rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 8**

# **Pension System Allocation in Domestic and Foreign Assets**

This figure shows the asset allocation of the pension system as a whole in domestic and foreign instruments for the entire sample period (July 1996 to December 2005). Allocation in foreign assets has increased. The major constraint for asset allocation of pension funds in foreign instruments has been quantitative limits imposed by the Central Bank of Chile according to the pension law (straight line shown on figure).

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70% Domestic<br>60%<br>50%<br>40%<br>30%<br>20%<br>10% Foreign<br>0%<br>Jul-96 May-97 Mar-98 Jan-99 Nov-99 Sep-00 Jul-01 May-02 Mar-03 Jan-04 Nov-04 Sep-05<br>Figure 9<br>Pension System Allocation by Broad Asset Class<br>This figure shows the asset allocation of the pension system as a whole in four broad asset classes, as<br>percentage, for the entire sample period (July 1996 to December 2005). Whereas asset allocation in the domestic<br>market has been mostly through fixed-income instruments, allocation in foreign investments has mostly been<br>through variable-income instruments.<br>100%<br>Domestic Variable Income<br>90%<br>80%<br>70%<br>Domestic Fixed Income<br>60%<br>50%<br>40%<br>30%<br>20%<br>Foreign Variable Income<br>10%<br>Foreign Fixed Income<br>0%<br>Jan-00 Jan-00 Jan-00 Jan-00 Feb-00 Feb-00 Mar-00 Mar-00 Mar-00 Mar-00 Apr-00 Apr-00<br> Fraction of Total Pension System Holdings<br> Fraction of Total Pension System Holdings<br><!-- End of picture text --> This figure shows the asset allocation of the pension system as a whole in four broad asset classes, as a percentage, for the entire sample period (July 1996 to December 2005). Whereas asset allocation in the domestic market has been mostly through fixed-income instruments, allocation in foreign investments has mostly been through variable-income instruments.
# **III. Results**

This section presents the micro-simulation results for each model variation proposed in the _Assessment_ sub-section. The performance of the different variations is measured as their capacity to predict three different aspects of interest: poverty, inequality, and changes along the income distribution. Each aspect's mean squared error (MSE) is calculated to facilitate the analysis. Hence, the best predicting model is the one with the lowest bias compared to the actual value of each measure and for each year. Notice that by using the MSE, top and bottom bias are equally weighted, then the model will be equally poor if it highly overestimates or underestimates these measures. Two time horizons are of interest for the analysis: the short run, which comprises the immediately following two simulated years (2017 and 2018 for most of the countries), and the middle run, which contains the last two simulated years (2019 and 2020 when available).

**Table 2 Public Transfers Programs Excluded for 2020 Assessment, by Country**

|**Country**|**Excluded COVID-19 relatedprograms**|
|---|---|
||No conditional cash transfers programs, this includes:<br>-<br>Cash social bonuses|
|Bolivia|-<br>Transfers from "Familia"<br>-<br>Transfers from "Canasta familiar"<br>-<br>Transfers from "Universal"|
||-<br>Disability bonuses|
|Brazil|Simulated transfers from "Bolsa Familia"<br>-<br>Transfers from "Bono de Emergencia COVID-19"|
|Chile|-<br>Transfers from “Ingreso Familiar de Emergencia”<br>-<br>Bonus "Ayuda Familiar"<br>-<br>Transfers from “Proteger”|
|Costa Rica|-<br>Cash transfers due to COVID-19 pandemic<br>-<br>Non-cash transfers due to the pandemic<br>-<br>Transfers from "Plan Quedate en Casa"|
|Dominican Republic|-<br>Transfers from “FASE – Fondo de Asistencia Solidaria al Empleado”<br>-<br>Transfers from “Asistencia al Trabajador Independiente”|
|Paraguay|-<br>Transfers from Tekopora<br>-<br>Transfers from Pytyvo<br>No conditional cash transfers programs, this includes:<br>-<br>Transfers from “Bono Yo me quedo en casa”|
|Peru|-<br>Transfers from "Bono Independiente"<br>-<br>Transfers from "Bono Rural"<br>-<br>Transfers from "Bono Familia"|

Source: Own elaboration based on SEDLAC.

