rafmacalaba/data-use-mentions-extended huggingface dataset:
# **2. The growth of EM local currency bond markets and foreign investor participation**

Local currency bond markets have grown rapidly in recent years across a wide array of developing countries. Nevertheless, reliable, comprehensive and up to date data in this area remain hard to come by. Here, we provide some views of the broad trend by drawing on data for two different subsets of EMs, prepared by JP Morgan and the Bank for International Settlements (BIS), respectively.

Figure 1 draws on data compiled by JP Morgan for 26 EMs (together accounting for over 50% of GDP of the 150 countries classified as “developing and emerging” by the IMF)<sup>i</sup> . It illustrates the rapid growth of EM domestic sovereign debt since 2000, both relative to external sovereign debt and in absolute terms. Until the economic slowdown in 2009 induced by the global financial crisis, GDP grew even faster than net debt issuance in these economies in aggregate, so the total government debt to GDP ratio dropped (also shown in Figure 1). This trend was interrupted by the crisis, but has since resumed, and the debt ratio remains moderate—and very low compared with that of advanced economies—at under 35%.

This view includes loans from the official and commercial sectors (not just bonds). The bulk of these loans will tend to be foreign currency-denominated, so the relative decline of external debt incorporating such financing supports the idea that a strong de-dollarization trend in EM public borrowing is underway. However, in some cases strong recorded rises in domestic financing may also reflect local commercial banking sector growth, rather than the domestic bond issuance which is our focus here.

We therefore also consider an alternative, securities-specific perspective: the trend in international and government debt securities outstanding since 1994 for a similar, but smaller, subset of 22 countries tracked by BIS (Figure 2).<sup>ii</sup> Again, the rapid relative and absolute increase in domestic
Table 3 derives the marginal effects over time, which are calculated by taking the difference between the maximum and the minimum value of each variable. As with the cross sectional marginal effects, the signs are those predicted and expected self-employed earnings is the most important variable to explaining the transitions change from formal to informal sector.

**Table 3: Marginal Effects Over Time**

||Variable Variation|Probability Variation|
|---|---|---|
|Formal Sector Wage|0,3654|-0,0315|
|Self-Employed Earnings|0,2411|0,29 13|
|Interest Rate|57,83|-0,0377|
|Interest Rate Lagged|64,03|-0,0429|

# **VI. Conclusion**

The paper has derived a methodology for analyzing logit models in a rotating panel context. Using data from Mexico, it then applied the technique to test between two theories of why salaried workers enter the informal self-employed sector. The evidence supports a view that self-employment is a desirable destination, but one that in the presence of credit constraints requires accumulated capital before the business is opened, over the more traditional view of self-employment as a safety net for those losing preferred formal sector jobs.

14
>IPPs Imports Tiers ONEE 13-09 Law<br>1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> _Source: Authors based on data from ONEE_ 35 (ONEE, 2017, p. 5) 17
accurate estimation of mean rain at the cost of variability estimation. The version 3.1 data is not homogenized and thus allows for better variability estimates. The estimates of rainfall near international boundaries are not less reliable as compared with those in the interior of the country, as the CRU estimation utilizes data from all the weather stations in the region.

To arrive at the Upazila level rainfall estimates from the CRU data, we use area weighted averages. For example if an Upazila covers two half degree grid cells for which CRU has rainfall estimates. We use the average rainfall of the two grid-cells, where the weights are the proportion of the area of the Upazila in each grid-cell. **Map 3** shows the locations of rainfall stations in Bangladesh in relation with the Upazilas and compares it with the 0.5 degree of the CRU rainfall data.

We use coefficient of variation as the measure of local rainfall variability.<sup>4</sup> Total rainfall for the monsoon season of June-September is calculated for each year for the 30 year period 1980-2009 for each Upazila. The long term historical local variability is then calculated as the coefficient of variation in total monsoon rain for this 30 year period. **Map 4** shows the spatial rainfall variability by Upazilas based on the CRU data.

