rafmacalaba/datause-encoder-data huggingface dataset:
-27-

evidence gleaned from growth accounting exercises which, in their quantification of the sources of growth, often attribute a small role to capital. Thus, Denison, in his well-known international investigations, "explains" only 18 per cent of Western Europe's growth from 1950 to 1962 through capital formation, and, even more surprisingly, limits the contribution of investment in Japan's case to barely 24 per cent for the years 1953-71 [Denison, 1967; Denison and Chung, 1976]. More recent estimates, however, suggest somewhat larger contributions from capital to - growth for the years 1950-73, these lie between 32 and 50 per cent, depending on the country, with much higher figures for earlier and later periods [Maddison, 19871.

Not too much should be read into these various results, given the well-known limitations of the growth accounting methodology, particularly as applied to capital [Scott, 19761, and its inability to incorporate dynamic interactions. Better evidence can be obtained from economic historians who have frequently pointed to the leading role of investment in the growth process: "The evidence ... does not suggest close correlation between rates of growth and levels of capital formation between countries. It is generally true, however, that within any one country increases in the proportion of resources devoted to capital formation tended to be correlated with increases in growth rates" [Milward and Saul, 1977, p.519, emphasis added]. And this summary judgment is broadly confirmed by most, if not all, the careful studies of individual countries' experience, contained in the Cambridge Economic History of Europe [Mathias and Postan, 1978] or by separate monographs (Fua, 1965; Matthews, 1968; Ohkawa and Rosovsky, 1973; Clismann et al., 1978; Minami, 19861.
# TABLE IV

# Teacher-Level Impact

(Continued)

||(1)|(2)|(3)<br>All te|(4)<br>achers|(5)|(6)|(7)|(8)|(9)<br>Target te|(10)<br>achers only|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||_Y_2|||_Y_3|||_Y_2|||_Y_3||
||Control|ITT|ITT|Control|ITT|ITT|Control|ITT|ITT|Control|ITT|ITT|
||mean|(simple<br>dif.)|(dif. in<br>dif.)|mean|(simple<br>dif.)|(dif. in<br>dif.)|mean|(simple<br>dif.)|(dif. in<br>dif.)|mean|(simple<br>dif.)|(dif. in<br>dif.)|
|Financial problems|0.50|-0.09<sup>_∗∗∗_</sup>||0.56|-0.09<sup>_∗∗∗_</sup>||0.48|-0.13<sup>_∗∗∗_</sup>||0.51|-0.16<sup>_∗∗∗_</sup>||
||[0.50]|(0.02)||[0.50]|(0.02)||[0.50]|(0.02)||[0.50]|(0.03)||
|Satisfed with|0.60|0.09<sup>_∗∗∗_</sup>||0.60|0.07<sup>_∗∗∗_</sup>||0.60|0.17<sup>_∗∗∗_</sup>||0.65|0.13<sup>_∗∗∗_</sup>||
|total income|[0.49]|(0.02)||[0.49]|(0.02)||[0.49]|(0.02)||[0.48]|(0.02)||
|Absent from school at|0.14|-0.00|-0.02|0.13|0.01|-0.00|0.12|-0.03<sup>_∗_</sup>|-0.04<sup>_∗∗_</sup>|0.10|0.00|-0.01|
|least once last week|[0.34]|(0.01)|(0.01)|[0.33]|(0.01)|(0.01)|[0.32]|(0.02)|(0.02)|[0.31]|(0.02)|(0.02)|
|_Baseline controls_||no|yes||no|yes||no|yes||no|yes|

_Notes:_<sup>_∗_</sup> _p <_ 0 _._ 10,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01. The table reports intent-to-treat effects at the teacher level. Column (1), (4), (7) and (10) report averages values in the control group for reference. Columns (2), (5), (8) and (11) reports treatment effects based on a model that includes district-triplet fixed effects, which are the strata used for randomization. Columns (3), (6), (9) and (12) report treatment effects based on a model that includes district-triplet fixed effects and baseline values as controls. Within-group standard deviations reported in brackets in columns (1), (4), (7) and (10). School-level clustered standard errors of the estimated treatment effects are reported in parentheses in columns (2), (3), (5), (6), (8), (9), (11) and (12). Empty cells in columns (3), (6), (9) and (12) correspond to variables for which we do not have baseline values.
# **4. Pure Premium Rates**

Under mNAIS premium rates are set at the crop/district level and so the pure premium rate is taken to be an actuarially sound estimate of the expected weighted average loss cost across the crop/district, that is, the expected claim payment divided by the sum insured, for the coming crop season. At this stage the reader may find it helpful to review Figure 1, which offers an overview of how the following steps fit together.

