rafmacalaba/data-use-mentions-extended huggingface dataset:
of reduced inequality.

56. **To illustrate this approach and the potential of fiscal redistribution in Colombia, a package of measures is proposed, but not necessarily recommended.** Before presenting the proposal, it is important to introduce a few methodological caveats (see Annex 2 for details). First, as an approximation of marginal program effectiveness, average rates of effectiveness reported in Table 7 are used. This may result in some lack of precision, which can be resolved through a more detailed analysis using the underlying household data. Second, the analysis is static as it does not account for possible behavioral responses of individuals to changes in taxes or public transfers. Third, the structure of existing programs is taken as given when computing the rates of effectiveness. In other words, the degree of progressivity, or the opposite, embedded in the existing tax and spending system is assumed unchanged. Finally, the approach is limited by the data available. No attempt was made to estimate the potential inequality reducing impacts of non-existing programs. As a consequence, there is no pretense that the policy package proposed is necessarily the optimal one.

57. **The static simulations presented here are intended to complement existing modeling efforts.** The objective of the simulations is to give a snapshot view of the relationship between the fiscal and income inequality reducing impacts. In this sense, the results are intended to complement other types of exercises, possibly based on CGE modeling, rather than a substitute 21
63 izer exposure measure (see equation 42). Sri Lanka’s NFS data also includes estimates of fertilizer prices and of each crop’s total cultivated area, which we combine with fertilizer requirements data to compute country-level fertilizer expenditure for each crop in 2022. Section VI.A.3 discusses how such fertilizer expenditures are used to estimate technological parameters.

NFS data on soybeans’ fertilizer intensity was deemed unreliable by agriculture specialists, so we complement the NFS data with data from the International Fertilizer Development Center and the International Fertilizer Association. Specifically, we use the ‘Asia’ entries in these databases to impute the fertilizer intensity of soybeans that appears in Appendix Table A2. Appendix Figure A2 shows that this alternative fertilizer intensity metric has a close linear relationship with our original metric from the NFS (with the exception of soybeans, which are a clear outlier in the graph), thus also serving as a validation for the latter.

Figure A2: Fertilizer Intensity: Sri Lanka vs Asia <!-- Start of picture text --> 0 200 400 600<br>Fertilizer intensity (kg/ha): Asia<br>3000<br>2000<br>1000<br>Fertilizer intensity (kg/ha): Sri Lanka<br>0<br><!-- End of picture text --> _Notes_ : The figure shows the fertilizer intensity of 22 crops (see Appendix Table A2 for the full list), measured in kg/ha, from two alternative data sources: Sri Lanka’s NFS (on the vertical axis) and the databases of the International Fertilizer Development Center and the International Fertilizer Association (on the horizontal axis). For the latter source, we use the estimates that correspond to Asia. The fertilizer intensity metrics add up the use of urea, TSP, and MOP. The outlier point on the top left corresponds to soybeans. The red line is the regression line when we exclude the outlier point from the sample, with a corresponding
0.9|0.9|1.1|0.8|0.8|0.7|0.5|
|HH that smoke tobacco, %|32|40|37|39|39|47|45|44|41|40|
|HH Size|3.9|4.0|3.8|3.6|3.4|3.3|3.6|3.2|3.0|2.8|
|Area (regional center), %|21|29|29|36|41|43|45|47|54|58|
|Female, %|0.55|1.55|2.55|3.55|4.55|5.55|6.55|7.55|8.55|9.55|
|Age 16-34, %|35|37|37|36|32|38|35|33|34|31|
|Age 35-54, %|35|31|32|31|32|33|33|36|34|36|
|Age 55+, %|30|32|31|34|35|29|32|31|32|34|
|Higher education, %|10|16|15|19|19|21|23|25|32|38|
|Years smoking (for smokers)|25|25|24|24|25|24|25|25|25|25|

_Source:_ Estimates based on Russia Longitudinal Monitoring Survey–HSE 2016. _Note:_ Deciles have been established using per capita household expenditure.

