GDP ratio is in integer percentage points (1 unit increase in the variable represents a 1 percentage point rise) as in the WDI database, whereas other variables are
58 of the World Development Indicators (WDI) database of the World Bank (2021).<sup>53</sup> More recent versions of the WDI do not report these GDP shares for earlier decades, only the older versions of the WDI do. The Beta version has the merit of showing all available estimates for all versions of the WDI. For each we yearly simultaneously country-year, then take the mean of the available estimates. To maximize the number of available estimates for the years 1960, 1965, ..., 2015, 2020, and in order to minimize fluctuations due to year-specific measurement issues, we rely on five-year moving averages.

After doing so, of the 1,508 observations in our data, for 189, 159 and 195 observations we still do not have an available estimate for the GDP share of MFG, services (SERV), and MFG+SERV, respectively. For the 2010-2020 period, we complete the data using estimates from Central Intelligence Agency (2021) and reports from international organizations or governmental agencies. Even after doing this, for 188, 153 and 189 observations we still do not have an available estimate, respectively.

For the long-difference regressions, we need data c. 1960-1970. For these years, we use United Nations (1960-1980). However, for 87, 75, and 88 observations, we do not have an available estimate for the GDP share of MFG, SERV, and MFG+SERV, respectively.

The _System of National Accounts_ (SNA) - _Analysis of Main Aggregates_ (AMA) database of United Nations (2020c) reports the GDP share of aggregated sectors, including MFG and services, for all countries from 1970 to 2020. When needed, we use this database to complete the missing country-years of our main data set, after verifying that the newly added estimates are consistent with the estimates that we already had for other years.<sup>54</sup> **GDP Share of FIRE.** The _National Accounts
* 0.9 = 0.522. Since this MPCF was estimated using variation in the timing of program exit across individuals, price responses are not a factor in this estimate. While this estimate is not necessarily biased upward, we take this to be an upper bound to be conservative in our calibration of the price response. These numbers are shown in the first and second rows of the first column of Table 8.

The second column of Table 8 restates the benefit-pass-through elasticity estimated in Table 6, column (3), row 1: 0.0724. Our aim is to check whether this number falls in line with the theory-based calibrations.

The third column of Table 8 reports the shift magnitude,<sup>_∂_</sup> _∂b_<sup>_<u>p</u>_</sup> _pb_<sup>=</sup> _εQ−D,SεD_ _<u>,b pQpQ</u>_<sup>_D,S_.As discussed</sup>

> 42Another possible reason making this a lower bound is imperfect designation of SNAP versus non-SNAP consumers in the consumer scanner data.

27
|0.000<br>(1.000)|0.174<br>(0.086)<sup>_∗∗_</sup>|0.131<br>(0.097)|0.218<br>(0.104)<sup>_∗∗_</sup>|5053|
|||[0.073]<sup>_∗_</sup>|[0.298]|[0.040]<sup>_∗∗_</sup>||
|(3) Satisfaction with village sanitation index|0.000|0.087|0.087|0.086|5051|
||(1.000)|(0.043)<sup>_∗∗_</sup>|(0.049)<sup>_∗_</sup>|(0.050)<sup>_∗_</sup>||
|||[0.073]<sup>_∗_</sup>|[0.298]|[0.059]<sup>_∗_</sup>||

_Notes:_ Variables are control-group normalized at endline (z-scored). Treatment effects are estimated using Missing-Indicator ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. The household index is comprised of water sources for drinking, for other uses, the existence and type of toilet facilities and the actions households take to make water safe to drink. The village index contains the existence and types of water sources for drinking and general use as well as the existence of public toilets and waste facilities. The satisfaction index consists of questions about sanitation and health services offered as well as village cleanliness. Significance: * is significant at the 10%