# _Loss Costs_

# <u>Description of step</u>

First calculate the historical loss costs for each insurance unit with the threshold yields calculated in Section 3. This is the same as the calculation of loss cost histories for the calculation of the indemnity level (see column [4] of Table 7), except that the indemnity limit is no longer necessarily 90%. For example, the indemnity level for cotton in district GJ8 of Gujarat was calculated to be 70% (see column [3] of Table 9). Threshold yields with indemnity level of 70% are reported in column [2] of Table 10 and historical loss costs, based on these threshold yields and using de-trended data, are given in columns [3] to [12] of Table 10.

Table 10. Cotton in district GJ8 of Gujarat: historical loss costs

|**Insurance**<br>|**Threshold**<br>**yield**||||**H**|**istorical**|**loss co**|**st**||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Unit**|<br>**(kg/ha)**|**1998**|**1999**|**2000**|**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|**2007**|
|[1]|[2]|[3]|[4]|[5]|[6]|[7]|[8]|[9]|[10]|[11]|[12]|
|GJ8_1|1,951|0%|21%|78%|67%|0%|0%|0%|0%|0%|0%|
|GJ8_2|1,842|0%|0%|16%|43%|82%|0%|0%|0%|0%|0%|
|GJ8_3|1,274|0%|46%|54%|44%|64%|0%|0%|0%|37%|17%|
|GJ8_4|1,543|0%|0%|56%|57%|75%|0%|0%|0%|0%|0%|
|GJ8_5|1,918|0%|42%|58%|0%|2%|0%|0%|0%|0%|0%|
|GJ8_6|1,900|0%|18%|48%|0%|13%|0%|0%|0%|0%|0%|
|GJ8_7|1,570|33%|29%|66%|36%|53%|0%|0%|0%|0%|10%|
|GJ8_8|1,576|0%|29%|66%|37%|53%|0%|0%|0%|0%|3%|
|GJ8_9|1,709|0%|35%|53%|32%|60%|0%|0%|0%|0%|0%|

Second calculate the weighted average historical loss costs for each district for each of the last ten years. This is the average loss cost that would have been payable in the district that year, if the portfolio had been as per the assumed portfolio weights. Table 11 gives these for each district in Gujarat.

18
In sum, the results from these simulations suggest that one can reject the hypothesis that there are no gains from further international diversification by holding more stocks within and across countries. In other words, the investment practices documented in the first part of the paper seem indeed to be restrictive. Although there is some heterogeneity in the results depending on the strategy used, the simulated global funds consistently yield higher returns and no greater volatility than the actual global funds, even when comparing funds within the same families.<sup>35,36</sup>

# **B. Benchmarking through a Tracking-Error Model**

Next we consider a different optimization strategy since the objective of most mutual funds is not necessarily to minimize the variance given some expected return, or to maximize returns given some variance. In particular, the performance of mutual funds might actually be evaluated in comparison to benchmark indexes. Moreover, managers might be compensated according to this relative performance. Thus, portfolio decisions should incorporate these managerial incentives. Furthermore, different mutual funds might follow different investment goals. It is possible that some mutual funds follow more passively benchmark indexes while others are more active in seeking returns. Therefore, the difference in returns between the actual global funds and the simulated global funds could, in principle, be explained by different investment practices across global and specialized funds. We address these two related issues in this sub-section.