# **5. Results**

To analyze the distributional effects of an increase in tobacco taxes, we estimate the effect on prices, medical expenditures, and working life years, aggregating these three into a single measure. The price elasticities estimated in table 1
likelihood of failing, while at the same time taking into consideration their systemic importance when budgeting the time and resources devoted to their monitoring. The information contained in PCRs can be used to enhance those risk-based supervisory practices. When bank supervisors have detailed data of bank loans, they can depict a more accurate risk profile of bank loan portfolios and perform more efficiently and effectively the two dimensions of the supervisory framework: the regular off-site monitoring and the less frequent on-site inspections.<sup>19</sup> According to survey data collected by the World Bank between 1999 and 2001,<sup>20</sup> of the 34 central banks or bank superintendences that had a PCR (of near 60 surveyed), 33 indicated that PCR data information is used for bank supervision, mainly to determine the total indebtedness of borrowers across the system. The PCR data also helped supervisors to revise, and when necessary rectify, the classification that financial institutions assign to their borrowers. PCR data also help to evaluate the sufficiency of provisions for problem loans, to identify lending trends and to flag banks that had significantly increased their exposure to riskier credits. Also, 31 authorities indicated that PCR data were reviewed on a periodic basis as a complement to off-site monitoring and prior to on-site inspections. When asked how important PCR data were for strengthening supervision, 23 central banks/superintendences indicated they were very important and 10 stated they were somewhat important.

# _On-site inspections_

The on-site supervision is a time-tested tool to monitor and assess banks solvency and resilience. The results of on-site supervisions are usually translated into a rating. For example, initiated by the US supervisors and now also used in other countries,<sup>21</sup> the CAMELS rating system yields a composite rating of an institution's overall condition and performance by assessing six components: capital adequacy, asset quality, management quality, earnings, liquidity, and sensitivity to market risk. Asset evaluation is of paramount importance: it measures to what extent the financial institutions are
<!-- Start of picture text --> Normalized GDP per capita<br>4<br>2<br>—— Data<br>—— Benchmark<br>1 —— Data trend<br>195 1975 1980 1985 1990 1995 2000 2005 2010<br>1<br><!-- End of picture text --> <!-- Start of picture text --> Sectoral GVA shares<br>75<br>70<br>65<br>60<br>55<br>50<br>2045 ——Benc farming<br>35 —==Benc industry<br>3 ee Bence Services:<br>.20<br>15<br>-1005 o_O<br>.00<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Average no. of workers per establishment<br>35<br>30<br>25<br>20 ———Benc farming<br>45 eee Bence Industry<br>———Benc services<br>10<br>5 ———<br>0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Grid capacity<br>0.30<br>0.25<br>0.20<br>0.15 == Benchmark<br>gi9<br>”<br>0.00<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text -->
dirt floors, has television, has fridge, has washing machine. We additionally compile data from various administrative records that allow us to observe at the municipal level: the number of users of electricity, the number of car registrations, and the number of building licenses for both industrial and residential use.

Given the frequency with which these data are available, panel A of table 5 uses a long difference specification in which we test whether the five year log change in the number of dwellings of the various characteristics previously described is related to five year change in log night lights. In all cases columns 1 to 4, we find that the log night coefficient is positive and that it is sharply estimated. The _R_<sup>2</sup> is in the 6 to 8% range. In panel B of the same table, we turn to administrative records available in yearly frequency. In this case we use a log log specification that includes both municipal and year fixed effects. As in the previous case we find that our measure of log nights is positive albeit less precisely estimated in columns 3 and 4 where the number of observations is limited.

Next, we investigate the relationship between log night lights and state level GDP.<sup>12</sup> Given that our primary purpose is to estimate an elasticity of light to GDP that would allow us to back out the impact of Fonden on local economic activity, we restrict the sample to the 26 states that have requested Fonden funding in the 2004-2011 period. We follow the approach taken by Henderson et al. (2011) and focus primarily on determining whether night lights are able to predict year to year growth, annual fluctuations, recession and expansions, and long term growth. Panel A of table 6 uses log night lights derived from annual composites. Panel B of the same table uses log night lights derived from pixel averaged monthly composites. The benchmark specification regresses log GDP on log night lights, state fixed effects and year fixed effects. Standard
.33**%**|101|11.0**%**|20|21.7**%**|86|2.40**%**|115|5.6**%**|100|4.52**%**|82|1.63**%**|
|**Total**|1,125||918||92||3,537||2,060||2,213||5,045||
|**Mean**|1.78||1.9||2.3||1.4||1.6||1.6||1.4||
|**Std dev**|1.01||1.1||1.2||0.7||0.9||0.9||0.7||
|**Median**|1||2||2||1||1||1||1||

