rafmacalaba/datause-encoder-data huggingface dataset:
shares from the 1950 data on expellees and post-1950 data on resettlers. The numbers of expellees and resettlers were then added to the existing totals.

# _APPENDIX 4. PROPENSITY MEASURES_

This appendix presents the propensity measures used to disaggregate the 236 aggregate origin regions/countries identified in the censuses. Let _Mo,d,t_ denote the number of migrants from origin country _o_ in destination country _d_ in year _t_ . These are the entries in the bilateral matrices that need to be completed. Now, instead of Mo,d,t , suppose a census in country _d_ gives the number of migrants originating from region _R_ (which includes country _o_ ), denoted as _MR,d,t_ . The problem is to find an allocation rule (  _o,d,t_ ) for estimating the bilateral stock from this aggregate amount. The allocation rule can be written as _Mo,d,t_ =  _o,d,t MR,d,t_ .

One type of aggregation problem occurs in the case of migrants from Czechoslovakia, the Soviet Union, and Yugoslavia and their successor states. For example, in many cases, migrants are recorded from Czech Republic, Slovakia, and Czechoslovakia in the same year. Belgium‘s 2001 reports 308 migrants from Czechoslovakia, 554 from the Czech Republic, and 412 from Slovakia. Presumably, migrants who left before the partition reported Czechoslovakia as their origin country, whereas most postpartition migrants reported the successor countries. In such cases, it is assumed that the distribution of migrants from these two countries was the same before and after the break-up of Czechoslovakia. Of the 308 migrants recorded as originating from Czechoslovakia, 177 migrants (308*[554/966]) were assigned to the Czech Republic and 131 (308*[412/966]) to Slovakia.

In other cases of aggregated migrant stock data, migrant data from other decades were used as the basis for disaggregation. Migrants were allocated according to a relative propensity, which is averaged over time. This can be formally written as: where _K_ denotes the set of census years other than _t_ for which bilateral data exist, and _n_ is the number of such observations in set _K_ . This propensity is simply the likelihood that a particular destination country will accept migrants from a specific origin country, relative to all the other countries comprising that aggregate origin region. For example, Australia records 29,311 migrants from the Soviet Union in 1966. This total needs to be disaggregated among the 15 successor countries in the master list. While the data for Australia cover census material for each of the five census rounds, only the 2001 census provides details for all 15 successor countries. According to the first method for allocating aggregate categories, the 2001 census is used to calculate the contribution of each of these countries towards the total. Those shares are then used to allocate the 29
|||_Table A- 1:_|_Credit constrain_|_ed firms and go_|_vernment-owne_|_d banks_|||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|CRCO|CRCO|CRCO|CRCO|CRCO|CRCO|CRCO|CRCO|
|SOB|0.1115|0.1205|0.027|-0.017|-0.231|-0.271|-0.143|-0.215|
||(0.1936)|(0.2178)|(1.221)|(1.390)|(0.858)|(0.883)|(0.671)|(0.751)|
|INTER|||0.124|0.195|0.564|0.637|0.357|0.452|
||||(1.952)|(2.122)|(1.423)|(1.422)|(0.978)|(1.025)|
|ROA||0.0010||-0.001||-0.002||-0.006|
|||(0.0412)||(0.047)||(0.042)||(0.044)|
|LTA||-0.0122||-0.013||-0.014||-0.014|
|||(0.0315)||(0.031)||(0.031)||(0.032)|
|lsize|-0.0821*|-0.0824*|-0.082*|-0.082*|-0.082*|-0.082*|-0.082*|-0.082*|
||(0.0426)|(0.0430)|(0.042)|(0.043)|(0.043)|(0.043)|(0.042)|(0.043)|
|lage|0.0100|0.0105|0.010|0.010|0.008|0.009|0.008|0.008|
||(0.0820)|(0.0823)|(0.083)|(0.084)|(0.082)|(0.082)|(0.082)|(0.083)|
|SOE|0.2156|0.2004|0.214|0.198|0.212|0.195|0.210|0.192|
||(0.2147)|(0.2245)|(0.214)|(0.224)|(0.214)|(0.224)|(0.213)|(0.224)|
|FEM|0.0916|0.0862|0.092|0.087|0.098|0.093|0.096|0.091|
||(0.1509)|(0.1529)|(0.151)|(0.153)|(0.150)|(0.152)|(0.151)|(0.153)|
|Constant|0.1894|0.3063|0.185|0.314|0.153|0.288|0.152|0.299|
||(0.2524)|(0.4482)|(0.263)|(0.437)|(0.231)|(0.443)|(0.237)|(0.446)|
|Observations|2,596|2,596|2,596|2,596|2,596|2,596|2,596|2,596|
|R-squared|0.0435|0.0437|0.043|0.044|0.044|0.044|0.044|0.044|

