rafmacalaba/datause-encoder-data huggingface dataset:
- _Adverse macroeconomic conditions_ , possibly reflecting deteriorating fiscal or external accounts.

- _Declining stock market prices or earnings._

- _Ratings downgrades for bank or competitors._

- _High banking competition_<sup>49</sup> . It can even be the case that in high levels of banking competition, banks may be reluctant to lend to competitors for fear of losing market share.

51. Bank managers have the duty to be alert and vigilant about changing circumstances in their competitive environment that might affect their bank, and to watch for changes in macroeconomic circumstances which might also impact them. This is a tall order of business since the future can be quite unpredictable. In view of this, it is prudent to build cushions—in the form of higher capital and higher liquidity cushions—which can mitigate unexpected events. Matz<sup>50</sup> envisages the different stages of a bank funding crisis (see Chart 7 above), where banks transition from ‘normal times’ to face increasingly severe impairments, before reaching a point of no return when the bank collapses.

52. The old case of the failure of the Continental Illinois bank in 1984 is illustrative (see Box 3) of a rapidly growing bank with a high sector concentration of its loan portfolio. On the other hand, the recent Northern Rock bank episode (see Box 4) is illustrative of the systematic risk posed by excessive dependence on securitization as a form of short-term funding (funding liquidity risk) and high reliance on smooth market functioning (market liquidity risk). Both cases highlight the consequences of poor liquidity management.

> 49 Banque de France, Financial Stability Review, ‘Overview: Liquidity in a time of financial turbulence’, February 2008

> 50 Op.cit., with adaptations from the author.
the payment system, making transactions more difficult and expensive. These mechanisms suggest that fragile banks hinder economic activity (the credit crunch hypothesis).

On the other hand, there are several channels through which exogenous adverse shocks to the economy might cause a decline in credit and economic activity even if the banking sector itself is relatively healthy. For instance, adverse shocks may trigger a fall in aggregate demand, leading firms to cut production and investment and, consequently, credit demand. Increased uncertainty may also cause firms to delay investment and borrowing decisions. Finally, adverse shocks might worsen agency problems and complicate lending relationships, for instance by reducing the net worth of borrowers. This, in turn, might cause banks to abandon high risk borrowers (flight to quality) or raise lending spreads. So output and bank credit may decelerate around banking crises even if there is no feedback effect from 14 bank distress to credit availability.

Existing studies of individual country experiences have found conflicting evidence on the relationship between bank distress and real activity. In a study of the so-called capital crunch in the United States in 1990, Bernanke _et al_ . (1991) argue that a shortage of bank capital had little to do with the recession. Domaç and Ferri (1999) reached the opposite conclusion for Malaysia and Korea during 1997–8. They found small and medium-sized firms to have suffered more than large firms during the crisis. Since these firms are usually more dependent on bank credit than large firms, this is evidence of a credit crunch. Data from a survey of Thai firms, on the other hand, suggest that poor demand rather than lack of credit caused the decline in production, although many firms complained about high interest rates (Dollar and Hallward-Driemeier, 2000). For Indonesia and Korea, Ghosh and Ghosh (1999) test an aggregate model of credit demand and supply and find evidence of a credit crunch, but only in the first few months of the crisis. Finally, using firm level data from Korea, Borensztein and Lee (2002) show that firms belonging to industrial groups ( _chaebols_ ) lost 24
<!-- Start of picture text --> Schooling log(Income PC)<br>2 0.1<br>--@- Age 11-16<br>15 --@- Age 6-10 a 0.08“ ry<br>@- Age0-5 0.06 pwn nn. weet<br>3 - -- - e<br>0 OO<br>1-3 years 4-7 years 8+ years 1-3 years 4-7 years 8+ years<br>Additional duration in Prospera Additional duration in Prospera<br>PCA Assets Receiving govt transfers<br>0.16 0.02<br>2<br>0.125 . r 0.01 ® _-)4<br>o. meee -0.02 en<br>0 TTSa es«# 08<br>-0.04 -0.04<br>1-3 years 4-7 years 8+ years 1-3 years 4-7 years 8+ years<br>Additional duration in Prospera Additional duration in Prospera<br>HH adults went hungry HH adults did not eat enough variety<br>0.01 0.01<br>A 0 SH-<br>A. -7-AO_o-*<br>~. sy<br>sling ~2222725*** -0.02 . e<br>-0.01 “er<br>-0.03<br>-0.02 -0.04<br>1-3 years 4-7 years 8+ years 1-3 years 4-7 years 8+ years<br>Additional duration in Prospera Additional duration in Prospera<br><!-- End of picture text -->
# FIGURE 11: RATIO OF INTEREST EARNING DEPOSITS TO MI

