rafmacalaba/datause-encoder-data huggingface dataset:
**Table 11: Results of the Regressions on Farm Size-Efficiency in Vaalharts, 1990/91**

|Regression/|||Variable|s||Eq|uation|
|---|---|---|---|---|---|---|---|
|Region|OP|OW|H|Z|M|F Value|||
|Ruens|-0.0067<br>(0.093)|0.0233<br>(0.114)|0.0142<br>(0.223)|0.0025<br>(0.002)|-|45.28<br>(0.000)|0.62|
|Vaalhartsl|-0.0428<br>(0.259)|0.0574<br>(0.008)|0.0811<br>(0.042)|0.0002<br>(0.2875)|-|37.53<br>(0.000)|0.55|
|Vaalharts2|-0.0735<br>(0.182)|0.0239<br>(0.094)|0.0756<br>(0.066)|0.0001<br>(0.365)|0.0533<br>(0.050)|74.25<br>(0.000)|0.69|

Notes: 1) Value in brackets indicates the significance of the coefficient or value. 2) R<sup>2</sup> of a regression model without an intercept measures the proportion of variability in the dependent variable about the origin explained by the regression, which cannot be compared to the R<sup>2</sup> for models which include the intercept.

Table 11 shows that, within a framework that accounts for many of the shortcomings of previous analyses on the farm size-efficiency relationship, farm profit (net of family labor) per unit of capital invested is negatively related to operated farms size (OP) (although it is not statistically significant in Vaalharts), but positively related to the percentage of the operated area owned (OW). This further complements the findings obtained from the TFP and non-parametric analyses: farm size tends to be inversely related to efficiency, however, larger owned areas are positively related to higher farm profits per unit of capital. In addition, the supervision component (H) is also positively related to farm profitability (although it is not statistically significant for the Ruens.

Inclusion of the managerial ability variable (M) yielded interesting results. As a whole, a better fit was obtained. Managerial ability is significantly positively related to efficiency. On the other hand, the z variable yielded mixed results. As expected, it has a highly significant positive relationship with efficiency for the Ruens, but is insignificant for Vaalharts.

In geneial, the results presented in table 11 are strictly according to prior expectations regarding the signs of the different coefficients. Some specific coefficients, in particular operated area (OW), however, are not significant.

38
9

# **Table 1: Ownership and Firm Performance Statistics** (all in percentages)

|||**_Firs_**|**_t Wave_**||**_Se_**|**_cond W_**|**_ave_**|
|---|---|---|---|---|---|---|---|
|**_Category_**|**_1992_**|**_1993_**|<br>**_1994_**|**_1995_**|**_19.93_**|**_199_**|**_4_**<br>**_1995_**|
|<br>A.**Aver**|**age Firm O**|**wnershi**|**p Share by S**|**ponsor***||||
|BankSponsoredlnvestmentFunds|23.1<br>(15.4)|23.8<br>(15.5)|19.8<br>(17.2)|21.2<br>(18.4)|5.8<br>(8.6)|7.8<br>(11.2)|9.8<br>(13.6)|
|Non-Bank Sponsored Investment Funds|23.8<br>(14.1)|24.6<br>(13.9)|26.1<br>(17.4)|27.6<br>(19.5)|31.0<br>(14.1)|30.3<br>(15.4)|30.9<br>(16.4)|
|Local Strategic Investors|0.1<br>(0.2)|0.1<br>(0.2)|5.1<br>(11.1)|6.2<br>(13.3)|2.1<br>(8.1)|6.3<br>(14.9)|7.6<br>(17.0)|
|Foreign Strategic Investors|0.0<br>(0.0)|0.0<br>(0.0)|2.9<br>(10.6)|4.3<br>(13.3)|1.0<br>(2.6)|1.2<br>(6.4)|2.4<br>(10.2)|
|National Property Fund|2.4<br>(7.4)|2.1<br>(7.1)|1.2<br>(5.7)|0.9<br>(0.4)|7.6<br>(17.5)|6.7<br>(16.6)|5.4<br> <br>(15.6)|
|Total**|49.5<br>|50.9<br>|53.9<br>|60.2<br>|46.9<br>|45.8<br>|55.9<br> <br>|
||(15.0)|(14.2)|(14.7)|(16.1)|(19.9)|(17.4)|<br>(21.1)|
||<br>**B. Concen**<br>|<br>**tration i**<br>|<br>**ndicators***<br>|<br>|<br>|<br>|<br><br>|
||**1992**||**1993**|**1994**|**1995**||**Panel**|
|Share oftop 5 investors|49.5<br>(15.0)|(|48.9<br>12.7)|49.6<br>(15.8)|57.8<br>(17.5)|<br>|52.1<br>(14.9)|
|Herfindahl Index|0.076||0.085|0.109|0.134||0.106|
||(0.057)|<br>(|0.081)|(0.104)|(0.125|)|(0.104)|
||**C. Firm per**|**formanc**|**e indicators**|*****||||
||**1992**||**1993**|**1994**|**1995**||**Panel**|
|Tobin's Q|96.7<br>(52.4)|<br>(|80.3<br>50.1)|78.2<br>(44.1)|76.9<br>(46.3)||81.2<br>(48.2)|
|Profitability|12.1<br>(11.4)|(|13.7<br>11.6)|15.1<br>(11.3)|17.1<br>(12.6)||14.8<br>(11.9)|
|Correlation (Tobin's Q: Profitability)|15.1||23.1|26.9|27.2||21.8|
|*****Standard Deviation in Parentheses.||||||||

