rafmacalaba/datause-encoder-data huggingface dataset:
|In contrast, indirect taxes<br>are regressive and<br>contribute to increasing|Figu|re 9.|Progres<br>(fr|sivity an<br>an<br>om marke|d Redistri<br>d contrib<br>t to cons|butive Effect of I<br>utions<br>umable income)|ndirect|Taxes|
|---|---|---|---|---|---|---|---|---|
|poverty and inequality in<br>2016. While it is true|0.15<br>0.20<br>essive||||||0.020<br>0.025|alizing|
|that regressive taxes can|0.10<br>nt<br>ogr||||||0.015|fect<br>Equ|
|be equalizing,<sup>18</sup>this was|000<br>0.05<br>ficie<br>> Pr||||||0010|e ef<br>-->|
|not the case in Romania<br>in 2016 (Figure 9). In|-010<br>-0.05<br>.<br>ni coef<br>--------||||||0.005<br>.|stributiv<br>g <-----|
|particular, VAT placed a|-0.15<br>.<br>kwa<br>e <-||||||0.000|Redi<br>lizin|
|large burden on low-<br>|-0.20<br>Ka<br>ssiv||||||-0.005|<br>equa|
|income households,|-0.25<br>gre||||||-0.010|Un|
|leading to an overall<br>increase in poverty and<br>inequality. Excise taxes<br>were also regressive, but<br>since they are not as|Re|All direct taxes|All contributions<br>s and contributions|VAT<br>Excises tobacco|Excises alcohol<br>Excises fuel|Excises energy<br>All indirect taxes<br>All taxes<br>s and contributions|||
|large as VAT, their|||taxe|||taxe|||
|impact on poverty and|||rect|||All|||
|inequality was<br>substantially smaller<br>(Table A2.1). When|Source: Own|estim|ates base<br>All di|d on the 20<br>Kak|16 Roma<br>wani Coef|nian HBS.<br>ficient<br>Marginal|Contribu|tion|

taken together, although indirect taxes are regressive and unequalizing, this effect is smaller relative to other countries, including many other middle-income countries (Figure 8B).

# **Social Spending**

Contributory benefits and direct cash and near-cash transfers are all progressive, reducing both poverty and inequality (Table A2.1). These programs are progressive in both relative and absolute terms – that is, they represent a larger share of the budgets of the poor, and are pro-poor, as most of the benefits are concentrated at the bottom 40 percent of the distribution for almost all categories of benefits (Figure 10). For instance, the state allowance for children spent 0.6 percent of GDP in 2016 and concentrated 59 percent of benefits to the bottom 40 percent of the distribution. Similarly, 65 percent of the 0.3 percent of GDP spending on disability benefits went to the bottom 40, while 70 percent of the 0.1 percent of GDP spending on the minimum social pension did so. Most progressive were the 0.11 percent of GDP spent on the guaranteed minimum income and the 0.07 percent of GDP spent on the support allowance for families with children, of which 99 and 97 percent benefited the bottom 40 percent of the distribution. The only exceptions are relatively small programs, including programs for persecuted persons for ethical or political reasons, and near-cash benefits for medicines and transportation.<sup>19</sup> Overall, 60 percent of the 1.4 percent of GDP spent

> 18 For instance, the case of Chile. See Martinez-Aguilar et al (2017) and Lustig (2017).

> 19 This last result should be interpreted with caution given the relatively small sample size of beneficiaries to these smaller programs.

20
rower, total value of the deal in USD, maturity of each tranche, the general industry group of the deal and the name of the lending banks. We also have information on deal type, specifically whether the syndicated loan is an investment grade, leveraged or highly leveraged loan.<sup>8</sup> Thanks to the information on the lender, we are able to distinguish the syndicated loans in which there is the participation of at least one MDB. The MDBs participate in the syndicated loans markets at commercial terms through their private sector windows to mobilize financing from domestic and foreign creditors. MDBs operate under a joint financial and development mandate, which differentiates them from commercial banks. In our sample, the largest players are the European Investment Bank (EIB), the European Bank for Reconstruction and Development (EBRD) and the International Finance Corporation (IFC)—see Table A1. Deals that involve MDBs are 9.2 percent of the sample and they are, on average, significantly larger, with longer maturities, and with fewer non-MDB banks involved (Table 1).