**RATIO** OF INTEREST EARNING BANH DEPOSITS TO **Ml** (1969.01 - 1989.12)

<!-- Start of picture text --> 4 .- . . . . . . . . . . . . . . . . . . . . . . . . . .<br>U. .... . .. ............<br>1969 1965 197: i975 1980 1985 1990<br><!-- End of picture text --> Regression (at in Table 11 shows the estimates of the **real** demand for interest earning deposits using OLS estimation method and monthly data covering the period 1978.01-1988.12. Regression (b) estimates the same demand for the later subperiod 1984.011988.12. We assume the real demand for deposits depends on the current month nominal interest rate and the expected inflation rate. The regressions shown use the current period inflation rate as the measure of the expected inflation rate. No significant changes were found when the next period inflation that actually took place was used. The high value and significance of the coefficient for the lagged endogenous variable strongly indicates the presence of a slow adjustment process. Regressions (a) and (b) are made with OLS estimation methods and use the data for the longer and shorter periods respectively. Regression (c) is done for the longer period using instrumental variables in order to correct for the problem of simultaneous determination of interest rates and inflation rates.

44
# **1. Introduction**

Solar PV has grown exponentially over the past decade. Cumulative PV installed capacity worldwide jumped from 39 GW in 2010 to more than 222 GW in 2015, driven by government-led incentives and falling costs (IEA, 2016a; IRENA, 2016).

Distributed solar PV systems, from here on referred to as distributed solar, account for about 42 percent of the global solar PV installations (IEA, 2016b).<sup>2</sup> Global investment in distributed solar has grown rapidly over the last decade, from US$9.4bn in 2006 to US$67.4bn in 2015, as shown in Figure 1 (UNEP & BNEF, 2015). However, distributed solar investments have been concentrated in OECD countries, especially Europe, Australia, Japan and the United States (IEA, 2016a). Emerging markets have struggled to unlock this market; instead, most investment remains focused on large, utility-scale power plants. In recent years, emerging markets such as India and China have set ambitious targets for distributed solar, but have not been able to tap into this market as intended, despite targeted policy incentives (e.g., ADB, 2015; Liang, 2014).

**Figure 1: Global renewable asset investment 2005-2015 (REN21, 2013).**

<!-- Start of picture text --> 300<br>266<br>257<br>243 249<br>250<br>216 212<br>200<br>Utility-scale<br>158 154 renewable asset<br>US$ bn 150 investment<br>124<br>94<br>100<br>63<br>50 63 76 79 54 60 67 Distributed solarinvestment<br>0 10 9 14 22 34<br>2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> Like many emerging markets, the Arab Republic of Egypt is keen to promote distributed solar but has struggled to create conditions for growth in the sector. Egypt has set a target of 20 percent of its electricity generating capacity coming from renewable sources by 2022, which represents more than doubling of the current share of 9.5 percent (including hydro which currently represents about 6% of the total capacity and is expected to decrease to 4% by 2022). In 2013, the country introduced a net metering program to 2 The IEA in this statistic uses a definition of systems below 100 kilowatts (kW). This paper uses a definition of up to 500 kW to be consistent with the Government of Egypt’s definition.

