rafmacalaba/data-use-mentions-extended huggingface dataset:
groups is statistically significant (Figure 4.A). Part of the explanation could be that governments in countries with higher political risk may be more focused on short-term outcomes to stay in power.

Likewise, commodity-exporting countries with lower-quality bureaucracy have often implemented more procyclical fiscal policies than those with higher-quality bureaucracies, although the difference between the two country groups is not statistically significant (Figure 4.B). Better bureaucracy may be expected to allow for better, rules-based, policy management and to limit unproductive discretionary spending.

Commodity exporters with better control of corruption have demonstrated lower fiscal procyclicality than those with weaker corruption control, and the difference between the procyclicality measures for the two country groups is statistically significant (Figure 4.C). Weaker control of corruption makes it easier to capture rents. Likewise, countries with more government stability have tended to demonstrate less fiscal procyclicality than those with less government stability—although the difference in cyclicality measures is not statistically significant (Figure 4.D). A more stable government allows for the formulation of fiscal policy with a longer horizon, weakening the incentive for procyclical policy. Better law and order has been associated with less procyclical fiscal policy (Figure 4.E). The difference between the procyclicality measures for countries ranked lower in the law-and-order index and countries ranked higher is statistically significant.

<mark>Many commodity exporters have enacted fiscal rules, often in conjunction with stabilization funds—which essentially put aside revenue in good times that can be tapped in bad times. Fiscal rules may signal the intent of the government to dampen, if not eliminate, fiscal procyclicality as well as to safeguard longterm fiscal sustainability. Over the past 40 years, t</mark> he presence of fiscal rules has been associated with lower procyclicality across the full sample of countries, although results are less clear for commodity exporters (Figure 4.F). Increased use of fiscal rules has not shielded EMDEs or commodity exporters from fiscal procyclicality, as evidenced by the continued procyclicality of fiscal policies in these countries
destinations served, the frequency of service, the number of seats per flight, and the size of the destination airport. Using a dataset similar to the one we use here (see further below), Pearce (2007) calculates connectivity measures for airports in 47 countries. He finds suggestive evidence of a relationship between connectivity thus defined, and important economic outcome measures such as labor productivity, and competitiveness of the travel and tourism sector.

In addition to the connectivity index developed by Pearce (2007), the applied mathematics and physics literature discloses a number of examples of the application of network methods to the air transport context, although they do not focus specifically on connectivity. For example, Guimerà et al. (2008) analyze the network structure of global air transport at the city level, focusing on the description of its mathematical properties and the identification of separate communities—an abstract analogue of huband-spoke systems—within the global network. There is also a number of applications of similar techniques to national or regional air traffic: Bagler (2008) examines India, and Li and Cai (2003) analyze China.

Another strand of the existing literature on air transport focuses on the impact of bilateral liberalization (Gönenç and Nicoletti, 2000; Doove et al., 2001; Intervistas, 2007; and Geloso Grosso and Shepherd, 2011). The general approach of these papers is to measure the restrictiveness of policy settings in a group of countries, and then to relate these policies to observed outcomes, such as bilateral traffic levels or international trade in goods. Although they thus provide important insight into the impacts of policy on sector performance, they do not explicitly consider the role that network structure and connectivity might simultaneously play.

Outside the air transport context, UNCTAD is continuing to develop a _Liner Shipping Connectivity Index_ (UNCTAD, 2007; Hoffmann and Wilmsmeier, 2008). They define connectivity in terms of access to regular and frequent transport services, then use factor analysis to bring together data on capacity and 6
**_Figure 3:_** _Share of shopping trips, by type of travel chain and main mode of travel (%)_

<!-- Start of picture text --> a. Bogota b. Buenos Aires<br>24% 24%<br>22% 22% 22.1%<br>20% 20%<br>18% 18%<br>16% 16.2% 16%<br>14% 14%<br>12% 12%<br>10% 10%<br>8% 8% 7.9%<br>5.7%<br>6% 5.6% 6%<br>4% 4%<br>2.8%<br>2% 2%<br>0% 0%<br>Non-motorized Public transport Private motorized Non-motorized Public transport Private motorized<br>Main trip mode Main mode<br>Round trips Chained trips Reduction potential Round trips Chained trips Reduction potential<br>Source: Team’s estimate based on EDM Bogota 2019 and ENMODO 2018<br>Percentage of shopping trips<br><!-- End of picture text -->