We group the information on the general industry of the syndicated loans into 9 sectors: agriculture, construction and real estate, finance, government, infrastructure, manufacturing, mining and metals, oil and gas, and services. The majority of loans belong to the finance, infrastructure, and manufacturing sectors, which are also the sectors with the majority of loans with MDB participation.

To prevent large countries from driving the results, we use nominal GDP in USD from the World Economic Outlook (WEO) as a scaling factor for the total value of the syndicated loan. Scaling by GDP also gives us a better understanding of the relative magnitude of the loan size with respect to the country’s economy. For data cleaning purposes we winsorize the top and bottom 1 percent of the total amount of syndicated loans, scaled by GDP .

With this information we build a balanced panel at the country-sector-year level. In total, we have 26,325 observations corresponding to 1,053 country-sectors (117 countries and 9 sectors) that we observe for 25 years. By constructing a balanced panel, we impute zeros to country-sectoryears where no syndicate lending takes place. Consequently, the averages of the main variables of interest become smaller in magnitude. For example, while the average number of participating banks per syndicate loan is around 6.2 (Table 1), the average number of banks participating in the syndicate lending market per country-sector-year is 1.6 (Table 2). To test whether syndicated loans with MDB participation crowd-out corporate bond financing, we also use the information

> 8This characterization refers to the borrower’s credit rating, with investment grade loans being issued to higherrated borrowers, while leveraged loans are for below-investment-grade borrowers. Most are investment grade loans (62.9 percent), the rest are leveraged. There is a small percentage (4.6 percent) of syndicated loans that are highly leveraged.

6
capita income we first determine individual incomes, allocating general household income evenly among all household members. Additionally, for farming households, their reported income is ignored in order to determine eligibility to receive the allowance for the first child and the predicted income is based on the amount of land belonging to the household is evenly distributed among all family members.<sup>18</sup> We therefore construct an income per capita measure in line with the eligibility criteria that will be used in order to qualify for the benefit for the first child, including income from wages, income from capital, income from pensions, and income from transfers categorized as alimony. This measure of income is then used to estimate monthly payments to be delivered across the distribution as part of the Family 500+. Finally, since households are expected to increase their spending, we also compute the likely increase in VAT and excise spending due to the Family 500+ program.

Since Family 500+ is not targeted to low-income households for families with 2 or more children, 13 percent of benefits are expected to benefit the top 40 percent of the distribution, pointing to potential efficiency gains if it were to be better targeted. However, since low-income households have more children, 44.5 percent of benefits will go to the bottom 20 percent of the distribution. Although the Family 500+ program is less progressive than the social assistance and family benefit program, it is more redistributive and has a greater impact on extreme poverty than either of those programs given its relative size (Figure 8).

Poverty and inequality are therefore expected to fall after the introduction of the Family 500+ program. Indirect taxes are also expected to increase, but assuming households spend on similar goods, the net effect on consumable income is still estimated to be positive, with extreme poverty declining from 8.9 to 5.9 percent relative to the situation in 2014 (Table 7).

<!-- Start of picture text --> Figure 8. Progressivity and Marginal Contribution – Family 500+<br>1.20 0.045<br>0.040<br>1.00<br>0.035<br>0.80 0.030<br>0.025<br>0.60<br>0.020<br>0.40 0.015<br>0.010<br>0.20<br>0.005<br>0.00 0.000<br>Kakwani Coefficient<br>Redistributive effect (Gini points, right)<br>Reduction of extreme poverty (percentage points, right)<br>Source: World Bank estimates based on HBS 2014.<br>Kakwani Index<br>--------------> Progressive Marginal contribution<br><!-- End of picture text --> The cost of the program is expected to amount to PLN 25.7 million per year (1.5 percent of GDP). However, when we take into account the likely increase in VAT and excise tax collections (assuming that households will consume a similar basket of goods after the program is put in

> 18 It is ignored only for the eligibility for all income concepts presented later the reported income is utilized. The value per hectare considered is PLN 2,506 per annum.

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Having set up the analytical framework, this section describes the data inputs that will be used to identify each damage channel and quantify the sectoral and aggregate impacts of climate shocks.

