rafmacalaba/datause-encoder-data huggingface dataset:
differences in the same direction. Shares of other consumption items such as transportation, furnishing and household equipment or expenditures on hotels, cafes and restaurants display the opposite trend, increasing for families along the income distribution. Overall, more that 70% of all expenditures of households from the first decile go to cover basic necessities, namely food, housing, education and health. This share drops to 41% for the typical household in the top income decile and is 54% for the median household.

Figure 2: Consumption patterns vary greatly across income groups (2019) <!-- 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>1 2 3 4 5 6 7 8 9 10<br>Food and non-alcoholic beverages Clothing and footwear<br>Housing, water, electricity, etc. Furnishings, household equipment, etc.<br>Transport Hotels, cafes and restaurants<br>Miscellaneous goods and services Other Expenditure Groups<br>Source : World Bank calculations using data from TUIK<br><!-- End of picture text --> The large differences in the structure of expenditures make some households more exposed to high inflation episodes depending on the productivity shocks underlying the spike in prices. Assuming everything else constant, high food or housing inflation, for instance, is felt relatively more by poor and vulnerable households than the rest of the population, leading to regressive effects. In fact, prices of the CPI basket’s subcomponents tracked by TUIK often follow different trajectories due to a host of external and domestic factors (for instance, international price shocks, exchange rate volatility, disruptions to international and domestic supply chains and taxes, among others) that affect different goods and services unevenly.

Figure 3 shows the level and evolution of the inflation experience for the first decile between 2011 and 2020 compared to the top decile (panel A) and the average inflation taking into account 13
<!-- Start of picture text --> 0.08<br>0.07 +<br>0.06 5<br>0.05 ~ —@— Mate<br>0.04 + —O=- Female<br>0.03neeoe+ oy<br>0.01 +<br>0.00 +<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text -->
(2007) also show a strong inverse relationship between earthquake propensity and mortality, we are able to improve on previous research designs by constructing measures of the magnitude and location of earthquakes and of quake propensity that take into account the exponential nature of the Richter scale. We also offer a different explanation for the result: propensity not only affects learning by doing, as they argue, but it also affects the opportunity costs of investing in earthquake mortality reduction.

In our empirical analysis of earthquake mortality over the period 1960 to 2005, we find ample support for the hypotheses generated from our theory. Not only is earthquake mortality lower in democracies, in high quake propensity countries and in rich countries; the conditional political and income effects predicted by the theory are also significant and large. For example, both income and political incentive effects are contingent on the level of earthquake propensity (or, equivalently, country responses to earthquake propensity depend on income and political incentives). Thus, we find that an elevated quake propensity lowers mortality more in relatively rich than in relatively poor countries. Likewise, democracies in which political competitors have greater incentives to provide public goods – older democracies – respond more to an elevated quake propensity and thus experience significantly less earthquake mortality than other democracies or autocracies. Autocracies in which leaders can make credible commitments to party members – autocracies with more institutionalized ruling parties – are better able to implement policies such as building regulations, are therefore better able to respond to an elevated quake risk and thus also experience lower earthquake mortality. The same applies to non-corrupt countries.

In the next section, we discuss why government intervention is necessary for the prevention of earthquake mortality despite the fact that most buildings are privately owned. We then develop our theory in several steps, first explaining why there is an inverse relationship between earthquake propensity and mortality and then demonstrating how income and political incentives condition this effect. After a detailed description of our 4
<!-- Start of picture text --> 0.3<br>i__.! Ethanol Use<br>02 ME Food Use<br>HE Exports<br>0.1<br>_l 7-77<br>-0.4 a.<br>a<br>05<br>1980 1985 1990 1995 2000 2005 2010 2015<br><!-- End of picture text -->
abroad. Hence, in both cases, our choice of instrument should be less relevant. We would therefore expect the 2SLS regression to yield largely insignificant results in countries that allow limited trade or international capital flows.

Tables 7 and 8 show that this is indeed the case. Our approach yields a significant effect of banking sector development on informality only in countries that are highly open to foreign trade (Table 7) and capital flows (Table 8). In these two tables, we split the sample into countries with above-median trade openness (defined as trade in percent of GDP) or capital account openness (defined as in Chinn-Ito 2006) and those with below- median trade openness or capital account openness. In the first-stage regression, the coefficient estimates are indeed somewhat smaller in countries that are less open to trade and capital flows than in those that are more open. In the second-stage regression, banking sector development, as instrumented here, has no statistically significant effect on informality in countries that are less open to trade and capital flows but does have a statistically significant effect in countries that are more open to trade and capital flows.

