rafmacalaba/datause-encoder-data huggingface dataset:
Figure 1.8a: Evolution in the share of Belarusian exports to Russia in the global exports to Russia, specific machinery goods (in units), % total <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Tractors (HS 8701) Refrigerators (HS 8418) Trucks (HS 8704)<br><!-- End of picture text --> Figure 1.8b: Belarus’s market share in total imports into Russia, % <!-- Start of picture text --> 35 31.5<br>30<br>25<br>19.7<br>20<br>15.7<br>15 13.0 12.4<br>8.4<br>10 6.3 5.1 4.0 5.8 6.3<br>5 7.3<br>5.7 5.4 5.3 4.8<br>0 3.2 3.2 3.2 2.9 3.1 3.4<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Machinery and mechanical appliances (HS 84)<br>Vehicles other than railway roll-stock, parts and accessories thereof (HS 87)<br><!-- End of picture text --> Figure 1.8c: Share of various trade partners in the global machinery exports to Russia, 2001, 2005 and 2010, % total <!-- Start of picture text --> 45<br>40<br>35<br>30<br>25 2001<br>20 2005<br>15 2010<br>10<br>5<br>0<br>Belarus Ukraine EU China<br><!-- End of picture text --> Source: World Bank staff calculations on WITS/UN COMTRADE data.

**The decline in the market share illustrates the difficulties faced by Belarus companies’ to maintain their participation in the expanding Russian market.** In the heavy truck segment, Belarus’s sales to Russia were stagnant for the second part of 2000s, despite the fact that Russian market for heavy trucks was growing on average by 11 percent per year between 2000 and 2008. Demand for more sophisticated products in Russia has increased. This demand cannot be served by Belarus more traditional product lines, and instead has been filled by imports of western-made higher quality trucks (see Table 1.9 and Figure 1.9).

12
## _Heterogeneity across Countries_

Next, we investigate whether the elasticity depends on the income level and the economic structure. To do so, we estimate the elasticity separately for countries with below and above median GDP per capita, share of agriculture, share of industry, and share of services. Table 5 shows the different relationships. First and foremost, we only find modest differences between the relationships among the different groups. However, with fewer observations in the different groups, the elasticity is not statistically significant even at the 10 percent level in some cases. Somewhat surprisingly, the elasticity is statistically significant at the 5 percent level for the poorer countries with a higher share of agriculture (and a lower share of industry and services). This could reflect the better measurement of nighttime lights in low-lit areas with VIIRS compared with DMSP-OLS sensors and suggests that VIIRS data offer great opportunities for economic analysis even in the least developed countries and regions.

Table 5: Elasticities between Nighttime Lights and GDP, by Economic Structure

||||Va|riance of ni|ght light|growth:|var(_z_)|||
|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
||EMDEs|GDP p|er capita|Agricultu|re share|Indust|ry share|Service|s share|
|||Below|Above|Below|Above|Below|Above|Below|Above|
|cov(_y, z_)|1.55***|1.52**|1.61|1.45|1.52**|1.66**|1.36|1.36**|1.81|
||(0.54)|(0.62)|(1.21)|(1.09)|(0.64)|(0.70)|(0.86)|(0.61)|(1.49)|
|1_/N_|522.0***|1056.0|565.0***|531.3***|1049.4|1390.2|513.6***|2168.0***|526.8***|
||(139.8)|(990.8)|(136.5)|(136.7)|(925.9)|(972.9)|(136.7)|(750.0)|(119.8)|
|Obs|327|173|151|161|166|169|158|175|152|
|Adjusted _R_<sup>2</sup>|0.056|0.029|0.097|0.081|0.029|0.031|0.082|0.058|0.11|

_Note_ . This table presents the results of regression (8) by economic structure. The first row represents estimates of the elasticity between nighttime lights and GDP. The sample includes 84 EMDEs between 2013Q2 and 2020Q4. Column (1) replicates column (2) in table 4 as our baseline estimate of elasticity. Columns (2)-(9) consider grouping countries based on their GDP per capita and sector share relative to the median of the sample. For each variable, below (above) indicates below (above) the median of that variable in the sample. For example, column (2) includes countries whose GDP per capita is below the median. Each time period used to estimate the variances and covariances covers two years, 2013-14, 2015-16, 2017-18, and 2019-20. Throughout, standard errors are in parentheses.<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01.