15
Table 1: Summary Statistics

||N|Mean|SD|Min|Med|Max|
|---|---|---|---|---|---|---|
|_Panel A: Firm Level_|||||||
|Months Active in CDR|2307|33.8210|14.80|1|45|45|
|Districts Active in CDR|2307|38.4252|41.30|1|24|259|
|Total Subscribers|2307|52.2466|287.67|1|4|10686|
|Total Calls|2307|1012.5899|8946.95|0|20|384749|
|Segment: Trade and Manufacturing|2307|0.4157|0.49|0|0|1|
|Segment: Construction|2307|0.1903|0.39|0|0|1|
|Segment: Transportation|2307|0.1183|0.32|0|0|1|
|Segment: Security|2307|0.0152|0.12|0|0|1|
|Segment: Finance|2307|0.0121|0.11|0|0|1|
|Segment: IT|2307|0.0056|0.07|0|0|1|
|Segment: Other|2307|0.2427|0.43|0|0|1|
|_Panel B: District Month Level_|||||||
|Total Killed|10642|1.396|6.29|0|0|246|
|District Insecure (=1)|10642|0.118|0.32|0|0|1|
|Total Firms|10642|81.402|127.82|0|41|1383|
|Total Subscribers|10642|391.364|1444.00|0|100|21278|
|Total Calls|10642|20914.475|154207.29|0|1594|2636652|

32
|**Label**<br>**Description**<br>Number of Rooms<br>Number of habitable rooms in the dwelling<br>Quality of Roof<br>Quality of the material used for the dwelling’s roof<br>(1=Low quality, 1=Medium quality , 3=High quality)<br>Quality of Wall<br>Quality of the material used for the dwelling’s walls<br>(1=Low quality, 1=Medium quality , 3=High quality)<br>Quality of Floor<br>Quality of the material used for the dwelling’s foors<br>(1=Low quality, 1=Medium quality , 3=High quality)<br>Type of Toilet<br>Type of main toilet facility (1=No facility, 2=Flush<br>toilet, 3=Improved pit latrine, 4=Uncovered pit la-<br>trine, 5=Other)<br>Main source of Lighting<br>Main<br>source<br>of<br>lighting<br>(1=Electricity/gas,<br>2=Kerosene, 3=Candles/battery, 4=Other)<br>Main heat source for cooking<br>Main heat source for cooking (1=Firewood, 2=Char-<br>coal, 3=Kerosene/oil, 4=Electricity/gas, 5=Other)<br>Type of garbage disposal<br>Type of garbage disposal (1=In bins collected by pub-<br>lic service; 2= Disposal in compound; 3=Discarded in<br>empty lots/streets/rivers)<br>TV<br>Household owns a TV<br>Fridge<br>Household owns a Refrigerator<br>Stove<br>Household owns a Stove<br>Bicycle<br>Household owns a Bicycle<br>Car<br>Household own a Car<br>Iron<br>Household own an Iron<br>Confict<br>Dummy variable equal to 1 if in the Local Government<br>Area at the time of GHS survey there have been fa-<br>talities due to confict events as reported in the Acled<br>Dataset|
|---|
|**Variable**<br>dwel<br>rooms<br>dwel<br>roof<br>dwel<br>wall<br>dwel<br>foor<br>dwel<br>toilet<br>dwel<br>fuellight<br>dwel<br>fuelcook<br>dwel<br>gdisp<br>own<br>tv<br>own<br>fridge<br>own<br>stove<br>own<br>bcycle<br>own<br>car<br>own<br>iron<br>confict|

28
in club constitutions include: i) members are expected to be sober, particularly during club meetings; ii) members are expected to be punctual during meetings (or face a monetary penalty); iii) membership is often denied to regionally mobile applicants; iv) in some clubs, members should have the means to procure their own inputs.

# **2.2 Tobacco Clubs: Services**

We begin with a brief description of tobacco farming in Malawi. The aim is to better understand the benefits behind the services provided by tobacco clubs. Burley tobacco farming is a very labor intensive activity and requires four times more labor than maize and twice more than groundnuts. The tobacco production cycle begins in September-October, just before the rainy season. Seeds are first sown in seedbeds for about two months, when they are transplanted to the field. Before transplantation, the soil is usually sterilized by burning or steaming, chemicals are used to control diseases, weeds, and insects, and fertilizer is applied. Later, in late January/early February, the tobacco is topped (flower stalks are broke off so that the energy of the plant is directed to the leaves). While topping, the growers may also pull off some of the largest immature leaves on the stalk to prevent waste of water and nutrients.

