rafmacalaba/datause-encoder-data huggingface dataset:
# **References**

Atkinson, Anthony B. (forthcoming), “Measuring top incomes: Methodological issues,” in A. Atkinson and T. Piketty (eds.), _Top Incomes over the Twentieth Century_ , Oxford: Oxford University Press.

Atkinson, Anthony B. and Thomas Piketty (eds.) (forthcoming), _Top Incomes over the_ _Twentieth Century_ , Oxford: Oxford University Press.

Bairoch, Paul (1993), _Economics and World History: Myths and Paradoxes_ , New York: Harvester Wheatsheaf.

Banerjee, Abhijit and Thomas Piketty (2005), “Top Indian Incomes, 1922-2000,” _World Bank Economic Review_ 19 (1), pp. 1-20.

Barnett, G. E. (1936), _Two Tracts by Gregory King_ , Baltimore: Johns Hopkins University Press.

Becker, Gary, T. Philipson and R. Soares (2006), “The quantity and quality of life and the evolution of world inequality,” _American Economic Review_ 95 (1): 277-91. Chen, Shaohua and Martin Ravallion (2007), “Absolute poverty measures for the developing world, 1981-2005,” World Bank Research Development Group, Washington, D. C.

Clark, Colin (1957), _The Conditions of Economic Progress_ , London: Macmillan and Co. Clark, Gregory (2007), _Farewell to Alms: A Brief Economic History of the World._ Princeton: Princeton University Press.

Deininger, Klaus and Lynn Squire (1996), “A New Data Set Measuring Income Inequality,” _World Bank Economic Review_ vol _._ 10, No. 3, pp. 565-91.

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between the two variables, though it is not extremely close. The correlation coe¢ cient between them is above 0.5, with a Spearman rank correlation of 0.42 in the period 1995-99. We turn to a regression analysis of the relationship between these two variables after presenting the stages of constructing the instrument.

# 5 Results

## 5.1 Sector-Level Gravity Estimation

In order to build the instrument, we estimate equation (13) for each industry and 5-year period between 1970-74 and 1995-99. Because all in all we have to run some 170 regressions, presenting the full regression output would be impractical. Thus, we summarize the results for the most part graphically. In the entire sample of our regressions, the smallest number of observations is 773, the largest is 6877, with the mean of 3677. The R-squared’s range from 0.14 to 0.56, with the mean of 0.32.

Because the right-hand side variables are the same in all regressions, our empirical strategy would only work if the estimated coe¢ cients di¤er signi...cantly across sectors. Thus, the ...rst important question we must answer is whether or not there is much variation in the estimated coe¢ cients. Figure 5 plots, for each of the 13 right-hand variables, the range of coe¢ cient estimates across sectors and years. There is clearly quite a bit of dispersion in virtually all of the coe¢ cients. For the period 1995-99 for example, the distance coe¢ cient ranges from -0.8 to 0.04 (though the latter is not signi...cantly di¤erent from zero). This variation in gravity coe¢ cients across industries is quite typical of sector-level gravity studies, which have focused for the most part on the distance coe¢ cient (see, among others, Rauch, 1999, Hummels, 2001, Evans, 2003, and Chaney, 2005).

We are also hoping to construct an instrument that varies meaningfully over time. Because none of our regressors except population changes over time, any time variation in the instrument will come from changes in the coe¢ cient estimates for each sector. We check whether our estimates have this feature in Figure 6. It plots, for each sector, the evolution of the coe¢ cient on the log of bilateral distance. Solid dots indicate the point estimates, while hollow dots are the point estimates plus and minus two standard errors.<sup>10</sup> It is clear that we do have time variation, and its extent varies across sectors. Nonetheless, in almost every sector, among the coe¢ cients for the individual time periods there is a pair that is signi...cantly di¤erent from each other. Another notable feature of this Figure is that the changes are not monotonic over time: the distance coe¢ cient within a sector often falls in some periods and rises in others. Distance coe¢ cient changes in gravity models over time have been examined elsewhere in the literature, though the conclusions di¤er across studies. While some

> 10 Similar plots for every other coe¢ cient are available upon request.