2
GDPPC is constant GDP per capita (2000 US$); DEMOG stands for a demographic variable; it is either the growth rate of population between 15-64 years old, or the age dependency rate; TRADE measures trade openness (exports plus imports in percentage of GDP); AGR is agriculture value added in percentage of GDP; GOVERNANCE QUALITY stands for bureaucracy quality index or corruption index; it is excluded in the basic specification.

We also include both regional and time dummies. The regions are defined in Table A4. The time dummies are annual.

World Bank’s World Development Indicators Database and the International Country Risk Guide (ICRG) Database are the main data sources.<sup>15</sup> The income level of a country is expected to be one of the significant factors determining actual tax collection.<sup>16</sup> As presented in Section II, higher-income countries tend to collect more taxes in percentage of GDP. Thus, it is expected that _GDP per capita_ to have a positive and significant impact on tax collection, as well as on fiscal revenue (Bahl, 1971; Fox et. al., 2005; Piancastelli, 2001).

_Higher age dependency_ and _higher population growth_ are expected to distort tax collection capacity of countries and lower the share of productive population (Bird et al., 2004). Thus, these two variables are expected to have a negative impact on taxes and total fiscal revenues.

_Trade openness_ is one of the variables commonly considered as an important determinant of taxation (Rodrik, 1998; Piancastelli, 2001; Norregaard and Khan, 2007; Aizenman and JinJarak, 2009). The changing size of international trade has expected to have two opposite effects on taxes. On the one hand, higher trade openness is expected to lower taxes collected on imports and exports; thus, it may have a negative impact on taxes and fiscal revenue. On the other hand, given that because higher trade openness is associated with higher economic growth rates, we expect open economies to grow faster; and as a result, more taxes can be collected with the increasing tax base. It is expected that the second effect dominates and trade openness has a positive impact on taxes and total fiscal revenue.<sup>17</sup>

> 15 See Table A2 in the Annex for detailed definitions.

> 16 See Le, Moreno-Dodson, and Rojchaichaninthorn (2008) for detailed information on variables and their expected effects on taxes.

> 17 Financial pressures on governments increased with globalization due to a higher demand for government spending and costly tax collection (Hines and Summers, 2009). Being an open market economy can affect tax policies such that higher international involvement increases the economic distortions created by taxation, but at the same time can increase the level of taxes due to higher economic growth rates as it is observed in open economies.

9
# **<u>Online Appendix</u>**

# **A1. Data collection and sampling procedure**

# _Baseline survey (2010)_

Table A1 presents the full timeline of our project. In early 2010, we selected six municipalities in Sorsogon for the baseline. These were selected to include both wealthier and poorer municipalities and both rural and urban areas. We then randomly selected 42 barangays: 11 from the capital of Sorsogon City, 7 from Casiguran, Castilla, Pilar, and Gubat, and 5 from Castilla and Irosin. Due to security and logistical considerations, three initially selected barangays were excluded and replaced with the next randomly selected barangay.

We obtained household rosters from each barangay captain, and we sorted households randomly then targeted the first listed households. Interviewers screened the first person they approached in the household. To be eligible for our study, the target respondent had to be between ages 20 and 45, and he or she must have not worked abroad in the past. Households that had current or past overseas Filipino workers (OFWs) were still eligible for the study. If the first household member was not eligible or did not want to participate in the survey, the interviewer asked if anyone else in the household might be eligible, and interviewed that person instead. We surveyed 5,008 individuals between March and August 2010, and 4,153 were ages 20-40.

# _Passport follow-up (2011)_

In 2011, we launched the second stage of our project to provide some respondents with assistance obtaining a passport. We revisited a subset of our baseline sample.