_Source:_ Authors‟ calculation based on Lao PDR customs data

**Table 5:** The number of years an FPD is present in the data, by destination and sector

|**Country**|**#FPDs**|**1year**|**2yrs**|**3yrs**|**4yrs**|**Sector**|**#FPDs**|**1year**|**2yrs**|**3yrs**|**4yrs**|
|---|---|---|---|---|---|---|---|---|---|---|---|
Ecuador.

Cyclone and droughts are the hazards with the lower share of provinces with high hazard classification. Potentially damaging cyclone-strength winds are a relevant hazard for Mexico and Dominican Republic, and to a lesser extent for Colombia. Alternatively, water scarcity (drought) is the natural disaster with the lowest hazard level province-wide across LAC; still, some countries have a relevant share of provinces with high or medium hazard level, namely Chile, Dominican Republic, and Mexico.

Information on hazard mapping is combined with data on the sector-specific geographical distribution of banking loans to estimate the banking sector's exposure to specific physical risks. We calculate the total amount/percentage of banking loans exposed to a specific physical risk, distinguishing between the different sectors' characteristics and their vulnerability to natural disasters. For instance, assuming landslides affect primarily the transport, storage and communication sector in Brazil, the amount of banking loans that are highly exposed to landslides consist of the sum of banking loan portfolios invested in the sector in Rio de Janeiro, Minas Gerais, and Espírito Santo, which are the Brazilian states where the hazard level of a landslide is assessed as high. Our measure of bank loan exposure to physical risks is presented in Table 6.

> 26 The value of goods produced and the amount of credit that banks allocate to economic sectors in specific provinces have a strong positive correlation. To measure this relationship, we leverage on countries with publicly available data for both banks’ outstanding loans and GDP disaggregated by sector and province: Mexico, Chile, and Bolivia. As we do with credit information, we map the economic sectors most affected by the climate hazards under analysis using the same approach described Annex C. The correlation coefficients show evidence of the strong positive and statistically significant relationship between these two variables. Results are available upon request.

> 27 As in Pagliari (2021) we focus on riverine floods. Of the type of floods cataloged by CRED EM-DAT, riverine floods accounts for more than 80 percent of the economic damage and more than 90
# Figure 6. Historical contributions of global shocks to global inflation around global recessions

## A. 1975 global recession

## B. 1982 global recession

<!-- Start of picture text --> Percentage points Percentage points<br>0.8 Oil price 0.8 Oil price<br>0.6 Global supply 0.6 Global supply<br>Global demand Global demand<br>0.4 0.4<br>Interest rate Interest rate<br>0.2 0.2<br>0 0<br>-0.2 -0.2<br>-0.4 -0.4<br>-0.6 -0.6<br>-0.8 -0.8<br>-3 -2 -1 t=0 1 2 3 -3 -2 -1 t=0 1 2 3<br><!-- End of picture text -->