13
|Grid cell-level model withgeospatial covariates|0.015|0.029|0.020|
|---|---|---|---|
|Area-level model withgeospatial covariates|0.015|0.025|0.018|
|Direct estimates with surveyweights|0.047|N/A|N/A|
|**Coverage rate relative to census-based estimat**|**es**|||
|Household-level model with geospatial covariates<br>(with Guadarrama et al.(2018)weights)|89.7%|97.4%|92.3%|
|Household-level model with geospatial covariates<br>(Unweighted)|86.3%|95.7%|89.5%|
|Grid cell-level model withgeospatial covariates|96.2%|97.4%|96.6%|
|Area-level model withgeospatial covariates|86.8%|87.2%|86.9%|
|Direct estimates with surveyweights|91.0%|N/A|N/A|
|**Average estimated headcountpoverty rate acro**|**ss Commu**|**nes**||
|Census-based estimates|45.7%|52.6%|48.0%|
|Household-level model with geospatial covariates<br>(with Guadarrama et al.(2018)weights)|49.3%|54.1%|50.9%|
|Household-level model with geospatial covariates<br>(Unweighted)|44.0%|47.6%|45.2%|
|Grid cell-level model withgeospatial covariates|48.5%|52.7%|49.9%|
|Area-level model withgeospatial covariates|39.1%|41.8%|40.0%|
|Direct estimates with WorldPopweights|48.2%|N/A|N/A|
|Direct estimates with surveyweights|46.8%|N/A|N/A|

Looking at the MSE estimates in Table 3, the household-level model generates estimates with lower MSEs on average compared to the estimates under the grid cell-level and area-level models. A further comparison between the estimates produced with geospatial covariates and the census-based ones is to compute coverage rates by treating the assumed gold standard census-based estimates as the truth. The coverage rate is the share of Communes for which a 95% normal confidence interval for headcount poverty, defined as the estimate plus/minus 1.96 times the square root of the estimated MSE, contains the census-based estimate. Overall, the coverage rate for estimates under the household-level model with geospatial covariates is 92.3%. Of course, this is not an ideal test because the census-based estimates are themselves estimates, derived from the same sample data as the geospatial based estimates. Nonetheless, the high coverage rate alleviates concerns about the validity of the estimates produced under the unit context model.

Recent research suggests that machine learning methods that allow for more flexible functional forms can improve small area prediction (Krennmair and Schmid, 2022, Merfeld and Newhouse, 2023). Exploring whether the use of machine learning methods improves prediction for out-ofsample areas is an area of current research focus. The performance for out-of-sample prediction will depend on the focus country and how well geospatial data predict poverty. Therefore, out-of-sample estimates under the unit context model in the four countries that lack recent census data should be interpreted with great caution and are likely to change when the next round of census-based estimates becomes available.

# **5. Assessment of experimental SAE estimates of head count poverty in Burkina Faso, Chad, Guinea, Mali and Niger**

16
_24 How_ **_Foat Has ChinueIndwr^y Grow.?_** TABLE **3:** BRANCH STRUCTURE OF GVIO AT 1980 **PRICES** (PERCENT)