|||_Table A-_|_2: Firms that in_|_vest and govern_|_ment-owned ba_|_nks_|||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|INV|INV|INV|INV|INV|INV|INV|INV|
|SOB|-0.1208|-0.2144|0.537|0.229|-0.176|-0.424|-0.180|-0.567|
||(0.1214)|(0.1471)|(0.607)|(0.706)|(0.982)|(1.001)|(0.634)|(0.679)|
|INTER|||-0.964|-0.628|0.091|0.341|0.083|0.476|
||||(0.935)|(1.025)|(1.681)|(1.688)|(0.971)|(1.001)|
|ROA||-0.0438*||-0.036||-0.045*||-0.051**|
|||(0.0240)||(0.027)||(0.024)||(0.024)|
|LTA||0.0005||0.003||-0.000||-0.002|
|||(0.0243)||(0.024)||(0.024)||(0.024)|
|lsize|-0.0279|-0.0264|-0.028|-0.027|-0.028|-0.026|-0.028|-0.026|
||(0.0315)|(0.0316)|(0.031)|(0.031)|(0.031)|(0.032)|(0.031)|(0.031)|
|lage|0.0247|0.0222|0.028|0.024|0.024|0.021|0.024|0.020|
||(0.0559)|(0.0558)|(0.055)|(0.056)|(0.055)|(0.055)|(0.055)|(0.054)|
|SOE|-0.3959***|-0.3866***|-0.386***|-0.379**|-0.396***|-0.389***|-0.397***|-0.396***|
||(0.1415)|(0.1484)|(0.143)|(0.149)|(0.140)|(0.147)|(0.140)|(0.146)|
|FEM|0.0231|0.0242|0.020|0.023|0.024|0.028|0.024|0.030|
||(0.1030)|(0.1025)|(0.103)|(0.102)|(0.103)|(0.103)|(0.102)|(0.102)|
|Constant|1.5373***|1.6456***|1.570***|1.622***|1.531***|1.636***|1.529***|1.638***|
||(0.2240)|(0.3260)|(0.219)|(0.327)|(0.237)|(0.334)|(0.234)|(0.329)|
|Observations|2,596|2,596|2,596|2,596|2,596|2,596|2,596|2,596|
|R-squared|0.0202|0.0225|0.022|0.023|0.020|0.023|0.020|0.023|

35
and occupations.

A final important element when analyzing the relationship between innovation and wages is the direction of causality. A particular strand of literature has focused on examining the links between incentive systems within firms and employee innovation activities. Although large-scale quantitative evidence is sparse, it appears that performance-based reward systems have a positive influence on the creativity and “innovative behavior” of individual employees (De Jong and Den Hartog (2007); Ederer and Manso (2013); Gibbs, Neckermann, and Siemroth (2017)) and, in some cases, on the firm’s overall innovation-related activities and outcomes (Leiblein and Madsen, 2009), including for example its patenting propensity (Chen et al., 2016) and quality (Mao and Weathers, 2019). In addition, human resource management and incentive systems play an important role in explaining performance in general (Bloom and Van Reenen, 2011; Bender et al., 2018). Therefore, it is possible that firms increase wages to attract more talented workers and incentivize innovation within firms. This is also consistent with efficiency wage theories (Katz, 1986). Entorf et al. (1999) find for the United States and computer-related occupations that these enjoy a wage premium, although these workers were already better paid before introducing the new technologies. Thus, there can be some “self-selection” of firms that pay higher wages to provide incentives to workers who can implement innovative projects.

Thus, in addition to estimating whether there is a wage premium associated with innovation, it is critical to understand whether this is the outcome of firms that “self-select” into innovation by paying higher wages already or, on the other hand, whether these higher wages of workers capture part of the rents and firm performance gains associated to innovation. The objective of this paper is to shed some light on these questions using a large national employer-employee census (RAIS) in Brazil, and employing a novel measure of innovation based on the share of technical and scientific occupations of the firm (PoTec).

