rafmacalaba/data-use-mentions-extended huggingface dataset:
# Table A.3: Renewable energy policies covered in the RISE dataset

|**Headings**|**RISE ID**|<br>**Our ID**|**Question**|
|---|---|---|---|
|Legal framework for|1.1.1|re_1_1|Does a legal framework for renewable energydevelopment exist?|
|renewable energy(LF)|1.2.1|re_1_2|Does the legal framework allowprivate sector ownershipof renewable energy generation?|
||2.1.1|re_2_1_1|Does an official renewable energytarget exist?|
||2.1.2|re_2_1_2|Is the target legallybinding?|
||2.1.3|re_2_1_3|Is the RE target linked to international commitments(e.g., NDC or regional commitment)?|
||2.1.4|re_2_1_4|Is the target based on a transparent methodology?|
||2.1.5|re_2_1_5|Is there a renewable energyactionplan or strategyto attain the target?|
||2.1.6|re_2_1_6|Is there any provision for consultation with thepublic on the renewableplan?|
||2.2.1|re_2_2_1|Is there an assessment of the role of renewables in the electricitysupply?|
||2.2.2|re_2_2_2|Is there a target for renewables in electricity?|
||2.3.1|re_2_3_1|Is there an assessment of the needs for heatingand coolingin buildings and industryin the countryand of how renewables can contribute?|
||2.3.2|re_2_3_2|Is there a specific target for renewables for heatingand cooling?|
||2.4.1|re_2_4_1|Is there an assessment of thepotential role for renewables in transport includings and electrification?|
||2.4.2|re_2_4_2|Is there a specific target for renewables in transport
geographical unit. Thus, satellites have enabled development economists to study geographic entities that were previously inaccessible because of insufficient data coverage. In future, new commercial satellite projects with continually improving spatial and temporal coverage will only reinforce the importance of remote sensing for academic studies.<sup>1</sup> Especially the use of nighttime lights as a proxy for economic activity is important to scholars in economics and other social sciences. Nightlight intensity has been used to approximate economic activity as light is believed to be a normal good with its consumption increasing at higher incomes. Henderson et al. (2012) have found strong correlation with GDP at the national level and researchers have gone on to use nightlights at smaller geographic units. For example, Bleakley and Lin (2012) use nighttime light intensity to measure economic activity in their study on the economic persistence of defunct portage sites and Alesina et al. (2016) combines them with sub-national ethnolinguistic maps. Similarly, Storeygard (2016) employs nightlights to study intercity transport costs and their impact on income of sub-Saharan African cities, while Harari (2016) uses them to measure shapes of urban areas in India.

However, the predictive power of nighttime light at geographies smaller than the national level has recently been disputed in the literature. For example, while Mellander et al. (2015) conclude that nightlight data have a reasonably high correlation with economic activity in the developed world context of Sweden, Bickenbach et al. (2016) find severe parameter instability between regions in India, Brazil, and the US, and cast doubt that the correlation between nightlights and GDP at the country level carries over to the subnational level. Chen and Nordhaus (2011) find similar issues when scaling down from national to grid cell levels. The reasons for this are threefold: The common nightlight data produced by the National Oceanic and Atmosphere Administration (NOAA) is only available at a geospatial resolution of 30 arc seconds (about 1km). This resolution might be too coarse to capture

> 1Private satellite
increase in informal competition should have a larger negative impact on employment growth in countries with higher corruption, more burdensome (to private formal firms) regulations, higher taxes and weaker rule of law. We test for this by using interaction terms between our informal competition variable and the following variables: Control of Corruption variable from the Worldwide Governance Indicators, World Bank; Rule of Law variable from the Worldwide Governance Indicators, World Bank; Profit tax rate taken from Doing Business project, World Bank; and Freedom from Business Regulations taken form the Heritage Foundation’s Economic Freedom of the World database.