# A. Branch Structure for **State Enterprisea** (SOE)

|Br|1978|1979|1980|1981|<br>1982|1983|1984|1985|<br>1986|1987|
|---|---|---|---|---|---|---|---|---|---|---|
|1|11.80|11.99|11.81|11.08|10.96|10.73|10.77|10.51|10.5|8 10.52|
|2|4.82|4.79|4.87|4.86|4.82|4.59|4.45|4.66|4.61|4.59|
|3|4.26|3.99|3.61|3.42|3.36|3.25|3.15|2.99|2 83|2.65|
|4|8.01|7.81|7.49|7.16|6.83|6.69|6.68|6.52|6.43|6.37|
|_5_|11.81|11.60|11.92|12.02|12.41|12.44|12.43|11.70|12.61|12.36|
|6|21.12|21.34|20.26|18.55|19.86|21.34|23.12|24.58|25.86|25.51|
|7|2.75|2.69|2.67|2.50|2.57|2.56|2.59|2.81|3.45|2.70|
|8|1.89|1.86|1.78|1.73|1.67|1.56|1.48|1.24|1.14|1.13|
|9|12.44|12.65|13.01|14.30|14.58|13.86|13.52|13.19|12.99|12.99|
|10|13.83|14.20|16.11|18.10|16.67|16.78|_15.56_|<br>15.19|<br>14.56|14.42|
|11|1.04|0.51|0.54|0.60|0.56|_0.55_|0.56|0.56|0.52|0.53|
|12<br>|0.00|0.53|0.58|0.63|0.57|0.52|0.48|0.47|0.48|0.46|
|13|3.07|3.18|3.18|2.93|1.44|1.43|1.46|1.48|1.60|1.64|
|14|0.00|<br>0.00|<br>0.00|<br>0.00|<br>1.48|<br>1.44|<br>1.42|<br>1.48|<br>0.46|<br>0.47|
|iS|3.14|2.85|2.17|2.12|2.24|<br>2.26|<br>2.32|<br>2.62|<br>1.89|<br>3.67|
|Sum|100.00|00.00|100.00|100.00|100.00|100.00|100.00|100.00|100.00|100.00|

# B. Branch Structure for CoUective Enterprises (COE)

|Br|1978|<br>1979|<br>1980|<br>1981|<br>1982|<br>1983|<br>1984|<br>1985|<br>198b|<br>1987|
|---|---|---|---|---|---|---|---|---|---|---|
|1|1.97|<br>2.28|<br>2.34|<br>2.17|<br>2.32|<br>2.47|<br>2.54|<br>2.89|<br>3.39|<br>_3.56_|
|2|0.11|0.15|0.17|0.21|0.23|0.25|0.22|0.20|0.20|0.20|
|3|2.27|2.12|1.94|<br>1.87|1.93|1.94|1.92|1.64|1.63|1.42|
|4|0.12|0.13|0.13|0.11|0.11|0.14|0.13|0.15|0.18|0.21|
|_5_|10.94|10.74|10.76|10.93|11.45|11.94|11.26|10.94|11.17|11.30|
|6|36.07|34.84|32.63|30.38|30.68|31.55|30.95|33.27|32.83|33.07|
|7|8.37|8.63|8.32|7.91|8.57|8.46|<br>8.17|7.50|8.89|7.51|
|8|2.55|2.73|2.72|2.64|2.71|2.52|2.33|2.23|2.85|2.28|
|9|2.96|3.39|3.70|4.39|4.69|4.71|5.04|4.96|5.37|<br>5.46|
|10|7.90|8.58|10.04|11.92|12.08|12.26|16.02|16.3|15.76|15.96|
|11|11.28|9.36|10.49|<br>10.96|9.59|9.22|8.44|7.10|6.39|6.22|
|12|0.uO|2.35|2.94|3.05|2.58|2.34|2.09|2.08|2.17|2.20|
|13|5.48|5.06|5.35|_5.53_|5.26|4.61|1.02|1.01|2.45|2.61|
|14|0.00|1.02|1.02|1.06|1.08|1.08|4.18|5.13|3.62|3.94|
|15|9.97|8.61|7.43|6.88|6.74|6.50|5.71|4.59|3.11|4.07|
||100.00|100.00|100.00|100.00|100.00|100.00|100.00|100.00|100.00|100.00|

**(continued)**
government reduces growth initially by 0.33 USD. The effect is most negative after four years, when it falls below -0.63 USD. It then recedes again and after seven years the cumulative effect on growth is negligible. The tax multiplier is insignificant for most of the years and the band of two standard errors after 10 years goes from -0.8 USD to 0.4 USD. The spending multiplier, on the other hand, implies that the effect of public spending on economic activity is positive and significant (Figure 1, right panel). An additional USD of government spending leads to an immediate increase in GDP of 0.34 USD. Over time, the effect builds up and each USD of additional spending results in 0.71 USD additional GDP three years later. In the long-run, the cumulative effect settles at 0.62 USD. The expenditure multiplier is statistically significant at the 5 percent level throughout all periods. However, the uncertainty is large and the two-standard-error band after 10 years goes from 0.3 USD to 0.9 USD. The size of the multipliers is very much in line with the one-year spending multiplier of 0.4 that Kraay (2014) finds in a large sample of 102 developing countries. Tang, Liu, and Cheung (2013) find similar results for Thailand but not for other Southeast Asian countries, for which the spending multiplier is insignificant.