# **6 Combining Nighttime Lights and National Accounts**

GDP growth as reported in national accounts may not always be an accurate measure of economic activity, in particular if statistical capacity is weak or if there is political pressure on statistical authorities. These concerns may be aggravated during periods 18
usage, property right protection, access to finance, and government expropriation. Thus, NSPF allows us to study how China’s private firms and their BEs vary over the two decades and across all the provinces. Table A1 in the appendix displays the distribution of observations across years and provinces, and Table A2, the sectoral distribution. As Table A1 shows, most private firms in this data set are from prosperous coastal provinces such as Beijing, Shanghai, Zhejiang, Jiangsu, Guangdong, and Shandong.

The second firm-level data set is an unbalanced panel of the Annual Survey of Industrial Firms (ASIF) from 1998 to 2008, compiled by China’s National Bureau of Statistics (NBS). ASIF contains an annual survey of two types of manufacturing firms: all state-owned enterprises (SOEs) and non-SOEs with annual sales over a certain threshold level (RMB 5 million before 2002 and RMB 2,000 million afterwards). On average, the sample accounts for over 95% of China’s total annual output in industrial sectors covering mining, manufacturing, and public utilities. The data set has 100+ variables from the firms' main financial statements, including balance sheets, income statements, and cash flow statements. ASIF will thus help us study the disparities in firm characteristics and their external environments, among firms with different ownership types. Table A3 presents the distribution of observations from ASIF.

The third firm-level data set consists of listed firms in China: the China Stock Market and Accounting Research (CSMAR) database constructed by GTA Information Technology. It covers all companies listed on the two major stock exchanges (Shanghai and Shenzhen Stock Exchanges) since 1990, covering both financial statements and corporate governance information. We rely on these three large-scale firm level data sets (i.e., NSPF, ASIF, and CSMAR) to make comprehensive comparisons among firms across sectors, sizes, and ownership types.

The fourth data set is the firm-level survey conducted by the World Bank in 2005, which includes 12,400 firms located in 120 cities covering all Chinese provinces except Tibet. The provincial capital of each province, usually the most populous city, is automatically covered, as well as additional cities according to the province’s economic size. One hundred firms from each city are included, except for the four provincial level cities of Beijing, Shanghai, Tianjin, and Chongqing, where 200 firms each are selected. The responses given during the survey 15
category that includes famines and wild res (which are indirectly related to the climate) there is also an increasing, albeit less sharp trend. This increasing incidence of climatic disasters is not concentrated in a few countries but is a prevalent phenomenon. In fact, in almost all countries the average incidence (number of disasters per year) during 1985-2006 is larger in the period than during 1960-1984 (not reported).

Data on macroeconomic performance and other types of external shocks (used as controls in part of the analysis) come from various sources. Real GDP per-capita is measured in constant 2000 U.S. dollars and obtained from the Bank (2008) World Development Indicators (WDI). The terms-of-trade index is the ratio of export prices to import prices computed using the current and constant price values of exports and imports from the national accounts component of the Penn World Tables (version 6.1) and updated using the terms-of-trade data from WDI. Real per capita aid ows include the ows of ocial development assistance (ODA) and ocial aid in constant 2000 U.S. dollars, and was obtained from the WDI. Aid as a fraction of Gross National Income was also obtained from the WDI. Summary statistics for these variables for the sample of countries during the period of analysis are presented in Table 1. To have good coverage on all macroeconomic and disaster variables, the nal sample used in the econometric analysis below is restricted to the post Bretton Woods, 1975-2006 period.

# 3 Methodology

The output impact of natural disasters across countries is estimated using a panel autoregressive distributed lags (PARDL) model that relates current output to its lagged values, and to contemporaneous and lagged indicators of the occurrence of various types of natural disasters. For a given country, the baseline specication of the model corresponds to where _yi,t_ is the (growth of) real GDP per capita (in constant 2000 US dollars) of country _i_ at time _t_ , and, in our baseline specication, _Di,t_<sup>_′_=(</sup><sup>_GEOi,,t,CLIMi,t,OTHi,t_)</sup><sup>_′_isavectorofvariables</sup> capturing the occurrence of geological, climatic, or other disasters, as described in the previous section. However, the impact of dierent types of climatic disasters will also be separately estimated, in which case where _WIND_ , _FLOOD_ , _DROUGHT_ , and _TEMP_ are indicators of the incidence of wind storms, oods, droughts, and extreme temperatures, respectively. The parameters _θi_ and _θt_ are country and year xed-eects that capture long run dierences in growth across countries and the impact of global factors that are common to all countries in the sample and can be understood as the 6
Figure 1. Exports of Goods and Services <!-- Start of picture text --> Ave. 1998-2000 Ave. 2008-2010<br>RUS CHL<br>BRA IND PER COL CHN ZAF CHL BRA COLINDARG PER RUSZAFCHN<br>ARG<br>0 25 50 75 100 125 150 175 0 25 50 75 100 125 150 175<br>200<br>200<br>150<br>150<br>100<br>100<br>50 50<br>0 0<br><!-- End of picture text --> _Source_ : World Bank. World Development Indicators.