32
|night<br>1_, X_)_−E_(_Y |T_ = 0_, X_)<br>(4)<br>(5)<br>OLS<br>IV<br>0.032<br>0.010<br>(0.036)<br>(0.046)<br>0.061**<br>0.067<br>(0.031)<br>(0.041)<br>0.131***<br>0.175***<br>(0.043)<br>(0.064)<br>0.118**<br>0.182**<br>(0.056)<br>(0.081)<br>0.069<br>0.083<br>(0.054)<br>(0.072)<br>0.040<br>0.033<br>(0.038)<br>(0.052)<br>Yes<br>Yes<br>. The outcome variable_Y_ is an indicator<br>nd zero otherwise. Columns (1) and (2)<br>olumns (3) and (4) report the difference<br>ifference between households who report<br>program participation with the treatment<br> Controls include gender and age of the<br>ion, information about access to water,<br>ving in the village).|
|---|
|e during the previous<br>_E_(_Y |T_ =<br>(3)<br>OLS<br>0.032<br>(0.047)<br>0.048<br>(0.039)<br>0.082**<br>(0.041)<br>0.097*<br>(0.054)<br>0.059<br>(0.056)<br>0.031<br>(0.045)<br>No<br>he selected sub-sample<br>ht before the survey a<br>viations in brackets. C<br>mn (5) estimates the d<br>idn’t by instrumenting<br>ported in parentheses. <br>ld size, tribe and relig<br>and share of women li|
|ndividual net us<br>(2)<br>Treatment<br>0.529<br>[0.500]<br>0.411<br>[0.492]<br>0.352<br>[0.478]<br>0.500<br>[0.501]<br>0.300<br>[0.459]<br>0.471<br>[0.500]<br>per individual in t<br>r a bed net the nig<br>roup, standard de<br>on (model2). Colu<br>s and those who d<br>illage level are re<br>spondent, househo<br>haracteristics (size|
|Dependent variable: I<br>Sub-sample<br>(1)<br>Control<br>Children under 5<br>0.497<br>[0.500]<br>Youth aged 5 to 20<br>0.362<br>[0.481]<br>Adult male workers<br>0.270<br>[0.444]<br>Adult female workers<br>0.403<br>[0.491]<br>Adult male unemployed<br>0.241<br>[0.429]<br>Adult female unemployed<br>0.441<br>[0.497]<br>Controls<br>Note: *** p_<_0.01, ** p_<_0.05, * p_<_0.1. One observation<br>variable equal to 1 if the individual reportedly slept unde<br>report sample means restricted to control and treatment g<br>between treatment and control groups using OLS regressi<br>to have received the spraying campaign in the last 5 month<br>group indicator (model 3). Standard errors clustered at v<br>individual, education, age and marriage status of the re<br>dwelling characteristics, sub-zone dummies and village c|

33
# **4 Data and Descriptive Statistics**

## **4.1 The PRIMA data set**

We constructed our panel of primary schools from six successive waves of the PRIMA longitudinal survey in the Netherlands. The survey was carried out every two years from 1994 to 2004 to follow the development of cognitive and non-cognitive skills of students throughout primary school. Participating schools were chosen to be representative of the entire population of Dutch primary schools.<sup>12</sup> As we have multiple observations per school, we pooled all grades and years to exploit within school variation in the proportion of immigrant students. We linked the successive waves of PRIMA to build a panel of Dutch primary schools, observed in grade two, four, six and eight every two years from 1994 to 2004. We obtain a panel of about 600 schools with 12,053 grade-level observations.<sup>13</sup> The data collected in PRIMA are based on answers to detailed questionnaires filled by teachers, parents, and school principals. As a result, the dataset contains rich information at the student, classroom and school levels. In particular, it contains detailed information on students’ socioeconomic and migration background. It reports whether the student is foreign born, the length of stay in the Netherlands, as well as the country of origin of the parents. We categorize as immigrants individuals for which the answer to the question “How long has the child been living in the Netherlands” is not “always”. Contrary to most work in the literature, our definition of immigrants is therefore restricted to first-generation migrants who were born abroad, and does not 12The full PRIMA dataset consists of a representative sample of about 420 schools and also includes an additional sample of about 180 schools with children from a low socio-economic background.

> 13We refer to a grade-level observation as a grade of a given school observed in a given year. For example, grade 2 of school 1 observed in 1994 is a grade-level observation.

12
domestic and foreign debt stocks. The same applies to the evolution of the stock of foreign reserves, which is computed on the basis of the initial stock and annual changes.