# **4.2.3 Alternative indicators of financial development**

We have deliberately chosen the number of commercial bank branches per 100,000 adults for the reasons laid out in Section 2. That said, an alternative indicator of financial development that has often been used in the literature is domestic credit to the private sector in percent of GDP. While this indicator is less suitable than the number of bank branches for our purposes, because it is “polluted” by cyclical movements and because it is mainly driven by formal-sector firms, Table 9 shows the results using domestic credit to the private sector (in percent of GDP) as a measure of financial development. The main results are robust: greater financial sector development leads to a lower share of informal output.

# **4.2.4 Alternative indicators of informality**

For our baseline results, we deliberately chose the share of informal output in GDP, for the reasons laid out in Section 2. That said, while conceptually less appropriate for the exercise conducted here, the DGE-based estimates on output informality of Elgin and Oztunali (2012) have some limitations. First, they rely on strong assumptions about the functional form of activity in the informal and formal sectors and about the relationship between formal and informal productivity (Orsi, Raggi, and Turino 2014; Schneider and Buehn, 2016). Second, like the MIMIC approach, they require base-year estimates of the informal economy from another independent study to calibrate the size of the informal economy (Ihrig and Moe 2004).<sup>11</sup> 11 In the case of Elgin and Oztunali (2012), productivity in the informal economy was assumed to grow at the average of the growth rates of formal sector productivity and the capital stock (SolisGarcia and Xie 2018). The model was calibrated by matching the productivity in the informal sector to the size of the informal sector in 2007 of the series reported in Schneider, Buehn, and Montenegro (2010).

12
|**Table 8. Persistence of key ma**|**cro aggregate**<br>**<1.5m**|**s, by popula**<br>**≥1.5m**|**tion**<br>**diff**|**<median**|**≥median**|**diff**|
|---|---|---|---|---|---|---|
|**GDP**|0.55|0.55|0.00|0.57|0.54|-0.03|
||0.02|0.02|0.03|0.02|0.02|0.03|
|**GNI**|0.52|0.51|0.00|0.52|0.51|0.00|
||0.05|0.02|0.07|0.03|0.02|0.05|
|**Trade balance/GDP**|0.46|0.42|-0.05|0.42|0.43|0.01|
||0.03|0.02|0.05|0.03|0.02|0.04|
|**Current account/GDP**|0.25|0.28|0.03|0.22|0.31|0.08**|
||0.04|0.02|0.05|0.03|0.03|0.04|
|**Real interest rate**|0.13|0.24|0.10*|0.16|0.23|0.07|
||0.05|0.03|0.06|0.04|0.04|0.06|
|**Terms of trade**|0.28|0.31|0.03|0.31|0.30|0.00|
||0.06|0.02|0.06|0.04|0.03|0.05|
|**Fiscal balance/GDP**|0.41|0.31|-0.10|0.20|0.33|0.13|
||.|0.04|.|0.10|0.04|0.10|
|**Inflation**|0.18|0.26|0.09**|0.20|0.26|0.06*|
||0.04|0.02|0.04|0.03|0.02|0.04|
|**Total investment**|0.45|0.55|0.10**|0.51|0.55|0.04|
||0.05|0.02|0.05|0.04|0.02|0.04|
|**Employment**|0.42|0.63|0.22|0.42|0.64|0.23**|
||.|0.03|.|.|0.03|0.09|
|**Government consumption**|0.53|0.52|-0.02|0.52|0.52|0.00|
||0.03|0.02|0.05|0.03|0.02|0.04|
|**Private consumption**|0.50|0.52|0.01|0.47|0.53|0.06|
||0.06|0.02|0.06|0.04|0.02|0.05|
|**Exports**|0.46|0.43|-0.03|0.46|0.43|-0.03|
||0.04|0.02|0.04|0.03|0.02|0.04|
|**Imports**|0.53|0.46|-0.07*|0.50|0.45|-0.05|
||0.04|0.02|0.04|0.03|0.02|0.04|
|**Private investment**|0.38|0.41|0.03|0.39|0.41|0.03|
||0.11|0.03|0.08|0.07|0.04|0.07|
|**Public investment**|0.39|0.47|0.08|0.42|0.47|0.05|
||0.21|0.04|0.12|0.12|0.04|0.10|
|**Public expenditures**|0.48|0.36|-0.12|0.23|0.38|0.15|
||.|0.04|.|0.11|0.04|0.09|
|**Public revenues**|0.64|0.43|-0.21|0.52|0.41|-0.11|
||.|0.03|.|0.07|0.03|0.07|

