rafmacalaba/data-use-mentions-extended huggingface dataset:
buildings and 0.2 percent less likely to buy equipment for each 1 percent increase in central government debt. On the intensive margin, investment by a given firm drops by 2.4 percent for each 1 percent increase in central government debt. The decline is larger in equipment investment (-2.3 percent), although the drop in land building investment is quite substantial (-0.9 percent). Interestingly, we do not identify the nonlinear effect of public debt investment.

Second, we examine the distributional effects of rising public debt. In other words, investment of which types of firms is more likely to be crowded out? We find that small, fully privately owned, service, and non-exporting firms reduce investment more when public debt rises. This finding suggests that higher public debt can deepen the unequal playing field among firms. This finding is the main contribution of our paper.

Third, we examine the channels of transmission from high public debt to reduced private investment. We confirm the access to finance channel: rising public debt makes access to finance more difficult. In addition, we identify for the first time a new channel that we refer to as a regulatory channel. We discover that when public debt rises, firms spend more time with regulatory and tax officials. The activities could suggest increasing efforts of the government to collect tax in more difficult times. Interestingly, the impact of this channel is much stronger for small and fully privately-owned firms.

Our paper uses firm-panel data from the World Bank’s Enterprise Surveys (ES). The standard ES sample collects information on a representative sample of formal (registered) private firms operating in manufacturing or service sectors. For countries with multiple waves of data, a subset of firms is reinterviewed over time, producing a panel component. This study exploits this panel component of about 14,000 unique forms in 57 economies between 2006 and 2019. The panel data allow for the empirical estimations to include firm-level fixed effects, thereby accounting for time-invariant firm-specific omitted
ads based on housing characteristics, we use the Facebook Lookalike Audience tool to target individuals living in non-solid dwellings. We show that this technique works to increase the number of respondents living in non-solid houses.

We apply this technique to recruit survey respondents into a longitudinal sample (N=5,205) and a cross-sectional sample (N=3,052), totaling 8,257 individuals in the three study states, balancing across 80 districts and the two dwelling types. To estimate the population-level effects of the malaria-prevention campaign, we work with the MNM advertising team and conduct a cluster randomized controlled trial, where we expose half of the 80 districts to the same ad content and delivery strategy used in the nationwide campaign. It is important to note that MNM employed conventional targeting techniques (i.e., based on socio-demographic characteristics available on the ad platform), optimizing ad delivery for engagement. Survey respondents in treatment districts were not necessarily exposed to the campaign, hence our results estimate intent-to-treat (ITT) effects.

We rely on the longitudinal sample to assess the impact of the campaign on a range of selfreported attitudes, behaviors, and malaria-related outcomes _during_ the campaign. This process was accomplished via an “experience” sampling method that gathered data across several survey rounds, for a maximum of 10 waves. We find an overall moderate impact of the intervention on bed net use among mosquito net owners. Overall, the likelihood of sleeping under a bed net both at the individual and household level increased by 3 percentage points (p.p.), a 4.5% increase compared

> 3The campaign cost 202,000 USD and reached users an average of 3.6 times over a 6-month period.

> 4In the context of India, houses constructed with durable materials are referred to as _pucca_ dwellings, while those made from temporary or non-permanent materials are known as _kutcha_ dwellings.

3
|0.870|0.870|

Source: OECD ICIO database. *** p<0.01, ** p<0.05, * p<0.1. Notes: Robust standard errors in parentheses. _FVAX_ and _DVAR_ lagged. All level variables in natural logarithms.

# **_Labor Standards_**

For our last three policy measures, we focus exclusively on the condition of inputs and output factors. This means we look at indicators that differentiate a country’s products based on specific standards in production. As a result, we expect on the one hand that the absorptive capacity channel is likely to become less prominent since our indicators do not necessarily look at the quality of input factors, as was done in the previous section. On the other hand, we expect these policy areas to matter much more for forward links through signaling effects.