Bangladesh Water Development Board characterizes the 1998 flood as: The 1998 flood in Bangladesh has been characterized as one of the catastrophic deluge on record. River water levels exceeded danger levels for country’s all of the major rivers. It was combined with local rainfall in catchment areas of small rivers. All these influences including overbank flow and drainage congestion resulted in a flood that extended over most of the country with duration of weeks to months.

Thus, the 1998 flood indicator identifies historically flood prone areas for the whole nation. We identify an Upazila to be historically flood prone if 50% or more of the area in the Upazila were flooded during any of the three periods of August 26, September 10,
.0*|-0.0**|
|esence: ura<br>(0.02)<br> <br>|(0.01)<br>|(0.04)<br>|(0.04)<br>|(0.04)<br>|(0.0)<br>|(0.0)<br>|(0.0)<br>|
|**Number of observations**<br>**1179**<br>**Note:**The standard errors are in parentheses, a<br>population weights are applied. The consumpti|**4644**<br>nd the difference<br>on data in NPS 2|**711**<br>s are estimated<br>019/20 and in|**701**<br>considering the<br>NPS 2020/21 are|**698**<br>complex survey<br>deflated to 2022|**1412**<br>design. * p < 0.1,<br>prices using the a|**1409**<br>** p < 0.05, ***<br>nnual WB CPI.|**1399**<br>p < 0.01. The|

**Note:** The standard errors are in parentheses, and the differences are estimated considering the complex survey design. * p < 0.1, ** p < 0.05, *** p < 0.01. The population weights are applied. The consumption data in NPS 2019/20 and in NPS 2020/21 are deflated to 2022 prices using the annual WB CPI.

73
can help facilitate more accurate measurement of business performance in emerging market economies. Given that administrative data are rarely available and expensive to collect, the AAA tool offers a more cost-effective method for obtaining reliable business metrics for small firms. As such, the tool will be valuable for researchers, policy makers, and businesses alike.

The remainder of the paper is organized as follows. Section 2 discusses the concept and design of the new survey tool. Section 3 describes the three field settings, the evaluation protocols, and presents the results. Section 4 concludes.

# **2. CONCEPTUAL FRAMEWORK AND METHODOLOGY**

To motivate the concept and design of the new survey methodology, this section first categorizes four main sources of error in survey-based business data. Subsequently, the discussion focuses on how the AAA survey tool reduces these errors through dynamic correction.

# **2.1. Most Common Survey Errors**

The first and perhaps most important source of noise is _respondent factors_ . Since formal records on business sales and profits are not usually kept by small firms in emerging market settings, survey respondents are typically asked to recall their firm sales for the previous month, quarter or year. De Mel, McKenzie, and Woodruff (2009) find that recall error is important, though they suggest the greatest loss in precision is from one month recall estimates to two months. In addition, resources between the business and household are fungible, making accurate measurement of business costs and income difficult to determine, resulting in more noise.

In addition, understanding the survey questions and reporting on sales and profits (particularly in the way these are asked in a survey) require a certain level of financial and numeric literacy that a typical entrepreneur may struggle with, which could lead to inaccurate responses. Furthermore, as de Mel, McKenzie, and Woodruff (2009) suggest, each survey carries not only a financial and time cost for the researcher and surveyor, but also an opportunity cost for the entrepreneur as time allocated to completing a survey
|Per capita revenue after transfer|75|75|
|---|---|---|
|Per capita net income|975|525|
|(income+transfer)|||

Case 2: Region A Reduces Its Per Capita Tax Base to $900

||Region A<br>($)|Region B<br>($)|
|---|---|---|
|Per capita income|900|500|
|Per capila local tax revenue|90|50|
|Per capila transfer|-20|20|
|Per capita revenue after transfer|70|70|
|Per capita net income<br>(income +transfer)|880|520|

# 3.4. Sources of **Data Required for Calculation**

][n selecting formulas for equalization transfer, a major consideration is the availability of data. In many developing countries, data constraints force the central governmen **t** o adopt relatively simple models with fewr variables. This section briefly discusses the possible channels through which data could be collected for the purpose of calculating equalization transfers.