****** Residual ownership is by individuals and smaller investment funds.

To calculate Tobin's Q, we use the secondary market prices for firms traded on the PSE at the end of January following the year for which we use accounting and ownership data. This way we can reasonably be assured that the market has incorporated all available information. Using these prices, we calculate Qs as the sum of market valuation and total debt outstanding, divided
# **1. Introduction**

Governance is crucial for central banks. According to Bossu and Rossi (2019), the concept of central bank governance is multifaceted. They contend that a clearly defined central bank mandate and decision-making structure are essential to support accountability and legitimacy. Such a mandate should elaborate on objectives, functions, and powers, specifying the legal tools available for a central bank to implement its functions. Clear decision-making structures are also necessary to ensure adequate implementation of the mandate. Given the complexity of central banks and the multiple responsibilities they assume, it is crucial to define what to decide, who must decide, and how decisions must be made (Bossu and Rossi 2019).

Our empirical analysis focuses on the governance of reserve management, a key part of central bank governance overall. As foreign currency reserves have significantly increased and represent an essential part of central bank assets, the process for making investment decisions and overseeing results becomes more relevant. We aim to contribute to the reserve management governance discussion with data-driven analysis using a unique survey data set on central bank reserve management practices. Most previous publications on the topic are prescriptive and qualitative. Notably, our research shows that governance arrangements do matter for the investment policy of foreign reserve portfolios.

As the first step in this analysis, we briefly review the literature on central bank governance, focusing on reserve management operations. We then describe the data that we use for empirical research. We use a unique data set collected by the World Bank's Reserve Advisory and Management Partnership (RAMP) using two surveys conducted across 99 central banks in 2018 and 105 central banks in 2019. The surveys collected data on central banks' reserves management activities, including their governance structures, components of their investment policies and asset allocations, and their accounting and profit-sharing methodologies. We also deploy data that describes the broader institutional and macroenvironment in which these central banks operate and proxies for reserve adequacies. We use this data set to address novel research questions to find links between specific governance arrangements and reserve management investment policies. Finally, we conclude and discuss the possible implications of our key findings for the management of reserves.

# **2. Literature Review**

Central banks have various roles and are responsible for a broad list of functions, which have grown significantly over time. While central banks vary substantially in terms of structure and purpose globally, all institutions have critical responsibility for monetary policy, financial system stability, and safeguarding the financial infrastructure's core elements. Managing foreign reserves is typically another essential central bank function (Anasashvili et al. 2020). This section briefly summarizes the literature around central bank legal frameworks, functions, and governance arrangements, focusing on reserves management.

2
_88_ _Figure 72: Zlurkey: Differences between Domestic andBorder PricesforDiesel Subsidy (-), Tax (+_ <!-- Start of picture text --> 300.00<br>250.00<br>2X00 f l<br>a' 150.00 .<br>0- 0- 0- O .-<br><!-- End of picture text --> Givi ie relnive importance of vanious energy sources for Turkish households, the changes m delcicity pnri during 1982 are significant Figure 7.3 gives the electricity expenditure shares for various roups of households.