Table 9 on the role of labor standards confirms this hypothesis. Such standards do not seem to affect economic upgrading through GVC integration as a buyer. This might in fact be an interesting result in itself. It shows that, despite their often cost-increasing nature, there is no evidence on an upgrading-preventing effect either. Therefore, a race to the bottom in terms of labor standards is not justified from a GVC perspective.

27
**-7-**

to compile nontariff barrier inventories. 6/ In this study we utilize summary statistics drawn from the GATT, UNCTAD, ICC and Commerce Department inventories by Walter (1969) (1972) to study longer-term trends in the level and pattern of nontariff measure use. Walter established a useful classification scheme for NTBs, based on the normal "intent" of these measures 'see Table 1), and published NTB two-digit SITC frequency and coverage indices for years around 1966 in 18 OECD countries. Since the methodology and data sources used in preparing the 1966 data were very similar to that employed in the UNCTAD Data Base the two sources can be linked to empirically assess changes in the frequency and coverage or nontariff measures. 7/

- 6/ The data on nontariff barrier use in the 1960s was drawr. from detailed surveys of NTBs made by a number of international and U-S. government organizations. The results of these surveys have been published in International Chamber of Commerce (1969), U.S. Office of the Special Representative for Trade Negotiations (1968), U.S. Bureau of International Comerce (1968), UNCTAD (1969) (1970) and a special GATT inventory of nontariff barriers compiled from submissions by each member country regarding the nontariff barriers facing its exporters. Given the extent of the surveys, and the amount of detail published on their findings, the information on nontariff barrier use in the 1960s appears to be as comprehensive as that compiled by UNCTAD for the 1980s. UNCTAD published detailed information on entries in its 1960s inventory. See UNCTAD (1973) (1974) for an example which illustrates how comprehensive the earlier statistics were.

- 7/ Several differences should be noted in the two data sources. First, Walter was unable to compile data on NTBs facing Finland and Ireland's agricultural imports, while New Zealand's import licensing requirements for industrial goods were not included in his inventory. These measures are included in the UNCTAD Data Base. Second,
power, whereas they reduced market power in the labor market, although the effect was not statistically significant. The data on industry import does not differentiate between intermediate and final goods imports. One possible explanation for the results is that the importation of final goods can increase the variety of products on the market; thus, product differentiation through imports can increase market power.

Table 5: Robustness Check: Identification Assumptions

||Expectat|ion Efect|Trade|Volume|
|---|---|---|---|---|
|VARIABLES|_μ_<sup>_m_</sup><br>_ijt_<br>(1)|_γijt_<br>(2)|_μ_<sup>_m_</sup><br>_ijt_<br>(3)|_γijt_<br>(4)|
|_τ_1991_× Post_1995|-0.0160*|0.0109***|-0.0228**|0.0109***|
||(0.00797)|(0.00329)|(0.00881)|(0.00241)|
|_τ_1991_×_ One year before WTO accession|-0.00992<br>(0.00826)|-0.000606<br>(0.00293)|||
|Total Industry Exports (log)|||-0.0299|-0.0211|
||||(0.0482)|(0.0278)|
|Total Industry Imports (log)|||0.203**|-0.0187|
||||(0.0757)|(0.0446)|
|_ωit_|1.571***|0.0836**|1.574***|0.0844**|
||(0.171)|(0.0286)|(0.168
_Table 4 Summary statistics of outcome variables for the control group_

|VARIABLES|N|mean|sd|min|max|
|---|---|---|---|---|---|
|_Main outcom_<br>|_es (survey‐ba_<br>|_sed)_<br>||||
|Employment index|763|0|1|‐2.314|6.401|
|Consumption index|763|0|1|‐1.580|5.037|
|Savings, investment and debt index|763|0|1|‐4.013|2.984|
|Business skills index|763|0|1|‐2.971|2.569|
|Psychological wellbeing index|763|0|1|‐2.625|3.606|
|Risk index|763|0|1|‐2.789|3.142|
|Trust index|763|0|1|‐2.982|3.147|
|Crime and violence index|763|0|1|‐1.214|5.667|
|Migration index|763|0|1|‐0.838|3.740|
|List experiment cattle index|763|0|1|‐3.360|3.095|
|List experiment argument index|763|0|1|‐3.666|4.163|