Our second check against endogeneity concerns is like in spirit to the previous one. We argue that in countries where there is a relatively high proportion of population that believes that cheating on taxes is justified, informality is more likely to result from the desire to avoid taxes and costly regulations rather than from failure to find good jobs in the formal sector. Thus, the negative spillover effect from informal to formal sector as suggested by the parasite view of informality can be expected to be stronger in countries where cheating on taxes in more strongly justified. Of course, this prediction assumes that the impact of informal competition on employment growth that we observe in the data is truly causal. There is little reason for the prediction to hold if informal competition is a mere proxy for other correlated drivers of employment growth or if our main result suffers from reverse causality. To test for the stated prediction, we use interaction term informal competition variable and a dummy variable equal to 1 if the reported justification level for cheating on taxes is above its median level across countries and 0 otherwise. Some adjustments are made in constructing the variable to avoid too many missing values. For details on how the dummy variable is constructed, see Table 1. Data source for the variable is World Value Surveys.

19
# **7 References**

Aklin, M., P. Bayer, S. Harish, and J. Urpelainen. 2015. “The Political Economy of Energy Access: Survey Evidence from India on State Intervention and Public Opinion.” Energy Research and Social Science 10: 250–58.

Alesina, A., A. Miano, and S. Stantcheva. 2020. “The Polarization of Reality.” AEA Papers and Proceedings 110: 324–28.

Alesina, A., A. Miano, and S. Stantcheva. 2023. “Immigration and Redistribution.” Review of Economic Studies 90 (1): 1–39.

Alesina, A., S. Stantcheva, and E. Teso. 2018. “Intergenerational Mobility and Preferences for Redistribution.” American Economic Review 108 (2): 521–54.

Amjad, B., H. Carrasco, and M. Meyer. 2023. “The Effects of Taxes and Transfers on Inequality and Poverty in Pakistan.” World Bank. Mimeo.

Bidani, B., R. Menon, S. Nguyen, R. Vakis, and Z. Afif. 2022. “Vaccine Hesitancy: 10 Lessons from Chatbotting about COVID-19 in 17 Countries.” World Bank Investing in Health Blog, March 17, 2022. https://blogs.worldbank.org/health/vaccine-hesitancy-10lessons-chatbotting-about-covid-19-17-countries.

Burgess, R., M. Greenstone, N. Ryan, and A. Sudarshan. 2020. "The Consequences of Treating Electricity as a Right." Journal of Economic Perspectives 34 (1): 145–69.

Calvo-Gonzalez, O., B. Cunha, and R. Trezzi, R. 2017. “When Winners Feel Like Losers: Evidence from an Energy Subsidy Reform.” World Bank Economic Review 31 (2): 329–50.

49
</mark></u> Bloom, N., R. S. Fletcher, and E. Yeh (2021). The impact of COVID-19 on US firms. _National Bureau of Economic Research Working Paper No._ 28314.

<mark>Bluedorn, J., F. Caselli, N.-J. Hansen, I. Shibata, and M. M. Tavares (2023). Gender and employment in the COVID-19 recession: Cross-Country evidence on “She-Cessions.</mark> _<mark>Labour Economics</mark>_ <mark>, 81: 102308.</mark> <mark>Bundervoet, T., M. E. Dávalos, and N. Garcia (2022). The short-term impacts of COVID-19 on households in developing countries: An overview based on a harmonized dataset of highfrequency surveys.</mark> _<mark>World Development</mark>_ <mark>, 153(May): 105844.</mark> <mark>Bruhn, M., A. Demirguc-Kunt, and D. Singer (2023). Competition and firm recovery postCOVID-19.</mark> _<mark>Small Business Economics</mark>_ <mark>. https://doi.org/10.1007/s11187-023-00750-w</mark> 40
were of school age during the

> 10 While studies quoted earlier in the paper emphasize the differences between the long and short-run employment outcomes and AWE (e.g. Fernandes and de Felicio 2005), such studies were based on the panel data which allows for such comparisons. My study of labor market outcomes in Tajikistan is based on the cross-sectional data for 2007. The cross sectional nature of the data limits the scope of the analysis of employment to short-term outcomes.

10
24

# **Literature**

Barrett, C.B., 2001, Livestock Pricing and Markets Performance, Cornell University – Department of Applied Economics and Management.

- Beck, T. , 2005, Learning Lessons from Disaster Recovery: The Case of Bangladesh, _Disaster Risk Management Working Paper Series No. 11_ : World Bank: Washington D.C.

- Carter, Michael R. and Christopher, Barrett, 2006, The economics of poverty traps and persistent poverty: An asset‐based approach, _Journal of Development Studies_ , 42‐ 2:178 — 199.

