rafmacalaba/data-use-mentions-extended huggingface dataset:
ranging from 0 (no light) to 63 (maximum light); the spatial resolution is 30 arc-seconds, and there is near-global coverage of 180oW to 180oE longitude and 65oS to 75o latitude (Li et al. 2020).

For this study, we use imagery recorded by satellite between 2000 and 2012, covering 29 Caribbean countries and territories: Anguilla; Antigua and Barbuda; Aruba; The Bahamas; Barbados; Bermuda; Bonaire, Sint Eustatius and Saba; British Virgin Islands; Cayman Islands; Cuba; Curaçao; Dominica; the Dominican Republic; Grenada; Guadeloupe; Haiti; Jamaica; Martinique; Montserrat; Puerto Rico; St. Kitts and Nevis; St. Lucia; St. Vincent and the Grenadines; St. Barthélemy; St. Martin; Sint Maarten; Trinidad and Tobago; Turks and Caicos Islands; and Virgin Islands (U.S.). Although some studies—such as Bertinellli and Strobl (2013) and Andreano et al. (2020)—use yearly frequency nightlight data to predict local economic activities and map poverty in the Caribbean, this paper is one of the first to use and exploit yearly NOAA frequency data to assess the role of mangroves as natural protection against the damaging effects of hurricane strikes on local economic activities in the Caribbean.

Many of the economists who use nightlights as a proxy for economic activity—such as Doll, Muller and Morley (2006) and Keola, Andersson and Hall (2015)—pay great attention to nightlight saturation in urban centers. Henderson, Storeygard and Weil (2012), however, who use a fixed-effects specification for a panel of 188 countries over 17 years to test whether sensor saturation impairs the capacity of nightlight to predictive GDP, find that the estimated elasticity of nightlight with respect to GDP and remains unchanged after controlling for the number of top-coded pixels.<sup>9</sup> In the same way, this study examines RR<sup>2</sup>

> 9 The R-squared reflects the contribution of lights
**Table 17. Interaction between Oil Prices and Political Stability for Fuel Exporters**

|Dependent variable:|Change in fiscal<br>balance/GDP (%)|Change in total<br>revenues/GDP (%)|Change in total<br>expenditures/GDP<br>(%)|
|---|---|---|---|
|Change in ln (real $ oil price)|0.364<br>(0.04)|11.65<br>(1.58)|7.04<br>(0.85)|
|Political stability|-0.0180|0.0296|0.0384|
|(0 to 100)|(-0.37)|(0.69)|(0.71)|
|Political stability *|0.265|-0.016|-0.195|
|change in ln (real $ oil price)|(1.39)|(-0.12)|(-1.18)|
|Time trend|-0.0.0771**|-0.0436|0.0395|
|(=0 in 2000)|(-2.06)|(-1.31)|(0.96)|
|n|328|336|328|
|No. of countries|33|33|33|
|RMSE|5.05|4.50|4.36|

_Note:_ Figures in parentheses are robust t-statistics. *, **, ***: significant at 10, 5, and 1 percent respectively. Annual data from 1996–2013 for low- and middle-income countries with fuel >25 percent of total exports. Country fixed effects are included in the regression. RMSE = root mean square error, or sample standard deviation.

# **8.
Information management was improved so that schools were responsible for systematically organizing information on enrollment, learning achievements, finance, and so forth, and reporting it to the division office. Schools were required to set improvement plans every year and compare them with actual achievement. This dynamic process was monitored by the division-level education department. School finance was also decentralized to some extent to relax the school budget constraints because Philippine public schools are not allowed to charge school fees. TEEP schools were free to raise their own funds from communities, parents, and others, although resources were admittedly limited in many poor communities. These reforms in public schools were expected to improve education quality, which would then in turn increase returns to schooling in the labor market (see for example, Yamauchi [2005] on returns to schooling).