Tobacco is ready for harvesting around late February/early March. Burley is harvested using machete-type knifes. After cutting, the large end of the stalk is fixed onto the sharpened end of a stick, which—when loaded with a number of stalks—is hung by hand in a tobacco barn for curing. Unlike flue- and fire-cured brands, which require firewood for leaf curing, burley tobacco is instead cured by air in open-sided thatched huts. The skill of the farmer on the curing process plays a major role in the final quality of the leaf. Once cured, the tobacco leaves are sorted by the farmer according to stalk position and leaf characteristics and packed into bales. The preparation of burley in bales requires special physical aptitude and specific knowledge. The selling season starts in April and ends in September-October.

The services delivered by the clubs increase the performance of members in key stages of the farming process. In order to document them, we use two sources of data. As before, some evidence comes from the Tobacco Clubs Survey of 2007. Other pieces of evidence come 10
model indicates that growth estimates are not significantly different before and after the transition, while during the transition itself, growth was slightly smaller.

# **VI. Additional Features**

An analysis of additional features can indicate the degree of robustness of the previous results. It is important to understand the arguments related to the endogeneity of the dummies used in the previous methodology.

In this kind of empirical analysis, several arguments support the presumption that the ownership dummies are endogenous. As was described by Ros (1999), the decision to privatize may not be viewed as an exogenous event that can be considered “fixed” in repeated sampling. Besides, there is the possibility of selection bias, though it can be argued that this possibility does not exist in this particular case.<sup>16</sup> Several different hypotheses can make the case for endogeneity, such as the theory that the government privatizes those firms that are more likely to be sold. Secondly, countries under worsening financial conditions may have higher incentives to enact these reforms. Additionally, private investors may be more interested in firms that allow them to earn higher returns on their investment.

The basic approach is to use Instrumental Variables (IV) procedures. However, it is difficult to find proper instruments. In this case, macroeconomic variables were selected, as they are linked to the factors that cause governments to make these kinds of reforms, and they are, in general, independent of the variables under study. However, an additional problem arises given the fact that, in most of the countries, the companies were privatized in different years. To accommodate this variation, additional instruments are required, ideally at the state and/or city level. As collecting this state or municipal level data would be very difficult, the IV approaches were run using only the federal level variables, and the results are consistent with the previous analysis.<sup>17</sup> Moreover, most of the coefficients resulted in higher absolute values, providing some evidence that the results of this paper are a lower bound than the total effect of the reforms.

> 16 See Ros (1999, pp. 79) for a detailed explanation of this argument.

> 17 These tables are available upon request.

_24 of_ 28
# **3.7 Improving multidimensional analysis**

Multidimensional policy analysis is another area where further progress could be made. After a multidimensional well-being indicator is constructed and its values are computed, there remains to decide what analyses to conduct with the indicator and what results should be reported to support evidence-based policy making. From this perspective, merely reporting that monetary poor individuals tend to also cumulate non-monetary deprivations falls short of what can be achieved with such indicator.

One key aspect of prioritarian policy making is to target individuals with low wellbeing. Another aspect is to identify the policy that delivers the largest increase in wellbeing at the lowest cost. These aspects could benefit from careful multidimensional policy analysis.<sup>72</sup> Admittedly, policy making is routinely done dimension-by-dimension, with limited interractions between, say, the health ministry and the housing ministry. Such approach to policy making has little use for summary indicators of well-being. However, this dimension-by-dimension approach is bound to be suboptimal, if only because it does not guarantee that individuals with low well-being are prioritized. We argue it could be worth standardizing multidimensional poverty analyses so as to provide the necessary information for more efficient policy-making.

In this section, we merely illustrate with a toy-example the added value that multidimensional analysis could bring to prioritarian policy making. Assume for simplicity that individual well-being depends on two dimensions. Further assume that individual i has low well-being only when she cumulates deprivation in both dimensions, namely when (si1, si2) = (1, 1). The country has three regions A, B and C, each with three individuals whose outcomes (si1, si2) are as follows:

- Region A: (1, 0), (1, 0) and (0, 1),

- Region B: (1, 1), (1, 0) and (0, 0),

- Region C: (1, 1), (0, 1) and (0, 0).

Assume that the government can enact a policy j that can pull an individual out of deprivation j. This policy can be easily targeted to individuals deprived in dimension j. For instance, an electrification policy can easily be targeted at individuals without access to electricity.

To serve as benchmark, we first analyze this country using a dashboard approach, which summarizes outcomes in each dimension:<sup>73</sup> • Region A: two individuals deprived in dim 1 and one individual deprived in dim 2,

> 72See for instance Azevedo and Robles (2013) on the multidimensional targeting of social protection programs.