We first test whether benchmark effects are important in explaining the gains from further international diversification found in the previous exercise. For the case of the variance minimization, we modify the objective function to take into consideration a benchmark index.<sup>37</sup> The benchmark is the appropriate MSCI index ( _B_ ), specific for each global fund as described in the Morningstar database or in the fund’s website. Instead of minimizing the variance of the portfolio, we minimize the variance of the difference

> 35 For robustness, we also perform these simulations with a more restricted sample. We use rolling windows of 240 business days. The results are robust to this change and are reported in Appendix Tables 6A and 6B.

> 36 These results are not driven by significant changes in the weights of the simulated global funds over time. The time series of the portfolio weights change smoothly and the simulated portfolios do not require large shifts in holdings, which could entail large transaction costs.

> 37 This is analogous to what others in the literature do. See, for example, Roll (1992) and Chan, Karceski, and Lakonishok (1999).

30
# **_Estimation_**

To identify the middle class, we use a regression-based approach which is organized in three stages (López-Calva and Ortiz-Juarez 2014).

In the first stage, we classify households in different groups depending on their transitions in and out of poverty. In this stage, we use the four-year intervals panel (the longest panel available) of the EU-SILC datasets. For each country and four-year interval and depending on their transitions, households are classified into four groups: poor-to-poor, poor-to-nonpoor, nonpoor-to-nonpoor, and nonpoor-to-poor (Tables A.1 and A.2 in the Appendix). Across all countries, the average share of households that become poor was around 4 percent, that means that 4 percent of households move from being non-poor to being poor.<sup>8</sup> We thus specify this 4 percent chance of falling into poverty in a four-year period as the “low” probability of falling into poverty.

In the second stage, using a logistic probability regression, we estimate the probability of being poor at the end of each panel (year _t2_ ) as a function of characteristics measured at the beginning of the panel (year _t1_ ).<sup>9</sup> Formally, this can be written as follows: where: _pic_ is the probability of household _i_ in country _c_ of being poor in year _t2_ ( _pooric,t2_ ) conditional on observable household characteristics in year _t1_ ; **X** is the vector of such household characteristics; is the associated vector of country-specific model parameters for the logistic probability function _F(.)_ . ββ The vector of household characteristics includes measures of human capital and occupational status of the household head, as well as demographic characteristics, the quality of housing, a subjective measure of welfare, and observed health and labor shocks at the household level. We refer to these characteristics, which include tangible and intangible variables, as _assets_ of households.

> 8 For middle-income countries, this average is almost 10 percent (Cruces and others 2011).

> 9 Note that this estimate is different from the probability of becoming poor between _t1_ and _t2_ . In terms of the matrices in tables A.1 and A.2, it is the sum of both being poor at time _t1_ and remaining poor at time _t2_ plus not being poor at time _t1_ and becoming poor at time _t2_ . Following López-Calva and Ortíz-Juarez (2014), we are interested both in those who enter poverty and those who remain in poverty. Additionally, restricting the analysis to only those that enter poverty shrinks the estimation sample to the point where estimates become statistically unreliable.