Note: Standard errors below statistics. * p<0.10, ** p<0.05, *** p<0.01 Table **9** investigates the significance of country size for the first-order autocorrelation coefficients of different variables. Column (i) presents the coefficient on size in univariate OLS regressions.

25
typical host country _h_ , _Kh_ , is with an internal solution for _Kh_ iff the equality holds. The first term here is familiar from the closed economy setting, being the post-tax marginal revenue product of capital in _h_ net of the tax-inclusive cost of capital. If there were no second term, this would have the familiar implication that the _KF_ - _METR_ is a sufficient statistic for tax effects on investment in _h_ , with the statutory rate itself having no independent impact. It is the second term which is the novelty introduced by the revenue structure introduced above, capturing the impact on the incentive to invest in _h_ of the consequent cross effect on the net revenues of affiliates elsewhere. If, for example, that aggregate cross-effect (after tax) is negative, then following the closed-economy rule of equating the marginal revenue product of capital to the _KF_ - _METR_ -inclusive costs of capital will lead (given the assumed negativity of the overall cross-effect) to too high a level of investment in _h_ , because doing so ignores the adverse impact of investing there on the profitability of other affiliates in the multinational group.

The key point is that interdependence in the circumstances of the affiliates within the multinational group means that one aspect of investing in _h_ is that may serve as an ‘implicit’ profit shifting device, in the sense of moving taxable profits across countries. Through this route the attractions of investing in _h_ depend (through the terms multiplying _Fh_<sup>_′_)on</sup> statutory tax rates in the host country relative to those _h_ and elsewhere. This has evident similarities with the more traditional idea of what we refer to here as ‘paper’ profit shifting as a matter of tax avoidance realized through artificial transactions. Here, however, the profit shifting is tied directly to, and implicit in, the real investment decisions that are the multinational’s core business.<sup>15</sup> The way in which taxation consequently affects the levels and allocation of investment

> 15One other aspect of the difference between ‘implicit’ and ‘paper’ profit shifting as the terms are used here is worth noting: while both shift profits in the sense of increasing them in some countries while reducing them in others, implicit profit shifting will generally affect the multinational group’s consolidated profit, whereas the essence of ‘paper’ profit shifting is that (apart from any cost associated with the artificial arrangements themselves) it does not.

8
**bcvIi;** SPECIAL Topic **1:** PROJECnNG AIDS **MORrALrY**

<!-- Start of picture text --> Figure S6. Total Population Cbillions)<br>Sub-Saharan Africa, 1985-2150<br>3.2<br>3<br>2.8<br>2.6<br>2.4<br>2<br>i.e<br>1.6<br>1.4<br>1.2<br>0.8<br>0.4<br>3.4 k9bt2bO 1 2d20 *%A2d40 1 20b 1% 20o 321b0 I 2120 l21'40<br>0.4 2010 0 2 20 2 0 2 110 2 0 21<br>No AIDS + AIDS<br><!-- End of picture text --> Figure S7. Life expectancy Cyears) **StIS,a5heran Ar Ica. 1985-2150** <!-- Start of picture text --> so N<br>75<br>70<br>90l-0 205W 290 223<br>65<br>so<br>53<br>50 I-<br>ligiso 20L 0 22-30 204-SO 2 204-)Wj90 20 10 211.30 21 50<br>o NDAIDS + AIDS<br><!-- End of picture text -->
Financial literacy is higher among the younger and the more highly educated populations and lower in rural areas and among those living outside of major cities. Importantly, we find that financial literacy is significantly positively related to retirement planning and the use of private pension funds and products, with financially literate individuals being 25%–30% more likely to plan for retirement using private pension funds.

The paper proceeds as follows: Section 2 describes our dataset, the main variables, and presents summary statistics; Sections 3 and 4 presents the empirical strategy and reports the results; and Section 5 concludes.