> 73To be sure, dashboards can also contain information on cumulative deprivation (Decancq, 2022).

36
20

# **Figure 7**

<!-- Start of picture text --> Education Expenditure and GDP<br>Learning Scores and (log) GDP per capita (per capita)<br>40000 32000<br>24000 28000<br>16000<br>12000 24000<br>8000<br>20000<br>4000 16000<br>2400 12000<br>1600<br>1200 8000<br>800<br>4000<br>400 0<br>100 200 300 400 500 600 0 500 1000 1500 2000 2500<br>SCORES EXPENDITURE<br>source: Herrera and Pang (2006)<br>GDPPC GDPPC<br><!-- End of picture text --> .

# **Figure 8**

<!-- Start of picture text --> Quality of Education and Educational Spending<br>(conditioned on GDP per capita)<br>500<br>450<br>400<br>350<br>300<br>250<br>200<br>150<br>400 800 1200 1600 2000<br>Spending<br>Scores<br><!-- End of picture text --> Source: Herrera and Pang (2006)
interact with other forms of wage rigidity, such as wage indexation, although in this case the consequences of operating in a low-inflation environment become less clear. On the one hand, indexation mechanisms might be easier to implement when inflation is low, considering that in a low-inflation environment the predictability of inflation is higher. On the other hand, indexation rules might become less necessary when inflation is under control, and the fight of central banks against indexation mechanisms are more likely to succeed.<sup>2</sup> This paper attempts to shed some light on the changing importance of real wage rigidities in Latin American countries, by looking at the evolution of real wage adjustments over the business cycle. Our interest in examining changes in the real wage cyclicality determines the methodology we apply to the data. In contrast to the previous macro literature that examines the cyclical movements of wages,<sup>3</sup> we can assess the time-varying nature in the comovement of real wages and output by estimating a vector autoregression model with both, time-varying coeffi cients and residuals volatilities. This model has become quite a popular tool in macroeconomics over the last few years, in studies addressing questions related to the evolution of the structure of the economy and the volatility of the shocks (see Primiceri, 2005; Canova and Gambetti 2009; Galí and Gambetti 2009). Our paper is a first attempt to apply these models to assess how the cyclical behavior of wages varies over time. We focus on the four Latin American economies—Brazil, Chile, Colombia and Mexico—for which suffi ciently long time-series of quarterly wage data for the manufacturing sector are available. The samples for Brazil and Mexico span from the early 1980s to 2010. In Colombia and Chile, the series are shorter, starting in 1990 and 1993, respectively.

> A fundamental advantage of the approach we adopt in this paper is that it properly accounts for the dynamic properties of the underlying data series and their comovement, as well as for changes in the underlying relationship between the different variables. nominal and real wage rigidities are pervasive when inflation is low, i.e., during the late 1990s and 2000s.

> 2 Messina and Sanz-de-Galdeano (2014) study the impact of disinflation on the extent of wage indexation in Brazil and Uruguay. While the two countries featured high indexation during the high inflation period, their responses to the disinflation of the 1990s was different. Wage indexation in Uruguay receded quite rapidly, while it was fairly persistent in Brazil.

> 3 See Abraham and Haltiwanger (1995) for an excellent literature review of the empirical literature and Messina, Strozzi and Turunen (2009) for a recent application to OECD countries.

2
(Watts, 2003). With this in mind, it is clear that the ways in which nodes are connected, and the role of specific nodes in integrating the network, need more understanding. This brings us to the issue of centrality.

# **4.3.4 Betweenness and centrality**

The extent to which a node lies between other nodes and has, or has not, high betweenness is linked to the broader issue of centrality or the importance of specific nodes in a network. Table 9 reports the betweenness shares - the extent to which nodes lie between other nodes - for each of the components (the columns sum to 100).<sup>16</sup> The betweenness shares are highest for politicians and political parties, as might be expected. Strikingly, betweenness is also high for SOEs; in Hungary and the Slovak Republic the share is higher than for either political parties or politicians. Private firms have low betweenness shares across the board.<sup>17</sup> Russia also looks different. The share for both political parties and political individuals is significantly lower than elsewhere, while the share for SOEs is significantly higher. If we extend this analysis to the neighbours of both SOEs and private firms, the betweenness of both SOE and private firm neighbours is particularly high in Russia (77% and 47% respectively).

