rafmacalaba/datause-encoder-data huggingface dataset:
# **2. Public Policy Experience in Promoting Distributed PV**

# **2.1. Global Experience**

## **_Grid-connected distributed solar_**

Grid-connected distributed solar has outpaced utility scale solar in several OECD countries, especially in Australia, where 94 percent of total PV capacity comes from distributed solar<sup>4</sup> ; Japan (85 percent) and Germany (56 percent), as shown in Figure 2.

**Figure 2: Share of utility-scale and distributed solar globally and in the seven largest markets (Gupta, Sharda, & Shrimali, 2016), ordered by total market size.  India data is for March 2016, other countries’ data for 2015.**

<!-- Start of picture text --> 232,000 MW 43,530 39,700 34,410 25,620 18,920 6,740 5,070<br>100%<br>6%<br>15%<br>44%<br>58%<br>71% 67%<br>86% 89%<br>Utility-scale solar<br>94%<br>85% Distributed solar<br>56%<br>42%<br>29% 33%<br>14% 11%<br>Global China Germany Japan US Italy India Australia<br><!-- End of picture text --> Investment in on-grid distributed solar has in part been driven by market forces, as solar costs have become competitive with retail electricity prices (i.e., they have reached ‘grid parity’) in many countries. Distributed solar remains more expensive than utility-scale projects<sup>5</sup> on a per-kW basis (REN21, 2016), but costs have fallen steeply over the past five years (see Figure 3). In Australia, China, Germany, Italy and the United States, the average levelized cost of electricity (LCOE) for residential PV systems fell by 42 to 4 Note that the solar PV market in Australia is small in comparison to other OECD countries. Thus, while the share of distributed solar is high, this is partly due to the fact that utility-scale solar PV was not supported with strong policies in Australia and therefore deployment of utility-scale solar has been very limited.

5 The capital cost of a PV system has two main components: (i) PV module cost, which includes raw materials for PV cells and manufacturing & assembly and (ii) the balance of system (BoS) costs. The BoS cost includes the structural system (e.g., site preparation), the electrical system costs (e.g., inverter, transformer), storage system if needed (e.g., a battery), and the soft costs of system development (e.g., permitting, labor costs for installation). Modules are traded globally and prices are relatively similar between countries, while the BoS differs from country to country and by end user category. Typically, large utility-scale projects have the lowest BoS costs per kW while the highest is the small residential rooftop systems due to economies of scale.

4
Therefore, replacing it in (A2.2): or We can re-write (A2.1) in the following way: Using (A2.2) on the LHS and (A2.3) on the RHS, we have the desired relation between the estimated VAR coefficients and the ones in the system of equations (13): An analogous exercise should be conducted for the case of a Japanese investor. To save space, we do not describe it here.

# **7 References**

Ait-Sahalia, Yacine, and Andrew Lo, “Non-Parametric Risk Management and Implied Risk Aversion,” _Journal of Econometrics,_ 94 (2000), 9–51.

41
|**1,503**<br>_All hosts_|**814 (622)**<br>_EMDE hosts only (same region)_|
|---|---|
|**Share ofgroup assets in host countries**||
|**8.4%**|**4.1% (3.2%)**|
|_All hosts_|_EMDE hosts only (same region)_|
|**Share ofgroup revenuegenerated in host co**|**untries**|
|**10.1%**|**7.4% (5.7%)**|
|_All hosts_|_EMDE hosts only (same region)_|

Source: Orbis Bank Focus; team analysis. Figures cover financial and real sector of majority-owned subsidiaries only. Caveats include significant missing financial information for subsidiaries. Direct ownership information was used in case total ownership was missing. The highest holder in the hierarchy of the corporate group is designated as the parent. Also, consolidation practices differ across countries, including within groups.

