rafmacalaba/data-use-mentions-extended huggingface dataset:
of the shocks could be on the real economy. _Ceteris paribus_ , when φ is larger, borrowing constraints are less likely to bind, and crises are less frequent. Without an empirical measurement of φ, the theoretical literature must resort to calibrating φ to match some business cycle moments. Thus, the value of φ is modelspecific, and not surprisingly, it varies markedly across studies (for example 15%-30% in Mendoza (2010), 5% in Korinek and Jeanne (2010), 32% in Bianchi (2011), 50% in Devereux and Yetman (2010)). An estimate of φ will provide the literature an empirical measure to serve as a benchmark value. More broadly, documenting and understanding the magnitude and the correlates of collateral’s extensive and intensive margins are crucial for both firms and governments to mitigate the negative aspects of collateralized borrowing.

Our paper is an attempt to fill the gap and it is divided into two parts. In the first part, we use data from 22,263 borrowing firms,<sup>3</sup> across 131 countries, and between 2005-2017 from World Bank’s Enterprise Surveys (WBES) to document the existence and prevalence of collateralized borrowing. We show that collateralized borrowing is widespread. On average, 77% of loans from financial institutions require collateral, and among loans with collateral, collateral value is about 167% of the loan value. This implies that for a $1 loan, the average collateral value is $1.67. Equivalently, the loan-to-value ratio φ is about 60%. When stocks, bonds and other financial assets are pledged as collateral, the collateral value is about 120% of the loan value. This implies that the value of φ is 83%. Either at 60% or 83%, the value is much larger than those commonly used in the theoretical literature. Our empirical estimate of φ has been cited and used in recent theoretical literature on macro-prudential policy, for example, by Bianchi
10 The MTF survey data has extensive information about households, dwellings and housing tenure, expenditures on various consumption and durable items, cooking equipment and habits, access to electricity, experience with electricity outages, equipment used for lighting, or to powering appliances during outages, and sources of energy use. The survey was conducted between July and November 2017, and resulted in 6000 completed questionnaires, with almost 90% of the interviews completed in August and September.

A total of 4047 out of 6000 households reported that they were connected to the NEA grid. Some 640 households produced the most recent electricity bill, which displays the exact consumption in kWh during the latest billing cycle. The remaining households reported their typical bill in Nepalese Rupees (NR), and we are able to compute their monthly consumption in kWh by matching the bill amount with the NEA tariffs.<sup>10</sup> Figure 2 shows two histograms of the distribution of monthly electricity consumption. Figure 2.a is based on the exact consumption in kWh from the bill. Figure 2.b is based on either exact consumption, when available, or calculated consumption if the respondent merely reported the bill in NR. The two distributions are very similar: The average consumption is 61 kWh, and the median is 34 kWh for the sample with exact consumption data, and 38 KWh for the blended exact and calculated consumption sample, indicating that Nepali households use about 1-2 kWh/day.

The questionnaire asks respondents to indicate whether there were some periods during the year that were worse than most in terms of unscheduled electricity outages. Based on a total of 1543 responses, people experience, on average, 76.8 outages (median 40.5) and 92.38 hours (median 63) per month during the worst months. In practice, this means that there are unscheduled outages every day, for a total of 2-3 hours a day with electricity gone without

> 10 At the time of the study the exchange rate of one United States dollar
5 Many questions in the MENA survey were directly drawn from the two previous surveys, allowing for comparisons of some of the main results. At the same time, the MENA survey has new questions, designed to cover topics that were not covered in the previous surveys, but that are important for MENA, or designed to provide granularity on some of the obstacles to SME lending. For example, the MENA survey has included a new question on long-run targets for SME lending that allows us to assess the additional room for SME finance by each country. The survey also provides more granularity on some of the problems with financial infrastructure such as creditor rights (an area where the MENA region fares very poorly), asks questions on gender and Islamic finance, and finally includes some questions on the status of Basel II implementation, because of its potential effects on SME finance.

The first version of the MENA survey was tested in a pilot set of interviews with 6 banks in 2 countries – Morocco and Egypt. The results of these interviews provided important insights for the final revisions. The UAB and World Bank teams finalized the revisions and launched the survey in December 2009. The survey was sent in English, French, and Arabic. The UAB also played a fundamental role in the follow-up phase (January-May 2010), through constant communication with member banks, collecting responses, checking for inconsistencies, and frequently requesting revisions. The final response rate was high – slightly less than half of the universe, due largely to the active follow-up of UAB staff in headquarters and regional offices.