# **5. The impacts of the pandemic on e-commerce: Comparative results**

The demand for e-commerce during the pandemic grew in both cities. In Bogota, the share of residents using the Internet to purchase products rose from 18.9 percent to 22.1 percent between 2019 and 2021, according to the Quality of Life Survey conducted annually by DANE. The survey data show a drop in e-commerce adoption in 2020 to 14.7 percent, a result that contradicts the general statistics of e-commerce growth in Colombia. This discrepancy may be related to a higher purchase frequency without expanding the number of users, to methodological differences in accounting for e-commerce transactions or to response biases in the surveys, among others. In Buenos Aires, the pandemic accelerated the
Empirically, we analyze U.S. equity mutual funds with an international investment scope as they have clearly defined investment mandates, allowing us to examine the extent that information asymmetries affect the portfolio decisions of foreign institutional investors with different mandates. There are mutual funds specialized in some countries or regions (s _pecialized funds_ ) and those with a wider, unrestricted investment scope (g _lobal funds_ ). Managers from both fund types can be considered equally sophisticated and skilled in acquiring and processing information.<sup>4</sup> However, as shown in our portfolio choice model, they might not be equally informed about the regions/countries/firms they are allowed to invest in as managers with different mandates have different incentives to pay for costly information. We then examine to what extent the firm-level portfolio choice of these funds differ from one another. We construct a unique micro dataset of actual asset-level portfolios for U.S. mutual funds to study in detail the behavior of their international investments from 1997 to 2003. Given the regular reporting requirements for mutual funds, asset-level portfolios can be constructed and traced over time. This characteristic of the mutual fund industry is unique and contrasts with other types of investors such as hedge funds, pension funds, and individual international investors, for which portfolio information is not publicly available.

We focus primarily on firm size, which has been extensively used as a proxy for information asymmetries.<sup>5</sup> One possible explanation is through its association with cash flow volatility. Coval (2003) argues theoretically that a greater variance of cash flows for small firms

> 4 Grinblatt and Keloharju (2000) emphasize that the degree of investors’ sophistication matters for portfolio choice and their consequent performance.

> 5 See for example Falkenstein (1996), Kang and Stulz (1997), Dahlquist and Robertsson, (2001), Edison and Warnock (2004), and Chan, Covrig, and Ng (2005), among many others.

4
- Doyle, Kate, R. Levtov, G. Bastian G. Barker, J. Bingenheimer, A. Nzabonimpa S. Kazimbaya, J. Pulerwitz, F. Sayinzoga, V. Sharma and D. Shattuck. 2018. “GenderTransformative Bandebereho Couples’ Intervention to Promote Male Engagement in Reproductive and Maternal Health and Violence Prevention in Rwanda.” _PLoS One_ 13(4).

- Duflo, Esther. 2012. “Women Empowerment and Economic Development.” _Journal of Economic Literature_ 50(4):1051–1079.

- Evans, David, Pamela Jakiela and Heather Knauer. 2021. “The impact of early childhood interventions on mothers.” _Science_ 372(6544):794–796.

- Facebook Data For Good. 2020. “Small Business Surveys.” _Report accessed from website address: https://dataforgood.facebook.com/dfg/tools/future-of-business-survey_ .

- Fafchamps, Marcel, David McKenzie, Simon Quinn and Christopher Woodruff. 2014. “Microenterprise growth and the flypaper effect: Evidence from a randomized experiment in Ghana.” _Journal of Development Economics_ 106:211–226.

- Gimenez-Nadal, Jose Ignacio and Almudena Sevilla. 2012. “Trends in time allocation: A crosscountry analysis.” _European Economic Review_ 56(6):1338–1359.

- Giurge, Laura M., Ashley V. Whillans and Ayse Yemiscigil. 2021. “A multicountry perspective on gender differences in time use during COVID-19.” _Proceedings of the National Academy of Sciences (National Academy of Sciences)_ 118(12).