_Note: Higher values of all indicators refer to higher scores in the respective outcome. For instance, higher values in the risk index imply a higher preference for risky behavior. Higher values in the list experiment cattle index imply a higher propensity to engage in cattle raiding, while higher values in the list experiment argument index imply a higher propensity to engage in arguments. Higher values of the migration index mark a higher propensity of having, or planning to migrate._
# **3.4 Sample Definition**

In order to arrive at the analytical sample summarized in Table 3, we implement several filters. First, we drop several countries for various reasons. We exclude the Central African Republic and the Democratic Republic of Congo, because the surveys were carried out exclusively in the capital cities. We exclude Argentina, Cambodia, the Arab Republic of Egypt, Iraq, Mozambique, Sierra Leone, Somalia and the Republic of Yemen because the survey data for these countries have not been approved for public disclosure. Second, we limit our analysis to the first survey wave in each country and the months of April to July. We exclude the Philippines because the Wave 1 data are from August. Finally, we limit our sample to respondents ages 15 to 64 and drop observations with missing age. We exclude Chad and Senegal because age is not available in the data.

# **3.5 Other Data**

In addition to the HFPS data, we use several data sets to describe pandemic severity, macroeconomic and labor market projections, and for weighting purposes.

**Pandemic severity data.** To describe pandemic severity, we use data from the University of Oxford’s Our World in Data (OWID) COVID-19 data set. Specifically, we use May 31 (the midpoint of our surveys) data on the number of cases, the number of cases per million, the number of deaths, and the number of deaths per million. The original source of the OWID COVID-19 case and death data is the European Centre for Disease Prevention and Control (ECDC) which collects data on countries around the world.

**Macroeconomic projections.** To relate our findings to macroeconomic projections on the economic impact of COVID-19, we use data from the IMF’s World Economic Outlook (WEO). To measure the pandemic’s impact on the macroeconomic outlook of countries, we compare October 2019 projections with October 2020 projections. This allows us to

16
16 specially-designed questionnaire forms to speed data entry and tabulation. This can be a more costly option in the short-run but it may save time and money if new survey work is anticipated in the future.

RepoingLd the Resul. Data should be presented in such a way that they can be easily used as instruments for decisionmaking. For effective use of reports, careful consideration is needed in the choice of the title and format. A survey covering different subjects and policy issues may need separate reports or working papers. A general presentation of the socio-economics of the survey area and the linkages between the survey findings and national policy should be included. Specific tables should reflect the policy issues that are important to the survey and not be just a description of the facts uncovered in the survey.

Reports should summarize the survey methodology (including sample stratification), conversion factors, technical aspects of energy data, results and findings. Use of tables, graphs, diagrams and charts should be made to clarify the data and major conclusions. The report should also provide information on the actual time spent gathering data, the period of the survey and a listing of pertinent unusual events which might affect the validity of the results. In any energy survey, certain findings are liable to be inconsistent or difficult to explain. These should be mentioned and their significance discussed. Lastly, while it is not practical or recommended that unconverted survey data be included in the report, a survey codebook should be prepared so that others can use the data, if requested.

Effective reporting requires apprepriate analysis. Results reported in terms of average energy consumption by end-use ad type of energy, as is done in most household energy consumption surveys, provide only a backgrou. or identifying needs and formulating broad energy policies and interventions. Information for policy analysis on prices, equity and economic growth should show whether significant differences or variations exist among households or types of energy consumers, as well as the factors influencing such differences.

# Costs and Timing

The pricetag of a household energy survey will
external rather than internal financing. Thus, we expect that as the transition moves along-China’s GDP per capita in PPP rose from around US$2,900 (in 2011 US$) in 1993 to about US$11,000 in 2012<sup>9</sup> -- Chinese firms will rely less on political connections and more on formal institutions such as courts and banks.

**Hypothesis 4** . As the Chinese economy grows, firms rely more on courts, contracts, and external financing and less on families, retained earnings, and political connections. Moreover, firms improve their corporate governance and adopt more formal governance mechanisms (i.e., the board of directors and board of shareholders).