Cavallo, Eduardo and Ilan, Noy, 2009, The Economics of Natural Disasters, Inter American Development Bank, Working Paper 124.

- Christiaensen, Luc, and Kalanidhi, Subbarao, 2005, Towards an Understanding of Household Vulnerability in Rural Kenya, _Journal of African Economies_ , 14‐4: 520‐558.

- Christiaensen, Luc, Nguyen Van Viet, and Ngo Tien Gian, 2009, Natural Hazards Events, Risks, and Vulnerability to Poverty, mimeographed.

- CIA (Central Intelligence Agency).  1972.  World Databank II.  Washington:  Central Intelligence Agency.

- CIESIN (Center for International Earth Science Information Network), Columbia University; IFPRI (International Food Policy Research Institute); The World Bank; and CIAT (Centro Internacional de Agricultura Tropical). 2004a. "Global Rural‐Urban Mapping Project (GRUMP), Alpha Version: Settlement Points". Palisades, NY: Socioeconomic Data and Applications Center (SEDAC), Columbia University. Available at http://sedac.ciesin.columbia.edu/gpw.  Downloaded March 17, 2005.

- CIESIN (Center for International Earth Science Information Network), Columbia University; International Food Policy Research Institute (IFPRI); The World Bank; and Centro Internacional de Agricultura Tropical (CIAT). 2004b. "Global Rural‐Urban Mapping Project (GRUMP), Alpha Version: Urban Extents". Palisades, NY: Socioeconomic Data and Applications Center (SEDAC
the 5% level (the t-statistic is less than 2). More dense US populations had higher fatalities. Cities in the US were more vulnerable to fatalities than more rural locations.

In order to understand the role of vulnerability, we turn to international data. The international regressions measure the relationship between damage and fatalities and population and income (using international data EMDAT 2009). Storm intensity is not recorded in the international data so it is an omitted variable. Table 3 reveals that damages increase with income but fall with population density. These coefficients are significantly different from zero. Perhaps more important, they are significantly different from unitary (1). The elasticities of income and population are less than 1, contrary to assumptions in the literature Pielke and Landsea 1998, (Pielke et al. 2008, and Nordhaus 2010). It is likely that people privately adapt to reduce damages as their incomes rise and as they urbanize. The damages consequently do not rise proportionally with income or population in a specific location. Note that the coefficients on income and population density are far more significant in Table 3 than in Table 2. This is probably because there 12
on Alquist, Kilian, and Vigfusson (2013), the forecast is obtained by regressing the future change of a commodity price against past independent variables: ∆ hh∆ tt tt tt,tt+hh YY = ββMM + εε where ∆∆ indicates the percent change of a commodity price within the next hh tt tt+hh tt _h_ months, and YY = YY −YY ∆ where ∆∆ _h_ months, and is one input. hh tt tt YY MM Six inputs are used separately: U.S. base money (M1 from the FRED database), 10-year Treasury bond yields (from the FRED database), and the CRB Raw Materials Index (from Bloomberg) in past percent differences equivalent to the horizon.<sup>11</sup> For example, the percent change over the last

> 10 Several studies employed different approaches to address the breaks introduced by the COVID-19 pandemic in forecasting (Schorfheide and Song 2021; Ng 2021; Lenza and Primiceri 2022). There was no need to make assumptions about the treatment of the pandemic in the econometric exercise here as the COVID-19 period formed part of the out-of-sample evaluation period in our comparison.

> 11 The CRB Raw Industrial Commodity Index includes 22 commodities. The 22 commodities are combined into an “All Commodities” grouping, with two major subdivisions: Raw Industrials, and Foodstuffs. Raw Industrials include burlap, copper scrap, cotton, hides, lead scrap, print cloth, rosin, rubber, steel scrap, tallow, tin, wool tops, and zinc.

11
are found for the UK (McIntosh and Vignoles 2001) and Canada (Finnie and Meng 2001). Using literacy scores (to proxy cognitive skill measures) and labor force behaviors from the International Adult Literacy Survey (IALS), Hanushek and Zhang (2009) finds that a one standard deviation increase in literacy scores increases earnings by 9.3 percent in a 13 country sample. The impact of school attainment falls from 7.1 to 5.9 percent after controlling for literacy scores. A small set of papers find an impact of cognitive skills on wages in developing countries as diverse as Ghana (Glewwe 1996), Kenya (Knight and Sabot 1990), Pakistan (Alderman, Behrman, Ross and Sabot 1996), South Africa (Moll 1998), and Argentina (Tetaz and Cruces 2009).