- Givord, Pauline and Claire Marbot. 2015. “Does the cost of child care affect female labor market participation? An evaluation of a French reform of childcare subsidies.” _Labour Economics_ 36(C):99–111.

- Goldin, Claudia. 1991. “The Role of World War II in the Rise of Women’s Employment.” _The American Economic Review_ 81(4):741–56.

- Goldin,
names into an English first name/last name format. Finally, BvD also provides a separate dataset providing information on the names and equity shares of owners.

# **_III.B. Product Complexity_**

Following Levchenko (2007) we rely on US data on the number of intermediate products used in production across 4-digit manufacturing industries to proxy for product complexity. We let the dummy Complex _k_ equal one for industries above the median. A ranking of these industries is provided in Table A2 of the Online Appendix.

# **_III.C. Contractual Institutions_**

To measure the quality of contracting institutions, we use (i) the Worldwide Governance Indicators rule of law index and (ii) a measure of trust from the World Value Survey (WVS).<sup>18</sup> While the rule of law index provides a good measure of contractual institutions across countries, unfortunately no similar exists that covers our sample at the subnational level. We therefore use trust as a proxy at the region level. We believe this captures similar variation in the relative cost of hiring professional or family managers. This is because in our model contractual institutions determine the probability agents can get away with acting against the interests of the principle. Whether the principal trusts the manager or whether they are sure they can enforce against any shirking in the courts should therefore be highly correlated. Indeed, this variable has been widely used as a proxy for rule of law by various researchers (Knack and Keefer 1997, La Porta et al. 1997, Zak and Knack 2001). Our trust measure is the share of people within a region that choose the first option in response to the question “Generally speaking, would you say that most people can be trusted, or that you can’t be too careful in dealing with people?”<sup>19</sup> To guage how reasonable the use of this proxy is empirically, Figure III explores the country-level relationship between trust and (i) the
# **3.5 Thailand**

_On the surface, Thailand does not appear to suffer from a serious liquidity crunch: monetary and credit variables along with interest rate spreads show no sign of it. However, the available interest rate data used here are inadequate for the purpose of our investigation. Additional data will have to be acquired before reaching afirm conclusion on the Thai situation._ As can be seen from the Chart TI, it appears that increases in interest rate (Chart T5 and Chart T6) have not been able to prevent the depreciation of the baht and restore investors' confidence. There is strong indication of a steep downturn: all output indicators are unfavorable. As illustrated in Chart T2, industrial production has been declining drastically: it registered a year-on-year growth rate of -13.7 % in February 1998 -- a much sharper decline than had been foreseen.

The evolution of the key monetary and credit variables do not show tight liquidity conditions. As depicted in Chart T3, the real domestic credit exhibits an upward trend. In addition, the growth rate of M2 definition of money is also strong, increasing by 15.4% (year-on-year) in March 1998.

These indicators can, however, be misleading if one concludes that there is no credit crunch in Thailand. Lending that is taking place could be mostly remedial -- i.e. extending lines of credit to existing clients in difficulty to prevent a further increase in the volume of non-performing loans.

<!-- Start of picture text --> Chart TI Chart T2<br>EVOLUTION OF STOCK-EXCHANGE INDEX CHANGE IN INDUSTRIAL PRODUCTION<br>AND THE EXCHANGE RATE (year-on-year)<br>01 in4a AOd WOO , OO , do io 90 9909 o ' .A2 _31<br><!-- End of picture text --> 3 1
# **4 Data**

In order to profile the relationship between labor market outcomes and social assistance in Turkey, we used the _Survey on Income and Living Conditions_ (SILC) that has been conducted annually by the Turkish Statistical Institute (TurkStat) since 2006. It provides information on labor market statuses and also detailed socioeconomic data on the Turkish population, covering demographics as well as household and income-related variables.

The SILC is designed in a novel fashion that allows for a specific individual to be followed for up to four years. It is conducted for four subsamples in each year, and each year the oldest subsample is replaced with a new one. Due to this rotating design, in any given year, of the individuals partaking in the survey, 75 percent can be traced back for two years, 50 percent for three years, and 25 percent for the previous four years.