Table 1 reports the number of banks and countries from MENA that responded to our survey along with their market share. We obtained responses from a total of 139 banks in 16 countries. On average the banks that responded account for 64% of the banking system loans. Among the 16 countries in our sample, 6 are from the GCC and remaining 10 are from outside the GCC. The 57 GCC banks account for 74% of loans in GCC banking systems
*|1.4939***|
||(0.0642)|(0.4084)|(0.2953)|(0.3386)|(0.2956)|(0.2891)|(0.3439)|
|Observations|40,468|40,468|40,468|40,468|40,468|40,468|40,468|
|R-squared|0.496|0.214|0.119|0.187|0.252|0.159|0.101|

Note: This table reports district fixed-effect RDD-DD regressions of income and poverty of households using household-level observations. The additional control variables include education levels of individuals (dummy variables), education levels (dummy variables), household size, the proportion of children in households, the proportion of older members in households. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

84
43 Such a restriction would also have the additional benefits of preventing abuse by over-taxing exporting firms as well as reducing the possibility of corruption and evasion.

Other possible problems might also be obviated by careful tax design. For example, the danger of tax exporting and, more importantly, ‘beggar-my-neighbor’ tax competition suggest that, as with some other subnational taxes on business such as property taxes, it may be advisable to place a floor (and perhaps also a ceiling) on such taxes as well as requiring that the same uniform rate should be imposed on all types of business. Similarly, compliance and administrative costs could be kept low by imposing the tax on the basis of information available on existing business income tax returns. The tax base for individual regions and localities might be determined on the basis of payroll data, which in many countries is already collected for purposes of social security and payroll taxation.

Although there is considerable experience around the world with variants of this sort of tax (Bird 2003), as yet the relative unfamiliarity of this idea appears to have discouraged its use in many countries at least until recently.<sup>86</sup> Most countries thus remain stuck with essentially presumptive forms of business levy. However, even these levies may often be improved by better design and implementation in order to improve their elasticity and to reduce both their distorting effects and their vulnerability to abuse (Bird and Wallace 2004). Many recent studies have highlighted the barriers to the growth of micro and small enterprises that may be created by badly designed and implemented systems of local business taxation (World Bank 2007). The potential solution offered by the BVT to this problem and to local government revenue problems, particularly in large and expanding urban areas, appears to deserve more detailed examination in many emerging countries.

# **7. Conclusion**

The intergovernmental fiscal arrangements in any country must be thought of as a _system._ The pieces of the system have to fit together properly if decentralization is to work properly
.2 and 46.3 percent in 2009, depending on the assumptions made about the pass-through rate from national accounts growth data to household consumption. The backcasted series suggests a small decrease in the poverty rate at the beginning of the decade and then stagnation, or even reversal, in poverty reduction following the 2016 recession. Qualitatively, these results remain unchanged even after running sensitivity analysis to test different assumptions about the pass-through rate and about the mapping between micro- and macro-data.

The survey-to-survey imputation approach constructs a consumption model using a set of comparable non-monetary variables, which are available in both the 2018/19 NLSS and the GHS, to impute consumption into the 2010/11, 2012/13, 2015/16, and 2018/19 waves of the GHS. This approach follows a wide literature on survey-to-survey imputation techniques (Christiaensen et al. 2012; Dang et al. 2017; Douidich et al. 2016; Newhouse et al. 2014; Stifel and Christiaensen 2007; Yoshida et al. 2015; Yoshida et

> 3 For a discussion on data quality concerns around the 2009/10 HNLSS surveys, see Nigeria Poverty Assessment 2016.

3
with annual DMPSOLS data (Henderson, Storeygard, and Weil, 2012), even though their study uses monthly VIIRS data. Most others are abstaining from the conversion and instead focus on differences between subnational entities (Beyer, Franco-Bedoya, and Galdo, 2021), thwarting quantitative economic conclusions.

So far, to the best of our knowledge, no cross-country study of the quarterly relationship between VIIRS nighttime lights and GDP exists. However, to exploit the full potential of VIIRS data for economic analysis, an elasticity to convert changes in nighttime lights into changes in economic activity is fundamental. This paper fills this important gap. In terms of the econometric framework, it is closely related to the seminal contribution of Henderson, Storeygard, and Weil (2012) and the more recent work of Hu and Yao (2021), which both estimate the annual elasticity between DMSP lights and GDP in a global sample. The elasticity estimated by the former has become the standard elasticity used to date, but its validity for higher-frequency VIIRS data is limited. In addition, identification and estimation of the elasticity depend crucially on assumptions about the signal-to-noise ratios of GDP data. Hu and Yao (2021) 4
materials and energy) as deviations from a single reference point. As the reference point, the index uses a hypothetical plant operating in the base time period (the first year of the data being 1983) and having average input cost shares, average logarithm of inputs and average logarithm of output. The index is calculated separately for each 4-digit ISIC sector.

The key input for our indicator of services sector policy restrictiveness is obtained from the OECD FDI regulatory restrictiveness index, disaggregated by type of restriction and by sector of activity. Input-output data allows us to generate an index of restrictiveness that varies at the level of each manufacturing sector, defined at the 2-digit ISIC 3 disaggregation (a precise description of the method is presented in the subsection that follows).