A regression analysis using the data for the 51 EMDE banks show the relationship between the international activities of these EMDE groups and various group-level banking outcomes (profitability, market-to-book values, cost-to-income ratio, and bank risk -proxied by NPL over loans) and funding strategies (deposit, short-term funding, and equity funding and well as banks’ positions in terms of liquidity and market capitalization). The analysis is built on the following econometric model: B B o o o o B i B + ii,jj , rr ii ii = αα+ θθ + ββB iiBBo iiB ooiiooB iiBBooiiooBB + γγB CCooBBooiiooiioo jj ii where δδCCo BBooiiCC CCooBBooiiooiiooB B o o o + εεo is the dependent variable for bank _i_ from country _j_ capturing profitability (return on average assets and net interest margin), market performance (market ii,jj value of equity over book value of equity) and risk-related outcomes (non-performing loans over gross loans) as well as variables related to costs (cost-to-income ratio), funding, capitalization or liquidity (deposit, short-term funding, equity, market value of equity and liquid assets –all over assets). B B i , the main variable of interest, is total subsidiary assets over total group assets. In other specifications, we also differentiate between regional vs. nonii iiBBo iiB ooiiooB iiBBooiiooBB regional or high-income vs. developing country subsidiary assets over total group assets. B B are bank-level control variables including natural logarithm of total assets (size), loans over assets (business model), equity over assets (capitalization) and overhead over assets ii CCooBBooiiooiioo (bank efficiency) and include standard country-level control variables GDP jj CCo BBooiiCC CCooBBooiiooiioo 15
# Inequality of Opportunity and Investment Choices

## J. Michelle Brock<sup>a,b</sup> , Maurizio Bussolo<sup>c</sup>

_aEuropean Bank for Reconstruction and Development, 5 Bank St, London E14 4BG, United Kingdom bCenter for Economic Policy Research, 33 Great Sutton Street, London, EC1V 0DX, United Kingdom cThe World Bank, 1818 H Street NW, Washington DC, 20433, United States_

**JEL codes:** D14, D31, D63, D91, G41

**Keywords:** inequality of opportunity, investment, risk, unfair inequality

∗This work was funded by a grant from the World Bank. We are grateful for the support of John Jensenius III and the Symons Hall Experimental Laboratory (SHEL) at the University of Maryland. We acknowledge research assistance from Yue Zhou. We benefited from early comments from David Smerdon. The pre-analysis plan can be found at EGAP Registry https://doi.org/10.17605/OSF.IO/R28JK . Views presented are those of the authors and not necessarily those of the European Bank for Reconstruction and Development or the World Bank.
# **I. Introduction**

Studying the effects of exchange rates is a hardy perennial of international macroeconomics. But nearly all the empirical research is focused on the impact of exchange rate changes on the country experiencing or undertaking them.<sup>1</sup> There is less evidence quantifying the effect of exchange rate movements on the exports of competitor countries, a classic case of spillover that in its adverse manifestation is dubbed the ―beggar-thy-neighbor‖ effect.

This paper examines the spillover effect of movements in China’s exchange rate on exports of other developing countries in third country markets. The Chinese currency provides a suitable opportunity to study the spillover dimension for three reasons. First, China, by virtue of being the world’s largest exporter of goods, is likely to have quantitatively more significant competitive consequences for other countries than nearly any other exporter. Second, China is also a highly diversified exporter so that it potentially competes with a broad range of countries and across the product spectrum. Finally, reflecting China’s dominant size and encompassing scope, its exchange rate policy has been one of the most controversial aspects of international macroeconomics during the 2000s. More recently, it has been in the spotlight because of the consequences of a possibly undervalued renminbi on demand and output in industrial countries experiencing high unemployment and excess capacity.

The spillover effect is estimated with the help of highly disaggregated trade data which facilitates the use of a novel methodology to exploit the variation across exporters, importers, products, and time. We use disaggregated trade data at the 6-digit level spanning 124 developing country exporters and 57 large importers over the period 2000-2008. Our empirical approach is motivated by an analytical framework that we develop based on Feenstra, Obstfeld, and Russ

> 1 This is generally true of the older, voluminous literature on the trade consequences of exchange rates (Goldstein and Khan (1985) provides a survey and other contributions include Hooper, Johnson and Marquez (2000); Thursby and Thursby (1987)). It is also true of the more recent micro-literature on trade and exchange rates (Dekle and Ryoo (2007); Das, Roberts and Tybout (2001); Forbes (2002); Berman, Martin and Mayer (2012)). It is also a characteristic of the recent literature on the growth consequences of exchange rates (Dooley et. al. (2003); Rodrik (2008); and Johnson et. al. (2010)).