A model like GEM‐Core can help analysts better understand the effects of a wide range of policies and exogenous shocks also when it is applied to a two‐sector macro database (the current case). To exemplify, in the fiscal area, it may address the space for government consumption and investment spending under alternative scenarios for TFP growth, foreign aid, and taxation, considering budgetary and sustainability constraints. Alternatively, it may consider the need and consequences of financing a planned spending program from different sources (foreign and domestic). Beyond the fiscal area, it may be used to assess the consequences of shocks affecting world (export and/or import) prices, migration flows, and worker remittances (current private transfers). It is straightforward to address demographic issues, including the impact on growth and living standards of changes in population size and age structure (endogenous or migration driven) and/or changes in age‐ and gender‐specific labor force participation rates. On the other hand, in a macro application, there is little scope for addressing issues related to income distribution and structural change in sectoral production structure. Poverty analysis would have to be done in the context of an unchanged (or exogenously changed) aggregate income distribution. In some cases, it may be helpful to explore the broader macro consequences of scenarios defined by parallel micro analysis that, for example, proposes policy packages with components related to spending, income distribution, and/or factor productivity.

# **Poverty Module**

To compute the poverty and distributional effects of each scenario, GEM‐Core implements a poverty module based on MAMS (Lofgren et al., 2013). The module offers a choice between the following approaches: (i) constant elasticity of poverty with respect to per‐capita welfare for each model household; (ii) log‐normal distribution of per‐capita welfare within each model household; and (iii) distribution of per‐capita welfare within each model household follows a real‐world household survey. In this macro application of GEM‐Core, we use approach (ii). The module is linked to base‐year poverty and distributional data for each of the representative households (RHs; one or more) in the database. It uses either household income or consumption as its welfare measure. The ability of the module to account for distributional change and its impact on poverty depends on the degree of ‐10‐
fell together with extraordinary monetary easing in advanced economies, the average distance has risen again.

# [ _Insert Figure 2 around here_ ]

Second, the US 3-month treasury bill rate is used as the benchmark global interest rate, given that a majority of sub-sovereign bonds was issued in U.S dollars and the outsized importance of the U.S Federal Reserve in influencing global financial conditions (Rey 2015). A low global interest rate will lower the financing cost for firms to access capital, and hence may widen the distance between the sovereign and sub-sovereign ratings. The second chart in Figure 2 shows that the Fed Funds rate was high in the 1990s, fell during the early 2000s following the dot-com bust (Ferguson and Schularick 2007), rose again in the subsequent years, and was sharply lowered during the global financial crisis (Taylor 2009).

# [ _Insert Table 3 around here_ ]

Country-level cyclical factors such as GDP growth and CPI inflation would also directly affect the performance of sub-sovereign entities (Dailami 2010). In our sample, average growth rate at the bond issuance is positive at 4.6 percent, while average inflation is 5.9 percent with a range of -1.4 to 85.7 percent. Higher GDP growth is usually associated with better performance of the private sector, which would help sub-sovereign entities service their debt obligations. But improved cash flows may also reduce the dependence of a sub-sovereign on the sovereign credit rating. The third chart in Figure 2 shows a general co-movement between the rating distance and GDP growth. High inflation reduces the real value of future earning of firms and jeopardizes macroeconomic stability, thereby reducing the willingness of sub-sovereign entities to plan future investments. Higher inflation is expected to be associated with a greater dependence of sub-sovereign ratings on the sovereign. Average inflation for our sample of countries has fallen from double-digit levels in the 1990s to 5-7 percent in recent years (fourth chart in Figure 2), and appears to show a negative relationship with average distance over time.

The country’s level of financial development and external openness affect corporate performance due to the difference in access to domestic and external financing. Domestic credit (as a share of GDP) is an indication of the depth of the domestic financial sector, and ranges from 3.9 to 192.7 percent. Despite the variation across countries, for the group of emerging market countries, average domestic credit as a share of GDP has risen almost steadily since the mid-1990s (fifth 7