Results of the SILC are published annually in both cross-section and panel formats. The samples are assigned with survey weights to be representative of the segments of the non-institutionalized Turkish population<sup>7</sup> . The panel component of the SILC is presented in a way that allows for the construction of three different balanced panel data sets from each year’s survey, namely, two-, three-, and four-year dimensional panels. Each balanced panel is assigned with corrected survey weights to represent the whole population. The two-year panel, which consists of the observations for the previous two years, includes 75 percent of all the individuals partaking in the latest year’s survey. The three- and four-year panels include 50 and 25 percent of individuals, respectively.

The SILCs are designed to produce reliable measures of social and economic inequality, poverty, and income distribution. Additionally, surveys offer a wide array of information on the labor market profiles and demographic characteristics of the individuals.

Using SILC data, we are able to classify each individual based on his/her labor market profile, such that we can identify if the individual is
the paper. We have also avoided references to names of individuals and documents that could identify the districts and/or interviewees. A reliance on interview data poses some methodological problems particularly with regards to the accuracy of the data collected (informants may not remember events correctly, interviewers may misinterpret their comments) and the potential for bias (informants may present only one side of a story). Accordingly, where possible, we have sought to double check information collected through interviews with other interviewees or secondary sources and to secure the views of a range of stakeholders. Secondary material was collected through online newspaper and media outlets, government publications, donor reports, and previous academic studies. We also examined, where relevant, laws and regulations provided to us by informants or that we were able to source online.

# _2.2.2 National vs. Regional Focus_

The focus of the paper is on the regional level given that, under Indonesia’s current decentralized system of government, primary responsibility for managing teacher management issues lies at this level. However, in examining this topic, the paper gives significant attention to the role of the central government and national level politics in shaping outcomes at the regional level. As Section 5 illustrates, the central government has played a crucial role in setting the incentives that regional governments face _vis-à-vis_ teacher management and the scope that they have to effectively resist reform through its use of central policy instruments. In some cases, it has designed effective policy instruments for promoting teacher management reform and remained committed to them in the face of political resistance. In other cases, it has either failed to devise such policy instruments in the first place or watered them down in the face of resistance. Whether the central government has played a disciplining role or effectively left regional governments to their own devices has thus been a crucial determinant of outcomes at the regional level, making it an important matter for the study.

# _2.2.3 Comparative Orientation_

The paper is focused solely on Indonesia’s experience with
these effects retain their causal interpretation, temporal autocorrelation in the observational data limits our ability to move beyond correlations without imposing further assumptions on the nature of the lag structure. The experimental data strongly suggests that the recall effects are stronger among those with a higher initial burden. These effects are significant for the total number of acute illnesses, the labor impact of sickness and the use of self-medication. They are also reasonably large for doctor‘s visits, but again, the small sample leads to imprecision in the estimated coefficients. In the observational data, the effects are uniformly large and significant and are actually quite close to the experimental estimates. The large size of the coefficients suggests that some portion of the recall effect is driven by those individuals and households with large morbidity burdens. In the observational results, the effect of recall on doctor‘s visits and the labor impact disappears entirely with the inclusion of the lagged morbidity interaction term, suggesting that when individuals and households have a large number of acute illnesses, they start to systematically forget entire episodes and it is precisely these households that drive the large observed differences between monthly and weekly reporting. This is consistent with our hypothesis 11
the country’s labor force across sectors, excluding agriculture. They were then asked to choose firms randomly within these broad sectors (for example, by choosing firms at regular intervals from an alphabetic list), and to pursue face-to-face interviews, following up for clarifications where necessary. The employment distribution was taken from data in the 1998 _Yearbook of Labour Statistics_ of the International Labour Office. The respondents to the survey are typically a company’s CEO or a member of its senior management.” The WCY uses a 115 question survey sent to executives in top and middle management of firms in all 49 countries of the WCY. The sample size of each country is proportional to GDP, and firms "normally have an international dimension." The firms are selected to be a cross section of manufacturing, service, and primary industries. There were 3532 responses to the Survey.