# **2. The Dataset**

We use information from the second wave of a dataset collected via face-to-face interviews<sup>3</sup> of 1,400 individuals in June 2009. The sample was designed to be nationally representative at the individual and the household level and was weighted by gender, age, education, and region (excluding the North-Caucasian [Chechnya] federal district).

As shown in the first column of Table A1, our sample consists of 42.2% male respondents, consistent with national census averages (Russia Census, 2002), with the average age of the sample at around 46 years.<sup>4</sup> Most individuals (62.3%) reported living in households with three or more individuals, with 13.5% living alone. A majority of our sample (56.3%) is in the workforce, and 31.9% of the sample live in urban regions, defined as settlements with a population greater than 500,000 (14.2% in Moscow, St. Petersburg, and nearby areas). The education level of individuals in our sample is higher than the level in comparative emerging markets: only 8.4% of the sample has less than a secondary education, and 22.7% have initiated or completed a higher education degree program.

> 3 It is interesting to note that most comparable financial literacy surveys, such as those conducted in the U.S. and other developed countries, have been conducted by telephone. We speculate that this might affect responses, in particular, the rate of  ̳Do not know‘ answers.

> 4 Summary statistics by gender, age, and education (% with secondary degrees) are very similar to those found in the Russia Longitudinal Monitoring Survey (LSMS), 2002, as well as the Russian National Census, 2002.  Relative to the census data, however, our survey appears to under-represent individuals in the highest income bracket.

4
The strong emphasis of public debt management on improving the currency and interest composition of the outstanding debt resulted in major changes in the structure of public debt in the later 1990s (Table 3.9). Non-marketable debt declined to 15 percent in 2001, while the share of treasury bills became negligible. Variable cost bonds, comprising variable-rate, foreigncurrency-indexed and inflation-indexed bonds, fell from 47 percent in 1995 to 29 percent of the total in 2001. Medium and long-term fixed-rate bonds emerged as major instruments and expanded from less than 2 percent in 1995 to reach 55 percent of the total in 2001. The average residual maturity of domestic tradable debt exceeded 5.6 years in June of 2001 and its duration and modified duration reached 4.0 and 3.8 years respectively (Ministry of Finance 2001:7-8). The volume of trading in Greek government securities also expanded rapidly. The monthly volume in the second half of 2003 amounted to 55 billion euro, corresponding to an annual volume of 440 percent of GDP.

Foreign debt experienced a substantial decline in 2001, falling to less than 6 percent of total debt, mainly because of the adoption of the euro and the reclassification as domestic of all existing foreign debt that was denominated in currencies of Euro zone countries. Cross currency swaps further reduced the exposure to foreign currency risk of debt that is denominated in other currencies.

|**Table 3.9: Composition of General G**|**overnmen**<br>1995|**t Debt, 19**<br>2000|**95-2001**<br>2001|
|---|---|---|---|
|Treasury Bills (% of Total)|26.3|3.4|1.5|
|Short-Term Fixed-Rate Bonds (% of Total)|3.1|0.0|0.6|
|Medium-Term Fixed-Rate Bonds (% of Total)|1.8|17.3|17.6|
|Long-Term Fixed-Rate Bonds (% of Total)|0.0|24.7|36.2|
|Variable Cost Bonds (% of Total)|47.2|36.4|28.8|
|Non Marketable Debt (% of Total)|21.5|18.2|15.4|
|Total|100.0|100.0|100.0|
|Domestic (% of Total)|79.4|75.8|94.4|
|Foreign (% of Total)|20.6|24.2|5.6|
|Total Central Government Debt in GRD billion|31982|48337|52275|
|Intergovernmental Debt in GRD billion|2379|4916|5381|
|Total General Government Debt in GDR billion|29603|43421|46894|
|Total Central Government Debt (% of GDP)|119.0|116.7|117.1|
|Total General Government Debt (% of GDP)|110.2|104.8|105.0|
|GDP in GRD billion|26884|41407|44638|

Source: Estimated on the basis of Bank of Greece, Ministry of Finance, IMF and OECD data.