The selection of TEEP provinces was purposive because the program was intended to cover the most depressed provinces identified in the Social Reform Agenda. TEEP allocation was rather different in the Philippines’ three macro regions. In the northern macro region of Luzon, TEEP was concentrated in the Cordillera Administrative Region, a mountainous region in the center of northern Luzon. In the central macro region of Visayas, TEEP provinces were relatively evenly distributed. In the southern Mindanao macro region, TEEP divisions were clustered, though not to the same degree as in northern Luzon. The TEEP provinces are highlighted in the map in Figure 2.1.

Using school-level data, Yamauchi and Liu (2011) show that national achievement test scores increased by about 12–15 points if students were exposed to TEEP for six years. The researchers used cohort panel data from grade 4 in school year 2002/03 to grade 6 in 2004/05 to difference out cohort-school-specific unobservables, combined with propensity score matching. This study demonstrates a significant increase in student learning performance. This study also demonstrates a significant increase in student learning performance due to (1) new constructions of school buildings,<sup>15</sup> (2) instructional training of
and expenditures. In Section 6, we suggest simple indicators of fiscal decentralization and subordination at the regional level and conduct their statistical analysis. Finally, in Section 7 we run a panel-data regression analysis of potential determinants of the fiscal decentralization process. We also explore relationships between these indicators and regional economic performance. Section 8 brings main conclusions.

# 2. Data

This paper was inspired by the work of Lavrov (1 996a), for which a special database on the structure of the Russian regional budgets for 1992-95 was collected'. Dr. Alexei Lavrov also shared with us the database, which derives from the standard reporting forms filed by regions with the federal Ministry of Finance. He also provided additional budget data for 1996. The database contains data on consolidated budgets for each level of subnational governments (regions, cities of regional status, rayons, cities of rayon status, rural municipalities) for all 89 regions. In a sense we had access to quite a unique data set. Systematic analysis of local budgets in Russia and their relationships with higher level budgets is usually limited by lack of representative data. As a result, the research is based on small samples or case studies (Freinkman and Titov, 1994; Mitcheneck, 1997a; World Bank, 1998; Zhuravskaya, 1997).

The data reflect actual outcomes of annual budget execution (i.e. it is not just agreed budget allocation). According to Russian budget accounting standards, the data include both cash and non-cash components of the actual budget flows, i.e. include budget revenues and expenditures occurred e.g. through barter or cancellation of mutual debts. Also, as is usual for Russian budget statistics, subnational ' Some results of this report were also presented in the paper by Kuznetsova, Lavrov and David (1997).
in Tables A2A through A2D of the Appendix. The results for the firm-bank-month level are summarized in Table A2E of the Appendix. Overall, we find that the outcomes of states of varying indebtedness levels indeed followed parallel trends before the FD Law was introduced.<sup>31</sup> We additionally check the validity of our estimates by relying on alternative identification strategies. First, we estimate the impact of the Law by exploiting differences in the alert system classification (i.e., sustainable vs under-watch) of states with ex ante similar debt-to-net-income ratios. We summarize these results in Internet Appendix Tables IA2 and IA3. Second, we adopt a difference-in-difference approach where the treatment variable is discrete and equal to one if a state's measure of public indebtedness in the quarter prior to the FD Law is above the median indebtedness and zero otherwise. This specification allows us to rule out that our results are driven by states with extreme values in their 2016Q1 ratios of total public debt over free disposable income. The results of this alternative specification are displayed in Internet Appendix Tables IA4 and IA5.

# **4.2. Bank Heterogeneity in the Impact of the FD Law**

Banks provide us with yet another layer of variation to study the impact that the FD Law, and debt ceilings in particular, had on credit markets. In particular, we expect that banks that lent a larger share of their funds to ex ante more indebted local governments, would likely experience more drastic changes to their lending once the debt ceilings were introduced. Descriptive evidence displayed in Figure 5 shows that there was a large divergence in lending across banks with varying ex ante exposures to local government debt after the Law.<sup>32</sup> In the figure, we split banks into two

> 31 We further check for differences in the non-linear trends of bank lending volumes to local governments and private firms across states before and after the reform. To examine bank lending volumes to local governments, we run a series of
“Voting Technology, Political Responsiveness, and Infant Health: Evidence from Brazil.” Econometrica 83 (2) (2015) p. 423–64.