## C. 1991 global recession

## D. 2009 global recession

<!-- Start of picture text --> Percentage points Percentage points<br>0.8 Oil price 0.8 Oil price<br>0.6 Global supply 0.6 Global supply<br>Global demand Global demand<br>0.4 0.4<br>Interest rate Interest rate<br>0.2 0.2<br>0 0<br>-0.2 -0.2<br>-0.4 -0.4<br>-0.6 -0.6<br>-0.8 -0.8<br>-3 -2 -1 t=0 1 2 3 -3 -2 -1 t=0 1 2 3<br>E. 2020 global recession<br>Percentage points<br>0.8<br>0.6<br>0
<!-- Start of picture text --> Figure 1.1a<br>Weighted Real GDP Growth Rates for ECA and subgroups<br>Data Source: World Bank Regional Tables<br>20<br>15<br>10 —<br>e——————>-_>: ———~<br>. a a=<br>arree ,<br>5 2001 2002 2003 2004 2005 2006 2007 2008Neo / 20100<br>-10 \/<br>-15<br>——ECA —— Oil & Gas Exporters ——EU10 + Croatia + Turkey —— LLMICs<br><!-- End of picture text -->
PSU level.<sup>33</sup> Perhaps the more interesting result is that the bias estimates from the gradient boosting model with georeferenced covariates exhibits considerable variation, with minimum and maximum biases reaching -0.43 and 0.43, respectively. That is, the standard implementation with remotely-sensed covariates is severely biased for some municipalities, even though its median bias is approximately zero. All other models, including the competing unit-context and area-level models, exhibit much less variation.

The performance of machine learning appears to depend heavily on the covariates used in the model, with census-based covariates considerably improving model performance. Another way to see that the census-based covariates have greater predictive power than the remotely-sensed covariates is by looking at feature importance. One popular measure of feature importance summarizes the gain (i.e., reduction of the loss function) associated with any given feature. That is, for a given run of the model, feature importance captures the average gain of a given feature, where the gains are averaged across all trees in the model.<sup>34</sup> Figure 3 plots the 20 covariates with the highest feature importance

> 31Note that the underlying model for CensusEB estimates relies on household level microdata. Consequently, the data requirements for CensusEB are even higher.

> 32A notable aspect of Fay-Herriot and Gradient Boosting models is their relative ease of application and the fact that these usually take considerably less time in implementing as opposed to CensusEB and unit-context models.

> 33The same issue for unit-context models is highlighted by Corral et al. (2021a) and Corral et al. (2022).

> 34In addition, the average gains for a given run of the model are normalized by dividing each by the sum of all average gains for that run.

22
more tropical climate that was inhospitable to European settlers and therefore fostered “extractive” institutions. We use data on Settler Mortality from Acemoglu, Johnson, and Robinson (2001) where settler mortality is a measure of the death rates faced by European settlers in former colonies and is calculated by the logarithm of annualized deaths per thousand Europeans. Since we have data on settler mortality for a smaller sample, we use latitude for our main results and present the results on settler mortality as a robustness test<sup>4</sup> .

> 4 Engerman and Sokoloff (1997) note that a second channel through which geographical endowments shape initial institutions is through openness and competition. They show that agriculture in southern North America and much of South America is conducive to large plantations. Thus, colonialists developed longlasting institutions to protect the few landowners against the many peasants. In contrast, northern North

6
LIS data highlights two main caveats: first, the variable used for instrumenting the FLFP was not available, which meant that we had to restrict the estimation to only the OLS model; secondly, the target welfare variable was consumption and not income. However, the results for Morocco and Jordan appear to be in line with expectations while Egypt has the right sign, but the coefficients are not significant.

# a. The FLFP and inequality macro-model

In Section 4, we proceed with a multivariate analysis to show the inverted U-shape relationship between FLFP and inequality. We follow a general formulation taken from the literature ( <mark>Förster and Tóth, 2015)</mark> that has enough explanatory variables but, at the same time, does not cover all the inequality determinants as they are not easily available for many developing countries and for all the years being analyzed. The scope of this regression is to show that – once controlling for a set of socio-economic variables and using different models – the relation between FLFP and inequality is concave, our focus is, thus, mostly on the signs and significance of the FLFP expressed in polynomial form.

From the World Bank database, we collected data about inequality (Gini index), labor force participation and other variables and included them in the analysis as “controls”. All in all, our data set had a total of 1,025 observations.<sup>9</sup> We estimate the following fixed effects panel model: where:

- _i_ = 1,  ,127 stands for the country and _t_ = 2002,  ,2018 is the year;

- _GINIi_ , _t_ is the value of the Gini index calculated on households’ income in country  at year _i t_ and is the dependent variable;

> 9 Data are not available for all nations and years, so the panel is not balanced. The World Bank produces the data on “International Migrant Stock, percentage of population” by intervals of five years (the last update was in 2015
, particularly in economies where output is highly volatile which, as we show, increases the cyclical effects on social indicators.