**Figure 1** Multipliers in South Asia: impulse response function to an unexpected one USD shock.

<!-- Start of picture text --> a) Tax multiplier b) Spending multiplier<br>1 1.2<br>1<br>0.5<br>0.8<br>0<br>0.6<br>-0.5<br>0.4<br>-1<br>0.2<br>-1.5 0<br>0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10<br>Years Years<br><!-- End of picture text -->

**Note:** Unbalanced panel VAR model with four lags and fixed effects is estimated using the annual data from 1987 to 2017 for six South Asian countries (Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka). The dashed lines show +/- two standard errors. **Sources:** ADB, IMF, World Bank, and own calculations.

# **6. Differences for current and capital expenditure**

As mentioned above, it has been shown that government consumption and government investment affect economic activity differently (Ilzetzki et al. 2012). We therefore analyze current and capital expenditure separately. First, we compare their cyclicality and, second, their impact on economic activity.

We re-estimate the error-correction (2) described above separately for current and capital expenditure. Especially in economic downturns, the former is typically harder to adjust due to political policy reasons and we hence expect it to be less pro-cyclical. In addition, some components like social security spending or work programs may be inversely related to economic growth. And indeed, the short-run coefficient for current expenditure is only 0.69 and not statistically significant at the 10 percent level.

11
expenditures represent extra costs of doing business that put especially the small firms of the region at a competitive disadvantage vis-à-vis their international counterparts.

# **5.2  Ineffective institutional architecture and weak capacity**

East Africa has an extensive architecture of regional political and technical bodies. So there is no shortage of regional institutions. However, they are of limited effectiveness because of overlapping memberships, weak technical capacity, and limited enforcement powers.

## _5.2.1  Incompatibilities of multiple memberships_

As discussed earlier, almost all countries in the region are members of the eight regional economic communities (RECs) recognized by the African Union and each of the countries belongs to at least two RECs (figure 6). Although these RECs have different structures, at least in principle they all share the common objectives of reducing trade barriers among the member states, promoting cooperation in all fields of human endeavor, coordinating and harmonizing policies, and creating a larger unified economic space. Yet, the complex patchwork resulting from the multiplicity and overlapping memberships has led to significant problems: wasteful duplication of efforts and resources, increased transaction costs and costly competition for resources; conflicting goals, policies, operational mandates and jurisdictions; legal uncertainties in cases, for example, where more than one trade agreement applies to two countries; fragmented economic spaces and approaches to regional integration; and reduced ability for the RECs to pursue coherent and effective integration strategies (AfDB, 2010).

**Figure 6  Overlapping membership in the institutional arrangements**

<!-- Start of picture text --> ECCAS<br>Tanzania SADC<br>COMESA<br>Burundi EAC<br>Rwanda Kenya<br>Uganda<br>IOC<br>IGAD<br>Comoros<br>Djibouti<br>Seychelles Eritrea<br>Ethiopia Somalia<br>Sudan<br><!-- End of picture text --> _Source_ : AfDB (2010).

19
**Figure 10**

<!-- Start of picture text --> Quantiles Gap (q90-q10) by gender (1992-2002)<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>-1.0<br>-2.0<br>-3.0<br>-4.0<br>Men Females<br>1992 1993 1994 1995 1996 997 1 998 1 1999 2000 2001 2002<br><!-- End of picture text --> For men, returns are higher towards the upper levels, thus signifying complementarity between education and observables. In the first instance, this may imply that raising the level of schooling for everyone will generally increase the inequality of earnings. However, the returns at the lower levels increase over time, and the gap between returns at the top and bottom has narrowed, thus leading us to reject the idea that expansion has brought more lower ability individuals into the system and reduced the returns. On the contrary, education is becoming a better investment at the lower ends of the distribution. For women, returns are highest at the bottom end of the distribution, implying that education is to a great extent a substitute for unobserved ability in the case of Argentine women. Since the returns at the lower ends of the distribution increased over time, and the gap between the top and bottom has narrowed, then education is a good investment.