# 5.1 Agricultural yields

**Data on direct losses to crop and livestock yields caused by changes in rainfall levels and seasonal timing are based on loss curves from a comprehensive disaster risk study** (IDB, 2019). Figure 7 shows the direct losses expected from droughts, expressed as a function of their periodicity (i.e. how frequent a drought of a given magnitude is expected, in years), for different climate scenarios (as represented by different RCP pathways). Loss curves were adjusted to be consistent with MGAP’s estimated losses in 2022/23 drought (MEF 2023) a one-in-a-hundred-year event according to Uruguay’s meteorological agency (INUMET, 2023).

**Droughts have historically had a significant direct impact on both livestock and agriculture.** The order of magnitude of damages in the historical/baseline scenario for droughts with return periods of 50 years or less is similar for crops and livestock, i.e. increasing rapidly until around US$600 million. For larger droughts, losses increase in the case of livestock to over US$1.6 billion for return periods of 200 years, while they increase at the margin in the case of crops (Figure 7).

**Climate change is expected to increase the magnitude of damages for crops** . While damages for crops increase monotonically with more extreme (i.e., higher RCP) climate scenarios, livestock damages are expected to change little with respect to historical experience (Figure 7 and Figure 8).  The differences can be explained by the definition of drought used in the IDB (2019) study. As is common in many studies of drought hazard, drought events are characterized using the three-month Reconnaissance Drought Index (RDI),<sup>8</sup> which is shown to replicate better the historical occurrence of “identified” drought events in Uruguay. This indicator tends to capture short duration agricultural droughts. A biological model of crop output and livestock pasture availability is then used to identify the loss of yield associated with drought conditions defined in this fashion. Water availability conditions that can lead to significant losses in production are different for different crops and for livestock. Short duration drought events in the summer have very negative implications for grain yields. Unlike crops, whose lifecycle covers only a few months, livestock can enter a period of compensatory growth if drought conditions reverse, which can partially make up for single acute drought losses. The climate model underlying IDB (2019) projects an increased occurrence of droughts during the summer months when rainfall is crucial for grain yields, but does not anticipate significant alterations in average precipitation patterns across various climate scenarios. Caution is, in any event, required to interpret these results, given the uncertainties in the projection of future precipitation patterns discussed in a previous section.

> 8 Droughts are defined as events where the normalized 3 month SDI is below -1.

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their local area. The majority of firms hired fewer than 10 workers, although large manufacturing enterprises had greater variation in number of employees (see figure 3).

**Figure 3. Cumulative distribution of enterprise workforce: by size and sector**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>0 50 100 150 200 250 300 350 400 450 500<br>Total number of employees<br>Cumulative distribution, %<br><!-- End of picture text --> <!-- Start of picture text --> Micro/Small-manuf Large-manuf Micro/Small-services<br>Note: excluding medium enterprises (there are only 4 in manufacturing and 1 in services); n= 614<br>enterprises.<br><!-- End of picture text --> Manufacturing dominated our sample in terms of share of jobs, with almost three out of every four jobs being concentrated in the sector. Among micro, small and medium enterprises (MSME), 65 percent of the jobs were in the manufacturing sector (figure 4). Among start-ups or firms that had been in operation for less than 5 years at the time of the survey,<sup>6</sup> the service sector contributed to a higher share in employment (figure 5).

**Figure 4. Share of employment (%): by sector and size of enterprise**

<!-- Start of picture text --> Registered MSMEs 65.2 34.8<br>All enterprises 72.4 27.6<br>Manufacturing Services<br><!-- End of picture text --> Note: n = 6601 employees who were holding jobs in our sampled firms at the time of the survey

**Figure 5. Share of employment (%): by sector and age of enterprise**

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# **_Prices and deficits_**

Given the tight supply positions to meet the growing demand, all the SEE countries have rapidly increased electricity tariffs in recent years. Albania and Bulgaria nearly tripled nonresidential electricity prices between 2000 and 2009 (Figure 8). Croatia and Romania have relatively high rates in the region. Macedonia has also adjusted the nonresidential price quickly in recent years. The nonresidential electricity prices exceed 10 U.S. cents per kWh in all SEE countries but Serbia and Bosnia and Herzegovina. This level of price is almost equivalent to or even higher than the world-highest “industrial” electricity prices in OECD countries, such as Ireland, the United Kingdom and the U.S. states of New York and California.<sup>13</sup> Compared with residential tariffs, many countries have proceeded with rebalancing between residential and industrial electricity prices, in favor of nonresidential customers. The relative nonresidential price to residential tariff declined from 1.2–1.6 to nearly or less than one (Figure 9). Romania is keeping nonresidential tariffs relatively low, compared with those for residential consumers. Particularly, some eligible industrial consumers are enjoying discounted prices. From the utility point of view, in fact, the cost of transmitting electricity to large-volume consumers with a higher voltage could be cheaper than low-voltage power supply. High voltage can reduce transmission losses. In addition, the cost of electrical transformers may not be required, because large-volume users may use high-voltage energy as it is. Otherwise, they may be equipped with private transformers in their factories or buildings. In Macedonia, the relative nonresidential price increased substantially, but the general level of electricity prices may remain relatively low for both residential and nonresidential customers.