Trade openness in Brazil, considering the level of per capita income, is among the lowest in the world. Larger economies do tend to be more dependent on their domestic markets, and thus it should not be a surprise to see Brazil below the predicted level in figure 2. Still, when compared to other BRICS, the level of trade integration remains below the expected line, with no signs of improvement. Over the past decade, trade flow (exports plus imports) to GDP in Brazil rose from 20.2 percent in 2000 to 22.8 percent in 2010, after peaking in 2005 at 29 percent.

Figure 2. Trade to GDP (%) <!-- Start of picture text --> CHL<br>ZAF RUSCHL IND PERCHNZAF RUS<br>CHN PER COL COL ARG<br>IND BRA ARG BRA<br>6 7 8 9 10 11 6 7 8 9 10 11<br>Log of GDP per capita (PPP, av. 1998-2000) Log of GDP per capita (PPP, av. 2008-2010)<br>250 250<br>200 200<br>150 150<br>100 100<br>50 50<br>0 0<br><!-- End of picture text --> _Source_ : World Bank. World Development Indicators.

The economic benefits of greater engagement in international trade have a long-established theoretical basis—gains to trade are as much derived from imports as from exports. Increased exports bring both static efficiency gains derived from the exploitation of comparative advantages and dynamic gains in the export sector, given productivity gains generated by increased competition, economies of scale, better 3
- 37 -

Table 8. The Peace Dividend: Simulated Effects of Decreasing Regional Militay Spending Ratios from their 197245 Aveage Levels to the 1986-90 Level for Western Hemisphere Developing Countries I/ Country Groups

||Industrial|||Develop|ing Countries|||
|---|---|---|---|---|---|---|---|
||Countries|Asia|Eastern<br>Europe|Middle<br>East|North<br>Africa|Sub-Saharan<br>Africa|<br>Western<br>Hemisphere|
||||(|In percent of G|DP)|||
|1. Averge Military||||||||
|SpaxdingRatio,<br>1972-1985 2/|3.97|6.35|11.75|10.34|8.13|3.12|2.16|
|2. "Minimum"Military||||||||
|SpandigRatio2/|1.94|1.94|1.94|1.94|1.94|1.94|1.94|
|3.Change in Militry<br>Spending Ratio|-2.03|-4.41|-9.81|-8.40|-6.19|-1.18|-0.22|
|_(2 minu_1)||||||||
|4.Asaociated Chanp in||||||||
|Ratioof Investnent|1.50|1.63|4.90|3.30|1.81|0.58|0.15|
|tDGDP 3/||||||||
|Time<br>Horizon||Simul|ated Minus Bas|eline Growth|Rates of Per C|apita GDP||
|(YOMrs)||||||||
||||(In|pecment pera|numn)|||
|I|0.010|0.071|-0.028|0.019|0.082|-0.013|0.015|
|5|0.049|0.339|-0.131|0.091|0.391|-0.063|0.073|
|10|0.265|0.535|0.570|0.609|0.639|0.140|0.064|
|25|0.348|0.542|0.908|0.815|0.657|0.243|0.043|
|S0|0.179|0.279|0.467|0.420|0.338|0.125|0.022|
|co|0.000|0000|0000|0000|0000|0000|0000|
|<br>Timo<br>Horizon<br>r||.<br>Si|.<br>mulated Minus|.<br>Baseline Leve|.<br>ls of Per Capi|.<br>ta GDP|.|
|(yeas)||||||||
||||(In pe|rcent of baseli|ne GDP)|||
|I|0.0|0.1|-0.0|0.0|0.1|-0.0|0.0|
|5|0.1|1.0|-0.4|0.3|1.2|-0.2|0.2|
|10|1.1|3.3|1.1|2.3|3.9|0.1|0.6|
|25|6.9|12.6|16.1|16.0|15.2|4.1|1.3|
|S0|13.2|224|32.4|30.7|27.0|8.5|2.1|
|ao|19.8|32.7|49.7|46.2|39.6|13.1|2.9|

1/ The simulation exmis is based on the parumeter estimates given in Table 5, column 4, and Table 6, column 4. Details on how the simulations wer pefoired ae given in Appendix II.