> 105 M. Kudamatsu, “Has Democratization Reduced Infant Mortality in Sub-Saharan Africa? Evidence from Micro Data.” Journal of the European Economic Association 10 (6) (2012) p. 1294–317.

16
machines. Capital utilization by apprentices varies according to the capital available in their training firm and their progression in the apprenticeship, and they generally learn each production technology in order of ascending productivity. In the **?** data, over 95% of apprentices can effectively use at least one type of sewing machine six months into their training. The pay structure for skilled and unskilled workers differs dramatically in this context. Apprentices are paid low weekly or monthly wages that are tied to the overall revenues of the firm, while skilled workers are much better paid piece rate employees. In the **?** baseline dataset, though skilled workers constitute only 9% of the workforce, they earn 61% of all the wages reported in the data.

Many features of the garment making industry mirror other small-scale manufacturing trades in Ghana. Apprenticeships are widespread in skilled construction, cosmetology, auto repair, cobblery, and artisan crafts. Variation in reliance on manual and electric equipment is also common in these trades. Perhaps not surprisingly, the profit distribution of the garment making firms in the sample studied in this paper is statistically indistinguishable from the distribution of incomes among all self-employed Ghanaians in the Ghana Living Standards Survey ( **?** ).

> 6This dataset includes other industries as well; here the study draws on descriptive statistics from the garment making portion of the sample and from the 2013 firm baseline survey before any experimental intervention.

6
them about the loan products may have primed them to be more accepting of the sharia-compliant product, leading us to over-estimate true demand.

The main outcome variable is applications for the loan but due to the implementation delays from the partner we were unable to see observe how this would have translated into actual loans made to those in the sample. It is possible that the preliminary application rates could have differed from the final disbursement. It is difficult to predict in which direction this potential difference may have gone.

Relatedly a lack of repayment data limits the ability to make strong statements about selection on unobservables and profitability for the bank. Although there is no evidence of compositional observable differences between those that apply for the sharia-compliant product and those that apply for the conventional loan, there may be unobservable differences. Unobservable characteristics could lead to adverse or advantageous selection. For example, those who take up sharia-compliant loans may be more pious and thus creditworthy, and thus less likely to engage in moral hazard behavior particularly at the repayment decision; on the other hand, those who take up shariacompliant loans may default more, as they could be used to more forgiving faith-based services. In the latter case, the increases in application rates would actually be detrimental to the financial institution.

6. Conclusion This paper shows that demand for sharia-compliant loans from borrowers in a Muslim-majority country is stronger than demand for conventional loans. This study also provides evidence that individuals are willing to pay more for this type of product, even though its financial characteristics are very similar to its conventional counterpart. While there seems to be no impact from different types of authorizing entities on demand for this product, this study finds that price elasticity differs by religiosity, as proxied by whether or not the respondent watches religious TV.

When comparing these impacts to what is implied by survey data, we show that survey data overestimate the exclusionary power of religious objectives by about one third, and that religious and social considerations are at least as important as economic factors like loan size and interest rates
preferences. We do not test the median voter theory per se but are interested in knowing how preferences over government spending on health care, education, pensions and transfers to poor households, which together account for the bulk of provision in advanced economies, change across age groups while controlling for other individual characteristics. Indeed, race, demographics, socio‐economic characteristics and gender are important determinants of attitudes towards redistribution and government spending on social welfare (Keely and Tan, 2008).

One of our main findings is that there is indeed a strong correlation between a person’s age and his or her preferences over policy, which is in turn reflected in how governments allocate scarce budgetary resources to competing programs. In particular, and in line with previous literature, we find that older individuals have stronger preference for allocating additional government spending to health care and pensions, whereas younger people tend to support additional spending on education. These findings are also fairly homogeneous among the countries covered in LITS II.