Similar results are found for the effect of non‐cognitive skills on wage earnings. Early work by Bowles and Gintis (1976) observes that a measurable part of the variance in earnings among observationally equal individuals with equal levels of education, are due to behavioral skills. Carneiro and Heckman (2003) report that participants in the Perry Pre‐School program, who received intensive non‐cognitive development interventions as children, have similar cognitive abilities but higher non‐cognitive abilities than non‐participants who were randomly selected out of the program; participants also have greater scholastic and labor market success as adults. Focusing on adults, Heckman, Stixrud and Urzúa (2006) find that both cognitive and non‐cognitive skills are important in explaining higher wages, shorter job search periods, and occupational choice, and that non‐cognitive skills are particularly important for those with lower levels of education, women, and youth.

> 1 Hanushek and Woessman (2008) identify two sources of measurement error.  First, there is a great deal of heterogeneity in the skills acquired at each level of schooling across countries, regions within countries, and schools within regions.  Second, much skill acquisition occurs outside of the classroom (
# **Expropriation of Minority Shareholders: Evidence from East Asia**

Stijn Claessens, Simeon Djankov, Joseph Fan, and Larry Lang

# **1. Introduction**

As many East Asian countries have plunged into a period of economic decline, policy advisers have pointed to the ownership concentration structure of corporations and associated corporate governance as one of the main culprits for the crisis and the weak performance of corporations.! Little empirical evidence exists, however, on the nature of the ownership structures in East Asia and its relation to corporate perfornance under the typical East Asian external environment, often including weak property and shareholders' rights, inefficient judicial systems, and corruption. This study is a first attempt to investigate this nexus. Using a unique, new data-set of 2,658 East Asian corporations in 1996, the year before the regional financial crisis, we study the relation between the concentration of cash-flow rights and control rights and the type of block ownership (family, state, widely-held financial institutions, and widely-held corporations), on the one hand, and corporate valuation, on the other hand. We also examine the evidence on minority shareholders' expropriation in East Asian corporations.

We find large differences in the concentration of cash-flow and control rights across the nine East Asian countries. Japanese and Taiwanese firms have the least concentrated ownership, on average only 6.90 (10.33) and 15.98 (18.96) percent of the total cash-flow (control) rights in these two countries is in the hands of the largest block-holder. In contrast, Thai, Indonesian, and Hong Kong fmns have the most ownership concentration in the hands of the largest blockI See, for example, Rajan and Zingales (1998) and Prowse (1998). Recovery plans for the corporate sector in East Asia are based on the need to change corporate governance, enhance property rights, minority rights, limit insidertransactions, and improve disclosure (World Bank, 1998).

2
to these five major areas to motivate the main hypotheses regarding the association between business environment indicators and productivity and innovation in Sri Lanka.

**_<mark>(1) Incentives for productive entrepreneurship.</mark>_** <mark>Whether entrepreneurial talent is allocated to productive entrepreneurship (wealth creation by increasing within-firm TFP through innovation), to unproductive entrepreneurship (wealth diversion via rent seeking), or to destructive entrepreneurship (wealth destruction via criminal or conflict-generation activities) depends on the relative payoffs facing individuals with entrepreneurial talent (Baumol 1990). For rapid, sustainable growth, it appears essential that the prevailing ‘rules of the game’ in the business environment allow transformational entrepreneurs to grow quickly and reap sufficiently generous rewards from innovation without fear of expropriation by public rent-seeking or private crime. We use the following business environment measures:</mark>

- **<mark>Competition.</mark>** <mark>Gorodnichenko, Svejnar and Terrell (2010) find robust evidence of a positive relationship between foreign competition and innovation using enterprise-level data similar to those employed in this paper. We hypothesize that foreign competitors not only pressure</mark>

> 5 See Trajtenberg (2009). See also Dutz (2007) for a similar broad definition of innovation and description of these four areas that provide key levers for innovation policy in addition to physical infrastructure.