The paper proceeds as follows. Section 2 resorts to a univariate time series approach to compute the relative weight of the cyclical and trend components in the variance of each social indicator. Section 3 links the cyclical component of social indicators to the business cycle and shows that, everything else equal, cyclical income volatility plays a crucial role in driving the cyclicality of social indicators. Using household survey data for a set of LAC countries, Section 4 decomposes the fall in monetary poverty during the Golden Decade into permanent income growth, cyclical income, and redistribution. Section 5 concludes.

# **2. How Cyclical are Social Indicators?**

When examining the evolution of social indicators over recent decades, we should always keep in mind that any change in the underlying indicator can be decomposed into a cyclical component, typically driven by short-term factors, and a trend component that responds to structural considerations. Taking this distinction into account is critical for policymakers since policies and programs implemented to deal with cyclical fluctuations of social indicators are intrinsically different

> 8 An alternative, naturally, would be to construct cyclically-adjusted monetary poverty measures.  However, as discussed in Riera-Crichton _et al_ . (2016) in the context of fiscal policy, cyclically-adjusted measures have also serious shortcomings.

4
program impact for cohort 2 rises to 0.31 with standard error 0.18 (significantly different than zero at 90 percent confidence) – regressions not shown. Standard errors fall considerably when disturbance terms are not clustered at the school level; for instance, the standard error on the overall effect for girls and boys in Busia and Teso (as in Table 5, Panel A) decreases from 0.05 to 0.02.

14 These are not included in the main specifications because they were only collected for a subsample of students, those present on the day of 2002 survey administration, and would thus reduce sample size.

15 To disentangle the effect of being in a Teso district school from the effect of the lightning strike (in a specification that pools the Busia and Teso data for all girls and boys), we included an indicator variable for Teso district, and an interaction of the Teso indicator with the program indicator, as well as an indicator for schools located with 6 km of the lightning strike, and the interaction of this distance term with the program indicator. The coefficient estimate on the lightning distance and program interaction term is large and negative but not statistically significant (-0.10, standard error 0.13), while the coefficient estimate on the Teso-program interaction term remains negative and marginally significant (-0.21, standard error 0.11) – suggesting that the lightning strike alone cannot account for the negative Teso effect. Thirteen schools, or approximately 10 percent of the sample, were located within 6 km of the strike. Interaction results are similar for distances between 5-7 km from the lightning strike.

20
**The possible noise in the "perceived" innovation measure.** Perception survey data are often criticized for noisy data that can arise because some firms may not fully understand the question or may project their wishes into the response. As another robustness check, we estimate our baseline regression model using an alternative and less noisy dependent variable: the firm's R&D spending. It is a binary variable measuring whether the establishment spent on in-house research and development activities. **Error! Reference source not found.** 6 in the Annex reports the estimation results and shows the positive effect of _patient capital_ on R&D spending disappears when we use R&D spending (input) instead of a measure of (perceived) innovation outcome. There are potential explanations for this result. R&D spending constitutes only one input among various other inputs related to innovation, such as the use of technology licenses, the existing human capital, the purchase of complementary equipment, or the company culture (de Nicola and Chen, 2022; Crespi et al., 2016; Bogliacino and Pianta, 2013). Likewise, Cirera and Maloney (2017) show that even after controlling for the usual innovation inputs such as R&D, firm capabilities— including managerial and organizational practices—are important predictors of innovation at the firm level. The lack of these capabilities helps explain the lower returns to R&D found in lower-income countries because capabilities facilitate R&D and enhance its effectiveness. This suggests that other factors besides R&D spending might co-determine innovation outcomes, such as our baseline measure. Our findings also reveal that large and medium-sized firms, as well as foreign-owned firms, allocate more resources toward research and development (R&D). However, this may be attributed to better financial management and reporting standards within these firms—that separately manage R&D spending—rather than solely reflecting their innovation efforts and outcomes relative to smaller firms. Finally, the 10% significance of the bank