Estimating the share of different types of investors in holding government debt in 2000 is even more difficult than for earlier periods (Table 3.10). This is both because of the absence of sectoral financial accounts and because of the use of nominee accounts in the central depository system. The holdings of banks, mutual funds, insurance companies and pension funds are estimated from published data on the assets of these institutions in the same fashion as for 1995. The estimate for retail investors is based on the value of outstanding saving bonds. The reported 37
# **1 Introduction**

It is a well recognized problem that policy makers must make decisions before all data about the current economic environment are available. Given this reality, there is considerable interest in short-term forecasting and nowcasting using intra-period data releases. For example, the forecaster can provide an estimate of GDP this quarter using other data that are available at a monthly frequency. This technique is called nowcasting, or predicting the present. Giannone et al. [2008] lays out three tenets of nowcasting. First, many data series are used. Second, nowcasts are updated as intraperiod data become available. Finally, nowcasting “bridges” higher frequency data releases with the nowcast of the lower frequency series of interest. This study is similar in spirit to that of Giannone et al. [2008]. However, while Giannone et al. [2008] are concerned with nowcasting GDP using a large number of economic data series, this paper nowcasts price series using Internet search keyword data from Google Trends<sup>1</sup> . Furthermore, we do not attempt to “bridge” higher frequency data with lower frequency data explicitly as part of a model. The Google Trends data are not all systematically available at a higher frequency than the series we wish to forecast. Instead, we are more concerned with the efficient aggregation of many series to help improve our nowcasts.

There are three main contributions of this study. First, it focuses on the countries of Central America. Almost the entirety of the nowcasting literature focuses on developed countries with one notable exception in Carri`ere-Swallow and Labb ́e [2013]. Second, this is a large scale study, approaching the problem of nowcasting with Google Trends from a data mining perspective rather than one solely grounded in economic theory. This approach gives us insights that will be useful for forecasters who wish to pursue similar ends. Third, we introduce methods from the statistical learning literature to compute the Google Trends keyword search index that are not yet commonly used in forecasting studies.

Given the large number of series included in this study, we rely heavily on automatic model identification procedures. Despite this potential shortcoming, we find that Google Trends can improve our ability to forecast certain series. These findings are notable and may be worth pursuing in more detail. The outline of the paper is as follows. Section 2 reviews some of the literature for nowcasting and the use of Google Trends data in forecasting. Section 3 introduces the data and includes a section that discusses the challenges of working with Google Trends data for the countries of Central America. Section 4 explains the framework used for forecasting and evaluating forecasts. Section 5

> 1 `http://google.com/trends`

2
expenditures in rural education, health, and other social sectors. All in all, rural public expenditures are disaggregated into 12 categories. The expenditure groups (including in brackets their respective average country share in total public expenditures for the year 1995) are the following: marketing assistance (28), irrigation (7.7), technological generation and transfers (4.7), soil conservation (1.2), forest promotion (0.09), plant and animal sanitation (4.2), communications and information (1.6), “focalized” expenditures directed to particular commodities (4.9), integral rural development (IRD) (4.1), rural public infrastructure (16.3), rural education, health, and other social services (25.5), and others (1.7).

To allocate the above expenditures into private versus public goods we first account for items that clearly fall into one or the other category. Next we deal with those categories that fall into a gray area where it is difficult to determine a priori whether they correspond to public or private goods. In these cases we have looked into more detailed components falling into these categories in the various country statistics available. This allowed us to obtain an educated guess as to what proportion of these expenditure items should be approximately allocated to public or private goods. Finally there are some minor items for which we were not even able to have a reasonable guess. In the latter cases we simply use a 50-50 rule.

Among the items that clearly qualify as public goods are technology generation and transfers, soil conservation, plant and animal sanitary protection, communications and information services, rural roads, and social services (public education, health, and anti-poverty programs). The above expenditures were classified as public goods, because they increase the supply of services that generate important positive externalities (technology generation and diffusion, soil conservation, plant and animal sanitation), were expenditures directed to at least in part palliate the impact of poverty (public education, health, and social programs), which can be regarded as a public “bad”, and expenditures such as communication infrastructure and public roads that may roughly satisfy both criteria for pure public goods (non-excludability and nonrivalry).

Items that were considered private goods are commodity-specific or focalized expenditures, marketing promotion (excluding expenditures in communications and market information that are among the public goods items), and irrigation expenditures. An important portion of “market promotion” includes direct subsidies and credit subsidies to mostly well-off producers. Similarly, most of the irrigation services are completely or almost completely 15