# **3. Data description**

Our key firm-level data consist of 10 waves of a nationwide survey of privately-owned firms (NSPF) in China, conducted in 1991, 1993, 1995, 1997, 2000, 2002, 2004, 2006, 2008, and 2012 jointly by the All China Industry and Commerce Federation, the Center for Private Economy Studies at the Chinese Academy of Social Sciences, and the CCP United Front Work Department. Our study ends in year 2012, which is the last year the NSPF data are publicly available. The sampling method used in NSPF was multistage-stratified random sampling to achieve a balanced representation of private firms across regions and industries. First, the total number of private firms to be surveyed was determined. Second, six cities or counties were selected from each province, including the capital city, one prefecture-level city, one countylevel city, and three counties. Next, the number of private firms to be surveyed in each province was determined as the product of the share of local private firms in the national total and the total sample size. The numbers of firms in each city, county, and sector were likewise determined. The survey thus covers firms in 19 sectors ranging from agriculture to service industries in 31 provinces, municipalities, autonomous regions
**Figure 1: Reported bribes vs. other costs (% of firms’ annual sales)**

<!-- Start of picture text --> 3.00<br>2.77<br>2.50<br>2.00<br>1.50<br>1.00<br>0.50<br>0.13<br>0.00<br>Bribes Electricity cost<br>% of firms' annual sales (median values)<br><!-- End of picture text --> Source: Authors’ own calculations based on Enterprise Surveys data. Note: The sample is restricted to only those firms that report positive bribes paid to public officials “to get things done”. It excludes firms with missing data on electricity cost of bribe amount paid. The figure uses sampling weights provided in the ES. The qualitative nature of the results shown in the figure remains unchanged even without the use of sampling weights.

31
are of little use in understanding intergenerational economic mobility?<sup>6</sup> Are the different measures of intergenerational persistence affected by coresidency bias to the same degree, or are some measures more robust than others with relatively small bias? This paper makes progress on these questions, providing evidence, analysis, and guidance for working with coresident samples that have wide ranging implications for research on intergenerational economic mobility.

To understand the implications of the coresidency restriction, the challenge is to find surveys that (i) include all of the children and the parents irrespective of their residency status, and (ii) identify the subset of individuals coresident in a household at the time of the survey. We take advantage of two high quality household surveys in villages of India and Bangladesh, and estimate the two most widely used measures of intergenerational persistence in the literature: intergenerational regression coefficient (henceforth IGRC) and intergenerational correlation (henceforth IGC).<sup>7</sup> The evidence on intergenerational schooling persistence presented below in this paper shows that IGRC, the most widely used measure of intergenerational persistence, suffers from large downward bias because of truncation due to coresidency.<sup>8</sup> In contrast, the downward bias in the estimated IGC in coresident samples is much smaller; in many cases, less than one-third of the bias in the corresponding IGRC estimate. In the sample of 13-60 years age range, the average bias in IGRC estimates is 29.7 percent in the case of Bangladesh, while the corresponding bias in IGC estimates is only 8.7 percent. The extent of truncation bias in India is smaller because of higher coresidency rates observed in the data. However, the IGRC estimates in India are also substantially biased downward; the

> 6The prevailing view, in fact, holds that the estimates from coresident samples are not useful, and it is partly driven by the fact that the researchers do not know the direction of bias.

> 7IGRC shows how a one year of higher schooling
imposed trade remedy in the case, which we define as the trade weighted-average final duty reported in the _Federal Register_ . We are admittedly agnostic as to how this variable would affect the likelihood of WTO dispute initiation. While an extremely high duty may also be more likely to indicate an egregious WTO violation (affecting the likelihood of legal success in the dispute, to be discussed in more detail below), there may be a substantial range through which even lowering that duty would still leave it as prohibitive, resulting in a zero market access benefit to the affected exporter for initiating a WTO-challenge. On the other hand, while a lower imposed duty may be less likely to be WTO-inconsistent, a marginal reduction could have a substantially positive market access impact.