Data on tariffs were obtained from the TRAINS database of the United Nations, and here again, input-output tables were used to calculate input tariffs and output tariffs.

> 10 We thank Beata Javorcik from sharing the STATA program to calculate this index.

9
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relationship between the international mobility of researchers and their performance. Finally, firm learning from diaspora has also increased sharply (Oettl and Agrawal. 2008).

# **V. Conclusions and Directions for Future Research**

As is evident from the literature review, application of international trade and FDI theories to global mobility of students and scholars is virtually nonexistent. Furthermore, lack of research on the impact of returning and non-returning international students on international knowledge diffusion needs to be addressed (King and Raghuram, 2013). Raghuram (2013) suggests that an analysis of student migration where the inducements that universities offer to prospective students and the subjective responses of such students to these invitations will throw light on how the spatiality of knowledge is achieved and highlight the distinctiveness of student migration in a skilled migrant world. Research on international students whether at the regional, national or international levels has been hampered by a lack of a comprehensive international student and scholar database. Although detailed data on international students are available to the public for at least five countries-US, Canada, Australia, the UK and Turkey, and to some extent for New Zealand and a few EU countries, they are fragmented, hampering any meaningful analysis. Furthermore, for countries such as Denmark, database on stay rates is very expensive. Discussed below are suggestions for future research on international student mobility.

_(i) Creation of an International Student Database_ There is a need to create a database on international student mobility in the context of trade in services and migration. The most detailed data are available for the United States. However, these data are from different sources such as the National Science Foundation and the Institute for International Education. Detailed data by different schools such as engineering, level of study such as PhD as well as field, university, country and nationality of professors are available from various data sources published by the NSF at the national and state levels. Birthplace of the professors are available from the faculty information of the various universities by schools and departments. In
_**|0.268|0.35|
|**_ES9_**|11.38|11.66||**_IGME9 _**|0.354|0.35|
|**_ES16_**|14.66|14.11||**_IGME16_**|0.558|0.35|

Notes: (1) The data used are CFPS 2016 with children aged 18-35. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling, (3) ESK is expected schooling when father has K years of schooling. K=0,6,9,16. (4) Robust standard errors in parentheses; standard errors clustered at province-level in square brackets; *** p<0.01, ** p<0.05, * p<0.1.
change on future agricultural productivity from the present-day cross-sectional relationship between climate and land values. This allows for as much (but no more) adaptation than is revealed in the data. Predictions based on reduced-form Ricardian regressions presuppose that the technological envelope along which farmers will adapt in the future will be the same as it was in the past.<sup>2</sup> In response to perceived empirical deficiencies of such cross-sectional analyses, Deschˆenes and Greenstone (2007) propose using panel data on farm revenue to estimate the response of agricultural productivity to year-toyear weather fluctuations. However, since there is relatively little scope for adaptation to short-run weather, damage estimates based on shocks can only provide an upper bound on the long-run impact of climate change on agricultural productivity; there is no guarantee, moreover, that this bound will be very informative.

Our Ricardian estimates indicate that warming of about 1.25<sup>_◦_</sup> C expected to occur in

> 2The Ricardian approach also ignores the costs of adjusting along the technological frontier (see, e.g., Quiggin and Horowitz, 2003; Kelly et al., 2005).

2
Policy Research Working Paper 9678

# **Abstract**

Robots are rapidly becoming a key part of manufacturing in developed and emerging economies. This paper examines a new channel for how automation can affect international trade: quality upgrading. Automation can reduce production errors, particularly of repetitive processes, leading to higher quality products. The effects of robot use on export quality are estimated, by combining cross-country and cross-industry data on industrial robots with detailed Harmonized System 10-digit trade data. Robot diffusion in (preexisting) foreign customers is used as an instrumental variable to predict robot adoption in the home country-industry. The findings show that robot diffusion leads to increases in the quality of exported products. Quality improvements are predominantly driven by the upgrading of developing country exports; and within countries, quality improvements are driven by upgrading of (initially) lower-quality exports of developed and developing countries. The paper also finds some differences in the type of robots—sophisticated or more basic—associated with quality gains in developing and developed economies.

This paper is a product of the Office of the Chief Economist, East Asia and the Pacific Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at tdestefano@hbs.edu and jtimmis@worldbank.org _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the
the regional level for the most populous regions in the country while Wave 2 and 3 expanded to include 1,500 households in urban areas. After data cleaning to remove urban and non-agricultural rural households, we are left with 7,272 household observations across three survey waves.