Second, the effect of ageing on preferences over policy is shaped by participation in the political process. As before, we find that older people are less likely than their younger counterparts to support an increase in government spending on education. In addition, older people who do not vote are even less supportive of raising spending on education than older people who do vote. The same is true for pensions: the positive relationship between age and support for increasing spending on pensions is less pronounced among people who vote than among non‐voters. In other words, preferences over policy tend to be more nuanced among voters than among non‐voters of different age groups.

Third, these findings are fairly homogeneous across countries and do not seem to be solely driven by cohort effects, a hypothesis that we tested using annual waves of Eurobarometer data between 2004 and 2011. In other words, the fact that older people are more (less) likely to support increases in spending on pensions (education) is not driven by a generational change, but
Of these, 74% had tenants.

The two interventions were implemented sequentially. The engagement intervention was implemented in September and October 2018, while the enforcement intervention was implemented in November and December of the same year.

# **6 Empirical Strategy**

The engagement intervention was individually randomized among compounds with tenants, and we estimate the intention to treat (ITT) effect by means of its sample analog: where _Yit_ is the outcome of interest for compound _i_ at month _t_ p _t_ “ 1 _,_ 9q after the intervention was completed; and _Ti_ is equal to 1 if compound _i_ is assigned to receive treatment and 0 otherwise. Note that treatment status does not change over time. We condition the analysis on settlement fixed effects and estimate robust standard errors.

The enforcement intervention was delivered as a clustered randomization. In this case we estimate the ITT by means of its sample analog: where _Cj_ is the cluster _j_ indicator which is equal to 1 if the cluster was assigned to treatment and 0 otherwise. The sample in both treatment and control clusters includes only disconnection-eligible compounds. Finally, to measure spillovers to the non-treated units (SNT) for the enforcement intervention, we estimate the sample analog of: In the estimation of the sample analogs of equations [2] and [3] we condition on settlement fixed effects given that the randomization was stratified at that level. Standard errors are clustered at the street level, which was the level at which the randomization was assigned.

# **7 Data and Outcomes**

We use high-frequency administrative billing and payment data from the utility to measure our primary payment outcomes. Jisomee Mita is a web-based ICT platform that enabled property owners to use a mobile phone to self-read meters, receive and pay water bills, and 14
reactions of oil-exporters’ stock markets to changes in oil prices. Quite surprisingly, a lower WTI oil price hurts the dollar value of the Gulf States’ stock markets but the impact is statistically insignificant: every 10% decline in WTI oil price, MSCI Gulf State index drops by 0.55%. The magnitude is relatively small and not statistically different to zero. Conventional

> 16 <mark>Euro Area; Canada; Japan; Mexico; China; United Kingdom; Taiwan, China; Republic of Korea; Singapore; Hong Kong SAR, China’ Malaysia; Brazil; Switzerland; Thailand; Philippines; Australia; Indonesia; India; Israel; Saudi Arabia; Russian Federation; Sweden; Argentina; República Bolivariana de Venezuela; Chile; and Colombia.</mark> Data are from the Federal Reserve.

24
).

> 10 These two studies illustrate the types of data sources used for this growing literature. Glewwe (1996) uses a household survey in Ghana which administered tests to household members of abstract reasoning (Raven's Coloured Progressive Matrices), mathematics, and (English) reading comprehension. Using the multi-country PIAAC data, Hanushek et al. (2015) find that in 22 countries a one-standard deviation increase in numeracy skills is associated with an average 18 percent wage increase among prime-age workers. They note that due to measurement errors in skills, these estimates may be lower bounds on the return to cognitive skill.

> 11 Among relatively recent studies are Kuhn and Weinberger (2005), Nyhus and Pons (2005), Wanberg et al. (2005), Heckman, Stixrud, and Urzua (2006), Mueller and Plug (2006), Losoncz (2007); Schmitt et al. (2007), Fortin (2008), Borghans, ter Weel, and Weinberg (2008), Heineck and Anger (2010), Lindqvist and Vestman (2011), and Segal (2013). Table 1 summarizes the studies on low- and middle-income countries.