The paper contributes to the literature in three dimensions. First, it contributes to narrow the knowledge gap in relation to estimating the innovation wage premium for a middle-income country like Brazil. Second, it empirically tests the hypothesis that firms pay higher wages before becoming innovative. Third, it measures the extent to which workers appropriate rents created by innovative activities. In the first part of the paper, we estimate the innovation wage premium in Brazil by sector, type of firm and type of skill during the period 2014-2017. To advance some of the main findings, we find a positive innovation wage premium that is robust across different definitions of skills and also across different firm sizes. The premium is higher for manufacturing compared to agriculture and services, and although larger in large firms, it is also positive in all firm size types. In the second part of the paper, using the empirical tools of the “self-selection” and “learning-by-exporting” literature (Bernard 3
transactions within the public sector. The unwillingness of many public and private actors to generate the basic policy relevant information in sectors catering to some of the most basic needs of the poorest populations of the poorest countries of the world and in which monopolies— and hence unilateral control of socially relevant information--tend to prevail is simply outrageous. The adoption of new international accounting standards scheduled for 2006 should address this need increased transparency but it will require a commitment of the international community to their strict enforcement in developing countries by all operators, local as well as foreign. In an ideal world, it would also lead to the adoption of decent regulatory accounting principles for regulated infrastructure services.

Ultimately, the internalization of these lessons will generate the accountability needed to get good weddings going and bad flirts abandoned. How many of the good flirts end up in weddings and how many in divorces will depend on how well these lessons are internalized.

# **<u>References</u>**

- Benitez, D., O. Chisari and A. Estache (2003), “Can the gains from Argentina’s utilities reform offset credit shocks?”, in Ugaz, C. and C. Waddams Price, ed.,

- Briceno, C., A. Estache and N. Shafik (2004), “Infrastructure Services In Developing Countries: Access, Quality, Costs and Policy Reform”, The World Bank, (forthcoming)

- Calderon, C. W. Easterly and L. Serven. (2003). “Infrastructure Compression and Public Sector Solvency in Latin America”, in Easterly, W. and L. Serven.

- Campos, J.,  A. Estache, N. Martin and L. Trujillo. (2003).  “Macroeconomic Effects of Private Sector Participation in Infrastructure”, in  Easterly, W. and L. Serven,

- Chisari, O., A. Estache and C. Romero (1999) “Winners and Losers from the Privatization and Regulation of Utilities: Lessons from a General Equilibrium Model of Argentina”, **The World Bank Economic Review,** Vol. 13, No 2., pp. 357-378

- Chong, Alberto and Florencio Lopez-de-Silanes. 2003. “The Truth about Privatization in Latin America”, Inter-American Development Bank Working Paper, October

- Coelli, T., A. Estache, S. Perelman and L. Trujillo (2003), **A Primer on Efficiency Measurement for Utilities and Transport Regulators,** World Bank Institute Publications,  Studies in Development Series

- Collignon, B and M. Vezina (2000), **Independent Water and Sanitation Providers in African Cities: Full Report of a Ten-Country Study,** Water and Sanitation Program. UNDP-World Bank. April 2000.

- DFID (2002), “Making connections: infrastructure for poverty reduction”, mimeo, available at _<u>http://62.189.42.51/DFIDstage/FOI/dc/7mar02_making_connections.pdf</u>_

18
# WPS4000

_Preliminary and incomplete Comments welcome_

## **Who Gets Debt Relief?**

Nicolas Depetris Chauvin Princeton University and Aart Kraay The World Bank _Paper prepared for the invited session "Developing Country Debt Crises" at the European Economic Association Annual Meetings Vienna, August 26-30, 2006_

**Abstract** We use preliminary results from an ongoing effort to construct estimates of debt relief to study its allocation across a sample of 62 low-income countries.  We find some evidence that debt relief, particularly from multilateral creditors, has been allocated to countries with better policies in recent years.  Somewhat surprisingly, conditional on per capita incomes and policy, more indebted countries are not much more likely to receive debt relief.  However, countries that are large debtors vis-a-vis especially multilateral creditors are more likely to receive debt relief.   We do not find much evidence that debt relief responds to shocks to GDP growth.  Finally, most of the persistence in debt relief is driven by slowly-changing country characteristics, indicating that it may be difficult for countries to "exit" from cycles of repeated debt relief.