2/ Derived from data in Hewitt (1992). Line 2 is the 1986-90 average military spending ratio of developing countries in the Western Hanisphee.

3/ The change in investment ratio is produced by the total change in military spending ratio with respect to the baseline military **spending** ratio (averge during 1972-85).
**Table 3: Robotics and Initial Quality Gap**

|Outcome:|(1)|(2)<br>Qu<br>OLS|(3)<br>ality|IV|(4)|
|---|---|---|---|---|---|
|Robots|0.006<br>(0.011)||0.137***<br>(0.028)|||
|Robots * Quality Gap|0.065***<br>(0.005)||0.113***<br>(0.008)|||
|Robots – Developing||0.005<br>(0.016)|||0.170***<br>(0.058)|
|Robots – Developing *||0.072***|||0.099***|
|Quality Gap||(0.007)|||(0.008)|
|Robots – Developed||0.008|||0.127***|
|||(0.013)|||(0.030)|
|Robots – Developed *||0.052***|||0.129***|
|Quality Gap||(0.007)|||(0.016)|
|Kleibergen-Paap F statistic|||59.9||16.6|
|# Observations|897,260|897,260|897,260||897,260|

_Note: Quality gap reflects the initial distance from the “quality frontier. The quality frontier is defined within a given product as the top 10% highest exporters quality. The quality gap is defined as the difference between export quality and the frontier export quality in the first year. Robots reflects log robot stock. The instrument reflects robot diffusion in (initial) foreign export partners, in other world regions, defined using 2000 export flows. Quality refers log quality, estimated via Khandelwal et al (2013). Country income status is defined by the World Bank (2000). Controls include (log) employment and (log) real value added per worker at the country-industry-year level. The regressions also include exporter*10-digit product and exporter*year fixed effects. The first stage has been omitted for brevity. Robust standard errors clustered at the exporter-industry level are reported in parentheses. *** p<0.01, ** p<0.05, * p<0.1._ We next extend the product dimension to see whether quality improvements are heterogeneous across other product characteristics.

We start by assessing whether product complexity matters for quality gains from robotics. We measure complexity as the variety of inputs used to produce a product, following Levchenko (2007). Robotics may be particularly important for complex products given that robots can enable manufacturers to reduce the size and increase the number of internal components used to produce goods (Popovic, 2009). Electronics today could not be produced without the fine precision of robots handling, soldering and assembly of numerous small components. Moreover, robots typically work in conjunction with computer-aided design/computer-aided manufacturing software (CAD/CAMs) allowing manufacturers to test virtual prototypes of new complex goods 27
Figure 3A. Lowess nonparametric fit of likelihood of school and log per capita expenditure, by years <!-- Start of picture text --> Prob. of school<br>rural boys rural girls<br>5 6 7 8 9 5 6 7 8 9<br>log per capita expenditure 98/99 value log per capita expenditure 98/99 value<br>bandwidth = .8 bandwidth = .8<br>urban boys urban girls<br>5 6 7 8 9 10 5 6 7 8 9 10<br>log per capita expenditure 98/99 value log per capita expenditure 98/99 value<br>bandwidth = .8 bandwidth = .8<br>...... 1998/99 ------- 2001/02 _____2005/06<br>1 1<br>.8<br>prob .5 prob .6.4<br>0<br>.2<br>-.5 0<br>1 1<br>.8 .8<br>prob .6.4 prob .6.4<br>.2 .2<br>0 0<br><!-- End of picture text --> Note: capita is adjusted with adult equivalence.

24
<!-- Start of picture text --> Oo<br>o<br>oO<br>oO<br>2<br>g<br>of<br>Sse<br>‘S Oo<br>a8<br>ge<br>co<br>Se<br>oD<br>a<br>Oo<br>° a a ms ......<br>Bus Train Walk Bike Motorbike —-Car Other<br><!-- End of picture text -->
# Introduction

In the secondarv mark.) for developing countrv debt, bank loans to manv countries trade_,at a discount. The discounts are tvpically assumed to reflect onlv the repavmernt prospects of the **borrower countries. In fact,** secondary market prices are taken as a guide in discussions of **the possible** debt **relief and valuation problems associated with developing country debt** (for example see Borensztein and Pennacchi (1989)). **Actual debt-reduction** schemes such as the offer of the Philippines to **repurchase between $1.2** billion and $1.4 billion of its own debt, **have also been guided by secondary** market prices (VSJ, September 18, 1989). To the extent **that secondary** market prices are svstematically affected by factors other than **countries'** repayment prospects, however, debt relief or buyback decisions **bas6d on** just the secondarv market prices will be contaminated and misguided.