8
17

**Table 4: Task and Mission Motivation, Controlling for Observables**

|Dependent variable:|Effort (Nu|mber of rou|nds)||
|---|---|---|---|---|
||I|II|III|IV|
|Treatment: Blank with Mission|1.320*|1.290**|1.477**|1.553***|
||(0.68)|(0.62)|(0.62)|(0.59)|
|Treatment: Slider, No Mission|0.048|0.071|0.247|0.373|
||(0.29)|(0.33)|(0.31)|(0.29)|
|Treatment: Slider with Mission|0.586|0.714|0.921*|1.244***|
||(0.41)|(0.46)|(0.49)|(0.45)|
|Treatment: Medical, No Mission|3.604***|3.862***|4.001***|4.418***|
||(0.58)|(0.58)|(0.65)|(0.68)|
|Treatment: Medical with Mission|3.660***|3.692***|3.808***|4.099***|
||(0.41)|(0.49)|(0.44)|(0.49)|
|Training: Midwife||-0.869|-0.425|-0.164|
|||(0.57)|(0.64)|(0.55)|
|Training: Doctor||-1.035|-1.492*|-1.638***|
|||(0.78)|(0.78)|(0.60)|
|Education level (years)||0.678|0.653|0.421|
|||(0.47)|(0.46)|(0.47)|
|Female|||-1.152***|-1.005**|
|1 = Female|||(0.41)|(0.39)|
|Age (years)|||-0.040|-0.059*|
||||(0.04)|(0.03)|
|Current state of personal finances|||0.264|0.376|
|4 = Excellent|||(0.53)|(0.54)|
|Risk preferences||||0.448***|
|5 = Risk seeking||||(0.14)|
|Clarity of instructions||||0.578***|
|5 = Always clear||||(0.13)|
|Confidence in payment to schools||||0.071|
|5 = Strongly Agree||||(0.27)|
|Motivation (dictator)||||0.001|
|CFA donated to school||||(0.00)|
|Constant|1.757***|-0.136|1.147|-2.166|
||(0.38)|(1.49)|(1.78)|(2.26)|
|Sigma|||||
|Constant|3.691***|3.658***|3.623***|3.524***|
||(0.35)|(0.35)|(0.33)|(0.35)|
|Pseudo R2|0.031|0.033|0.037|0.047|
|Log Likelihood|-666.2|-664.2|-661.8|-655.0|
|P|0.000|0.000|.|.|
|Observations|248|248|248|248|
|Right censored observations|9|9|9|9|
**Figure 1:** Overall Trends in Attitudes

<!-- Start of picture text --> 2004 2006 2008 2010 2012 2014 2016 2018<br>2004 2006 2008 2010 2012 2014 2016 2018 year<br>year Income Distribution Perceived as Unfair<br>President Municipal Gov. Generalized Trust<br>National Leg. Judicial Syst. Democracy is Better<br>Media Church Satisfied with Democracy<br>(a) LAPOP (b) Latinobar ́ometro<br>1 1<br>.8 .8<br>.6<br>.6<br>.4<br>.4<br>.2<br>.2<br>0<br>0<br><!-- End of picture text -->

**Figure 2:** Urban vs Rural

<!-- Start of picture text --> 2004 2006 2008 2010 2012 2014 2016 2018 2004 2006 2008 2010 2012 2014 2016 2018 2004 2006 2008 2010 2012 2014 2016 2018<br>year year year<br>Rural Urban Rural Urban Rural Urban<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br><!-- End of picture text --> (a) Trust in President (b) Trust in Municipal Gov. (c) Trust in Nat. Legislature <!-- Start of picture text --> 2004 2006 2008 2010 2012 2014 2016 2018 2004 2006 2008 2010 2012 2014 2016 2018 2004 2006 2008 2010 2012 2014 2016 2018<br>year year year<br>Rural Urban Rural Urban Rural Urban<br>(d) Confidence in Jud. Syst. (e) Trust in Media (f) Trust in Church<br>1 1 1<br>.8 .8 .8<br>.6 .6 .6<br>.4 .4 .4<br>.2 .2 .2<br>0 0 0<br><!-- End of picture text --> 8
4.33 Even though taxing resource rents would not suffice to provide revenue stability, this realization should not deter the government from assessing resource rent and taking steps to capture it when it is positive. Stability of revenues requires diversifying revenue sources and efficiency in resource allocation requires removing tax distortions. A balance must be struck between these two potentially conflicting objectives.

# V. CONCLUSION

5.1 Despite their importance in the Russian economy, natural resources do not contribute as much as they should to public revenues and private interests appropriate significant resource rents. Several natural resource taxes are set regardless of market prices or production costs. Such taxes favor profitable deposits and penalize marginal ones. The natural resource sector supplies a large amount of revenues to the budget, but mostly through taxes on the value that is added to natural resources, not the value, i.e. rent, of resources themselves.

5.2 The appropriated rent on crude oil, natural gas and standing timber was estimated. What was estimated was the current average rent. Through rent should theoretically be estimated in an intertemporal framework, the data necessary to carry out such an analysis, in particular reliable discount and inflation rates and marginal cost and revenue stream estimates, were not available. Auction data, which could help measure and collect the rent, were equally absent, except in the case of timber in Novgorod Oblast. For the other estimations, the method consisted of netting out costs from commodity prices.