'The central government agency in charge equalization transfer can use a variety of data sources. In most countries, the easiest source is the statistics provided by the central government's statistical agency. In addition to the central statistical agency, line ministries can often provide more detailed statistics on need indicators such as demographic composition, land areas, student enrolment rates, health indicators, length and quality of roads, electricity consumption, number of policemen, etc. Data on local tax bases are often supplied by local tax authorities.20 In cases where the central and local governments share the same tax bases and the center is responsible for tax collection, tax base data can be easily obtained from the central tax authorities. In cases where local tax bases differ from central tax bases, local tax authorities should be required to provide annual
reported by each EO product for each country. Mean estimates are generated using a fractionalpolynomial and graphs include 95% confidence intervals on the mean estimates. We again make note of several trends. One trend (T3) is that ARC2, CHIRPS, and CPC form a cluster, measuring more days without rain, relative to the other EO sources. Another trend (T4) is that the two reanalysis data sets, ERA5 and MERRA-2 form a different cluster, measuring fewer days without rain, relative to other EO sources. A final trend (T5) is that TAMSAT reports substantially fewer days without rain than the first group but substantially more than the second, ending up somewhere in the middle.

Again, there are exceptions to these trends. In Ethiopia, Nigeria, and Tanzania, T3, T4, and T5 hold. While MERRA-2 reports the fewest days without rain in all four countries, the EO product that reports the most days without rain varies from country to country. Additionally, the size of the gap between the products reporting drier conditions and TAMSAT varies, with TAMSAT reporting nearly as little rain in Tanzania as the others while it reports nearly half the number of days without rain relative to the other products in Ethiopia and Nigeria. Turning to Malawi, T4 and T5 (TAMSAT in the middle) hold, though T3 does not. Instead, CPC measures about 20 percent more days without rain than ARC2 or CHIRPS. Next, looking at Niger, T3 and T5 hold. Unlike in other countries, in Niger ERA5 does not agree with MERRA-2 but rather reports very few days without rain, similar to ARC2, CHIRPS, and CPC. Finally, in Uganda, only T4 (ERA5 and MERRA-2 cluster) holds. TAMSAT and CPC report the fewest days without rain while ARC2 and CHIRPS fall in between this high reporting cluster and the low reporting re-analysis cluster.

In Figure 6 we present the distribution of mean daily seasonal temperature (measured in<sup>_◦_</sup> C),
**N** umber of observations 148 19 148

_Note_ :<sup>1</sup> Used for re‐randomization;<sup>2</sup> used for stratification.

_Source:_ Household data set 2011.

If we look at the small group of non‐compliers, who declared their kit to be sold, lost or stolen, we see that they are generally poorer than complying households: They have more children, own less land, have less cows and goats, and have less radios and cell phones.

**Table 3. Balance of Outcome Related Characteristic between Treatment and Control Group (Baseline Values)**

||Tre|atment<br>‐|Control|_t_‐test/chi‐2‐<br>test<br>|
|---|---|---|---|---|
||total<br>(SD)|non<br>compliant<br>(SD)|total<br>(SD)|(total treated<br>vs. control<br>_p_‐values)|
|Lighting hours, categorized (%)<sup>2</sup>|||||
|No lamps or candles|19|26|19||
|Less or equal 3h/day|51|42|51||
|More than 3h/day|30|32|30|1.000|
|Lighting hours per day, continuous<sup>1</sup>|3.1|2.7|3.2|.910|
|Usage of hand‐crafted LED<sup>1</sup>(%)|37|26|35|.628|
|Usage of mobile LED<sup>1</sup>(%)|4|5|3|.520|
|Consumption of candles<sup>1</sup
049<sup>_∗∗_</sup><br>(0.013)|
|NonAgShock (Crime only)||||0.046<sup>_∗∗_</sup><br>(0.020)|0.041<sup>_∗∗_</sup><br>(0.041)|
|HH FE|yes|yes|yes|yes|yes|
|Wave FE|yes|yes|yes|yes|yes|
|Controls|yes|yes|yes|yes|yes|
|Observations|19,653|19,653|19,653|19,653|19,653|
|R-squared (within)|0.026|0.025|0.026|0.026|0.027|