In this paper we demonstrate that creditor country **factors have a major** impact on secondary market prices. Our empirical investigation suggests a systematic relationship between secondary market prices and the **size** distribution of banks' portfolios. Large U.S. banks' exposures to developing countries and discounts in the secondary market are found **to be** strongly negatively correlated, even when borrower characteristics and the size of the market are controlled for. The magnitude of this effect **is** substantial: a four billion U'.S.$ increase in the exposure of the **large** banks to a country is estimated to reduce the discount 10-15 cents on the dollar. The empirical investigation also suggests that discounts **and bank** capital aggregates are strongly positively related over time: an **eight** billion U.S. $ increase in the capital of largest U.S. banks is found to increase di;counts bv near 25 cents on the dollar.

An explanation for the empirical results is provided with the aid of a
hence would make identification of the effect of different TAs problematic.

We combine the 2018 and 2016 editions of the TiVA database to obtain data on 62 countries, for goods and services, between 1995 and 2015. The 2016 edition covers the 1995-2011 period, while the 2018 edition covers the 2005-2015 period. We employ data from the 2016 edition from 1995 to 2004, and data until 2015 from the latest edition. The sector classifications in the two editions are not fully compatible. The 2018 edition uses the ISIC Rev. 4 classification, whereas the 2016 edition is based on the ISIC Rev. 3 one. While harmonization of the two classifications at the sector-level can be problematic<sup>21</sup> , the definitions of the goods and services aggregate sectors, and of the ‘buying’ sectors composing government entities did not change. We therefore conduct our analysis with the aggregates of goods and services. Table A1 in the Appendix lists the goods and services sectors that are included in the data. As for the country composition, Kazakhstan is included in the 2018 edition only, whereas Thailand never reports imports in public procurement (no imports are reported also in the Government Expenditures column). Therefore, we exclude both countries from the analysis. For simplicity, we also exclude the Rest-of-the-World aggregate that is part of the dataset.

To measure government procurement flows, we have to define the perimeter of the public sector. The OECD defines public procurement as “intermediate consumption (goods and services purchased by governments for their own use, such as accounting or IT services), gross fixed capital formation (acquisition of capital excluding sales of fixed assets, such as building new roads) and social transfers in kind via market producers (goods and services produced by market producers, purchased by government and supplied to households)” (OECD, 2013a, p.130). We cannot measure gross fixed capital formation by the public sector because the TiVA database, like other ICIO data, does not provide a split between public and private gross fixed capital formation. We measure social transfers in kind with the entries under the “General Government Expenditure” component of final demand. To measure intermediate consumption, we take the column vectors from the input-output matrix that correspond to government entities. The European Commission (2017) proposes three ways to define these entities in an input-output matrix: (i) a “narrow” classification that includes only the “Public Administration” column; (ii) a “typical” definition that adds the “Health” and “Education” columns to the “Public Administration” one; and (iii) a

> 21See this note: `http://www.oecd.org/industry/ind/tiva-2018-differences-tiva-2016.pdf` from the OECD on the subject.

16
# **CASTE AND PUNISHMENT**

## **The Legacy of Caste Culture in Norm Enforcement**

Karla Hoff Mayuresh Kshetramade Ernst Fehr _The World Bank Affinnova Inc, Waltham, MA University of Zurich_ <u>khoff@worldbank.org mayurvk@yahoo.com efehr@iew.uzh.ch</u>

### **JEL Classification :    D02, D64 Key Words :   Norms, informal sanctions, third-party punishment, altruism, endogenous preferences, social exclusion, caste**

_Acknowledgements._ We thank Robert Boyd, Robert Keohane, Vijayendra Rao, and Rob Willer for valuable discussions and comments. We are indebted to Sonal Vats for superb research assistance at every stage of this project, and to Siddharth Aryan, Manoj Gupta, Mukta Joshi, Shiv Mishra, Priyanka Pandey, and Dinesh Tiwari for their help in implementing the experiment. We benefited from participants’ comments at seminars at Cornell,Georgetown University Law School, George Washington University, Harvard, the Indian Statistical Institute,the Institute for Advanced Studies, the MacArthur Foundation Research Network on Inequality, Princeton,the University of Texas at Dallas, and the World Bank. We acknowledge research support from the World Bank (for Hoff) and from the Research Priority Program on the “Foundations of Human Social Behavior—Altruism vs. Egoism” at the University of Zurich (for Fehr). Hoff thanks the Princeton University Center for Health and Wellbeing for its hospitality in 2008-9.