_figure 7.3: 7brkey: Household Income and EIectricity Expenditure Shares. 1987_ <!-- Start of picture text --> (CaWatsdan twheboi t htaauhd ncwco endcm ejmKUwmjWr*Uft 1917. Stat df3flM.at AftA .<br>3509 -T T*wy. hin4Tui tTUrl)<br>250%<br>II.<br>2K1XJ 7s0m0 12500 175000 2200 275000 3250W 375000 425O 475=<br>I* AN G uban Rur<br><!-- End of picture text -->
CONTENTS

|GOA|LS|2|
|---|---|---|
|THE|DEFINITION OF TAXPAYER|4|
||Domestic Coverage<br>|4<br>|
||International Jurisdiction|6|
||Foreign-source income|6|
||The definition of resident|7|
||Source rules|8|
|THE|CONCEPT OF IN(COME|8|
||General Definition|8|
||Interest Income|9|
||Capital Gains|11|
||Fringe Benefits|13|
||Income of Permanent Establishments|14|
||Income of Foundations and Cooperatives|15|
||Income of "Hard to Tax" Groups|16|
|DED|UCTIONS FROM INCOME|17|
||Pensions and Life Insurance|19|
||Depreciation|20|
||Interest Expense|22|
|TAX|RATES|23|
||Residents|23|
||Nonresidents|25|
|INV|ESTMENTTAX INCENTIVES|27|
|IND|EXING FOR INFlATION|28|
|THE|TAX TREATMENT OF THE FAMILY|29|
|ADM|INISTRATIVE PROCEDURES|31|
||Withholding Mechanisms|32|
||Employment income|32|
||Capital income|33|
||Current Payment (P.A.Y.E.) System|33|
||Self- vs. Official Assessment|34|
||Refunds|35|
||Enforcement Mechanisms|36|
||Objections and Appeals|37|
||<br>Books and Records|38|
The estimation of a quantile regression relies on the minimization of the sum of residuals. The residuals are weighted asymmetrically depending on the quantile, , estimated (Koenker and Hallock 2001). Other financial studies using the quantile regression approach include Koenker and Bassett (1978), Engle and Manganelli (2004), and more recently Adrian and Brunnermeier (2009) and Boyson, Stahel and Stulz (2010). Figure 3 plots the betas calculated by estimating equation (5) for each year for all the banks in our sample using the 0.90 quantile.

In contrast to the second measure which focuses on large changes in default risk in the banking sector, our final measure focuses on collective behavior that may precede these very large changes. Asset correlations increase dramatically during crisis periods when there are large swings in asset prices (see for instance Ang and Chen 2002). In other words, correlations tend to increase when the magnitudes of changes in prices are large. Since we are interested in interdependence in the banking sector, we also want to analyze periods when co-dependence may be high even when the magnitude of changes in default risk is low. With the final measure, following Harmon et al. (2011), we focus on the fraction of banks whose default risk moves in the same direction. This measure can more accurately capture collective behavior and mimicry that may culminate in a crisis. We slightly modify the methodology used by Harmon et al. (2011) and measure the 52 week rolling standard deviation of the fraction of banks that have positive change in their default probability in a given week: Above is an indicator function. First we compute the fraction of banks with a positive increase in credit risk and then compute the time series standard deviation of this measure. If the changes in credit risk are random across banks then the standard deviation will be zero. As codependence increases so does our measure. Figure 4 plots this co-movement measure calculated on an annual basis using all banks in our sample.

8
**Figure 2.1. Tariff rates have dropped sharply, especially in manufactured products.**

**Figure 2.2. Trade flows have increased sharply.**

Cambodian tariff rate of all products in percentage, 2000-2020 Exports and imports of goods and services, constant millions 2015 US$ <!-- Start of picture text --> 20 25,000<br>Exports of goods and services<br>15 (constant 2015 US$) 20,000<br>Imports of goods and services 15,000<br>10 (constant 2015 US$)<br>All products (%) 10,000<br>Manufactured products (%) 5 5,000<br>Primary products (%)<br>0 0<br>2001 2002 2003 2005 2006 2007 2008 2010 2011 2012 2014 2016 2020 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020<br><!-- End of picture text -->

**Source:** Authors’ elaboration using data from Our World in Data (https://ourworldindata.org/)

**Source:** Authors’ elaboration using data from Our World in Data (https://ourworldindata.org/)

As Figure 2.3 shows, export growth has primarily been driven by an increase in trade volume rather than a price effect. Notably, while exports have grown more than imports in volume, imports are relatively more expensive than Cambodia's export basket, resulting in a negative current account balance (Figure 2.5). Despite the rise in trade value, Cambodia’s share of exports and imports as a percentage of GDP remained relatively constant in the second half of the 2000s.