9
Elsewhere, roads data present a different source of error. Chamberlin (2013) has noted that roads data sets in models are only “snapshots” of present conditions that fail to capture changing dynamics, a charge that applies to our analysis. This is problematic in Nepal, where even a casual observer will note that road quality changes frequently due to poor construction standards and maintenance practices. Deteriorating roads impact travel times, particularly during monsoon season and therefore we anticipate some error where road quality data are out of date.

All service facilities data sets were exclusive to Nepal and the lack of cross‐border facilities represents a possible source of error in border communities. However, these communities are either very small in the High Mountains, or usually already well served by Nepal‐based services in the Terai. So, the influence of border‐ based errors on aggregate numbers is likely minimal, though possibly locally impactful in some border municipalities. Care is advised when interpreting numbers from border municipalities with clear roads or paths to China or India.

We also acknowledge the importance of service quality, although our analysis ignores it for lack of data. Frequent staff absenteeism, inadequate supplies and/or poor quality services are common problems for schools, clinics and government offices in Nepal, particularly in remote areas, and impact usage patterns and development outcomes (RAP 2018, IDEA 2018). We therefore caution readers applying our findings in small‐ scale areas to research the impact of quality on usage of local services.

The assumptions listed above naturally impact the model’s accuracy, especially those regarding unencumbered travel and waiting for vehicles, both of which would slow down travel times in practice. Conversely, local residents tend to walk faster than visitors on local paths, somewhat offsetting these factors (RAP 2018). Users should remember that details of the local context like bus schedules and the frequency of landslides will cause departures from the model findings; we recommend careful analysis of such conditions when applying our findings on a small scale. Road‐blocking
25-250 employees in five federal districts: Central (excluding Moscow oblast and Moscow city), Northwestern (excluding Leningrad oblast and Saint Petersburg city), Southern, Volga and Ural.<sup>8</sup> The sample frame for our work is sourced from Ruslana database of Bureau van Dijk. Survey data on managerial practices is aggregated into a single management score following Bloom et al. (2019a) and merged with data on firm performance, profitability, employment growth and other relevant metrics from Ruslana.

Analyzing this newly collected data for Russia, we draw the following four key conclusions. _First_ , an average Russian manufacturing firm in our sample scores 0.43 in adoption of structured management practices, a value very similar to that in Mexico and Pakistan but behind the one observed in Croatia (0.54) the U.S (0.62). Management score is measured on a scale from 0 to 1.0, where 1 reflects adoption of all structured management practice, and 0 adoption of none. Thus, a score of 0.43 may be interpreted as if firms adopt 43 percent of structured management practices.

_Second_ , the weaknesses in management practices particularly relating to data driven performance monitoring pulls down the overall score in Russia. Structured management relating to monitoring practices include, for example, a larger number of key performance indicators that are tracked and reviewed systematically and more frequently by managers as well as non-managers. It appears that Russian manufacturing firms are weaker on these aspects of structured management.

_Third,_ there is much heterogeneity in adoption of structured management practices among Russian manufacturing firms in the surveyed federal districts. As in Mexico, Pakistan and Croatia, the distribution of management score has a fat left tail, implying that a large share of firms are poorly managed. Nearly 3 percent of the surveyed firms adopt no structured management practices, 60 percent of them score below 0.5 but only 3.5 percent of these firms score over 0.75. By comparison, establishments in
/privatesector/ic/.

> 20The survey questionnaires also included some country speci fi c questions. For our purposes, the relevant survey questions relate to the workforce, technology and innovation, production and costs, ownership structure, trade and manager characteristics.

> 21In Brazil, the sampling frame consisted of the listings of manufacturing establishments provided by IBGE (the national statistical office). The states covered in Brazil are: Amazonas, Bahia, Ceará, Goiás, Maranhão, Mato Grosso, Minas Gerais, Paraíba, Paraná, Rio de Janeiro, Rio Grande do Sul, Santa Catarina and São Paulo. In China, the sampling frame consisted of an electronic list of fi rms in each of the cities covered: Beijing, Chengdu, Guangzhou, Shanghai and Tianjin.

> 22Size groups are de fi ned according to a plant’s total number of permanent employees.

10