2
15 <!-- Start of picture text --> DOMESTIC SAVING AND JVESTkNT.<br>(in 1980 pesos as % of GOP)<br>0.35<br>0.3<br>0.25<br>K 0.2 ,<br>0.15.<br>0.1<br>1980 198t 1982 t983 19184 1985 1I86 1987 198 1989 1990<br>--- Ae +- OomA lc Svg I<br><!-- End of picture text -->

**Figure 3**

<!-- Start of picture text --> OISPOSABLE INCOME COIONENTS.<br>(in 1980 pesos as % of GDP)<br>0.08<br>0.06<br>0.04<br>0.02<br>-0.02<br>-0.04<br>-0.06 -<br>1960 I9It 1982 198319841985 1986 1987 1188 I989 I990<br>Ye"<br>-r i' -F-a)<br>... ~ ~~~.<br><!-- End of picture text -->

**Figure 4 a)**
# **3 Research Design and Sample**

As discussed above, local cadres’ promotion incentives, the lack of portability, the lack of certainty in policies relating to the future for migrants and their families, and the complexity of social insurance schemes may all contribute to low participation rates among China’s migrant workers. This paper focuses on the extent to which information about schemes and enrollment processes affects participation in urban pension and health insurance programs. The data used in this study come from the Rural-Urban Migration in China (RUMiC) survey.

## **3.1 The RUMiC Survey**

The Rural-Urban Migration in China (RUMiC) survey is a longitudinal study with nine rounds: the initial wave was carried out in 2008, with additional rounds conducted annually during each of the last 8 years. Migrants are surveyed in 15 cities, including such coastal migrant destinations as Guangzhou, Shenzhen, Dongguan, Shanghai, Wuxi, Nanjiang, Hangzhou, and Ningbo, as well as major cities in interior regions, including Chengdu, Chongqing, Wuhan, Hefei, Bengbu, Zhengzhou and Luoyang. Unlike other surveys of migrant workers in China, in which migrants are sampled primarily by urban residential address, the RUMiC uses a workplace sampling strategy. In contrast with urban local residents, rural migrants frequently move to cities alone and often live in factory dormitories or other workplaces. Even in cases in which migrants bring their families to the city, high urban rents deter them from living in the type of urban residences that comprise standard sample frames (such as those maintained by the National Bureau of Statistics). More conventional urban household sampling frames tend to yield a biased sample of migrants, over-representing those who are more affluent, have longer tenure and more secure positions in the city than the “representative” migrant. By using a sampling frame based on a census of work-places, RUMiC avoids this bias.<sup>9</sup> Although RUMiC is designed as a longitudinal survey, the young and mobile nature of the migrant population leads to some attrition between annual survey waves. To maintain sample size, the RUMiC team randomly draws a refreshment sample for each wave, designated as a “New Household Sample.” Thus, the RUMiC survey has two sub-samples in each year: the non-attrited households from the previous year’s sample (“old-households”), and a new representative refresher sample (“new households”) (Meng, 2013). The implementation of the RUMiC surveys normally begins in late March to early April each year, after migrants return from visiting their homes during the Chinese New Year. Because tracking takes time and there is also a need to draw and enumerate refresher samples, the survey typically runs for 6-8 months and is completed by November. This paper uses RUMiC survey data from

> 9For detailed discussion of RUMiC sampling procedure, see Gong et al. (2008).