**Figure 2.3. Exports are driven by increases in volume rather than price.** Unit and volume index, 2000-2021

<!-- Start of picture text --> 250 1,200<br> 1,000<br> 200<br> 800<br> 150<br> 600<br> 100<br> 400<br> 50<br> 200<br> - -<br>Export unit value index (2000 = 100)<br>Import unit value index (2000= 100)<br>Export volume index (2000 = 100)<br>Volume index 2000=100<br>Unit value index 2000=100<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020<br><!-- End of picture text -->

**Source:** Authors’ elaboration using data from the World Development Indicators.

**Figure 2.4. Trade flows remain relatively moderate in the second half of the 2000s.** Imports & exports, % of GDP, 2000-2020

<!-- Start of picture text --> 80<br> 75<br> 70<br> 65<br> 60<br> 55<br> 50<br>Exports of goods and services (% of GDP) 45<br>Imports of goods and services (% of GDP)<br> 40<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020<br><!-- End of picture text -->

**Source:** Authors’ elaboration using data from the World Development Indicators.

3 | P a g e
30

# Notes to Chapter **1**

1. For information on these matters the reader is directed to various publications of the International Labour Office, particularly Introduction to Social Security (third edition, 1984).

2.In developing countries particularly, financial markets are usually insufficiently developed to offer on a private basis the insurance and annuity coverage of social security systems.

3. This is usually done initially through provisions in the labor code which make employers liable to furnish or arrange private insurance for such benefits. Statistics are not readily available to indicate when public administration of such benefits began in various countries. Hence, Tables 1.1 and 1.2 count employer-liability programs for work-injury coverage, although they do not count employer-liability programs for other types of '-Jn AO J -a.

4. In this chapter's tables, the figures for medical care include only those countries in which such care is provided through the social security system. Countries which provide free or low-cost medical care under other administrative arrangements are not counted here, although some of them are tallied below in Table 3.

5. In actual operation a social security system may perversely give a positive transfer to some who are well off. This is documerted for the United States' Old Age and Survivors Insurance in Boskin, Kotlikoff, Puffert, and Shoven (1987).

S. Government may contribute also, out of general revenues or through ear-marked taxes.

7. See note 3 above concerning the inclusion of employer-liability work-injury programs in Table 1.

8. For an analysis of how this possibility became politicallyexpedient policy in Latin America see Mesa-Lago (1986), p. 143. Also, in several cases pay-as-you-go financing has resulted from the loss of reserve funds, either through their effective confiscation f'or other governmental expenditures or through high inflation and financial crisis. The latter happened, notably, to the original social-insurance pension scheme, that of Germany, in Germany's hyper-inflation of 1923 and in the financial crisis which, in the Federal Republic of Germany, followed World War II. The related experience of Latin American and other developing countries is discussed below in chapter 3.

9. If population and real wages grow fast enough, benefits can be raised above the value of contributions indefinitely without the maintainance of any reserve fund. If growth is slower,
ensure that the inferences from any given model mimic closely traditional frequentist ones, and accordingly we set g to be small, i.e. g=0.01, so that the shrinkage factor is very close to one.