In most other countries increasing returns with quantiles have been observed: Austria, Denmark, Finland, France, Ireland, Italy, Netherlands, Norway, Portugal, Spain, Sweden, 25
back office in the same department, while another third of institutions place them in completely separate departments. The remaining central banks opt for a hybrid approach (Anasashvili et al. 2020).

# **3. Approach and Objectives**

Little quantitative research is available on the link between overall central bank governance and reserve management, despite the critical role that sound reserve management plays in helping, supporting, and maintaining confidence in monetary management (Al Hassan, Farahmand, and Papaianou 2014). As shown above, most publications on reserve management governance are prescriptive and qualitative. Therefore, we contribute to the reserve management governance discussion with data-driven analysis using RAMP's unique survey data on governance and organizational arrangements of central banks and asset allocation and risk measures. Specifically, we investigate whether governance arrangements impact investment policies and central bank risk taking and, if so, precisely which arrangements matter. We also analyze whether organizational arrangements impact central banks' investment policies and whether reporting structures influence central banks' investment policies and risk taking in their reserve management operations. This paper's ultimate goal is to empirically analyze the relationships between a central bank's governance structure for its reserve management operations and its investment policies and risk taking.

# **4. Methodology and Data**

We use correlations and regression analysis to find links between specific governance arrangements and variables related to central banks' reserves investment policies. We start by analyzing correlations between the variables described above and testing for their statistical significance. We then use regression analysis to analyze whether some of the correlation results hold when adjusting for reserve adequacy and indicators that describe the macroenvironment.

We use data groups that capture the governance arrangement, investment policies, and measures of risk taking of individual central banks to test whether governance affects investment policies. First, we use data on governance arrangements for central banks' reserve management operations as an independent variable collected through the RAMP surveys. Second, we utilize RAMP survey data on the composition and risk of reserve portfolios as dependent variables describing a central bank's investment policy. We then deploy three types of control variables to isolate empirically the impact of the governance structure and investment policies.

# **4.1 Governance and Macroeconomic Variables**

We compiled governance variables from multiple sources, collected at the national level, to assess the broader governance environment as a control variable to isolate the effect of the governance arrangements at the central bank level. We also use macroeconomic variables and data on reserve adequacy to further isolate the effects (see Table 1 for a summary).

7
# **11. Using the dialog boxes to specify desired settings for the simulation**

The _INFRISK_ Main Dialog Once the main proj ect data are entered in the input sheet, the user can specify the settings regulating the functioning of the _INFRISK_ simulation. This is done through the main dialog's key sections, which cover the following areas:

- Macroeconomic Parameters

- Construction Cost _(Note:_ Information on construction costs can be viewed but not changed in the dialog box. It can only be changed on the input sheet.)

- Risk Variables

- Debt Capital Info

- Equity Capital Info

- Output Options

Clicking the mai button in any of these sections allows the user to modify the simulation settings. For example, to set the _tNxrISK_ simultionedit button in the macroeconomic parameters section, which will open a dialog where the tax rate can be changed.Thefnothngeoin pages give a more detailed breakdown of the capabilities of each of the sections of the main dialog box.

15
0.6, and average inter-item covariance from 0.5 to 0.12. The overall PSM index, however, shows higher Cronbach’s alpha of 0.76.

The Integrity index, comprised of eight items, has an alpha of 0.61 and average interitem covariance of 0.14.

Each of the Big Five components show rather low alphas, between 0.2 and 0.4. These results, however, are consistent with those found by Pierre et al. (2014) using the same instrument in different low-income countries. It should be noted that we use a simplified Big Five Instrument, with only 15 questions instead of the usual 44-question instrument. The overall Big Five Index presents moderate reliability (alpha = 0.57). Finally, the overall Non-Cognitive Index shows higher reliability with alpha = 0.68.