_Source:_ Authors’ analysis based on survey data from the LSMS-ISA database (World Bank, 2018). _∗ ∗∗ ∗∗∗ Notes: p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01. _p_ -values in parentheses. Standard errors are clustered at the stratum level (consisting of 24 clusters defined as region _×_ rural). Included shock items are as follows: Health (“Illness or accident of HH member”, “Death or disability of HH member”), Income (“Loss of employment”, “Reduction in non-farm income”, “End of assistance/remittances”), Crime (“Theft of money/valuables”, “Conflict/violence”). As controls, full sets of dummies are included for the following variables: adults, children, education of HH head, age of head, male head.

Additional robustness checks with respect to clustering of standard errors, alternative variable definitions, and exclusion of household fixed effects are reported in Appendix C. As shown in Table A5,
. The analysis is based on a sample of 118 countries for which data was available from the PKF International Worldwide Tax Guide 2015, recent EY International Tax Alerts, PWC Tax Summaries, or the secondary sources referenced in the introduction. The results are robust to using dierent denitions of GDP (e.g. purchasing power parity), limiting the sample to the 109 countries for which information was available from the PKF Guide, coding countries for which the guide does not specically mention withholding either as missing or as not having withholding, and dierent ways of dening targeted withholding systems.

38
the excellent _Handbook_ chapters 40 and 44 by Anne Krueger on policy lessons from post war experience, and by Corbo and Fischer on structural adjustment.

# 7.2) "Stylized Facts" of Development and the Fashion for Cross-Country Regressions

Development economics is indebted to the economists who, using patience and simple tools, built up and analyzed the quantitative record of development. An excellent chapter 7 by Moshe Syrquin reminds us of the insights that we owe to Kuznets and to Chenery in this area of research, and to the purchasing power data set up by Kravis, Summers, and Heston and later by Summers and Heston; as well as to Paul Bairoch's work on the historical role of agriculture and trade, Angus Madison's work on the historical record of growth, Goldsmith's work on financial data; and many other contributions. Development economics started out with the fact that development implies industrialization; much of the rest we owe to those men.

18 For example Vogel (1991), pp 48-9.

> 19 The source is Helen Hughes, who has extensive and long standing contacts in that island.

_13_
governance and policies were likely better than those of other Taiping areas, as Olson’s theory of stationary banditry implies (Olson 1993). Third, the rebellion occurred in years of solid technological progress. We examine whether the loss of life associated with the rebellion ultimately led to the subsequent Malthusian transition, characterized by income growth accompanied by lower population growth (Galor and Weil 2000; Galor 2005; Voigtlander and Voth, 2013b). Fourth, we note that the Qing government instituted fiscal decentralization to finance local militias against the rebel army; thus, we examine whether this fiscal decentralization acted as taxation that hindered trade, or state capacity that facilitated development (Besley and Persson 2009).

We analyze a data set of 266 prefectures in China that were and were not affected by the rebellion. Our data cover the years from 1820 to the present day. We focus on population levels and cross-sectional development outcomes between 2000 to 2010. We examine the rebellion impact on population growth because it is a common measure of long-term development in the 3
# **VI. Conclusions and Policy Implications**

Manufacturing processes are being increasingly automated throughout the world. In recent years, automation has accelerated across many developing economies, from initially low levels of use, which has led to convergence in robot diffusion across countries. Despite this marked trend, there is limited empirical evidence on the effects of these technologies beyond developed economies.

In developing economies much of the attention has been around the possible adverse effects of robots, such as reshoring of production back to advanced economies, unemployment due to laborsaving and the future of manufacturing-led growth models (De Backer and DeStefano, 2021). This paper considers one of the potential positive implications: quality upgrading. Automation can reduce production errors by allowing repetitive tasks to be performed to a consistent level of accuracy. Many of these robots operate with limited human interaction, and so one potential upside of any labor-saving technological change is reducing human error in production.