KKZ (2002) updates the data on governance that were developed in Kaufmann, Kraay and Zoido-Lobaton (1999) “Governance Matters.” The database contains more than 300 governance indicators for 175 countries compiled from a variety of sources in 2000/2001. Six aggregate 28
# **Figure 1.  Trends in Food Access and Violence**

<!-- Start of picture text --> Share of Respondents Using Food Coping Strategies<br>0.75<br>0.7<br>0.65<br>0.6<br>0.55<br>0.5<br>0.45<br>2016‐Q1 2016‐Q2 2016‐Q3 2016‐Q4 2017‐Q1 2017‐Q2 2017‐Q3 2017‐Q4 2018‐Q1 2018‐Q2 2018‐Q3<br>Reduce Meals Restrict Consumption Limit Portions<br>Conflict in Yemen<br>8000<br>6000<br>4000<br>2000<br>0<br>2016‐Q1 2016‐Q2 2016‐Q3 2016‐Q4 2017‐Q1 2017‐Q2 2017‐Q3 2017‐Q4 2018‐Q1 2018‐Q2 2018‐Q3<br>Fatalities Incidents<br><!-- End of picture text --> Sources: WFP mobile phone survey; ACLED conflict data base.

In addition to the lack of alignment between the geographic distribution of violence and food access in the June 2018, this lack of alignment persists throughout the entire conflict. Figure 3 presents the geographic distribution of violence over time. One can see that the regions experiencing the most violence has evolved over time. Initially, regions along the front lines in the eastern and southern parts of the country experienced the most violence. However, this evolved over time, where governorates on the front lines in the west and the north began experiencing more severe conflict.

However, figure 4 presents the geographic distribution of food access over time. The regional distribution of food access has been relatively fixed‐ the regions that had poorer food access in 2016 still had poorer food access in 2018. Furthermore, the regions with the poorest access to food were not necessarily those that experienced the most violence. Specifically, regions in the north that
|Observations|89|90|97|276|
|R2|0.170|0.113|0.081|0.048|
|R2 adjusted|-0.015|-0.081|-0.103|-0.011|
|F|1.031|1.241|0.666|1.746|
|Prob>F|0.436|0.259|0.819|0.364|

Note: The table presents the F statistics and the R2 of running a linear regression of our instruments on four lags of real GDP growth, inflation, the change in ECRR, and the change in IR, for each country and all together as a panel. See Section 6 for details. ECRR stands for effective constant legal reserve requirements. IR stands for policy rate. The panel regression includes country fixed effects. Source: Author's calculation. See Appendix for a detailed description of data sources.
# **Table 5 – Impact of FD Law on Balance Sheets of Banks Given Their Exposure to Local Public Debt**

This table reports OLS estimates of the impact of the FD Law on the balance sheet of banks of varying ex ante exposure to local public indebtedness. The variable _Loansb,m_ is the total volume of outstanding loans (in logs) extended by bank b in month m. _Loans-NonGovtb,m_ is the total volume of outstanding loans (in logs) extended by bank _b_ in month _m_ to all borrowers excepting the government. _Loans-Govtb,m_ is the total volume of outstanding loans (in logs) extended to the government by bank _b_ in month _m_ . _IntRate-NonGovtb,m_ is the average interest rate (percent) charged by bank _b_ in month _m_ to all borrowers except government. _IntRate-Govtb,m_ is the average interest rate (percent) charged by bank _b_ in month _m_ to the government. _BankExposureGovb,Mar16_ is a measure of the exposure of a bank to local public entities in the month prior to the FD Law and is calculated as the ratio of bank lending to local public entities over total lending. _Postm_ is an indicator variable that equals one from April 2016 onwards. Observations at the bank-month level. Standard errors are reported in parentheses and are doubled clustered at the bank and month levels. *, **, *** denote significance at the 10, 5 <u>and 1 percent levels. Detailed variable definitions are provided in Table A1 in the Appendix.</u>

||Loansb,m<br>(1)|Loans-Non-Govtb,m<br>(2)|Loans-Govtb,m<br>(3)|IntRate-Non-Govtb,m<br>(4)|IntRate-Govtb,m<br>(5)|
|---|---|---|---|---|---|
|Postm*BankExposureGovb,Mar16|0.01|1.26*|