### World Bank Policy Research Working Paper 4000, August 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ _____________________________________

* 1818 H Street NW, Washington, DC 20433, akraay@worldbank.org.  We would like to thank Nevin Fahmy for her continued guidance through the World Bank's external debt statistics, and Nancy Birdsall, Carolyn Geginat, and Mark Thomas for helpful comments.
Finally, the international diffusion of technologies is likely to be grounded in creative rather than passive adoption (Antonelli 2006). Technological congruence, institutional setting and governmental arrangements shape a country’s capacity to absorb knowledge and technologies produced elsewhere (Dosi and Nelson 2013). Social capabilities represent the set of cultural, political, commercial, industrial and financial institutions that create the conditions for catching-up countries to absorb and exploit the technologies developed elsewhere (Abramovitz 1986). For example, a study conducted on Brazil, Russia, India, and China (the so-called BRIC) confirmed that their institutional specificities play a major role in shaping their rapidly growing economies (da Rocha, Ferreira da Silva, and Carneiro 2012; Kim, Park, and Lee 2013; Gupta et al. 2014). Nevertheless, in most DCs and even in BRIC, the role of R&D-driven new firms and domestic NTBFs<sup>19</sup> is extremely limited. Therefore, it is not surprising that very few studies attempt to link innovation with entrepreneurship within a DC context.

Santarelli and Tran (2011) studied entrepreneurship in Vietnam using a panel of regional-level data for 61 provinces over the 2000–8 period. Among other outcomes, the authors found that an innovative climate (proxied by the share of technical/R&D personnel in the province) significantly and positively affects the regional net entry rate. As for post-entry performance, in the previously cited study by Goedhuys and Sleuwaegen (2010) on Africa, the innovative capability (proxied by a dummy for the introduction of new products) was found to increase a firm’s annual employment growth by 2 percent on average.

# _Unemployment_

Regarding unemployment (or the fear of becoming unemployed), the literature notes two stylized facts: 1) those who start a new business as an escape from unemployment exit to a higher extent than those who enter from paid employment (see Carrasco 1999; Pfeiffer and 20
# **1 Introduction**

Satellite-recorded nighttime light data are used extensively as a proxy for economic activity. However, surprisingly little economic analysis employs data from the Visible Infrared Imaging Radiometer Suite (VIIRS).<sup>1</sup> These new nighttime light data, with a better resolution and a higher frequency than the previous generation of data, have the potential to facilitate our understanding of rapid and spatially heterogeneous economic changes, such as those during the COVID-19 pandemic.<sup>2</sup> One reason for the hesitancy to use these data in the economic literature may be the difficulty of converting changes in nighttime light intensity into changes in economic activity. To the best of our knowledge, no properly estimated and widely accepted quarterly elasticity between VIIRS nighttime lights and economic activity exists to date.<sup>3</sup> In this paper, we attempt to fill this gap.

VIIRS nighttime light data are an imprecise measure of man-made lights. Even after aggregating the data to the country level and to quarterly frequency, substantial statistical noise remains. It mainly stems from atmospheric conditions like cloud cover that impact the effective number of observations. For instance, there are only five effective observations at the pixel level on average each month for a median developing country. Equally important, missing observations in the summer months and occasional satellite sensor recalibrations contribute to the noise as well.

In this paper, we provide a novel framework to estimate the elasticity between nighttime light intensity and gross domestic product (GDP) at quarterly frequency that takes measurement errors of nighttime lights explicitly into account. The elasticity can be identified because countries exhibit varying noise in their nighttime light growth. Using information from the average number of effective observations, we provide a regression equation that estimates the elasticity precisely. In emerging markets and developing economies (EMDEs), a 1 percent change in GDP is associated with a 1.55 percent change in nighttime lights. While the elasticity varies somewhat with a country’s income status and economic structure, it does not deviate much from this

> 1A recent survey by Gibson, Olivia, and Boe-Gibson (2020) reviews more than 150 economic studies using nighttime lights and argues that economists seem slower than others to switch to VIIRS data.

> 2For a detailed explanation of the superiority of the VIIRS data, see Elvidge, Baugh, Zhizhin, and Hsu (2013) and Gibson, Olivia, Boe-Gibson, and Li (2021).