Table A1: Reliability of Selection Traits

||Number of items|Cronbach’s alpha|Avginteritem covar.|
|---|---|---|---|
|Attraction to Policy Making|4|0.198|0.056|
|Commitment to Public Interest|7|0.426|0.057|
|Social Justice|5|0.319|0.049|
|Civic Duty|7|0.549|0.080|
|Compassion<br>|7|0.441|0.089|
|Self Sacrifce|6|0.613|0.115|
|PSM Index|36|0.765|0.056|
|Integrity Index|8|0.608|0.143|
|Extraversion|3|0.257|0.060|
|Conscientiousness|3|0.278|0.044|
|Openness|3|0.246|0.053|
|Emotional Stability|3|0.197|0.036|
|Agreeableness|3|0.437|0.114|
|Big Five|15|0.576|0.042|
|Grit|3|0.280|0.067|
|Decision Making|4|0.591|0.120|
|Hostile Bias|2|0.216|0.070|
|Non Cognitive|24|0.680|0.042|

# **9.3 District Level Administrative Data**

_Uganda Certificate of Education (UCE) scores_ : we use data for 2011-2013 on average total scores and division allocation of students at the end of lower secondary school. Students take exams in six mandatory subjects and may choose four additional ones . Note that 67
34

**References.**

Almeida dos Reis, J. G. and R. Paes de Barros (1991): "Wage Inequality and the Distribution of Education: A Study of the Evolution of Regional Differences in Inequality in Metropolitan Brazil", _Journal of Development Economics,_ 36, pp. 117-143.

Bonelli, R and G. L. Sedlacek (1989): "Distribuicao de Renda: evolucao no ultimo quarto de seculo", in Sedlacek, G. L. and R. Paes de Barros, _Mercado de Trabalho e Distribuicao de Renda: Uma Coletanea,_ (Rio de Janeiro: IPEA; Serie Monografica #35).

Bourguignon, F., F. Ferreira and N. Lustig (1998): "The Microeconomics of Income Distribution Dynamics in East Asia and Latin America", World Bank Research Proposal, (Washington, DC: World Bank, April).

Buhmann, B., L. Rainwater, G. Schmaus and T. Smeeding (1988): "Equivalence Scales, Wellbeing, Inequality and Poverty: Sensitivity Estimates Across Ten Countries _using_ the Luxembourg Income Study Database", _Review of Income and Wealth,_ **34,** pp. 1 15-142.

Cowell, F. A. (1995): _Measuring Inequality (2nd_ Edition), (Hemel Hempstead: Harvester Wv7heatsheaf).

Duryea, S. and M. Szekely (1998): "Labor Markets in Latin America: A Supply-Side Story", Paper prepared for the IDB/IIC Annual Meeting, Cartagena de Indias, Colombia.

Ferreira, F. H. G., P. Lanjouw and M. Neri (1999): "The Urban Poor in Brazil in 1996: A New Poverty Profile Using PPV, PNAD and Census Data", background paper for the World Bank's Brazil Urban Poverty Assessment, 1999; (Washington, DC: The World Bank).

Ferreira, F. H. G. and J. A. Litchfield (1996a): "Growing Apart: Inequality and Poverty Trends in Brazil in the 1980s", LSE - STICERD - DARP Discussion Paper No.23, London (August).

Hoffiman, R. (1989): "Evolucao da Distribuicao da Renda no Brasil, Entre Pessoas e Entre Familias, 1979/86", in Sedlacek, G. L. and R. Paes de Barros, _Mercado de Trabalho e Distribuicao de Renda: Uma Coletanea,_ (Rio de Janeiro: IPEA; Serie Monografica #35).

Juhn, C., K. Murphy and B. Pierce (1993): "Wage Inequality and the Rise in Returns to Skill", _Journal of Political Economy,_ 101 (3), pp. 410-442.

Macrometrica (1994): "Inflacao: Primeiros Meses do Real", _Boletim Mensal Macrometrica,_ #111, (July-August).

Mookherjee, D. and A. F. Shorrocks (1982): "A Decomposition Analysis of the Trend in UK Income Inequality", _Economic Journal,_ 92, pp. 886-902.

Ramos, L. (1993): _A Distribuicao de Rendimentos no Brasil: 1976/85_ (IPEA: Rio de Janeiro).