> 13 According to IEA database, the average industrial electricity price in OECD member countries in Europe was estimated at 11.6 U.S. cents per kWh in 2007, which was twice as high as the average in 2002, i.e., 5.9 cents.
slightly lower coefficient (-0.05) than the mark-up model I (-0.09), suggesting a somewhat slower return to the long-run equilibrium compared with the mark-up model II.

# **4.2 Money-gap Model**

In the case of Turkey, the money gap measure can be constructed for almost any definition of money. As a result, I consider three definitions of monetary aggregates, namely the monetary base (mb), M1, and M2.<sup>20</sup> I construct a real money gap variable, measured as the proportionate deviation of the actual real money supply from its trend value, obtained using the Hodrick-Prescott (HP) filter. The corresponding money gap measures for the three monetary aggregates will be referred to as mgapmbt, mgapm1t, and mgapm2t.

I rely on the AIC to select the appropriate lag length for each variable in estimating equation (7). In this respect, the investigation considers all possible combinations up to 8 lags and then employs the combination that minimizes the AIC.<sup>21</sup> Given that the objective of the paper is to examine models that can be employed both to explain and forecast inflation, I decided to use the parsimonious specifications chosen by the AIC.

Table 4 reports the results of this analysis. As expected, the impact of an increase in the money gap on inflation is positive and is estimated at around 0.4 percent, 0.081 percent, and 0.11 percent (including both the lagged and contemporaneous coefficients), respectively for the monetary base, M1, and M2 based measures of money gap.

> 20 M2Y definition of money supply, which includes foreign exchange deposits, is not considered since this aggregate is affected by the evolution of the exchange rate.

> 21 It should be noted that this exercise involves running 130,305 regressions.  The general formula for the number of regressions involved can be written as (2<sup>n</sup> -1)(2<sup>n+1</sup> -1)<sup>m</sup> where n and m are the number of lags considered and number of independent variables involved, respectively.

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influence. Adapting ideas from the economic theory of stochastic dominance,<sup>3</sup> we provide criteria for robust qualitative comparisons of publication records, based on our assumptions about the theoretical properties of the influence function.

The arguments for and against the _h_ -index, as reviewed in the next section, provide a motivation for our approach. We then present our approach in an intuitive (largely nonmathematical) way, before providing a more formal exposition. This is followed by examples.

# **For and against the** **_h_ -index**

We define a publication record as a list of the publications by a given author (or other entity being compared). Each element of this record has its own citation count. The list of the citations received ranked in descending order of citations can be called the citation profile. These are the data provided by standard sources such as SCOPUS, the _Social Science Citation Index_ (SSCI), and Harzing’s _<u>Publish or Perish</u>_ (POP), which uses Google Scholar.

There are a number of ways one can use these data to assess the scientific impact of a publication record. One way is to calculate the average number of citations per publication. This has the advantage that it measures quality independently of scale (not penalizing small university departments for example). But this reflects a disadvantage too: a researcher or institution that published just one well-cited paper could hardly be considered very productive.