> 3The use of nighttime light data from the Defense Meteorological Program Operational Line-Scan System (DMSP-OLS) picked up only after Henderson, Storeygard, and Weil (2012) provided such an elasticity.

2
**Table 6.** CDM Projects Registered and in the Process of Registration

|Project type|(regis|T<br>tered a|otal<br>ndinproce|ss)||Reg|istered||
|---|---|---|---|---|---|---|---|---|
||Proj|ect|’000 CE|Rs|Proje|ct|’000 C|ERs|
||No.|%|No.|%|No.|%|No.|%|
|Renewables|2,747|63|228,142|38|785|60|53,312|22|
|Hydro|1,150|26|118,015|20|287|22|19,722|8|
|Biomass energy|660|15|39,996|7|236|18|13,266|5|
|Wind|621|14|53,412|9|180|14|15,847|7|
|Biogas|275|6|13,242|2|70|5|2,528|1|
|Solar|27|1|704|0.1|4|0.3|43|0.02|
|Geothermal|13|0.3|2,457|0.4|7|1|1,590|1|
|Tidal|1|0.02|315|0.1|1|0.1|315|0.1|
|Methane, Cement & Coal mine|682|16|103,769|17|262|20|41,067|17|
|Supply-side energy efficiency|451|10|76,968|13|105|8|17,858|7|
|Demand-side energy efficiency|203|5|7,753|1|52|4|1,634|1|
|Fuel switching|139|3|44,226|7|35|3|13,077|5|
|HFCs, PFCs & N2O reduction|97|2|132,747|22|58|4|115,980|48|
|Afforestation & Reforestation|36|1|1,888|0.3|1|0.1|26|0.01|
|Transport|9|0.2|981|0.2|2|0.2|288|0.1|
|Total|4,364|100|596,473|100|1,300|100|243,242|100|

_Note_ : CER refers to certified emission reduction units, 1 CER = 1 tons of CO2 equivalent _Source_ : URC (2009a).

# 9. Concluding Remarks

This study presents the current status and future prospects of wind power at the global level, considering various aspects such as resource potential, installed capacity, economics, financing, physical barriers, intermittency, grid interconnections, and policies related to climate change. We find that global wind power generation capacity expanded rapidly from only 10 MW in 1980 to 94,124 MW by the end of 2007, with an average annual growth rate of about 40%. The growth is also facilitated by the improving economics of wind power as it is becoming increasingly competitive with traditional sources of electricity generation, such as coal, gas, hydro and nuclear. Despite the phenomenal growth of installed capacity, however, wind power still accounted for only 1% of global electricity supply as of 2007. Moreover, the distribution of installed capacity and ongoing investment are preponderantly concentrated in developed countries, with the exception of China and India. Existing studies 26
error term.

Coefficients of monthly dummies before and after lockdown policies were imposed by trading partners of the Philippines from Equation 1 are plotted in section 5. The coefficients are interpreted as percentage changes having been transformed into elasticities.<sup>13</sup> We present results for aggregate trade, product categories, and trade partners. Later, we conduct three sets of robustness checks. We start by establishing the effect of domestic lockdowns on import and export trade of the Philippines. Next, we test the effect of different measures of restrictions in partner countries on exports and imports of the Philippines. Lastly, we check for product-level differences by analyzing the effect of external lockdowns on imports and exports of similar commodities with similar trading partners.

# **5 Empirical Results**

## **5.1 Results for aggregate trade**

This section reports the results of the event-study model in Equation 1 for total monthly export and import trade in the Philippines. In general, the average monthly drop for exports by value and quantity during the lockdown was 7% and 13% respectively as shown in Figure 6.

The upper panel of Figure 6a displays coefficient plots of export values for the Philippines. We find that export values dropped immediately lockdowns were imposed. They contracted by 9% a month into the lockdown and consistently fell up to the third month when they started to recover. Coefficients of export values attained pre-lockdown levels by the eleventh month although they were statistically insignificant past the fifth month. Borrowing from Figure 1, this could mean that the Philippines’ exports positively reacted to the initial efforts by trading partners to relax lockdown measures but the irregularity in suspending these policies mildly affected export trade by values. Coefficients of exports by

> 13 Coefficients _βj_ were transformed into elasticities as follows _βj_ =(exp _βj_ -1)*100. Refer to Ullah et al. (2021) and Clarke and Tapia-Schythe (2021) for a guide on conducting event-study analysis in Stata.

21