17 For evidence on exporting countries ability to “deflect” U.S. trade remedy-affected exports to third markets, see Bown and Crowley (2004).

> 18 Since we are comparing the foreign exports to the non-U.S. markets relative to foreign exports to the world (at the product level), we can no longer use the 10-digit HTS import data as our level of aggregation. Thus, for this variable we use the 6-digit, product level Harmonized System (HS) data derived from the UN’s _COMTRADE_ database, as this is the most disaggregated trade data that is readily comparable across countries.  Nevertheless, this data is only consistently available for 15 of the world’s 30 largest importing countries for the 1992-2003 period required for our sample; therefore, in constructing the ratio, the foreign country’s exports to the “rest of the world” are proxied for by the imports of the following 14 other countries: Australia, Brazil, Canada, Switzerland, China, Germany, Denmark, Japan, Mexico, Malaysia, Singapore, Sweden, Turkey and Taiwan.

11
to armed conicts ever since. In the case of Sri Lanka, terrorist groups such as the Liberation Tigers of Tamil Eelam (LTTE) and Janatha Vimukthi Peramuna (JVP) have remained active after being defeated in the Sri Lankan Civil War (1983 to 2009), motivated by ethnic disputes between a Tamil minority and the Sinhalese majority. As a result, terrorism in SriLanka and India has concentrated in specic areas This paper aims to estimate the relationship between violence and women's decisions to work in four South Asian countries. In doing so, we aim to extend the literature in two directions. First, while other studies use national-level data, we look at within-country dierences in violence using micro-data on employment and (i) number of attacks by administrative division; (ii) number of deaths, and (iii) number of people wounded as proxy measures for violence. Second, we pay particular attention to the distinction between the eect over the extensive and intensive margins; that is, whether women work or do notwork or to not work vis-Ã -vis the number of hours supplied, respectively. As such, our paper extends Menon and van der Meulen Rodgers [2015] and Kreibaum and Klasen [2015] by allowing for the possibility that the added worker hypothesis is at work in the context of terrorism in South Asian countries. In Section 2 we present two simple models that illustrate the relationship between violence and the extensive and intensive margins of female labor supply. Section 3 describes the data use in this paper. Section 4 explains the methods used to estimate the eect of our three violence measures on FLFP. Section 5 describes the results and their implications. Section 6 draws conclusions.

# 2 Theoretical Considerations

Labor supply decisions are distinguished between the extensive margin, the decision to work or not, and the intensive margin, the decision of how much to work. In this section, we describe the theoretic intuition for each concept in turn.

## 2.1 The
because (say) no one was home or the respondent was too busy—then enumerators made as many as two return visits. If the enumerator was unable to complete the interview after three attempts, a new household from the same neighborhood was selected.

During the interview, respondents were asked to show their electricity and water bills. The enumerator recorded respondents’ utility customer ID numbers on the questionnaire form. In the case of electricity, the ID number allowed us to verify that the household interviewed was the same household that had been randomly selected; we were then able to match this sample household with their EEU customer billing record. In the case of water, the customer ID number helped us identify the sample household in the AAWSA customer billing records. We were able to match 99% of the completed interviews to the electricity utility records.<sup>17</sup> Yet we could match only 70% of our observations of water private connections to the water utility database; as mentioned in note 3, for the remaining 30% we relied on self-reports and back-calculations.<sup>18</sup>

# **4. Analytical framework for calculating the distribution of household electricity and water subsidies**

# **_Basic subsidy incidence calculation_**

This section describes how we estimated (a) the electricity and water subsidy received by each of the 366 households with both private electricity and private water connections and (b) the incidence of subsidies across these 366 households. For each household _i_ , we calculated the subsidy it received for

> 17 Only nine electricity customer ID numbers could not be matched to the electricity utility records. These observations were not included in our final sample.

> 18 The remaining sample households had either shared connections (394) or no water connection (211 households). Most households with shared connections were not able to show a water bill to the enumerator, which prevented us from obtaining the billing ID that would have allowed for matching with the water utility’s billing