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- 46 -

**Chart 11**

**Net Equity Investments in Turkey by Foreign Investors**

<!-- Start of picture text --> as % of free-float<br>(million US$) capitalization<br>6000 60%<br>5000~~~~~~~~~~~~~~~ 50%<br>4000 ~~~~~~~~~~~~~40%<br>0 ~~~~~~~~~~~~~0%<br>1989 1990 1991 1992 1993 1994 1995 1996 1997<br><!-- End of picture text --> Source: Istanbul Stock Exchange

# **VI.3 Capital Flows and Financial Vulnerability**

While increased financial integration may convey substantial economic benefits for developing countries, free capital flows also expose these countries to the risks and costs imposed on the economy by abrupt and large reversals of capital flows because of changes in creditor perceptions. A country may become financially vulnerable externally if there are large risks in its financial system that constrain foreign exchange liquidity. As we can see from earlier sections, foreign loans and credits to Turkey have been rather volatile, and Turkey experienced a sharp reversal of capital flows and a severe currency crisis in 1994. As a result, there was a sharp contraction of credit to the real sector and real economic activity contracted by 6% in that year. In this section we assess the liquidity risks, particularly foreign exchange liquidity risks, currently facing the Turkish banking system, and the ability of the commercial banks and the authorities to handle those risks.

**Adequacy of International Reserves** In traditional models of balance of payments, the vulnerability to a speculative attack usually results from a drainage of reserves following an excessive flow domestic credit expansion. Recent developments in the literature suggest that a currency can be subject to attack even without a flow fiscal deficit that causes a drainage of reserves, if the stock of broad money greatly exceeds the stock of foreign exchange reserves. When capital inflows suffer a reversal, not only do gross inflows dry up, but also holders of liquid domestic liabilities try to convert them into foreign exchange and flee the country. Thus reserves must be compared with a broad measure of liquid monetary assets in order to determine a country's vulnerability to panic.
<mark>individual and country-level factors that drive account ownership and use of accounts such as for savings. The Global Findex team reconsidered that analysis u</mark> sing data from the 2021 edition of the Global Findex database, specifically to assess consumer protection regulations as potential determinants of account ownership at financial institutions. To do so, we combine Global Findex 2021 data with data from the World Bank's 2017 Financial Inclusion and Consumer Protection (FICP) Survey, which tracks the supervisory activities and enforcement powers of the consumer protections agencies in a country (World Bank Group 2017). The final merged dataset consists of more than 91,000 observations covering 89 economies. Data on GDP per capita are from the World Bank’s World Development Indicators Database.

We construct three indices based on FICP indicators on the following themes: (1) if there are simplifications or exemptions to the documentation (KYC) requirements for certain types of applicants or products; (2) if the country has strong and enforced consumer protection provisions, such as collection of data on consumer complaints, and market monitoring of provider’s advertisements and websites, etc.; and (3) if tax incentives for saving schemes have been implemented to promote financial inclusion.<sup>7</sup> A higher score indicates a more extensive set of monitoring activities undertaken by consumer protection authorities and enforcement powers held by them.

The results of the country-level analysis show a positive correlation between pro-inclusion regulations—such as tax incentivized savings schemes—and increases in account ownership. In all, an increase in the quality of regulations is associated with a 4.3 percent increase in the share of adults in the country who have an account. When we in turn examine the individual observations, however, consumer protection regulations appear to be the only ones associated with increases in financial account ownership. These results highlight the importance of transparent terms and conditions and clear recourse to increase trust in financial institutions enough to encourage take-up of financial products.

> 7 We exclude a fourth category identifying explicit regulations on the cost of maintaining an account, financial accounts, since fewer than 10 economies are identified as having these regulations.

17
Figure 8: Low cost annual abatement potential by 2030 in Turkey <!-- Start of picture text --> 2030 abatement potential in Turkey<br>Potential (CO2e, megatonnes)<br>200<br>20 to 100 €/tCO2e<br>0 to 20 €/tCO2e<br>Win-Win<br>150<br>100<br>50<br>0<br>Power Buildings Transport Industry Waste Agriculture<br><!-- End of picture text --> <!-- Start of picture text --> Source: McKinsey (2009) reported in EBRD (2011)<br><!-- End of picture text -->