5.3 The sum of appropriated rents on oil and natural gas alone represented about 18% of consolidated tax revenues in 1999. In other words, if the appropriated rent on oil and gas had been captured by the government, all taxes at the federal, regional and municipal levels could have been cut by 18%.

5.4 The current tax reform explicitly aims to reduce the tax burden on capital and labor so as to stimulate investment and employment. Green tax shifting would help realize these objectives without running a budget deficit. The Putin Administration seems aware that natural resources are inadequately taxed and has proposed that the public finance role of rental payments should be enhanced and these payments should be differentiated according to the objective conditions.

5.5 Failure to tax away the rent negatively affects the economy in several ways. First, public revenues must be raised elsewhere, _i.e._ on capital and labor. Most taxes discourage what is being taxed. Capital taxes discourage investments and labor taxes discourage employment. Investment and employment are exactly what should be stimulated to foster the reorganization of the Russian economy. As long as rents remain insufficiently taxed, resource extraction will continue to attract resources that could otherwise be allocated to activities with higher value added and greater labor intensity, such as manufacturing and services.

47
Table 3: The evolution of the economy and demographic variables, D = 0.

|t|λ1,t|lt|c1,t|e<sup>p</sup><br>1,t|y1,t|λ2,t|kt|c2,t|st|e<sup>p</sup><br>2,t|e<sup>s</sup><br>2,t|y2,t|y<sup>′</sup><br>t|y<sup>′′</sup><br>t|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|1|1.400|1.000|0.573|0.264|3.171|2.800|0.947|0.794|1.409|0.723|0.491|6.671|0.491|0.515|
|2|1.823|0.702|0.707|0.782|3.599|3.053|1.567|1.135|1.233|0.968|0.657|7.291|0.761|0.836|
|3|2.588|0.429|0.930|1.000|4.307|3.181|1.540|1.516|1.400|1.000|1.000|9.485|1.014|1.091|
|4|3.359|0.362|1.375|1.000|6.259|4.686|1.987|2.192|1.808|1.000|1.000|12.614|1.603|1.682|
|5|4.446|0.362|2.140|1.000|9.447|5.983|2.984|3.239|2.600|1.000|1.000|18.357|2.586|2.665|
|6|5.824|0.468|3.376|1.000|16.096|8.112|5.095|4.765|3.886|1.000|1.000|28.096|4.016|4.102|
|t|q<sup>c</sup><br>i,t|q<sup>a</sup><br>i,t||N <sup>c</sup><br>1,t|N <sup>c</sup><br>2,t|N <sup>o</sup><br>1,t|N <sup>o</sup><br>2,t|M <sup>a</sup><br>t|ξt|F1,t|F2,t|P1,t|P2,t|Pt|
|0|0.076|0.174|||3.840||||0.423|4.51|0.65||||
|1|0.062|0.150||4.485|3.768|1.400|0.990|0.519|1.124|5.97|1.25|47.09|8.47|55.56|
|2|0.052|0.120||3.850|3.321|1.295|0.431|1.079|1.578|8.51|5.36|60.77|30.81|91.58|
|3|0.041|0.090||3.237|2.891|1.145|1.128|0.200|0.108|13.94|8.91|88.97|53.62|142.60|
|4|0.031|0.060||2.645|2.475|1.611|1.034|0.631|0.373|16.51|16.44|103.31|90.56|193.87|
|5|0.020|0.040||2.083|2.083|1.941|1.354|0.508|0.220|16.49|23.55|99.37|128.04|227.41|
|6|0.020|0.040||2.083|2.083|2.529|1.693|0.451|0.158|12.78|27.27|84.50|157.49|241.98|

> 1 The rural sector: A1,1 = 1.000, gA1,t = 0.222, λ1,0 = 1.150, ω = 0.800, c1,min = 0.500, ep1,min<sup>= 0.001,b1= 0.350,θ1= 0.400.</sup>