A consensus appears to have emerged in the biological, physical and social sciences that the Hirsch (2005) index (the _h_ -index hereafter) is a useful comprehensive measure of the scientific impact of a publication record, based on its citation profile. An _h_ -index of _x_ means that _x_ is the highest rank in the citation profile such that the first _x_ items received at least _x_ citations. Hirsch argues that this index is a robust and relevant measure of “..the importance, significance and broad impact of a scientist’s cumulative research contributions.” Hirsch’s paper has been widely cited (1200 times in Google Scholar), and the _h_ -index has its own (substantial) <u>Wikipedia</u> entry. The index has also been popularized by citation software such as POP, Scopus and Web of

> 3  The theory of stochastic dominance has been mainly used for comparing risky portfolios and in comparing income distributions in terms of social welfare or poverty. An important early contribution in the context of portfolio choice was Hadar and Russell (1971). Applications to social welfare and the measurement of poverty and inequality include Atkinson (1970, 1987) , Dasgupta et al. (1973) and Shorrocks (1983). A difference to past applications of dominance theory (that we know of) is that in the present case one cares about the number of objects being compared (the number of publications) as well as their “results” (citations in our case, returns to an investment or incomes in other applications).

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Table 2: Tests of _β_ -convergence conditional on total public spending, 1992-2014

||**(1)**<br>**1992-2014**|**(2)**<br>**1992-2000**|**(3)**<br>**2000-2014**|**(4)**<br>**2000-2005**|**(5)**<br>**2005-2010**|**(6)**<br>**2010-2014**|
|---|---|---|---|---|---|---|
||||**a. All mu**|**nicipalities**|||
|_ln yit−τ_|_−_0_._012***<br>(0.001)|_−_0_._016***<br>(0.005)|_−_0_._020***<br>(0.001)|_−_0_._047***<br>(0.003)|_−_0_._020***<br>(0.004)|_−_0_._015***<br>(0.003)|
|Public spending|0.003***<br>(0.001)|0.012***<br>(0.004)|0.004***<br>(0.001)|0.008**<br>(0.003)|_−_0_._008*<br>(0.004)|0.024***<br>(0.005)|
|Obs.|2,234|2,234|2,193|2,193|2,116|2,045|
|R<sup>2</sup>|0.166|0.056|0.342|0.318|0.089|0.061|
||||**b. Urban m**|**unicipalities**|||
|_ln yit−τ_|_−_0_._013***<br>(0.002)|_−_0_._020***<br>(0.006)|_−_0_._021***<br>(0.002)|_−_0_._049***<br>(0.003)|_−_0_._020***<br>(0.004)|_−_0_._014***<br>(0.004)|
|Public spending|0.003**<br>(0.001)|0.012***<br>(0.005)|0.006***<br>(0.002)|0.011***<br>(0.004)|_−_0_._006<br>(0.005)|0.028***<br>(0.006)|
|Obs.|923|923|971|971|985|937|
|R<sup>2</sup>|0.216|0.067|0.345|0.333|0.086|0.066|
||||**c. Rural m**|**unicipalities**|||
|_ln yit−τ_|_−_0_._020***<br>(0.001)|_−_0_._044***<br>(0.005)|_−_0_._031***<br>(0.002)|_−_0_._083***<br>(0.003)|_−_0_._035***<br>(0.005)|_−_0_._077***<br>(0.009)|
|Public spending|0.002***<br>(0.001)|0.016***<br>(0.002)|0.001<br>(0.001)|0.009***<br>(0.003)|_−_0_._013***<br>(0.004)|0.012**<br>(0.005)|
|Obs.|1,311|1,311|1,222|1,222|1,131|1,108|
|R<sup>2</sup>|0.253|0.112|0.417|0.405|0.095|0.220|

_Source_ : World Bank calculations. _Note_ : The table presents estimates of parameters _β_ and _γ_ in equation (4), weighted by municipal population at the initial year of each period under study. The dependent variable is the annualized growth rate in the mean per capita income of municipalities over the period. _ln yit−τ_ and public spending are for the initial year and are in log-scale and in real per capita terms at August 2014 prices. Urban (rural) municipalities are defined as those with more (fewer) than 15,000 inhabitants. The intercepts are shown in tables 2–11 in the ancillary file. Robust standard errors are in parentheses. *** _p < ._ 01, ** _p < ._ 05, * _p < ._ 1 meaning that the initial level of public spending exerts a positive influence on income growth through, for instance, the allocation of resources to public investment or transfers and subsidies. If the model in (4) controls for the latter components instead of total public spending, it can be verified that both public investment and transfers and subsidies exhibit a negative and significant sign during 2005–10 (see tables 2–11 in the ancillary file). Hence, it seems that the initial level of per capita public spending in 2005 was not sufficient to promote income growth through those channels in an environment of economic and fiscal contraction toward the end of the 2000s and thereby accelerate the pace of convergence.

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