**Figure 9: Low cost marginal abatement potential in Russia**

<!-- Start of picture text --> 2030 abatement potential in Russia<br>Potential (CO2e, megatonnes)<br>250<br>20 to 100 €/tCO2e<br>0 to 20 €/tCO2e<br>Win-Win<br>200<br>150<br>100<br>50<br>0<br>Forestry BuildingsPower & PetroleumIndustry Agriculture Transport Waste<br><!-- End of picture text --> Source: McKinsey (2009) reported in EBRD (2011) 63
20 better for poorer countries is accepted for both cereal and noncereal aid. Replacing the GNP per capita measure of poverty with more food-related poverty measures, such as per capita food production or per capita daily cereal consumption, did not produce any significant results. The /i coefficient varied greatly with the average level (as a share of domestic production) of regular food aid received: countries with a high share of food aid received more aid in crisis situations. If low-income countries tend to receive more nonemergency food aid, however, the correlation between emergency aid and income per capita could be erroneous (given the high collinearity between the two components of food aid). Nevertheless, the income effect in the response equation remained unchanged even when income per capita was controlled for in the estimation. This robust result can be interpreted as follows: The response to emergency needs is correlated positively with the ease (low cost) of delivering food aid, and this cost of transportation and delivery tends to be lower for countries that receive high levels of nonemergency aid. This premise is further supported by the significant contribution of geographical location (inland or on the sea) to the variance of the response to output shocks. The emergency needs of the inland countries do not meet with the same generosity as those with seaports.

A completely different interpretation of the positive partial correlation of / and regular food aid is possible. Emergency food aid is not only an additional food supply, part of it is in fact regular food aid under a different name. This nonadditionality may be more significant in countries that receive more regular food aid. We cannot test this interpretation, nor do we address the related hypothesis that emergency food aid is not additional but is part of nonfood financial aid.
This paper has employed a common methodology of estimating continuous time Markov processes on panel data from three countries with three purposes. First, we generate a set of stylized facts about LDC labor market dynamics and find a remarkable degree of similarity in sectoral duration and transition patterns across the three countries. Second, we explore the possible uses for and limitations of a set of statistics for drawing inferences from Markov transition matrices. Third, as an example of intrinsic interest in itself, we ask to what degree these statistics can shed light on the nature on reason for being of the informal sector in developing countries. In the process, we highlight problems of heterogeneity and of identification: that transitions statistics have as fundamentally reduced forms that conflate comparative advantage in a sector with ability to enter the sector. We explore additional identification strategies exploiting variation across age, education, gender and time to this end. Overall, we find patterns of transition suggestive that a substantial part of the informal sector, particularly the self-employed, corresponds to voluntary entry although informal salaried work appears may correspond more closely to the standard queuing view, especially for young workers.

23
# **Table 4.  Draft Empowerment Indicators, Mexico Lifelong Learning Project**

|DOMAIN|SUB-DOMAIN|DEGREE OF EMPOWERMENT INDICATOR|SAMPLE QUESTIONS|
|---|---|---|---|
|SOCIETY|Family|•<br>Percentage of (married) women who have a say over how<br>the household income is spent<br>•<br>Percentage of women who have taken action to stop<br>domestic violence by (1) speaking up against the husband,<br>(2) leaving the husband, (3) seeking help from friends or<br>family, (4) seeking help from authorities, (5) other<br>•<br>Level of women’s involvement in reproductive decisions<br>such as (1) family size, (2) child spacing, (3) use of birth<br>control|•<br>Who in your household decides how the household<br>income is spent on (list investment options) (Husband<br>decides. Wife decides. Joint decision). Have you ever<br>wanted to manage a larger part of the household income?<br>What was the outcome?<br>•<br>Would you say that there have been incidences of<br>domestic violence in your household? Have you ever<br>taken action to stop it? If no, why not? If yes, what did<br>you do? What was the outcome?<br>•<br>Who in your household decides (1) family size, (2) child<br>spacing, (3) use of birth control? (The husband decides.<br>The husband consults the wife but makes the final<br>decision. The husband and the wife make a joint decision.<br>The wife decides.) Have you ever wanted to assume a<br>greater degree of control of 1-3? What was the outcome?|

57