> 2 The urban sector: A2,1 = 2.000, gA2,t = 0.000, λ2,0 = 2.300, ω1 = 0.539, ω2 = 0.458, ω3 = 0.001, ω4 = 0.001, c2,min = 0.584, e<sup>p</sup> 2,min<sup>= e</sup> 2<sup>s</sup> ,min<sup>= 0.001,b2= 0.450,θ2= 0.400.</sup>

> † du = 0.500, αi = 0.667, γ = 0.40, δ = 0.20, β = 0.60, ρ = 0.60, φ = 0.75. yt′<sup>growsat2.06%perannum.Fi,t,Pi,tandPtin</sup> millions.
10 One set of entries highlights the problem associated with the macro approach utilized here. By far the largest change in the relative importance of transfers was in Thailand where the percent of central government expenditures being allocated as transfers declined from over 13 percent to less than 4 percent. This large change was due to the fact that in FY 1981 the Government of Thailand shifted the responsibility for primary education from the local government to the center with an offsetting reduction in central-to-local government transfers.7 Summary data as shown here mask such policy chaiges.

Another way to measure the relative importance of grants is to consider their importance within the context of the economy as a whole. Such a measure depends upon two factors--the relative importance of the granting government within the economy and the willingness of the granting government to allocate money through the grant system. The final column of Table 1 sumn.-rizes changes in these factors by displaying the ouoyancy of transfers (as measured by the IMF) relative to GDP. The entries are buoyancies since they do not necessarily reflect automatic changes in transfer programs in response vo changes in GDP; instead, discretionary choices regarding both the size of government and the relative importance of grants each partially caused the changes. Entries greater than unity indicate that grant programs were growing more rapidly than GDP while 7 The World Bank, Thailand: Managing Public Resources for Structural Adjustment (Washington, DC: The World Bank, 1984), p. 73.
against the FAO data on tractors.<sup>21</sup> The prevalence of vertical clusters suggests that the data on tractors were more stagnant than the data on the value of agricultural fixed capital.

Looking at Table 1, we see from the decade growth rates that the data series for tractors display the same pattern of slowing growth as fixed capital. However there are remarkable differences between the growth rates of tractors and fixed capital by income groups. In higherincome countries, there was very little growth in tractors, while fixed capital stocks grew at over 6%. It is clear from these comparisons that tractors are not a convincing proxy for agricultural fixed capital, and, by inference, of total agricultural capital.

We compare the data on agricultural capital to other economic variables to obtain a sense of its relevancy for economic growth. Figure 6 shows the relationship between average labor productivity (output-labor ratio) and the fixed capital-labor ratio for agriculture.<sup>22</sup> This scatter diagram traces the production function in terms of capital intensity, without allowing for the effects of other pertinent variables. In Figure 7, we plot capital intensity in the 1980s (the ratio of fixed capital stock to GDP in agriculture) against structural transformation in the 1990s (as measured by the percentage decline in the ratio of agricultural labor to total labor). Both figures suggest positive relationships between our data set on agricultural fixed capital stocks and measures of economic growth. However, plotting tractors per worker against agricultural GDP per worker (Figure 8) shows a weaker and much more volatile relationship.

# **Agricultural production function**

Mundlak, Larson, and Butzer (1999) first utilize the panel data set on agricultural capital in an analysis of agricultural productivity for 37 countries for the time period 1970-1990. They present a model of production under heterogeneous technology, where the implemented technology is chosen jointly with the level of inputs. The empirical formulation allows for the dependence of parameters of the function, as well as the inputs, on a set of state variables, where state variables are defined to be a set of variables which characterize the environment in which the production decisions are

> 21 For ease in displaying the figure, data from the United States were excluded in Figure 5. Including the US would not alter the finding of the lack of a strong relationship between the two series.

> 22 Since livestock and treestock are included in agricultural GDP, it would be more appropriate to examine the relationship between agricultural GDP and _total_ agricultural capital, which includes these components. We compromise here for the purpose of a more direct comparison with Figure 8. Both fixed capital and capital of agricultural origin (livestock and treestock) are included in the productivity analysis which is reviewed in the next sections.

11