6
_Source_ : author's green dictionary

# Step 2: Application to the ISCO-08 task database

The green dictionary is then applied to a task database, created from the occupational manual of the International Standard Classification of Occupations 2008 (ISCO-08). ISCO is a hierarchically structured system that classifies and aggregates occupational information for all jobs in the world into 433 4-digit level occupations (referred to as occupations here on) to be used for the collection of occupational data in statistical census, surveys, and administrative databases (See appendix D for more details on ISCO).<sup>50</sup> For each occupation in ISCO-08 there is an occupational title, a job description that delimits the scope of the occupation, a list of up to 14 tasks performed,<sup>51</sup> and examples of job titles included within the occupation.<sup>52</sup> Hence, the created occupational database contains 433 occupations; 3,245 task statements; and 46,184 words (equivalent to 8,010 unique words). Since ISCO-08 has been widely adopted in several countries, the results of the analysis described here can be applied to any country that uses ISCO-08.

The green dictionary captured 78 unique terms and 329 tasks from the ISCO-08 occupational database. Out of the 347 unique terms in the green dictionary, 78 terms were also present in the occupational database (figure 3.1 panel a).<sup>53</sup> Many of the captured terms were related to greenhouse gas reduction, pollution reduction and removal, and natural resource conservation. Out of the 3,245 task statements, the green dictionary captured 329 green tasks (or 10 percent of the total tasks) (figure 3.1 panel b – examples in table 3.3), of which 83 are strictly green.<sup>54</sup> These tasks are related to natural resource conservation (16%), climate change (14%), recycling and reuse of waste
1|
|household with disability recipient|44.1|43.5|40.5|57.2|
|household with pensioner|52.1|59.9|45.3|63.9|
|household with social aid|87.9|80.9|77.3|-|
|national AROPE+ rate|45.0|46.2|38.6|53.6|

Note: Percentage of individuals (using survey weights). AROPE rates of 2022 are not anchored to 2019, as in Figure 1 and Figure 2. Children are defined as those younger than 18 and elderly, those 65+. Social aid is defined as those households that report incomes from social assistance captured in the SILC survey by social exclusion not elsewhere classified. This captures households receiving the main poverty-targeted programs (mean-tested) as well as other mean-tested programs. Households with social aid cannot be identified as a vulnerable group in the Romania EU-SILC. Pensioners are defined as those receiving old-age benefits. Statistics presented are unconditional AROPE means. Since households with disabled members cannot be identified, we use as a proxy those receiving disability benefits. Source: Own estimates based on 2022 EU-SILC. We report the survey year, and not the income year. AROPE definition based on EU2030 target.

27
The unique identifier included in the ENIA allows us to follow plants over time and identify exit of plant A in year _t+1_ if plant A is part of the ENIA in year _t_ but is not part of the ENIA in year _t+1_ and after. Another advantage of our data is that we can identify multi-plant firms, which may exhibit important differences relative to single-plant in terms of survival (Disney et al., 2003).<sup>10</sup> Table 1 shows that the average yearly exit rate in the Chilean manufacturing sector is about 9%. Exit rates differ across industries ranging from 6% in the basic metals industry to 11% in textile, wearing apparel and leather, wood and wood products.

The crucial feature of our dataset is that it provides for each plant and year information on the entire set of products manufactured and sold classified at the 7-digit ISIC level (revision 2).<sup>11</sup> This information allows us to construct two novel measures of innovation. Our first main measure of product innovation is a dummy variable that equals one for a plant in year _t_ if the plant sells one or more new 7-digit products while our second main measure explores the quantitative aspect of innovation and is a continuous measure capturing the number of new 7-digit products sold by a plant in year _t_ . For both measures a new 7-digit product is one that the plant has never sold prior to year _t-1_ , but that product may not be new to the market or the world. Table 1 shows that the average percentage of plants introducing new products is 14%. The innovation rate is lowest for the food, beverage, and tobacco industries - where only about 8% of plants innovate - and well-above average for a diverse set of industries from textiles, wearing apparel and leather, wood and wood products to chemicals, basic metals and fabricated metal products. The largest numbers of new products are introduced in the basic metals and fabricated metal products and machinery and equipment