In Malawi, the LSMS-ISA data includes two separate surveys: the cross-sectional Integrated Household Survey (IHS), and the longitudinal Integrated Household Panel Survey (IHPS) (NSO, 2012; NSO, 2015; NSO, 2017). This analysis relies on the data from the IHPS, which is representative at the national-, urban/rural-, and regional-level. Data comes from 2010/11, 2013, and 2016/17. A key IHPS design feature is that starting in 2013, the survey attempted to track all individuals that changed locations between the survey waves, and brought into the sample the new households that the movers formed/joined. Our analysis relies on households that did not move vis`a-vis the baseline interview location, since it is not obvious what the appropriate reference location historical weather data should be drawn from for calculating seasonal deviations from long-term trends. Should the long-term trend be the weather in the location the households lived in the past

> 6LSMS-ISA has supported nationally-representative cross-sectional surveys in Mali in 2014 and 2017. The data from Mali will be incorporated as part of future work, per the analysis plan (Michler et al., 2019). While Burkina Faso has also been supported by the LSMS-ISA, the resulting survey data cannot be used in our analysis since the sampled households were not georeferenced.

10
poverty rate in the country, being representative at the national, provincial, and urban/rural levels. Across the four provinces, 24,809 households were interviewed, broken down into 15,936 rural and 8,873 urban households.<sup>4</sup> Table 1 shows that the official (national) poverty rate in FY19 was 21.9,<sup>5</sup> but it varied between the provinces and urban and rural areas. The spatial distribution of the poor varies, as the province with the lowest population share (Balochistan) also has the highest poverty rate, almost twice as high as the national poverty rate.

**Table 1: Official Poverty rate in Pakistan FY19**

||**National**|**Punjab**|**Sindh**|**KPK**|**Balochistan**|**Urban**|**Rural**|
|---|---|---|---|---|---|---|---|
|Population (%)|-|53.9|23.0|17.1|5.9|36.4|63.6|
|Poverty rate|21.9|16.3|24.1|29.5|42.7|10.9|28.1|
|Share of thepoor|-|40.0|25.4|23.0|11.6|18.12|81.9|

Source: Author calculations using HIES 2018/19 Note: Population shares use the survey expanded weights Table 2 provides information on the macroeconomic indicators, including growth rates, inflation, and population. PBS publishes data on population growth rates, inflation, and the GDP series between FY20 to FY22.<sup>6</sup> Growth rates, along with inflation forecasts,
2001|62,201.5|98,012|11,615.3|15,911.0|118.5|18.7|162.3|
||||Average Per<br>|Annum Growth|Rate (%)<br>|||
|1960-1970|32.4|26.9|32.1||3.4||n.a.|
|1970-1980|34.2|18.1|29.3||10.2||n.a.|
|1980-1990|18.0|5.5|16.5||10.4||14.1|
|1990-2001|7.6|-2.0|3.8||5.9||16.4|
|||Percentage of To<br>|tal Manufacturing<br>|||||
|1960|3.7|4.6|5.6|na||||
|1970|5.5|9.3|9.1|na||||
|1980|16.9|25.1|19.6|7.8||||
|1990|39.1|34.9|35.7|20.8||||
|2001|45.0|28.4|36.4|33.1||||

_Sources_ : Economic Development Board, Report on the Census for Industrial Production, various years; Economic Development Board, Report on the Census of Manufacturing Activities 2000.

10
that a significant fraction of the growth in these countries might have come from more efficient private firms that displaced the SOEs (Hsieh and Klenow, 2009).

# **4. Firm dynamics and the use of capital markets**

We now investigate the link between firm dynamics and their use of equity and bond markets. It is well-known that the larger firms within an economy have greater access to capital markets, due at least in part to cost and liquidity considerations. In practice, these considerations render the minimum issue size rather large for smaller firms (Beck et al., 2006). But even among the publicly listed companies, not all firms actually raise capital in capital markets on a recurrent basis, as shown above. Therefore, we analyze which firm characteristics are related to the probability of raising capital in bonds or equity markets. We also study the firm dynamics around the capital raising activity and the implications of our findings for the distribution of firm size.

# **4.1.  Which firms use capital markets?**

To conduct the analysis, we rely on our merged data set that combines the SDC Platinum database on the use of capital markets with firm-level balance sheet information on the post-IPO period from Orbis and Worldscope.<sup>22</sup> We split firms into users and non-users of capital market financing, according to whether firms issue equity or bonds within our sample period. Because the firm-level balance sheet information is only available for the 2003-2011 period, we classify a firm as a user of equity or bond markets if it had at least one non-IPO capital raising between 2003 and 2011. Non-user firms are those that do not issue any equity or bond after their IPO. Based on the matched SDC-Orbis data set, in China, 425 firms are equity users, 195 are bond users, and 1,915 are non-users; in India, those numbers are 727, 291, and 3,428, respectively.

> 22 Because we work with listed firms, all firms