We note however that fixing the prior parameters based on these objectives of course does not avoid the problem of sensitivity of results to prior choices. Of primary concern here is the choice of g, which as noted above plays two roles in the assignment of posterior probabilities across models: lower values of g increase the model size penalty for adding additional regressors, and also increase the sensitivity of the posterior probability to improvements in R‐squared. Together these two forces imply that when g is small, the posterior probability will tend to concentrate on models with few regressors, and among these, on models with high R‐squareds. This concentration of posterior model probabilities on a few models can be extreme, which Feldkircher and Zeugner (2009) label the “supermodel effect”. And this in turn can lead to a strong concentration of high PIPs on just a few variables. Potentially, this “supermodel effect” can lead to very large changes in posterior model probabilities and PIPs as we move from one dependent variable to another. However, as we discuss further below, our main finding of instability across outcomes will not be driven by this effect.<sup>14</sup> We note that implementing BMA in principle poses major computational problems, as the number of models to be estimated and averaged increases in the number of explanatory variables at the rate 2<sup>K</sup> . When K=303 as is the case in GII, this is an astronomically large number of models (1.6 followed by 91 zeros!). Even for the more moderate K=41 in DB, there are still over two trillion (2.2x10<sup>12</sup> ) potential models to consider. Fortunately, fast and accurate algorithms for identifying and sampling only those models with the largest posterior probabilities have been developed, greatly reducing the computational burden, and we rely on them here.<sup>15</sup> Following the BMA literature, the posterior distribution is approximated by simulating a sample from it by applying MC3 sampler (Madigan and York 1995, as described in Fernandez, Ley and Steel (2001)). We also follow Fernandez, Ley and Steel (2001) in using the correlation between analytical and empirical posterior model probabilities as a criterion for convergence of the sampling chain. We will report results in the next section from a simulation run with a burn‐in of 100,000 discarded drawings and N million recorded drawings, where N is 0.2, 0.5, 1, 0.1, and

> 14 See also Ciccone and Jarocinski (forthcoming) who find that the set of cross‐country growth determinants identified as robust using BMA changes drastically as the dependent variable changes using different revisions of the Penn World Tables.  Feldkircher and Zeugner (2010) argue that this sensitivity is largely driven the authors’ choice of a small value for g.

> 15 We are very grateful to Martin Feldkircher and Stefan Zeugner  whose R‐code (available at http://feldkircher.gzpace.net/links/bma) we used to implement BMA in this paper.

16
to their faith, (ii) eat basic food twice a day, (iii) have different clothing for school/work/home, (iv) have a floor not made out of earth, and (v) have access to modern medical care for children or access to modern contraceptive methods. This prosperity measure, which was developed in the context of a family planning program designed to promote modern contraception, was not entirely appropriate for administering SSN programm. Given the non-availability of survey data with up-to-date information on household socio-economic status at below the district level, there was no alternative but to use it. The BKKBN collects this information on a census basis at regular intervals [13].

Village and municipality level committees were responsible for allocating the healthcards to the eligible population in each community. Eligibility was determined on the basis of the prosperity measure described above, although local leaders used their discretion in identifying who was poor and vulnerable. Healthcards were usually distributed through local health centers or village midwives. The healthcard entitled the owner and family members to free services at public health care providers for outpatient and inpatient care, contraceptives for women in child bearing age, pre-natal care and assistance at birth [3].

Ownership of the healthcard among Indonesian households grew to 10.6% in 1999 and to 20% in 2000 [14, 15]. The healthcard was not very well targeted, especially initially when less than 5% of the poorest quintile was covered. Pritchett et al. [14] note that as the program went from being a “regular” program to a crisis program, it moved from the typical pattern of middle class capture to being quite pro-poor in the early phase of 8
# **4. The Seigniorage Sheet and Steady State Fiscal Consistency Approach (as in Anand and van Wijnbergen)**

## **4.1. Seigniorage**

The sheet “Seigniorage” calculates seigniorage for each year of the projection period and in the long run, assuming that real growth and inflation remain constant at their values as in last year of projection period. To account for link between seigniorage revenue and inflation, we benchmark seigniorage revenue using a Cagan money demand function, where nominal base money stock, M at the end of each period t-1 is a function of nominal income and expected average inflation during the period t: Where _M teop_ − 1 is Base Money at the end of period _t-1,_ Pt,AV is the average CPI in year _t,_ π t,AV=(Pt,AVPt-1,AV)/Pt,AV, Yt is the real GDP in year _t,_ η is the semi-elasticity of money demand with respect to inflation and A is a constant term. In sheet “Seigniorage” (Fig. 3), data to calculate base money are reported in cells A9:I15. All data are linked to the input sheets.

The sheet “Seigniorage” also calculates the inflation maximizing seigniorage revenue for each year of the projection period, using the Cagan money demand formula and assuming a semi-elasticity of inflation of 0.3 (see cells B20:H20; Seigniorage sheet, Fig. 4): For the purpose of the derivation of the long –run sustainability condition at the end of the projection period, steady state seigniorage revenue σ is calculated, assuming that money demand grows at a constant rate, given that both inflation and real growth rate are at their constant long-run values: (cell I5:I23; Seigniorage sheet): 19