We examine how robot diffusion affects export quality – distinguishing upgrading effects between developed and developing economies, and between initially high- and poor-quality products. To do so we rely on cross-country and cross-industry data which cover 59 countries and 16 manufacturing sectors over a span of 16 years. Our instrumental variable, robot diffusion in (preexisting) foreign export customers, strongly predicts robot adoption in the home country-industry.

We find that robots lead to export quality upgrading, however the gains are not spread evenly. The majority of the quality upgrading is achieved by developing countries, more so than developed economies. Looking within exporters, we find that robots lead to the upgrading particularly of initially low-quality products – and this is true in both developed and developing countries. Since developing countries tend to export lower quality goods, we find stronger effects overall for developing economies. These results suggest that developing countries may have greater potential for quality catch-up through automating their production.

There are several important implications for policy. First, technology always creates winners and losers. While automation poses risks, it also presents new opportunities for developing countries
traditional, and less inclined to assume lead roles in loan syndicates. Equity/assets is the bank’s total common equity to total assets ratio lagged by one year with a mean of 0.05. Less capitalized banks may be less aggressively entrepreneurial, and hence less inclined to take on lead roles in loan syndicates. Finally, Past relationship is a dummy variable that indicates whether a bank has already provided at least one syndicated loan to the borrower in a previous year with a mean of 43.6%. A past relationship with a borrower makes it less necessary to provide a loan through a foreign subsidiary to facilitate monitoring, and it makes it easier for a bank to take on a lead role in new loan syndicate as the bank has an information advantage regarding the borrower.

A multinational bank with foreign subsidiaries can engage in regulatory arbitrage in the area of capital regulation by originating syndicated loans through foreign subsidiaries located in countries with less stringent capital regulation. Figure 3 provides information on the extent to which loans have been ‘arbitraged’ over the 1995-2016 period in the sense that they have been originated through foreign subsidiaries located in countries with less stringent capital regulation than in the parent country. The share of ‘arbitraged’ loans reached a peak of about 8% in 2000, and generally was relatively high in the period 2000-2007 preceding the financial crisis, but has been more modest at less than 1% since then. The much lower share of arbitraged loans in recent years could conceivably reflect international convergence in capital regulatory standards, which would reduce the scope for such arbitrage. To see whether such convergence has occurred, Figure 4 displays the mean value and 10<sup>th</sup> percentile and 90<sup>th</sup> percentile values of the capital regulatory variable across countries for each of the World Bank regulation and supervision survey waves. The figure fails to provide evidence of international capital regulatory convergence over time.<sup>15</sup>

> 15 <mark>Alternatively
Table 6: Evidence from Intermediate Inputs Data

||(1)<br>Pr(switch)|(2)<br>Share Imp. Inp.|(3)<br>Avg. Inp. Price|
|---|---|---|---|
|Input PSI|-0.000|-0.001|-0.054|
||(0.003)|(0.004)|(0.045)|
|Import tarif|0.153***|-0.170***|-6.835***|
||(0.043)|(0.057)|(0.709)|
|Observations|138,464|138,464|138,464|
|R-squared|0.354|0.830|0.704|
|Plant-input FE|yes|yes|yes|
|5-digit ind-year FE|yes|yes|yes|
|Region-year FE|yes|yes|yes|
|Other NTMs|yes|yes|yes|

**Note:** This table shows the effect of pre-shipment inspections (PSI) applied to inputs, on plant’s probability of switching to domestic inputs (col. 1), share of imported inputs (col. 2) and average input price (col. 3) in the years between 2008 and 2015. _Input PSI_ is a dummy taking value 1 if a NTM is applied on an intermediate input _m_ at time _t_ . The coefficients are obtained by the estimation of Equation 2. All specifications include controls for other NTMs and import tariffs applied on plant’s inputs, plant-input fixed effects, 5-digit industry-year fixed effects and region-year fixed effect. Standard errors are robust to heteroskedasticity and clustered at the plant level. *, **, *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.