# **4.3.5 Summary**

All countries are marked by the presence of a big island. The second largest component is very small in all cases. But the properties of the big island differ significantly across – – countries. The most obvious difference reflecting differences in political systems concerns political parties. These differences relate not only to the number and scale, but also the nature of connections flowing to and from those parties. Mostly, the links are from political individuals to parties. SOEs are in almost all instances a significant component of the big island. In autocratic contexts, such as Russia (but also its neighbours, Belarus and Kazakhstan)<sup>18</sup> , SOEs have relatively high betweenness shares. In the case of private firms, betweenness shares are lower than for SOEs but there is also less difference across 16The betweenness share is defined as the ratio of the sum of the betweenness of all nodes in a particular group (parties, SOEs, etc.) over the total network betweenness, i.e. over the sum of the betweenness of all the nodes in the network.

> 17Regarding closeness – which measures how close nodes are to one another and hence of the ability to connect to many, even when not between – shares are higher for both political individuals and other individuals across all countries. For both private firms, and particularly SOEs, the closeness shares are very much higher in Russia than elsewhere.

> 18The betweenness share for Belarus and Kazakhstan is 60% and 50% respectively, while the closeness shares are 17% and 10% - all significantly higher than in either Spain or the other economies covered in this paper.

16
Commerce, tourism and transport have been the fastest growing sectors in the past 10 years, while the share of the primary sector declined. However, during the Argentina crisis, commerce, tourism and transport were also the sectors that grew the least. The primary sector, on the other hand is more vulnerable to adverse climatic conditions and commodity price shocks. On the demand side, private consumption continues to be the main driver of the economy.

**Figure 3: Sectoral shares of real GDP, 2000-2012 Figure 4: Real sectoral growth rates, 2001-2012**

<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Other services<br>Commerce, tourism, transport<br>Manufacturing, electricity, gas, water, construction<br>Primary sector<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012<br><!-- End of picture text --> <!-- Start of picture text --> Source: BCU<br><!-- End of picture text --> <!-- Start of picture text --> 20<br>10<br>0<br>200120022003200420052006200720082009201020112012<br>-10<br>-20<br>Primary sector<br>Manufacturing, electricity, gas, water, construction<br>Commerce, tourism, transport<br>Other services<br>Source: BCU<br>percent<br><!-- End of picture text --> However, despite these positive developments, significant risks remain. The economy remains vulnerable to shocks related to Uruguay’s trade dependence on Argentina and Brazil. While Uruguay has diversified its export markets and reduced export concentration, merchandise exports continue to be concentrated in primary products and the five main trading partners concentrate 50 percent of total merchandise exports. Brazil is still the country’s main trading partner with 19.9 percent of total merchandise exports in 2012, while Argentina is in the fourth place with 5.7 percent of total merchandise exports. A hard landing in Argentina and lower growth in Brazil could have a significant negative impact on Uruguay’s economy.

A possible worsening of the Euro Zone crisis and a prolonged economic downturn in developed countries would likely have negative effects on Uruguay. A potential global economic slowdown would affect Uruguay via similar channels as during the 2008/2009 crisis, namely: lower external demand and a drop in commodity prices, lower FDI inflows and heightened risk aversion of investors leading to a reduction in short-term private portfolio inflows, loss of access to global financial markets leading to difficulties in securing financing and increased liquidity risks; and lower fiscal revenues due to reduced economic activity and higher expenditure due to counter-cyclical stimulus. An analysis carried out in 2012, estimated real growth in Uruguay to fall by about 1.7 percentage points under a mild crisis scenario and almost 5 percentage points in the case of a global recession (World Bank 2012).

In addition, inflationary pressures continue to be a recurring problem for the economy. Although prices were successfully stabilized in the 1990s, strong inflationary pressures, including rising 4
**Figure 4 – Scatter plots of relative (%) and absolute (acres) bias over plot size (acres)**

<!-- Start of picture text --> Plot Size & Relative Bias (%)<br>Ethiopia Tanzania Nigeria<br>100 100 100<br>50 50 50<br>0 0 0<br>-50 -50 -50<br>-100 -100 -100<br>0 2 4 6 0 1 2 3 0 5 10 15 20<br>CR(acres) CR(acres) CR(acres)<br>Plot Size & Absolute Bias<br>Ethiopia Tanzania Nigeria<br>1 1 1<br>.5 .5 .5<br>0 0 0<br>-.5 -.5 -.5<br>-1 -1 -1<br>-1.5 -1.5 -1.5<br>0 2 4 6 0 1 2 3 0 5 10 15 20<br>CR(acres) CR(acres) CR(acres)<br>Bias (acres)<br><!-- End of picture text --> 45